esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The System's Dynamics Modeling
Then according to the Lagrange equations: \(L=T-U\) and \(\frac{d}{dt}\left(\frac{\partial L}{\partial Q}\right)-\frac{\partial L}{ \partial Q}=F\), the system's dynamic equation is:  
\[\mathbf{J}_{\theta}\ddot{\mathbf{\theta}}-\mathbf{K}_{\theta}(\mathbf{q}_{\theta}-\mathbf{\theta}) =\mathbf{\tau} \tag{4}\]  
\[\mathbf{M}_{\theta}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{H}_{\theta}(\mathbf{q},\dot{\mathbf{q}})\dot{ \mathbf{q}}+ \begin{bmatrix}0\\ \mathbf{K}_{\theta}(\mathbf{q}_{\theta}-\mathbf{\theta})\end{bmatrix} = \begin{bmatrix}0\\ \tau_{d}\end{bmatrix}  \tag{5}\]  
Figure 2: The free-floating flexible-joints space manipulator. where, \(\mathbf{K}_{\Theta}=\text{diag}(k_{\Theta 1},k_{\Theta 2},\cdots,k_{\Theta n})\in\mathbf{R}^{n \times n}\mathbf{J}_{\theta}=\text{diag}(J_{\theta 1},J_{\theta 2},\cdots,J_{\theta n})\in\mathbf{R}^{n \times n}\) is the motor rotors' inertia matrix, \(\mathbf{M}_{\Theta}(\mathbf{q})\in\mathbf{R}^{(n+1)\times(n+1)}\) is the system's inertia matrix, \(\mathbf{H}_{\Theta}(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}\in\mathbf{R}^{(n+1)\times 1}\) is a column vector that contains the Coriolis force and centrifugal force. \(\tau_{\text{d}}\in\mathbf{R}^{n\times 1}\) is an external disturbance signal. Decompose matrix \(\mathbf{M}_{\theta}(\mathbf{q})\) and \(\mathbf{H}_{\Theta}(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}\) into the following form:  
\[ \begin{bmatrix}\mathbf{M}_{011}&\mathbf{M}_{\theta 12}\\ \mathbf{M}_{021}&\mathbf{M}_{022}\end{bmatrix} \cdot \begin{bmatrix}\dot{q}_{0}\\ \dot{\mathbf{q}}_{\theta}\end{bmatrix} + \begin{bmatrix}N_{\theta 1}\\ N_{02}\end{bmatrix} + \begin{bmatrix}0\\ \mathbf{K}_{\theta}(\mathbf{q}_{\theta}-\mathbf{\theta})\end{bmatrix} = \begin{bmatrix}0 \\ \tau_{\dot{\mathbf{q}}}\end{bmatrix}  \tag{6}\]  
where, \(\mathbf{M}_{\theta 11}\in\mathbf{R}^{1\times 1}\), \(\mathbf{M}_{\theta 12}\in\mathbf{R}^{1\times n}\mathbf{M}_{\theta 21}\in\mathbf{R}^{n \times 1}\) and \(\mathbf{M}_{022}\in\mathbf{R}^{n\times n}\) are the sub-matrices of \(\mathbf{M}_{\Theta}(\mathbf{q})\). \(N_{\Theta 1}=\begin{bmatrix}1&0_{1\times n}\mathbf{H}_{\Theta}(\mathbf{q},\dot{\mathbf{q}}) \dot{\mathbf{q}},\,N_{02}= \begin{bmatrix}0_{n\times 1}&\mathbf{I}_{n\times n}\end{bmatrix} \mathbf{H}_{ \Theta}(\mathbf{q},\dot{\mathbf{q}})\dot{\mathbf{q}}\). By eliminating \(\ddot{q}_{0}\) in (6), we can obtain:  
\[\mathbf{D}_{\theta}(\mathbf{q})\ddot{\mathbf{q}}_{\theta}+\mathbf{C}_{\theta}(\mathbf{q},\dot\mathbf{{q}}_{\theta},\dot{\mathbf{\theta}})+\mathbf{K}_{\theta}(\mathbf{q}_{\theta}-\mathbf{\theta} )=\tau_{\text{d}} \tag{7}\]  
where, \(\mathbf{D}_{\theta}(\mathbf{q})=\mathbf{M}_{\theta 22}-\mathbf{M}_{021}\mathbf{M}_{\theta 11}^{-1} \mathbf{M}_{\theta 12}\), \(\mathbf{C}_{\theta}(\mathbf{q},\dot{\mathbf{q}}_{\theta},\dot{\mathbf{\theta}})=-\mathbf{M}_{021} \mathbf{M}_{\theta 11}^{-1}\mathbf{N}_{\theta 1}+\mathbf{N}_{02}\).
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 3 Methodology  
Due to the effect of perspective in the real scenario, there are some different scales of feature between the distant view and the close-up view, such as crowd density, the scale of head and shape, etc., which decreases the accuracy of counting. Motivated by the above shortcomings, a scene adaptive segmentation network (SASNet) is proposed for crowd counting as shown in Figure 1.  
Figure 1: The Architecture of SASNet. The SASNet mainly covers two stages. The first stage is the image segmentation, and the second stage is the crowd counting.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 4 Overview of Recent and Existing Commercial SATis5 Testbeds and Projects  
A survey on recent and existing SATis5 testbeds and commercial projects was conducted using available public sources. Based on the survey results [13, 14, 15, 16, 17, 18, 19, 20], this section provides an overview of the ESA ARTES project (Section 4.1), EU-Commissioned SaT5G project (Section 4.2), Starlink Enhancement with 5G (Section 4.3), Avanti Communications and ST Engineering iDirect on SATis5 (Section 4.4), GateHouse SATis5G mMTC-NB-IoT (Section 4.5), NOKIA 5G from Space and Edge Slicing in Next-Generation Virtual Private Network (Section 4.6), and IoT and Unmanned Aerial Vehicle (UAV) Integration in 5G Hybrid Terrestrial-Satellite Networks (Section 4.7).
## 3 Results
### Different Grassland Types of Carbon Fluxes  
The carbon flux variations in the alpine meadow and alpine steppe, the primary grassland categories in the TRSR, were calculated. The multiyear average GPP of the alpine meadow and alpine steppe, as shown in Figure 9, was 177.33 g C/m\({}^{2}\)/yr and 5.73 g C/m\({}^{2}\)/yr, respectively. Significant increases of 1.48 g C/m\({}^{2}\)/yr (R\({}^{2}\) = 0.67, \(p\) < 0.001) and 0.68 g C/m\({}^{2}\)/yr (R\({}^{2}\) = 0.73, \(p\) < 0.001) were seen in the alpine meadow and alpine steppe, respectively. The multiyear average NEP of the alpine steppe was 4.82 g C/m\({}^{2}\)/yr from 1985 to 2000, with an annual average total of 0.22 Tg C/yr. In 1985, 1987, 1995, and 1998, the alpine steppe was a carbon source, but in the other years, it was a carbon sink. During the research term, the alpine meadow NEP averaged 13.00 g C/m\({}^{2}\)/yr, with a multiyear average total of 2.43 Tg C/yr. The multiyear average carbon sequestration in the alpine meadow accounted for surpassing 60% of the total carbon sequestration in the TRSR.  
Carbon fluxes were measured across the three sub-park regions (Figure 10). The LRSP alpine meadow and alpine steppe had the highest GPP, at 133.02 and 106.79 g C/m\({}^{2}\)/yr, respectively, followed by the HRSP, at 109.32 and 53.44 g C/m\({}^{2}\)/yr, respectively, and the YRSP alpine meadow and alpine steppe had the lowest GPP, at 76.69 and 33.48 g C/m\({}^{2}\)/yr, respectively. Carbon sequestration was more significant in the alpine meadow than in the alpine steppe, and the HRSP's alpine meadow had the highest NEP, at 8.09 g C/m\({}^{2}\)/yr, followed by the LRSP (7.31 g C/m\({}^{2}\)/yr) and YRSP (5.39 g C/m\({}^{2}\)/yr). The NEP of the alpine steppe in the LRSP, HRSP, and YRSP was 5.74, 3.80, and 3.51 g C/m\({}^{2}\)/yr, respectively.  
Figure 9: Alpine meadow and alpine steppe carbon fluxes in the TRSR.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 4 Methodology
### Preprocessing  
There are a number of log parsing methods, such as Spell [23], IPLoM [24], Drain [25], and so on. According to previous studies [26], weak log parsing methods can negatively affect the performance of subsequent anomaly detection tasks. Drain has become a common method  
Figure 2: Examples of preprocessing system log (HDFS dataset).  
used by researchers with its superior accuracy and efficiency [27; 28]. Therefore, in this work, we use Drain for log message parsing. As shown in Figure 2, the HDFS dataset is used as an example. Each raw log message consists of a constant part (also known as \"log keys\" or \"log events\") and a variable part (also known as \"parameters\"). The logs are first parsed into log keys using the method of Drain, then each log is mapped to its key to obtain structured logs, and finally, a vocabulary of log keys is constructed, and a log sequence is obtained by sorting the log data in time order according to the block of the log data.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 4 Security Analysis
### Unforgeable Record  
We implemented a blockchain-based system in the proposed scheme shown in Figure 15. Each CA had a ledger, and the blocks in the ledger were synchronized with the PBFT consensus algorithm. When an accessing party executes a chaincode function, the modification of the chaincode will be chained to the ledger. The latest ledger will be validated from every CA department that participates in the blockchain center. It is hard to modify and forge the data in the ledger.  
Furthermore, we designed the structure of data as shown in Figures 4 and 5. The structure of the chaincode stores the complete information of tobacco including the manufacturer and logistics record with GPS location. Therefore, all information and the logistics records of tobacco can be tracked. Consequently, the logistics records cannot be forged and can be tracked correctly in our blockchain-based system.
## Appendix B Parameter Estimation for Distribution Functions
### Detecting Circular Dependencies  
The algorithm checks for circular dependencies (e.g., Parameter A depends on B, and B depends on A or even indirectly):  
* If any parameter remains unresolved after iterative resolution (dependency level remains 0), a circular dependency exists.
* The algorithm flags an error and stops execution for correction. We illustrate this approach using an example of dependency resolution in practice. Given the previous dependency matrix, the resolution order is as follows:  
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Depends On** & **Dependency Level** \\ \hline
**Age** & None & 1 \\ \hline
**Sex** & None & 1 \\ \hline
**SBP** & Age & 2 \\ \hline
**Menopause** & Age, Sex & 2 \\ \hline
**Hypertension** & SBP & 3 \\ \hline \hline \end{tabular}  
Resolution sequence is then as follows: Level 1: Resolve Age, Sex Level 2: Resolve SBP, Menopause Level 3: Resolve Hypertension This structured approach ensures logical coherence, clinical plausibility, and accurate, consistent patient cohort generation.
## 4 Temperature and Humidity Control System  
### Functions of Temperature and Humidity Control System  
The temperature and humidity control system mainly controls and monitors the air temperature and humidity in the enclosed chamber. Its main functions include:  
1. Display the temperature, humidity in the enclosed chamber and operating state of the system, and realize man-machine interaction through the touch screen;
2. Control the constant-temperature water tank, recirculating heating/refrigerating machine, and control the on/off of solenoid valves over the water supply (intake) pipeline/water return pipeline and other waterways to accomplish the accurate temperature control in the enclosed chamber;
3. Control the humidifier or recirculating dehumidifier, and control the on/off of the solenoid valves over the steam pipeline to achieve the precise control of the humidity in the enclosed chamber;
4. Monitor the air temperature and humidity in real time in the enclosed chamber, display the distribution of temperature and humidity, and let the operators know whether the temperature and humidity in the enclosed chamber meet the calibration conditions;
5. Communicate with, obey the management of, execute the operation instructions from, and feedback status parameters such as temperature and humidity in the enclosed chamber to the master control computer.
# Effect of Thermal Treatment on Nano- and Micro-Copper Particles for Jewelry Making  
[PERSON]  
1 Faculty of Science, Burapha University, Chonburi 20131, Thailand  
2 Faculty of Gems, Chanthaburi Campus, Burapha University, Chanthaburi 22170, Thailand  
3 Faculty of Science and Arts, Chanthaburi Campus, Burapha University, Chanthaburi 22170, Thailand  
4 Correspondence: [EMAIL_ADDRESS]  
[PERSON]  
2 Faculty of Gems, Chanthaburi Campus, Burapha University, Chanthaburi 22170, Thailand  
Hypothetically, the nano-sized metal particle is considered a novel material because the reduced particle-sized can decrease the melting point of the metal. In addition, the nano-sized material is expected to advocate a dense structure and suitable mechanical properties, particularly at low temperatures [3; 4; 5]. During the process, we successfully produced nano-silver clay utilized for jewelry applications [4]. The nano-silver clay has sintering temperatures significantly lower than micro-silver clay, and the obtained nano-silver jewelry exhibited desirable mechanical properties for wearing jewelry. Although silver clay has promising properties for handmade jewelry, it is rarely available on a commercial scale for the jewelry industry and artwork and is expensive. Accordingly, copper clay is an alternative material for creating handmade jewelry, small sculptures, and artworks because it is less expensive than metal clay. Some reports have revealed the promising properties of nano-copper particles in interconnecting applications, biosensors, and electrochemical sensors, and they supper hard coatings for electronic device production [6; 7; 8]. However, the application of nano-copper clay for jewelry or artwork has not yet been revealed.  
In this article, we propose nano-copper particles for nano-copper clay, a new jewelry fabrication material. Since both nano- and micro-sized metal particles can be utilized for creating metal clay [4], we assessed nano- and micro-copper clays' fabrication capability for jewelry applications. It is well known that heating parameters dramatically affect finished metal clay jewelry's physical and mechanical properties. Our objective for this research was to determine the effects of thermal treatment on some physical and mechanical properties of both nano- and micro-sized copper particles in clay material. Since wearable jewelry fabricated from metal clay is committed to various physical properties, i.e., hardness, shrinkage, and density of the fired specimen, we determined and compared the suitable firing conditions for nano- and micro-copper clays in this study. In addition, firing conditions, such as heating temperature, heating rate, and holding time, were examined to determine the optimal firing conditions. Furthermore, we conducted DTA analysis and captured SEM images of the fired specimens to determine the microstructure of the sintered specimens.
## 3 System Reliability Evaluation Method That Considers Multiple Failure Criteria
### AHP(Analytic Hierarchy Process) Method  
AHP is a subjective decision-making method for solving the complex problems of multiple objectives [41]. Its core idea is to determine the weight of evaluation indexes according to the experience of experts. Here, we take three failure criteria as the evaluation indexes of the system reliability.  
The specific steps are as follows.  
(1) Establish the hierarchical mode for system reliability evaluation:  
In order to evaluate the bridge system reliability by considering multiple failure criteria, a target layer and an index layer are established. The target layer represents the system reliability by considering multiple failure criteria. Each index in the index layer represents the system reliability of each failure criterion. Then, a hierarchical structure between the target layer and the index layer is established, as shown in Figure 5.  
(2) Construct the judgment matrix \(\mathbf{A}\).  
Figure 5: The hierarchical mode of system reliability evaluation.  
The judgment matrix is constructed based on the scores of peer experts for each index. Equation (13) shows the definition of the element \(a_{ij}\) in the judgment matrix \(\mathbf{A}\), in which \(a_{ij}\) indicates the relative importance of the \(i\)th index over the \(j\)th index.  
\[\mathbf{A}=\left[\begin{array}{cccc}a_{11}&a_{12}&\ldots&a_{1n}\\ a_{21}&a_{22}&\ldots&a_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ a_{n1}&a_{n2}&\ldots&a_{nn}\end{array}\right] \tag{12}\]  
\[a_{ij}=\frac{s_{i}}{s_{j}},i,j=1,2,\ldots,n, \tag{13}\]  
where \(s_{i}\) and \(s_{j}\) indicate the scores of the \(i\)th index and the \(j\)th index, respectively. \(a_{ij}\) is an element of the judgment matrix.  
(3) Calculate the weight vector \(\mathbf{W}=(w_{1};w_{2};\ldots;w_{n})\):  
\[w_{i}=\frac{\prod\limits_{j=1}^{n}\sqrt{a_{ij}}}{\sum\limits_{i=1}^{n}\prod \limits_{j=1}^{n}\sqrt{a_{ij}}},i,j=1,2,\ldots,n, \tag{14}\]  
(4) Calculate the maximum eigenvalue \(\lambda_{\max}\):  
\[\lambda_{\max}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{(\mathbf{AW})_{i}}{w_{i}}. \tag{15}\]  
(5) Check the consistency of the judgment matrix \(\mathbf{A}\):  
\[CI=\frac{\lambda_{\max}-n}{n-1}, \tag{16}\]  
\[CR=\frac{CI}{RI}. \tag{17}\]  
where \(CI\) and \(CR\) represent the consistency index and the consistency ratio, respectively. \(RI\) is the random consistency index shown in Table 2. \(n\) is the number of indexes.
## 2 Current Status of Support Instability in the Working Face of SDCS
### Establishment of Numerical Model  
FLAC3D is selected as the numerical simulation software for this study, as it can simulate the mechanical behavior of geological materials when they reach the strength limit or yield limit, as well as complex mining engineering problems. FLAC3D is an explicit solution based on the finite difference method. It adopts the dynamic relaxation method for time-step iteration without forming the whole stiffness matrix and is suitable for simulating nonlinear, large deformation and material failure problems.  
A numerical model was developed based on the 1123 working face, with dimensions of 500 m (length) \(\times\) 200 m (width) \(\times\) 560 m (height), as shown in Figure 5. Mesh discretization was determined according to the physical and mechanical properties of different strata, as derived from mine geological data. To account for material heterogeneity, hard rock masses were discretized into 5 m \(\times\) 5 m \(\times\) 5 m elements, while the weaker coal seam was assigned a finer resolution of 1 m \(\times\) 1 m \(\times\) 3 m to better capture deformation and failure  
Figure 4: Stress environment at the boundary of TLEZ in the fully mechanized top-coal cavity face in SDCS.  
mechanisms. Local mesh refinement was applied to optimize computational efficiency, resulting in a total of 1,711,200 elements.  
The Mohr-Coulomb constitutive model was selected to simulate the mechanical behavior of the rock mass, as it is widely adopted in geotechnical and mining engineering. This criterion effectively characterizes the shear failure of geomaterials (e.g., soil and rock), where plastic yielding occurs upon reaching a critical shear stress threshold, consistent with Coulomb's friction law. Additionally, the model accounts for both compressive and tensile strength, making it particularly suitable for geotechnical materials, which typically exhibit significantly higher compressive strength than tensile strength.  
Boundary conditions included vertical displacement constraints at the model base to restrict downward movement, along with horizontal displacement constraints on all lateral boundaries (front, back, left, and right) to simulate confinement from surrounding rock formations. The average buried depth of the coal seam is 330 m, which is consistent with the buried height of the coal seam in the numerical model, and the gravity acceleration of the model is set at 9.8 m/s\({}^{2}\).
## 2 Materials and Methods  
### Sample Preparation  
The composition of initial matrix in this work is given in Table 1, including WC (99.9% purity, Zhuzhou, China), bronze (99.9% purity, Beijing, China), Ni (99.9% purity, Beijing, China) and Mn (99.9% purity, Beijing, China).  
Two series of samples (size: 38 \(\times\) 8 \(\times\) 5 mm\({}^{3}\)), matrix and impregnated diamond composites samples, were fabricated by power metallurgy methods of hot-pressing sintering. For purpose of obtaining uniform mixture, initial matrix powder was first mixed in a ball milling machine (Focucy, P400, Changsha, China) with WC balls for 24 h at a speed 120 rpm. Nano-WC (99.9% purity, average particle size of 80 nm, Qinhuangdao, China) with different mass percent concentration were then mixed with initial powder that was milled already using the same ball milling machine for 8 h at a speed 120 rpm. The resultant mixture with various concentration of nano-WC were sintering by hot-pressing in graphite moulds at 980 \({}^{\circ}\)C for 5 min. During the sintered process, the samples were forced by a uniaxial pressure of 35 MPa. The sintering apparatus was an intermediate frequency furnace (KGPS, Ezhou, China).  
In impregnated diamond samples preparation process, initial matrix powder and different mass percent concentration nano-WC were mixed by the same way as matrix samples. The diamond grits (20 vol% concentration, synthetic, 270-325 \(\upmu\)m, Change, China) were added into the resultant mixture through a three-dimension mixer (JH2D-6, Zhengzhou, China) for 4 h. The diamond composites samples were prepared by hot-pressing sintering at the same parameters as matrix samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Composition** & **WC** & **Bronze** & **Ni** & **Mn** \\ \hline Content (wt\%) & 55 & 35 & 5 & 5 \\ Average particle size (\(\upmu\)m) & 10 & 50 & 75 & 60 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of initial matrix.
## Materials and Methods
### Data Source and Pre-Processing  
The satellite remote sensing data of the study include two main parts: land use/cover data and Landsat TM/OLI images. First, the land use/cover data in 2005, 2010, 2015, and 2020 were collected from the Land Use and Land Cover in China (CN-LUCC) dataset with a spatial resolution of 30 m. CN-LUCC was provided by the National Resources and Environment Database of the Chinese Academy of Science, which is based on the manual visual interpretation of multitemporal Landsat images and is widely used in published research [36]. The overall accuracy of the classified CN-LUCC dataset was over 92% [37], hence the land use/cover data used in the study was fairly credible. CN-LUCC includes six main land cover types: forestland, grassland, waterbody, cropland, built-up land, and unused land.  
Landsat images (path 118, row 28) in 2004-2020 were freely available from the United States Geological Survey (USGS, [[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)), accessed date September 2022) of the Earth Resources Observation and Science Center (EROS). Due to the desirable properties of moderate spatial resolution and shorter revisit time, Landsat images have been widely used for the research of urban planning, urban heat islands, and impervious surfaces [38; 39; 40]. The dates of images were selected and acquired during the growing season to reduce the effects of differences in phenological status. In the study, we assembled a set of cloud-free Landsat images of similar dates (September 2004-2020). Before downloading the data (L1T level of systematic geometric accuracy), georeferencing was performed at the USGS and no additional refinement was conducted in this study. Atmospheric corrections were undertaken to the Landsat datasets using the FLAASH model to remove the effect of atmospheric disturbance on the pixel values. A geometric precision correction was conducted based on the 1:50,000 topographic maps, and correction error was controlled within 1 pixel using ENVI 5.3 software. The parameters in the metadata file were used to perform radiometric calibration, which involved converting the pixels values to reflectance values. Finally, all satellite images were clipped according to the boundary of the study area using \"extracted by mask\" tools in ArcGIS Pro 2.5 software. Additionally, we calculated the greenness-related NDVI for each Landsat image. NDVI images were produced by calculating the ratio between the red (R, 630-690 nm) and near-infrared (NIR, 760-900 nm) values of the satellite image using Equation (1):  
\[\mathrm{NDVI}=(NIR-R)/(NIR+R) \tag{1}\]  
Figure 1: The location of the study area in Harbin.