esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 In Situ Observation Experiment of Shale Microfractures  
In order to investigate the microfracture characteristics of shale, some studies have conducted static observation experiments on microfractures in shale after fracturing [16; 25; 26; 27; 28; 29; 27]. However, merely observing post-fracture microfractures cannot reflect the influence of the loading process on them, nor can it reveal the initiation and extension process of microfractures in shale. Continuous and dynamic observation of the propagation characteristics of microfractures is necessary in order to obtain a clear and comprehensive understanding of the fracture propagation process and discover general rules [17; 28; 29].  
Currently, there are several in situ observation techniques available for continuously and dynamically observing microfracturing in shale, including optical microscopy with an in situ loading stage, scanning electron microscopy (SEM), and computer tomography (CT) scanning (Figure 2). In situ compression experiments were conducted under an optical microscope to observe the initiation and propagation process of microfractures [30]. However, the low magnification of optical microscopy cannot represent the formation and evolution characteristics of microfractures. SEM equipped with an in situ mechanical loading device has made it possible to dynamically observe microfractures. Some studies have used SEM to observe the continuous propagation process of main fractures and branch fractures in shale and analyze their fracture toughness [17; 28]. In addition to SEM, some studies have utilized micro-CT to conduct in situ loading experiments on shale at the microscopic level, observing the generation of extensive microfractures. As the load increases, these microfractures either connect with the main fracture or form new fractures independently [31]. With the advancement of testing methods, some studies have also employed advanced instruments such as positron emission tomography-computed tomography (PET-CT) and high-energy CT to carry out experimental studies on the extension of micro-fractures [32; 33].  
The aforementioned in situ experiments have successfully observed the dynamic process of microfracture initiation and propagation. Microfractures are typically discontinuous and widely generated. As the load increases, these discontinuous fractures connect with each other to form the main fracture and also generate branch fractures connected to the main fracture, while microfractures that are not connected to the main fracture eventually close [7, 16, 17, 34, 35, 36, 37, 38, 39]. What are the mechanisms behind the formation and evolution of these discontinuous microcracks?
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 4 Experimental Results
### LEO Network Topologies for Experiments  
In this experiment, TLE data predicted at a future epoch for satellites passing over a designated observation point were collected, and only those satellites that met predefined selection criteria were retained for analysis. A total of 150 selected satellites participated in the communication experiment, and the K value was set to 15. Subsequently, all satellites located within a specified communication radius were connected by edges of equal cost (1), and each satellite was assigned a sequential identifier for analysis. This procedure produced a graph-based satellite network topology representing connectivity information.
## 2 Materials and Methods
### Main Parameters  
To analyze the mechanical characteristics of a TCC, the water blocking rate, area ratio, inclination ratio and water temperature stratification ratio are mainly considered. Figure 3 shows the geometry of the TCC and the main parameters are defined as follows:  
(1) Water blocking rate (\(Pr_{h}\)): water blocking area divided by cross-sectional area.  
(2) Area ratio (\(Pr_{a}\)): total area divided by vertical projection area.  
(3) Inclination ratio (\(Pr_{l}\)): inclination distance divided by vertical height.  
Figure 1: Working program.  
Figure 2: Comparison of monthly average water temperature in downstream areas of the hydropower station before and after reservoir construction.  
4. Water temperature stratification ratio (\(\beta\)): temperature difference between surface and bottom divided by water depth. The formulas are shown as follows: \[Pr_{h}=\frac{0.5*(D+b)*h}{0.5*(D+B)*H}\] (1) \[Pr_{a}=\frac{0.5*(D+b)*l_{a}}{0.5*(D+b)*h}=\frac{l_{a}}{h}\] (2) \[Pr_{l}=\frac{l}{h}\] (3) \[\beta=\frac{T_{t}-T_{b}}{H}\] (4)  
where \(Pr_{h}\) is the water blocking rate; \(Pr_{a}\) is the area ratio; \(Pr_{l}\) is the inclination ratio; \(\beta\) is the water temperature stratification ratio; H is the water depth (m); \(h\) is the water blocking height of the TCC (m); \(l_{a}\) is the arc length of the TCC (m); \(l\) is the slope distance (m); D is the base width of the cross section (m); B is the top width of the cross section (m); \(b\) is the top width of the TCC (m); \(T_{t}\) is the surface water temperature of the reservoir (\({}^{\circ}\)C); and \(T_{b}\) is the bottom water temperature (\({}^{\circ}\)C).
# SybilHP: Sybil Detection in Directed Social Networks with Adaptive Homophily Prediction
## 3 Problem Formulation
### LBP-Based Sybil Detection
#### 3.3.1 LBP Framework  
As shown in Figure 2, LBP-based methods operate in the following steps. The process starts with the OSN user interaction dataset, where we associate each node \(u\in V\) with a binary random variable \(x_{u}\), whose state could either be \(-1\) or \(1\), corresponding to benign or Sybil, respectively.  
We then model the joint probability distribution over all binary random variables \(\mathbf{x_{V}}=\{x_{u}\}_{u\in V}\) as a pMRF. Specifically, pMRF factors the joint distribution \(P(\mathbf{x_{V}})\) into the multiplication of a series of unary and pairwise potential functions:  
\[P(\mathbf{x_{V}})=\frac{1}{Z}\prod_{u\in V}\phi_{u}(x_{u})\prod_{(u,x)\in E}\varphi _{uv}(x_{u},x_{v}), \tag{1}\]  
where \(Z=\sum_{\mathbf{x_{V}}}\prod_{u\in V}\phi_{u}(x_{u})\prod_{(u,v)\in E}\varphi_{uv} (x_{u},x_{v})\) summing over all possible combinations of \(\mathbf{x_{V}}\) is the partition function used for probability normalization. The node potential \(\phi_{u}(x_{u})\)  
Figure 1: Sybil attack model.  
Figure 2: Overview of LBP-based methods.  
defined by Equation (2) incorporates the node prior information. Furthermore, the edge potential \(\varphi_{uv}(x_{u},x_{v})\) will be elaborated in the next section.  
\[\phi_{u}(x_{u})=:\left\{\begin{array}{ll}q_{u},&\text{if }x_{u}=1\\ 1-q_{u},&\text{if }x_{u}=-1,\end{array}\right. \tag{2}\]  
where \(q_{u}\) is the prior probability of node \(u\) being a Sybil. Without further prior knowledge, for those nodes labeled with Sybil, we set a soft probability \(q\) for them, and \(1-q\) for those labeled with benign. We set the prior probability to be neutral at 0.5 for the unlabeled nodes.  
After initializing the prior probability that \(x_{u}=1\) as \(q_{u}\) for all nodes according to the given labels, we then implement the LBP algorithm on pMRF to estimate the posterior probability of each node \(x_{u}\) being a Sybil, denoted by \(p_{u}=P(x_{u}=1\mid x_{\Psi})\).  
The LBP algorithm can be summarized as the following two steps [36, 37], that is, update messages on edges until convergence and then calculate \(p_{u}\) by message aggregation:  
(1) For each edge \((u,v)\in E\), a message is sent from \(u\) to \(v\) in the \(t\)th iteration:  
\[m_{uv}^{(t)}(x_{v})=\sum_{x_{u}}\phi_{u}(x_{u})\varphi_{uv}(x_{u},x_{v})\prod _{k\in N(u)\setminus v}m_{ku}^{(t-1)}(x_{u}). \tag{3}\]  
We iteratively apply Equation (3) until the difference between \(m_{uv}^{(t)}\) and \(m_{uv}^{(t+1)}\) is negligible.  
(2) For each node \(u\), the posterior probability distribution of \(x_{u}\) can be estimated from the aggregation of all the converged messages received from its neighbors:  
\[p(x_{u})=\frac{1}{Z}\phi_{u}(x_{u})\prod_{k\in N(u)}m_{ku}^{(t)}(x_{u}), \tag{4}\]  
where \(Z=\sum_{x_{u}}\prod_{k\in N(u)}m_{ku}^{(t)}(x_{u})\) summing over possible \(x_{u}\) for probability normalization.
## 3 Experiments and Results
### Study of the Applied Classifiers
#### 3.1.1 Naive Bayes  
Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes theorem with the \"naive\" assumption of conditional independence between every pair of features given the value of the class variable [37]. According to [37], for a given class variable \(Y\) and dependent feature vector \(X_{1}\) through \(X_{p}\) [PERSON] theorem states that  
\[P(Y|X_{1},...,X_{p})=\frac{P(Y)P(X_{1},...,X_{p}|Y)}{P(X_{1},...,X_{p})} \tag{3}\]  
Applying the conditional independence assumption which implies that  
\[P(X_{j}|Y,X_{1},...,X_{j-1},X_{j+1}...,X_{p})=P(X_{j}|Y) \tag{4}\]  
For all \(j\), the relationship can be simplified as  
\[P(Y|X_{1},...,X_{p})=\frac{P(Y)\prod_{j=1}^{p}P(X_{j}|Y)}{P(X_{1},...,X_{p})} \tag{5}\]  
Naive Bayes classifiers tend to yield posterior distributions that are robust to biased class density estimates, particularly where the posterior is 0.5 (the decision boundary) [38]. Naive Bayes classifiers assign observations to the most probable class and the algorithm can be explicitly described as follows [38]:  
a. Estimates the densities of the predictors within each class.  
Models posterior probabilities according to Bayes rule. For all \(k=1,...,K\), it can be mathematically expressed as: \[\hat{P}(Y=k|X_{1},...,X_{p})=\frac{\pi(Y=k)\prod_{j=1}^{p}P(X_{j}|Y=k)}{\sum \limits_{k=1}^{K}\pi(Y=k)\prod_{j=1}^{p}P(X_{j}|Y=k)}\] (6) here \(Y\) denotes the class index of an observation, \(X_{1},...,X_{p}\) are the random predictors of an observation and \(\pi(Y=k)\) represents the prior probability that a class index is \(k\).
3. Classifies an observation by estimating the posterior probability for each class, and then assigns the observation to the class yielding the maximum posterior probability. If the predictors constitute a multinomial distribution, then the posterior probability \[\hat{P}(Y=k|X_{1},...,X_{p})\propto\pi(Y=k)P_{mn}(X_{1},...,X_{p}|Y=k)\] (7) where \(P_{mn}(X_{1},...,X_{p}|Y=k)\) is defined as the probability mass function [38]. Naive Bayes classifiers require a small amount of training data to estimate the necessary parameters [37]. These classifiers can be extremely fast compared to more sophisticated methods, since the decoupling of the class conditional feature distributions means that each distribution can be independently estimated as a one dimensional distribution [37]. Thereby the problems associated with dimensionality can be alleviated.
## 4 Results and Discussion
### Image-Sensor-Based Spatial Object Extraction
#### 4.3.2 Utility Tunnel Data Plane Object Analysis  
To identify line objects from the tunnel data, the variables of the algorithm were fine-tuned to align with the data, generating the corresponding results. Figures 11-13 illustrate the outcomes of each variable adjustment. Figure 11 illustrates the result when the minimum value for plane recognition was defined. In this instance, planes within the data were recognized, but each plane was treated as a separate object. This suggests that the algorithm identified different planar elements within the tunnel system, but it may not have correctly grouped or classified these elements together where appropriate. In Figure 12, the K-nearest neighbor value was set to 100. The K-nearest neighbor algorithm is a type of instance-based learning where the function is approximated locally, and all computation is deferred until classification. By setting this value to 100, planes were again recognized as individual objects. This might indicate that the algorithm was able to classify or group similar planar elements together more accurately. Figure 13 shows the final results of recognizing plane objects in the tunnel data after the algorithm variables were further adjusted. The specifics of these adjustments are not detailed here, but the implication is that the algorithm was able to accurately identify and classify the planar objects within the digital twin of the tunnel system, potentially leading to a more precise and insightful analysis of the tunnel data. These figures demonstrate how adjusting variables within an algorithm can influence the results obtained, particularly in complex tasks such as identifying and classifying different elements within a large, detailed dataset like a digital twin of an underground utility tunnel.
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 5 Discussion  
Nanosatellites have become increasingly popular due to their size and cost-effectiveness, making them more affordable to build, launch, and operate than larger satellites. This affordability allows organizations with limited budgets to engage in space-based remote sensing. Nanosatellites' smaller size and more straightforward design also enable quicker development cycles and faster deployment, which is advantageous for addressing rapidly evolving situations, such as disaster response or monitoring dynamic environmental changes [22].
# Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
###### Abstract  
Once, bird respiration was thought to be responsible for the 10 dB-level fluctuations in the radar signals of birds. Although, recently, many researchers provide evidence against this, there are almost no quantification measurements of the contribution of respiration to bird signals in microwave anechoic chambers. Here, we first measured the radar signals modulated by the respiration of birds in a microwave anechoic chamber. Theoretically, the simulated signal fluctuation caused by the respiration of a 1 kg standard avian target (SAT) duck is approximately 1.2 dB based on the water sphere model. Then, experimentally, in a microwave anechoic chamber, we measured the signal fluctuations produced by the respiration movement of ducks using a dynamic system composed of a network analyzer and a high-speed camera. We tracked continuous radar data of a living duck and a dead duck within the S-band, X-band, and Ku-band, and then presented them using low-resolution range profiles (LRRP) and high-resolution range profiles (HRRP). The results indicate that respiration movement causes periodic signal fluctuation with a respiration rate of approximately 0.7 Hz, but the amplitudes within S-band, X-band, and Ku-band are approximately 1 dB level, much less than the 10 dB level. Respiration is not responsible for the 10 dB-level periodic signal fluctuation in radar echoes from birds.  
high-resolution range profiles (HRRP); microwave anechoic chamber; low-resolution range profiles (LRRP); radar signals of birds; respiration movement +
Footnote †: journal: Medical Imaging  
_Applied_  
1
###### Abstract
The above examples utilized floaters' motions to detect mooring-line failures so that much fewer sensors can be used compared to the conventional deterministic methods. They [12; 13; 14; 15] also assumed that floating structures are rigid to simplify the analyses of motion-sensor signals. In this study, we developed a ML-based mooring-failure-monitoring system for a long and highly flexible SFT with DNN algorithms to detect mooring-line failures in real time without employing human effort or any devices. The effects of variable number of sensors (accelerometers) and their locations were analyzed. The training data for the developed ML algorithm was produced by running the author-developed time-domain SFT simulation program [7] using the commercial software, OrcaFlex [16]. The simulations of SFT's wave-induced motions and mooring tensions were partly validated through comparisons with an independent SFT hydro-elastic simulation program [7; 17] and a series of experimental results with small SFT sections [18]. To validate the developed smart monitoring system, various intact/failure scenarios and wave conditions were considered for training and testing of the algorithm. In the algorithm optimization stage, not only the numbers of the hidden layers, neurons, and epochs but also the activation function and optimizer were properly utilized to enhance the detection accuracy. Contrary to previous researches [12; 13; 14; 15], in the case of highly elastic SFT, a single sensor cannot cover the entire motion, so several sensors are required, as presented in [19]. In this regard, in the testing stage, we checked the detection accuracy of the developed algorithm with different numbers and arrangements of sensors. Machine learning algorithms for the failure detection of submerged deformable structures like SFT have rarely been investigated in the open literature. In this regard, this study using DNN will help other researchers to investigate similar problems in the future.
## 4 Experiment and Results
### Experimental Training Setting  
We used the transfer learning method to load the pre-train weight so that the model has stable parameters for training from the beginning, which speeds up the training speed and reduces the amount of data required for model fitting. The default input image resolution of the model is 608 pixel \(\times\) 608 pixel \(\times\) 3. Because there is only one category in the citrus dataset, the default feature dimension is 18.  
In training, we trained 50 epochs on the citrus data set, and set the constraint model using CIOU loss function and adam optimization algorithm \(\beta\)1 = 0.89, \(\beta\)2 = 0.99, \(\varepsilon\) = 109. Set batch size to eight. All training was conducted on NVIDIA RTX 2080 Ti graphics card. We used the operating system is Ubuntu 18.04, and the in-depth learning parallel acceleration Library of cuda10.2 and cudnn8.0 is used for acceleration training; PyTorch version is 1.7.
## 2 Data and Methodology
### Dataset  
The data sources and processing flow of this study are as follows. Meteorological data including temperature and precipitation were obtained from a nationwide monthly 1 km resolution spatiotemporal dataset (2000-2020) developed by [PERSON] et al. (2019). This dataset integrates nationwide meteorological station observations through the thin-plate spline interpolation method, with topographic correction applied to enhance spatial accuracy. The vegetation data include a 250 m resolution NDVI dataset (2000-2020) based on the MODIS MOD13Q1 product, processed through cloud removal and spatiotemporal filtering with an R\({}^{2}\) exceeding 0.85, as well as 868 basic vegetation units mapped in the Chinese 1:4 million vegetation atlas. The solar radiation data were sourced from the homogenized Chinese land surface climate observation grid dataset (2000-2020) ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)), accessed on 12 June 2025). This dataset integrated MODIS 0.1-degree resolution cloud cover, aerosol retrieval, and ground-based sunshine duration observation data using the Geographically Weighted Regression (GWR) method, ensuring the spatiotemporal consistency of surface solar radiation. All the raster data were obtained from the National Tibetan Plateau Science Data Center ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)), accessed on 12 June 2025) and the Gansu Ecological and Environmental Science Data Center ([[http://eco.gssdc.cn](http://eco.gssdc.cn)]([http://eco.gssdc.cn](http://eco.gssdc.cn)), accessed on 12 June 2025). Dataset resampling to 250 m spatial resolution was performed using bilinear interpolation, followed by geospatial extraction of the Yellow River Basin boundaries to ensure multi-source data compatibility. This preprocessing workflow guaranteed pixel-level spatial consistency across heterogeneous datasets.  
Model validation was performed using the GLASS vegetation net primary productivity product (GLASS NPP, 500 m/8 days), which was generated based on 10 dynamic vegetation models simulating plant autotrophic respiration to GPP ratios from the TRENDY model comparison program ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020). The data are available through the GLASS product release website of the University of Hong Kong ([[https://www.glass.hku.hk/](https://www.glass.hku.hk/)]([https://www.glass.hku.hk/](https://www.glass.hku.hk/)), accessed on 12 June 2025).
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
### Estimation of Forest Structure Parameters and Biomass
[72] developed a CNN model using ALS data and Landsat imagery and found that CNN was more accurate than an extreme learning machine (ELM), a backpropagation neural network, a regression tree, RF, SVR, KNN, and other standard machine learning techniques. The stacking algorithm significantly increased prediction accuracy when compared with base models.
## 4 Result and Validation
### Comparison with Wet Refractivity Derived from Radiosonde Data  
It is accepted that the radiosonde data can provide precise atmospheric profile information vertically [40; 47; 48]. Fortunately, there is a radiosonde station (57816) located in the tomographic area, where the radiosonde balloon is launched twice a day at UTC 12:00 a.m. and 12:00 p.m., respectively. Therefore, such data was also introduced in this paper to validate the accuracy of the tomographic result derived from different schemes. In this experiment, the data of fifteen days from Doy 303 to 317, 2015 was selected for tropospheric tomography and compared with that derived from radiosonde data. Thus, there is a total of thirty sets of data are used and analyzed over the experimental period.  
The tomographic wet refractivity value of voxels derived from two schemes were compared with those from radiosonde at the location of the overlapped voxels. The RMS error and Bias of the differences between the radiosonde and different schemes for two epoch (UTC 12:00 a.m. and 12:00 p.m.) of tomography are calculated and showed in Figure 8. It is clearly shown that the values of RMS error and Bias obtained from Scheme 2 are smaller than those from Scheme 1. In addition, the statistical results of wet refractivity between radiosonde and tomography for schemes 1 and 2 was also given and the result reveals that the mean RMS error and Bias of their differences for two schemes are 13.8/8.2 ppm and 3.9/2.8 ppm, respectively.  
To further assess the performance of the proposed method considering the weightings of different input information of tomography modeling, the mean wet refractivity profiles at various altitudes derived from two schemes were compared with those from radiosonde over the tested period. In addition, the RMS error of those differences and relative error (RE) are also calculated to analyze the relationship of tomographic modeling error with respect to altitude in Figure 9. It can be clearly seen that the mean wet refractivity profiles of Scheme 2 has a better agreement with that of radiosonde data, especially at the bottom altitudes, compared to that of scheme 1. They also show a smaller value of mean RMS at different altitudes. For the comparison of relative error, the values of Scheme 2 are smaller than those of Scheme 1 below 4 km and shows the opposite case for the above altitudes. From Figure 9 (left), we can see that the water vapor density is mainly focused on the bottom layers, which changes from more than 60 ppm to about 10 ppm at the height of 0-4 km. Therefore, the proposed method in this paper can effectively improve the accuracy of the tomography result, especially at low altitudes. As for the slightly larger relative error above 4 km (Figure 9, right), it is accepted because of the small wet refractivity value in the upper layers, and even a small difference can result in a large relative error.  
Figure 8: The RMS (Root Mean Square) error and Bias between raidosonde and two schemes for the Doy 303 to 317, 2015.