esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies
### Case-1 at 07:00 UTC on 23 April 2018  
The NRT SWIPE processing system successfully captured a medium sudden local convective storm case at 07:00 UTC (Beijing time 15:00) on 23 April 2018 in the Hainan province of China. This island is one of the southernmost islands of China with a mean latitude of 19\({}^{\circ}\)N, which has a typical tropical monsoon climate and tropical marine climate [33]. It is not surprising that this area often suffers from the attack of severe convective storm weather systems, in particular in the summertime. In addition, we also find many convective storm samples tracked by SWIPE from April to October in Figure 1. For precipitation, the ground station test results are the most accurate. The precipitation products are tested with the results of the ground test as the true value [16; 38].  
This convective storm case lasted about 3 hours. Its appearance and development is shown in Figure 5. From this figure, the SWIPE model initially marked a baby or newborn local convective storm system on the western side of Hainan Island at 07:00 UTC. This recognition result by the SWIPE algorithm disappeared immediately at 07:10 UTC (not shown here) due to the stable development of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Variable Score** & **Ranking** & **Variable Score** & **Ranking** \\ \hline \multirow{5}{*}{\(\begin{array}{c}\text{CIN min}=0.0104\\ \text{925}\text{min}=0.0094\\ \text{MR}\text{ 925}\text{min}=0.0078\end{array}\)} & \multirow{2}{*}{\(\begin{array}{c}\text{17}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text{}\text  
convective cloud cluster. According to the continuous records of the 21 ground-based rainfall gauge observations within 1 min intervals, the precipitation induced by this medium convective storm system initially occurs at 08:23 UTC in the Northern part of the island. In contrast, the H08/AHI can only take a picture for this convective storm system at 08:30 UTC. Therefore, the SWIPE model, in fact, captures this local sudden medium convective storm system one hour and twenty-three minutes earlier than the ground rainfall gauges (or radar). The sub-figures in the last column of Figure 5 exhibit the related results with the maximum rain rate of 10.8 mm/h at 09:40 UTC. It explicitly shows that the retrieved SWIPE index was two hours and 40 minutes earlier than the occurrence of the maximum rain rate.
## 1 Introduction
However, the transmission failures may occur due to path fading and interferences in WSNs during CFP, and sensor devices buffer the data to transmit in the next superframe. Under such a circumstance, retransmission delays and the packet drop rate increase due to the buffer constraints of the devices. Hence, it is crucial to investigate the current problems and to find solutions to reduce their impact on the overall network performance. The main contributions of our paper are summarized as follows. * A new frame structure is designed for data transmission, which can identify and avoid the hidden terminal problem. * A new superframe structure is designed for the transmission failure due to interference between WiFi and ZigBee devices. * A modified channel access mechanism is proposed to reduce the packet drop rates, energy consumptions and to enhance the throughput. * A Markov chain model is designed to analyze the performance of our proposed MAC. * Performance analysis of our proposed methods is made to study the reliability, latency, throughput and energy consumption. The rest of the paper is organized as follows. The overview of IEEE 802.15.4e general superframe structure and channel access mechanism are briefly reviewed in Section 2. We review the related works in Section 3. The network model and our proposed methods are presented in Section 4. We develop the analytical models in Section 5. Performance analysis of different network parameters based on our models is studied in Section 6. Simulation results are discussed in Section 7, and concluding remarks are made in Section 8.
###### Abstract  
China has built four stations in Antarctica so far, and Zhongshan Station is the largest station among them. Continuous power supply for manned stations mainly relies on fuel. With the gradual increase in energy demand at the station and cost of fuel traffic from China to Zhongshan station in Antarctica, reducing fuel consumption and increasing green energy utilization are urgent problems. This research considers a standalone renewable energy system. The polar environments and renewable energy distribution of area of Zhongshan station are analyzed. The physical model, operation principle, and mathematical modeling of the proposed power system were designed. Low-temperature performance and state of charge (SOC) estimation method of the lead-acid battery were comprehensively tested and evaluated. A temperature control strategy was adopted to prevent the battery from low-temperature loss of the battery capacity. Energy management strategy of the power system was proposed by designing maximum power point tracking (MPPT) control strategies for wind turbine and PV array. The whole power system is broadly composed of a power generator (wind turbine and PV array), an uploading circuit, a three-phase rectifier bridge, an interleaved Buck circuit, a DC/DC conversion circuit, a switch circuit, a power supply circuit, an amplifier, a driver circuit, a voltage and current monitoring, a load, battery units and a control system. A case study in Antarctica was applied and can examine the technical feasibility of the proposed system. The results of the case study reveal that the scheme of standalone renewable energy system can satisfy the power demands of Zhongshan Station in normal operation.  
**Keywords: renewable energy; low-temperature energy storage; SOC; simulation**
# A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method
## 4 Results and Discussion
### Short-Term Forecast Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**GM** & **ES2** & **ES2 + SW** & **IMP 1 (\%)** & **ES2 + GM + SW** & **IMP 2 (\%)** & **ES3** & **ES3 + SW** & **IMP 1 (\%)** & **ES3 + GM + SW** & **IMP 2 (\%)** \\ \hline
**RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** & **RMS** & **Range** \\ \hline
19.57 & 32.22 & 5.56 & 8.99 & 3.18 & 7.38 & 42.80 & 17.90 & 2.83 & 4.19 & 11.00 & 43.20 & 6.77 & 16.13 & 4.67 & 13.86 & 31.00 & 14.10 & 2.95 & 8.13 & 36.80 & 41.30 \\ \hline \hline \end{tabular}  
* \({}^{1}\): IMP represents the improvement of the ES2 + GM (ES3 + SW) compared with the single ES2 (ES3) model (Unit: \%).
* \({}^{2}\): IMP represents the improvement of the ES2 + GM + SW (ES3 + GM + SW) compared with that of the ES2 + SW (ES3 + SW) model (Unit: \%).  
\end{table} 
Table 5: Statistics of the average prediction (RMS), stability (Range), and improvement of the different models for 24 h (ns). In general, apart from the prediction algorithm, the MEO satellites accomplish a fairly superior forecast accuracy, followed by IGSO satellites. The worst is GEO satellites, which are probably related to the poor observed structures and observations.
## 3 Methodology
### Feature Selection
#### 3.2.3 Chi-Square-Based Feature Selection  
The chi-square test assesses the dependence between stochastic variables [36]. This function helps identify and exclude features that are more likely to be independent of the class, thereby making them irrelevant for classification [37]. The following formula calculates the chi-square statistic:  
\[X^{2}=\sum_{i}\frac{\left(Observed_{i}-Expected_{i}\right)^{2}}{Expected_{i}}\]  
Figure 4: Mutual information values for the top 100 predictors and the target variable.  
Figure 3: Top 100 features selected by ElasticNet, ranked by their coefficient magnitudes.  
where:  
* \(X^{2}\) represents the chi-square statistic, which is a measure of how much the observed values deviate from the expected values.
* \(Observed_{i}\) is the observed or actual values.
* \(Expected_{i}\) is the expected value if the two categories are independent.  
If the categories are independent, the observed and expected values will be close. However, if there is a relationship between them, the chi-squared value will be high, indicating that the variables are likely not independent.  
In this study, SelectKBest was used with the chi-squared function for feature selection, allowing independent ranking of features based on their relationship to the output variable. The sklearn.feature_selection.chi2(X, y) function calculates the chi-squared statistic for each feature, identifying those with the highest statistical significance in re-lation to the target class. This function works with non-negative features, such as Booleans or frequencies, and helps ensure that only relevant features are retained.  
Figure 5 illustrates the chi-square statistics of the top 100 features selected using the chi-square feature selection technique. Features with higher chi-square values have a stronger relationship with the target variable.
## 4 Numerical Simulation Applied to the FFF Process  
Based on the stages illustrated in Figure 5, simulations are grouped into four categories: melt flow, cooling and solidification, thermal-mechanical behavior, and material property characterization [3]. Melt flow simulations focus on shear forces, viscosity variations, and flow stability, ensuring precise material deposition and interlayer bonding [36]. Cooling and solidification studies explore thermal gradients, heat dissipation mechanisms, and their effects on mechanical properties, which are key to preventing warping and structural defects [37]. Thermal-mechanical models predict stress distribution, distortion patterns, and deformation risks, aiding in process optimization and defect mitigation [1; 38]. Finally, material characterization simulations allow for the correlation between process parameters and final material properties, ensuring that predictions align with experimental validation [3].  
The following sections provide an in-depth review of each numerical modeling domain, detailing key methodologies and significant findings that contribute to advancing FFF-based additive manufacturing.
## 4 Discussion
Our study is limited, as the determined material and mechanical properties of the PCL and scaffolds are only valid for the chosen molar weight of the PCL, process temperature and testing speed. Furthermore, eccentric load application can lead to inaccuracies in results. The polymers were processed in this study at the same day as fabrication. Embrittlement due to UV radiation exposure was not investigated and cannot be ruled out. For numerical modeling, various assumptions are made, such as the presence of an isotropic and constant material. These inhomogeneities of scaffolds are not represented by the numerical approximation. The developed models provide sufficient approximation in linear-elastic ranges up to 3% strains. For calculations at higher strains, further investigations and adapted non-linear material models are necessary. Furthermore, the collagen layer and seeded cells were not modeled. Due to their small dimensions compared to the scaffolds, it can be assumed that the effects of the collagen layers and cells on the mechanical behavior are negligible. Nevertheless, an influence cannot be ruled out based on this study. This study focused on the parametric numerical modeling and shows only a qualitative growth of bone cells on the fabricated scaffolds. For a detailed description and analysis of the cell growth, further quantitative data are needed. Furthermore, calcium staining or the detection of calcium or mineral deposits on cell-seeded scaffolds are needed to prove the final suitability for bone tissue engineering applications. As Giemsa staining and no L/D assays for MSC, MG-63 and MLO-Y4 cells were performed, no statement can be made concerning the vitality of these cells. The present study did not explore the effects of varied pore size on the biological behavior of scaffolds in vivo. This was published previously for comparable scaffolds [25; 27].
# Similarity Model Test on Rainfall Scouring Mechanism of High-Speed Railway Subgrade Slope
## 3 Test Results and Discussions
### Occurrence and Development Process of Subgrade Slope Erosion of G4  
Unlike the previous three groups, the permeability coefficient of G4 increased significantly and the ponding time decreased due to the removal of the minimum soil particle size R1. Local ponding occurred at 10 min 5 s, and the overall ponding occurred at 14 min 30 s, with a depth of 0.34 cm.  
Due to the lack of cohesion provided by the soil particles of R1, water flow runoff and scouring erosion occurred on the surface of G4 faster than G2. In the first 14 min, no erosion occurred. Until 14 min 33 s, soil particles, transported locally, formed sheet erosion, as shown in Figure 11. At this stage, the water flow first scoured and transported the soil particles of R2, then R3 particles also underwent erosion, while R4 had a larger particle size and was less likely to be eroded under a rain intensity of 1.0 mm/min. The eroded pits generated at 15 min 12 s. After scouring, eroded pitting of a similar size was finally formed, with a maximum diameter of 4 cm and a depth of 2.5 cm, as shown in Figure 12. If we extend the scouring time, this pitting erosion will further develop into gully erosion, leading to the overall collapse of the slope.  
Figure 10: Pits after erosion of G3: (**a**) Panorama, (**b**) Enlarge.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle Size (mm)** & **Incipient Flow Velocity (cm/s)** & **Erosion Quantity (g)** \\ \hline
0.075–1 (R1) & 7.50 & 3.3 \\
1–2.5 (R2) & 11.31 & 13.8 \\
2.5–5 (R3) & 16.33 & 8.0 \\
5–10 (R4) & 21.14 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Erosion and collecting condition of G3.  
The erosion and collecting condition of G4 is shown in Table 6. Due to the low cohesion of soil filler, G4 was prone to be eroded under a 1.0 mm/min rainfall intensity. R2 and R3 were scoured and transported at 14 min 33 s and 16 min 51 s, respectively. Compared with R2, the incipient flow velocity of R3 increased by 41.5%, and the erosion quantity decreased by 55.2%.  
Compared with G2, G4 experienced less flake erosion, but had more eroded pits, shorter ponding time, and more scouring R3 and R2 particles. Compared with the R2 of G4 and G2, the incipient flow velocity decreased by 15.9%, and the erosion quantity increased by 87.5%. The soil particles of R3 in G4 were not transported and scoured, unlike in G2, due to the loss of cohesion provided by the R1 in G4. Thus, appropriate fine particles can enhance the anti-scouring ability of the slope and improve the incipient flow velocity of the same soil particle size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Particle Size (mm)** & **Incipient Flow Velocity (cm/s)** & **Erosion Quantity (g)** \\ \hline
1–2.5 (R1) & 11.09 & 10.5 \\
2.5–5 (R2) & 15.65 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Erosion and collecting condition of G4.  
Figure 11: Sheet erosion occurs after erosion of G4.  
Figure 12: Pits after erosion of G4: (**a**) Panorama, (**b**) Enlarge.
## 2 Materials and Methods
### Rationale
#### 2.1.3 Variables for Data Selection  
In order to provide the most accurate identification of archaeological sites in prospective reservoir areas, it is essential to consider two types of variables: (1) temporal; (2) environmental.  
The temporal variable relates to the timeframe of the dam construction and how satellite data can provide sufficient temporal coverage of the pre-, during and post-construction phases. Therefore, this variable influences the selection of the number and type of archive and new images to be acquired. \"Tunable data\" (i.e., COSMO-SkyMed see Section 2.1.2) are therefore advantageous to fill documentation gaps, although this cannot be done retrospectively.  
The environmental variable regards the physical properties of the natural setting where the dam is constructed and that may impact on the visibility of archaeological sites in the satellite data. Above all, seasonality. Indeed, it is well known that highly vegetative landscapes may hamper visibility in both optical and SAR data. Therefore, if the prospective reservoir area is located in an arid or semi-arid region (e.g., desert areas in Northern Africa or the Middle East), seasonality may not be a constraint for satellite images collection. On the contrary, humid regions (e.g., most of continental Europe, Amazonian region or central Africa) may require specific satellite data tasking during either low or high vegetative periods.
## 2 Materials and Methods
### Mathematical Model
 \tag{6}\]  
in which the following relations are applied:  
\[\overline{T}=\frac{T+T_{0}}{2};\overline{T}TT_{0}\approx\overline{T}^{3} \tag{7}\]  
With such a linearization the exchange coefficient by radiation, depending on the cube of the average value of the absolute temperature between the source and environment, is given by:  
\[h_{RAD}=4\varepsilon\sigma\overline{T}^{3} \tag{8}\]  
in which \(\varepsilon=\) [0, 1] is the emissivity of the emitting surface and \(\sigma=\) 5.67 \(\times\) 10\({}^{-8}\) [W m\({}^{-2}\) K\({}^{-4}\)] is the Stefan-Boltzmann constant. For example, around room temperature (T average equal to 300 [K]) and with an emissivity value \(\varepsilon=\) 0.9, the exchange coefficient by radiation is \(h_{RAD}=\) 5.5 [W m\({}^{-2}\) K\({}^{-1}\)], while at \(T\) average equal to 373.15 [K], \(h_{RAD}\) is equal to 10.6 [W m\({}^{-2}\) K\({}^{-1}\)]. After switching on the radiant heating panel, a heat front starts to propagate along the \(x\) axis. Because the material has small thickness, heat reaches the other side of the layer almost immediately,and temperature increases according to Equation (1) [21]. It is then possible to write the ordinary differential equation:  
\[\theta^{\prime}+\frac{h_{CONV}+h_{RAD}}{\rho c_{p}L_{x}}\theta=0 \tag{9}\]  
in which:  
\[\theta=\frac{1}{\rho c_{p}L_{x}}[q_{E}-(h_{CONV}+h_{RAD})(T-T_{0})] \tag{10}\]  
The solution is:  
\[\theta=\frac{q_{E}}{\rho c_{p}L_{x}}e^{-\frac{k_{CONV}+h_{RAD}}{\rho c_{p}L_{x }}t} \tag{11}\]  
that could be rewritten as:  
\[T(t)-T_{0}=\frac{q_{E}}{h_{CONV}+h_{RAD}}\bigg{[}1-e^{-\frac{k_{CONV}+h_{RAD}} {\rho c_{p}L_{x}}t}\bigg{]} \tag{12}\]  
Two groups of parameters, \(\alpha\) and \(\tau\), could be identified:  
\[\alpha=\frac{q_{E}}{h_{CONV}+h_{RAD}};\tau=\frac{\rho c_{p}L_{x}}{h_{CONV}+h_{RAD}} \tag{13}\]  
The parameter \(\tau\) is the time constant of the system. This value is of particular interest for the designers, as it defines the mode of operation of the panel and significantly contributes to establishing the thermal comfort in the rooms in which the panel is applied.
Although the authors declared a high efficiency of fault detection (over 90% of cases) and the possibility of using the procedure for remote monitoring of the connection status in real time, it should be noted that the system can detect the loosening of fasteners when the height of the protruding part of a bolt exceeds 0.5 cm, which, in the case of prestressed connections, may not be sufficient. The problem of bolted connection change detection, based on image analysis, was also dealt with in [15], where the proposed approach combined a cascade bolt detector with Support Vector Machines (SVMs). Work [16] presents algorithms for the automatic identification of a loosened bolt location; the algorithm used the comparison of photos taken during various inspections (undamaged and with a loose connector). Some other algorithms that are based on the detection of washer edges are presented in [17; 18]; the identified location of washers in relation to the initial state enabled the detection of changes in bolt tightening. It is not only the detection of fastener loosening that is important--some other phenomena may also be dangerous for bolted connections; these include stress concentration points, cracks (propagating from the edge of the holes), and corrosion. The work [19] analyzed the change of stress (measuredby strain gauges) in bolts, depending on the change in the bolt head surface (the head was cut off to simulate a corrosion defect). Stress concentration issues, especially around welds of connections subjected to cyclic loading (e.g., seismic), were analyzed in [20]. The problem of detecting changes in connections is also dealt with in the aviation industry. For example, in work [21], looseness of connectors in the turbine-plane connection was detected, and in work [22] a decision system for monitoring the condition of bolted joints for large-size aluminum plates used in aircraft plating was proposed. The system was developed on the basis of Lamb wave propagation analysis; the waves were recorded by a piezoelectric transducer system. The described vision-based methods allow for damage detection, as a result of image-processing, on which the connectors are visible. The level of loosening, considered in these papers, was significant. In many cases, changes in connectors were visible with the naked eye, whereas the procedure presented here enables the detection of very small changes, impossible to be detected without manual inspection of every connector. It is not necessary to observe the connectors directly, but the near surrounding of the node should be visible. These two reasons make the presented research original. A similar problem of anomaly detection in nodes has been admittedly presented in [23], where the anomaly detection was supported by SVMs, but in the now proposed procedure a different tool is used, namely, Artificial Neural Networks (ANNs).