esa-sceva/satcom-chunk-collection huggingface dataset:
# In-Situ Testing of Methane Emissions from Landfills Using Laser Absorption Spectroscopy
###### Abstract  
Field monitoring of methane emissions from landfills is of great importance for both environmental concern and economic benefit. This study presents a highly effective method to measure methane emissions from landfills based on tunable diode laser absorption spectroscopy (TDLAS). Methane concentration is obtained by analyzing the absorption spectrum of the laser after passing through the landfill gas. The relationship between methane concentration and the optical signal was calibrated in the laboratory. As the methane concentration increased from 400 ppm to 500 ppm, the absorption spectrum amplitude increased linearly from 0.0005 to 0.0046. In situ testing of methane emissions at a large-scale landfill in China demonstrated the accuracy of the TDLAS method. The methane concentrations in the well-covered areas were generally below 100 ppm. In the working area or the coverage area with holes, the methane concentration was about 700 ppm. The methane concentration was up to 1900 ppm, where the gas collection pipe is disconnected. Due to the accuracy and simplicity, the TDLAS method is suitable to detect methane emissions on a large-scale from landfills.  
methods: modelling, landfill gas; laser absorption spectroscopy; cover system; field monitoring +
Footnote †: journal: Applied Sciences
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 3 The Design and Calculation of the Zero Bending Moment Shield Tunnel Cross-Section
### The Calculation of the Internal Force of the Zero Bending Moment Shield Tunnel  
For shield tunnels whose cross-section is rational axis, the internal force of the cross-section is only composed of the axial force and the shear force. Assume that the axial force of the cross-section \(S\) is \(Q_{x}\) and the shear force is \(Q_{x}\). Given that the angle of the section will change as the coordinates change, the internal force of the cross-section \(S\) can be disintegrated as the horizontal loading \(F_{hx}\) and the vertical loading \(F_{vx}\) (in Figure 9 the leftward and the upward are set as positive), as shown in Figure 9. First calculate the position angle of the section \(S\), then disintegrate the horizontal loading as \(F_{hx}\), vertical loading \(F_{vx}\) as axial force \(N_{x}\), and shear force \(Q_{x}\) (in Figure 8 the pressure exerted on the section \(S\) direction and the direction where the slider rotates clockwise are set as positive).  
\(\sum X=0\), \(F_{hx}\) is calculated as Equation (14).  
\[F_{hx}=P_{1}y \tag{14}\]  
where \(F_{hx}\) is the loading of section \(S\) in a horizontal direction.  
\(F_{vx}\), \(F_{vx}\) is calculated as Equation (15).  
\[F_{vx}=P_{2}x+\frac{P_{3}}{2a}x^{2}-N_{1} \tag{15}\]  
where \(F_{vx}\) is the loading of section \(S\) in a vertical direction.  
Derivation of Equation (8) to \(x\), \(\psi\) is calculated as Equation (16).  
\[\psi=\frac{dy}{dx}=\frac{1}{2P_{1}}\cdot\sqrt{\frac{P_{1}}{2N_{1}x-P_{2}x^{2} -\frac{1}{3a}P_{3}x^{3}}}\cdot\left(2N_{1}-2P_{2}x-\frac{1}{a}P_{3}x^{2}\right) \tag{16}\]  
The expression of the angle \(\alpha\) can be calculated as Equation (17).  
\[\alpha=\arctan\frac{dy}{dx}=\arctan\psi \tag{17}\]  
where \(\alpha\) is the angle between the tangent of cross-section \(S\) and the horizontal line.  
According to the equivalent relation between \(F_{hx}\), \(F_{vx}\), and \(N_{x}\), \(\mu\), \(N_{x}\), \(Q_{x}\) can be calculated as Equations (18) and (19).  
\[N_{x}=F_{hx}\text{cos}\alpha-F_{vx}\text{sin}\alpha \tag{18}\]  
where \(N_{x}\) is the axial force of cross-section \(S\).  
\[Q_{x}=-F_{hx}\text{sin}\alpha-F_{vx}\text{cos}\alpha \tag{19}\]  
where \(Q_{x}\) is the shear force of cross-section \(S\).  
Figure 9: Internal force analysis for a section of zero bending moment shield tunnel cross-section 3.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
###### Abstract  
Multi-object tracking (MOT) has gained significant attention in computer vision due to its wide range of applications. Specifically, detection-based trackers have shown high performance in MOT, but they tend to fail in occlusive scenarios such as the moment when objects overlap or separate. In this paper, we propose a triplet-based MOT network that integrates the tracking information and the visual features of the object. Using a triplet-based image feature, the network can differentiate similar-looking objects, reducing the number of identity switches over a long period. Furthermore, an attention-based re-identification model that focuses on the appearance of objects was introduced to extract the feature vectors from the images to effectively associate the objects. The extensive experimental results demonstrated that the proposed method outperforms existing methods on the ID switch metric and improves the detection performance of the tracking system.  
computer vision; deep learning; multiple object tracking; re-identification +
Footnote †: journal: Medical Imaging
## 3 Results
This range represents normal conditions where no seismic events are detected. Any measured value outside this interval, whether lower than 98.73 or higher than 98.96, is considered a deviation from the norm. When such a deviation occurs, the system registers it as a potential earthquake, triggering an alert to signal the abnormal movement. This approach ensures accurate seismic activity detection by monitoring even slight variations in acceleration. Figure 7a illustrates an example where the acceleration coordinates are recorded as 0.19 m/s\({}^{2}\), 9.39 m/s\({}^{2}\), and 3.31 m/s\({}^{2}\) for the X, Y, and Z axes. These values indicate that movement has been detected in the system. The monitored total acceleration value is 9.96 m/s\({}^{2}\), which exceeds the standard range threshold. As a result, an alert is generated and displayed, signaling a potential seismic event. In contrast, Figure 7b presents a case where the acceleration coordinates remain within the accepted normal range, and the monitored total value does not exceed the threshold. In this scenario, no unusual movements are detected, and no alert is triggered, indicating stability. \begin{table}
\begin{tabular}{c c c} \hline \(P_{\text{EMM}}\) & **Number of Devices Reporting a Possible Event** & **P(EQ)** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 5: Simulation results of random values indicating potential earthquake events from devices within 200 km of the Vrancea seismic center with actual data predictions for past events. The comparison of these figures demonstrates how the system operates by continuously monitoring accelerometer data. When the calculated total acceleration surpasses predefined limits, it triggers an alert, potentially signaling an earthquake, and sends this event to the database through Azure API Request. This method ensures that even minor movements, which may not be immediately noticeable, are detected and recorded in real-time. Multiple devices recording the same event will increase the certainty of a real event.
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 4 AI-Assisted Sensing and Survivability Techniques
### Statistical Analysis  
AI is a collective name, covering a variety of individual technologies such as decision tree, association rules, support vector machine (SVM), artificial neural networks (ANNs), Bayesian networks, genetic algorithms, and k-nearest neighbor (KNN). Among these technologies, ANNs inspired by biological neural networks have a powerful nonlinear modeling ability when introducing hidden layers. Table 6 presents the applied AI techniques in optical sensing on learning, reasoning, and self-correction. It can be seen that ANN is the most widely used AI technique.
## 4 Results and Discussion
### Results
#### 4.2.7 Forward and Backward Linkage Effects  
The purpose of the forward linkage effect indicated by the sensitivity of dispersion is to identify the output of the NG supply sector as a raw material for production in other industries. The purpose of the backward linkage effect that can be confirmed by power of dispersion is to identify the output of other industries as a raw material for production in the NG supply sector. The average of power of dispersion and sensitivity of dispersion is 1. Therefore, if it is lower than 1, it is an industry that is lower than the average, and if it is higher than 1, it is an industry that is larger than the average. The respective analysis results of forward and backward linkage effects of the NG supply sector in South Korea and Japan are presented in Table 8. The results of the sensitivity of dispersion show that 0.77970 and 0.63836 were found in South Korea and Japan, respectively; that is, the values are less than 1. This means that the NG supply sector in Korea and Japan has the characteristics of final demand. The results of power of dispersion were 0.58638 and 1.06613 in South Korea and Japan, respectively. Therefore, the NG supply industry in South Korea, the value of which is less than 1, is a basic industry type. On the other hand, in Japan, the NG supply industry is a manufacturing type because its value is greater than 1. In summary, the NG supply sector is the final demand basic industry type in South Korea, and it is the final demand manufacturing type in Japan.
## 4 Results and Discussion
### Screening and Sieving of TBM Muck  
As indicated in Section 4.1, TBM muck gradation does not comply with the specification's requirements for concrete coarse aggregates. Thus, prior to using TBM muck in concrete mixtures, it should be initially classified based on a sieve analysis in order to have aggregates with acceptable sizes (10-20 mm (Figure 3a and 4-10 mm (Figure 3b). Using the appropriate sieves, the muck particles were classified into five categories:  
* Particles size from 20 to 75 mm.
* Particles size from 10 to 20 mm
* Particles size from 4 to 10 mm
* Particles size from 0.063 to 4 mm.
* Particles size less than 0.063 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sieve Size (mm)**} & \multicolumn{2}{c}{**\% Passing by Weight**} \\ \cline{2-3}  & **Test Result** & **QCS 2014 Limits** \\ \hline
75 & 100 & \\
63 & 100 & \\
50 & 86 & \\
37.5 & 74 & \\
25 & 43 & \\
20 & 25 & \\
14 & — & \\
10 & 19 & \\
6.3 & — & \\
5 & 18 & Max size should not be more than 75 mm \\
3.35 & — & \\
2.36 & 17 & \\
2.00 & — & \\
1.18 & — & \\
0.600 & — & \\
0.425 & 15 & \\
0.300 & — & \\
0.212 & — & \\
0.150 & — & \\
0.075 & 13 & \(\leq\)20\% (buildings filling) \\ \(\leq\)30\% (Road filling) & \\
0.063 & — & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Particle size distribution (PSD) of TBM muck as building or /road fill material according to ASTM D6913 and ASTM D1140.  
Figure 3: (**a**) Particles size from 10 to 20 mm, (**b**) Particles size from 4 to 10 mm.  
This classification will be considered a pre-step for future work where TBM muck is used as partial or full replacement of concrete coarse aggregates.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Carotenoids  
More than 600 carotenoids have been identified to date, 60 of which are present in the daily diet. They inhibit free radical activity through reduction and oxidation mechanisms. Unlike plants, animals and humans cannot synthesize carotenoids, making dietary intake essential. Carotenoids are absorbed through the intestines into the bloodstreamand transported to various tissues via lipoproteins. Carotenoids and their derivatives are widely used in cosmetics, dietary supplements, and pharmacological agents due to their potent antioxidant properties, which help inhibit excessive cellular proliferation. While the skin possesses intrinsic defense mechanisms, factors such as natural aging and prolonged exposure to ultraviolet (UV) radiation lead to an increased concentration of free radicals. This oxidative stress triggers apoptosis and necrosis in skin cells, resulting in photoaging, excessive dryness, and wrinkle formation. Carotenoids effectively absorb UV radiation, functioning as natural filters that help mitigate photoaging, reduce skin irritation, and lower the risk of skin cancer. Additionally, these compounds are used in the prevention and treatment of skin discoloration, contributing to a more even skin tone and enhanced complexion. They are commonly included in self-tanning formulations and therapies targeting hyperpigmentation. Furthermore, carotenoids exhibit anti-acne properties by normalizing sebaceous gland activity, cleansing hair follicles, and promoting epidermal exfoliation. They are also effective in reducing the appearance of freckles, further supporting their role in dermatological care [55; 56].
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 4 BackCom: Signal Processing and Detection  
This section covers signal processing aspects such as channel coding techniques, interference problems, and decoding schemes in BackCom. BackCom channel coding schemes are used to efficiently transmit data over wireless channels. The decoding techniques for backscattered signals and signal detection schemes including coherent and non-coherent detection are described in the following subsections, respectively.
# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 3 Main Work
### SCGFormer
#### 3.2.2 AcChebGConv Module  
The second crucial module is the Chebyshev graph convolution [43]. We introduce second-order neighborhood information into the prior constraints of Chebyshev graph convolution. Specifically, the adjacency matrix that combines both first-order and second-order information is employed as the primary constraint. This enhanced module is referred to as the AcChebGconv module, denoting Chebyshev graph convolution with a fusion of adjacency matrix information. The AcChebGConv module comprises two instances of AcChebGconv and two rectified linear unit (ReLU) layers, as depicted in Figure 4. Building upon the globally refined preliminary features extracted in the preceding module, this module further refines local feature information by expanding the receptive field, as illustrated in Figure 3c.  
The output \(X^{l+1}\) at the L-th layer of the AcChebGConv module is given by:  
\[X^{l+1}=\sum_{k=0}^{K-1}T_{k}(\bar{L})X^{l}\theta_{k}, \tag{6}\]  
where \(T_{k}\) is the k-th order chebyshev correlation function (i.e., \(T_{k}(x)=2 xT_{k-1}(x)-T_{k-2}(x)\)), \(k\geq 2\), \(T_{0}=1\), and \(T_{1}=x\). \(\theta_{k}\in R^{D\times D_{l+1}}\) is the matrix of trainable parameters in the graph convolution of this layer. \(L=2L/\lambda_{max}-I\) denotes the Laplacian operator based on the maximum eigenvalue rescaling, and \(\lambda_{max}\) is the maximum eigenvalue in the normalized graph Laplacian \(L\). The normalized graph Laplacian \(L\) is formulated as follows:  
\[L=I-\bar{D}^{-\frac{1}{2}}\bar{A}\bar{D}^{-\frac{1}{2}}, \tag{7}\]  
Figure 4: Structure of AcChebGconv module.  
where the adjacency matrix \(\tilde{A}=A+A^{2}\), it incorporates more extensive adjacency information by summing the first-order adjacency matrix with the second-order adjacency matrix.  
In alignment with the existing literature [28], the updated Chebyshev graph convolution layer inherits the K-order polynomial function from its foundational Chebyshev graph convolution, thereby affording the advantage of an expanded receptive field. This extension allows for the aggregation of information from the K nearest neighbors. Furthermore, the innovative constraint introduced here, which integrates the second-order adjacency matrix, enhances the local structural constraints of the human body. This augmentation, in conjunction with the semantic graph convolution, leads to a noteworthy enhancement in the model's performance, as will be substantiated in the forthcoming experimental sections.  
Our model incorporates two variants of graph convolution: the semantic graph convolution and the Chebyshev graph convolution enriched with fused adjacency matrix information. In comparison to prior graph convolution methods [28], although the computational load is notably increased, the required dataset remains relatively uncomplicated, involving only 17 nodes. As a result, the computation entails the calculation of a \(17\times 17\) matrix.
## 2 Materials and Methods
### Outcome Variables
#### 2.4.1 EMG Outcome Variables  
Two variables were extracted from the actEMG analysis: the peak actEMG and the average actEMG during the MVIC of each muscle tested. The sampling rate registration was 2000 Hz, and the common mode rejection ratio was 100 dB. The raw EMG signal was processed to enable the comparison of the statistical means. The band-pass filter ranged from 10 to 500 Hz, the full wave was rectified, and the root-mean-square (RMS) of the signal was derived [18; 49]. The signal normalization was performed relative to the maximum of maximum values [50]. To determine this maximum of maximum, the highest value observed for each muscle across three MVIC repetitions was selected. To calculate peak actEMG variables, the mean of peak actEMG values was calculated, and then it was divided by the maximum of maximum value, selected before, and multiplied by 100. Thus, the peak actEMG variable is expressed as the percentage of maximal activation. To calculate average actEMG, the mean of all activation values during each maximal voluntary isometric contraction was calculated and divided by the maximum of maximum value, selected before, and multiplied by 100. In this way, the average actEMG variable is also expressed as the percentage of maximal activation.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis
### Multi-Focus Fusion Methods Based on Transform Domain
#### 2.1.1 Multi-Scale Decomposition (MSD)-Based Methods
[13] extracted the six-dimensional feature vectors of the source image using the dual-tree complex wavelet transform (DT-CWT) coefficient sub-bands and then projected them onto the class tags by training a two-class (focused and unfocused) support vector machine (SVM). The common methods based on wavelet transform include contour wave [36], shear wave [37], non-subsampled contour wave transform (NSCT) [14], and non-subsampled shear wave transform (NSST) [15], etc. NSCT method and NSST method are two widely used methods at present. Figure 5 shows the image decomposition process of NSCT. In general, the advantage of the MSD method lies in extracting more accurate feature information and having a better fusion effect. However, the decomposition information is too much, leading to a large amount of calculation. Figure 5: Image decomposition process of NSCT [38]. Reprinted/adapted with permission from Ref. [38]. 2019, MDPI.
###### Abstract  
The aim of the present study was to determine the vertical root fracture (VRF) resistance of roots obtuated with TotalFill BC Sealer and AH Plus sealer using lateral condensation and single cone techniques in comparison to untreated controls. Sixty single rooted mandibular premolars were sectioned and divided into six groups. Ten teeth were left untreated (positive control-Gp 1) and fifty teeth were cleaned and shaped. Ten root specimens were left unfilled (negative control-Gp 2) and the remaining roots were divided into 4 groups. Gp 3, GP and AH Plus sealer (AH Plus) using the cold lateral compaction (LC) technique; GP 4, GP and AH Plus using the Single Cone (SC) technique; GP 5: TotalFill GP and TotalFill BC sealer using the LC technique; GP 6: TotalFill GP and TotalFill BC sealer with SC. VRF was performed for all specimens using a universal testing machine. Analysis of variance (ANOVA) and [PERSON]'s post-hoc multiple comparison test was used to compare the means among tested study groups. Group 1 (positive control) displayed the highest fracture resistance (946.61 \(\pm\) 166.465 N); however, the lowest fracture strength was demonstrated by the specimens in group 2 (negative control) (433.31 \(\pm\) 129.350 N). Specimens treated with AH plus using different obturation techniques (group 3 and 4) showed comparable outcomes (_p_ > 0.05). Similarly, specimens treated with TotalFill BC sealer with different obturation techniques showed statistically similar outcomes (_p_ > 0.05). It was also observed that specimens in groups 3, 4, 5 and 6 demonstrated comparable outcomes of fracture strength (_p_ > 0.05). The use of TotalFill-BC sealer showed similar vertical root fracture resistance as AH plus sealer in root canal treated teeth. Use of totalfill-BC and AH Plus sealer in root canal treatment showed vertical root fracture resistance comparable to untreated natural teeth (positive controls).  
sealers; vertical fracture; root filling; fracture resistance; obturation technique
# Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN)
## 2 Methods
### Methods for Evaluating Input Feature Importance
#### 2.2.4 SHAP Values  
SHAP (Shapley additive explanations) values use principles from game theory to measure the contribution of each feature to the final prediction [29; 41]. They aim to fairly allocate the contributions of each feature towards achieving the overall result [31]. SHAP values can be applied in machine learning to measure the contribution of each feature to the model's prediction, providing insights into how each feature collectively influences the final outcome [42]. In evaluating feature importance, SHAP values compare the model's output with and without a specific feature for a given data point, accounting for all possible combinations of the other features. The average difference in the output is then computed. The SHAP value for feature \(X_{j}\) in a model is represented by Equation (6):  
\[\text{Shapley }(X_{j})=\sum_{S\subseteq N\backslash\{j\}}\frac{K!(p-k-1)!}{p!}( f(S\cup\{j\})-f(S)) \tag{6}\]  
where  
\(p\)--The total number of features.  
\(N\backslash\{ji\}\)--A set of all possible combinations of features excluding \(X_{j}\).  
\(S\)--A feature set in \(N\backslash\{jj\}\).  
\(f(S)\)--The model prediction with features in \(S\).  
\(f(S\cup\{jj\})\)--The model prediction with features in \(S\) plus feature \(X_{j}\).  
According to Equation (6), the SHAP value of a feature represents its marginal contribution to the model's prediction, averaged across all possible models with varying combinations of features [42]. Equation (6) determines feature importance for a single data point. In our study, which involves multiple data points, this process is repeated for each data point. The average difference in output across all data points is then used to measure the importance of a single feature. The calculation process of SHAP values for multiple data points in our dataset is illustrated in Figure 4.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline ECT & Thickness & 1 & w & d & BCS \\ \hline lb/in & in & in & in & in & lb \\ \hline
46.8 & 0.197 & 29.75 & 26.25 & 47.5 & 1780 \\ \hline
52 & 0.200 & 31.5 & 29.5 & 47 & 1720 \\ \hline... &... &... &... &... &... \\ \hline
46.83 & 0.206 & 12 & 6 & 8 & 541.5 \\ \hline
44.32 & 0.215 & 12 & 6 & 15 & 549.8 \\ \hline \end{tabular}
\end{table}
Table 1: Principle of permutation feature importance.  
In this study, the explainer.shap_values() function from the SHAP library in Python 3.11.5 was employed to compute the SHAP values for each BCS feature.