esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 1 Introduction
This study proposes a method of content-based communication load reduction in an IoMT, based on detection of Regions-of-Motion (ROM) through a consecutive frame differencing mechanism. Notice that frame differencing is also known as background subtraction [8,9]. However, herein the intention is to apply this established method to the emerging field of sensor networks. The research focuses on the implementation of an efficient video compression technique, which reduces the communication load of camera sensors in an IoMT. The proposed mechanism is of a modular nature comprising of: Data acquisition; ROM identification, with trimming of changed video frames; load reduction, featuring image compression; and confidential transmission through video encryption. The contributions are:  
* Enabling sensors to transmit only changing motion information, established through inter-frame differing. * Consequently, reducing communication overhead by limiting the data to be sent. * Accommodation of limited resources in the IoMT by managing excessive data. * Efficient data compression based on the contents of individual frames. * Bitrate reduction through compression, while preserving the video content and its quality of representation. * Configuration of IoT testbed with two Intel NUC Core i5-6260U mini computers for experiments and performance evaluation of proposed load reduction scheme. Analysis of the proposed method in comparison with five other related methods for load reduction. The relative merits and demerits of these methods are indicated. This paper is arranged as follows: Section 2 gives the context to this paper, reviewing the research literature in this field and recording challenges in IoT including overloaded networks, and the need for resource management, video streaming, and IoT security. The Section also reviews the video processing background, including video compression and motion detection. Section 3 describes the methodology pursued in the paper, while experimental results are presented in Section 4, along with a discussion of their significance, including in relation to other recent similar systems. Finally, Section 5 draws some conclusions as a result of this study.
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 5 Conclusions  
In this paper, we focus on exploring the channel estimation algorithm for OTFS systems and propose a novel approach that incorporates the Transformer model. The inclusion of Transformer in our work stems from its exceptional capability to handle time series data more effectively when compared to other neural network architectures. Initially, we employ the threshold method to obtain preliminary channel estimation results. However, in order to achieve superior channel estimation performance, we consider the inherent temporal correlation present in channel responses. To leverage this correlation, we treat the data as time series and utilize them for predicting channel responses. The proposed Transformer estimation algorithm demonstrates better NMSE and BER performance than the threshold method proposed in the literature and conventional neural networks under the same \(SNR\), \(SNR_{p}\), and velocity. Moreover, through simulations, we validate the robustness of OTFS in scenarios with high Doppler spread. Additionally, our work highlights the potential of employing time series-based channel response prediction methods for accurate channel estimation. We also take into account the time complexity and spatial complexity of different models, where our proposed algorithm achieves the highest accuracy while maintaining acceptable computational complexity. Overall, our findings contribute to advancing the field of channel estimation in OTFS systems, demonstrating the effectiveness of incorporating Transformer models and highlighting their benefits in handling time series data.  
Conceptualization, T.Z.; methodology, J.L.; software, T.S.; validation, T.S.; investigation, T.S. and J.L.; data curation, J.L.; writing--original draft, T.S. and J.L.; writing--review and editing, T.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Beijing Natural Science Foundation under Grants 4212006 and L212030, and the National Natural Science Foundation of China under Grants 62071031 and 62341102.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
## 5 Simulation and Experimental Results
### Study Example 1  
A second order nonlinear system with mismatching condition is considered for study [13].  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}+e^{x_{1}}+\omega(t)\\ \dot{x}_{2}=-2x_{1}-x_{2}+u(t)\\ y=x_{1}\end{array}\right. \tag{31}\]  
being \(d(x,\omega(t),t)\triangleq e^{x_{1}}+\omega(t)\) with \(\omega(t)=0,\ 0\leq t<5\) s and \(\omega(t)=3,\ t\geq 5\) s. The system matrices are:  
\[A=\begin{bmatrix}0&1\\ -2&-1\end{bmatrix},B_{u}=\begin{bmatrix}0\\ 1\end{bmatrix},B_{f}=\begin{bmatrix}1\\ 0\end{bmatrix},C=\begin{bmatrix}1&0\end{bmatrix} \tag{32}\]  
The modified sliding surface with reaching phase elimination as per (20) and (24) is considered as  
\[\dot{\sigma}_{m} =\sigma-\sigma(0)e^{-\kappa_{s}t}\] \[=c_{1}\hat{x}_{1}+\hat{x}_{2}+\hat{x}_{3}-(c_{1}+\hat{x}_{3})e^{ -\kappa_{s}t} \tag{33}\]  
where \(\hat{x}_{3}\) is an estimation of disturbance \(d\). Equivalent control law \(u_{eq}\) and disturbance caring term \(u_{n}\) is obtained as  
\[u_{eq}= -[c_{1}\hat{x}_{2}-2\hat{x}_{1}-\hat{x}_{2}+\alpha_{s}c_{1}e^{- \kappa_{s}t}-\hat{d}\alpha_{s}e^{-\kappa_{s}t}]-k_{s}\dot{\sigma}_{m}-\dot{d} \tag{34}\] \[u_{n}= -[c_{1}d+\hat{d}\alpha_{s}e^{-\alpha_{s}t}] \tag{35}\]  
where the sliding surface coefficient \(k_{s}=5\), control gain \(c_{1}=20\), observer poles kept at \(s=-5\) (same, as placed in [14]), \(\alpha_{s}=4.5\), step size of simulation = 0.001 s, solver used in simulation is ode4 (Runge-Kutta), saturation limit for control signal considered as +30 to \(-40\). It should be noted that the sliding surface coefficient \(k_{s}\) should satisfy the Equation (30) which was generally considered at least 10 times greater than that of bounds \(\mu\). Whereas, \(c_{1}\) is dependent on the location of the pole to be placed in such a way that the sliding surface will always be stable. This system is a non-minimum phase and it is observed that the proposed controller gives better performance even after adding constant disturbance from \(t\geq 5\) s as shown in Figure 2.  
If the control law is designed with \(\sigma\) instead of \(\hat{\vec{\sigma}}_{m}\), then sliding surface starts initially from 20, whereas with reaching phase elimination it started from 0, which provides less control efforts at initial stage and better performance as shown in Figure 3.  
The error dynamics of actual and estimated states are given in Figure 4. The quick and perfect convergence of estimated states towards original states provides less error bound \(\mu\). As this is an example of regulating type, the set point is zero. The performance indices for this system are shown in Table 1. From the performance indices, it is clarified that the proposed controller provides less error than other techniques in all cases.  
Figure 3: Sliding surface response for Example 1.  
Figure 2: Response of output and control signal for Example 1.
## 4 Communication Networks and Technologies in NMGs
Given resource limitations in smart meters [57], computationally low-cost communication technologies are necessary in HANs [58]. Both wired and wireless technologies are utilised to gather data from homes and buildings. Typical communication technologies in HANs include ZigBee, Wi-Fi, Bluetooth, cellular, and Ethernet [59]. In Ireland, Electricity Supply Board networks (ESBN) and Greencom networks employ Wi-Fi-based units for smart meter monitoring in HANs in the Dingle Peninsula electrification project [60].
## 2 Datasets  
In this paper we evaluate four datasets, as detailed in Table 1. Three of these datasets are publicly accessible, namely: (1) NIST-DB4 [20], (2) SOCOfing [21], and (3) NIST-302 [22]. Additionally, we incorporated a privately obtained dataset from the Israeli police, henceforth referred to as IsrPoliceDB. To the best of our knowledge, these datasets represent the only publicly available resources that include gender information in conjunction with fingerprint images. The NIST-DB4 dataset [20] comprises 4000 plain fingerprints, captured at a resolution of 500 dpi, sourced from 2000 individuals (380 females and 1620 males), where each subject contributed two impressions of their index finger for this dataset. The SOCOfing database [21], established in 2007, consists of 6000 fingerprints collected from 600 African subjects (123 females and 477 males). Each participant provided a single impression of all ten fingers. The images in this dataset measure 96 \(\times\) 103 pixels and have a resolution of 500 dpi. Furthermore, a relatively recent dataset, NIST-302 [23], published by the NIST agency in 2019, includes fingerprint impressions from 200 Americans (132 females and 68 males). Each individual contributed 20 impressions of all ten fingers. The images in this dataset are at a resolution of 500 dpi, with dimensions of 256 \(\times\) 360 pixels.
## 4 Results and Discussion
### Further Experiments and Results  
Our proposed method falls under the category of cost-sensitive learning. The evaluation compares DLWP against other SOTA methods; LDAM [47], Focal loss [46] and Class Balanced loss [23] as baselines. To compile a comprehensive list of experiments, we combine different reweighting strategies with the loss functions and obtain Tables 1 and 2. The combination is structured as follows: LOSS type--LOSS WEIGHT strategy--LOGIT WEIGHT strategy.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 6 Visualization of Convolutional Neural Networks
### Visualization of the Outputs of Hidden Layers  
In order to understand the process of CNN processing data, the input data, output of convolutional layer, output of pooling layer, and output of fully connected layer are visualized. In this section, the single damage test dataset is used as the input data. The shape of input data is \(50\times 12\). The data of each channel represent the interlayer displacement data of each node. The number of the data of each channel is 50, and the acquisition frequency is 100 Hz. The curves of each channel of the input data are shown in Figure 16. These curves are similar, and all have the characteristics of two cycles.  
Figure 16: Input data.  
Figure 15: The curve of the first twelve channels. (**a**) Kernel 1 (**b**) Kernel 2.  
Convolution is the process of feature extraction in CNN, so the convolutional layer is also the most important part of the CNN. Taking the first convolutional layer as an example, the input shape is 50 \(\times\) 12, the convolution kernel size is 5 \(\times\) 12, and the number of the convolution kernel is 32. The output shape of the convolutional layer is 45 \(\times\) 32, and each channel is generated by convolving the 12 channels of input data. The curves of the first twelve channels of output are shown in Figure 17. It can be seen that most of the twelve channels still maintain the characteristics of two cycles.  
The role of the pooling layer is mainly to reduce the size of the feature map. Its input shape is 46 \(\times\) 32, the pooling kernel size is 2 \(\times\) 32, and the number of the pooling kernel is 32. Additionally, there are no parameters in the pooling kernel. The output shape of the pooling layer is 23 \(\times\) 32. The curves of the first twelve channels of output are shown in Figure 18. The pooling layer reduces the size of the input data, but the outputs still maintain the shape and characteristics of the input data. At the same time, the curve becomes smoother. Thus, the pooling layer is similar to a filter.  
After the second pooling layer, data are entered into the Flatten layer. The data are converted from a matrix to a vector. Then, the vector is inputted into the fully connected  
Figure 17: Output of the first convolutional layer.  
Figure 18: Output of the first pooling layer.  
layer. The last fully connected (Dense) layer output the predicted probabilities of all sample. The probabilities of the first ten samples in single damage test dataset are shown in Table 6. As can be seen, each sample can be identified with high probability. The result showed that the model has a strong ability to detect structural damage.
###### Abstract  
Reliable wireless communications are crucial for ensuring workers' safety in underground tunnels and mines. Visible light communications (VLC) have been proposed as auxiliary systems for short-range wireless communications in underground environments due to their seamless availability, immunity to electromagnetic interference, and illumination capabilities. Although multiple VLC channel models have been proposed for underground mines (UM) so far, none of these models have considered the wavelength dependence of the underground mining VLC channel (UM-VLC). In this paper, we propose a single-input, single-output (SISO), wavelength-dependent UM-VLC channel model considering the wavelength dependence of the light source, reflections, light scattering, and the attenuation due to dust and the photodetector. Since wavelength dependence allows us to model VLC systems more accurately with color-based modulation, such as color-shift keying (CSK), we also propose a wavelength-dependent CSK-based UM-VLC channel model. We define a simulation scenario in an underground mine roadway and calculate the received power, channel impulse response (CIR), signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), root mean square (RMS) delay, and bit error rate (BER). For comparison, we also calculate these parameters for a monochromatic state-of-the-art UM-VLC channel and use it as a reference channel. We find that the inclusion of wavelength-dependency in CSK-based UM-VLC systems plays a significant role in their performance, introducing color distortion that the color calibration algorithm defined in the IEEE 802.15.7 VLC standard finds harder to revert than the linear color distortion induced by monochromatic CSK channels.
## 4 Experiments
### Ablation Studies  
To better understand the contributions of different components in our MVSD(GCN) and MVSD(LLM) models, we perform an ablation study by systematically removing key elements and evaluating their impact on performance. Table 5 presents the average Macro F1  
Figure 5: Comparison of node distributions in graphs generated by different LLMs (Qwen-2.5, Llama-3.1, and GPT-3.5) and their impact on stance detection performance on SEM16. gcn denotes MVSD(GCN) and llvm denotes MVSD(LLM).  
scores after ablating different components. Specifically, \"w/o R\" replaces RGCN with a standard GCN, which does not differentiate between stance relationships (favor, against, or neutral). \"w/o H-node\", \"w/o U-node\", and \"w/o E-node\" remove hashtag, user, and named entity nodes, respectively, while \"w/o Att\" removes the self-attention mechanism.  
For MvSD(GCN), replacing RGCN with a standard GCN (w/o R) causes a substantial performance drop from 75.4% to 62.4%, highlighting the importance of relational modeling in stance detection. The inability to distinguish between stance relationships weakens the model's ability to capture nuanced interactions. Removing specific node types also degrades performance, with the most significant drop occurring when entity nodes are removed (71.5%), underscoring the importance of entity information in stance reasoning. Eliminating self-attention (w/o Att) leads to a moderate decline (72.7%), indicating that attention-based interaction modeling refines the model's representations.  
For MvSD(LLM), we observe similar trends, with the removal of hashtag (70.2%), user (71.4%), or entity nodes (68.6%) consistently reducing performance. MvSD(LLM) is notably more sensitive to the removal of entity nodes than MvSD(GCN), suggesting that LLM-based features benefit more from explicit entity modeling. This highlights the critical role of structured knowledge in enhancing LLM-driven stance classification.  
Overall, these results confirm that both relational graph modeling and multi-view representations are crucial for stance detection. Removing any key component causes significant performance degradation, reinforcing the need to integrate structured knowledge and multi-view information in our approach.  
Figure 6: Comparison of entity word clouds extracted by different LLMs on SEM16. Each subfigure corresponds to a different large model, where the word size represents the frequency of the extracted entity. Larger words indicate higher extraction frequencies in the model’s output. (**a**) GPT-3.5; (**b**) Llama-3.1; (**c**) Qwen-2.5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Average Perf.** \\ \hline MVSD(GCN) & 75.4 \\ w/o R & 62.4 \\ w/o H-node & 72.2 \\ w/o U-node & 73.0 \\ w/o E-node & 71.5 \\ w/o Att & 72.7 \\ \hline MvSD(LLM) & 75.0 \\ w/o H-node & 70.2 \\ w/o U-node & 71.4 \\ w/o E-node & 68.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of MvSD(GCN) and MvSD(LLM) on SEM16. The table shows the average Macro F1 performance (%).
## 2 Source and Toxicity of Heavy Metal Ions
### Heavy-Metal-Mediated Toxicity  
#### 2.2.1 Exposure to Heavy Metals and Environmental Pollution  
ROS are generated in living cells when heavy metals bond to sulfhydryl groups and enter the cell. As a result, macromolecules are inactivated, ROS stress is caused, and glutathione levels are depleted [33]. When toxic metals enter the body, they initiate several processes, including interfering with metabolic pathways or inhibiting them. As a result,both humans and animals suffer from a variety of toxic effects of heavy metals [21]. A variety of problems may result, including organ dysfunction, metabolic abnormalities, hormone changes, congenital disorders, immune system problems, and cancer [34]. Metals in the environment, food, and water have therefore been subject to standards established by several international organizations. Food and water heavy metals are subjected to risk assessment studies [35]. Approximately 20% of 193 Ayurvedic medicines and famous Indian herbal medicines were found to contain Hg, Pb, and As, according to [PERSON] et al. [36]. Pb, Hg, and As were detected in nearly 21% of samples [36]. The level of acceptable daily metal intake was exceeded by all metal-containing products. This would cause Pb and/or Hg levels to exceed acceptable levels by 100 to 10,000 times [37]. The presence of heavy metals in dietary supplements is a matter of considerable concern, and as such, these potentially toxic substances must be subjected to daily dosage limits as established by regulatory authorities [38]. It is important to note that the establishment of such dosage limits is crucial for ensuring the safety and well-being of individuals who consume such supplements and that any deviation from these limits could result in severe health consequences. Thus, manufacturers and regulatory authorities must work together to ensure that the levels of heavy metals in dietary supplements are strictly controlled and monitored to minimize any potential risks. Ultimately, the establishment of and adherence to daily dosage limits for heavy metals in dietary supplements is a critical component of ensuring public health and safety and should be given the utmost priority by all stakeholders involved in the production and regulation of such products [39]. Manufacturers must assess these standards as well [40]. The phenomenon of environmental pollution has a profound impact on areas that possess an unspoiled quality. This can be observed in the case of Mount Everest, a location that is isolated and remote [38; 39; 40]. There have been reports of heavy metal contamination in this area associated with Pb, Cd, Cr, As, and Hg [41]. As and Cd levels in all snow samples collected from Mount Everest have exceeded the drinking water guidelines outlined by the US EPA, according to [PERSON] and [PERSON] [42]. Moreover, a substantial amount of As contamination was detected in soil samples taken from the heavy-metal-contaminated sites located on Mount Everest.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 2 Characteristics of Snow and SAR
### SAR Sensor Characteristics
Even though the temporal resolution is yet insufficient to provide daily imagery, the spatial resolution of recent SAR missions such as Sentinel-1 (\(5\times 20\) m) is more than satisfactory, given that the minimum requirements expressed by GCOS are between 100 and 500 m.  
Contrary to optical sensors, the geometry of SAR observations is more complex due to their synthesized multi-beam echoes transmitted and received at both, side-looking slant-range direction and azimuth direction [81]. The significance and type of the resulting distortions varies by landscape and sensing angle and thus leads to foreshortening, layover and shadow effects [82]. In addition, as the received signal for each pixel of the SAR imagery is the sum of the random constructive and destructive phase interferences reflected from countless ground features, the resultant speckles can degrade the image quality considerably [82; 83]. Consequently, the interpretation and analysis of SAR images are more challenging than conventional optical observations. The overview of different characteristics of SAR and optical sensors as well as advantages and drawbacks for snow cover monitoring are summarized in Table 1 and illustrated in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **SAR** & **Optical/Multispectral** \\ \hline Sensing mode & Active & Passive \\ \hline Wavelengths & 0.01–0.3 m & 0.3–1 μm \\ \hline \multirow{4}{*}{Spatial resolution} & PALSAR-2: 3–10 m & Landsat-8: 15–30 m \\  & COSMO-SkyMed: 3–15 m & Sentinel-2: 10–20 m \\  & Sentinel-1: \(5\times 20\) m & MODIS: 250–500 m \\  & (Stripmap mode) & (not included thermal band) \\ \hline \multirow{4}{*}{Temporal resolution} & PALSAR-2: 14 days & Landsat-8: 16 days \\  & COSMO-SkyMed: -5 days & Sentinel-2: 5 days \\  & Sentinel-1: 6 days & MODIS: 1 day \\ \hline \multirow{4}{*}{Recorded snow characteristics} & Surface roughness, & \multirow{2}{*}{Surface reflection} \\  & dielectric property & \\ \hline \multirow{4}{*}{Advantages} & Day-and-night sensing under any & \multirow{2}{*}{Visually natural to interpret;} \\  & weather condition; & \\ \cline{1-1}  & Possibility of interferometric and & \\ \cline{1-1}  & polarimetric information & \\ \hline \multirow{4}{*}{Drawbacks} & Low temporal resolution; & \multirow{2}{*}{Hindered by cloud, darkness;} \\ \cline{1-1}  & Challenging to interpret due to its & \\ \cline{1-1}  & imaging geometry; & \\ \cline{1-1}  & Significant geometric distortions and & \\ \cline{1-1}  & speckles & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of SAR and optical/multispectral sensors regarding their ability to detect snow cover.