esa-sceva/satcom-chunk-collection huggingface dataset:
# TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria
###### Abstract  
Scintillation and total electron content (TEC) are the two major examples of the top-side ionospheric parameters that are recorded differently by most Global Positioning System (GPS) receivers. The new GPS sensor created by the Atmospheric and Space Technology Research Associates (ASTRA), Cornell University, and the University of Texas, Austin have capability to record scintillation and TEC fluctuations simultaneously. Hence, the Connected Autonomous Space Environment Sensor (CASES) from ASTRA is a software-defined GPS receiver with the dual frequency of L1 C/A and L2C codes for space-weather monitoring and can be remotely programmed via an internet source. The receiver employs numerous novel techniques that make it suitable for space-weather studies compared to other nearby GPS receivers, such as different methods for eliminating local clock effects, an advanced triggering mechanism for determining scintillation onset, data buffering to permit observation of the prelude to scintillation, and data-bit prediction and wipe-off for robust tracking. Moreover, the CASES hardware is made up of a custom-built dual frequency, a digital signal processor board, and a \"single board computer\" with an ARM microcontroller. We have used the CASES GPS receiver newly installed at Bowen University, Iwo, Nigeria, to investigate the TEC and the rate of the TEC index (ROTI) around the equatorial region. Measurements of the TEC and ROTI showed similar variation trends in monthly, seasonal, and annual periods when compared to TEC and ROTI measurements from a nearby station, BJCO at Cotonou, Benin Republic. The newly installed GPS receiver looks promising for scientific use as it is the only one operational in Nigeria at the moment.  
TEC; ROTI; ionosphere 23 January 2023  
1
# A Gamified AI-Driven System for Depression Monitoring and Management
## 4 Results: Simulation-Based Evaluation of Depression Detection Models
### Data Simulation Framework  
A synthetic dataset was generated to simulate 3000 virtual users across multiple sessions. Each user profile contains both static (such as baseline cognitive score) and dynamic variables (such as mood score and reaction time). Environmental conditions (such as temperature and humidity) and task metrics (such as accuracy and response latency) were also included, informed by empirical ranges from prior clinical and digital mental health studies [4; 12; 16].  
Variables were sampled using Gaussian or uniform distributions depending on empirical variability, with relationships between features (such as sleep and mood, temperature and stress) encoded based on findings from previous literature [23, 25, 27]. Environmental parameters (temperature, humidity) and performance metrics (accuracy, latency) were correlated with vital variables using probabilistic mappings to simulate realistic behavioral patterns.  
A continuous _depression-level_ score was assigned to each user based on the aggregation of relevant behavioral and environmental indicators. This score was binarized using the median as a threshold to create a balanced binary classification task. Users with scores above the median were labeled as depressed, and those below as healthy. This approach establishes a synthetic ground truth for classification and enables model performance evaluation under controlled conditions.
## 2 Methods
### Few-Shot Fine-Tuning  
After self-supervised pretraining, in order to perform the classification task, it is necessary to fine-tune the query encoder on a large number of labeled WBMs. However, manually labeling WBMs is highly costly and time-consuming. This study proposes a few-shot fine-tuning method that uses a prototypical network in the fine-tuning period, which can significantly improve the efficiency of labeled data and reduce the demand for labeled samples.  
The prototypical network is an effective few-shot classification method with high accuracy with limited labeled samples. In the prototypical network, a small-scale labeled WBM dataset \(L\) is given as \(L=\{(x_{1},y_{1}),\cdots,(x_{N},y_{N})\}\), where each \(x_{i}\in R^{L}\) is the \(D\)--dimensional embedding of a WBM and \(y_{i}\in\{1,\cdots,K\}\) is the label. \(L_{k}\) denotes the WBMs set labeled with class \(k\).  
Then, a prototype is an \(M\)--dimensional representation \(c^{k}\in R^{M}\) of each class through the query encoder function \(f_{\phi}:R^{D}\to R^{M}\) with learnable parameters \(\phi\). Each prototype is the mean of the embedded support set belonging to its class [40]:  
\[c_{k}=\frac{1}{L_{k}}\sum_{(x_{i},y_{i})\in L_{k}}f_{\phi}(x_{i}) \tag{3}\]  
Prototypical networks generate a probability distribution over classes for a given query point \(x\) by applying the softmax function on the distances between the query point and the class prototypes in the embedding space. The loss function is the negative log-probability:  
\[J(\phi)=-\log\frac{\exp(-d(f_{\phi}(x),c_{k}))}{\sum_{k^{\prime}}\exp(-d(f_{ \phi}(x),c_{k^{\prime}}))} \tag{4}\]  
The squared Euclidean distance is adopted as the distance metric:  
\[-d(f_{\phi}(x),c_{k})=\parallel f_{\phi}(x)-c_{k}\parallel_{2}^{2}. \tag{5}\]  
Fine-tuning the query encoder is achieved by minimizing the loss function of the true class \(k\) via stochastic gradient descent (SGD) [40]. In the training process, a subset of classes from the training dataset is randomly selected in each episode. For each selected class, a subset of WBMs is chosen to form the support set, while a subset of the remaining WBMs is selected as query points. The prototypical network procedure is shown in Figure 5. We set the sample number of a support set as five and the class number as three. After fine-tuning, the classification was performed by computing the distance between the input and the prototypes.  
Figure 5: The prototypical-network-based few-shot procedure.
## 5 Conclusions  
The source identification technology based on the Clean-SC algorithm was successfully used in this study to investigate the noise reduction effect of serrated TE treatment. Clear and quantitative sound radiation results from the TE noise source were obtained. According to this study, the conclusions are outlined below.  
The current array data reduction program based on Clean-SC algorithm was assessed using [PERSON]'s benchmark case. As indicated by [PERSON], although Clean-SC algorithm has been proposed for a long time, it still needs special attention to its correct application to achieve the best effect. The assessment results show that the present array data processing method has a good accuracy with an error less than 0.5 dB in a wide frequency range. The correct implementation of the Clean-SC algorithm is guaranteed for the study of noise reduction using TE serrations.  
In the present study, it is found that the noise level of the sound source identified by the Clean-PSF algorithm is approximately 2 dB higher than that identified by the Clean-SC algorithm. This result shows the necessities to consider the interference effect of the sound sources on the identification results using phased microphone array.  
The serrated TE treatments are found to be an effective noise reduction modification. The TE noise can be considerably reduced by the serrations with a maximum noise reduction effect up to 15 dB. However, the serrations can only achieve significant noise reduction effects in the low- to mid-frequency range, while they obtain negligible noise reduction effects in the high-frequency range or even increase the high-frequency noise. In addition, the noise reduction effect is sensitive to both the serration amplitude and wavelength. In general, the noise reduction effect increases with the serration amplitude, while no obvious noise reduction law is observed for the serration wavelength. Moreover, the noise reduction effect can be affected by the inflow conditions such as the incoming velocities. The results show that the use of TE serrations for noise reduction is a very complicated issue. To obtain the best noise reduction, the sawtooth configuration should be carefully designed according to the actual working conditions and airflow parameters.  
[PERSON] designs the experiment and writes the manuscript. [PERSON] conducts the experiment and data processing. [PERSON] conducts the experiment and helps the installation of the setup. [PERSON] designs the array and guides the data processing. [PERSON] provides valuable comments and guides on the results analysis and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant numbers 51936010 and 51776174), and the National Science and Technology Major Project of China (Grant number 2017-II-0008-0022).  
The authors declare no conflict of interest.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 3 Passive Barriers  
The previous sections describe the need for an oxygen-free environment. As polymeric packaging always allows for some oxygen permeation, this has prompted many researchers in the past to decrease oxygen permeability of commodity plastics by adding a barrier material. Usually, applying the pure barrier material as a packaging material is impossible due to insufficient mechanical properties and/or high cost. Passive barriers specifically attempt to physically slow down oxygen ingress in the packaging wall. In contrast, the goal of active oxygen barriers is chemically removing the oxygen and is very little related to permeability. This will be discussed in Section 4. The passive barrier property of a material is best described by its permeability. Equation (3) from Section 2.2 is therefore revisited here.  
This equation can be rewritten to provide for a formula that can model the total oxygen ingress in a packaging construct of a material with known permeability. Equation (6) captures the oxygen flux Q (cm\({}^{3}\) day\({}^{-1}\)) diffusing into the contents of the package.  
\[\mathbf{Q_{package}}=\int_{\mathbf{A}}\frac{\mathbf{P}}{\mathbf{L}}\mathbf{ \sigma}\;(\mathbf{p_{O_{2,outside}}}-\;\mathbf{p_{O_{2,inside}}})\mathbf{d}\sigma \tag{6}\]  
with \(\sigma\) representing an infinitesimally small portion of surface area of the package.  
For films, most often the permeability is approximately equal across the entire surface. However, bottles for example own several zones in which the permeability can severely change. The cap, usually consisting of HDPE, allows for more oxygen to permeate than a PET-bottle wall [79; 80]. Additionally, due to the blowing process, the bottom and neck of a PET-bottle are more amorphous and thicker than the body. Both P and L are therefore functions of bottle area, even for the same polymer material [80].  
Passive barrier solutions are all attempting to decrease permeability across the PET-wall. Three different techniques have been developed to incorporate low permeability materials in plastics and thereby raising the passive barrier. A visual representation is given in Figure 7.
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
###### Abstract  
Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.  
cloud computing; big data; spark; resource allocation +
Footnote †: journal: Applied Sciences
## 3 FMMs for Discrete Channel Modeling
### The Fritchman-Markov Model  
[PERSON] [22] used the partitioned Markov chain model to examine the characteristics of the discrete communication channel in 1967. This Fritchman-Markov model (FMM), a finite-state model, statistically describes the channel states and the occurrences of burst errors in a digital communication channel. [PERSON] experimented with high-frequency radio channel statistical modeling to demonstrate that Markov chain models can be applied to burst binary communication channels and found that this model can describe actual communication channels.  
According to [PERSON]'s framework, data transmission can be divided into error-free states (\(g\)) and error states (\(N-g\)). An error state is one in which one or more bits have been received incorrectly, implying a transmission error; in an error-free state, all of its bits have been successfully received without error.  
Figure 4 depicts an FMM with three states: two error-free states and one error state. The states \(S(1)\) and \(S(2)\) are considered error-free as they do not produce any errors, resulting in a representation of zero in the error sequence. In contrast, the state that generates errors, denoted as \(S(3)\), is indicated as a value of one in the error sequence due to its error-generating nature. Considering that both cases show error-free transmission, it can be inferred that the two error-free states are identical, as the received bits are accurately received in both instances.  
According to Figure 4, it can be observed that the three-state FMM does not permit transitions between states that belong to the same group. This suggests the presence of diverse levels of memory, thus facilitating the modeling of a realistic communication channel model. The choice of two error-free states is based on the fact that the length of the error-free runs in the obtained error sequences is long. A single error-free state would not show this adequately, so the use of two error-free states is justified.  
Figure 3: Elements of a discrete communication system [31].  
Indoor PLC channels in [24], visible light communication channels in [39], and underwater acoustic channels in [40] were successfully modeled using an FMM with a single-error state. In these works, two or three error-free states with reasonable precision represented the channel burst behavior. As a result, a three-state FMM approach was used in this study.
## 2 Materials and Methods
### Experimental Tests on GFRP Piles
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Test Pile ID** & **Tube Type** & **Eccentricity** & \begin{tabular}{c} **Span** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Axial Load in** \\ **Failure (KN)** \\ \end{tabular} &
\begin{tabular}{c} **Moment Load** \\ **in Failure** \\ **(KNm)** \\ \end{tabular} \\ \hline BE 1-1 & Tube 1 & \(\infty\) & 4.50 & 0 & 112 \\ BE-AX 1-1 & Tube 1 & 647 & 1.60 & 198 & 176 \\ BE-AX 2-1 & Tube 1 & 322 & 1.60 & 326 & 144 \\ BE-AX 3-1 & Tube 1 & 239 & 1.60 & 312 & 126 \\ BE-AX 4-1 & Tube 1 & 144 & 1.60 & 427 & 237 \\ BE-AX 5-1 & Tube 1 & 54 & 1.60 & 344 & 117 \\ AX-1-1 & Tube 1 & 0 & 1.0 & 677 & 0 \\ BE 1-2 & Tube 2 & \(\infty\) & 4.50 & 0 & 96 \\ BE-AX 1-2 & Tube 2 & 547 & 1.55 & 446 & 156 \\ BE-AX 2-2 & Tube 2 & 417 & 1.55 & 557 & 160 \\ BE-AX 3-2 & Tube 2 & 310 & 1.55 & 612 & 211 \\ BE-AX 4-2 & Tube 2 & 189 & 1.55 & 643 & 206 \\ BE-AX 5-2 & Tube 2 & 21 & 1.55 & 811 & 53 \\ AX-1-2 & Tube 2 & 0 & 0.97 & 901 & 0 \\ BE 1-3 & Tube 3 & \(\infty\) & 4.50 & 0 & 116 \\ BE-AX 1-3 & Tube 3 & 733 & 1.75 & 288 & 136 \\ BE-AX 2-3 & Tube 3 & 522 & 1.75 & 401 & 255 \\ BE-AX 3-3 & Tube 3 & 319 & 1.75 & 522 & 274 \\ BE-AX 4-3 & Tube 3 & 199 & 1.75 & 622 & 144 \\ BE-AX 5-3 & Tube 3 & 71 & 1.75 & 848 & 202 \\ AX-1-3 & Tube 3 & 0 & 0.94 & 1002 & 0 \\ \hline \end{tabular} BE: bending load, BE-AX: combined axial compression load and bending moment, AX: axial compression load.  
\end{table} 
Table 1: Experimental data and specifications of specimens, including beams, concentrically and eccentrically loaded columns. \begin{table}
\begin{tabular}{c c c c} \hline
**GFRP Type** & **T-1** & **T-2** & **T-3** \\ \hline Average E\({}_{\text{Axial}}\), (GPa) & 14.3 & 16.6 & 19.5 \\ Average E\({}_{\text{Hoop}}\), (GPa) & 15.2 & 17.3 & 21.4 \\ Outer Thickness (mm) & 4.31 & 4.87 & 5.24 \\ Poisson Ratio & 0.071 & 0.085 & 0.092 \\ Tensile strength \({}^{*}\) (MPa) & 178 & 201 & 263 \\ Compressive strength \({}^{*}\) (MPa) & 164 & 187 & 255 \\ Diameter (mm) & 227 & 236 & 241 \\ \hline \end{tabular} \({}^{*}\) Tensile and Compressive Stresses were measured in the Longitudinal Direction.  
\end{table} 
Table 2: Tested Specifications for GFRP tubes.
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 3 Robust ENF Estimation Techniques
### Robust ENF Enhancement Techniques
#### 3.3.2 Robust Filtering Algorithm  
The RFA was proposed to improve ENF estimation in noisy interference [58]. Instead of reducing the noise after the spectrum is computed, the RFA approach enhances the estimation accuracy by improving the SNR and minimizing in-band noise prior to the ENF estimation.  
In the RFA [58], a time-domain preprocessed input signal is encoded into the Instantaneous Frequency (IF) of the Sinusoid-Frequency-Modulated (SFM) signal. A kernel function is utilized to generate the Sinusoidal Time-Frequency Distribution (STFD) of the encoded signal, where the peaks correspond to the denoised ENF. For an optimal selection of the kernel function and the signal encoding, we recommend readers refer [58] for a detailed description of the algorithm. With the help of the RFA, the ENF can be reliably estimated under a \(-20\) dB noise level. For deepfake videos, the underlying ENF, although captured by the imaging sensor from the artificial light, is interfered by the pixel noise and subject movement [56]. Therefore, the RFA technique can be used to minimize the noise, and then, a suitable spectrum estimation technique can be used to estimate the ENF.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC  
The security functionalities of the presented ECSSP-SC can be divided into two logical parts--security properties fulfilled by the proposed protocols and the resistance capability of the proposed protocol from different attacks.
# Design of Triple-Band (DSRC, 5G, 6G) Antenna for Autonomous Vehicle Telematics
## 1 Introduction
We reported the preliminary and simulated results for the first time in [13]. To fully introduce and describe the proposed triple-band antenna, this paper includes the introduction, discussion, and simulated performance of a one-by-eight and an eight-by-eight 6G patch antenna array, Wilkinson power divider, coplanar waveguide to microstrip (CPW to MS) transitions, and developed triple-band antenna. The simulated performance of the designed triple-band antenna at DSRC and 5G frequency bands is validated through measurements.
## 2 Related Work
### Activity Analysis Datasets  
#### 2.1.1 Human Action Datasets  
The UCF101 dataset [7] is a classic RGB action recognition dataset which contains 101 action categories (diving, typing, biking, basketball, etc.) and 13,320 videos collected from YouTube. Researchers have been studying this dataset for a long time, and the reported results of this dataset have already reached very high accuracy [8; 9].  
The MSR-Action3D dataset [10] is one of the earliest depth-based action recognition datasets. It records 20 kinds of actions (forward punch, high throw, hand clap, bend, etc.) and 10 subjects. It provides a total of 567 depth map sequences and corresponding skeleton coordinates. It is often regarded as a challenging common benchmark dataset in the field of action recognition based on skeleton depth data [11; 12].  
The NTU RGB+D dataset [13] is one of the most popular public human action recognition datasets. It contains 60 action classes performed by 40 distinct subjects in total, which are divided into three groups: 40 daily actions (drinking, eating, reading, etc.), 9 health-related actions (sneezing, staggering, falling down, etc.), and 11 mutual actions (punching, kicking, hugging, etc.). State-of-the-art action recognition methods [14; 15] often use this dataset as a benchmark.  
Basketball action recognition is a sub-task of human action recognition. All of the above-mentioned datasets are famous human action recognition datasets, which cover a variety of data modalities, and they have greatly contributed to human behavior analysis. However, they only contain small amounts of basketball action data, which is inadequate to support the related research.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 6 Evaluations
### Performance of Layer Dropping in a 1 Gbit Network Environment  
The first experiment was conducted in an environment consisting of one parameter server machine and two worker machines. Each worker machine consisted of two NVIDIA Geforce GTX 1080 Tis. The parameter server uses the CPU to update and maintain model parameters. Each worker uses the GPU to compute gradients and send computed gradients to the parameter server. The network is composed of 1 Gbit Ethernet. We used ResNet v1 50 as a training model. Furthermore, we implemented a distributed environment using OpenMPI 3.0.0 and TensorFlow 1.13.  
Figures 5 and 6 show the training accuracy and loss over the training steps. As shown in Figure 5, when layer dropping is applied, the training loss is larger than that when layer dropping is not applied  
by about 15% from 0.073 s to 0.085 s. However, in the case of communication time, it decreased about 95% from 3.457 s to 0.182 s. Therefore, the total execution time was reduced by about 82%, so that training can be performed faster than the existing distributed deep learning.  
Figure 8: Training accuracy over time in a low bandwidth network.  
Figure 7: Training loss over time in a low bandwidth network.  
Figure 9: Communication time and computation time in a low bandwidth network.