esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Evaluation on the Perceptual Color Correction
#### 4.3.1 The Function Analysis on User Parameters  
In \(l\alpha\beta\) space, \(l\) denotes an achromatic channel, and \(\alpha\) and \(\beta\) are chromatic yellow-blue and red-green opponent channels. Therefore, our three user parameters \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) in Equations (8)-(10) can be used to effectively adjust the perceptual colors. The vividness and intensity of a color are represented by its saturation. Depending on the presence or absence of this saturation, colors can be broadly divided into chromatic and achromatic colors. The achromatic color is one that lacks hues such as white, gray, and black, and the chromatic color is a color that has even the slightest amount of hue. The effects of user parameters are shown in Figure 14. We adjust one parameter for each row increasingly, while the other two parameters are set at 1. The first image of each row is the result of the OCC. From the first row, we can see that the color becomes more vivid with the increasing of the \(\lambda_{1}\). The image will saturate if the value of \(\lambda_{2}\) becomes too large. With the increase of \(\lambda_{2}\) and \(\lambda_{3}\), the adjusted image will turn red and yellow, respectively.  
Figure 13: Distribution of the color central of the tongue images captured in the open and closed environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(X_{mean}\) & \(Y_{mean}\) & \(X_{var}\) & \(Y_{var}\) \\ \hline Open environment & 0.373 & 0.343 & 0.00096 & 0.00015 \\ Closed environment & 0.408 & 0.346 & 0.00012 & 0.00003 \\ Corrected closed environment & 0.415 & 0.348 & 0.00017 & 0.00003 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average coordinates and their variances of image centers for three groups of images.
## 2 System Model  
In this section, we present the model for implementing pilot sequences and outline the methodology for simulating phase noise using a random-walk process. We then describe the application of linear regression to perform linear interpolation. In addition, we explore advanced techniques for outlier elimination, focusing on cosine filtering in conjunction with robust local weighting methods for smoothing. These techniques play a crucial role in reducing the impact of noise-induced distortions. In general, this discussion provides a comprehensive understanding of the key steps required for effective phase recovery in the implementation of pilot sequences.
###### Abstract  
The displacement-time curve of a landslide serves as a critical indicator of its movement state, with precise deformation prediction being essential for effective disaster early warning. While numerous studies have employed machine learning techniques to predict deformation at individual monitoring points, they often overlook the spatial correlations among monitoring points arranged along horizontal and vertical cross-sections. To address this limitation, this paper employs the Temporal Graph Convolutional Network (T-GCN) model, which integrates the strengths of Graph Convolutional Networks (GCNs) and Gated Recurrent Units (GRUs). The GCN captured spatial correlations among monitoring points, while the GRU modeled the temporal dynamics of displacement. The T-GCN model was applied to predict the spatio-temporal deformation of the Dawuchang landslide in the Three Gorges Reservoir area. Experimental results demonstrated that the T-GCN model effectively predicted the spatio-temporal displacement of landslides, offering a robust approach for landslide monitoring and early warning systems. The model also incorporated the temporal influence of external factors, such as rainfall and reservoir water levels, enhancing prediction accuracy and providing valuable insights for future research in landslide deformation forecasting.  
l +
Footnote †: journal: Applied sciences  
Article
## 4 Proposed Method  
Based on the previous analysis, the magnetic interference of UAV airborne electronic equipment is mainly caused by power supply current, and the magnetic interference vector of UAV electronic equipment should be similar to the characteristics of permanent magnet materials. So, we established a linear compensation model based on current interference sources under inspiration of the T-L modeling method of the constant magnetic field. The model uses UAV current data, fluxgate magnetometer data and OPM data obtained during the ground calibration experiment to calculate the model coefficients. The coefficients and the current interference source matrix can be used to estimate the magnetic interference and then subtract the modeling interference from the OPM data to achieve compensation.
# Launch Dynamic Simulation of a Compressed-Air Launcher for Fire Suppression
## 1 Introduction  
High-temperature and high-pressure gases are mainly generated via the combustion of solid propellants as a power source in traditional barrel launch systems [1, 2, 3]. The high temperature, high noise, and significant damage in the launching process pose hidden risks to the safety of operators and equipment environments [4]. The storage, transportation service requirements, and associated costs are high because of the inclusion of explosive and energetic materials [5]. This traditional launch technology cannot be directly applied in non-military fields. Forest fires are often complex and powerful and cannot be fought and controlled at a close range [6]. Forest fires require a large amount of an extinguishing agent, and it is impossible to use explosive and energetic materials on a large scale. All countries in the world are committed to developing forest fire devices and launchers without energetic materials [7].  
At present, compressed-air launch systems have been widely applied in sea, land, and air weapon systems, including torpedoes, missiles, rockets, unmanned aerial vehicle (UAV) ejections, etc. [8]. A compressed-air launch system utilizes normal-temperature compressed air as the working medium, avoiding the drawbacks associated with pyrotechnics and the combustion of energetic propellants during the launching process. Consequently, it eliminates the presence of high-temperature, toxic, and harmful gases. A compressed-air launch system offers several advantages, including a simple principle, broad applicability, high safety, low cost, environmental friendliness, a short preparation time, a large energy power density, and a high muzzle velocity.  
This paper introduces the application of compressed-air launching technology in the field of fire suppression. A launch dynamics model of a compressed-air launcher is established, and the variations in interior ballistics are analyzed using numerical calculations.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Continuous Water-Softening Stage
#### 2.4.1 Deformation and Damage Characteristics of Surrounding Rock in Water-Rich Geology  
In practical engineering, the water-softening effect on rock is usually accompanied by previous unloading softening. Moreover, differential water-rock interaction is caused due to the initial variability of fracture development and its permeability. The surrounding rock in section K14 + 885-K14 + 935 of Xujiadi tunnel is dolomitic limestone. The groundwater in this section is highly developed. After excavation, there is a large amount of water flowing out from the vault in a sprinkling or gushing way, as shown in Figure 3. The rock mass before encountering water can maintain a specific shape. However, after water immersion, the rock mass softens and disintegrates into \"melon pieces\" of debris, as shown in Figure 4.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
###### Abstract  
Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency's (ESA) Sentinel-2 satellite--delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres--could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements--the mean R\({}^{2}\) and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R\({}^{2}\) and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two--the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices.  
S 2019 11 2050 29 August 2019; Published: 31 August 2019
## 2 Literature Review
The authors concluded that superior results were obtained when gathering an LSVM classifier with the IDFT approach. [PERSON] et al. [45] proposed an automatic classification model for Hadith. The proposed model was used to organize Arabic text Hadith into related categories: Sahih, Hasan, Da'if, and Maudu. Several classifiers, such as LSVC, SGD, and LR, were investigated to build a classification model. IG, Chi2, and Gain Ratio (GR) are feature selection methods to remove irrelevant features. The outcomes showed that LSVC outperforms other classifiers. Moreover, in [46], the authors evaluated the Hadith dataset using DT, RF, and NB classifiers, and they isolated redundant features using IG and Chi2. Binary boolean algebra and TF-IDF were used to extract features. Experimental results demonstrated that the best classifier investigated was DT. [PERSON] et al. [1] performed an extensive analysis to evaluate the effectiveness of Deep Neural Networks (DNN) models and a word2 vec embedding model on new constructed large corpora for Arabic document classification. The corpus was SANAD (Single-label-Arabic- News- Articles Dataset), collected by [PERSON] et al. [47]. The evaluation experiments showed the effectiveness of the proposed models on single-label categorization and multi-label categorization. [PERSON] et al. [48] studied the effects of stemming strategies on ATC. Several classifiers, including NB, KNN, and SVM, were used to build the classification model. Chi2 was used to extract essential features in different sizes. An available public dataset, namely, CNN, was used to evaluate the classification model. The outcomes demonstrated that the SVM classifier gathered with ARLStem stemmer outperforms other classifiers when increasing the features. Moreover, in [49], the authors studied the effects of stop word removal in several classifiers and feature extractions using the CNN public dataset. Chi2 was applied as a feature selection method. The TF-IDF and BoW methods were used to extract features. They concluded that the best results were achieved when removing stop words gathering with TF-IDF and the VM classifier. Researchers reported several classifiers and feature selection methods to improve the ATC system from the literature. Therefore, our approach is used to automatically find an effective solution to improve the ATC system among this literature, such as choosing the optimal feature selection method with an optimal number of features alongside the classifier using supervised machine learning. Furthermore, to clear up the differencesbetween existed works with our approach, we conclude these differences in Table 1. This table includes preprocessing, normalization, stop word removal, stemming technique, feature selection, and classification algorithms, abbreviated as PR, NR, SR, ST, FS, and CA, respectively.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 6 Performance Evaluation
### Cost Comparison
#### 6.2.1 Computation Cost  
When analyzing the computation cost of the proposed protocol, we deliberately omit the time complexity measurement of the setup phase since it is considered to be done offline and infrequently. We basically privilege the operations that dominate the speed of signature generation and verification. We adopt the implementation parameters in [50; 51] with embedding degree 6, \(\{\mathbb{G},q\}\) represented by 161 bits and 160 bits respectively. The implementation was performed on a 3.5-GHz, core i-5, 16 GB RAM desktop computer with \(crypto++\) library 5.6.5 [52]. The cost of respective security primitives are depicted in Table 2.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 1 Introduction
### The Need for XAI in Clinical Settings  
AI tools are meant to assist both general physicians and non-specialists in diagnosing in the absence of specialists. AI might augment the ability of non-expert doctors to make accurate diagnoses, especially in crowded clinical settings. The need for explainable artificial intelligence (XAI) in detecting AD is crucial. Explainability refers to the ability of AI models to provide transparent and understandable reasons for their decisions. Without this, even the most accurate AI models as [10; 13; 14; 15; 16; 24; 25] may be met with skepticism and hesitation from general physicians and non-specialists. Current efforts in interpretable AI, including technologies such as Shapley additive explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), in [14; 15; 25; 26; 27; 28; 29; 30] are steps in the right direction. However, these methods often fail to provide the kind of intuitive understanding necessary for clinical application. These models may provide insights into which features made the AI make the decisions, but these insights cannot always be easily interpreted by human users, especially in a high-stakes medical context, and presented without explaining the outputs making AI tools inapplicable in clinical decision-making. The lack of large datasets and truly interpretable AI data has hampered the implementation of such contributions in clinical settings [31; 32]. It can be difficult to trust traditional AI in critical applications. If a model makes an error, it is often difficult to diagnose why, leading to potential risks and a lack of accountability.
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
## 6 Security Tests and Evaluation
### Brute-Force Attack Analysis
After defining \(y=17\) randomly, the FakeProver computes \(x=y^{p}\cdot f^{e_{fake}}=17^{47}\cdot 16^{20}\mod 65=38\). Thus, the Verifier receives \(x=38\) and checks that \(x^{\prime}=y^{p}\cdot f^{e}=17^{47}\cdot 16^{2}=38\). As \(x=x^{\prime}\), this round of the protocol is passed successfully. This error occurs due to the fact that \(16^{2}\equiv 16^{20}\ mod\ 65=61\); these flukes will be less likely the bigger the value of the modulus \(n\). Table 9 shows the average number of times needed for the different parameter settings for each of the protocols. As expected, the number of tries necessary to break their security increases as the number of trials requested by the verifier increases. However, it is important to note that the values assigned for the parameters in this experiment are much smaller than the ones that would be used in real life, as the computing power available to the user is not enough to support such numbers. Therefore, while these results provide a useful first practical experience with ZKP, they should not be taken as indicative of the performance of these protocols in real-world scenarios. Nonetheless, this experiment serves as a good starting point for understanding the strengths and weaknesses of these protocols and their potential applications in various fields. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a)**} \\ \hline \(\mathbf{t}\) & **Number of Tries (\(p=3\), \(\mathbf{q=5}\))** & **Number of Tries (\(p=17\), \(\mathbf{q=31}\))** & **Number of Tries (\(p=233\), \(\mathbf{q=173}\))** \\ \hline
1 & 4 & 7 & 59 \\
2 & 5 & 39 & 469 \\
3 & 10 & 230 & 633 \\
4 & 17 & 1355 & 3365 \\
5 & 69 & 7989 & 15,840 \\ \hline \multicolumn{4}{c}{(b)} \\ \hline \(\mathbf{t}\) & **Number of Tries (\(p=3\), \(\mathbf{q=5}\))** & **Number of Tries (\(p=17\), \(\mathbf{q=31}\))** & **Number of Tries (\(p=233\), \(\mathbf{q=173}\))** \\ \hline
1 & 3 & 2 & 4 \\
2 & 3 & 6 & 17 \\
3 & 10 & 14 & 247 \\
4 & 16 & 223 & 4449 \\
5 & 160 & 3564 & 71,209 \\ \hline \multicolumn{4}{c}{(c)} \\ \hline \(\mathbf{t}\) & **Number of Tries (\(p=11\), \(\mathbf{q=5}\), \(\mathbf{a=3}\))** & **Number of Tries (\(p=29\), \(\mathbf{q=7}\), \(\mathbf{a=11}\))** & **Number of Tries (\(p=373\), \(\mathbf{q=31}\), \(\mathbf{a=8}\))** \\ \hline
1 & 3 & 48 & 137 \\
2 & 3 & 282 & 314 \\
3 & 10 & 399 & 693 \\
4 & 16 & 4 & 5548 \\
5 & 160 & 5 & 34,560 \\ \hline \end{tabular}
\end{table} 
Table 9: Performance measurement tables. (**a**) FFS, (**b**) GQ, (**c**) Schnorr. Figure 7: FakeProver test—GQ.
# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 4 Signal Preprocessing: Denoising and Amplitude Normalization
### Denoising Strategies  
The raw PPG signals are usually corrupted by noise and motion artifacts, which can undermine their interpretation and use [59]. In the CLAS dataset, the signals had already been preprocessed by the authors during the acquisition phase [35], and thus, no further denoising procedure was applied.  
Concerning the CLAWDAS, the PPG raw signals of each subject were preprocessed by a multiresolution wavelet denoising strategy, as suggested by [59; 60]. The signal was divided into frequency sub-bands using the Stationary Wavelet Transform (SWT) [61] with the Fejer-Korovkin mother wavelet [62] and four levels of decomposition. A soft thresholding was applied to the detail coefficients of each sub-band. The universal threshold calculated by the formula \(T_{k}=\sqrt{2 log(N_{j})}\) was adopted, where \(N_{j}\) is the length of the \(j\)-th wavelet coefficient and \(k\) is the sub-band [63]. The SWT was implemented with the a trous algorithm [64]. A preliminary operation of replicate padding was applied to the signal in order to obtain a length divisible by \(2^{level}\)[61], with \(level=4\).
# CEPP: Perceiving the Emotional State of the User Based on Body Posture
## 3 Overview of CEPP  
In this section, we explain in detail the proposed algorithm. Figure 1 represents the overview of _CEPP_ algorithm. _CEPP_ consists of two fundamental components: feature extraction and computation of similarity distance. Based on these two components, it recognizes the emotional state of the user by comparing the image of the user with the ground truth images. The procedure of the _CEPP_ algorithm works as follows. The first two steps are high-level approaches which rely on image processing techniques. Firstly, we identify the user within the sight of the camera and create a silhouette of the user. Afterwards, we acquire the edge of the user from the user's silhouette. Based on the high-level approach for obtaining the silhouette and the edge of the user, we proceed to the low-level feature based approach by obtaining the center point of the user and compute the similarity distance. We determine the center point of the user and extract the features based on the user's body posture once we obtain the edge of the user. After the feature extraction is complete, we compare the user's body posture image with the ground truth image's feature by computing the Euclidean distance based on the center point to the detected edge of the body posture from the 0 to 360 degrees. When the comparison of Euclidean distance is done, we analyze the similarity distance to find the best match with the ground truth image and classify the emotional condition. Details on each design choice will be explained later in this section.  
Figure 1: Operation flow of Computerized Emotion Perception based on Posture (_CEPP_).
## 3 EDFA's Space Qualification and Test  
The 2-W EDFA was conceived to be operated in LEO orbit within a CubeSat, which means that the exposure to the space environment can be even more intense compared with other conventional satellites. Therefore, the space qualification and test are critical steps before proceeding with the integration with the spacecraft prior to launch. The ISO 15864:2004 (Space systems--General test methods for space craft, subsystems and units) and ISO 19683:2017 (Space systems--Design qualification and acceptance tests of small  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Functional Test** & **Initial-Final** & **Consumed** & **Initial-Final** \\
**Name** & **Power (dBm)** & **Power (W)** & **Temp. (\({}^{\circ}\)C)** \\ \hline FT0 & 32.82 & 18.58 & Ambient \\ FT1H & 32.03 to 30.99 & 18.67 & 56.5 to 86.9 \\ FT1C1 & 31.6 to 33.2 & 17.92 & \(-\)10 to 25 \\ FT1C2 & 31 to 33.12 & 18.52 & \(-\)20 to 15 \\ FT1C3 & 30 to 32.97 & 17.99 & \(-\)30 to 5 \\ FT2H & 32.99 to 32.01 & 18.71 & 57.6 to 87.6 \\ FT2C & 30 to 32.99 & 17.79 & \(-\)30 to 5 \\ FT3H & 31.90 to 30.82 & 18.66 & 56.8 to 88.1 \\ FT3C & 30 to 32.92 & 17.74 & \(-\)30 to 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numerical results of the EDFA’s full TVT campaign.  
Figure 5: Dependency of the EDFA’s output optical power with wavelength for different levels of input optical power.  
spacecraft and units) standards were followed as a reference guide to comply with the qualification for sinusoidal and random vibration tests, thermal vacuum tests (TVT) and total ionization dose (TID) test. Three identical EDFA modules were produced, two of which went through the full qualification tests to certify that the third one is prepared for operation in the space environment. One of the units was used to make a preliminary investigation under partial high-temperature vacuum test prior to the complete TVT, as well as a preliminary TID in facilities with simpler equipment than the final one, which was the Center for Nanosatellite Testing (CeNT) of the Kyushu Institute of Technology in Japan.