esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Easy Cases
It is also straightforward to verify that when \(\mu\) is a locally stable matching of \((I,G)\), \(\mu_{1}\) and \(\mu_{2}\) are locally stable matchings of \((I_{1},G_{1})\) and \((I-\mu_{1},G_{2})\) respectively. Let us now argue the converse. Suppose \(\mu_{1}\) and \(\mu_{2}\) are locally stable matchings of \((I_{1},G_{1})\) and \((I-\mu_{1},G_{2})\) respectively but \(\mu\) has a local blocking pair \((f,w)\) whose point of contact is \(w_{1}\). Thus, in \(\mu\), \(f\) has an opening or prefers \(w\) over its worst employee \(w_{2}\), and that \(w\) is either unmatched or prefers \(f\) over her current employer. Furthermore, since \(G_{2}\) is an empty graph, \(w\) and \(w_{1}\) must both be in \(W_{1}\) and neighbors in \(G_{1}\). If \(f\) has an opening in \(\mu\) or \(w_{2}\) is in \(W_{2}\), then \(f\) has an opening after the matching \(\mu_{1}\) so \((f,w)\) is a local blocking pair of \(\mu_{1}\). On the other hand, if \(w_{2}\) is in \(W_{1}\), then \(f\) prefers \(w\) to its worst employee \(w_{2}\) in \(\mu_{1}\) so that \((f,w)\) is again a local blocking pair of \(\mu_{1}\). All cases lead to a contradiction. Thus, \(\mu\) must be a locally stable matching of \((I,G)\). To find a maximum locally stable matching of \((I,G)\), we do what is essentially a brute force method. We consider all possible matchings of \(I_{1}\). For each such matching \(\mu_{1}\), we check to see if \(\mu_{1}\) is a locally stable matching of \((I_{1},G_{1})\). If it is, we construct \(I-\mu_{1}\) and then find a maximum matching \(\mu_{2}\) of the instance. According to Theorem 3, \(\mu_{2}\) is a maximum locally stable matching of \((I-\mu_{1},G_{2})\) since \(G_{2}\) is an empty graph. If \(\mu=\mu_{1}\cup\mu_{2}\) is currently the largest locally stable matching of \((I,G)\) we have seen, we store \(\mu\); otherwise, we move on to the next matching of \(I_{1}\). There are at most \(2r\) workers in \(W_{1}\). Each one is either unmatched or employed by one of the \(|F|\) firms. Thus, the number of possible matchings of \(I_{1}\) is \(O((|F|+1)^{2r})\). Verifying if a matching \(\mu_{1}\) of \(I_{1}\) is locally stable in \((I_{1},G_{1})\), constructing \(I-\mu_{1}\), and finding a maximum matching of the instance can all be done in \(O(poly(|F|,|W|))\) time. Hence, finding a maximum locally stable matching of \((I,G)\) takes \(O((|F|+1)^{2r}poly(|F|,|W|))\) time, which is polynomial in \(|F|\) and \(|W|\) when \(r\) is a constant.
# Inorganic Waste Generated in Kraft Pulp Mills:
## 3 Main Properties of the Inorganic Wastes
### Potentially toxic metals
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & **GLD** & & **SG** & **LM** & & **BFA** & & & **Limit FF** & **Limit CM** & **Crust \({}^{*}\) \\ \hline  & [4] & [25] & [30] & [19] & [25] & [28] & [41] & [25] & [42] & [47] & [47] & [19] & [48] \\
**Pb** & 6.12 & 46.8 & 13 & \textless{}3 & 34.1 & 6.79 & \textless{}3 & 44.3 & 28.7 & 31 & 150 & 300 & 12.5 \\
**Cd** & 3.81 & 5.19 & 9.4 & 0.3 & 4.75 & 0.91 & \textless{}0.3 & 4.7 & 2.9 & 3.3 & 25 & 15 & 0.2 \\
**Cu** & 229 & 80.9 & 102 & \textless{}10 & 4.6 & 0.73 & 4.1 & 25.8 & 63.6 & 72 & 700 & 400 & 55 \\
**Cr** & 295 & 56.0 & 118 & 12.6 & 12.4 & 16.7 & 7.0 & 24.1 & 66.9 & 74 & 300 & 400 & 100 \\
**Ni** & **233** & **189** & 84 & 23.9 & 25.2 & ni & 4.0 & 97.4 & 32.4 & 33 & 150 & ni & 75 \\
**Zn** & **3197** & 160 & 1000 & 9.9 & 15.0 & ni & 36 & 68.9 & 295.3 & 320 & 4500 & 2000 & 70 \\
**Hg** & \textless{}0.05 & ni & \textless{}0.03 & ni & \textless{}0.04 & \textless{}0.03 & ni & 0.03 & 0.1 & 1.0 & ni & ni \\
**V** & ni & ni & 1.9 & 39.0 & ni & ni & ni & ni & 92.7 & ni & ni & 400 & 135 \\
**Mo** & 0.29 & ni & 1.7 & \textless{}1 & ni & ni & 2 & ni & 3.8 & ni & ni & 50 & 1.5 \\
**As** & \textless{}0.1 & ni & 0.3 & \textless{}3 & ni & 0.38 & 2.7 & ni & 13.0 & 14 & 40 & 50 & 1.8 \\ \hline \hline \end{tabular}  
* ni-not indicated; Limit FF–current Finnish limit values for ash used as a forest fertilizer; Limit CM–limits in Finnish legislation for ashes use as an earth construction material; \({}^{*}\) Average crustal abundance  
\end{table} 
Table 5: The total content of potentially toxic elements (mg/kg). construction agent. In addition, the leached concentrations of heavy metals, chloride, fluoride from SG were lower than the EU limits for disposed waste in inert waste landfills. Nevertheless, sulfate can exceed the limit for inert waste landfills (1000 mg/kg dw), since the value found was 5250 mg/kg. Additionally, [PERSON] and [PERSON] [32] concluded that fly ash from pulp and paper mills is in general considered non-hazardous, since PTM are mainly held in the amorphous aluminosilicate phases, and thus with low solubility. [PERSON] et al. [49] produced a glass from BFA and the material exhibited a satisfactory leaching behavior. Moreover, [PERSON] et al. [43] highlighted that biomass ashes produced in Portugal contain very low metals concentration. [PERSON] et al. [25] tested GLD, SG, and LM as alternative liming materials and concluded that metals do not appear to be a limiting factor for their use in the soil.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 1 Introduction
### Methods and Scope of Study
#### 1.2.2 Development of the AI Model  
Two AI models were developed for EEG-based emotion classification. The first model is a CNN-LSTM hybrid model, designed to extract spatial and temporal features from EEG signals. CNNs were used for feature extraction, while LSTMs captured temporal dependencies, making this model a benchmark for EEG classification [11].  
The second model is a fine-tuned LLM, trained on the channel-agnostic JSONL EEG dataset, allowing it to process EEG signals in a structured data format [14]. This approach investigates whether LLMs can effectively interpret EEG signals and infer affective states, diverging from conventional deep learning models that rely on spatial mappings [15].  
Additionally, the feasibility of a hybrid model combining CNN-LSTM and LLM inference was explored. The hybrid approach integrates CNN-LSTM-extracted features with LLM-based reasoning, examining whether LLMs can complement traditional EEG classification models by leveraging structured data representations [18].
## 1 Introduction
[16] used two pre-trained DL CNN models (vgg16 and resnet50) for fire detection and enhanced the depth of network learning by fine-tuning the network on the basis of a fully connected layer. Although this improved the detection accuracy, it also increased the network training time. However, the deep CNN performed well on complex data sets. At present, research on forest fire risk detection based on ML mainly involves flame detection and smoke detection [17; 18; 19; 20; 21]. The forest fire risk monitoring methods proposed by most scholars are for specific scenes, with several problems that need to be solved. How to recognize the color, form, and texture of fire in different chaotic backgrounds is the key to forest fire risk recognition [22; 23; 24; 25; 26]. The purpose of this study was to effectively extract the complex upper layer features of fire risk images and improve the robustness of input conversion by constructing an unmanned aerial vehicle (UAV) image-based forest fire risk prediction model based on a deep learning back-propagation neural network. The problems to be solved in the process of fire risk identification are feature extraction from training image data sets and training with a large number of fire risk image data sets [27; 28]. In this study, we attempted to solve the above problems as follows. First, the dynamic CNN (DCNN) network was trained in combination with transfer learning, and multiple pre-trained DCNNs were used to extract features from forest fire images. The traditional DCNN model was improved and the recognition speed and accuracy were compared and analyzed with the other three DCNN model algorithms with different architectures. The difficulty of using DCNN to monitor forest fire risk was solved and the model's detection accuracy was further improved. Therefore, the forest fire risk model based on the improved DCNN could accurately identify and classify the risk of a forest fire under natural light conditions. The main research contents of this paper were as follows:1. The DCNN network was trained in combination with migration learning, and multiple pre-trained DCNN models were used to extract the features of forest fire images. 2. Principal component analysis (PCA) reconstruction technology was used to enhance image category differentiation. 3. A 15-layer DCNN model called \"DCN_Fire\", as an improvement on the traditional DCNN model, was constructed and analyzed. 4. The recognition speed and accuracy of the improved DCNN model were compared with three other DCNN models with different architectures. 5. The DCNN model could accurately recognize the risk of a forest fire risk in natural light; thus it was suitable as an early warning system for forest fires. In Section 2, we discuss the extraction and enhancement of pertinent image features using the DCNN. Section 3 presents the improved DCNN model and its construction process. Section 4 provides an analysis of the model verification and experimental results. Finally, conclusions based on the study findings are presented in Section 5.
## 5 Future Works  
While the SECF model is promising as shown in this work, the sample size used in this work is too small. In order to increase the confidence of this model, larger sample size and with LiB from different manufacturers as well as different type of LiB are to be used. This requires large resources and long test time. Collaboration with other research organizations on LiB is desired.  
As mentioned in the introduction, the proposed model does not include the effect of the depth of discharge, which is known to affect SoH. This is one of the limitations of the proposed model and the inclusion of the depth of discharge in the model will also be our future work. Another limitation is the possible change of the values of the k parameters when the SoH becomes low as the internal structure of the LiB could have changed so significantly that a new set of k parameters are to be extracted from the ECBE model which has shown to be accurate even at low SoH [14]. Such investigation will also be our future work.  
Besides the above-mentioned applications, other applications of SECF model are possible and yet to be verified. One of them is the development of acceleration model of LiB lifetime. If we can perform the same four set of test conditions at higher temperature and discharge rate, the k values can be obtained in a short time, and hence, the complete SoH estimation equations can be obtained and applied to compute the lifetime of LiB cell in other operating conditions, in the same way as in the above examples, for a given heat capacity, weight, and thermal design of the cells. Thus, battery manufacturers can simply provide a lifetime model for each batch of their batteries for their users. Also, as k values determined the lifetime of LiB, statistical distribution of the k values from a set of LiB samples in a production batch can also provide information on the lifetime distribution of the batch of LiB, and hence a process control methodology can be developed.
# VFLD: Voxelized Fractal Local Descriptor
## 2 Related Works
### Traditional Handcrafted Descriptors  
Traditional handcrafted local features rely on normal surfaces, curvatures, distances, and angles between points, constructing features to describe the geometrical information of the local neighborhood of each keypoint. These descriptors should be robust to occlusion and clutter but sensitive to changes in the neighborhoods around keypoints [7].  
3D Shape Context (3 DSC) [8] is an extension of the 2D shape context descriptor for 3D point clouds. It uses a spherical support region centered on the keypoint aligned with its surface normal. It then divides the space into a set of bins by equally dividing azimuth and elevation and logarithmically distributing the radial dimension. The descriptor captures the distribution of points in every bin to construct the feature vector. However, this method needs the computation of different descriptors, one for every azimuth division, as there is no reference frame.  
In order to avoid the computation of different descriptors, ref. [9] proposed Unique Shape Context (USC). This descriptor defines a unique, unambiguous local reference frame to align the points. The further construction of the descriptor is like the previous 3 DSC.  
Point Feature Histogram (PFH) estimates surface normals to represent relationships between neighboring points. First, it creates pairs between the keypoint and its neighbors, and between each neighbor and the others. It then defines a Darboux reference frame between point pairs and calculates three angular features as the difference between normals, and the Euclidean distance between points. The final PFH descriptor is created by discretizing these four features into a histogram divided into bins [10; 11].  
The computational complexity of PFH for every point is exponential, so it is not applicable for real-time scenarios. In order to improve its efficiency, refs. [12; 13] introduced Fast Point Feature Histogram (FPFH). One of the improvements is related to the number of point pairs generated: in FPFH, the keypoint is paired with its neighbors in a radius, and its neighbors are only paired with their respective neighbors in a radius. The angular features are then computed as in PFH. Finally, the resulting descriptor is calculated as a weighted sum of the descriptors of the neighbor's keypoints, weighted by the inverse of every neighbor of the keypoint.  
Signature of Histogram of Orientation (SHOT) [14] is a combination of signatures and histograms. First, it creates a local reference frame (LRF) using eigenvalue decomposition of the covariance matrix of the neighbor points. It then creates an isotropic spherical grid with equal bins along the radial, azimuth and elevation axes. For every bin sector, a histogram of point counts is created according to the angle between the keypoint's normal and the neighbor's normal. Finally, the descriptor is generated by juxtaposing every bin histogram.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 2 Problem Definition  
Figure 1 presents a schematic diagram of capturing non-cooperative targets in aerospace missions. Accurate pose data are provided by the navigation system. The capturing task is completed by the robot arm at the appropriate position.  
A schematic diagram of the composite pose measurement system built in this paper is shown in Figure 2. The visual image is acquired by grayscale cameras, and the point cloud data are acquired by a TOF camera. The projection points of the space points \(P(X_{\mathrm{w}},Y_{\mathrm{w}},Z_{\mathrm{w}})\) in the world coordinate system on the left and right camera imaging planes can be expressed as \(P_{\mathrm{l}}(u_{\mathrm{l}},v_{\mathrm{l}})\) and \(P_{\mathrm{r}}(u_{\mathrm{r}},v_{\mathrm{r}})\), respectively. The left and right camera coordinate systems are \(O_{\mathrm{l}}-X_{\mathrm{l}}Y_{\mathrm{l}}Z_{\mathrm{l}}\) and \(O_{\mathrm{r}}-X_{\mathrm{r}}Y_{\mathrm{r}}Z_{\mathrm{r}}\), respectively. The TOF camera coordinate system is \(O_{\mathrm{p}}-X_{\mathrm{p}}Y_{\mathrm{p}}Z_{\mathrm{p}}\).  
The final pose of the non-cooperative target can be determined by Equation (1).  
\[\begin{bmatrix}X_{\text{P}}\\ Y_{\text{P}}\\ Z_{\text{P}}\end{bmatrix}=[\textbf{R}|\textbf{T}]\begin{bmatrix}X_{\text{w}}\\ Y_{\text{w}}\\ Z_{\text{w}}\end{bmatrix} \tag{1}\]  
where \(\textbf{T}\) is the translation vector of the target spacecraft. The rotation matrix, \(\textbf{R}\), of non-cooperative targets in the world coordinate system can be represented by the following formula:  
\[\textbf{R}=\textbf{R}_{Z}(\gamma)\textbf{R}_{Y}(\beta)\textbf{R}_{X}(\alpha)= \begin{bmatrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix} \tag{2}\]  
Figure 1: Schematic diagram of the capture mission.  
Figure 2: Schematic diagram of the pose measurement system.  
Then, the Euler angle of the three axes can be calculated by Equation (3).  
\[\left\{\begin{aligned} \alpha=\arctan(r_{32},r_{33})\\ \beta=\arctan(-r_{31},\sqrt{r_{32}^{2}+r_{33}^{2}})\\ \gamma=\arctan(r_{21},r_{11})\end{aligned}\right. \tag{3}\]  
where \(\alpha,\beta,\gamma\) represent the X, Y, and Z three-axes angle transformation of the target. \(r_{ij}(i=1,2,3;\,j=1,2,3)\) are the corresponding elements in the rotation matrix. The essence of solving the pose relationship of non-cooperative targets is the calculation of \(R\) and \(T\).
# A Review of Evaporite Beds Potential for Storage Caverns: Uncovering New Opportunities
## 2 Evaporites and Bedded Evaporite Deposits
### Evaporite Minerals
Halite, which precipitates at the highest density of approximately 1.22 g/cm\({}^{3}\), forms when the brine concentration reaches 10 to 12 times that of seawater [97]. To understand the mineralogy of local evaporites, it is necessary to investigate specific evaporite deposits in various locations. Examples of such research include studies on the Saline Valley in California [85], the Upper Miocene Solifferia Series in Sicily, Italy [71], the Permian Salado Formation in Texas [74], the evaporites of the Abaqiq Field in eastern Saudi Arabia [91], and evaporite mineral formation in lakes of Western Canada [98]. Various methods are used to identify evaporite minerals and their precipitation sequences, such as X-ray powder diffraction (XRD) analysis [99], scanning electron microscopy (SEM) [100], and remote sensing [101]. According to [102], halite is the dominant mineral in rock salts, though other saline minerals like gypsum, anhydrite, carallite, kieserite, and polyhalite may also be present. Salt beds can be associated with shale and sandstone. In potash beds, as defined in [103], the dominant potassium-bearing minerals are sylvite and carallite, with polyhalite also reported in some deposits. In tron beds, trona is the most common mineral, with additional sodium carbonate minerals like nahcolite and wegscheiderite found in some deposits. Some trona beds also contain a mix of halite and trona [104]. Evaporites contain both soluble and insoluble minerals. Soluble minerals in evaporites include halite, potash, and trona [56]. Halite (NaCl) and sylvite (KCI), the most common potash mineral, are simple chloride salts, while trona (NaHCO\({}_{3}\cdot\)Na\({}_{2}\)CO\({}_{3}\cdot\)2H\({}_{2}\)O) is a carbonate double salt [63]. However, evaporites also contain insoluble minerals, such as gypsum and anhydrite [48]. This study focuses on evaporite beds containing halite, potash, and trona. These beds are significant for two main reasons: their economic importance due to the industrial use of these minerals and the potential to use caverns left by solution mining for storage purposes [105].
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 5 Simulation Results
### Optimized Design Point for Polar Codes  
In this sub-section, a simulation comparison for polar code construction using GA method for different coefficient of variation \(C_{v}\) is established. Table 1 summarizes the used simulation parameters.  
Figure 6 shows the BER performance versus \(E_{b}/N_{0}\) at code-rate \(R=3/5\) for various coefficients of variation \(C_{v}\). It is observed that the minimum BER performance happened at coefficient of variation \(C_{v}=\frac{1}{\sqrt{3}}=0.577\). In addition, to check the validity of the relation proved in Equation (16), BER performance is evaluated versus coefficient of variation \(C_{v}\) for different code-rates \(R\).  
The results shown in Figure 7 demonstrate that the coefficient of variation \(C_{v}\) that achieves minimum BER performance is very close to the calculated \(C_{v}=\frac{1}{\sqrt{3}}\). This small variation is expected since the GA method approximates the exact transition probability of each binary input channel to overcome difficulties in calculating the actual values as shown in Equations (5) and (6). However, in our mathematical calculations we assume that the exact relation is used. Therefore, this value of \(C_{v}\) can be used in the construction of polar code with GA method after a small fine tuning for each code rate as we did in the upcoming simulation results for optimized polar codes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Construction** & **GA Technique** \\ \hline Decoder & FA-SCL \\ Max. List size (\(L_{max}\)) & \(L_{max}=32\) \\ Implementation & Full \\ CRC Size in bits & 32 \\ Codeword size (N) & 65,536 \\ Frame size (N\({}^{\prime}\)) & 64,800 \\ Modem Type & BPSK \\ Channel Type & AWGN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for polar code.  
Figure 6: Polar code BER versus \(E_{b}/N_{0}\) for code rate \(R=3/5\) and different coefficient of variation \(C_{v}\).
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation  
Based on our investigation into voxel-based navigation, we have identified four key aspects--modeling, segmentation, analysis, and data storage--that are closely linked to the efficient operation of the navigation system. As shown in Figure 5, voxel-based modeling serves as the foundation of the navigation system. Constructing voxel maps in three-dimensional space effectively represents the geometric information of the environment and provides a spatial framework for subsequent path-planning and navigation algorithms. Voxel segmentation is used to identify different types of spatial areas (such as corridors, obstacles, or passable areas), providing the navigation system with precise data on accessibility and the location of obstacles. The voxel-based analysis involves further processing of voxel data to extract richer spatial information, including connectivity analysis, visibility analysis, spatial analysis, etc., which provides decision-making support for navigation algorithms. Voxel data storage and management is crucial for ensuring the efficient operation of the navigation system, relying on efficient storage structures and update mechanisms to ensure stable operation under different operating conditions, especially in dynamic environments. We briefly overview the applications and related literature on voxel-based modeling, voxel segmentation, voxel-based analysis, and voxel storage and management (see Table 2). In the following sections, we discussed these four relevant issues in detail.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Techniques** & **Application** & **Example** \\ \hline \multirow{4}{*}{Voxel-based modeling} & Indoor environment & [PERSON] et al. [31], [PERSON] et al. [32], [PERSON] et al. [33], [PERSON] et al. [34], [PERSON] et al. [35] & \\  & Outdoor environment & [PERSON] et al. [36], [PERSON] et al. [37], [PERSON] et al. [38], [PERSON] et al. [39], [PERSON] et al. [30], [PERSON] et al. [40] & \\ \hline \multirow{4}{*}{Voxel segmentation} & Segmentation of static objects & [PERSON] et al. [41], [PERSON] et al. [42], [PERSON] et al. [27], [PERSON] et al. [43], [PERSON] et al. [44], [PERSON] et al. [45], [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [48], [PERSON] et al. [49], [PERSON] et al. [50] & \\  & Segmentation of static and dynamic objects & [PERSON] et al. [51], [PERSON] et al. [52; 53], [PERSON] et al. [54], [PERSON] et al. [55], [PERSON] et al. [47], [PERSON] et al. [56], [PERSON] et al. [57] & \\ \hline \multirow{2}{*}{Voxel-based analysis} & \multirow{2}{*}{Spatial analysis} & [PERSON] et al. [58], [PERSON] et al. [59], [PERSON] et al. [60], [PERSON] et al. [61], [PERSON] et al. [62], [PERSON] et al. [19], [PERSON] et al. [63], [PERSON] et al. [64], [PERSON] [65] & \\ \hline \end{tabular}
\end{table}
Table 2: Overview of key techniques for voxel-based navigation.  
Figure 5: The relationship between modeling, segmentation, analysis, and data storage in voxel-based navigation.