esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In recent years, the technique has gradually matured in all-round non-destructive scanning imaging and characterize the three-dimensional spatial structure of the pore-throat [25; 26; 27]. Compared with cast thin sections and the high-pressure mercuric pressure method, X-CT scanning has the advantages of non-destructive, high efficiency, and fine calculation of reservoir microstructural parameters [28], which can be combined with rock thin sections and scanning electron microscopy to accurately characterize the microscopic features of the gas layer in dense sandstone. To investigate the microscopic characteristics and factors influencing the development of the tight sandstone gas layer in the southwestern part of the Hangjinqi Prospect area in the Ordos Basin, in this paper, basic features such as petrological characteristics of the first member of the Lower Stone Box Formation in the D-well Zone of the Xinzhao East Zone of the Hangjinqi Prospect area were studied, based on rock sheets, field emission scanning electron microscopy, helium porosimetry, a gas permeability meter, and coring data. X-CT scanning technology was used to establish a three-dimensional spatial digital core and extract the reservoir space data. The quartz grain size was segmented and analyzed based on an adaptive approach. The above methods were used to characterize the microscopic features of the tight gas layer in the study area. We also analyzed the effect of lithological features and grain size on the microscopic characteristics of the reservoir space. This paper provides the scientific basis for further oil and gas exploration and development and advantageous reservoir prediction.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 5 Performance Evaluation
### Comparison with Existing Schemes  
This section compares the performance of the proposed scheme with the random channel selection (RCS) scheme and the greedy channel selection (GCS) scheme based on throughput, reliability and SoC. The RCS randomly chooses a channel from the available pool of channels with probability \(1/N\) and allocates it to an SU without considering its QoS, whereas the GCS selects a locally-optimal channel for each user with the hope to reach a globally-optimal solution. The cumulative distribution function (cdf) plots are shown for (a) min-max throughput, (b) max-min BER and (c) min-max SoC. For the current comparisons, we select the high traffic mode and carry out the simulations at 500 iterations, and then, the average values are shown by adopting the Monte Carlo principle. Figure 7 compares the proposed scheme with the RCS and GCS schemes based on the overall throughput of the network. The combined throughput of SUs is plotted in comparison with the existing schemes to illustrate the performance gain of the proposed scheme. It is clear from the line plot that the proposed scheme provides approximately 75% and 16.25% higher throughput as compared to RCS and GCS, respectively.  
Figure 5: Average objective function for the low traffic scenario.  
Figure 6: Average objective function for the high traffic scenario.  
The proposed scheme can achieve throughput within the range of 0.74 to 1.75 Mbps, whereas RCS shows variation across 0.378 to 1.0 Mbps. Hence, the proposed scheme leads both schemes. The comparison based on reliability is illustrated in Figure 8. The result is simulated 500 times, and then, the average reliability index is plotted to show the performance gain of the proposed scheme. The RCS lags the proposed scheme in the reliability index by 39.68%. Although the GCS shows a leading pattern as compared to the RCS, it is still lagging behind the proposed scheme. Hence, our scheme is equally good for error-sensitive applications.  
Figure 9 highlights the performance gain of the proposed scheme across the min-max SoC. For the current simulation result, we plotted the average stability index acquired using different schemes at 500 iterations. The proposed scheme shows the leading behavior by selecting the channels with a higher stability index as compared to RCS and GCS. The higher SoC values are achieved through the mechanism depicted in Section 2.3. The proposed scheme selects the best channels in terms of least PU activity for the stable and smooth operation of SUs. Furthermore, it selects the channels within the range of 0.66 to 0.964 SoC, whereas the RCS shows values in the range of 0.52 to 0.72. Similarly, the range of SoC values acquired through GCS is higher than RCS, but lower than the proposed scheme. Hence, the proposed scheme provides the most stable channels to SUs.  
Figure 8: Cumulative distribution function of reliability.  
Figure 7: Cumulative distribution function of throughput.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### Implementation Framework for Multi-Regional Load Forecasting Integration  
Regional grid dispatch centers face significant challenges when attempting to integrate forecasting data from multiple provinces. Direct superimposition of provincial forecasts introduces substantial errors, as it fails to account for inter-regional interactions and data quality variations. Our comprehensive implementation framework for multi-regional forecast integration consists of four key components, presented below.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 3 MEC of Square Inductor  
### MEC of Square Inductor  
By using the local corner MEC model, the MEC model of the square core-type inductor is established, as shown in Figure 9, where the core limb branch and winding leakage flux branch reluctance are \(R_{e}\) and \(R_{limb}\), respectively. The main flux path flows through the limb and is divided into four sub-fluxes into the corner sub-branches. The winding leakage flux of each winding flows through adjacent core limb and winding. These flux paths assure that the flux density \(\mathbf{B}\) of MEC is correctly distributed with a simple model and high precision.  
The limb reluctance \(R_{limb}\) is calculated by the core material and geometry [18], as shown in Equation (7).  
\[R_{limb}(\Phi_{limb})=\frac{l_{limb}}{A_{limb}\cdot\mu_{B}\big{(}\frac{ \Phi_{limb}}{A_{limb}}\big{)}}, \tag{7}\]  
Figure 8: The local MEC of a corner.  
Figure 9: The MEC of a square core-type inductor.  
where \(A_{limb}\) and limb are the cross-sectional area and the length of the core limb branch, respectively. \(\mu_{B}(\cdot)\) is the branch apparent permeability, which is represented by the function of the branch flux due to the nonlinear characteristics of the magnetic core material.  
The leakage reluctance \(R_{e}\) is calculated in Equation (8) [19].  
\[R_{e}=\frac{2\sqrt{w_{e}(w_{w}+w_{e})}+2d_{w}}{\mu_{0}w_{e}l_{c}}+\frac{256w_{w} ^{2}d_{w}^{2}}{\mu_{0}l_{c}\Big{[}16k_{2}^{4}+16\sqrt{2}k_{1}k_{2}^{3}+4k_{1}^{ 2}k_{2}^{2}-2\sqrt{2}k_{1}^{3}k_{2}+k_{1}^{4}ln\Big{(}1+\frac{2\sqrt{2}k_{2}}{k _{1}}\Big{)}\Big{]}}, \tag{8}\]  
where  
\[\begin{array}{l}k_{1}=|w_{w}-2d_{w}|,\\ k_{2}=\frac{1}{\sqrt{2}}min(2d_{w},w_{w}),\end{array} \tag{9}\]
## 4 Results  
### Item Bank Evalutioan  
First, the quality of the item bank was evaluated to see if all of the items were appropriate to be used in this study. The unidimensionality, model fit, local independence, and item fit were assessed (Table 2). The variance explained by the first factor was 69% in the exploratory factor analysis, which means that there was a dominant factor and unidimensionality was assumed. In addition, the model fit statistics of the one-factor model were computed using confirmatory factor analysis. CFI was 0.95 and RMSEA was 0.04, indicating that the one-factor model is acceptable. For the local independence evaluation, the Q3 values of all of the items were computed, all of which were below 0.36, indicating that all of the items are locally independent based on the criterion that Q3 values below 0.36 represents local independence [38]. Lastly, the item fit statistics were computed using S-X\({}^{2}\) statistics. The p-value of the S-X\({}^{2}\) statistics of all items was above 0.01; hence, all items were appropriate enough to be used. Therefore, 40 items in the item bank were qualified to be used in CAT. Figure 4 shows the information and standard error of measurement curve of the item bank.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Stopping Rule** & **CBIAS** & **CRMSE** & **T** \\ \hline None & \(-\)0.004 & 0.114 & 1 \\ SE (theta) \(<\) 0.2 & 0.002 & 0.147 & 0.69 \\ SE (theta) \(<\) 0.3 & 0.006 & 0.217 & 0.39 \\ SE (theta) \(<\) 0.4 & 0.008 & 0.311 & 0.28 \\ \hline \end{tabular} Note. CBIAS, conditional measurement bias; CRMSE, conditional root mean squared error; T, test overlap rate; SE, standard error.  
\end{table}
Table 2: CBIAS, CRMSE, and test overlap rate (T) calculation.
## 5 Results
### PCA and HCA Results  
#### 5.3.1 PCA-1 Results  
The initial Principal Component Analysis (PCA) was conducted on a limited set of properties, including both the first (mode) and second-order statistical moments (skewness and kurtosis) as well as the sorting index (Table 3). The correlation matrix revealed mostly weak correlations among the variables, except for a negative correlation between kurtosis and sorting value. Consequently, the variables exhibited weak correlation and redundancy. The first two PCs account for approximately 71% of the total variance, with PC1 representing 44.68% and PC2 representing 25.88% (Table 4). PC1 primarily captures the variation in grain size samples and is predominantly influenced by the mode values. On the other hand, PC2 contrasts sorting and skewness with kurtosis, reflecting the distinction between homogeneous and heterogeneous samples.
## 6 Conclusions  
In this study, the mathematical model of the relationship between drying accumulated temperature and moisture ratio of rice was established, and the results of two experiments with and without the tempering process were fitted. The results of the two kinds of fitting were compared and analyzed, and the optimal tempering ratio suitable for the drying operation of high-quality indica rice was obtained. During the research process, it was found that:  
1. The relationship between the drying accumulated temperature and the moisture ratio of rice was exponential, which made it possible to establish a mathematical model. Seven existing mathematical drying models were used to fit the non-tempering test results. After selecting the model with the best fitting degree, the model was reconstructed. The reconstructed model was named the AT-MR model.
2. The AT-MR model was used to fit the test data both with tempering and without tempering, and the new evaluation parameters of the two model coefficients were obtained, as follows; Non-tempering test: \(R^{2}\) = 0.998615-0.999964, \(\chi^{2}(10^{-4})\) = 0.0280372-1.408121, and \(RMSE(10^{-4})\) = 0.0227802-1.106381; Tempering test: \(R^{2}\) = 0.997548-0.999979, \(\chi^{2}(10^{-4})\) = 0.016273-2.0163, and \(RMSE(10^{-4})\) = 0.014239-1.6979. Thus, the AT-MR model had high accuracy and achieved good predictive performance.
3. The influence of the tempering process on the rice drying process was analyzed by comparing the AT-MR model fitting results of the non-tempering test (our previous experiment) and the tempering test (the experiment in this study). The results showed that the optimal tempering ratio was effectively three under the conditions of regular hot air intermittent drying.
4. In our future research, we plan to model the batch drying process of rice in order to explore the changes in various indexes of rice during actual drying operations.  
**Author Contributions:** Conceptualization, Y.J. and Z.Z.; methodology, Y.J.; software, J.Y.; validation, Y.J., J.Y.; formal analysis, H.X.; investigation, Y.J.; resources, Y.J.; data curation, H.X.; writing--original draft preparation, Y.J.; writing--review and editing, Z.Z.; supervision, Z.Z.; project administration, Y.J.; funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Academy of National Food and Strategic Reserves Administration, grant number JY2005. The APC was funded by YJ2005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank the reviewers for their valuable suggestions that helped improve the quality of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments  
The proposed LSA-Net and AE-YOLO networks were implemented using MATLAB, and trained with four Titan-XP GPUs on a Windows operating system. Two experiments were conducted in this study. One was leaf disease identification, and the other was leaf disease detection. Leaf disease identification is to predict label values corresponding to leaf diseases from input leaf images, and leaf disease detection is to determine the bounding boxes surrounding diseased leaves. First, to compare the performance of the leaf disease identification, state-of-the-art classification models, that is, VGG [13], ResNet [14], and SqueezeNet [43], feature pyramid network (FPN) [23], attention gated network (AGN) [22], and pyramid vision transformer (PVT) [24] were tested. Second, to compare the performance of the leaf disease detection, state-of-the-art detection models such as YOLO [33], RCNN [30], Fast RCNN [31], RetinaNet [44], and Faster RCNN [32] were tested. For quantitative evaluation, the correct recognition accuracy (CRC) [40] and mean averaged precision (mAP) [30] were calculated. The dataset and source codes of the proposed LSA-Net and AE-YOLO can be downloaded at [[https://github.com/cvmllab/](https://github.com/cvmllab/)]([https://github.com/cvmllab/](https://github.com/cvmllab/)) (accessed on 27 August 2021).
## 1 Introduction  
Multiagent systems with swarm control have broader utility in missions, such as for exploration or environmental characterization, than single-agent systems. Additionally, because the former employs multiple agents simultaneously, it exhibits an enhanced performance, shorter mission window, and increased cost efficiency and energy savings [1; 2; 3; 4]. Existing research broadly classifies swarm-control methods into behavior-based swarm control, virtual-structure swarm control, and leader-follower swarm control [5]. According to studies [6; 7; 8], behavior-based swarm control has advantages such as minimal communication loss between unmanned vehicles and ease of establishing control power when multiple tasks are assigned. However, it is noted that behavior-based swarm control faces challenges in terms of mathematically representing control algorithms and maintaining desired swarm formation. Virtual-structure swarm control has the advantage of not requiring multiple rules or behaviors and making it easy to mathematically represent the behavior of the entire swarm formation. However, it is noted that to maintain swarm formation,wide-band communication is necessary, and significant computing power is required for path generation. Lastly, leader-follower swarm control offers advantages such as ease of mathematical representation and comprehension of swarm control algorithms, as well as easy implementation of algorithms. Theoretically, it is possible to expand the number of followers infinitely. However, a critical drawback is that the failure of the leader can lead to severe issues across the entire swarm formation. This limitation becomes prominent in marine environments with numerous environmental variables and a high probability of thruster system failure due to floating debris.  
Therefore, in this study, we address the shortcomings of leader-follower swarm control by monitoring the state of unmanned surface vehicles (USVs) using various sensors. To handle an event of a failure, we propose a fault-coping algorithm that enables follower entities to assume the leader's role and continue their missions. In addition, we developed a USV encoded with a leader-follower swarm-control algorithm. Through experiments in actual maritime regions, we verified its performance and confirmed the activation and functioning of the fault-coping algorithm during malfunctions.
# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
###### Abstract  
In this work, we propose a road pavement damage detection deep learning model based on feature points from a local minimum of grayscale. First, image blocks, consisting of the neighborhood of feature points, are cut from the image window to form an image block dataset. The image blocks are then input into a convolutional neural network (CNN) to train the model, extracting the image block features. In the testing process, the feature points as well as the image blocks are selected from a test image, and the trained CNN model can output the feature vectors for these feature image blocks. All the feature vectors will be combined to a composite feature vector as the feature descriptor of the test image. At last, the classifier of the model, constructed by a support vector machine (SVM), gives the classification as to whether the image window contains damaged areas or not. The experimental results suggest that the proposed pavement damage detection method based on feature-point image blocks and feature fusion is of high accuracy and efficiency. We believe that it has application potential in general road damage detection, and further investigation is desired in the future.  
local minimum of grayscale; feature fusion; pavement damage detection; deep learning
# In Silico Investigation of the RBC Velocity Fluctuations in Ex Vivo Capillaries
## 2 Physical and Mathematical Description of the Problem
### Boundary Conditions  
For the main simulations, a fixed Zou-He density is used at the capillary outlet, a bounce-back condition at the walls, and a transient Zou-He velocity condition at the inlet. This transient condition is provided by a periodic feeding section where the RBCs are initialized and fed into the main simulation domain. In this periodic domain, an external force drives the fluid, specifically using [PERSON]'s forcing method [29]. The outlet of this periodic feeding section intersects with the feed section of the main simulation domain, and the values from its outlet are extrapolated to the main simulation domain, as shown in Figure 2(top). The RBCs are randomly fed into the main simulation domain; they are initially positioned off the center at varying angles to ensure that no bias is introduced into the main domain. Figure 2(bottom) shows an example feed setup. Different sets of boundary conditions are used in validation studies, which are detailed in their corresponding sections.  
Before initializing the RBCs, pure plasma flow is solved to obtain a converged velocity field in the capillaries. Since LBM is inherently transient, convergence is achieved by running the simulations until the plasma flow becomes steady, after which the RBCs are initialized. Due to singularity problems near the wall [45], discrete particles of the RBCs never touch the wall boundaries. This is accomplished by employing a repulsive potential between the wall boundary and the RBC, which decays with distance.
# Natural Frequencies and Modes of Electrostatically Actuated Curved Bell-Shaped Microplates
## 1 Introduction
From another side, knowledge of the plate spectral content is necessary for the construction of compact yet reliable approximate RO models, which use the vibrational modes as the base functions. In the next section, the equations governing the axisymmetric behavior of a bell-shaped curved plate are derived based on the [PERSON] plate theory. To obtain the solution, a reduced-order model based on the Galerkin decomposition is built. While the nonlinear static version of the equations is employed for defining the plate equilibrium state under an applied tuning load, the eigenvalue problem resulting from the linearization of the equations around the static equilibrium is solved for the natural frequencies and modes. The results of the RO model are compared with results of an FE analysis for the cases of a curved plate with and without a transverse uniformly distributed mechanical load. The FE solution of the coupled electromechanical static problem for the electrostatically actuated plate is presented up to the snap-through, and then compared with the RO model. The latter is then employed for investigating the effect of the electrostatic load on the spectral response, illustrating the feasibility of the electrostatic modulation of the plate stiffness and accompanied resonance frequencies and modes. Finally, the role of the base functions choice on the RO model quality is investigated. The simplest single-degree-of-freedom (DOF) RO model with the numerically obtained fundamental mode of a curved plate serving as the base function is shown to provide better accuracy than the five DOF model with the flat plate modes as the base functions.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 4 Experimental Design  
The key to the MongoDB distributed cluster is choosing an appropriate shard key. Therefore, to verify the practicability and availability of the proposed method for remote sensing data, several groups of comparison experiments are designed, including remote sensing metadata storage and access under different shard key strategies, image data storage, and access with the GridFS mechanism of MongoDB and PostgreSQL.