GNSS interference and spoofing dataset.

GNSS signals are vulnerable to spoofing and interference, which poses a threat to the security of critical national infrastructure. GNSS data sets with spoofing and jamming are lacking, which hinders the research of GNSS anti-spoofing and anti-interference techniques. This data article presents a dataset recorded by a low-cost sensor deployed on the balcony at the 5th floor of the Science Hall of Yunnan University (25°3'26'' N, 102°41'55'' E). The sensor suite includes a GNSS antenna, a u-blox GNSS receiver and an embedded computer. In the experiment, interferences including spoofing and jamming were irregularly emitted using a SDR HackRF One and a commercial jammer, respectively. The dataset collected by the receiver consists of two parts: (1) raw data; (2) processed data. The types of the raw data include hardware information, satellite information and receiver parameters of GPS, Campass, Galileo, GLONASS and QZSS systems. The processed data are extracted from the raw data, including the signals, Doppler shift, pseudorange observations, carrier phase, position (latitude, longitude, and altitude), satellite azimuth and elevation angles, etc. The provided datasets are interesting for the GNSS security, anti-jamming and anti-spoofing mechanisms based scientific communities.

Influence of Neoadjuvant Therapy on Success of Endoscopic Vacuum Therapy in Anastomotic Leakage after Rectal Resection Because of Rectal Cancer.

Background: For locally advanced rectal cancer, neoadjuvant therapy (NT) is an established element of therapy. Endoscopic vacuum therapy (EVT) has been a relevant treatment option for anastomotic leakage after rectal resection since 2008. The aim was to evaluate the influence of NT on the duration and success of EVT in anastomotic leakage after rectal resection for rectal cancer. Methods: This was a monocentric, retrospective cohort study including patients who underwent rectal resection with primary anastomosis because of histologically proven carcinoma of the rectum in the Department for General and Visceral Surgery of Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin over a period of ten years (2012 to 2022). Results: Overall, 243 patients were included, of which 47 patients (19.3%) suffered from anastomotic leakage grade B with consecutive EVT. A total of 29 (61.7%) patients received NT and 18 patients (38.3%) did not. The median duration of EVT until the removal of the sponge did not differ between patients with and without NT: 24.0 days (95% CI 6.44-41.56) versus 20.0 days (95% CI 17.03-22.97); p = 0.273. The median duration from insertion of EVT until complete healing was 74.0 days with NT (95% CI 10.07-137.93) versus 62.0 days without NT (95% CI 45.99-78.01); p = 0.490. Treatment failure-including early persistence and late onset of recurrent anastomotic leakage-was evident in 27.6% of patients with NT versus 27.8% without NT; p = 0.989. Ostomy was reversed in 19 patients (79.2%) with NT compared to 11 patients (68.8%) without NT; p = 0.456. Overall, continuity was restored in 75% of patients in the long term after EVT. Conclusion: This trial comprised-to our knowledge-the largest study cohort to analyze the outcome of EVT in anastomotic leakage after rectal resection for rectal cancer. We conclude that neoadjuvant therapy neither prolongs EVT nor the time to healing from anastomotic leakage. The rates of treatment failure of EVT and permanent ostomy were not higher when neoadjuvant therapy was used.

Effects of Recombinant α1-Microglobulin on Early Proteomic Response in Risk Organs after Exposure to 177Lu-Octreotate.

Recombinant α1-microglobulin (A1M) is proposed as a protector during 177Lu-octreotate treatment of neuroendocrine tumors, which is currently limited by bone marrow and renal toxicity. Co-administration of 177Lu-octreotate and A1M could result in a more effective treatment by protecting healthy tissue, but the radioprotective action of A1M is not fully understood. The aim of this study was to examine the proteomic response of kidneys and bone marrow early after 177Lu-octreotate and/or A1M administration. Mice were injected with 177Lu-octreotate and/or A1M, while control mice received saline or A1M vehicle solution. Bone marrow, kidney medulla, and kidney cortex were sampled after 24 h or 7 d. The differential protein expression was analyzed with tandem mass spectrometry. The dosimetric estimation was based on 177Lu activity in the kidney. PHLDA3 was the most prominent radiation-responsive protein in kidney tissue. In general, no statistically significant difference in the expression of radiation-related proteins was observed between the irradiated groups. Most canonical pathways were identified in bone marrow from the 177Lu-octreotate+A1M group. Altogether, a tissue-dependent proteomic response followed exposure to 177Lu-octreotate alone or together with A1M. Combining 177Lu-octreotate with A1M did not inhibit the radiation-induced protein expression early after exposure, and late effects should be further studied.

Design of a Cavity for the High-Power Radio-Frequency Quadrupole Coupler Test for the ANTHEM Project.

The ANTHEM (Advanced Technologies for Human-centered Medicine) Radio-Frequency Quadrupole (RFQ) will employ eight coaxial power couplers, which will be magnetically coupled to the device through a loop antenna. The coupler design can support up to 140 kW in continuous wave operation. This paper presents the design of the cavity used for high-power testing, with the primary objectives of both optimizing the coupling between the couplers and ensuring operations at the designated operating frequency. Furthermore, the paper encompasses thermal and structural assessments conducted through numerical simulations.

Recent Advances on Jamming and Spoofing Detection in GNSS.

Increased interest in the development and integration of navigation and positioning services into a wide range of receivers makes them susceptible to a variety of security attacks such as Global Navigation Satellite Systems (GNSS) jamming and spoofing attacks. The availability of low-cost devices including software-defined radios (SDRs) provides a wide accessibility of affordable platforms that can be used to perform these attacks. Early detection of jamming and spoofing interferences is essential for mitigation and avoidance of service degradation. For these reasons, the development of efficient detection methods has become an important research topic and a number of effective methods has been reported in the literature. This survey offers the reader a comprehensive and systematic review of methods for detection of GNSS jamming and spoofing interferences. The categorization and classification of selected methods according to specific parameters and features is provided with a focus on recent advances in the field. Although many different detection methods have been reported, significant research efforts toward developing new and more efficient methods remain ongoing. These efforts are driven by the rapid development and increased number of attacks that pose high-security risks. The presented review of GNSS jamming and spoofing detection methods may be used for the selection of the most appropriate solution for specific purposes and constraints and also to provide a reference for future research.

The Application of Commercial Surface Acoustic Wave Radio Communication Filters as Transducers for DMMP Sensors.

In the present study, we used two popular radio communication SAW resonators as a base for gas sensors and tested their performance. Taking into account issues related to sensor sensitivity, the possibility of applying a sensor layer, the availability of devices, and other related issues, we selected two popular single-port resonators with center frequencies of 315 and 433 MHz (models R315 and R433, respectively) for testing purposes. Both resonators were equipped with a sensitive film of hexafluoroisopropanol-substituted polydimethylsiloxane, a material that selectively absorbs molecules with a high ability to form basic hydrogen bonds. Fabricated sensors were used to detect trace amounts of dimethyl methylphosphonate (DMMP) vapor, which has often been used in similar studies as a nerve chemical warfare agent simulant. Sensors using both devices loaded with sensor layers of an optimal thickness rapidly reacted to a gas containing DMMP at a concentration of 3 mg/m3, generating a stable analytical signal ranging from several to several dozen kilohertz. In the case of R433, the frequency signal was 20.5 kHz at 1 min from the beginning of exposure to DMMP. The obtained results showed that the used transducers exhibited good performance as a base for gas sensors. Finally, their suitability for sensing applications was confirmed by a comparison with the results obtained in previous similar studies.

Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning.

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm.

Effect of ultrasound combined with chemical pretreatment as an innovative non-thermal technology on the drying process, quality properties and texture of cherry subjected to radio frequency vacuum drying.

To obtain high-quality cherry products, ultrasound (US) combined with five chemical pretreatment techniques were used on cherry prior to radio frequency vacuum drying (RFV), including carboxymethyl cellulose coating (CMC), cellulase (CE), ethanol (EA), isomaltooligosaccharide (IMO), and potassium carbonate + ethyl oleate (PC + AEEO). The effect of different pretreatments (US-CMC, US-CE, US-EA, US-IMO, US-(PC + AEEO)) on the drying characteristics, quality properties, texture, and sensory evaluation of cherries was evaluated. Results showed that the dehydration time and energy consumption were decreased by 4.17 - 20.83 % and 3.22 - 19.34 %, respectively, and the contents of individual sugars, soluble solid, total phenolics (TPC), natural active substances, total flavonoids (TFC), and antioxidant properties (DPPH, ABTS and FRAP) were significantly increased after US combined with five chemical treatments (P < 0.05). Moreover, the pretreatment played important role in improving texture properties and surface color retention in the dried cherries. According to the sensory evaluation analysis, the dehydrated cherries pretreated with US-CMC exhibited the highest overall acceptance, texture, crispness, color, and sweet taste showed lower off-odor, bitter taste and sour taste compared to control and other pretreatments. The findings indicate that US-CMC pretreatment is a promising technique for increasing physicochemical qualities and dehydration rate of samples, which provides a novel strategy to processing of dried cherry.

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

BACKGROUND: It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed promise in assessing bone structure characteristics and determining the risk of fragility fracture. AIMS: To evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragility fractures retrospectively in postmenopausal women, and compared it with the traditional parameter of QUS, speed of sound (SOS), and bone mineral density (BMD) acquired with dual X-ray absorptiometry (DXA). METHODS: Using QUS, RF signal and SOS were acquired for 246 postmenopausal women. An MResNet was utilized, based on the RF signal, to categorize individuals with an elevated risk of fragility fracture. DXA was employed to obtain BMD at the lumbar, hip, and femoral neck. The fracture history of all adult subjects was gathered. Analyzing the odds ratios (OR) and the area under the receiver operator characteristic curves (AUC) was done to evaluate the effectiveness of various methods in discriminating fragility fracture. RESULTS: Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was competent to discriminate any fragility fracture (OR = 2.64; AUC = 0.74), Vertebral fracture (OR = 3.02; AUC = 0.77), and non-vertebral fracture (OR = 2.01; AUC = 0.69). After being modified by clinical covariates, the efficiency of MResNet was further improved to OR = 3.31-4.08, AUC = 0.81-0.83 among all fracture groups, which significantly surpassed QUS-SOS (OR = 1.32-1.36; AUC = 0.60) and DXA-BMD (OR = 1.23-2.94; AUC = 0.63-0.76). CONCLUSIONS: This pilot cross-sectional study demonstrates that the MResNet model based on the ultrasonic RF signal shows promising performance in discriminating fragility fractures in postmenopausal women. When incorporating clinical covariates, the efficiency of the modified MResNet is further enhanced, surpassing the performance of QUS-SOS and DXA-BMD in terms of OR and AUC. These findings highlight the potential of the MResNet as a promising approach for fracture risk assessment. Future research should focus on larger and more diverse populations to validate these results and explore its clinical applications.

Targeted axillary dissection reduces residual nodal disease in clinically node- positive breast cancer after neoadjuvant chemotherapy.

BACKGROUND: Any advantage of performing targeted axillary dissection (TAD) compared to sentinel lymph node (SLN) biopsy (SLNB) is under debate in clinically node-positive (cN+) patients diagnosed with breast cancer. Our objective was to assess the feasibility of the removal of the clipped node (RCN) with TAD or without imaging-guided localisation by SLNB to reduce the residual axillary disease in completion axillary lymph node dissection (cALND) in cN+ breast cancer. METHODS: A combined analysis of two prospective cohorts, including 253 patients who underwent SLNB with/without TAD and with/without ALND following NAC, was performed. Finally, 222 patients (cT1-3N1/ycN0M0) with a clipped lymph node that was radiologically visible were analyzed. RESULTS: Overall, the clipped node was successfully identified in 246 patients (97.2%) by imaging. Of 222 patients, the clipped lymph nodes were non-SLNs in 44 patients (19.8%). Of patients in cohort B (n=129) with TAD, the clipped node was successfully removed by preoperative image-guided localisation, or the clipped lymph node was removed as the SLN as detected on preoperative SPECT-CT. Among patients with ypSLN(+) (n=109), no significant difference was found in non-SLN positivity at cALND between patients with TAD and RCN (41.7% vs. 46.9%, p=0.581). In the subgroup with TAD with axillary lymph node dissection (ALND; n=60), however, patients with a lymph node (LN) ratio (LNR) less than 50% and one metastatic LN in the TAD specimen were found to have significantly decreased non-SLN positivity compared to others (27.6% vs. 54.8%, p=0.032, and 22.2% vs. 50%, p=0.046). CONCLUSIONS: TAD by imaging-guided localisation is feasible with excellent identification rates of the clipped node. This approach has also been found to reduce the additional non-SLN positivity rate to encourage omitting ALND in patients with a low metastatic burden undergoing TAD.

Radio-miRs: a comprehensive view of radioresistance-related microRNAs.

Radiotherapy is a key treatment option for a wide variety of human tumors, employed either alone or alongside with other therapeutic interventions. Radiotherapy uses high-energy particles to destroy tumor cells, blocking their ability to divide and proliferate. The effectiveness of radiotherapy is due to genetic and epigenetic factors that determine how tumor cells respond to ionizing radiation. These factors contribute to the establishment of resistance to radiotherapy, which increases the risk of poor clinical prognosis of patients. Although the mechanisms by which tumor cells induce radioresistance are unclear, evidence points out several contributing factors including the overexpression of DNA repair systems, increased levels of reactive oxygen species, alterations in the tumor microenvironment, and enrichment of cancer stem cell populations. In this context, dysregulation of microRNAs or miRNAs, critical regulators of gene expression, may influence how tumors respond to radiation. There is increasing evidence that miRNAs may act as sensitizers or enhancers of radioresistance, regulating key processes such as the DNA damage response and the cell death signaling pathway. Furthermore, expression and activity of miRNAs have shown informative value in overcoming radiotherapy and long-term radiotoxicity, revealing their potential as biomarkers. In this review, we will discuss the molecular mechanisms associated with the response to radiotherapy and highlight the central role of miRNAs in regulating the molecular mechanisms responsible for cellular radioresistance. We will also review radio-miRs, radiotherapy-related miRNAs, either as sensitizers or enhancers of radioresistance that hold promise as biomarkers or pharmacological targets to sensitize radioresistant cells.

Prospective assessment of peripapillary microvasculature using optical coherence tomography angiography in para-optic intracranial and sinonasal tumors treated with proton therapy.

PURPOSE: Radiation-induced optic neuropathy (RION) is rare but may lead to blindness. The mechanisms by which this occurs include endothelial and neuronal damage, but RION has been assessed very little in the case of extraocular tumors treated with high-energy proton therapy, the use of which is expanding worldwide. We assessed peripapillary microvascular changes by optical coherence tomography angiography (OCT-A) in patients undergoing high-energy proton therapy for para-optic intracranial or head and neck tumors. MATERIALS AND METHODS: In this prospective institutional review board approved study, patients receiving>40Gy_RBE maximal PBT dose to their optic nerve between 2018 and 2020 underwent quantitative OCT-A analyses. ImageJ software was used to assess changes in the peripapillary superficial vascular complex (SVC) using vascular area density (VAD), vessel length density (VLD) and fractal dimension (FDsk). Uni- and multivariate analyses were performed. RESULTS: Of 47 patients (78 eyes) with 29±6 months of follow-up (range 18-42), 29 patients (61.7%) had previously undergone surgery and 18 (32.1%) had microvascular abnormalities prior to proton therapy. Total radiotherapy dose was the most relevant factor in decreased peripapillary microvasculature. Duration of follow-up was associated with lower VAD (P=0.005) and mean retinal nerve fiber layer (RNFLm) thickness also decreased. There was no significant correlation between OCT-A changes and mean visual defect. CONCLUSION: Peripapillary microvasculature changes may occur from tumor compression or surgery and proton therapy for extraocular tumors. OCT-A may provide quantitative and mechanistic insights into RION before the occurrence of clinical symptoms.

Episodic-like memory in wild free-living blue tits and great tits.

Episodic-like memory in non-human animals represents the behavioral characteristics of human episodic memory-the ability to mentally travel backward in time to "re-live" past experiences. A focus on traditional model species of episodic-like memory may overlook taxa possessing this cognitive ability and consequently its evolution across species. Experiments conducted in the wild have the potential to broaden the scope of episodic-like memory research under the natural conditions in which they evolved. We combine two distinct yet complementary episodic-like memory tasks (the what-where-when memory and incidental encoding paradigms), each targeting a different aspect of human episodic memory, namely the content (what-where-when) and process (incidental encoding), to comprehensively test the memory abilities of wild, free-living, non-caching blue tits (Cyanistes caeruleus) and great tits (Parus major). Automated feeders with custom-built programs allowed for experimental manipulation of spatiotemporal experiences on an individual-level basis. In the what-where-when memory experiment, after learning individualized temporal feeder rules, the birds demonstrated their ability to recall the "what" (food type), "where" (feeder location), and "when" (time since their initial visit of the day) of previous foraging experiences. In the incidental encoding experiment, the birds showed that they were able to encode and recall incidental spatial information regarding previous foraging experiences ("where" test), and juveniles, but not adults, were also able to recall incidentally encoded visual information ("which" test). Consequently, this study presents multiple lines of converging evidence for episodic-like memory in a wild population of generalist foragers, suggesting that episodic-like memory may be more taxonomically widespread than previously assumed.

A comprehensive review of 5G NR RF-EMF exposure assessment technologies: fundamentals, advancements, challenges, niches, and implications.

This review offers a detailed examination of the current landscape of radio frequency (RF) electromagnetic field (EMF) assessment tools, ranging from spectrum analyzers and broadband field meters to area monitors and custom-built devices. The discussion encompasses both standardized and non-standardized measurement protocols, shedding light on the various methods employed in this domain. Furthermore, the review highlights the prevalent use of mobile apps for characterizing 5G-NR radio network data. A growing need for low-cost measurement devices is observed, commonly referred to as "sensors" or "sensor nodes," that are capable of enduring diverse environmental conditions. These sensors play a crucial role in both microenvironmental surveys and individual exposures, enabling stationary, mobile, and personal exposure assessments based on body-worn sensors, across wider geographical areas. This review revealed a notable need for cost-effective and long-lasting sensors, whether for individual exposure assessments, mobile (vehicle-integrated) measurements, or incorporation into distributed sensor networks. However, there is a lack of comprehensive information on existing custom-developed RF-EMF measurement tools, especially in terms of measuring uncertainty. Additionally, there is a need for real-time, fast-sampling solutions to understand the highly irregular temporal variations EMF distribution in next-generation networks. Given the diversity of tools and methods, a comprehensive comparison is crucial to determine the necessary statistical tools for aggregating the available measurement data.

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.

BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE: Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS: The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS: According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION: From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

Radio frequency drying on functional diversity of tiger nut flour: Effects on physicochemical, structural, and rheological properties.

Tiger nut (TN) is a valuable nutrient and gluten-free tuber. To achieve high-quality TN flour as functional ingredients in food, it is essential to develop effective drying technologies for TN. Five drying methods including natural drying (Control), hot-air drying (HD), radio frequency single drying (RFSD), RF assisted hot-air drying (RFHD), and RF- vacuum drying (RFVD) were selected and compared to determine their effects on physiochemical, structural, and rheological properties of TN flour. Results showed that RF drying (RFD) significantly improved the hydration, oil-absorbing, and antioxidant activity capacity, especially for RFVD. RFHD exhibited greater color (BI = 13.80 ± 0.05 and C = 10.26 ± 0.05) and reducing sugar content (253.50 ± 2.27 mg d.b.) than RFSD and RFVD. The gelatinization temperature, enthalpy value, and particle size (57.30-269.33 µm) of TN flour were reduced. The structural property results indicated that RFD reduced the relative crystallinity and short-range ordering of the flour, altered protein secondary structure, and caused the damaged microstructure in comparison with Control and HD groups. All sample gels exhibited a weak strain overshoot behavior (type III) under large amplitude oscillations, and RFD resulted in a reduced viscoelastic behavior. RFD could be an effective method to produce functional TN flour.

Fast Thermodynamic Study on a Silicon Nanotransistor at Cryogenic Temperatures.

Cryogenic temperatures are crucial for the operation of semiconductor quantum electronic devices, yet the heating effects induced by microwave or laser signals used for quantum state manipulation can lead to significant temperature variations at the nanoscale. Therefore, probing the temperature of individual devices in working conditions and understanding the thermodynamics are paramount for designing and operating large-scale quantum computing systems. In this study, we demonstrate high-sensitivity fast thermometry in a silicon nanotransistor at cryogenic temperatures using RF reflectometry. Through this method, we explore the thermodynamic processes of the nanotransistor during and after a laser pulse and determine the dominant heat dissipation channels in the few-kelvin temperature range. These insights are important to understand thermal budgets in quantum circuits, with our techniques being compatible with microwave and laser radiation, offering a versatile approach for studying other quantum electronic devices in working conditions.

Linearity and intermodulation distortion analysis of single and dual metal gate junctionless transistor.

Linearity and intermodulation distortion are very crucial parameters for RFICs design. Therefore, in this work, a detailed comparative analysis on linearity and intermodulation distortion of single metal (SMG) and double metal (DMG) double gate junction less transistor (JLT) is done using TCAD silvaco suite. Furthermore, the effects of temperature fluctuation, gate length variation, and gate material engineering on the linearity performance of both devices are also studied. A few significant figures of merit, including Voltage Intercept Point 2 (VIP2), Voltage Intercept Point 3 (VIP3), Third Order Intercept Power (IIP3), 1 dB Compression Point (P1dB), Third Order Intermodulation Distortion (IMD3), and the transconductance derivative parameters First Order Transconductance (gm1), Second Order Transconductance (gm2), and Third Order Transconductance (gm3) are used to assess the device linearity and intermodulation distortion of SMG and DMG JLT's. The findings show that higher VIP2, VIP3, IIP3, 1-dB compression point and lower gm3, IMD3 values are obtained for the SMG JLT device when compared to its counterpart DMG JLT. SMG JLT, which assures strong linearity and low distortion.

Loom: A Modular Open-Source Approach to Rapidly Produce Sensor, Actuator, Datalogger Systems.

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom's development.

Transferring Learned Behaviors between Similar and Different Radios.

Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations. This paper seeks to evaluate the feasibility and performance trade-offs when transferring learned behaviors from functional RFML classification algorithms, specifically those designed for automatic modulation classification (AMC) and specific emitter identification (SEI), between homogeneous radios of similar construction and quality and heterogeneous radios of different construction and quality. Results derived from both synthetic data and over-the-air experimental collection show promising performance benefits from the application of TL to the RFML algorithms of SEI and AMC.