esa-sceva/satcom-chunk-collection huggingface dataset:
# Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning
## 4 Experiment
### Evaluation
#### 4.3.4 Result on MS COCO Dataset  
It is worth mentioning that model with Aligned Contrastive Loss and Multi-Scale Fusion has achieved an accuracy improvement in MS COCO dataset, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Method** & **Copper** & **Cutting** & **Fatigue** & **Severe Sliding** & **Spherical** \\ \hline \multirow{3}{*}{5 way-5 shot} & Baseline & 17.926 & 3.155 & 11.529 & 14.821 & 4.868 \\  & Baseline+Aligned \({}^{1}\) & 20.572 & 2.356 & 12.776 & 16.697 & 4.940 \\  & Baseline+Fusion \({}^{2}\) & 21.065 & 4.751 & 15.082 & 16.515 & 4.932 \\ \hline \multirow{3}{*}{5 way-10 shot} & Baseline & 20.077 & 7.880 & 11.174 & 14.137 & 6.433 \\  & Baseline+Aligned & 24.154 & 6.239 & 12.052 & 14.886 & 5.785 \\  & Baseline+Fusion & 29.270 & 7.755 & 14.299 & 14.304 & 6.654 \\ \hline \multirow{3}{*}{5 way-30 shot} & Baseline & 31.969 & 14.150 & 13.690 & 14.191 & 6.085 \\  & Baseline+Aligned & 34.841 & 14.859 & 15.697 & 17.677 & 6.149 \\ \cline{1-1}  & Baseline+Fusion & 36.083 & 13.202 & 18.857 & 18.474 & 8.290 \\ \hline \hline \end{tabular}  
* \({}^{1}\) Aligned: Aligned Contrastive Loss. \({}^{2}\) Fusion: Multi-Scale Fusion.  
\end{table}
Table 4: AP accuracy for each category.  
Figure 9: Average bounding box size by defect category.
## 3 Grassmann MTS Clustering
### Extrinsic Grassmann Clustering
#### 3.2.2 Embedding MTS Points on the Manifold into an RKHS via Kernel Mapping  
The main idea of this approach is to perform the MTS clustering on a feature space. This is due to the property that positive definite kernels can be defined and computed on the Grassmann manifold. Thus, any clustering method that can be kernelized while still taking into account the geometry of the manifold can be deployed. Examples include, but are not limited to, \(k\)-means, spectral clustering, and support vector clustering. Before we go further in detail, it should be noted that this approach may not be appropriate for large-scale data.  
Figure 3: The relation of the Grassmann manifold and its tangent space in which \(\mathbf{U}\) is chosen as the Karcher mean.  
Moreover, there is no theoretical basis for choosing kernel functions and the associated parameters [48].  
Assume points \(\mathbf{U}\in\mathcal{G}\) are mapped to a feature vector \(\phi(\mathbf{U})\) in a Hilbert space through the kernel learning method [47]. Considering that a kernel function \(k:(g\times g)\rightarrow\mathbb{R}\) is used to define the inner product on a Hilbert space, an RKHS is thus formed. According to [PERSON]'s theorem, however, only positive definite and symmetric kernels delineate a valid RKHS. A few Grassmann kernel functions have been devised such as projection kernel, which is derived from the Frobenius norm and defined as follows:  
\[\begin{split}\mathbf{K}^{(i,j)}_{proj.}&=\|\mathbf{U }_{i}^{T}\mathbf{U}_{j}\|_{F}\\ &=tr\{\left(\mathbf{U}_{i}\mathbf{U}_{i}^{T}\right)\left( \mathbf{U}_{j}\mathbf{U}_{j}^{T}\right)\}.\end{split} \tag{8}\]  
The canonical correlation kernel is defined as follows:  
\[\mathbf{K}^{(i,j)}_{\mathrm{CC}}=\Sigma(1), \tag{9}\]  
where \(\Sigma(1)\) denotes the first singular value obtained by singular value decomposition of \(\mathbf{U}_{i}^{T}\mathbf{U}_{j}\) (_e.g._, use \([u,\Sigma,v]=\mathit{svd}\left(\mathbf{U}_{i}^{T}\mathbf{U}_{j}\right)\) in Matlab) [25]. Algorithm 5 provides a detailed description of kernel Grassmann clustering.  
```
1:Input:\(n\) points \(\{\mathbf{U}_{i}\}_{i=1}^{n}\) on the Grassmann manifold, number of clusters \(K\)
2: Construct projection kernel matrix \(\mathbf{K}_{proj.}\) (8) or canonical correlation kernel \(\mathbf{K}_{\mathrm{CC}}\) (9).
3: Compute the normalized kernel matrix \(\mathbf{K}_{norm}=\mathbf{D}^{-\frac{1}{2}}\mathbf{K}\mathbf{D}^{-\frac{1}{2}}\) where \(\mathbf{D}_{(i,i)}=\Sigma_{j=1}^{n}\mathbf{K}_{proj.}^{(i,j)}\) or \(\Sigma_{j=1}^{n}\mathbf{K}_{\mathrm{CC}}^{(i,j)}\).
4: Compute the top \(K\) eigenvectors of \(\mathbf{K}_{norm}\).
5: Form a matrix \(\mathbf{V}\in\mathbb{R}^{n\times K}\) containing the \(K\) eigenvectors as columns.
6: Let the new set of points be \(\{\mathbf{z}_{i}\}_{i=1}^{n}\); \(\forall\mathbf{z}\in\mathbb{R}^{1\times K}\) corresponding to the \(i\)-th row of \(\mathbf{V}\).
7: Apply \(k\)-means clustering on \(\{\mathbf{z}_{i}\}_{i=1}^{n}\).
8:Output: Clusters \(\{C_{1},...,C_{K}\}\) with \(C_{k}=\{i|\mathbf{z}_{i}\in C_{k}\}\)
```  
**Algorithm 5** Kernel Grassmann clustering
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Resistance to Statistical Attacks  
Resistance to statistical attacks is the most basic objective of an image encryption algorithm. [PERSON] suggested that maximum confusion and diffusion should be employed in a cryptosystem to remove the correlation characteristics of image pixels to frustrate powerful statistical analysis. In the proposed compression and encryption algorithm, we achieve maximum confusion and diffusion at various levels via nonlinear inverse operations, K\({}^{2}\)-tree and multiple chaotic maps in the last stage. After many confusion and diffusion operations were performed through the joint compression and encryption operations, the cipher image shows a truly random distribution. This random distribution of the cipher image was evaluated using histograms, adjacent-pixel correlations, and the information entropy of the cipher image.
## 2 Materials and Methods
### Principal Component Analysis (PCA) for Noise Filtering
To calculate SNR, the residual noise is determined as follows:  
\[\text{Resid\_Noise}=\text{Noisy\_data\_temperature}-\text{Denoised\_data\_temperature}\]  
SNR is then calculated by:  
\[\text{SNR}=\text{(mean (Noisy\_data\_temperature^{2})/mean (Resid\_Noise^{2})}\]  
The SNR value is then converted to decibels (dB) using the equation:  
\[\text{SNR\_dB}=10\times\text{log}10\text{ (SNR)}\]  
It is found that the SNR_dB is equal to 47.48 and 32.25 for the PCA denoising and wavelets signal denoising methods, respectively. It is clear that denoising the temperature signal is more effective using the PCA filter. Figure 3: Application of wavelet signal denoising to temperature data. Figure 2: Application of PCA-based noise filtering to temperature data.
###### Abstract  
The growing global demand for sustainable energy solutions has led to increased interest in kinetic energy harvesting as a viable alternative to traditional power sources. High-foot-traffic environments, such as public spaces and religious sites, generate significant mechanical energy that often remains untapped. This study explores energy-harvesting technologies applicable to public areas with heavy foot traffic, focusing on Al-Haram Mosque in Saudi Arabia--one of the most densely populated religious sites in the world. The research investigates the potential of piezoelectric, triboelectric, and hybrid systems to convert pedestrian foot traffic into electrical energy, addressing challenges such as efficiency, durability, scalability, and integration with existing infrastructure. Piezoelectric materials, including PVDF and BaTiO\({}_{3}\), effectively convert mechanical stress from footsteps into electricity, while triboelectric nanogenerators (TENGs) utilize contact electrification for lightweight, flexible energy capture. In addition, this study examines material innovations such as 3D-printed biomimetic structures, MXene-based composites (MXene is a two-dimensional material made from transition metal carbides, nitrides, and carbonitrides), and hybrid nanogenerators to improve the longevity and scalability of energy-harvesting systems in high-density footfall environments. Proposed applications for Al-Haram Mosque include energy-harvesting mats embedded with piezoelectric and triboelectric elements to power IoT devices, LED lighting, and environmental sensors. While challenges remain in material degradation, scalability, and cost, emerging hybrid systems and advanced composites present a promising pathway toward sustainable, self-powered infrastructure in large-scale, high-foot-traffic settings. These findings offer a transformative approach to energy sustainability, reducing reliance on traditional energy sources and contributing to Saudi Arabia's Vision 2030 for renewable energy adoption.  
kinetic energy harvesting; piezoelectric; triboelectric; hybrid energy systems; Al-Haram Mosque; sustainable energy solutions; high-foot-traffic energy conversion 2020 IEEEexample: accepted.com
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 4 Conclusions  
This paper proposes an ESLM-AE network to reduce the PAPR value for the DCO-OFDM system without reducing the BER performance. The constellation mapping and de-mapping of the symbols and phase factor of each subcarrier are trained adaptively using a combined loss function of the AE with two different loss components. The simulation results show that our proposed scheme significantly outperforms conventional schemes in terms of both the PAPR and BER. By using our proposed technique, a distinct PAPR reduction of more than 10 dB is achieved for the VLC system, which significantly relieves the linear requirement of the front-end devices. Moreover, the decrease in the PAPR transforms to a BER performance gain both in the LOS channel and the DOW channel. The proposed scheme is a primary study on the application of deep learning networks in VLC systems. Its simulation results are very promising, and we are working on its implementation in digital signal processing It can give insights on combining a deep learning framework with conventional communication methods for different applications.  
Figure 8: BER comparison of the clipped DCO-OFDM, SLM, Autoencoder and the proposed scheme under the DOW channel with/without ISI. The clipping ratios, \(\gamma\), used for clipping, ESLM-AE and Autoencoder are 1.5.  
**Author Contributions:** Data curation, L.H.; Formal analysis, L.H. and D.W.; Funding acquisition, W.C.; Investigation, L.H.; Methodology, L.H. and D.W.; Project administration, J.L.; Software, L.H.; Supervision, Y.T.; Visualization, Z.L.; Writing--original draft, L.H.; Writing--review & editing, D.W. and Y.T.  
**Funding:** This research was funded by the Fundamental Research Funds of Shandong University (Grant No. 2015 JC043) and the Independent Innovation Program of Universities and Institutes in Jinan City (Grant No. 201303007).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Dataset and Statistical Analysis  
Experiments were conducted using real-life data obtained from the Gauteng Department of Community Safety (GDCS). The collected dataset was compiled over a four-year duration. Ethical clearance was obtained from the ethics committee and the department to collect the historical dataset (Ref: 2020 SCiiS04). The dataset included recordings of road traffic accidents over major highways in Gauteng province. Some features were omitted during data preparation because they contained insufficient entries. Then, features with 5% missing values were used during the analysis and handled using missing data strategies. Extensive data pre-processing was performed, resulting in a cleaned dataset containing 46,692 instances and 8 attributes, as shown in Table 1. The data had missing values that were handled using several missing value methods, as discussed in Section 3.2 above.
## 3 Research Methodology
### Performance Evaluation
#### 3.3.8 End to End Delay  
The time a node takes for data transfer to the target node is called an end-to-end latency. In the end, a high value of the end-to-end hindrance refers to the optimal cluster heads selection.  
Total delay = No. of hops (1 st packet total delay) + (Transmission delay + Processing delay + Queuing delay).
## 6 Discussion  
_Major Findings and Future Scope_  
The study discussed in this paper primarily focused on the effectiveness and interpretability of employing ML algorithms and using XAI techniques for GNSS signal classification and detection tasks. The results demonstrated that ML models performed at the average level for signal classification; however, the decision-making process of the model was entirely dependent on interpretability and finding the inner relationship between the features. By incorporating the LIME and SHAP algorithms into feature importance and model predictions, improved transparency and easier model understanding can be achieved. Furthermore, the research findings demonstrated that the model's prediction effectively leveraged domain knowledge and created causal relationships, leading to an enhanced comprehension of the attributes influencing abnormal behaviour in the data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Model** & **Class** & \multicolumn{3}{c}{**SHAP**} & \multicolumn{3}{c}{**PCA**} & \multicolumn{3}{c}{**Backward Elimination**} & \multicolumn{3}{c}{**Forward Selection**} \\ \hline  & Metric & Acc & Prec & Rec & F1 & Acc & Prec & Rec & F1 & Acc & Prec & Rec & F1 & Acc & Prec & Rec & F1 \\  & MCWI & 100 & 100 & 100 & 97 & 95 & 97 & 92 & 97 & 95 & 97 & 92 & 97 & 95 & 97 & 92 \\  & MP & 95 & 100 & 97 & 99 & 98 & 89 & 91 & 90 & 98 & 89 & 91 & 90 & 98 & 89 & 91 & 90 \\  & Chirp & 24 & 19 & 23 & 20 & 21 & 19 & 21 & 20 & 23 & 17 & 32 & 23 & 25 & 22 & 26 & 23 \\  & Clean & 72 & 76 & 78 & 83 & 82 & 18 & 12 & 12 & 12 & 18 & 15 & 12 & 16 & 22 & 23 & 09 \\  & CWI & 100 & 100 & 100 & 100 & 98 & 88 & 90 & 9 Overall, the results showed that anomaly detection can be performed in a better way, and the system has the ability to grasp different behaviours in the data when the ML algorithms are combined with explainability techniques.  
The major challenges in classifying the GNSS signals captured in a real-time scenario were that it was completely different from the simulation environment, and the signal reception could be completely deteriorated by narrow-band and wide-band interference [5]. Furthermore, in an urban short-delay MP environment, from analyzing the time domain characteristics, it is very difficult to understand the inner relationship between the signal attributes in the MP scenario. Similarly, the frequency domain characteristics also failed to capture the variations in the spectrum of the pure GNSS and MP categories. Analyzing characteristics from both domains may not be sufficient to identify the abnormalities in the signal. Based on the spectrogram/scalogram image features, it may be possible to find the differences concerning time and frequency observations by using advanced XAI techniques such as saliency maps or the GradCAM and EigenCAM models, which would be helpful in analyzing the variations in the signal behaviour effectively.
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
## 3 Simulation Results  
In this section, the proposed STC OTDM technique is evaluated and compared to conventional OFDM [5] and recent multicarrier techniques like STC OFDM [7] and OTDM [14]. Set of experiments are conducted in terms of modulation techniques, number of subcarriers and channel taps. These experiments are discussed in the following subsections. The simulation results are displayed and discussed. An STC OTDM system is considered with N = 16, 64, and 128. The modulation schemes QPSK, 64-QAM, and 256-QAM are investigated with a number of channel taps equal to L = 9, 12 and 15. For the sake of comparison, a standard OFDM system, STC OFDM and standard OTDM are also considered with N = 16, 64, 128 sub-carriers. In this section, also, the effect of the modulation scheme on all the techniques is studied. The study includes the effect of increasing the number of subcarriers and the effect of increasing the number of channel taps. All the parameters used in simulations are collected in Table 1.
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 2 How Does the ALP Work?
### The Reduced Move Coefficient  
The RMC is in the range [0, 1] and it defaults to 0.5 based on experimental observations [6], but 0.5 may not be the best for every problem. An optional golden section search algorithm is added to progressively narrow the range of the RMC by cutting off the sub-range with large deviations or larger violations of the constraints until the range is too small to reduce, Figure 6. The golden section search algorithm is given as follows - visualized in Figure 6 and summarized a Algorithm 2.  
```
#Define Performance function using RMC to linearize the model and obtain the merit function value FUNCTION Performance (Model, RMC) Linearize Model using ALP with RMC into Linear_Model Solve Linear_Model using Dual Simplex RETURN Z #Define Golden Section Search function FUNCTION GoldenSectionSearchForRMC (Rmin, Rmax, Th): RMCa = Rmin + (0.382)*(Rmax - Rmin) RMCb = Rmin + (1 - 0.382)*(Rmax - Rmin) WHILE (RMCb - RMCa > Th): #Compare the performance of using RMCa versus RMCb IF Performance (Original_Model, RMCa) < Performance (Original_Model, RMCb): RMC = RMCa Rmax = RMCb ELSE: RMC = RMCb Rmin = RMCa RETURN RMC #Initialize parameters Rmin = 0 Rmax = 1 Th = 0.0001 #Call the Golden Section Search Function GoldenSectionSearchForRMC (Rmin, Rmax, Th)
```  
**Algorithm 2** Golden section search for updating the RMC  
Figure 6: The golden section search for the RMCs in the ALP.  
With the golden section search approach, the desired sub-range of the RMC may be missed if the performance oscillates in the range of the RMC. The criterion to evaluate the approximation performance is oversimplified--the deviations and the constraint violations. Given these limitations, in this paper, we propose to use parameter learning to determine the relation between the RMC value and solution quality with more evaluation criteria. Machine learning techniques are used to improve algorithms [32; 33], so we leverage this idea to improve the ALP.  
We hypothesize that by incorporating parameter learning in the ALP, we can update the RMC based on richer performance criteria. There are three steps to verify this hypothesis. In Section 3, we propose ALPPL to realize the three steps.  
* Step 1. Identifying the criteria--to evaluate approximation performance.
* Step 2. Developing evaluation indices (EIs)--to quantify the approximation performance with RMC values.
* Step 3. Learning the desired range of each EI (DEI)--to tune the RMC.  
In the next section, we discuss how parameter learning is used to dynamically change the value of the RMC.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 2 Research Methods
### Three-Dimensional Model of the Filling Body  
The numerical simulation in this study was conducted based on the actual stope parameters from the \(-\)730 m level of Dongguanashan Copper Mine, with dimensions of 50 m in length, 16 m in width, and 50 m in height. To accurately simulate real mining conditions, a 0.5 m gap was deliberately incorporated between the backfill material and the stope roof in the model, reflecting the common scenario where backfill fails to achieve complete contact with the roof in actual mining operations. The model boundaries were carefully designed to minimize boundary effects, extending to three times the excavation dimensions in all directions. The bottom and surrounding boundaries of the rock mass were fixed to represent geological constraints, resulting in a final model size of 316 m (x) \(\times\) 350 m (y) \(\times\) 350 m (z). The finite element mesh consisted of 989,677 elements, with particular attention paid to optimizing element aspect ratios to ensure computational accuracy. A refined mesh was implemented in the excavation zone to better capture stress concentration phenomena, while a coarser mesh was adopted for the outer regions to improve computational efficiency.  
The Mohr-Coulomb elastoplastic constitutive model, available in FLAC3D, was selected for this simulation to appropriately represent the mechanical behavior of the rock mass. The complete numerical model configuration is illustrated in Figure 3, providing a clear visualization of the modeling approach and geometric setup [25; 26]. This modeling methodology was designed to achieve a balance between computational accuracy and  
Figure 2: Vertical stress comparison diagram.  
efficiency while maintaining fidelity to actual mining conditions. The careful consideration of boundary conditions, mesh refinement strategies, and material constitutive relationships ensures the reliability of the simulation results for subsequent analysis and interpretation.
## 3 Results and Discussion
### Physical Stability of Aqueous Dispersions of Flaxseed Fiber Submitted to Ultrasonic Homogenization  
In Figure 4, the results of delta-backscattering percentage (%\(\Delta\)BS) versus the height of the measurement cell containing the sample as a function of the aging time for the U-2 min-40% and LU-20 min-100% samples are shown as an example. In the mentioned figure, it is observed that the main changes occur during the first 24 h of aging, after which the scans undergo only minimal changes. This result suggests the need to wait at least one day to stabilize the sample's structure. However, these initial changes seem to be more significant the less shear is applied. This result may primarily be due to the fact that the greater the applied energy, the greater the destruction of the microstructure, leading not only to lower viscosity values but also to a significant loss of elasticity. As a consequence, there is less structure to stabilize. It should be noted that there is a slight syneresis process, characterized by a decrease in \(\Delta\)BS% in the upper zone of the turbiscan vial.  
To quantify the physical stability of these aqueous flaxseed fiber dispersions, the global TSI value was calculated, considering the entire height of the sample and therefore, accounting for all destabilization processes occurring within it. Figure 5 shows these values as a function of aging time and the processing variables studied. An analysis of this figure reveals that the greater the energy applied to the sample, the higher its physical stability, as inferred from the lower TSI values. However, in general, it can be stated that all the samples studied are quite stable, since the \(\Delta\)BS% values are below 5% [58; 59].  
Figure 4: Evolution of the backscattering of (**a**) U-2 min-100% and (**b**) LU-20 min-100% with aqueous flaxseed dispersions stored at 5 \({}^{\circ}\)C.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.3.5 Electrospensitive Hydrogels  
Hydrogels that can respond to an electrical stimulus undergo a conformational change in response to an applied external electrical field [46]. Many synthetic copolymers have been reported with electrosensitive applications such as poly (vinyl alcohol) (PVA). Natural polyelectrolytes such as proteins and polysaccharides also respond to an electric stimulus. The main principle involves the movement and reorganization of ions after applying an electric field, resulting in the formation of an ion concentration gradient through the solution and hydrogel, leading to swelling or shrinkage driven by osmotic pressure. Electrosensitive hydrogels contain ionizable groups within their backbone, which generate a response to the electric stimulus. Their main applications include controlled drug delivery, and fabrication into artificial muscles for biomedical application [46].