esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## Anomaly Detection Method
### Selection of the Detection Threshold  
There are several practical **challenges** that undermine the selection of a suitable threshold in a semi-supervised training setting, such as for our study. An incorrectly selected threshold (either too low or too high) might cause misclassifications. It must be noted that assembling a \"reliable\" database of normal points for training purposes is a complex matter. For example, the normal points might be fraught with uncommon behaviors or outliers being accidentally included within the normal points. The labeling might be imperfect, i.e., intrusions being occasionally labeled as normal behaviors. The rationale behind our method is to clear out as many \"strange\"--although normal--training points as possible _before_ computing the threshold: though belonging to normal data, spurious \"out-of-the-crowd\" behaviors will be more similar to intrusions than to normal points.  
The threshold is computed by relying on a small, i.e., 10%, disjoint subset of the training set, which we call the **threshold set**. A representation of the method is in Figure 2 and consists of four steps:  
1. **AE training**: the autoencoder undergoes the typical semi-supervised training procedure described above, which allows it to learn the normal profile of the data points;
2. **Outlier detection**: an _outlier detection_ algorithm is applied to the threshold set in order to discriminate **inliers** from **outliers**;
3. **RE computation**: inliers and outliers are fed to the autoencoder: this step produces two separate vectors of reconstruction errors, i.e., \(RE_{IN}\) and \(RE_{OUT}\) of inliers and outliers, respectively;
4. **Threshold selection**: the detection threshold is obtained through a sensitivity analysis performed with \(RE_{IN}\) and \(RE_{OUT}\).  
At first, the threshold is initialized with the maximum RE in \(RE_{OUT}\); then, the threshold is progressively lowered until it finds an \"equilibrium\" between inliers and outliers, i.e., inliers whose RE is below the threshold against outliers characterized by an RE above the threshold. In this study, we use the **isolation forest**[35], although the threshold selection method _does not_ mandate a specific outlier detection algorithm.
# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 3 Presenting the Yolo V5 Network Architecture Model and Introducing Other Detection Models
### Training and Detection Process of Yolo V5 in RTI IMS Software  
#### 4.2.1 YOLO V5's Object Training Algorithm  
The object training process of YOLO V5 requires a large enough dataset and computational resources to train a model with good performance. YOLO V5's object training algorithm includes the following steps:Prepare data: First, it is necessary to prepare the training data. The training data must contain the input image and the bounding boxes containing the object to be detected. These bounding boxes are contained in the label files corresponding to each image.  
Build configuration file: Next, it is necessary to create a configuration file to configure the training parameters. The configuration file defines the model architecture and training parameters, such as learning rate, epochs, batch size, and other parameters.  
Conduct training: Based on the data and configuration file, we proceed to train the YOLO V5 model. The training process consists of passing the corresponding images and bounding boxes through the neural network, calculating the model's predictions, comparing them with the actual labels, and calculating the loss. This process will be repeated over many epochs to improve the model performance.  
Evaluate and adjust: After training, the model needs to be evaluated to measure performance. This is usually done by measuring metrics such as accuracy, recall, and F1. Based on the evaluation results, we can adjust the training parameters to improve the model's performance.  
Test and deploy: Finally, the model is tested on independent test data to ensure generality and accuracy. The model can then be deployed and used in real applications.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 2 Experimental Investigation
### Materials
#### 2.1.2 Filler Materials  
The experimental study employed Manufactured Sand (M-Sand or MS), finely crushed material smaller than 4.76 mm in size, meeting the specifications of the Zone II category. According to the \"The \"IS 383:2016--Specifications for Fine and Coarse Aggregate from Natural Sources for Concrete of the Indian Standard code of practice, Zone II sand is expected to possess a fineness modulus ranging between 2.2 and 2.6, as well as a specific gravity falling within the range of 2.5 to 2.9. Concrete mixtures with compressive strengths up to 50 MPa are deemed appropriate for using Zone II sand. The physical characteristics of MS were determined following IS 383 (2016) [29]. Coarse aggregates (CA) in this study consisted of crushed stones with a size of 12 mm and below. To partially replace MS, an optimal amount of EPA was added, equivalent to 2.5% of the MS content. The properties of the aggregates can be found in Table 3, while Figure 1 depicts the particle size distribution of both MS and CA. To ensure the desired flow characteristics of fresh concrete while minimizing the powder content, a polycarboxylate ether-based SP was employed. Additionally, a VMA was incorporated to prevent particle segregation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Oxides**} & \multicolumn{5}{c}{**Chemical Properties (\%)**} \\ \cline{2-5}  & **OPC** & **FA** & **GGBFS** & **EPA** \\ \hline SiO\({}_{2}\) & 20.12 & 60.20 & 38.72 & 73.50 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.41 & 3.42 & 1.12 & 1.21 \\ Al\({}_{2}\)O\({}_{3}\) & 8.53 & 28.24 & 14.43 & 7.56 \\ CaO & 61.31 & 4.96 & 37.35 & 0.92 \\ MgO & 2.26 & 0.31 & 8.12 & 1.12 \\ Na\({}_{2}\)O & 1.81 & 0.55 & 0.17 & 2.86 \\ K\({}_{2}\)O & 2.71 & 1.27 & 0.92 & 5.6 \\ SO\({}_{3}\) & 0.24 & 2.25 & 0.26 & 6.2 \\ Loss on ignition & 0.05 & 0.09 & 0.01 & 0.025 \\ \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{5}{c}{**Physical Properties**} \\ \cline{2-5}  & **OPC** & **FA** & **GGBFS** & **EPA** \\ \hline Specific gravity & 3.15 & 2.38 & 2.45 & 2.12 \\ Density (kg/m\({}^{3}\)) & 3200 & 1580 & 1786 & 675 \\ Surface area (m\({}^{2}\)/kg) & 3310 & 892 & 1200 & - \\ Colour & Pale grey & Light grey & Whitish & White \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical and physical characteristics of SCM and EPA.  
Figure 1: Granulometric curve of MS and CA.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 2 Related Works
### Conventional Methods  
Most conventional approaches rely on building a background model for a specific video sequence. To model the background model, statistical (or parametric) methods using Gaussians are proposed [20; 25; 26] to label each pixel as a background or foreground pixel. In [27], an improved Gaussian mixture model compression technique is proposed to separate background images and moving objects in video. However, parametric methods are computationally inefficient. To alleviate this problem, various nonparametric methods [28; 29; 30] have been proposed.  
In order to reduce the impact of lighting changes, shadows cast by foreground objects, dynamic background motion, camera motion, camouflage or subtle areas on the performance of background subtraction, [PERSON] et al. [31] proposed the idea of background modeling based on frame difference. Furthermore, ref. [32] used Genetic programing to select different change detection methods and combined their results. Most of the current CNN-based methods are also based on background subtraction. However, these methods may cause overfitting due to the loss of redundant pixels and higher context information in patches, and require a large number of patches for training.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 2 Theoretical Background  
### Degradation Model of a Haze Image  
In the hazy condition, the light scattered by various types of particles in the atmosphere decreases the visibility of the acquired image. In addition, the amount of image degradation is proportional to the distance between the object and camera. [PERSON] et al. proposed the transmission-based haze removal method in reference [1], where the image degradation model is defined as \[g(x)=f(x)t(x)+A(1-t(x)), \tag{1}\]  
where \(g(x)\) represents the observed hazy image; \(f(x)\) is the haze-free image; \(A\) is the atmospheric light; and \(t(x)\) is the transmission. The transmission (\(t(x)\)) is defined as  
\[t(x)=e^{-\beta d(x)}, \tag{2}\]  
where \(\beta\) represents the scattering coefficient of the atmosphere, and \(d(x)\) is the scene depth.  
[PERSON] et al. analyzed the statistical properties of various haze-free images based on a histogram. They introduced the DCP which is estimated by selecting the minimum intensity value among the R, G, and B pixels. In this method, the haze-free image is obtained by solving the degradation model using properly estimated \(A\) and \(t(x)\) values. The haze-free image was obtained as  
\[f(x)=\frac{g(x)-A}{t(x)}+A. \tag{3}\]  
If \(A\) and \(t(x)\) are inaccurately estimated, color distortion and brightness saturation many occur in the resulting image.  
On the other hand, [PERSON] et al. presented the localized haze model using a small patch in a hazy image in reference [11], where the image degradation model is defined as  
\[g_{i}(x)=t_{i}(x)R_{i}+m_{i}A, \tag{4}\]  
where \(g_{i}(x)\) represents the \(i\)-th patch in \(g(x)\); \(t_{i}(x)\) is the \(i\)-th patch in the transmission; \(R_{i}\) is the RGB vector representing the reflected light from \(g_{i}(x)\); and \(m_{i}\) a constant magnitude of the \(A\). This model assumes that the vectors of the principal component of the patches in a haze image intersect the vector of atmospheric light, which passes through the origin in the RGB color space. [PERSON] et al. estimated the orientation and magnitude of the atmospheric light vector using the PCA of the selected haze patch satisfying the degradation model of (4) [11].
# A Distributed RF Threat Sensing Architecture
## 5 Performance Evaluation
### Limitations  
The system design was based on an RF wideband logarithmic amplifier in order to provide flexibility on the band monitoring while keeping the costs low. It is primarily focused on jamming detection and for this scope it appears to serve its purpose. However, it cannot be used for detecting very low RF signals at long distances.
## 4 Experiments and Discussion
### Validation of Proposed Noise Reduction Approach  
#### 4.1.1 Experimental Setup  
To investigate the performance of the proposed noise reduction approach, we used non-stationary noise data obtained from two noise databases that are widely used in robust speech recognition tasks: NOISEX-92 and AURORA [36; 37]. NOISEX-92, which was produced by the NATO research study group on speech processing, comprises a set of recordings of eight different noises, e.g., babble, factory noise, and F16 fighter jet noise. AURORA, which was developed by the ETSI, comprises more types of background noises, e.g., subway, car, and airport noise. Among the noise types, we selected five non-stationary noises: babble, F16 fighter jet, subway, airport, and street.  
We simulated noise-contaminated speech by adding the noise signals to speech data recorded in clean environments. To consider the noise levels, we varied the intensity of the noises, thereby producing noise-contaminated speech with four SNR conditions: 0 dB, 5 dB, 10 dB, and 15 dB. The 0 dB SNR demonstrates a very severe noise condition, and the 15-dB speech is close to clean speech with little noise.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 2 Materials and Methods
### Radionuclides in Liquid Effluents from Nuclear Power Plants  
The primary radionuclides discharged from Ningde NPP in liquid form are \({}^{110\text{m}}\)Ag, \({}^{51}\)Cr, \({}^{54}\)Mn, \({}^{137}\)Cs, \({}^{134}\)Cs, and \({}^{124}\)Sb. The full energy peak count rate n of a particular \(\gamma\)-ray is proportional to the product of the emission probability \(I_{E}\) and the source peak detection  
Figure 1: External appearances of the aluminum alloy case and the PVC container of the detector.  
Figure 2: Location of the experiment site.  
efficiency \(\varepsilon_{E}\). Therefore, for each monitoring radionuclide, the \(\gamma\)-ray with the largest \(I_{E}\times\varepsilon_{E}\) should be selected.  
For radionuclides that emit only one type of \(\gamma\)-ray, such as \({}^{51}\)Cr, \({}^{54}\)Mn, \({}^{137}\)Cs, that ray is chosen as the characteristic \(\gamma\)-ray. Since that the \(\gamma\)-ray with energy 602.726 keV for \({}^{124}\)Sb and the \(\gamma\)-ray with energy 604.721 keV for \({}^{134}\)Cs are very close and may overlap in the spectrum, the \(\gamma\)-rays with energies 1690.971 keV and 795.864 keV were selected in the energy spectrum processing as the characteristic \(\gamma\)-rays for \({}^{124}\)Sb and \({}^{134}\)Cs respectively to calculate their activity concentrations. \({}^{137}\)Cs \(\gamma\)-rays with the energy of 661.657 keV and \({}^{1100}\)Ag \(\gamma\)-rays with the energy of 657.76 keV are also closer together in the spectrum, but \({}^{137}\)Cs emits only one major \(\gamma\)-ray, so the energy of 884.678 keV was chosen as the characteristic \(\gamma\)-ray of \({}^{1100}\)Ag in the process of spectrum processing.
## 2 Electromagnetic Radiation Testing Method
### Determining the Test Antenna Type and Model
Specifically, the distribution characteristics of the antenna gain in this particular angular range indicate that positioning the base station and satellite in the optimal direction of the vehicle antenna gain can significantly enhance signal transmission efficiency and quality. Thus, this research outcome not only aids in optimizing the antenna layout, but also lays the foundation for improving the performance of the entire wireless communication network.
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 3 Load Transfer Analysis
### Lateral Load Transfer Analysis
In Figure 5c,d, the variation in the vertical reaction force of the left and right wheels can be obtained by taking the moment balance at the touch point of each wheel:  
\[\Delta F_{zff}W=F_{ay}\frac{l_{r}}{l_{f}+l_{r}}h_{f}+T_{ff}+F_{ufy}h_{uf} \tag{19}\]  
\[\Delta F_{zfr}W=-F_{ay}\frac{l_{r}}{l_{f}+l_{r}}h_{f}-T_{ff}-F_{ufy}h_{uf} \tag{20}\]  
\[\Delta F_{zrl}W=F_{ay}\frac{l_{f}}{l_{f}+l_{r}}h_{r}+T_{fr}+F_{ury}h_{ur} \tag{21}\]  
\[\Delta F_{zrr}W=-F_{ay}\frac{l_{f}}{l_{f}+l_{r}}h_{r}-T_{fr}-F_{ury}h_{ur} \tag{22}\]  
where \(\Delta F_{zfl}\), \(\Delta F_{zfl}\), \(\Delta F_{zrl}\), and \(\Delta F_{zrr}\) are the variations in the vertical reaction forces of the left and right wheels of the front and rear axes; \(F_{ufy}\) and \(F_{ury}\) are the lateral forces generated by the non-suspended mass of the front and rear axes; \(h_{uf}\) and \(h_{ur}\) are the height from the ground of the mass center of the non-suspended mass; \(h_{f}\) and \(h_{r}\) are the height of the center of mass of the front and rear axes. From the (19)-(22), it can be seen that the variation in the vertical reaction force of the wheel is related to the centrifugal force \(F_{ay}\); the centroid height \(h_{f}\), \(h_{r}\) of the front and rear axes; the front and rear suspension roll torque \(T_{\phi f}\), \(T_{\phi r}\); the lateral force \(F_{ufy}\), \(F_{ury}\) generated by the unsuspended mass; and the off-ground height \(h_{uf}\), \(h_{ur}\) of the unsuspended mass centroid. Compared with the load of AGV, the unsuspended mass is smaller, and the lateral force caused by the unsuspended mass is ignored. \(T_{\phi f}\) and \(T_{\phi r}\) are related to the roll angle and roll angle stiffness of the suspension, and it is difficult to adjust. Therefore, the load transfer can be reduced by optimizing \(F_{ay}\). Because \(F_{ay}=ma_{y}\) and the total mass \(m\) of AGV is large, \(a_{y}\) must be optimized. The lateral acceleration of the centroid can be expressed as  
Figure 5:0  
where \(\dot{v}_{y}=\dot{v}\text{sin}(\beta)\), \(v_{x}\dot{\theta}=v^{2}\cos^{2}\beta\frac{\text{tan}\,\delta_{f}-\text{tan}\, \delta_{r}}{l_{f}+l_{r}}\). When the centroid sideslip angle \(\beta\) is zero,  
Figure 5:1In order to reduce the lateral load transfer of heavy-duty AGV and improve the driving stability, it is necessary to optimize the lateral acceleration \(a_{y}\).
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 2 Results  
Grapevine plants were exposed to five different radiation regimes from budbreak to harvest (see Section 4.1): P (only photosynthetically active radiation, PAR), PA (PAR + UV-A), PB (PAR + UV-B), PAB (PAR + UV-A + UV-B), and PAB\(\dagger\) (PAR + UV-A + enhanced UV-B). At the end of the exposure period, phenolic compounds and VOCs were measured in both grapes [9] and the resulting wines (Tables 1 and 2).
## 4 Discussion  
Probiotic bacteria must be effective in keeping the favorable balance of microflora and in benefiting the host's health. They should lack the capacity to transfer antibiotic resistance, which is one of the greatest threats to human health [7; 18]. Bacteria such as _S. aureus_, _E. coli_, and _H. pylori_ can be intrinsically resistant to certain antibiotics, and also acquire resistance through genes mutations and horizontal gene transfer [26]. In this study, all the strains showed resistance to vancomycin at its highest concentration. Some LAB, such as _Leuconstoc mesenteroides_ C4, _Leu. mesenteroides_ C10, _Leu. mesenteroides_ F27, _L. plantarum_ C182 and _L. casei_, have shown resistance to vancomycin [16; 18]. The vancomycin-resistant phenotype is perhaps the best-characterized intrinsic resistance among LAB, and is explained by the relatively poor binding of vancomycin to the peptidoglycan with D-alanyl-D-lactate termini, which some lactic acid bacteria have [27]. Acid and bile tolerance were evaluated to predict the survival of probiotics after oral administration. The gastric juice secreted in the stomach reduces the presence of most microorganisms due to its low pH (about 2.0) [28]; meanwhile, bile salts disorganize the structure of the cell membrane [29]. Therefore, resistance to these conditions is ideal for the selection of probiotic strains. Several LAB have exhibited high survivability in simulated gastric juice with pepsin at pH 3.0; however, cell viability decreased below the detection limit when exposed to pH 2 [5; 7; 8; 10; 30]. In our study, isolated LAB showed high viability (between 63.2-96.3%) in the presence of 1% pepsin and pH 2. In acidic conditions, bacteria maintain the pH homeostasis by discharging H+ from the cell via the membrane H+-ATPase [31; 32], with this enzymatic activity being higher in acid-tolerant bacteria [33]. Some lactobacilli utilize other responses to resist hostile stomach conditions, such as repair proteins for DNA damage and changes in cellular envelope and altered metabolism [34; 35]. Other studies demonstrated that some LAB can have levels of resistance against bile salt concentrations of 0.3 to 1.5% [10; 16; 36]. Exopolysaccharides (EPS) production may provide certain tolerance to simulated gastrointestinal conditions since it is lightly attached to the cell wall [6]. Furthermore, pre-exposure of lactobacilli strains to acidic environments, such as the stomach or food matrices, could enhance their performance as probiotic strains by improving their adhesion to mucosal cells and thus increasing bacteria colonization in the gut [34; 37].
###### Abstract  
Hyperspectral imaging (HSI) technology is able to provide fine spectral and spatial information of objects. It has the ability to discriminate materials and thereby has been used in a wide range of areas. However, traditional HSI strongly depends on the sunlight and hence is restricted to daytime. In this paper, a visible/near-infrared active HSI classification method illuminated by a visible/near-infrared supercontinuum laser is developed for spectra detection and objects imaging in the dark. Besides, a deep-learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. By applying the method to a selection of objects in the dark successfully, we demonstrate that with the active HSI classification method, it is possible to detect objects of interest in practical applications. Correct active HSI classification of different objects further supports the viability of the method for camouflage detection, biomedical alteration detection, cave painting mapping and so on.  
active hyperspectral imaging; supercontinuum laser; classification; deep-learning +
Footnote †: journal: Applied Sciences
## 4 Discussion  
This study demonstrated the integration of advanced artificial intelligence models, including convolutional neural networks (CNNs) and vision transformers (ViTs), with  
\begin{table}
\begin{tabular}{l l l l} \hline
**Performance Metric** & **ResNet-50** & **MobileNet V1** & **EfficientNetV2 B0** \\ \hline Accuracy & 0.9500 & 0.9000 & 0.9250 \\ Sensitivity & 1.0000 & 1.0000 & 1.0000 \\ Specificity & 0.9000 & 0.8000 & 0.8500 \\ Precision or True Positive Rate (TPR) & 0.9091 & 0.8333 & 0.8696 \\ True Negative Rate (TNR) & 1.0000 & 1.0000 & 1.0000 \\ True Positives & 20 & 20 & 20 \\ True Negatives & 18 & 16 & 17 \\ False Positives & 2 & 4 & 3 \\ False Negatives & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 6: List of CNN metrics for the test dataset.  
\begin{table}
\begin{tabular}{l l} \hline
**Performance Metric** & **ViT** \\ \hline Accuracy & 0.9545 \\ Sensitivity & 1.0000 \\ Specificity & 0.9000 \\ Precision or True Positive Rate (TPR) & 0.9231 \\ True Negative Rate (TNR) & 1.0000 \\ True Positives & 24 \\ True Negatives & 18 \\ False Positives & 2 \\ False Negatives & 0 \\ \hline \end{tabular}
\end{table}
Table 7: List of ViT metrics for the validation dataset.  
\begin{table}
\begin{tabular}{l l} \hline
**Performance Metric** & **ViT** \\ \hline Accuracy & 0.8000 \\ Sensitivity & 1.0000 \\ Specificity & 0.6000 \\ Precision or True Positive Rate (TPR) & 0.7143 \\ True Negative Rate (TNR) & 1.0000 \\ True Positives & 20 \\ True Negatives & 12 \\ False Positives & 8 \\ False Negatives & 0 \\ \hline \end{tabular}
\end{table}
Table 8: List of ViT metrics for the test dataset.  
thermographic imaging for the diagnosis of diabetic peripheral neuropathy and peripheral arterial disease.  
The comparative analysis revealed that ResNet-50 outperformed the other CNN architectures (MobileNet V1 and EfficientNetV2 B0) and the visual transformer network, achieving an accuracy of 95.00%, a sensitivity of 100.00%, and a specificity of 90%. These results are also superior to those of existing diagnostic methods, which report a sensitivity of 53% and specificity of 88% for DPN (monofilament test) and a sensitivity of 35.48% and specificity of 97.55% for PAD (ankle-brachial index).  
Despite the promising results, the study is constrained by certain limitations. First, the relatively small size of the dataset--despite the inclusion of healthy cases from the INAOE public database--may limit the generalizability of the results. Although acquisition protocols between datasets were harmonized, subtle domain shifts cannot be entirely ruled out. Second, the lack of explainability in the AI predictions may hinder clinical adoption. Employing explainability techniques could address this issue by providing clinicians with insights into the decision-making process of the models.