esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The \(\Delta\alpha_{B}/\alpha_{B}\) Scattering Mechanism Classification Diagram  
From the above analysis, it shows that for both helix and double-bounce type scatterers, \(\Delta\alpha_{\text{B}}<0\) is always satisfied. We should note that the co-polarization ratio is affected by the target orientation angle, which would affect the scattering mechanism interpretation [15]. Thus before extracting \(\rho_{r}\), a de-orientation procedure should be applied to rotate the scattering coherency matrix to be with \(0^{\circ}\) orientation angle [15].  
\(\alpha_{\text{B}}\) and \(\Delta\alpha_{\text{B}}\) can be used to interpret the scattering mechanism and the scattering randomness, respectively, which have similar physical interpretations for target characterization as the polarization \(H\) and \(alpha\). In [13], based on three scattering models and the alpha angle, a scattering segmentation plane was proposed as shown in Figure 1. By using this diagram, target physical scattering mechanisms can be classified into 8 classes. This diagram has similar interpretation for targets as the \(H/alpha\) plane. The main difference is that the \(H/alpha\) plane could not tell the difference between high entropy reflections from vegetated areas and urban areas, because both areas can generate multiple backscatters. The \(\Delta\alpha_{\text{B}}/\alpha_{\text{B}}\) diagram integrates the phase difference to represent the scattering randomness. Two different zones, i.e., Zone 4 and Zone 2, are used to represent the multiple backscattering processes from the urban area and the vegetated area, respectively.
## 2 System Model
### FSO Channel Model  
The optical signal received by the target node from the relay through the FSO link can be represented as [26; 27]:  
\[\begin{split}\text{y}_{D}=I_{FSO}^{\frac{2}{2}}G[1+\eta_{e}^{ \frac{\tau}{2}}(h_{S_{G}R}x_{S_{G}R}+n_{S_{G}R})]+n_{RD}\end{split} \tag{15}\]  
where \(\eta_{e}\) represents the electro-optical conversion efficiency, and G represents the gain factor. The value of r represents the type of detection method (r = 1: heterodyne detection (HD); r = 2: direct detection (DD)), \(y_{S_{G}R}\) indicating the signal received by the source node. \(I_{FSO}\) represents the channel coefficient of the FSO link and n\({}_{RD}\) represents the zero-mean AWGN with variance \(\sigma_{RD}^{2}\).  
The PDF and CDF of the FSO fading channel are expressed as follows [28]:  
\[\begin{split}\text{f}_{\gamma_{RD}}(\gamma_{RD})=\frac{\xi_{\text {mod}}^{2}}{r\Gamma(a)\Gamma(b)\gamma_{RD}}G_{2,2}^{2,1}\left[\frac{a\phi\gamma _{RD}^{\frac{1}{2}}}{(b-1)\mu_{r}^{\frac{1}{2}}}\bigg{|}\begin{array}{c}1-b,\xi_{\text{mod}}^{2}+1\\ \text{a},\xi_{\text{mod}}^{2}+1\end{array}\right]\end{split} \tag{16}\]  
\[\begin{split}\text{F}_{\gamma_{RD}}(\gamma_{RD})=1-\frac{\xi_{ \text{mod}}^{2}}{r\Gamma(a)\Gamma(b)}H_{3,3}^{3,1}\left[\frac{a\phi\gamma_{RD}^ {\frac{1}{2}}}{(b-1)\mu_{r}^{\frac{1}{2}}}\bigg{|}\begin{array}{c}(1-b,1),( \xi_{\text{mod}}^{2}+1,1),\Big{(}1,\frac{1}{r}\Big{)}\\ \Big{(}0,\frac{1}{r}\Big{)},(a,1),(\xi_{\text{mod}}^{2},1)\end{array}\right] \end{split} \tag{17}\]  
where \(\mu_{r}\) is the average telecom noise ratio, r = 1 represents the heterodyne detection (HD), and r = 2 represents the direct detection (DD).  
\[\begin{split}\mu_{1}=\mu_{HD}=\overline{\gamma_{RD}}(HD),\;\mu_{2 }=\mu_{DD}=\frac{\text{a}\phi\big{(}\xi_{\text{mod}}^{2}+2\big{)}(b-2) \overline{\gamma_{RD}}(DD)}{\big{[}(b-1)(a+1)\big{(}\xi_{\text{mod}}^{2}+1) \big{]}},\;\phi=\frac{\xi_{\text{mod}}^{2}}{\xi_{\text{mod}}^{2}+1}\end{split}\]  
In the above expressions, \(\xi_{\text{mod}}^{2}\) is related to the equivalent beam width \(w_{Leq}\), and a and b denote the turbulence parameters. The value of \(\xi_{\text{mod}}^{2}\) increases as the pointing error decreases. When there is no pointing error, \(\xi_{\text{mod}}^{2}\rightarrow\infty\).
## 3 Materials and Methods
### Feature Tokenized Transformer Architecture  
The feature tokenizer transformer [4] introduces an innovative approach to tabular data processing by treating each numerical feature as a distinct token requiring specialized embedding.  
Feature-specific tokenization: each feature undergoes individualized transformation through dedicated embedding networks:  
\[E_{i}(x_{i})=\text{GELU}(\text{LN}(W_{i}x_{i}+b_{i})) \tag{11}\]  
\[E(x)=[E_{1}(x_{1}),E_{2}(x_{2}),\ldots,E_{d}(x_{d})]+P_{pos} \tag{12}\]where each feature \(x_{i}\) is independently embedded through a dedicated transformation \(E_{i}\), followed by the incorporation of learned positional embeddings \(P_{pos}\in R^{1\times d\times h}\) that enable the architecture to maintain feature ordering information.  
Feature interaction modeling through multi-head attention:  
\[\text{Attention}_{FT}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}+ M\bigg{)}V \tag{13}\]  
where \(M\) represents a masking matrix enabling control over allowed feature interactions. For this implementation, full attention was permitted (\(M=0\)).  
Multi-layer transformer encoding: \(\text{TransformerOutput}=\mathcal{T}\text{L}(\mathcal{T}L-1(\ldots\mathcal{T}_{ 1}(E(x))))\).  
Where \(\mathcal{T}_{i}\) represents the \(i\)-th transformer layer, and \(L\) denotes the total number of layers.  
Final classification pathway via dimensionality reduction:  
\[z=\text{Flatten}(\text{TransformerOutput}) \tag{14}\]  
\[h=\text{GELU}(\text{LN}(W_{h}z+b_{h})) \tag{15}\]  
\[y=\sigma(W_{o}h+b_{o}) \tag{16}\]  
The flattening operation concatenates the embedded representations across all features to form a comprehensive representational vector. The FTTransformer architecture is particularly effective for capturing intricate relationships between water quality parameters through its specialized feature tokenization approach.
## 4 Discussion
### Effects of Flexibility Training on Hamstring Injury Incidence  
When hamstring flexibility decreases, the length of the hamstring during the demonstration of peak torque is reduced [43]. Further, [PERSON] et al. [36] proved that short muscle length during the demonstration of peak torque is a risk factor for the occurrence of a hamstring injury. Flexibility is an intrinsic property of body tissue and determines the range of motion achievable without injury at a joint or group of joints [44]. Increase in the joint range of motion is associated with decrease in passive resistance to stretching. This decrease in resistance is called decrease in muscle stiffness or increase in musclecompliance [45]. Increased passive stiffness of the hamstrings and decreased knee ROM are both risk factors for hamstring injury [46; 47].  
The purpose of stretching before an athletic event or training is to ensure that the athlete has a sufficient range of motion in his joint to optimally perform the athletic activity and to decrease muscle stiffness or increase muscle compliance. Therefore, stretching is used to affect both injury risk and performance [24; 45].  
However, the practice of static stretching has been demonstrated to have a potentially negative impact on performance that directly follows the stretching [48; 49]. In contrast, dynamic stretching has been shown to have a positive effect on performance; it has recently been endorsed as a replacement for static stretching [50; 51]. It is recommended to do dynamic stretching as rapidly as possible before the performance [52]. Dynamic stretching should be part of warmup routines because of its similarity to the movement patterns of the subsequent activity, in addition to the promotion of greater muscle activation [52].  
Performing dynamic stretching, the neuromuscular system is allowed a softer musculotendinous system with increased length and the ability to perform larger movements [53]. The objective of training with dynamic stretching is to acquire flexibility in the lumbopelvic region muscles and to adapt the hip joint to a mobile state where dynamic flexibility is secured. The hip joint movement adapted to supramaximal running is likely acquired by stretching the hamstring, quadriceps femoris, and other muscles while actively moving the joints.  
Dynamic stretching is conjectured to function effectively in preventing hamstring injury while also assisting athletes to perform at a high level. The practice of dynamic stretching is potentially effective as a method of preventing hamstring injuries in supra-maximal running. However, [PERSON] and [PERSON] [54] found that few studies had been conducted regarding the methodological rules and effects of dynamic stretching; hence, future studies are hoped for in this area.
## 3 The Proposed CCBA Model and Distillation Techniques
### Knowledge and Feature Distillation
#### 3.2.1 Teacher Classification System  
In this study, we trained the proposed end-to-end classifier using both knowledge and feature distillation techniques, leveraging a teacher classification system--specifically, a dual-stream ResNet50-CNN-LSTM model trained on the UrbanSound8K dataset with SPWVD-MFCC features. The architecture of the teacher system is depicted in Figure 7. This system extracts SPWVD-MFCC features with 16 coefficients and 256 timestamps from the 1.024 s raw audio segments sampled at 8 kHz and then concatenates the delta and delta-delta features. The computation of SPWVD is based on a Python library called \"tftb\" [35], and notably, this library can run on CPU only. Then, the feature is fed into ResNet50 and CNN-LSTM pathways separately for classification. The SoftMax layers convert the outputs from fully connected layers into probability distributions, which are then averaged to generate the final prediction. This classifier effectively combines the strengths of ResNet50 for spatial feature extraction and CNN-LSTM for processing temporal information. The system demonstrates exceptional urban sound classification accuracy, even in noisy environments, by utilizing SPWVD-MFCC features, which offer high resolution in both time and frequency domains. This method significantly enhances the robustness and precision of the classification system.
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Securing Authentication for Mobile Networks
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**3G**} & \multirow{2}{*}{**4G**} & \multirow{2}{*}{**5G**} & \multicolumn{3}{c}{**Privacy-**} & \multirow{2}{*}{**Authentication**} & \multirow{2}{*}{**Remark**} \\  & & & & & & & & \\ \hline \multirow{2}{*}{[21]} & \multirow{2}{*}{0} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{\(\surd\)} & \multicolumn{2}{c}{Investigated authentication and privacy-protection methods for 4G and 5G wireless mobile networks.} \\ \cline{6-6}  & & & & & & & & \\ \hline \multirow{2}{*}{[26]} & \multirow{2}{*}{X} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{0} & \multicolumn{2}{c}{Outlined the 3 GPP standard’s security structures and mechanisms.} \\ \cline{6-6}  & & & & & & & & \\ \hline \multirow{2}{*}{[27]} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{Outlined the security and privacy issues that 5G networks face.} \\ \cline{6-6}  & & & & & & & & \\ \hline \multirow{2}{*}{[28]} & \multirow{2}{*}{X} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multicolumn{2}{c}{Long term evolution (LTE) jamming and spoofing mitigation strategies were investigated.} \\ \cline{6-6}  & & & & & & & & \\ \hline \multirow{2}{*}{[29]} & \multirow{2}{*}{0} & \multirow{2}{*}{\(\surd\)} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{X} & \multirow{2}{*}{Proposed the cryptographic algorithms for LTE.} \\ \cline{6-6}  & & & & & & & & \\ \hline \hline \end{tabular} Remarks: \(\surd\): indicates fully supported; X: indicates not supported; 0: indicates partially supported.  
\end{table} 
Table 3: Comparative analysis of security and privacy of 3G, 4G and 5G cellular networks.
## 6 CFD Analysis of Drying as a Mechanically Aided Process
### Fluid-Bed Drying and Spray Drying as Coupled Heat-Mass Transfer and Mechanical Systems
In fluid-bed systems, simulations have helped improve airflow uniformity, reduce coating damage in agglomerated powders, and enhance thermal efficiency. In spray drying, CFD supports the design of chamber geometries that minimize wall fouling and improve powder recovery, as well as the selection of atomization parameters for better droplet control. These applications contribute to energy savings, improved product consistency, and more effective scale-up from laboratory to industrial systems [116]. Despite significant progress, several challenges remain in the CFD analysis of drying operations. High computational demands, especially when coupling with DEM or modeling individual droplet behavior in spray systems, limit practical implementation. Additionally, thermo-physical property data for complex food matrices are often lacking, and experimental validation of internal process variables remains difficult due to opacity and heterogeneity. Future developments are expected to focus on integrating CFD with real-time process control, using surrogate models enhanced by machine learning, developing multiscale frameworks linking droplet or particle microstructure with macroscopic behavior, and implementing digital twins for predictive maintenance and optimization [102].
## 2 Materials and Methods
### Research Methodology  
#### 2.3.1 D-InSAR Technology Fundamentals  
Differential Interferometric Synthetic Aperture Radar (D-InSAR) is an advanced technique that utilizes different time-phase SAR images of the same area to measure and analyze small ground surface deformations through differential interference. This tech  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & **Data Name** & **Resolution** & **Time** & **Source** \\ \hline Radar image & Sentinel-1A & 5 m \(\times\) 20 m & 2017–2021 & European Space Agency (ESA) \\ \hline Optical remote & Sentinel-2A & 10 m \(\times\) 10 m & 6 August 2017 & \\ sensing image & Sentinel-2B & 10 m \(\times\) 10 m & 22 July 2018 & \\  & GF-1 & 8 m \(\times\) 8 m & 20 July 2020 & China Resources Satellite \\ \hline DEM data & & 30 m \(\times\) 30 m & & \begin{tabular}{c} United States Geological \\ Survey (USGS) \\ \end{tabular} \\ \hline Vector data & \begin{tabular}{c} Zhaogu Mining Area boundary \\ Zhaogu No. 1 Mine boundary \\ Zhaogu No. 2 Mine boundary \\ \end{tabular} & 2021 & \begin{tabular}{c} Coking coal Group \\ Coking coal Group \\ \end{tabular} \\ \hline Socio-economic data & \
\begin{tabular}{c} Huixian City Statistical Yearbook \\ Huixian City Statistical Bulletin \\ \end{tabular} & 2017–2021 &
\begin{tabular}{c} Statistical yearbook \\ sharing platform \\ Huixian City \\ government portal \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Source of data for the study area.  
nology is an extension of InSAR and is used to obtain precise information about ground movements [30]. Differential Interferometric Synthetic Aperture Radar (D-InSAR) employs two-track, three-track, and four-track approaches based on the required number of images. Due to its practicality and widespread use in many applications [31; 32], this paper selects the two-track approach of differential interferometry for processing radar data in D-InSAR. This method only requires two radar images. The phase information of the interferogram acquired using the D-InSAR technique can be represented by the following equation for \(\varphi\):  
\[\varphi=\varphi_{def}+\varphi_{topo}+\varphi_{flat}+\varphi_{atm}+\varphi_{noise} \tag{1}\]  
where \(\varphi_{def}\) indicates that land surface is along the line of sight of the satellite observation (LOS) phase of deformation in this direction; \(\varphi_{topo}\) indicates the topographic phase of the target point; \(\varphi_{flat}\) indicates the flat earth phase; \(\varphi_{atm}\) indicates the delayed phase due to atmospheric effect; and \(\varphi_{noise}\) indicates other noise phases.
## 3 Frequency Domain Methods of Calculation
### The Vehicle-Track Transfer Functions  
The dynamic stiffness \(K_{\mathrm{T}}(\omega)\) of the track is coupled with the dynamic stiffness \(K_{\mathrm{V}}(\omega)\) of the vehicle to analyse the combined vehicle-track system. The force \(F_{T}\) that acts on the track is calculated as:  
\[F_{\mathrm{T}}(\omega)=-\frac{K_{\mathrm{V}}(\omega)K_{\mathrm{T}}(\omega)}{K _{\mathrm{V}}(\omega)+K_{\mathrm{T}}(\omega)}s(\omega)=H_{\mathrm{V}}(\omega) \;s(\omega) \tag{6}\]  
for the geometric vehicle-track irregularities \(s(\omega)\). In the case of a varying track stiffness \(K_{\mathrm{T}}(x)\) (see Section 3.4), a similar transfer function \(H_{\mathrm{V}}\mathrm{*}(\omega)\) for the effective track irregularities \(s^{\mathrm{*}}(\omega)\) yields:  
\[F_{\mathrm{T}}(\omega)=-\frac{K_{\mathrm{V}}(\omega)K_{\mathrm{T}0}}{K_{ \mathrm{V}}(\omega)+K_{\mathrm{T}}(\omega)}s^{\mathrm{*}}(\omega)=H_{\mathrm{ V}}\mathrm{*}(\omega)\;s^{\mathrm{*}}(\omega) \tag{7}\]  
The only difference is that the static track stiffness \(K_{\mathrm{T}0}\) is used in the numerator [34]. Equation (7) holds also for the effective irregularity \(s_{D}\)* due to the stiffness variation on and between the sleepers.  
Figure 2: Track model consisting of rail, rail pad, sleeper, sleeper pad, ballast, ballast mat, ballast and continuous soil.  
In general, the influence of the track mass must be introduced by the force transfer function \(H_{T}(\omega)=F_{S}/F_{T}(\omega)\) in Equation (5) from the top to the bottom of the track. Finally, the force \(F_{S}\) on the soil, which excites the ground vibration, can be calculated as:  
\[F_{S}(\omega)=H_{T}(\omega)F_{T}(\omega)=H_{T}(\omega)H_{V}(\omega)s(\omega). \tag{8}\]  
For the standard ballast track and the interesting frequency range (8-30 Hz), the track transfer \(H_{T}(\omega)\) is almost 1, so that the force on the track is nearly the force on the soil \(F_{T}\approx F_{S}=F\) (the dynamic axle load).
###### Abstract  
Since MANET consists of only nodes and is in wireless communication with limited resources, cooperation between nodes is very important. Multicasting technology supports various applications that allow data to be immediately transmitted to target nodes. In environments such as MANET, where nodes are constantly moving, finding an efficient path from the source to the destination is a critical challenge. Providing integrity for the data transmitted from the source to the target node set is also an important part. Maintaining the state of neighbor nodes not only increases the communication and processing overhead but also requires more memory. In this paper, we propose a zone-based secure routing algorithm to improve the performance of routing protocols. We will also prove efficient management of node groups in an area and better scalability, performance, and security despite frequent topology changes by using group keys. To evaluate the proposed technique, detailed and extensive simulation performance with PAST-DM and MSZRP are evaluated. The simulation results show that the proposed technique, compared to other routing protocols, can achieve scalability by maintaining the routing load even if the speed of nodes, the number of sources, the number of group members, and the size of the network increase.  
MANET; multicast protocol; routing protocols; zone-based secure multicast Article
## 1 Introduction  
The Frequency-Modulated Continuous-Wave (FMCW) radar has emerged as a dominant sensing modality in automotive safety, indoor localization, gesture recognition, and industrial monitoring due to its low power requirements, simple hardware architecture, and ability to provide simultaneous range and velocity measurements [1, 2]. The basic operation of an FMCW radar involves transmitting a chirped signal and measuring the frequency difference (beat frequency) between the transmitted and received echoes. This beat frequency corresponds directly to the range of the target.  
The core of FMCW radar signal processing lies in the detection of peaks in the frequency spectrum of the beat signal, where each spectral peak corresponds to a reflecting object. Accurate and robust detection of these peaks is vital for ensuring reliable target estimation. However, this task becomes increasingly difficult in the presence of low signal-to-noise ratios (SNRs), strong clutter, multiple closely spaced targets, and hardware nonidealities such as phase noise, leakage, and nonlinearity in the frequency sweep.  
Figure 1 illustrates the impact of noise on beat signal observability in FMCW radar systems by comparing clean and noisy signals in both time and frequency domains. In the top-left subplot, the clean beat signal exhibits a well-defined sinusoidal waveform modulated by a Gaussian envelope, enabling straightforward peak detection. In contrast, the top-right subplot shows the same signal corrupted by additive noise, resulting in a distorted waveform with an obscured structure. The corresponding frequency-domain representations in the bottom row further emphasize the challenge: while the clean spectrum displays a sharp, dominant peak at the true beat frequency, the noisy spectrum reveals a flattened and fluctuating profile where the peak is partially masked by noise and side clutter. This visual highlights the difficulty faced by conventional FFT-based peak detectors under low SNR conditions and motivates the need for more robust algorithms such as CA-CFAR, ITSP, and subspace-based methods that can better distinguish true targets from spurious spectral features.
## 7 Discussion, Limitations, and Future Research Trends
### Discussion
This change in direction is attributed to DL's exceptional work, reliable outcomes, and breadth of industry experience. [PERSON] and [PERSON] and [PERSON] (2019) also mainly concentrated on defining DL methods. Peer review has been applied to DL one-stage and two-stage detectors from the investigations. Currently, the leading methods are YOLOv8 from the YOLO series and Faster R-CNN from the R-CNN family, renowned for their superior accuracy and performance. Figure 15 shows the number of summarized papers for one-stage and two-stage detectors, which shows that one-stage detectors are frequently used for vehicle detection, while, for pedestrian detection, both are equally applied. Two-stage detectors are frequently used for road lanes compared with single-stage detectors. In order to improve OD, Faster R-CNN was integrated into the RPN. This resulted in shorter evaluation times, but it also required a significant amount of processing power and had difficulties maintaining consistent performance for small and variously scaled objects.YOLOv8 is designed to cater to specific requirements. The YOLOv8 structure comprises backbone, neck, and head modules, enhancing feature extraction with C2f and employing the PAN feature fusion technique. It also adopts a decoupled head architecture and introduces an anchor-free strategy for OD. Furthermore, we analyzed high-quality datasets related to AVs, and some standard evaluation metrics were also reviewed and summarized. Our discussion underscores the advancements in OD for autonomous vehicles under adverse weather, paving the way for enhanced safety and reliability in ADs.