esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture and Design Principles
### RS Coding Design Based on FPGA
As depicted in Figure 4, the decoding journey begins with the incoming data \(r(x)\) being stored in an FIFO buffer. Concurrently, the Syndrome module diligently computes the syndromes \(S_{0}\) to \(S_{15}\), which are essential for error detection. The syndromes serve as a pivotal trigger for state transitions within the finite-state machine. In the fortunate scenario where \(S_{j}\) is equal to zero, it signifies  
Figure 3: RS system code encoding circuit. the absence of transmission errors, allowing for the immediate and direct output of the error value \"\(\gamma_{i}=0\)\" without starting the error correction algorithm by turning on the \"_Switch_\". However, when non-zero syndromes are encountered, it cues the initiation of the error correction algorithm. The BM algorithm is then engaged to derive the error locator polynomial \(\delta_{x}\) from the syndromes. Armed with this polynomial, the Chien search algorithm systematically identifies the precise locations \(X_{i}\) of errors within the data stream. Subsequently, the Forney algorithm is applied to pinpoint the error value \(\gamma_{i}\). In the final stage of the process, the Error Corrector module meticulously applies the XOR operation between the error values and the data extracted from the FIFO. This operation rectifies the errors, yielding the decoded data \(c(x)\). The use of syndromes to gauge the system's state enables the system to bypass unnecessary decoding computations when no errors are present, thereby enhancing both the speed and efficiency of the decoding process.
## 2 Methodology
### Minimum Spanning Tree  
Network planning is based on the determination of energy station sites and the division of energy supply scope. As a general rule, the network is the basic representation of space, which can accurately represent the relationship between two connection points. Figure 4 is a graphical model of the studying area.  
The model can be expressed as follows:  
\[G(V,E,W) \tag{5}\]  
where \(V\) represents a group of points, including the points of geographic coordinates of users (\(V_{1}\)) represented by blue dots and road nodes (\(V_{2}\)) represented by black dots. \(E\) is the edge set, representing the connection between two points, and \(W\) is the weight set. This is the relationship between the start point and end point on the edge.  
Minimum spanning tree (MST) is often used to solve practical problems related to graph theory, and has extensive use in transportation, communication, architecture, and other fields [22]. MST requires obtaining a spanning tree, that is, to select n-1 edges from a weighted undirected network graph, which is still connected, and the weight of the tree should be minimized. At present, Prim algorithm [23] and Kruskal algorithm [24] are widely used in solving MST problems. In this paper, Prim algorithm is used to set up the minimum spanning tree for the layout of the energy supply network. MATLAB programming is employed to implement the process of MST. The task consists of the following steps:  
**Step 1**: Input a weighted undirected network graph, where V is the set of vertices and E is the set of edges.  
**Step 2**: Initialize V\({}_{n\
eq v}\).  
Figure 4: Graph model of the studying area.  
**Step 3**: Select the edge \((u,v)\) with the smallest weight in the set E, where \(u\) is the element in the set V\({}_{new}\) and \(v\) is not in the set V\({}_{new}\), \(v\in V\).  
**Step 4**: Add \(v\) to the set V\({}_{new}\) and add the edge \((u,v)\) to the set E\({}_{new}\).  
**Step 5**: Repeat Step 3 and Step 4 until V\({}_{new}\) = V.  
**Step 6**: Output V\({}_{new}\) and E\({}_{new}\), which are the MST.
## 7 Signal Processing:  
### Single-Degree-of-Freedom Acceleration Response  
Signal conditioning was applied before analyzing the deck response with the SID algorithm to extract useful information and reduce noise. The deck response was recorded as an acceleration time history using accelerometers at the ends of the bars, but the recorded response had a lot of noise, including system noise and mechanical noise from wind tunnel vibrations at high speeds. Figure 12 shows the deck response for SDOF heave and pitch at wind speeds of 0 m/s and 10 m/s.  
Noise affects the response more at high wind speeds due to increased wind tunnel vibrations. The initial cycles contain extra modes due to the catapult effect. The pitch response at zero wind speed shows clean harmonics after the initial cycles, but the heave response has noise even at zero wind speed, possibly caused by friction in the bearings. The response was resampled, normalized, and conditioned by removing the mean and applying a lowpass filter at 10 Hz and a Butterworth band pass at 2 Hz and 6 Hz to remove noise and produce clean time histories. The dominant heave frequency was 3 Hz, and the pitch was 5 Hz, with high-frequency noise at 40 Hz. The filtered response of the heave and pitch motion is shown in Figure 13. The signal conditioning produced clean and smooth time histories that were used to identify SDOF parameters for heave and pitch motions.
# A Proposal for a Zero-Trust-Based Multi-Level Security Model and Its Security Controls
## 3 Introduction of MLS Classification Criteria and Proposed Zero-Trust-Based MLS Model
### Comparison Analysis Between Proposed Zero-Trust-Based MLS Model with Other Models
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Questions** & **ZT-Based MLS Model** & **CISA Maturity Model [39]** & \multicolumn{2}{c}{**Bell–Lapadula Model [40]**} \\ \hline What is the type of the model? & \begin{tabular}{c} Convergence model (ZT+MLS) \\ \end{tabular} & \begin{tabular}{c} Single model \\ \end{tabular} & \begin{tabular}{c} Single model \\ \end{tabular} \\ \hline Does it propose a specific model? & \begin{tabular}{c} To implement a Zero-Trust-based \\ network, we propose a dynamic \\ access control model based on the \\ importance of key components \\ and data \\ \end{tabular} & \begin{tabular}{c} Presents a simple pillar-based \\ maturity model \\ \end{tabular} & \begin{tabular}{c} A model that describes a set of access \\ control rules using security labels on \\ objects and permissions on subjects \\ \end{tabular} \\ \hline Does it consider additional technologies or strategies for improving security? & \begin{tabular}{c} Considered \\ In particular, a flexible network \\ separation control strategy for \\ introducing new technologies \\ such as AI for the Classified level \\ is presented \\ \end{tabular} & None & None \\ \hline Does it propose security controls or capabilities? & \begin{tabular}{c} Specific security controls for each \\ component \\ (e.g., PEP: AC-3-2: Dynamic \\ Information Flow Control) \\ \end{tabular} & \begin{tabular}{c} High-level capabilities for each \\ pillar (Not specific. Vague and \\ abstract) \\ \end{tabular} & None \\ \hline Does it classify and provide security controls or capabilities focused on data protection? & \begin{tabular}{c} Provides security controls for the \\ sensitivity of data used, utilised, \\ and processed in the organisation \\ \end{tabular} & \begin{tabular}{c} Presenting capabilities focused \\ on maturity \\ \end{tabular} & None \\ \hline What is the basis for the classification of security labels? & \
\begin{tabular}{c} Classification by maturity \\ - Traditional, Initial, Advanced, \\ Optimal \\ \end{tabular} &
\begin{tabular}{c} Classification of subject and object \\ - Top secret, Secret, Confidential, \\ Unclassified \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of the other models.
## 6 Conclusions
\begin{tabular}{c c c c c} \hline \hline
**Sampling Point** & **Longitude** & **Latitude** & **Altitude (m)** & **Soil** \\ \hline Tributary 2--to & 39.439889 & 69.542361 & 2075 & Regosol \\ Zarafshon (Isizi poyon) & 39.431222 & 69.483639 & 1999 & Regosol \\ Tributary 3--to & 39.415750 & 69.418694 & 1985 & Regosol \\ Zarafshon (Isizi poyon) & 39.413306 & 69.376833 & 1927 & Regosol \\ Zarafshon 11 (Pastigov) & 39.413833 & 69.269194 & 1919 & Regosol \\ Kallakhona-tributary to & 39.418194 & 69.264861 & 1913 & Regosol \\ Zarafshon & 39.425611 & 69.167250 & 1875 & Regosol \\ Zarafshon 12 (Pastigov) & 39.417056 & 69.156833 & 1820 & Regosol \\ Zarafshon 13 & 39.401222 & 69.060500 & 1742 & Regosol \\ (Shamutch) & 39.402833 & 68.954028 & 1660 & Regosol \\ Zarafshon 15 (Shavadki poyon) & 39.391472 & 68.853306 & 1619 & Regosol \\ Zarafshon 16 (Rarz) & 39.380639 & 68.773361 & 1508 & Regosol \\ Zarafshon 17 (Fatmev) & 39.385861 & 68.673861 & 1462 & Regosol \\ Zarafshon 18 & 39.387250 & 68.570250 & 1405 & Regosol \\ Shahriston 1, tributary & 39.520806 & 68.553389 & 2728 & Regosol \\ right site of tunnel & 39.519972 & 68.554889 & 2744 & Regosol \\ Shahriston 2, tributary & 39.519972 & 68.554889 & 2744 & Regosol \\ left site of tunnel & 39.440583 & 68.541389 & 1682 & Luvisol \\ Zarafshon 19 & 39.419455 & 68.509311 & 1366 & Regosol \\ Zarafshon 20 & 39.439194 & 68.441583 & 1306 & Regosol \\ Zarafshon 21 & 39.439194 & 68.441583 & 1284 & Regosol \\ Zarafshon 22 & 39.440806 & 68.273250 & 1226 & Regosol \\ Zarafshon 23 & 39.443472 & 68.087750 & 1206 & Regosol \\ Artuch 1 (Alpine camp) & 39.274889 & 68.139167 & 2142 & Luvisol \\ Artuch 2 & 39.335278 & 68.087750 & 1573 & Luvisol \\ Artuch 3 & 39.397806 & 68.043250 & 1267 & Regosol \\ Zarafshon 24 & 39.471667 & 67.991833 & 1113 & Gleysol \\ Zarafshon 25 & 39.466472 & 67.887139 & 1072 & Gleysol \\ Obj Sara 1 & 39.379278 & 67.880028 & 1775 & Regosol \\ Obj Sara 2 & 39.415889 & 67.865639 & 1315 & Regosol \\ Obj Sara 3 & 39.468389 & 67.872833 & 1079 & Regosol \\ Zarafshon 26 & 39.497306 & 67.785056 & 1035 & Gleysol \\ Lake 1 & 39.113250 & 67.855250 & 2393 & Regosol \\ (Khazorchashma) & 39.140972 & 67.859528 & 2106 & Regosol \\ Lake 3 & 39.166083 & 67.838972 & 1888 & Regosol \\ Lake 4 & 39.197278 & 67.819333 & 1811 & Regosol \\ Lake 5 & 39.203564 & 67.810650 & 1718 & Regosol \\ Lake 6 & 39.210056 & 67.805028 & 1701 & Regosol \\ Lake 7 & 39.220306 & 67.008006 & 1610 & Regosol \\ Shing 1 & 39.240806 & 67.790028 & 1565 & Regosol \\ Shing 2 & 39.273667 & 67.802778 & 1382 & Regosol \\ Shing 3 industrial zone & 39.306917 & 67.783778 & 1282 & Regosol \\ Mogiyon 1 & 39.112611 & 67.733639 & 2383 & Gleysol \\ Mogiyon 2 & 39.147750 & 67.737000 & 2144 & Gleysol \\ Mogiyon 3 & 39.215194 & 67.711944 & 1722 & Gleysol \\ Mogiyon 4 & 39.273722 & 67.688639 & 1474 & Gleysol \\ Mogiyon 5 & 39.315583 & 67.770167 & 1250 & Luviysol \\ \hline \hline \end{tabular}
## 2 Materials and Methods
### Laser Assisted Crown Retrieval Process  
An apical channel was prepared through the furcation of the tooth into the pulpal chamber to insert the thermometer probe for a microthermal couple element (Adv. Thermocouple Therm. w/RS232 Output Datalogger Type K- 800008, Super Scientific Works Pvt. Ltd., Gujarat, India) to measure the temperature changes during the laser-assisted crown retrieval process (Figures 1 and 2). The cemented crown/abutment specimen was mounted into a 3-D printed typodent with adjacent natural teeth to simulate a clinical situation during laser irradiation (Figure 2).  
Similar to previous studies [23; 24; 25], Er:YAG laser (Lightwalker, Fotona, Slovenia) was set at: 300 mJ, 15 Hz, 4.5 W, spot size 0.9 mm, fluence per pulse 47.16 J/cm\({}^{2}\), power density 707.4 W/cm\({}^{2}\)operation mode: quantum square pulse (QSP) mode (50 us pulse duration) 2 water/2 air. Using a non-contact continuous motion tipless handpiece (HO2, Lightwalker, Fotona, Slovenia), the crown was irradiated bucally, lingually, occlusally, and indirectly interproximally at a distance of 6-10 mm. Following 2 min of irradiation time, the crown was gently tapped with a traditional crown removal instrument on the buccal and lingual margins, simulating clinical access to those surfaces. If removal could not be achieved, the tooth was subjected to additional 30 s intervals of irradiation followed by the attempt of crown removal after each of the intervals. The irradiation time was recorded until the crown was successfully retrieved either by tapping, light digital manipulation or passively lifting off during the irradiation (Figure 2). Before initiating the crown removal, pulpal temperatures were recorded at the baseline and throughout the entire procedure at 30 s intervals until the crown was retrieved.  
After the first, second, and third crown retrieval experiment, one specimen for each group was withheld for SEM analysis. To examine the surface integrity of the crown after each irradiation and crown retrieval, the underlying intaglio surface of the crown was examined using scanning electron microscopy (SEM) analysis (JEOL 6610 LV, JEOL, Japan). Similar to the previous studies [23; 24; 25], secondary electron imaging (SEI) to topography of the structure analysis was applied to enhance structural assessment and visualization of possible cracks using back-scattered electrons (BSE).
## 5 Implications and Conclusions  
Earth-observation and characterization of anthropogenic disturbance regimes of forests will become requisites for understanding global patterns of forest use during the Anthropocene. The substantial increase in energy development and consumption over recent decades, and in particular fossil fuels,  
Figure 4: Nighttime light intensity for North America collected in 1996 and 1997 by the Operational Linescan System of the Earth-Observing Defense Meteorological Satellite Constellation.  
will likely undermine ecosystem-based management of forested ecosystems. Several ecological components have already been exacerbated by energy development including biodiversity and habitat loss. The current state of knowledge of energy development in forests is primarily limited to persistent linear corridors and surface mining. As the number of _in situ_ projects rapidly increase in forested ecosystems, research programs should focus on the non-discrete impacts of energy development. Moreover, research into the monitoring of pipeline impacts to forested ecosystems will be critical during this decade of new expansions.  
To date, the majority of impacts of oil and gas activities have been characterized by research from outside the industry. The forestry industry is often a primary stakeholder in landscapes undergoing rapid energy development and is usually responsible for maintaining forest structure, function, and composition as part of sustainable forest management objectives. Thus, most monitoring research on the impacts of energy development is conducted by the forestry industry in order to meet internal and regulatory planning mandates. The development of a planning framework that brings all stakeholders together will be a necessary first step towards mitigating the negative impacts on multiple use landscapes undergoing rapid energy development.  
Furthermore, future remote sensing of energy development should be focused around integrating observations from a range of spectral, spatial, and temporal domains. Relating those data to recurring ground campaigns will provide a sound basis for monitoring, assessing, and establishing reclamation standards that are suited to forested ecosystems. There remain a number of unique opportunities for exploring the impacts of energy development on forested ecosystems. For example, nighttime lights can be used as a proxy for the level of oil and gas activities based on flaring, and disturbance trend algorithms can provide new insights into the persistence and recovery success of abandoned mines, well sites, and cut lines. These and other new indicators will form the basis toward monitoring forest change in landscapes undergoing rapid energy development.
_applied_  
_sciences_  
_Article_  
**Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
**Abstract: Remote sensing for the monitoring of chlorophyll-a (Chl-a) is essential to compensate for the shortcomings of traditional water quality monitoring, strengthen red tide disaster monitoring and early warnings, and reduce marine environmental risks. In this study, a machine learning approach called the Gradient-Boosting Decision Tree (GBDT) was employed to develop an algorithm for estimating the Chl-a concentrations of the coastal waters of the Beibu Gulf in Guangxi, using Landsat 8 OLI image data as the image source in combination with field measurements of Chl-a concentrations. The GBDT model with B4, B3 + B4, B3, B1 - B4, B2 + B4, B1 + B4, and B2 - B4 as input features exhibited higher accuracy (MAE = 0.998 \(\upmu\)g/L, MAPE = 19.413%, and RMSE = 1.626 \(\upmu\)g/L) compared with different physics models, providing a new method for remote sensing inversion of water quality parameters. The GBDT model was used to study the spatial distribution and temporal variation of Chl-a concentrations in the coastal sea surface of the Beibu Gulf of Guangxi from 2013 to 2020. The results showed a spatial distribution with high concentrations in nearshore waters and low concentrations in offshore waters. The Chl-a concentration exhibited seasonal changes (concentration in summer > autumn > spring \(\approx\) winter).  
ch chlorophyll-a; gbdt model; Guangxi Beibu Gulf; remote sensing inversion
## 3 Dynamic Model
### Electromagnetic Force Modeling  
The force exerted on the MPM by the current-carrying wires beneath it is modeled with the Lorentz force equation. The current-carrying wires are considered as thin filaments, obtaining Equation (2), with \(I\) the current intensity through the wire and \(\mathbf{d}\mathbf{l}\) the vector along the wire length and \(\mathbf{B}_{\mathrm{ext}}\) the external magnetic flux density field.  
\[\mathbf{F}_{\mathrm{electromagnetic}}=I\int_{wire}\mathbf{d}\mathbf{l}\times \mathbf{B}_{\mathrm{ext}} \tag{2}\]  
This expression models the driving, holding and return currents interactions with the MPM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Dimensions (mm)** & **Mass** & **Material** & **Mag. (T)** \\ \hline MPM & \(2.0\times 2.0\times 1.2\) & 34 mg & NdFeB & 1.22 \\ FPM & \(2.2\times 2.2\times 1.0\) & 45 mg & NdFeB & 1.43 \\ Structure & \(9.9\times 9.9\times 1.0\) & 104 g & Si & - \\ Glass layer & \(4.0\times 4.0\times 0.1\) & 64 mg & Glass & - \\ Object & \(20\times 20\times 0.1\) & 416 mg & Glass & - \\ \hline \hline \multicolumn{5}{c}{Distances (mm)} \\ \hline d\({}_{1}\) = 2.6 & d\({}_{2}\) = 0.185 & d\({}_{3}\) = 0.253 & d\({}_{4}\) = 0.2 & Stroke = 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EDA characteristics.  
Figure 4: EDA’s free body diagram (**a**) Side view. (**b**) Top view.
## 3 Results
### Interpretable Modeling Approaches
#### 3.3.2 SHAP Analysis  
To further clarify the contributions of various digital technology tools to teaching quality prediction in physical education, SHAP was employed for interpretability analysis. Figure 4A illustrates the average impact of each feature on predicted teaching quality across the full sample. Management software, websites, and multimedia materials showed strong positive effects, with higher values generally corresponding to increased predictions of teaching quality (as indicated by red rightward shifts). Figure 4B displays the cumulative contribution trajectories of individual features. The color gradient from blue to red indicates a progression from lower to higher predicted values. Figure 5A presents an SHAP force plot for the sample with the highest predicted teaching quality. In this case, aside from multimedia courseware and question banks (both used at frequency = 1), websites, subject software, and management software--when used more frequently--exerted stronger positive influences. Among these, websites emerged as the most impactful contributor. Figure 5B offers a detailed breakdown of feature-wise contributions, tracing the progression from the model's expected baseline prediction (E[f(x)] = 0.496) to the final predicted value of 1.162. The most substantial positive contributions came from websites (+0.26), multimedia courseware (+0.14), and question banks (+0.10), while multimedia materials had a slight negative effect (\(-\)0.01), and e-textbooks had negligible influence.  
Figure 4: SHAP-based interpretation of feature contributions in the ridge regression model. (**A**) Summary plot; (**B**) SHAP decision plot.  
Figure 5: SHAP-based local interpretation of feature contributions for a high-scoring sample. (**A**) SHAP force plot; (**B**) SHAP waterfall plot.
## 1 Introduction
### Three-Step Development and Validation of a Numerical Model  
Considering the uncommon geometry and material characteristic of an arch, development of a FEM model of such a structure required a specific approach and its validation at different stages of advancement. The development of a FEM model of the full-sized construction made of the self-supporting arch has been heavily supported by the experiments performed by means of 3D DIC within three steps. At first, the tests have been carried out on single sections of the arch in order to determine which geometric model of the surface shape (planar, corrugation or corrugation and wavy model) should be applied in the further steps for FEM analysis. The considered sections were 1 m long and 0.7 m wide. In the next step, a structure composed of four individual segments with the geometry selected at step 1 has been used in laboratory tests with controlled loading conditions in order to investigate the global stability and to determine the mechanical behavior of supports, in particular to define the rotational stiffness of supports used in a FEM model of arches. The measured object was of 12 m span and 2.8 m wide. The final step was based on the tests of a full-scale object: 8 m high covering the area of 18 \(\times\) 18 m. The outcomes of this test is the validation of a complete FEM model in which the knowledge gained in steps 1 and 2 had been implemented. It has been proven that the model accurately simulates construction deformations under the environmental loads. The model can be scaled to larger constructions. The general procedure supporting the development and validation of a FEM model has been summarized and presented in Figure 2. This procedure is described in detail in Sections 5 and 6.  
Figure 1: (**a**) Outside image of the measured hall as an example of typical self-supporting arch-shaped covering, (**b**) section surfaces.  
Figure 2: Flowchart of the 3-steps updating and validation of numerical model of self-supporting arch structures with utilization of 3D digital image correlation method.