esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 5 Security Analysis  
The consensus roots system is designed to foster global Internet sharing, DNS domain name resolution, and the resolution of authority issues. It operates on the assumption that all participants are partially trusted, meaning they will not falsify the shared domain name resolution information. The system's cross-chain platform ensures all cross-chain participants are authorized, thus eliminating the need to consider security threats from malicious nodes or cross-chain participants.  
Incorporating the Hyperledger Fabric-permissioned consortium blockchain, the system's main chain relay platform verifies the trustworthiness of cross-chain nodes using the CA certificate mechanism. Given the system's consortium chain features, it can bypass more verification processes in the cross-chain interaction than conventional cross-chain systems. However, the access and information transmission of the sub-chains still require stringent verification.  
One key aspect of this system is the implementation of privacy domain name protection through staged atomic exchange in the DNS domain name synchronization process. This is conducted prior to the cross-chain interaction on the main chain relay platform, where the system executes a negotiation phase. During this stage, the cross-chain sub-chains decide whether to continue with cross-chain domain name synchronization based on the negotiation content. This staged cross-chain process not only ensures the autonomous choice interaction of multiple parties, but it also avoids transactions that do not align with the participants' wishes, thereby enhancing the system's security. The data involved in the cross-chain process are safeguarded by blockchain, making them tamper-proof. The transaction detail information is endorsed and stored on the main chain relay platform, allowing system participants to query and verify as required.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 2 Materials and Methods
### Calibration of Soil Moisture Measurements  
The SM raw data is collected as a voltage (mV) from the EC-5 sensor attached to a sensor board via an ADC (analog-to-digital-converter). The raw data needs to be converted to SM using a conversion equation. Typically, the conversion equation is presented as a linear equation \(\theta=c1\times\) ADC + \(c0\), where ADC is the raw voltage output (in mV) from the EC-5 sensor and \(c1\) and \(c0\) are the slope and intercept of the fitted linear regression model, respectively. The standard coefficients for non-Decagon data loggers at an excitation of 2.5 V for mineral soils, are \(c1=0.00119\) and \(c0=-0.401\).  
Estimates of SM based on the standard equation showed a bias towards drier conditions. To increase the accuracy of the estimation, the field data collected at the validation locations were calibrated by a linear regression using the ordinary least squares (OLS) method. The targets values are from the validation data logger and the input is the raw data (i.e., ADC in mV from EC-5 sensor) from the validation nodes. The EC-5 sensors were calibrated using an intercept of \(-0.360\) and \(-0.367\) for depths of 10 and 30 cm, respectively, and slopes of 0.0011 and 0.0012 for depths of 10 and 30 cm, respectively. The slope values were found to be similar to the standard value (i.e., 0.00119), while the intercept values are lower in magnitude.
## 2 Materials and Methods
The RF force instead, was input into the model with a predefined amplitude and time course and was modulated, in the subsequent simulations, as to represent a progressive weakening of the muscle or, at the contrary, an exceeding activation. Ground reaction forces and moments obtained from gait analysis were applied to the foot. To analyze the effects of changing the RF force, we first computed the moments produced by its complementary synergistic muscles: the VA at the knee, and the liiopsoas at the hip. This was done by running the simulation in an inverse dynamics mode, with normal kinematics and RF force as input. Since the RF force produced a moment at hip and knee, the joint moments computed by the model represented the additional contribution required to other muscles than RF. These moments were named the \"residual\" moments. When the model was used in a forward dynamics mode, the residual moments plus the moments produced by the RF force reproduced exactly the previous kinematics. To simulate the effect of different RF forces we just changed the RF force in a predetermined time window and we obtained different kinematics. This was the objective of our study. In this approach, the complementary muscles were supposed to be unaffected by changes of the RF force, so that this condition was considered: \"without compensation\". We also considered the possibility of compensation by other muscles to the change of RF force. Of course, it is difficult to imagine what strategy the motor control system can adopt. For the sake of simplicity, we assumed that compensation consisted in reproducing a percentage of the moment originally produced by the RF. For example, 0% compensation means that kinematics will be determined by the residual moments only; 50% compensation means that the residual moments will be incremented by 50% of the moments originally produced by RF; 100% compensation means that the moments originally produced by RF are entirely added to the residual moments so that the resulting kinematics are exactly the original ones. This criterion of simulating compensations to the altered RF force was applied in two different conditions: hip joint constrained by the predefined, normal kinematics (this represents a full compensation at the hip joint) and both hip and knee free to move under the effect of the forces and moments applied. According to the purpose of our study, RF force alterations were applied in the time window from 50% to 80% of the gait cycle, which corresponded to the late stance-early swing phase.
# Towards Interactive Analytics over RDF Graphs
## 5 Applicability of HIFUN over RDF
### Prerequisites for Applying HIFUN over RDF Data  
Two assumptions should hold to apply HIFUN over a data set \(D\), (i) the unique identification of its data items and (ii) the functionality of its attributes.  
**RDF data.** The first assumption, the unique identification of the data items, is satisfied by the RDF data, since each resource is identified by a distinct URI. Consequently, \(D\) can be any subset of the set of all the available URIs. The second assumption, the functionality of attributes, is partially satisfied by the RDF properties. The functional (i.e., owl:FunctionalProperty) or the effectively functional properties (i.e., even if they are not declared as functional, they are single-valued for the resources in the data set \(D\)) have only one unique value for each instance. However, there are also properties in RDF with (i) no or (ii) multiple values; a non-value property implies that a value may not exist (or it is unknown even if it exists) or it is incomplete, while a multi-valued infers that a property has more than one values for the same resource. Such cases require transforming the original data before applying HIFUN to it. These transformations can be made using the operators that will be described in Section 5.4.  
**RDF Schema**. Each resource of an RDF schema is identified by a distinct URI; therefore, its data items are uniquely identified. However, a property (e.g., rdf:type, rdfs:subClassOf etc.) may appear more than once by relating different classes or classifying concepts in more than one classes (i.e., a class might be sub-class of several super-classes). Nevertheless, these relationships are considered distinct since they have different domain and/or range. Therefore, HIFUN allows analytics not only over the data, but over RDF schema(s) as well. Inference is supported, too, since it refers to automatic procedures that generate new relationships based on a set of rules; a process that is independent of HIFUN.
## 4 Results Analysis
### Ablation Experiment
#### 4.4.1 R-I Fusion Layer  
The selection of real-imaginary fusion layers is critical for optimizing classification performance within the PRI-SWT model. This study compared two fusion approaches--2D convolutional layers and linear projection layers--while also analyzing the effect of varying the number of layers. Figure 13 presents the confusion matrices, where categories 1, 2, and 3 represent cracking, delamination, and low-velocity impact damage, respectively.  
In the early stages of training, the linear projection fusion layer showed a clear advantage, with a steeper increase in accuracy compared to other methods. As training progressed, the accuracy across all models stabilized at around 95%, with the PRI-SWT model achieving the highest accuracy of 97% using a single linear mapping layer. The detailed multi-classification metrics for each model are summarized in Table 3.  
Figure 12: Accuracy curves of Vi-T and PRI-SWT models for the first 50 training epochs.  
Notably, the data indicate that increasing the number of fusion layers did not enhance performance, which was likely due to overfitting. The PRI-SWT model, utilizing a single linear mapping layer, demonstrated superior metrics, with a macro-F1 score 1.7% higher  
Figure 13: Model’s accuracy curves and confusion matrix when using different fusion layers. (**a**) Confusion matrix; (**b**) training curves. The performance of the linear fusion layer surpasses that of the CNN fusion layer. However, increasing the number of fusion layers tends to decrease the model’s overall performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fusion Layer** & **Num** & **macro-P** & **macro-R** & **macro-F1** & **Kappa Coefficient** \\ \hline \multirow{2}{*}{Linear} & 1 & 0.952 & 0.938 & 0.945 & 0.924 \\  & 2 & 0.889 & 0.901 & 0.895 & 0.828 \\ \hline \multirow{2}{*}{Convolution} & 1 & 0.934 & 0.923 & 0.928 & 0.898 \\  & 2 & 0.928 & 0.891 & 0.909 & 0.863 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effects of the fusion layer.  
and a Kappa coefficient 2.6% higher than the corresponding scores of a model with a single convolutional fusion layer. These results highlight the effectiveness of the linear projection approach in capturing relevant features while maintaining model robustness, establishing the PRI-SWT model as a superior choice for CFRP defect classification. The superior performance of the linear mapping layer over the convolutional layer in the PRI-SWT model can be attributed to several key factors. First, the linear layer enables a simpler, more direct transformation of the real and imaginary components, preserving crucial cross-modal relationships. Second, it reduces the risk of overfitting because it has fewer parameters, as evidenced by the drop in performance with additional convolutional layers. Finally, the linear projection efficiently merges magnitude and phase information, resulting in faster convergence and improved feature extraction, leading to higher classification accuracy.
## 6 Case Study
### Efficiency of the Proposed DL-DBIS  
The second experiment studies the labeling time between DL-DBIS and human labeling from Figure 18. On human labeling, an expert of defective bean removal is trained to label around 35 bean images, and the average time of labeling an image is used to estimate the required time from 100 to 800 images. Thus, the estimated human-labeling time is a straight regression line. On the DL-DBIS, since two networks (the DIDN and the BSDN) need to be trained, the consumed time is still over a few hours with our computer. From the result, we can see the DL-DBIS spends similar but a bit less time than that by human labeling. It shows that our DL-DBIS replaces human labeling tasks with similar time cost. The lower time cost can be achieved if more equipment resources are invested. Most importantly, a bean-picking proficient who is also skilled in the labeling tool only spends merely 175 min on labeling 35 images (around 2100 beans); then the proposed DL-DBIS works with approximately 70\(\sim\)80% accuracy, which meets the goal of reducing human effort. To the best of our knowledge, this is a pioneer work on removing defective beans with robotic arm plus deep learning techniques. Our following publications would take this version as a basis for performance comparison.
# Dynamic Impedance Estimation: Challenges and Considerations
## 4 Dynamic Impedance Estimation
### Illustrative Results  
For illustrative purposes, the ICA technique was used to estimate the source and the load impedance of the IEEE 15 bus system (see Figure 11), with the addition of two capacitor banks on buses 1 and 3.  
As the ICA is a passive method, the harmonic impedance of order h will only be accurately estimated if this harmonic is present in the system. To assess the performance of the estimation, the existence of all harmonic components up to 50 th was assumed. A window containing 2 min of each harmonic samples was acquired at the PCC and processed by the ICA algorithm. Figure 12 shows a typical variation for the 5  
Figure 11: 15 bus system to illustrate the load and source side impedance estimation.  
Figure 13a shows the impedance's magnitude on the source side and Figure 13b on the load side. Note that the ICA method can estimate both impedances with good precision. The phase, although not shown, was also estimated with accuracy.  
The ICA technique has some limitations that must be considered for its application. The first limitation is that the harmonic sources must be statistically independent or weakly correlate with each other. Furthermore, ICA is a passive estimation technique, and consequently, it is not possible to estimate the impedance in the frequency with very low energy. Finally, the impedance is assumed constant within the window used for the estimation, and if it changes during the estimation, the results are inconsistent.  
Figure 12: Magnitude variation for the 5 th harmonic voltage and current.  
Figure 13: Harmonic impedance estimation results: (a) magnitude on the source side; (b) magnitude on the load side
## 5 Adaptive Dual-Mode Routing-Based Mobile Data Gathering Algorithm
### Direct Upload Mode Threshold  
According to the analysis above, the upload mode of each node should change from direct to multi-hop mode as the upload distance increases. Based on the distribution of charging cells, the distance threshold for choosing the upload mode can be calculated as follows:  
\[d^{th}=\frac{1}{N}\sum_{i\in V}d_{i,a_{i}},a_{i}\in AS, \tag{10}\]  
where \(AS\) is the anchor point set, which records the central locations of all charging cells in \(CV\). \(a_{i}\) and \(d_{i,a_{i}}\) are the nearest anchor point of node \(v_{i}\) in \(AS\) and the corresponding upload distance, respectively. \(d^{th}\) is distance threshold for all nodes, and it denotes the average upload distance for all nodes. Compared with this threshold, the nodes with longer upload distances should choose the multi-hop mode to avoid high energy consumption. On the other hand, the nodes with shorter upload distances should choose the direct upload mode to avoid energy waste caused by the multi-hop mode.
# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 2 Multi-Threshold OTSU Segmentation Model  
The OTSU image segmentation method is the maximum inter-class variance method, which aims to determine the optimal threshold for image segmentation. In OTSU, the image is divided into foreground and background by threshold, and the threshold with the largest class variance in the foreground and background regions is taken as the best segmentation threshold. Assume that the gray scale range is \(\{0,1,2,\cdots,L-1\}\), representing \(L\) different gray levels in a digital image, whose size is \(M\times N\) pixels. Let \(B(i)\) be the proportion of grayscale \(i\) in the entire image, calculated as  
\[B(i)=a(i)/(M\times N), \tag{1}\]  
where \(a(i)\) is the number of pixels with grayscale \(i\). Let the threshold \(t\) divide the image into two parts, foreground and background.  
\[\begin{cases}h_{0}=\sum\limits_{i=0}^{t}B(i)\\ h_{1}=\sum\limits_{i=t+1}^{L-1}B(i)\end{cases} \tag{2}\]  
\[\begin{cases}\rho_{0}=\sum\limits_{i=0}^{t}i\times B(i)/h_{0}\\ \rho_{1}=\sum\limits_{i=t+1}^{L-1}i\times B(i)/h_{1}\end{cases} \tag{3}\]where \(\rho_{T}\) is the average grayscale of the image, which can be expressed as:  
\[\rho_{T}=\sum_{i=0}^{L}i\times B(i) \tag{4}\]  
where \(h_{0}\) and \(h_{1}\) are the proportion of the foreground and background in the image, respectively, and \(\rho_{0}\) and \(\rho_{1}\) are the average grayscale of the image foreground and background, respectively. \(\sigma_{B}^{2}\) is the inter class variance, which can be expressed as:  
\[\sigma_{B}^{2}=h_{0}(\rho_{0}-\rho_{T})^{2}+h_{1}(\rho_{1}-\rho_{T})^{2} \tag{5}\]  
when the interclass variance \(\sigma_{B}^{2}\) reaches the maximum, \(t\) is the optimal threshold. In the case of multiple thresholds, a threshold set of \(\{t_{1},t_{2},\cdots,t_{K-1}\}\) is the optimal combination of thresholds. The optimization problem can be expressed as:  
\[T^{*}(t_{1},t_{2},\cdots,t_{K-1})=argmax_{B}^{2}(t_{1},t_{2},\cdots,t_{K-1}) \tag{6}\]  
The OTSU multi-threshold method uses the exhaustive method to solve the optimal thresholds combination with a total computational cost of O(\(L^{K}\)). It can be seen that the computational cost will increase exponentially with the increase in the number of thresholds. In order to improve the efficiency of image segmentation, this paper proposes an improved particle swarm optimization algorithm to optimize the optimal threshold combination and then perform multi-threshold image segmentation.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
###### Abstract  
This paper investigates the performance of a full-duplex (FD) relaying-enabled satellite sensor network under residual loop interference, where the satellite uplink and the downlink transmissions simultaneously occur over the same frequency band. Specifically, the closed-form expressions for the outage probability and ergodic capacity of the FD relaying satellite sensor network are derived by considering residual loop interference, channel statistical property, propagation loss, geometric satellite antenna pattern, and terminal elevation angle. Simulation results show the achieved performance gains of a full-duplex relaying satellite sensor network over traditional half-duplex relaying, and highlight the impact of key system parameters on the performance of the considered FD relaying satellite sensor network.  
s 2019 19 5453 3 December 2019 11 December 2019
## 3 Analysis of Fatigue Damage Law of Base Plate Structure
### Parametric Analysis
#### 3.2.2 Fatigue Damage Evolution under Varying Axle Loads  
To obtain the mechanical behavior and fatigue evolution of CRTSIII SBT with various axle loads, the structure's adaptability to axle loading was studied. According to developing tendency of heavy load trains and heavy haul railways, the axle loads were taken as 25, 27, 30, 32.5, 35, 37.5 and 40 t respectively. The action mode of train load is that single axle and double wheels act in the slab middle.  
(1) Mechanical characteristics of CRTSIII SBT structure under different axle loads  
For different axle loads, the base generated a maximum stress value on its bottom just below the loading point. Due to many working conditions, only the cloud picture of longitudinal tensile stress of base when the axle load is 25 t is shown (Figure 24). At the same time, the maximum tension on the longitudinal direction of the base subjected to varying axle loads is indicated in Table 8. It can be observed from the Table that the stress on the base gradually increased with the increase in axle loads.  
(2) Comparative analysis  
The variations of the greatest tension on the longitudinal direction of the base applied varying axle loads with loading times are shown in Figure 25.  
The structure layer stress varied with different axle loading. The stress of base concrete increased with the axle load, and the maximum longitudinal tensile stress increased with the action times.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Working** & **Working** & **Working** \\  & **Condition 1** & **Condition 2** & **Condition 3** \\ \hline Base plate (ten thousand times) & 1900 & 1600 & 1300 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The earliest cracking loading time of base plate under different working conditions.  
Figure 24: Diagram of calculation of longitudinal tensile stress of base (25 t).  
During the fatigue process of CRTSIII SBT, tension stress changes were observed strictly. The tensile stress was usually high longitudinally, which plays an important part in the fatigue evolution. Crack numbers of the base subjected to different axle loads are summarized in Table 9. With the increase of axle load, the stress of each structural layer increased, and the damage rate of concrete also increased, resulting in earlier cracks in concrete. An increase in axle load to about 35 t, resulted in an obvious the damage of the base. The results illustrate that to use CRTSIII SBT in heavy load railways with greater axle loads, and to improve durability and service performance, attention should be paid to structural design and detailing to strengthened slabs and diminish concrete stress.
## 3 Model Reconstruction
### Drying Accumulated Temperature-Moisture Ratio Model  
In the previous study, 36 groups of experimental data were substituted into the seven mathematical models shown in Table 4, and the three indices described above were used to evaluate the models, as shown in Table 5. The Midilli model yielded an extremely significant fit to the test data, with an \(R^{2}\) ranging from 0.998632 to 0.999964. Hence, the Midilli model was selected to describe the relationship between the moisture ratio and drying accumulated temperature.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model No.** & **Name** & **Model Equation** & **Modified Equation** \\ \hline
1 & Linear equation & \(MR\ =\ at+b\) & \(MR\ =\ atT+b\) \\
2 & Polynomial equation & \(MR\ =\ at^{2}-bt+c\) & \(MR\ =\ aAT^{2}-bAT+c\) \\
3 & Page & \(MR\ =\ exp(-kt^{n})\) & \(MR\ =\ exp(-kAT^{n})\) \\
4 & Modified Page II & \(MR\ =\ a\exp(-kt^{n})\) & \(MR\ =\ a\exp(-kAT^{n})\) \\
5 & Simplified modified Page II & \(MR\ =\ a\exp(-t^{n})\) & \(MR\ =\ a\exp(-AT^{n})\) \\
6 & Weibull II & \(MR\ =\ exp[-(t/a)^{b}]\) & \(MR\ =\ exp\left[-(AT/a)^{b}\right]\) \\
7 & Midilli & \(MR\ =\ a\exp(-kt^{n})+bt\) & \(MR\ =\ a\exp(-kAT^{n})+bAT\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Several commonly used mathematical models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model No.**} & \multicolumn{3}{c}{**Range**} \\ \cline{2-4}  & \(R^{2}\) & \(\chi^{2}\) (\(10^{-4}\)) & \(RMSE\) (\(10^{-4}\)) \\ \hline
1 & 0.973769–0.999161 & 0.5720432–19.53701 & 0.0444922–17.26623 \\
2 & 0.99196–0.9999697 & 0.0273277–7.422666 & 0.218622–5.832094 \\
3 & 0.984013–0.998321 & 1.061333–14.11593 & 0.928666–12.50415 \\
4 & 0.986226–0.998438 & 1.034447–12.90039 & 0.840489–10.42148 \\
5 & 0.17752–0.330753 & 588.84853–879.66005 & 510.48454–787.06426 \\
6 & 0.984013–0.998321 & 1.061333–14.11593 & 0.928666–12.50415 \\
7 & 0.998632–0.9999964 & 0.0301823–1.515054 & 0.0226367–1.082181 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of evaluation indicators.