esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Multinomial Logit-Based Analysis  
Although individuals in a respective cluster were considered relatively homogenous, heterogeneity might have remained among individuals in the respective subgroup. Furthermore, the respective cluster tended to make different driving decisions in a wide variety of driving scenarios.  
In the selection of the multinomial logistic (MNL) model as the analytical framework for this study, a meticulous evaluation was conducted to appraise its suitability and advantages. Firstly, the MNL model, acclaimed for its statistical robustness, facilitates modeling based on the joint probability distribution of multiple classification outcomes. This structural characteristic allows for the simultaneous consideration of various factors influencing classification outcomes, contributing to a nuanced and comprehensive understanding of the intricate relationship between demographic characteristics and driver classification. Secondly, the MNL model showcases a notable capability of addressing sample imbalances, especially in scenarios where different types of driver samples may be unevenly distributed. This capability ensures a balanced and unbiased modeling approach, establishing a robust foundation for analyzing diverse classification outcomes. Additionally, the MNL model is distinguished by its flexibility in accommodating diverse variable types, encompassing counts, continuous, and categorical variables. This allows for the incorporation of a wide range of driver characteristics into the model, thereby fostering a more comprehensive analysis of their impact. Lastly, the intuitive nature of parameter interpretation in the MNL model facilitates a clear understanding of how demographic characteristics impact the classification and behavioral choices of different driver types. Hence, an MNL analysis was employed in our study to comprehend the correlation between driver groups and individual socio-demographic characteristics, as well as to analyze the behavior choices of different clusters in the face of distinct driving scenarios.  
The MNL model presents a number of equivalent formulations of simple logistic regression, which can be expressed as follows:  
\[\ln\frac{P(Y=j|X)}{P(Y=J|X)}=a_{j}+\sum_{i=1}^{I}\beta_{ji}x_{i} \tag{6}\]  
where \(X\) is a set of independent variables; \(Y\) is a set of dependent variables; \(P(Y=j|X)\) is the probability of \(j\)th dependent variable; \(x_{i}\) is the \(i\)th independent variable; and \(a_{j}\) and \(\beta_{ji}\) are the regression coefficients.
###### Abstract  
Protected areas (PAs) play a crucial role in safeguarding biological resources and preserving ecosystems. However, the lack of standardized and highly operational criteria for evaluating their conservation effectiveness, particularly across different ecological types, remains a significant gap in the literature. This study aims to address this gap by constructing a conservation effectiveness evaluation model for two distinct types of PAs in Heilongjiang Province, China: the Zhalong National Nature Reserve (ZINNR), a wetland ecological reserve; and the Mudanfeng National Nature Reserve (MdfNNR), a forest ecological reserve. We employed various methods, including land use dynamic index, visual analysis of landscape patterns, remote sensing inversion, and a multi-factor comprehensive assessment model, to assess changes in conservation effectiveness from 2000 to 2020. Our findings reveal a contrast between the two PAs. In the ZINNR, croplands and water bodies increased significantly by 4069.4 ha (\(K\) = 1.5820%) and 2541.58 ha (\(K\) = 3.2692%). In the MdfNNR, impervious lands increased greatly by 65.35 ha (\(K\) = 7.4021%), whereas forest lands decreased by 125 ha (\(K\) = \(-\)0.067%). The core area of the two PAs displayed increased landscape regularity, whereas the experimental area showed heightened landscape diversity. In ZINNR, the MPSL value increased by 134.91%, whereas the PDL value decreased by 57.43%, indicating a more regular landscape pattern. In MdfNNR, the SHDIL value decreased by 110.7%, whereas the PDL value increased by 52.55%, indicating a more fragmented landscape pattern. The area with improved vegetation trends in ZINNR was 8.59% larger than in MdfNNR, whereas the area with degraded vegetation trends was 4.86% smaller than in MdfNNR. In all years, the high effectiveness area was larger in ZINNR than in MdfNNR, whereas the medium and low effectiveness areas were smaller in ZINNR compared to MdfNNR. This study provides a scientifically rigorous assessment method for evaluating the conservation effectiveness of different types of PAs, laying a solid theoretical foundation and practical guidance for future conservation strategies.  
Zhalong National Nature Reserve; Mudanfeng National Nature Reserve; conservation effectiveness; Google Earth Engine
# A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems
## 1 Introduction
In our simulations, the results showed that the CNN-based equalizer could achieve a higher performance than conventional equalizers in previous studies. In the ideal case, when 0% misalignment, our proposed model can obtain a gain of 3.5 dB at a BER of \(10^{-3}\). When the level of misalignment increases to 10%, the gain achieves 2.5 dB at a BER of \(10^{-3}\). Finally, when we change the level of blur in the HDS channel, we recognize that our proposed model can resist the blur effect up to 2.8. Our contributions are as follows:  
* We proposed a CNN-based equalizer for the HDS systems. * We optimized the best structure of the CNN (between straight and branch structures) for the 2D equalizer. * We analyzed and explained the properties of CNN structures. * We presented simulation results to verify the performance of the CNN-based equalizer. The remainder of this paper is organized as follows. In Section 2, we briefly introduce the background of the equalizer and the GPR target. The channel modeling of HDS systems and the proposed scheme are presented in Section 3. Simultaneously, we also analyze and explain the properties of CNN structures in Section 3. Simulation results are presented and discussed in Section 4. The complexity and latency/delay of the proposed model are also measured in Section 4. Finally, the conclusions are presented in Section 5.
# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 3 Methodologies  
To realize the detection target of PCB electronic components based on machine vision, we should not only identify the electronic components by category but also locate the device location. According to the previous review of target detection technologies, we selected YOLO V3, which has both accuracy and timeliness, as the basic model. We decided to implement algorithm improvement from the following aspects:  
Figure 3: Size and distribution diagram of the nine anchors of COCO dataset.  
* Establishment of image database: PCB electronic component image database is the foundation of object detection based on deep learning. The training database is established by combining the real data with the virtual data;
* Size analysis of detection targets: The slicing technique is adopted to separate the labeled image database training targets into different pictures, and the category and size database of detection targets is established through statistical analysis of this picture's size and quantity;
* Network structure improvement: We will analyze the output data content of the detection layer of YOLO V3, determine the feature fusion method according to the detection target and design the network structure improvement scheme;
* Anchor box generated by cluster analysis: A new anchor box is generated by clustering according to PCB electronic components training database to predict bounding box.
## 1 Introduction
Compared to numerical simulations, physical simulations provide a more direct and consistent method of quantitatively recording data with real stratigraphic information and contain fewer assumptions and limitations [30]. Since the development of the first modern seismic physical modeling system by French [31], seismic physical modeling has been widely applied to investigate wave propagation in complex media in exploration geophysics [32; 33; 34; 35; 36; 37]. To investigate the SBLR of fractured reservoirs, several seismic physical simulations have been carried out [38; 39; 40]. Established studies have not carried out targeted research on the SBLR of carbonate fractured reservoirs in the Tarim Basin based on physical models by synthesizing real regional geology. By comprehensively considering the real geology and petrophysical parameters of the Tarim Basin, a seismic physical model that is more compatible with the real observing system was designed to further investigate the fracture reservoir response. In the physical model, fracture reservoirs that include a variety of different dip angles, different fracture layers, and different overlying strata were designed and fabricated. After acquiring seismic data for the physical model, the SBLRs of the fractured reservoirs involved in the carbonate of the Tarim Basin were investigated, and the correspondence between the SBLRs and their corresponding fractured reservoirs was clarified. Finally, the influence of factors such as the fracture dip angle, number of fracture layers, and overlying strata on the SBLR was analyzed, and the fracture reservoirs were further analyzed based on planar attributes.
## 3 Methodology and Models
### SHAP Analysis for Model Interpretability  
#### 3.6.1 SHAP Value Calculation  
To interpret the model's predictions, Shapley additive explanation (SHAP) values were computed. These values quantify the contribution of each input feature to the model's output, offering insights into which meteorological factors most significantly impact energy production.  
* Kernel Explainer: The SHAP Kernel Explainer was utilized to approximate SHAP values for the TimeMix model, providing a detailed breakdown of feature importance.
* SHAP Value Interpretation: The computed SHAP values identified the most influential features, such as temperature and wind speed, enabling a deeper understanding of the factors driving energy production predictions.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 5 Process Flow
### Creating the Sectors  
The algorithm operates with sectors, each representing a 30 by 30 m area, which can be adjusted by modifying the values. In the case of raster division, the detection times of coordinates within a specific raster determine the behavioral characteristics of the particular area unit. The first step is to establish the sectors, which proceeds as follows. The algorithm identifies the boundaries of the area, both north-south and east-west, and then, divides the enclosed area according to the desired area size. This results in a scale where the individual x- and y-values can be grouped. Each sector is assigned a unique identifier for future reference. The created sectors are assigned coordinates on the basis of their location. Each coordinate is classified into x- and y-intervals, and based on these, the corresponding sector can be determined. As a result, each record contains the following: the number of the respective sector, the hour of measurement, the level of congestion strength.
## 3 Results and Discussions  
### Scanning Electron Microscope and Energy Dispersive X-Ray Spectroscopy Analysis  
Scanning electron microscope (SEM) images of plagioclase samples taken using 1.00 and 80.00 KX magnification ratios, the Energy Dispersive X-ray Spectroscopy (EDS) analysis spectrum, and the results of the quantitative elemental analysis obtained as a result of the EDS analysis are given in in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Step** & **Treatment** & **Observed** \\ \hline
1 & Preheat (up to 450 \({}^{\circ}\)C) & - \\
2 & TL measurement (up to 450 \({}^{\circ}\)C) & First background TL data \\
3 & Give radiation dose 1 & - \\
4 & TL measurement (up to 450 \({}^{\circ}\)C) & TL data \\
5 & TL measurement (up to 450 \({}^{\circ}\)C) & Background TL data \\
6 & Return to Step 3 & - \\ \hline \end{tabular} \({}^{1}\) Starting with the minimum dose deliverable by the device, the procedure was repeated with different doses (from 0.1 Gy to 550 Gy).  
\end{table}
Table 1: The measurement protocol used for TL measurements and dose–response experiments.  
As can be seen in Figure 1, when the SEM images, EDS spectra, and elemental composition results are examined, the spectra of potassium (K) in addition to sodium (Na), aluminum (Al), silicon (Si), and oxygen (O) belonging to plagioclase are observed in certain energy regions of the samples. As expected, this indicates the presence of plagioclase and K-feldspar in igneous rocks containing more silica. The reason for the spectrum of the carbon (C) element seen in the EDS results is due to the carbon tape on which the analyzed samples were glued.
## 4 Experimental Outcomes and Evaluations
### Evaluation Parameters
The predicted results are shown in Table 4. We achieved better results in applying the proposed CNN+LSTM model in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC-ROC (99%), respectively. The CNN+LSTM model, which is a hybrid architecture that leverages the respective advantages of the LSTM and CNN, exhibited superior performance, with an accuracy rate of 98.73% as shown in Table 4. Figure 5 depicts the graph illustrating the relationship between accuracy and loss over the course of multiple epochs. Figure 6 displays a heat map, while Table 3 provides advantages and shortcomings along with the applied and suggested DL or ML method used by different researchers. The graphical representation of the comparison of various DL models with the proposed model is shown in Figure 7.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 2 Related Works
This supports functions such as group communication, device-to-device direct communication (D2D), proximity service, and network survivability. However, these functions are primarily developed for situations where there is surviving network infrastructure in a disaster area. Considering the variety of disaster situations, it is necessary to develop a technology for scenarios where the communication infrastructure has been completely destroyed. In addition, deep-learning technology is applied to the field of UAV/drone detection algorithms [28; 29]. The work [28] developed a method to classify the types of drones, called drone classification. For the scheme, the authors utilized millimeter wave (mmWave) and a deep-learning network, which presents high drone-classification accuracy. The authors of [29] proposed a deep-learning-based framework to detect malicious drones, which outperforms existing state-of-the-art drone detection methods. However, our work is different from drone-detection research. Using wireless signals, our proposed scheme recognizes the number of devices around an UAV. Particularly, our novel deep-learning-based scheme can detect the number of asynchronous wireless signals.
## 2 Literature Review Methodology
### Development of the Review Protocol
#### 2.2.1 Criteria for Selection and Rejection  
* **Subject Relevance:** Research must be directly relevant to the context of this study.
* **Publication Date (2020-2025):** Only research published between 2020 and 2025 is included. Studies published before 2020 are excluded.
* **Publisher:** Selected research must be published in one of the three renowned scientific databases (IEEE, Springer, or Elsevier). To ensure comprehensive coverage, the first 10 pages of Google Scholar were searched for each key term, allowing consideration of articles from other databases.
* **Impactful Contributions:** Selected research must present key advancements in UWA communication, using ML/DL to enhance channel estimation, adaptive modulation or modulation recognition.
* **Results Oriented:** Studies with proposals and findings supported by solid evidence, facts, and experimental validation are favored.
* **Repetition:** Identical or redundant research within the same context is excluded.
# Slip Estimation for Mars Rover Zhurong Based on Data Drive
## 3 Method
### Input Features Selection  
The goal of input features selection was to select the relevant features from available sensor inputs for good model accuracy [28]. In this work, four features were chosen to form input feature vector to the algorithms: (1) the rover's pitch; (2) the rover's roll; (3) the average current \(I\) of six-wheel driving motors; (4) the average angular velocity \(w\) of six-wheel driving motors.  
(1), (2), and (3) were used for slip prediction in previous work [16; 17; 29; 30]. The rover's pitch and roll corresponded to the terrain geometry [16], and directly reflected the vertical loads acting on wheels. Additionally, there was a linear relationship between driving motor current and torques applied to the wheels. According to the complex relation between force and the wheel-soil interface, changes in vertical load and driving torque would affect wheel slip, so (1), (2), (3), and slip could be related [30]. There was some correspondence between wheel angular velocity and slip because of non-ideal motor behaviors [31]; it was useful to select (4) as one of the input features.
# Enhanced Upward Translations for Systems with Clusters
## 3 Enhanced Translations
This completes the modified translation suggested. The total algorithm of the modified translations, including its clustering stage described in the previous section, is summarized in Appendix A. Emphasize, that the first two steps of the algorithm are performed only once and before starting iterations. As mentioned, merely _intensities_ of sources, comprising the clusters, are updated at an iteration. Meanwhile, the two remaining steps, involving modified matrices, do not differ in complexity from the conventional method. Consequently, the time expense is actually the same for the modified and conventional approaches. We find the far field (2) approximately in the way, different from that used in conventional S2M translations. This path provides the gain in accuracy due to exact evaluation of the far fields of the representative sources, whose input into the field \(\mathbf{f}(\mathbf{x})\) is highest. This is clearly demonstrated by examples in Section 5. The additional gain is due to favorable for the accuracy fast decreasing of the modified (residual) fields. Emphasize that if a cluster, say for certainty the first (\(p=1\)), contains only one (leading) source, then \(M_{cl1}=1\) and the leading source becomes the representative source. In this case \(y_{1}=y_{cl1}\), \(q_{1}=q_{cl1}\). Hence, the right-hand side of (9) is zero. For kernels used in practice, the equations arising for different check nodes are linearly independent. Therefore, the determinant of the system (9) is non-zero; consequently, for zero right-hand side, the solution, giving the modified equivalent intensities, is zero, as well: \(q_{mes1}=\mathbf{0}\) (\(s=1,\ldots,N_{e}\)). Then approximation of \(f_{m1}(x)\) by the right-hand side of (7) is zero. This is the exact modified field \(f_{m1}(x)\) defined by (6), because in the case considered, \(f_{m1}(x)=K(x,y_{1})q_{1}-K(x,y_{cl1})q_{cl1}\equiv 0\). We see, that when the cluster radius \(r_{cl}\) or/and intensities of each of its sources, except for the leading one, are very small (in the limit, go to zero), and the error of the modified translation becomes negligibly small. In a general case, it is less than the error of the conventional SZM translation applied to sources of the cluster. The modified translations have been suggested above by using quite general considerations regarding \"fast decreasing\" of a field, \"gain in accuracy\", etc. Explaining these concepts in quantitative terms may be performed theoretically or/and numerically. Theoretical study requires special investigations, which would be especially involved for 3D Navier kernels (singular and hypersingular). In this paper, we prefer to avoid rigorous mathematical analyses and to obtain useful for practical applications estimations numerically. Specifically, the gain in accuracy is provided by direct calculations and comparison of the errors of the conventional and modified methods. The maximal and average errors are found for the kernels of direct BIE of 2D and 3D Laplace and Navier equations, solved by using the KI-FMM.
# Anisotropic Shear Strength Behavior of Soil-Geogrid Interfaces
## 3 Experimental Results  
### Shear Strength Behavior  
For the fine sand-geogrid interfaces, taking the biaxial geogrid SS30 and the triaxial geogrid TX160 as examples, the interface shear test results for different shear directions are shown in Figures 4 and 5, respectively. The shear stress-shear strain curves of the fine sand-biaxial geogrid (SS30) interface and the fine sand-triaxial geogrid (TX160) interface both show no obvious peak values under different normal stresses, all indicating strain hardening behavior. In the initial stage of shearing, the shear stress increases rapidly. As  
Figure 3: Geogrid specimen and shear direction. (**a**) Biaxial geogrid, (**b**) triaxial geogrid.  
Figure 2: Interface shear test apparatus.  
the shear strain increases, the increasing rate of shear stress gradually decreases, and there is an inflection point between the rapid increase and gradual increase. As the normal stress increases, the shear strain where the inflection point of the curve appears also increases.  
In this study, the peak value on the shear stress-strain curve is taken as the shear strength. If no obvious peak is observed on the curve, the shear stress at the shear displacement of 30 mm (i.e., 15% shear strain) is taken as the shear strength of the interface. Figure 6 presents shear strength envelopes for the fine sand-biaxial geogrid (SS30) interface and the fine sand-triaxial geogrid (TX160) interface under different shear directions. It can be observed that the interface shear strengths under the same normal stresses are different for the three different shear directions for both biaxial and triaxial geogrids. This indicates that the shear direction has a certain influence on the shear strength of fine sand-geogrid interfaces.  
Figure 4: Shear stress–strain curves for fine sand–biaxial geogrid (SS30) interfaces. (**a**) Shear direction of 0\({}^{\circ}\), (**b**) shear direction of 45\({}^{\circ}\), (**c**) shear direction of 90\({}^{\circ}\).  
Figure 5: Shear stress–strain curves for fine sand–triaxial geogrid (TX160) interfaces. (**a**) Shear direction of \(0^{\circ}\), (**b**) shear direction of \(45^{\circ}\), (**c**) shear direction of \(90^{\circ}\).  
Figure 6: Strength envelopes for fine sand–biaxial geogrid (SS30) interfaces and fine sand–triaxial geogrid (TX160) interfaces. (**a**) SS30, (**b**) TX160.