esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 5 Discussion  
### Ablation Comparison Experiment  
The SID-DN proposed in this paper introduces two key innovations: the Double-Branch Adaptive Coarse Detection and the integration of the SID concept into Diffusion networks. To validate the effectiveness of these two innovations, ablation experiments were conducted on each dataset, with results summarized in Table 5. In Table 5, the first row of each dataset shows the detection results with a fixed threshold. The second row presents results after applying the Double-Branch Adaptive Coarse Detection, while the third row includes results after applying both the Double-Branch Adaptive Coarse Detection and the incorporation of SID. The results show that the quantitative detection results considering the SID loss outperform the quantitative detection results obtained without considering the SID. This finding highlights the robustness of the Double-Branch Adaptive Coarse Detection and SID loss in enhancing the Diffusion network for reconstructing HSI. By thresholding the adaptive coarse detection and integrating the SID concept into the network, the influence of potential targets on background learning is minimized. This reduces interference from target spectra on background spectra, ensuring a smaller discrepancy between the reconstructed and original background spectra, which enables accurate target-background separation. This work not only introduces novel approaches to the generative coarse detection process but also significantly improves the effectiveness of Diffusion-based generative spectral learning.
# Analytical Attitude Guidance Planner for Multiple Ground Targets Acquisitions
## 4 Attitude Point-to-Point Generator
### Near Optimal Time
#### 4.1.2 Maneuver Time for Step 2  
The solution of \(t_{f_{2}}\) is carried out from the second set of equations in Equation (42). The two equations can be written as a function of the only variable \(t_{f_{2}}\). In fact, the \(b_{j}\) coefficients of the polynomials, \(t_{\omega_{max_{2}}}\) and \(t_{\dot{\omega}_{max_{2}}}\) are functions of \(t_{f_{2}}\). Therefore, the two equations can be written in an explicit form as a function of the time maneuver \(t_{f_{2}}\) by the following steps:  
1. The \(b_{j}\) coefficients, stated in Equation (32), are replaced in Equation (29). As a consequence \(\omega(t)\) and \(\dot{\omega}(t)\) can be written as: \[\begin{split} 5\left(\frac{6\,\theta_{f}}{t_{f_{2}}^{5}} \right)t^{4}-4\left(\frac{15\,\theta_{f}}{t_{f_{2}}^{4}}\right)t^{3}+3\left( \frac{10\,\theta_{f}}{t_{f_{2}}^{3}}\right)t^{2}=\omega_{max}\\ 20\left(\frac{6\,\theta_{f}}{t_{f_{2}}^{5}}\right)t^{3}-12\left( \frac{15\,\theta_{f}}{t_{f_{2}}^{4}}\right)t^{2}+6\left(\frac{10\,\theta_{f}}{ t_{f_{2}}^{3}}\right)t=\dot{\omega}_{max}\end{split}\] (46)
2. The conditions \(t=t_{\omega_{max_{2}}}=t_{f_{2}}/2\) and \(t=t_{\dot{\omega}_{max_{2}}}=(3-\sqrt{3})t_{f_{2}}/6\) are respectively substituted in the first and in the second expression of Equation (46). Thus, their final expressions as functions of \(t_{f_{2}}\) are obtained.  
\[5\bigg{(}\frac{6\,\theta_{f}}{t_{f_{2}}^{5}}\bigg{)}\bigg{(}\frac{t_ {f_{2}}}{2}\bigg{)}^{4}-4\bigg{(}\frac{15\,\theta_{f}}{t_{f_{2}}^{4}}\bigg{)} \bigg{(}\frac{t_{f_{2}}}{2}\bigg{)}^{3}+\ 3\bigg{(}\frac{10\,\theta_{f}}{t_{f_{2}}^{3}}\bigg{)} \bigg{(}\frac{t_{f_{2}}}{2}\bigg{)}^{2}=\omega_{max} \tag{47}\] \[\qquad 20\bigg{(}\frac{6\,\theta_{f}}{t_{f_{2}}^{5}}\bigg{)}\bigg{(} \frac{(3-\sqrt{3})t_{f_{2}}}{6}\bigg{)}^{3}-12\bigg{(}\frac{15\,\theta_{f}}{t_ {f_{2}}^{4}}\bigg{)}\bigg{(}\frac{(3-\sqrt{3})t_{f_{2}}}{6}\bigg{)}^{2}\] \[\qquad\qquad\qquad\qquad+\ 6\bigg{(}\frac{10\,\theta_{f}}{t_{f_{2}}^{3}} \bigg{)}\bigg{(}\frac{(3-\sqrt{3})t_{f_{2}}}{6}\bigg{)}=\dot{\omega}_{max}\]
3. After several simplifications, Equation (47), can be written as follows \[t_{f_{2}}^{\omega}=\frac{15}{8}\frac{\theta_{f}}{\omega_{max}},\quad t_{f_{2 }}^{\omega}=\sqrt{\frac{10\sqrt{3}}{3}\frac{\theta_{f}}{\dot{\omega}_{max}}}.\] (48) The rest-to-rest guidance law gives two different solutions. \(t_{f_{2}}^{\omega}\) and \(t_{f_{2}}^{\omega}\) are the final times which allow to verify \(|\omega(t=t_{\omega_{max}})|=\omega_{max}\) and \(|\dot{\omega}(t=t_{\omega_{max}})|=\dot{\omega}_{max}\) respectively.
## 2 System Architecture, Theoretical Methods, and Experiments
### Using MFCC for Speech Recognition and Normalization  
The acoustic signal captured in this study was subjected to the MFCC for speech recognition after discrete square-wave Fast Fourier Transform (FFT). The number of filters is 30, the MFCC has 20 dimensions, the frequency range is 0-44,100 Hz, the Fast Fourier Transform (FFT) has 2048 points, and the size of the sound frame used for the sound file is 5 s. In addition, to avoid overly drastic changes between sound frames, two sound frames can be overlapped by 20 milliseconds (ms) to obtain the time-spectrum data graph, as shown in Figure 3. The three axes are amplitude, frequency, and time, respectively. Figure 3a is the time-frequency characteristic of environmental audio, and Figure 3b is the time-frequency characteristic of PVC pipe leak detection.  
Figure 2: Intelligent cloud water leakage signal diagnostic and management platform (mobile interface).  
Each acoustic signal must be normalized before being trained with deep learning algorithms. The normalization method used in this study is min-max normalization. The readings obtained by sampling 13 times at a certain time point \(n\) form a vector (or a one-dimensional array); the maximum and minimum values of the readings measured each time are formed into two vectors, \(X[n]\in R^{13\times 1}\) and \(X_{max}[n]\in R^{13\times 1}\). \(X[n]\) is normalized with the following formula:  
\[X_{norm}[n]=\frac{X[n]-X_{min}[n]}{X_{max}[n]-X_{min}[n]} \tag{1}\]  
Using the difference method, the normalized measurement reading at the current time point \(X_{norm}[n]\) is subtracted from the measurement reading at the previous time point \(X_{norm}[n-1]\) (as shown in Formula (2)):  
\[X_{diff}[n]=|X_{norm}[n]-X_{norm}[n-1]|\in R^{13\times 1} \tag{2}\]  
where \(X_{diff}\) is a differential signal.  
The sum of the differential signals is calculated, and a threshold is established, as shown in Formula (3):  
\[\sum_{n=2}^{16}X_{diff}[n]^{T}[1\:1\ldots 1]^{T}>\text{threshold} \tag{3}\]  
If the sum of the differential signals is greater than the threshold, it can be determined that the input sound wave signal is a transient signal with a sharply changing waveform; otherwise, it can be determined that the input sound wave signal is a steady-state signal with stable and moderate waveform changes.
# Networking of Multi-Robot Systems: Architectures and Requirements
## 2 Types of MRS Systems and Their Applications
### MRS Applications  
There are several applications that can be improved using MRS. Some of these applications are search and rescue [19; 20], detection of forest fires [21], hazardous waste removal [22], farm operations [23], mining [24], constructions [25], disaster management [26], security applications [26; 27], warehouse management [28], moving containers within harbors and airports [29], and gaming and entertainment such as soccer [30].
## 3 Proposed Joint Decoding (JD) Framework for Video
### Joint Decoding Framework
#### 3.2.1 Frame Estimation  
This step proposes a frame estimation technique using registration and fusion methods. The idea is to utilize the frames' correlations to estimate the J-1 non-key frame (F\({}^{\prime}\)NK) in the GoP from the keyframes (F\({}^{\prime}\)K). The proposed method assists in utilizing the inter and intra-view correlations between the frames and produce a set of predicted non-key frames.  
Registration Approach  
The frame estimation step is initialized by performing registration (intensity) on the two independently generated keyframes F\({}^{\prime}\)K in the GoP. The aim is to align the F\({}^{\prime}\)K on the same plane as of F\({}^{\prime}\)NK (F\({}^{\prime}\)K is aligned to F\({}^{\prime}\)NK, and the redundancies between them are exploited). Firstly, a phase correlation method (finding the gross alignment) is used on the F\({}^{\prime}\)K and F\({}^{\prime}\)NK frames to calculate the initial transformation matrix. Next, the translation transformation over affine is used for aligning F  
Figure 3: Reconstruction of frames using the proposed joint decoding (JD) framework.  
Figure 2: Correlation analysis of CS measurements among the adjacent frames of various CIF video sequences.  
w.r.t. \(\text{F}^{\prime}_{\text{NK}}\) to produce transformed \(\text{F}^{\prime}_{\text{K}}\) called \(\text{F}^{\prime}_{\text{KT}}\). The reason for using a translation transform is that affine transform makes sense when multiple images are not on the same plane and are to be rectified, whereas, in the case of a video sequence, each frame is usually on the same plane, so it would be sufficient to consider translation transform.  
The similarity metric and optimization function is then applied on the transformed frame \(\text{F}^{\prime}_{\text{KT}}\) to estimate the registration precision and generate the final registered image \(\text{F}^{\prime\prime}_{\text{K}}\), as shown in Equation (6):  
\[\text{F}^{\prime\prime}\text{K}=\text{argmin}_{\text{N}}\left\|\sum\
olimits_{y \in\text{L}_{\text{F}^{\prime}_{\text{KT}}}}\sum\
olimits_{x\in\text{L}_{ \text{F}^{\prime}_{\text{NK}}}}P_{\text{F}^{\prime}_{\text{KT}}\text{F}^{ \prime}_{\text{NK}}}(y,x;a)log\left(\frac{P_{\text{F}^{\prime}_{\text{KT}} \text{F}^{\prime}_{\text{NK}}}(y,x;a)}{P_{\text{F}^{\prime}_{\text{KT}}}(x)P_ {\text{F}^{\prime}_{\text{NK}}}(y;a)}\right)\right\| \tag{6}\]  
The similarity metric makes use of the mutual information (MI) method where the optimizer uses one evolutionary (OE). The evolutionary optimizer is used to exploits the alike alignment that usually occurs within the frames of a video sequence that better helps the OE optimizer than the other optimizers such as gradient descent (GD). The optimizer is one of the vital parameters of the registration process that defines the method to exploit the attained similarity metric M and produce a final result. Once both the keyframes \(\text{F}^{\prime\prime}_{\text{K}}\) is registered, a wavelet-based fusion process is applied on them to produce the predicted frame \(\text{F}_{\text{P}}\).
## 4 Discussion
### Which of the NMES Modes Is More Effective?  
NMES features two modes, the EMG-triggered mode and the cyclic mode. As well as EMG-triggered NMES, the post-stroke application of cyclic NMES to UE paresis after stroke was considered effective for UE improvement [16]. Most of the included studies (21/23 trials), however, used EMG-triggered NMES; therefore, we could not compare the NMES benefits between the two modes, leaving us unable to address the issue of whether cyclic or EMG-triggered NMES is more effective.  
EMG-triggered NMES can provide weaker contraction of target muscles in severe stroke patients than cyclic NMES, whereas survivors with moderate or mild paresis experience stronger contractions with EMG-triggered NMES than with cyclic NMES. Actually, EMG-triggered NMES for severe paretic patients cannot enhance the fully volitional contraction of target muscles. It is possible that insufficient muscle contraction by electrical stimulation may be nonproductive, resulting in unsatisfactory benefits from NMES. NMES might be more effective for motor recovery when it is appropriately decided which mode of NMES is applied to each of the paretic muscles, including the shoulder, elbow, wrist and fingers during the early acute phase of stroke. The selection of mode may depend upon the volitional contraction of each of the paretic muscles. Accordingly, a recent study examined the effect of coupled cyclic and EMG-triggered NMES with whole UE on motor function [32]. In other words, EMG-triggered NMES was applied to each of the relatively moderate paretic muscles (stroke impairment assessment set (SIAS) level 3), while cyclic NMES was applied to severe paretic muscles (SIAS 1-2). The results suggested that the PR of the FMA-UE scores differed significantly between the coupled NMES group and the standard care group (2.54 for the coupled NMES group and 1.10 for the standard care group) (\(p\) = 0.036). The NMES group demonstrated a large effect size (r = 0.50) on FMA-UE, suggesting the high clinical significance of FMA-UE. These findings suggest that a new strategy of coupled NMES, depending on the severity of targeted paretic muscles, might be more effective than the application of the alternative mode.
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 1 Introduction
### Prediction of the Measurement: Forward Process  
The forward and optimization processes are described in detail in previous studies [2; 23; 24; 25; 26; 27; 28]. The predicted measurements to be compared with the actual measurements are obtained by computing the light measured at the surface of the object based on the theory of light propagation inside biological media. Light propagation involves the transport of photons, accompanied by scattering and absorption. In this case, the radiative transport equation (RTE) describes the light propagation and can be used to obtain predicted measurements with a set of given optical properties. Some approximation methods of the RTE, such as \(P_{N}\) approximation, diffusion approximation (DA) with the photon diffusion equation (PDE), and the Monte Carlo (MC) method are used in the computation of light propagation to reduce the computational difficulties of the RTE. Except for the MC method, general numerical computation methods such as the finite element method (FEM) and the finite difference method are used to compute light propagation and predicted measurements.
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 2 Formulation  
This section presents a mode-matching formulation to analyze the wave propagation in a 2D periodic (along \(x\) and \(y\) directions) holey structure composed of equilateral triangular holes. In the present study we focus on the analysis of the mirror- and glide-symmetric configurations shown in Figure 1. The metal is assumed to be a perfect conductor in both structures, and no dielectric losses are present. Furthermore, the structures are completely shielded, and then without material and/or radiation losses. Figure 1b illustrates the glide-symmetric configuration, with a zoom of its unit cell (the bottom and upper planes are perfect electric conductors). The mirror-symmetric configuration would have the triangular holes aligned along the vertical direction. Given the symmetry plane that this structure has in this vertical direction, the even modes of the mirror-symmetric structure would be studied considering the configuration shown in Figure 1a, where a perfect electric conductor is placed at \(z=0\). The equilateral triangular holes can be filled with a dielectric of relative permittivity \(\varepsilon_{r\,\text{hole}}\). The gap region between top and bottom metallic plates is filled with a dielectric of relative permittivity \(\varepsilon_{r}\).  
Figure 1: (**a**) Mirror-Symmetric and (**b**) glide-symmetric periodic metallic structures with equilateral triangular holes and a zoom showing their unit cells.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 3 Camera Movement Errors  
In the stereo-vision system, \(\ A_{t},\ A_{r},\ R_{lr}\ \text{ and}\ \ T_{lr}\) have been calibrated before measurement. It is generally assumed that the intrinsic parameters \((\ A_{l},\ A_{r})\) are unaffected during slight camera movements. However, the extrinsic parameters \((\ R_{lr},\ T_{lr})\) and the global world coordinate system coinciding with the left camera coordinate system have been altered. The movement of a camera is composed of both translation and rotation, which correspond to the change of position and orientation. To clarify how the 3D coordinate error is varied with the changes of the positions and orientations of two cameras, we have divided the camera movements into four categories as below.  
1. Identical translations of the left and right cameras when the positions and orientations of their optical centers have the same changes.
2. Identical rotations of the two cameras when the orientations of two cameras have the same changes.
3. Relative translation when the position of the left camera relative to that of the right camera is changed.
4. Relative rotation when the orientation of the left camera relative to that of the right camera is changed.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
###### Abstract  
This study proposes an interpretable hybrid deep learning framework for classifying attention deficit hyperactivity disorder (ADHD) using EEG signals recorded during cognitively demanding tasks. The core architecture integrates convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) layers to jointly capture spatial and temporal dynamics. In addition to the final hybrid architecture, the CNN-GRU-LSTM model alone demonstrates excellent accuracy (99.63%) with minimal variance, making it a strong baseline for clinical applications. To evaluate the role of global attention mechanisms, transformer encoder models with two and three attention blocks, along with a spatiotemporal transformer employing 2D positional encoding, are benchmarked. A hybrid CNN-RNN-transformer model is introduced, combining convolutional, recurrent, and transformer-based modules into a unified architecture. To enhance interpretability, SHapley Additive exPlanations (SHAP) are employed to identify key EEG channels contributing to classification outcomes. Experimental evaluation using stratified five-fold cross-validation demonstrates that the proposed hybrid model achieves superior performance, with average accuracy exceeding 99.98%, F1-scores above 0.9999, and near-perfect AUC and Matthews correlation coefficients. In contrast, transformer-only models, despite high training accuracy, exhibit reduced generalization. SHAP-based analysis confirms the hybrid model's clinical relevance. This work advances the development of transparent and reliable EEG-based tools for pediatric ADHD screening.  
ADHD; EEG; hybrid deep learning; SHAP; transformer; CNN-GRU-LSTM
# Physical Layer Security for Military IoT Links Using MIMO-Beamforming at 60 GHz
###### Abstract  
This paper discusses the concept and practicality of internet-of-things (IoT) link security enhancements using multiple-input multiple-output (MIMO) and beamforming solutions at the physical layer of the wireless system. Large-scale MIMO and beamforming techniques have been studied extensively in the context of 5G cellular systems. The concept of utilizing these transmission techniques for security enhancements in cellular IoT systems, however, have not yet been fully explored. This article will list a variety of options that may be explored in realizing more secure IoT links using MIMO-beamforming techniques. The paper provides a valuable tutorial for both engineers and researchers working in this field.  
physical layer security; military IoT; MIMO-beamforming 2022
# Carbon Footprint Assessment on the Viability of Utilizing Brewer's Spent Grain to Produce Biochar
## 1 Introduction
### Brewing Industry Waste  
BSG, the major waste stream generated during beer production, primarily consists of the husk, pericarp, and seed coats of crushed malted grains. It may also contain trace amounts of silica, polyphenols, insoluble protein, and oils [14]. The composition of BSG can vary, depending on factors including grain quality, malting and mashing conditions, the use of adjuncts, and salt incorporated during the mashing stage.  
The high water activity and fermentable sugar concentration of BSG are major limiting factors for valorization, as they make it highly unstable and prone to rapid deterioration from microbial activity. Drying BSG can extend its shelf life and reduce weight, thereby limiting transportation costs and storage needs, making it a potentially valuable biomaterial. However, the drying process is often energy-intensive, and, therefore, costly.  
During the cleaning-in-place (CIP) process of brewing equipment, caustic chemicals, such as sodium hydroxide (NaOH) or potassium hydroxide (KOH), are commonly used. These chemicals often maintain a relatively high pH at the end of their use, indicating the need for neutralization before disposal. Instead of neutralizing and discarding these aqueous solutions, they can be recovered and reused as a pretreatment to enhance the quality of BSG for biochar production. Soaking a lignocellulosic material in a caustic chemical solution is a common precursor to biochar production. This form of pretreatment induces lignin removal, which yields more pure cellulose and hemicellulose fractions, and ultimately improves pyrolysis efficiency and biochar quality [15; 16].
## 3 Results and Discussion
### Evaluation of Environmental Effects  
#### 3.3.1 Environmental Effects of Single-Axis Loading  
By comparing the temperature stress and coupling stress in different seasons, it is found that the change rule of coupling stress and temperature stress with time is basically the same, where the peak coupling stresses lag with depth. The maximum temperature stresses and coupling stresses at different depths are compared, respectively, and the results are shown in Table 5.  
As can be seen from Table 5, the coupling stresses from the road surface along the depth direction show an increase and then a decrease in the spring and winter seasons. It reaches its maximum at 4 cm of road surface. And the influence of temperature stress gradually becomes smaller with increasing depth, and the contribution of traveling load  
Figure 14: Evolution of triple-axis load-temperature coupling stress under different seasons.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & \multicolumn{3}{c}{**Winter Stress (MPa)**} & \multicolumn{3}{c}{**Spring Stress (MPa)**} & \multicolumn{3}{c}{**Fall Stress (MPa)**} \\ \cline{2-10}  & **Temperature** & **Coupling** & **Stress** & **Temperature** & **Coupling** & **Stress** & **Percentage** & **Temperature** & **Coupling** & **Stress** & **Percentage** \\ \hline
0 & 1.028 & 1.233 & 0.83 & 0.422 & 0.787 & 0.54 & 0.532 & 0.873 & 0.61 \\
0.04 & 0.816 & 1.337 & 0.61 & 0.212 & 0.815 & 0.26 & 0.250 & 0.853 & 0.29 \\
0.09 & 0.500 & 1.019 & 0.49 & 0.073 & 0.601 & 0.12 & 0.081 & 0.603 & 0.13 \\
0.15 & 0.178 & 0.551 & 0.32 & 0.060 & 0.391 & 0.15 & 0.066 & 0.396 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Temperature stresses and coupling stresses under uniaxial loading.  
action gradually increases. A comparison of the relative magnitude of temperature stress and coupling stress reveals that with the change of annual seasons, the law of action of temperature stress shows an increase and then a decrease from fall to spring. In addition, the effect of temperature stress on the coupling stress in the spring and fall seasons shows a tendency to decrease and then increase. The temperature stress at 9 cm below the road surface had the least effect on the coupling stress. It shows a gradual decrease in winter. Overall, the degree of temperature influence on the road surface along the depth direction is 66.0%, 38.7%, 24.7%, and 21.3% in that order.