esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Influence of the Load Eccentricity  
It has been observed that the most influential parameter on the ultimate bearing capacity, relative to the bearing capacity that would be obtained without the existence of the cavity, is the position of the load (of the foundation) with respect to the crown of the cavity. The eccentricity E/a has been varied from zero to a value in which its influence becomes negligible, for three cases with a given cavern depth H, measured from its crown to the ground surface.  
The following parameters have been kept constant:  
\begin{tabular}{  
Figure 4: Case IEH-2. Eccentricity E/a = 2 (mixed failure mechanism, by punching shear with inclined cut surfaces, plus classic plastic failure wedges).  
Figure 5: Case IEH-2. Eccentricity E/a = 6 (still a mixed failure mechanism, but with a clear predominance of classic plastic-failure wedge formation on both sides of the footing. Little influence of the cavern).  
Each figure also represents the relation between the load applied (q\({}_{\rm{ij}}\)) and the vertical displacement of the central point of the load surface, which allows the estimation of the value of the ultimate bearing capacity (q\({}_{\rm{u}}\)), as the load value of the horizontal asymptote of the diagram.  
Only the figures corresponding to the case IEH-2 are shown.  
The values obtained for all the cases analysed are included in Figures 7 and 8. Thus, the influence of the load eccentricity(E/a) on the relative ultimate bearing capacity (q\({}_{\rm{u}}\)/q\({}_{\rm{u}}\)H\({}_{\rm{e}}\)I) can be observed.  
Figure 6: Case IEH-2. Eccentricity E/a = 12 (classic plastic failure mechanism, with formation of complete yield wedges on both sides of the footing. Minimum influence of the cavern).  
Figure 7: The load eccentricity on the ultimate bearing capacity for values between E/a = 0 and E/a = 12.  
By analysing the curves of the above figures, the following conclusions can be drawn:  
* For all practical purposes, the \"reduction\" curve obtained for different cover thicknesses H/b is independent of the crown depth, except for the eccentricity range E/a between 0 and 1, in which the influence of the depth entails a difference between 10% and 20%.
* For eccentricity values E > 2a, the relative ultimate bearing capacity is higher than 70% and it varies little with the cover H/b.
* For eccentricity values E < 2a, the relative ultimate bearing capacity increases with the cover H/b, with values that are almost proportional (for the cases studied with B < 2a), as can be observed in Figure 9.  
Figure 8: The load eccentricity on the value of the ultimate bearing capacity. Detail for values between E/a = 0 and E/a = 2.  
Figure 9: The relative ultimate bearing capacity with the cover H/b for E/a < 2.
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
###### Abstract  
Non-intrusive load monitoring (NILM) is a cost-effective technique for extracting device-level energy consumption information by monitoring the aggregated signal at the entrance of the electric power. With the large-scale deployment of smart metering, NILM should ideally be designed to operate purely on the low-rate data from smart meters. In this paper, an approach based on Graph Shift Quadratic Form constrained Active Power Disaggregation (GSQF-APD) is proposed, which is built upon matrix factorization and introduces graph shift quadratic form constraint according to piecewise smoothness of the power signal. In addition, a two-step iterative optimization method is designed to solve this problem. The first step minimizes the regularization term to find the signal with minimum variation, and then the second step uses the simulated annealing (SA) algorithm to iteratively minimize the objective function and constraint based on the total graph variation minimizer. Using one open-access dataset, the strength of GSQF-APD is demonstrated through three sets of experiments. The numerical results show the superior performance of GSQF-APD, with Graph Laplacian Quadratic Form constrained Active Power Disaggregation (GLQF-APD) and the state-of-the-art NILM methods as benchmarks.  
Keywords:non-intrusive load monitoring; load disaggregation; matrix factorization; graph shift quadratic form constrained +
Footnote †: journal: Applied
# Key Action Mechanisms of Intentional Mistuning
## 3 Verification of AMM Results  
Simulations of the forced response of two blisk models, rotors A and B, have been performed to test the validity of the AMM results from the previous section, and to apply these results to the design of effective intentional mistuning patterns.  
Rotor A has \(N=24\) blades and its finite element method description has approximately 5800 DOF per sector. Rotor B model is slightly more reduced, with \(N=21\) blades and about 3600 DOF per sector. In both cases the forced response of the complete blisk can be computed in a reasonable time.  
For both rotor models, the mistuning is implemented as a small variation of the mass matrix of each blade, without varying any other property of the structure  
\[\mathbf{M}_{blade\text{-}j}\rightarrow(1+m_{j})\mathbf{M}_{blade\text{-}j},\]  
where \(m_{1},\dots,m_{N}\) is the mistuning distribution. The material damping is modelled by changing the FEM stiffness matrix:  
\[\mathbf{K}\rightarrow(1+\mathrm{i}2\xi)\mathbf{K}\]
## 1 Introduction  
With the scaling down of the integrated circuit process and the increase of the metal layers, circuit-level defects are more and more difficult for an optical microscope to observe. Therefore, nondestructive Failure Analysis (FA) for Integrated Circuits (IC) has gradually become necessary. Defect localization is a fundamental step in FA. Accurate and efficient localization makes a great contribution to the analysis of the failure's physical causes. At present, several optical based technologies, such as Emission Microscopy (EMMI), Photoelectric Laser Stimulation (PLS), Thermal Laser Stimulation (TLS), and Laser Voltage Probing (LVP), have been proven to meet the present requirements [1]. Among all the advanced technology, TLS is an efficient and non-destructive method used in back-side localization [2; 3; 4]. TLS technology has proved that it is sensitive to leakage current [5], short-circuit [3], and open-circuit [6]. It is applicable to metallic, polysilicon, and highly doped silicon structures [7]. However, TLS still has some limitations. Because of the high reflectivity of the metal layer to the laser, it is difficult for TLS to locate a defect at a deep level [8]. In 2017, a modified technology called Lock-in TLS was proposed to locate the defect in multilevel interconnects [9].  
TLS technology contains three techniques, Optical Beam-Induced Resistance Change (OBIRCH), Thermally-Induced Voltage Alteration (TIVA), and the Seebeck Effect Imaging (SEI). OBIRCH was firstly proposed by [PERSON] et al. [10], while TIVA and SEI were proposed by [PERSON]. [11]. A simulation model of OBIRCH and TIVA was proposed in 2001 [12]. In 2005, reports showed that the SEI signal would be affected by the OBIRCH signal in experiments [13,14]. The three techniques mentioned above are used in different test situations. Each technique has its unique test mechanism. However, the comprehensive theoretical model of TLS technology is still deficient.  
In this paper, a comprehensive model of TLS technology is proposed. This model describes the whole process of the laser beam interacting with the IC and the joint influence of the laser-induced resistance change effect and Seebeck effect. Next, a defect localization and analysis experiment is carried out. The experimental results are consistent with the predictions of the theoretical model. The comprehensive model provides theoretical guidance for the efficient and accurate defect localization of TLS technology.
## 1 Introduction  
Transient receptor potential vanilloid 1 (TRPV1) is an ion channel protein expressed in sensory neurons. Activation of TRPV1 in response to stimuli, such as heat, capsaicin, or other chemicals, triggers an influx of cations such as calcium, magnesium, and sodium [1; 2; 3]. Capsaicin, a naturally occurring vanilloid, is a well-known TRPV1 agonist. TRPV1 is a major contributor to pain and is a potential target for analgesic agents [3]. It is also expressed in various non-neuronal organs, such as the bladder, lungs, cochlea, and epidermis, where TRPV1 activation is responsible for the pathological development ofystitis, asthma, hearing loss, and skin diseases, respectively [3; 4]. The FDA approved Qutenza, a synthetic trans-capsaicin with a patch application system for postherpetic neuralgia [5]. Several TRPV1 antagonists, including ACD440 (NCT05416931), have been evaluated in preclinical and clinical studies for pain relief [6; 7]. However, few studies have applied a machine learning (ML)-based approach for identifying candidate TRPV1 antagonists.  
The use of artificial intelligence (AI) has increased in various areas, including the pharmaceutical industry [8; 9]. ML technology--a major branch of AI--uses algorithmsthat can be trained with known datasets to predict new outputs based on trained weights. In drug discovery, ML techniques can be used to predict the physicochemical properties, bioactivity, and toxicity of small-molecule bioactive compounds and are more cost-effective and rapid than conventional methods [8; 9; 10; 11]. Using ML, the present study aimed to identify natural compounds or derivatives that could act as transient receptor potential vanilloid 1 (TRPV1) antagonists. Based on a molecular transformer (MT) drug-target interaction (DTI) model--previously used for predicting antiviral agents for SARS-CoV-2 [12; 13]--we identified the natural flavonoid compound troxerutin as a potential TRPV1 antagonist from a target-based drug repurposing perspective. Furthermore, we investigated the bioactivities and potential of troxerutin in ameliorating skin sensitization and/or skin irritation.
## 5 Conclusions  
This study introduced a novel ensemble intrusion detection framework that effectively combines ANN, SVM, and RF to enhance cybersecurity threat detection in real time. Unlike traditional intrusion detection systems that rely on static rules or isolated classifiers, the proposed hybrid model leverages the strengths of both deep learning and classical machine learning within a unified architecture. The system achieved a high detection accuracy of 99.40% on the NSL-KDD dataset, outperforming individual classifiers. The integration of SHAP-based explainable AI not only enhanced transparency but also provided actionable insights into model decisions, fostering trust among security analysts. To further validate the robustness of the ensemble model, an additional experiment was conducted using the more recent UNSW-NB15 dataset. This dataset includes nine distinct attack types, and the ensemble model maintained a high performance with a binary classification accuracy of 99.80%. The results demonstrated superior generalization compared to standalone classifiers (ANN: 98.30%, SVM: 95.60%) and were consistent with prior benchmark studies. The corresponding confusion matrix and ROC curve highlighted the model's strong discriminative ability. Beyond its strong performance, the system was then successfully deployed using a Flask-based web interface in an AWS cloud environment, demonstrating its readiness for real-time monitoring and practical deployment. By continuously analyzing live network traffic and delivering interpretable alerts, the proposed framework proves its applicability across critical real-world domains such as healthcare, finance, industrial IoT, and government networks. Each phase of the system, from data preprocessing to explainability, was designed to ensure scalability, interpretability, and operational relevance. Moving forward, expanding the model to support multiclass attack detection, integrating it with enterprise-grade SIEM platforms, and exploring continuous online learning will further enhance its robustness and adaptability in complex security environments.  
This research was funded by Qassim University (QU-APC-2025).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2025).  
The authors declare no conflicts of interest.
# Wireless Communication: Applications Security and Reliability--Present and Future
###### Abstract  
Information security is of great importance nowadays. This is related to the widespread use of new technologies in the development of long-distance communications and the use of increasingly \"faster\" signals. Information security can be considered in many areas of information processing, including wireless systems and wired systems that are based on electronic devices. Each of such devices is a source of electromagnetic disturbances but may also be sensitive to such disturbances. This Special Issue titled \"Wireless Communication: Applications, Security, and Reliability\" covers a broad spectrum of information security related to cybersecurity, cryptography, and electromagnetic protection. In each case, the information protection solutions used must keep up with the development of technologies used in information processing. The broad thematic area of this Special Issue was intended to encourage scientists and researchers to present their research results and the goals that this research was intended to serve. At the same time, it could allow for the exchange of knowledge and experience gained during the work on information security.  
Keywords:MIMO; 5G; cyberattack; security; side-channel attack; cognitive radio; quantitative metrics +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Method Validation
#### 2.1.4 Accuracy and Precision  
Accuracy conveys the closeness of the test results generated by the method to the true value [47]. The accuracy was determined by six measurements of spiked samples, using magnesium stearate and wastewater as the matrix, at different concentration levels (10 \(\upmu\)g/mL for wastewater and 20 \(\upmu\)g/g for commercial tablets) (Table 3). Acceptable accuracy was within the range of 80% to 120%, and the precision of the %RSD was not more than 2.0%.  
The method's precision (repeatability) is defined as the closeness of the agreement among a series of measurements attained from several samples of the same homogeneous sample under the established conditions, and it is usually stated as the relative standard deviation [47]. Our system's precision was determined by analyzing six independent replicates at two concentration levels, for each matrix (magnesium stearate and wastewater), over the working range of the method on the same day (intraday). Amox and Dox concentration levels were 60.0 and 30.0 \(\upmu\)g/g for magnesium stearate and 50 and 20 \(\upmu\)g/mL for wastewater (Table 4). In order to evaluate the interday precision, two lots of spiked samples containing a placebo matrix and a standard of Amox and Dox at different concentrations (20.0 and 35.0 \(\upmu\)g/g, and 10.0 and 25.0 \(\upmu\)g/mL, respectively) were determined over five consecutive days. In terms of method precision, the %RSD of the assay results for both studied antibiotics (Amox and Dox) in the calculation of repeatability and intermediate precision were less than 2% [45; 47].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multicolumn{4}{c}{**Capsules**} & \multicolumn{4}{c}{**Wastewater**} \\ \cline{2-9}  & **Fortified** & **Found** & **Recovery** & **RSD** & **Fortified** & **Found** & **Recovery** & **RSD** \\  & **(\(\upmu\)g/g)** & **(\(\upmu\)g/g)** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** & **(\(\%\))** & **(\(\%\))** \\ \hline Amox & 20.1 & 18.6 \(\pm\) 0.21 & 92.8 & 1.11 & 10.1 & 9.92 & 98.6 & 1.03 \\ \hline Dox & 20.1 & 18.6 \(\pm\) 0.29 & 92.5 & 1.54 & 10.1 & 9.95 & 98.4 & 1.37 \\ \hline \hline \end{tabular}  
* Average of six determinations.  
\end{table}
Table 3: Results of Accuracy.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 3 Deformable Attention-Guided Network (DAGN)
### Illumination Preprocessing for Testing  
Uneven illumination of the test data is also a critical issue for pedestrian detection. Instances with low illumination often fail to be detected. To improve the illumination conditions of the testing images, we adopted a simple tone mapping method in the preprocessing procedure. In this work, we choose ALTM [15] for illumination preprocessing, which can improve the visibility of dark regions while keeping the detailed informationof bright regions. ALTM takes a small computational cost and can be easily integrated into the test pipeline. Other tone mapping techniques with similar properties can also be used. In ALTM [15], a global tone mapping is applied using Equation (9), according to the Weber-Fechner law [38]. \(L_{g}(x,y)\) is the global adaptation output. N is the total number of pixels of the image. \(L(x,y)\) is the input luminance. \(L_{max}\) denotes the maximum luminance value. \(\overline{L}\) is the log-average luminance that is given as Equation (10). The guided filter is applied to the global adaptation to preserve the edge details, and is denoted by \(H_{g}(x,y)\). The local adaptation \(L_{out}(x,y)\) is computed by using Equation (11).  
\[L_{g}(x,y)=\frac{\log\Bigl{(}\frac{L(x,y)}{\overline{L}}+1\Bigr{)}}{\log \Bigl{(}\frac{L_{max}}{\overline{L}}+1\Bigr{)}} \tag{9}\]  
\[\overline{L}=\exp\Biggl{(}\frac{1}{N}\sum_{x,y}\log(\delta+L(x,y))\Biggr{)} \tag{10}\]  
\[L_{out}(x,y)=\alpha(x,y)\log\biggl{(}\frac{L_{g}(x,y)}{H_{g}(x,y)}+\beta \biggr{)} \tag{11}\]  
where \(\delta\) is a small value to avoid the singularity of black pixels in the image, \(\alpha(x,y)=1+\eta\frac{L_{g}(x,y)}{L_{max}}\) is the contrast enhancement factor, \(\eta\) is the contrast control parameter whose default value is 10, \(\beta=\xi\overline{L}_{g}\) is the adaptive nonlinearity offset relying on the log-average luminance of the global adaptation \(\overline{L}_{g}\), and \(\zeta\) is the nonlinearity control parameter (default \(\zeta=10\)). Figure 5 presents two examples with the original test images (Figure 5a) and the preprocessed test images (Figure 5b) after applied ALTM. The detections of pedestrians missed in the original images can be detected after ALTM preprocessing, as shown in Figure 5c.  
Figure 5: Detection comparisons before and after applying ALTM. (**a**) Original test images, (**b**) preprocessed images with tone mapping, and (**c**) detection of pedestrians. Red boxes denote the detections.
## 5 Discussion  
To assess the effectiveness of the two components of the model, ablation experiments are carried out, as detailed in Table 3. The quantitative evaluation metrics presented in Table 3 demonstrate that the proposed method consistently surpasses all other models across various experimental conditions, achieving outstanding results in accuracy, recall, precision, and F1 score. This suggests that the incorporation of comprehensive fire point features greatly enhances the model's accuracy. While the accuracy of the BiLSTM model experiences a slight decline, it maintains a high recall rate. However, this drop in accuracy is linked to an increase in the error detection rate, indicating that an exclusive focus on temporal features may result in a higher occurrence of false positive predictions. In comparison, the Swin Transformer exhibits a weaker performance. Although it achieves high accuracy, its F1 score suffers due to its inadequate precision and recall, leading to larger prediction errors for fire points and an increase in false positives.  
From the perspective of fire point detection across different models, the proposed method excels in identifying fire points. The integration of comprehensive fire point features significantly boosts the model's capability to detect fire points while minimizing both false negatives and false positives. The BiLSTM model effectively captures fire point information, and despite a slight decrease in accuracy, it still maintains a high recall rate, indicating its continued effectiveness in fire point detection. Conversely, the Swin Transformer struggles with fire point detection, as it tends to produce larger prediction errors, resulting in more false negatives, highlighting the drawbacks of relying solely on global fire point features.  
In conclusion, the proposed method significantly outperforms networks that rely solely on BiLSTM or the Swin Transformer in terms of accuracy and fire point detection capability, underscoring the importance of comprehensive feature fusion for effective fire point detection.
## 3 Results and Discussion
### Discussion on the Effectiveness of KNN in Handling Anomalies  
Three improved percentage indicators of predictions are given in Table 5, including \(P_{RMSE}\), \(P_{MAE}\) and \(P_{R2}\) between outlier treatment (better performance) and no outlier treatment (worse performance). Through comparative analysis, it can be found that KNN outlier processing has a great impact on the prediction results. The RMSE and MAE of the SRU were reduced by 16.60% and 21.85%, respectively, and the \(R^{2}\) was increased by 1.58%. The RMSE and MAE of the SVR were increased by 12.56% and 7.83%, respectively, and the \(R^{2}\) was increased by 1.47%. By introducing KNN outlier processing, the model shows significant improvement in prediction accuracy.
## 2 Methods
### Description and Calculation of VIs  
The NDVI is an intrinsic vegetation index that simply accounts for the chlorophyll absorption feature in the red (R) and the structural information inherent in high NIR reflectance of a green vegetation canopy. It does not involve any external factor other than the measured spectral reflectance.  
\[\mathrm{NDVI}\,=\,(\mathrm{NIR-R})/(\mathrm{NIR}\,+\,\mathrm{R}) \tag{1}\]  
The TSAVI incorporates slope and intercept of the soil line together with an adjusted coefficient to account for first-order soil background variation.  
\[\mathrm{TSAVI}\,=\,a(\mathrm{NIR-aR-b})/[(\mathrm{R}\,+\,a(\mathrm{NIR-b})\, +\,0.08(1\,+\,a^{2})] \tag{2}\]  
where \(a\) is the slope and \(b\) is the intercept of the soil line. The value 0.08 is an adjusted coefficient.  
The TBVI is a general formulation of the NDVI and was used to examine the predictive accuracy of other provided band combinations than used by the NDVI.  
\[\mathrm{TBVI_{i,j}}\,=\,(\mathrm{Ref_{j}-Ref_{i}})/(\mathrm{Ref_{j}}\,+\, \mathrm{Ref_{i}}) \tag{3}\]  
where i, j = 1,..., N, where N is the number of narrow bands and Ref is the reflectance measured in a narrow band.  
EVI, originally developed as a standard satellite vegetation product for the Moderate Resolution Imaging Spectroradiometer (MODIS), combines atmospherically corrected blue (B), R and NIR reflectance with coefficients of an aerosol resistance term and a soil-adjustment factor. The use of the EVI has been motivated by studies reporting a trend to more linear relationships with vegetation biophysical parameters such as standing biomass and LAI of crops and a wider range of values at the same time [4; 37].  
\[\mathrm{EVI}\,=\,2.5\,((\mathrm{NIR-R})/(\mathrm{NIR}\,+\,\mathrm{C_{1}R-C_{2 }B}\,+\,\mathrm{L})) \tag{4}\]  
where 2.5 is a gain factor. C\({}_{1}\) and C\({}_{2}\) are coefficients of the aerosol resistance term and L is the soil background reflectance adjustment where C\({}_{1}\) = 0.06; C\({}_{2}\) = 0.08 and L = 1.  
In order to investigate the potential of the six available bands in the NIR, six variations of the NDVI, the TSAVI and the EVI were calculated. Variations of the TBVI were calculated for all band combinations except those covered by the NDVI variations (the R band in combination with the six NIR bands).  
Figure 11: Relationship between ASD Fieldspec measurements of topsoil reflectance in the wavelengths corresponds to Mini-MCA 12 bands b\({}_{658}\) and b\({}_{756}\).