esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Threshold FOV of the Satellite Based on the Improved Observation Mode
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Revisit Period**} & **AOFOV\_**(\_\)**(deg)** & **Daily Laps** & \multicolumn{2}{c}{**Orbit Altitude**} & **Orbit Inclination** \\ \hline \multirow{6}{*}{1 day} & & 14 + 1/2 & 725,6459 & 98.2876 \\  & & 14 + 1/3 & 780.6078 & 98.5156 \\  & 12.000” & 14 + 1/4 & 808.4000 & 98.6343 \\  & & 14 + 1/5 & 825.3501 & 98.7051 \\  & & 14 + 1/6 & 836.6453 & 98.7533 \\ \hline \multirow{6}{*}{2 days} & & 14 + 1/3 & 780.6078 & 98.5156 \\  & 6.207” & 14 + 1/4 & 808.4000 & 98.6343 \\  & & 14 + 1/5 & 825.3501 & 98.7051 \\  & & 14 + 1/6 & 836.6453 & 98.7533 \\ \cline{2-5}  & 6.000” & 14 + 2/3 & 671.7269 & 98.0680 \\  & & 14 + 2/5 & 758.4958 & 98.4235 \\ \hline \multirow{6}{*}{3 days} & & 14 + 1/4 & 808.4900 & 98.6334 \\  & 4.186” & 14 + 1/5 & 825.3501 & 98.7051 \\  & & 14 + 1/6 & 836.6453 & 98.7533 \\ \cline{1-1}  & 4.091” & 14 + 2/3 & 671.7269 & 98.0680 \\  & & 14 + 2/5 & 758.4958 & 98.4235 \\ \cline{1-1}  & 4.000” & 14 + 3/4 & 645.1487 & 97.9614 \\  & & 14 + 3/5 & 693.1714 & 98.1549 \\ \hline \multirow{6}{*}{4 days} & & 14 + 1/5 & 825.3501 & 98.7051 \\  & 3.158” & 14 + 1/6 & 836.6453 & 98.7533 \\ \cline{1-1}  & 3.104” & 14 + 2/5 & 758.4958 & 98.4235 \\ \cline{1-1}  & 3.051” & 14 + 3/5 & 693.1714 & 98.1549 \\ \cline{1-1} \cline{2-5}  & 3.000” & 14 + 4/5 & 629.3215 & 97.8984 \\ \cline{1-1} \cline{2-5}  & 2.535” & 14 + 1/6 & 836.6453 & 98.7533 \\ \cline{1-1} \cline{2-5}  & 2.900” & 14 + 5/6 & 618.8195 & 97.8568 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The relationship between the revisit period, the along-orbit FOV, the daily laps, the orbit altitude, and the orbit inclination. sensor's COFOV is set to 15\({}^{\circ}\). Corresponding to the orbital altitude and inclination of the satellite, the AOFOV of the sensor is set to the minimum value in each case. The simulation results for all the cases in Table 3 are shown in Figure 7.  
The simulation results in Figure 7 intuitively verify our theoretical derivation. When the AOFOV is 12\({}^{\circ}\), it only takes one day to revisit the GEO target to be observed. When the AOFOV is 6\({}^{\circ}\), it takes two days to revisit the GEO target to be observed. When the AOFOV is 4\({}^{\circ}\), it takes three days to revisit the GEO target to be observed. When the AOFOV is 3\({}^{\circ}\), it takes four days to revisit the GEO target to be observed. As the AOFOV gradually decreased, the number of revisit days required increased. When the revisit period is fixed, changes in satellite orbital altitude and inclination due to the different daily laps will not affect the satellite coverage.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.2 Interaction Mechanism  
Nineteen papers mentioned that AR application is an interaction mechanism between different parties. Within a construction environment, on-site users can communicate with off-site users allowing for a natural flow of information [11; 30]. It has also been mentioned that this interaction mechanism can be conducted with multiple people at the same time allowing for better collaboration [42]. In addition, mobile AR and head-mounted AR create a context-immersive space that facilitates social interactions among users, construction places, and objects, and provide multi-user-based contents in the linked structure [42]. Therefore, the papers suggest that there is consensus about the beneficial aspect of AR being an interaction tool that allows for a visual experience between users to communicate verbal and contextual information of the site, allowing for collaborative decision making.
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 4 Statistical Analysis of Channel Propagation Characteristics
### Discussion  
Based on the above analysis, the following observations could be highlighted:  
* The 5G system operating with SSB-based transmission, the echoes arrived at the receiver with smaller mean excess delays but were relatively weaker. While in the 5G system operating with the wide beam-based transmission, echoes arrived at the receiver with large excess delays but were relatively much stronger.
* The RMS delay spread is lower for the 5G system, using a single wide beam, than the multiple SSBs-based transmission systems.
* The system with multiple SSB-based transmission, the measured PL is on average about 20 dB higher than the PL predicted by the 3 GPP model. While in case of single wide-beam-based transmission, the PL estimation by 3 GPP model is more accurate. A possible explanation for this difference is that the 3 GPP model does not account for SSB-based transmission, leading to a PL estimate for single wide-beam-based transmission more accurate than for the SSB-based transmission.
* The 5G system employing a single wide beam gives a higher K-factor than the multiple SSBs-based transmission systems.
# Multi-Feature Guided Low-Light Image Enhancement
## 4 Experiment and Result Analysis
### Result Analysis
#### 4.2.2 Comparison with State-of-the-Art Methods  
We also compare the performance of our method with current state-of-the-art methods based on deep learning. Results are demonstrated in Figure 4; we zoom in on some details in the bounding boxes to get a better observation.  
After comparing the pictures, we can see that our method can restore detailed information and suppress noise in a relatively dark scene with good contrast. The Zero-DCE method fails to enhance background details in darker scenes. The KinD method is unsatisfactory in enhancing contrast and detail.
# UVsq-Sat NG, a New CubeSat Pathfinder for Monitoring Earth Outgoing Energy and Greenhouse Gases
## 3 Description of the Uvsq-Sat NG Space-Based Mission
### Calibration and Validation--Cal/Val  
Cal/Val constitutes a vital phase in assuring the precision and dependability of the Uvsq-Sat NG's measurements and observations. Figure 3 depicts the observation principle of the Uvsq-Sat NG satellite.  
Hereafter is a detailed description of the Uvsq-Sat NG Cal/Val process. It consists of the following phases.  
* Pre-Launch Calibration: Before Uvsq-Sat NG is launched, its payloads (ERSs, NIR Spectrometer, and NanoCam) will be calibrated in controlled laboratory conditions in a clean-room class 1000. Calibration involves exposing the instruments to known physical parameters, such as temperature, radiation, and light sources, and comparing the instrument's response to the expected values. In this stage, reference standards are used to determine the instrument's sensitivity, linearity, and accuracy.
* Pre-Launch Validation: It encompasses testing and validating the satellite's data processing algorithms through simulated data and mimicking real-world measurements,  
Figure 3: Schematic illustration of radiation transfer in passive remote sensing, applied to Uvsq-Sat NG. The observation approach of the Uvsq-Sat NG NIR Spectrometer relies on measuring spectra of sunlight scattered back via the Earth’s surface and atmosphere in the NIR spectral range.  
processed with mission algorithms and compared against known outcomes for data processing accuracy.
* In-Orbit Calibration: Following its launch and early operations, In-Orbit Calibration is conducted to optimize the instrument's calibration. This involves using internal calibration sources (laser light source for the Uvsq-Sat NG Spectrometer calibration, for instance) or external known references (Sun, Moon, stars, use of the Lunar Laser facility at Calern (Observatoire de la Cote d'Azur, France), or specific Earth locations, such as Palm Jumeirah in the United Arab Emirates, for satellite instrument calibration). The calibration coefficients are validated and adjusted as needed by comparing the In-Orbit Calibration data with the Pre-Launch Calibration results.
* In-Orbit Validation: Once the satellite is operational, its data undergo validation through a comparison with measurements taken from models, ground-based measurements, or data from other satellite missions. This process will be implemented to validate the quality and consistency of the future Uvsq-Sat NG data. Cross-validation with data from independent sources is essential for establishing the credibility of the Uvsq-Sat NG measurements and for detecting any potential biases or errors.
## 3 Position Tracking Controller Design
### Observer Gain Selection Using Optimal Control  
For the gain tuning guide, the LQR method is applied to the observer (17). The estimation error dynamics can be rewritten as  
\[\underbrace{\begin{bmatrix}\hat{\theta}\\ \dot{\omega}\\ \dot{t}_{l}\end{bmatrix}}_{\hat{x}}=\left(\underbrace{\begin{bmatrix}0&1&0\\ 0&-\frac{R}{7}&-\frac{1}{7}\\ 0&0&0\end{bmatrix}}_{A_{o_{1}}}-\underbrace{\begin{bmatrix}l_{1}\\ l_{2}\\ l_{3}\end{bmatrix}}_{L_{o}}\underbrace{\begin{bmatrix}1&0&0\\ \tilde{\tau}_{L}\end{bmatrix}}_{C_{o}}\right)\underbrace{\begin{bmatrix}\tilde {\theta}\\ \tilde{\omega}\\ \tilde{\tau}_{L}\end{bmatrix}}_{\hat{x}}. \tag{20}\]  
Because the estimation error dynamics (20) is observable, the LQR method can be applied to it. The cost function is defined as  
\[J_{o}=\int_{0}^{t}\left(\tilde{x}^{T}Q_{o}\tilde{x}+u^{T}R_{o}u\right)d\tau. \tag{21}\]  
where \(Q_{o}\) and \(R_{o}\) are positive definite. To obtain the observer gain for optimal control, the Riccati equation is used as follows:  
\[\begin{split}& A_{o_{1}}P_{o}+P_{o}A_{o_{1}}^{T}-P_{o}C_{o}^{T}C _{o}P_{o}+Q_{o}=0,\\ & L_{o}=-R_{o}^{-1}C_{o}P_{o}.\end{split} \tag{22}\]
## 3 Results
It predicted 103 of 108 test images belonging to Class 2 correctly and 5 of them incorrectly. KNN predicted 85 correctly and 4 incorrectly out of 89 test images belonging to Class 3. KNN predicted 82 correctly and 6 incorrectly out of 88 test images belonging to Class 4. KNN predicted 139 correctly and 9 incorrectly out of 148 test images belonging to Class 5. The KNN classifier was the most successful among the classifiers used in this study to classify hydatid cyst images because the KNN method is a simple and understandable classification algorithm. Compared to the training and implementation processes of other complex machine learning algorithms, KNN is easier to implement and understand. KNN relies on data without making any biases or assumptions. This allows KNN to offer a flexible classification approach, given that hydatid cyst images can be complex and variable in nature. The accuracy values reached by the proposed model in six different classifiers are shown in Table 2.  
In the proposed model, the fine KNN classifier's greatest accuracy value was attained. The performance metrics obtained in the fine KNN classifier and the training times of the models are presented in Table 3.  
The proposed model achieves the maximum accuracy in Class 5 when Table 3 is reviewed. Class 5 images classification accuracy of the proposed model was 97.88%. Class 3 was the group in which the suggested model failed the most. Class 3 image classification accuracy of the proposed model was 93.18%. The AUC/ROC curve obtained in the proposed hydatid cyst model is in Figure 11. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**FT** & **NB** & **LD** & **SVM** & **ES** & **KNN** \\ \hline
49.1 & 66 & 82 & 89.2 & 94 & 94.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classifier accuracy values of the proposed model (%). Figure 11: AUC/ROC curves of proposed hydatid cyst model. As can be seen in Figure 11, it has been observed that the proposed hydatid cyst model has achieved a high performance.
## 4 Results and Discussion
### Evaluation Metrics  
**A confusion matrix** is a structured table that illustrates the performance of a classification model on a dataset with known true values. It classifies prediction into four distinct categories: true positive (TP), true negative (TN), false positive and false negative (FN).  
**The classification report** provides detailed insights into the precision, recall, F1-score, and support for each class. It is very useful for understanding the performance of the classifier across different classes.  
**Accuracy** measures the overall correctness of a model by determining the ratio of correct predictions to the total number of instances. It is calculated by:  
\[\text{Accuracy }=\ \frac{\mathbf{TP}+\mathbf{TN}}{\mathbf{TP}+\mathbf{TN}+\mathbf{FP}+\mathbf{ FN}}\]  
**Precision** is the accuracy of positive predictions. It is represented as the ratio of true positive predictions to the total number of positive predictions made. It is defined by:  
\[\text{Precision }=\ \frac{\mathbf{TP}}{\mathbf{TP}+\mathbf{FP}}\]  
**Recall** or sensitivity measures the ability of a model to identify all relevant instances. it is the ratio of true positive predictions to all positive samples. It is represented by:  
\[\text{Recall}\ =\ \frac{\boldsymbol{TP}}{\boldsymbol{TP}+\boldsymbol{FN}}\]  
**F1-score** or F1-measure is the harmonic mean of precision and recall, providing a balance between the two metrics. The value of F1-score is a number between 0 and 1, and it is calculated by this formula:  
\[\text{F1-score}\ =\ \boldsymbol{2}\times\frac{\boldsymbol{Precision}\times \boldsymbol{recall}}{\boldsymbol{Precision}+\boldsymbol{recall}}\]  
**Area under the curve (AUC)-receiver operating characteristics (ROC)** is a widely used method for evaluating classification model performance. The ROC curve illustrates the balance between the true positive rate (**TPR**) and the false positive rate (**FPR**). The TPR, also called recall, shows the percentage of actual positive cases correctly identified by the model. In contrast, the FPR shows the percentage of negative cases incorrectly classified as positive. It is defined by:  
\[\text{FPR}\ =\ \frac{\boldsymbol{FP}}{\boldsymbol{TN}+\boldsymbol{FP}}\]  
The AUC quantifies the overall area under the ROC curve, representing the model's ability to distinguish between classes. An AUC value closer to 1 signals a highly effective model.
## 6 Conclusions  
Aiming at the TVR problem during the whole life cycle of the structure, an active learning TVR analysis method based on EDF-FCTP-Kriging is proposed. The Kriging surrogate model can ensure the accuracy and efficiency of the solution by using as little sample information as possible. The specific conclusions are as follows:  
1. Based on the probability distribution model of the FCTP, the cumulative failure probability of structure in the whole life cycle is obtained, which solves the problem of estimation difficulty of the traditional crossing rate method.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Position (m)** & **Err (\%)** \\ \hline
20.5 & 9.09 \\
21 & 6.09 \\
21.5 & 2.04 \\
22 & 3.60 \\
22.5 & 0.08 \\
23 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: EDFK-FCTP relative error of wing structure.  
Figure 11: Failure probability trend of wing structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \(\textbf{N}_{\textit{all}}\) & \(\textbf{N}\) \\ \hline MCS-FCTP & \(2.97\times 10^{7}\) & \(10^{5}\) \\ EDFK-FCTP & 7806 & 62 \\ ASVR & 19,636 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of calculation results of various methods for Aircraft Wing Structure.  
Figure 10: FCTP probability density function of wing structure.  
2. The EDF active learning function is proposed based on model error and sample distance and the geometric mean of probability density of training samples and the maximum error of the first four order origin moments of two adjacent iterations are taken as convergence conditions, which accelerates the speed of convergence and significantly improves the efficiency of model construction. 3. The proposed method improves the fitting effect in the region of low failure probability compared with other methods and ensures accuracy for complex LSF while markedly enhancing computational efficiency. 4. The proposed method calculates the failure probability during life cycle of structure not only limited to single time interval, which significantly enhances the efficiency of assessments and holds particular engineering importance for the reliability evaluation and design of intricate structures.  
Methodology, S.Y.; Validation, X.W.; Writing--original draft, F.Y. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China: 52405253.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 5 Analytical Methods to Assess VOCs
### GC-MS for VOCs Detection from Wood and Wood-Based Panels  
Gas chromatography (GC) is today the most important analytical method in organic chemical analysis for the determination of individual low molecular substances in complex mixtures. For compounds detection, conventional flame ionization detector (FID) can be used. However, mass spectrometry (MS) is a universal and sensitive detection method, providing data for both the identification of compounds based on their mass spectra and also for their quantification when providing both quantification and confirmation ions in one run [120]. A suitable GC capillary column needs to be selected for the separation of analytes in the sample--the most often used types are nonpolar columns (-5% or 1% modified polydimethylsiloxane) or polar wax columns (Table 2). According to ISO 16000-6 [121], columns of a length of 30 m are common, which are characterized by an internal diameter of 0.25 to 0.32 mm and phase thickness of 0.25 to 0.5 \(\upmu\)m.  
Comprehensive two-dimensional gas chromatography (GC \(\times\) GC) is allowing better sensitivity due to a combination of two columns, usually of a different polarity, and a modulation step, where an eluate from a first column is cryo-focused before injection onto a second column. This way, coelutions appearing in single dimension analysis can be resolved, and matrix components can be separated from target compounds. Longer columns can be used for the same purpose but unavoidably prolonging the total run time [42; 62; 116; 122; 123; 124; 125]. For basic measurements, a widely used quadrupole mass spectral analyzer is sufficient. However, advanced analyzers such as time of flight (TOF; either unit or high resolution) can offer beneficial properties in case of the nontarget type of analysis. Combined instruments coupling either quadrupole and TOF or multiple quadrupoles can increase the sensitivity of determination. A higher resolving power of detection can increase samples' throughput, since for chromatographic separation, a faster ramping can be used [125]. In mass spectrometric detection, electron impact ionization is used as a first-choice option, since the initial identification of chemical compounds can be based on mass spectral similarity with the in-built mass libraries (NIST, Wiley) or various online sources. For confirmation of target compounds identity, retention times of respective standards could be used, or calculated Kovats retention indices (KI) may be compared with literature data [116; 118; 123; 126; 127]. The amount of compounds present in the solid material or emitted to the air can be expressed exactly using calibration curves or as an equivalent of one compound (e.g., toluene) [71]. For comparison, peak areas in the total ion current (TIC) chromatogram or sum of peak areas can be used [30; 70].
## 2 Related Work and Methods
### Proposed Method
#### 2.4.3 Optimization Method
```
Input: Data matrix \(X\in R^{m\times n}\) hidden layer size: \(m\) KNN parameter \(k\) learning rate \(\gamma\) balance parameters \(\beta,\lambda,\alpha\) The selected subset of the features. Initialize the parameters \(\textbf{W}_{1}\in R^{m\times d}\), \(\textbf{W}_{2}\in R^{d\times m}\), \(b_{1}\in R^{m}\), \(b_{2}\in R^{d}\) of auto-encoders; repeat

Calculate loss of auto-encoder by Equation (17);

Update \(\textbf{W}_{1}\) by Equation (18);

Update the \(j\)-th diagonal element of D by \(\frac{1}{2\times\left|\left|w_{j}^{(1)}\right|\right|_{2}^{3/2}}\);

Update \(\textbf{W}_{2}\) by Equation (23)

Update the jth diagonal element of B by \(\frac{1}{2\times\left|\left|w_{j}^{(2)}\right|\right|_{2}}\);

Update \(\textbf{b}_{1}\) by Equation (26);

Update \(\textbf{b}_{2}\) by Equation (27);

until ConvergenceReturn Selected features corresponding to the top k values of \(\mid\mid W_{1}\mid\mid^{2}\), which are sorted by descending order
```  
**Algorithm 1** Unsupervised feature selection method based on dual-graph autoencoder (DGA)
## 3 Intelligent Cluster-Based Routing Approach
### The Optimal and Secure Route Minimizing Communication Latency to the Fullest Extent  
This paper introduces a resolution that articulates an ideal and secure pathway, minimizing the communication delay within the designated vehicle network. VN is the vehicular node, and N = {N\({}_{1},N_{2},\ldots.N_{m}\),} is the number of vehicles. Data are sent from the VN\({}_{8}\) source node to the VN\({}_{d}\). We collect the information through all the nodes between the source and destination nodes using \(\sum_{i=0}^{n}\text{Vn}^{i}\). We set the location of the vehicles using V\({}_{\text{cl}}^{\text{Loc}[x,y]}\) under the velocity of nodes using V\({}_{\text{cl}}^{\text{v}}\). There are several nodes on the maps that provide the directions from one node towards others, so we use map\({}_{\text{lane}}\) under timestamps to provide the node's location through any distance from RSU. The threshold distance is set using D\({}_{\text{threshold}}\) under D\({}_{\text{RSU}}\) nodes selections.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 6 Experimental Results  
The experiments are conducted for two scenarios. In scenario 1, handover from WiMAX network to UMTS network is considered, and in scenario 2, handover from UMTS network to WiMAX network is considered. In both scenarios, the number of service request is varied as 50, 100, 150, 200, and 250, and the performance metrics are evaluated for both the techniques.