esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Luoyang Site Case Study
#### 4.1.3 Reconnaissance of Archaeological Marks in Luoyang
The corresponding result is illustrated in Figure 9. Rectangular and linear features highlighted by red arrows correspond to an ancient palatial structure and settlement foundations of walls or buildings, respectively, as interpreted by local archaeologists. A numerical comparison of radar backscattering in sigma-nought between natural dry soil and damp marks (highlighted by green and blue polygons, respectively, in Figure 9) was also conducted using sample-averaged backscattering values, indicating a difference of 3.65 dB _vs._ 3.28 dB.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 5 Discussion  
Firstly, the simulation results proved the realizability of the proposed method, given the accordance between the resulting S-curves and target S-curves. Apparently, better consistency can be acquired if the strobe width is smaller and the waveform length is larger. However, a smaller strobe width means a higher sampling frequency in the receiver, which would increase the hardware cost. Therefore, the results in the infinite bandwidth cases are not practical, as it demands a sampling frequency above 100 MHz. However, it shows the capability of the LS method in finding suitable CCRWs with a given pre-correlation bandwidth and strobe width. For example, the acquired LS coherent CCRW for the BPSK signal infinite bandwidth case is similar to the W2 waveform, which is designed theoretically. Furthermore, the non-coherent CCRW of the BPSK signal with infinite bandwidth produces an S-curve more similar to the ideal S-curve than the non-coherent W2 discriminator.  
Secondly, the optimization process of the LS method is a balance between the multipath error envelope, linear region length and the false lock points. The multipath error envelope of the S-curve is determined by the left side of the linear region, while the false lock points occur at the right side of the linear region. Thus, the whole area of the S-curve is constrained during the approximation. A close confinement on one aspect may deteriorate another aspect, e.g., a strictly restricted S-curve for the smallest MEE area could lead to the very existence of false lock points. Therefore, the resulting non-coherent S-curve without false lock points usually has larger MEE than W2, which is designed without considering the false lock points. However, the multipath mitigation capability of LS coherent discriminators is better than W2 for the BPSK signal. Furthermore, they are all capable of resisting the multipath signals with medium or large time delays and perform worse with multipath signals arriving within 0.1 chips than the direct signal.  
Thirdly, the ideal S-curve may not be the best approximation target, because the restrictions of the right side to the linear region can be relaxed to be just above zero, rather than a positive  
Figure 47: The tracking jitters for the S-curves of the BOC(7,1) signal.  
constant. In a similar manner, this relaxed restriction of the S-curve can also avoid false lock points. However, this vague description is difficult to realize in the LS process and can be studied in future research. The reason to relax the restriction is that it is difficult to find a perfect reference waveform that meets the demands of being a non-coherent discriminator with a small MEE area, high tracking precision, no false lock points and narrow bandwidth simultaneously, especially for the high-order BOC signals. Thus, a relaxed restriction may improve the performance.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results
### Analyzing the Image Quality of the Proposed System Under Different BERs  
Figure 9 illustrates the quality of received images for a range of BERs (0 to 37.3%) under different trained decoders. The observation shows that each decoder has high PSNR levels at zero BER, but the decoders trained with different noise levels have lower PSNR levels compared to the decoder trained with no noise. As the BER increases, the PSNR of the decoder trained with no noise decreases significantly compared to the other decoders. All other decoders have more or less similar performance, indicating that their behavior is mostly independent of the noise levels they were trained with.
## 4 Control Algorithm
### Leader-Follower Swarm-Control Algorithm
#### 4.1.1 Virtual Point Based on Leader USV  
The error in the distance between the leader and the followers was considered the control value. When the distance between the USVs exceeded a certain threshold, the leader would decelerate. When this distance decreases, it would revert to the original speed. This approach enhanced the stability of the swarm formation.  
According to [17; 18; 19], the equation of error in the distance between the follower and the virtual point is given by (15).  
\[\begin{bmatrix}e_{X}(t)\\ e_{Y}(t)\\ e_{\theta}(t)\end{bmatrix}=\begin{bmatrix}X_{F}^{d}(t)-X_{F}(t)\\ Y_{F}^{d}(t)-Y_{F}(t)\\ \theta_{F}^{d}(t)-\theta_{F}(t)\end{bmatrix}, \tag{15}\]  
where \(X_{F}^{d}(t)\) and \(Y_{F}^{d}(t)\) represent the positions of the virtual points that are intended for the follower USV to follow. Using (15) and the non-holonomic dynamic model, the equation of error between the follower and the virtual point can be expressed in the forms of (16)-(18).  
\[\dot{e_{X}} =v_{L}(t)\text{cos}(\theta_{L}(t))-v_{F}(t)\text{cos}(\theta_{F}( t))-d_{ref}\text{sin}(\phi_{ref}+\theta_{L}(t))\omega_{L}(t), \tag{16}\] \[\dot{e_{Y}} =v_{L}(t)\text{sin}(\theta_{L}(t))-v_{F}(t)\text{sin}(\theta_{F} (t))+d_{ref}\text{cos}(\phi_{ref}+\theta_{L}(t))\omega_{L}(t),\] (17) \[\dot{e_{\theta}} =\omega_{L}(t)=\omega_{F}(t). \tag{18}\]  
Similarly, the nonholonomic dynamic model in the form of Equations (16)-(18) can be condensed to (19)-(21), respectively.  
\[\dot{e_{X}} =-K(X_{F}^{d}(t)-X_{F}(t)), \tag{19}\] \[\dot{e_{Y}} =-K(Y_{F}^{d}(t)-Y_{F}(t)),\] (20) \[\dot{e_{\theta}} =-K\text{sin}(\theta_{L}(t)-\theta_{F}(t)), \tag{21}\]  
where \(K\) is the speed control constant used to adjust the speed of the USV. Expanding (16)-(18) using (19)-(21), the equation for the leader's speed control quantity is expressed as follows:  
\[v_{L} =-Ke_{X}^{c}(t)\text{cos}(\theta_{L}(t))-Ke_{Y}(t)\text{sin}( \theta_{L}(t))+\] \[d_{ref}\text{sin}\left(\phi_{ref}(t)\right)\omega_{L}(t)+v_{F} \text{cos}(\phi_{\theta}(t)) \tag{22}\] \[\omega_{L} =-K\text{sin}(\varepsilon_{\theta}(t))+\omega_{F}(t),\]  
where \(v_{L}\) represents the speed-control quantity of the leader USV, \(v_{F}\) indicates the approach speed of the follower USV, \(\omega_{L}\) denotes the angular velocity-control quantity of the leader USV, and \(\omega_{F}\) represents the angular velocity of the follower USV.
# Experimental Study on Shear Creep Characteristics of Residual Soil with Different Stone Content
## 5 Creep Model and Parameterization  
The component model is widely used in creep analysis and consists of three types of components: Hookean solid, Newtonian fluid, and Saint-Venant's body. The Hookean solid represents pure elastic behavior, the Newtonian fluid simulates viscosity, and the Saint-Venant's body represents ideal plasticity. The Burgers model, a type of component model, effectively describes attenuation creep and stable creep. It consists of a series arrangement of a Kelvin body and a Maxwell body. The Maxwell body is composed of a series arrangement of Newtonian fluid and Hookean solid, representing elastic and viscous deformation. The Kelvin body consists of a parallel arrangement of Hookean solid and Newtonian fluid, as depicted in Figure 8.  
Based on the results of the creep test conducted on residual soil, it is evident that there is a clear and stable creep process. To accurately represent the creep curve observed in the test, the Burger model is employed. The one-dimensional creep equation for the Burger model is as follows:  
\[\epsilon(t)=\frac{1}{\eta_{1}}\sigma_{0}t+\frac{\sigma_{0}}{E_{1}}+\frac{ \sigma_{0}}{E_{2}}\bigg{(}1-\exp^{-\frac{E_{2}}{\eta_{2}}t}\bigg{)} \tag{1}\]  
where \(E_{1}\) and \(E_{2}\) are the elasticity coefficients of the Maxwell and Kelvin bodies, respectively; \(\eta_{1}\) and \(\eta_{2}\) are the viscosity coefficients of the Maxwell and Kelvin bodies, respectively. In the direct shear creep test, the applied stress is shear stress, and the following equation is obtained by transformation:  
\[\gamma(t)=\tau_{1}\bigg{[}\frac{1}{G_{1}}+\frac{t}{\eta_{1}}+\frac{1}{G_{2}} \bigg{(}1-\exp^{-\frac{G_{2}}{\eta_{2}}t}\bigg{)}\bigg{]} \tag{2}\]  
where \(G_{1}\), \(G_{2}\), \(\eta_{1}\), and \(\eta_{2}\) are the unit shear modulus and viscous coefficient of the creep model, respectively. Through the nonlinear least squares method, four sets of test data were fitted to determine the values of the two unit shear modulus, \(G_{1}\) and \(G_{2}\), as well as viscosity parameters \(\eta_{1}\) and \(\eta_{2}\). These values are presented in Table 4. The fitting performance of the Burgers model, as shown in Figure 9, is particularly strong for the first three load levels, exhibiting a high degree of accuracy. The fitted samples demonstrate a favorable steady-state creep effect. The predicted results align well with the experimental findings, effectively illustrating the creep behavior of samples with varying stone contents. While there exists a slight discrepancy between the fitted and experimental results, the error is relatively minor. The fitting correlation coefficient, \(R^{2}\), exceeds 85%. Figure 8: Burgers creep model.
# Empirical Study of Test Case and Test Framework Presence in Public Projects on GitHub
###### Abstract  
Automated tests are often considered an indicator of project quality. In this paper, we performed a large analysis of 6.3 M public GitHub projects using Java as the primary programming language. We created an overview of tests occurrence in publicly available GitHub projects and the use of test frameworks in them. The results showed that 52% of the projects contain at least one test case. However, there is a large number of example tests that do not represent relevant production code testing. It was also found that there is only a poor correlation between the number of the word \"test\" in different parts of the project (e.g., file paths, file name, file content, etc.) and the number of test cases, creation date, date of the last commit, number of commits, or number of watchers. Testing framework analysis confirmed that JUnit is the most used testing framework with a 48% share. TestNG, considered the second most popular Java unit testing framework, occurred in only 3% of the projects.  
testing culture; code quality; testing framework; GitHub; test case presence +
Footnote †: journal: Applied Sciences
## 3 Experiment and Results  
In order to prove the effectiveness of the method, three different PolSAR sensors are adopted, including E-SAR, GF-3, and RADARSAT-2. In the case of a convolutional neural network using the same sample and the same structure and training mode, we compare the results of our method with those of other methods. Further, we also compare the convolutional neural network method for PolSAR classification and the method we propose. In this section, the experimental data, experimental sample construction, and training patterns are described, and the experimental results are illustrated.
## 5 Case Study
### Evaluating of Machine Criticality for the Automotive Part Production Line  
In this case study, the number of uniform design factor \(S\) was equal to 20 (the number of machines in the automotive part production line) and the value of level \(N\) (\(N\) is 3-5 times \(S\)) took 70, so the uniform design table \(U_{20}\) (\(20^{70}\)) was gained and used. It was assumed that the preventive maintenance had the same influence on the failure rate of each machine, namely, the failure rate of each machine uniformly reduced on the basis of current level as showed in Table 1. The 70 groups of factor levels for the machines' failure rate are listed in Table 2.  
The uniform design table was created using the DPS (Data Processing System) software (DPSv17.0, Hangzhou Ruifeng Information Technology Co., Ltd., Hangzhou, China), and 70 groups of combined  
\begin{table}
\begin{tabular}{c c} \hline
**Machine** & **Failure Rate \(\lambda\) (I/h)** \\ \hline M01 & 0.0022 \\ M02 & 0.0017 \\ M03 & 0.0026 \\ M04 & 0.0026  
schemes were gained, which were taken into the simulation model for the production line based on the Plant Simulation software (Plant Simulation 13.0, Siemens Ltd. China, Beijing, China). Then, the operating rate \(Y\) of the production line with different groups of the machine failure rates was obtained. The regression equation between the failure rate of each machine and the operating rate of production line was established using MATLAB software (MATLAB 7.0, MathWorks Inc., Natick, MA, USA). The coefficients of regression equation are shown in Table 3.  
According to the coefficients of regression equation as shown in Table 3, it can be found that the coefficients of regression equation are all negative except for the constant term, that is, there is a negative correlation between the failure rate of each machine and the operating rate of the production line. The bigger the absolute value of the regression equation coefficient, the greater the influence of the machine failure rate on the operating rate of the production line. Therefore, the absolute value of the regression equation coefficient can be used as the criterion to evaluate criticality of the machines. On the basis of the ABC analysis method as mentioned in Section 2, four machines such as M02, M05, M15, and M20 were evaluated as the most critical machines and six machines such as M01, M08, M12, M16, M18, and M19 were evaluated as the secondary critical machines.
# Predicting Pedestrian Crashes in Texas' Intersections and Midblock Segments
## 2 Data Description  
### Crash Count Data  
The Texas Crash Records Inventory System is called CRIS [16], and contains records from police crash reports across Texas' 254 counties and 268,597 square miles. Crash variables include time and location, persons and vehicles involved, injury severities, and road conditions. Many crashes are never reported to police or are not flagged for CRIS inclusion. These are typically proper-damage-only or no-injury crashes, but drivers and pedestrians will leave the scene for other reasons as well. Police officers who deem a reported crash to be worth less than the USD 1000 minimum crash cost threshold (for recording purposes) often do not record the crash formally.  
From January of 2010 through December of 2019, 5.63 million crash incidents were recorded in the TxDOT CRIS database. A total of 78,498 crashes involving a pedestrian (including those caused or contributed to by a pedestrian, such as a driver swerving into a tree to avoid a pedestrian) are analyzed here. These are 1.40% of all reported and then recorded crashes.
## 3 DTN Solutions for IoT
### Taxonomy of DTN Solutions for IoT  
There are a few surveys in the literature to review DTN solutions for IoT [15; 17; 20]. Each one proposes a classification and a discussion based on predefined performance criteria related to their point of view and field of interests. In addition to these most recent surveys, the present work introduces a new taxonomy based on the adopted approach in each solution. We classify the solutions into three main categories: **BP-based** solutions, **Routing-based** solutions and solutions combining DTN with other technologies, which we call **X-DTN**. The latter is considered as the novelty of this survey, since it is the first work to review the combination of Delay Tolerant schemes with another technology in order to insure a reliable IoT. Figure 3 illustrates our classification.
# Kinematic Analysis of Dynamic Coactivation During Arm Swing at the Shoulder and Elbow Joints
## 3 Results  
Table 1 presents the ANOVA results for each dependent variable by joint, showing the mean values of kinematic variables based on walking speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Joint (Motion)**} & \multirow{2}{*}{**Kinematic Variable (Unit)**} & \multicolumn{4}{c}{**Walking Speed (km/h)**} \\ \cline{3-6}  & & **3.6** & **4.2 (PWS)** & **5.4** & **7.2** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Shoulder (flexion–extension) \\ \end{tabular} } & Range of motion \({}^{*}\) & & 27.1 (8.8) & 24.9 (8.4) & 28.4 (8.5) & 30.0 (9.9) \\  & \begin{tabular}{c} Maximum joint angular velocity ** \\ (deg/s) \\ \end{tabular} & 78.2 (24.4) & 68.2 (19.4) & 77.9 (22.9) & 102.2 (40.8) \\  & \begin{tabular}{c} Minimum joint angular velocity ** \\ (deg/s) \\ \end{tabular} & \(-86.9\) (23.0) & \(-88.7\) (25.5) & \(-104.0\) (26.9) & \(-128.9\) (40.3) \\  & \begin{tabular}{c} Maximum joint angular acceleration ** \\ (deg/s) \\ \end{tabular} & 680.2 (221.2) & 723.4 (203.4) & 979.8 (298.7) & 1102.8 (447.1) \\  & \begin{tabular}{c} Minimum joint angular acceleration ** \\ (deg/s) \\ \end{tabular} & \(-453.9\) (174.3) & \(-478.1\) (167.6) & \(-681.7\) (181.2) & \(-987.9\) (217.2) \\ \hline \multirow{6}{*}{\begin{tabular}{c} Shoulder (abduction–adduction) \\ \end{tabular} } & Range of motion & 6.9 (2.7) & 6.6 (3.1) & 6.7 (4.2) & 6.4 (2.3) \\  & \begin{tabular}{c} Maximum joint angular velocity ** \\ (deg/s) \\ \end{tabular} & 18.1 (8.8) & 16.1 (9.0) & 18.6 (8.9) & 23.5 (10.1) \\  & \begin{tabular}{c} Minimum joint angular velocity ** \\ (deg/s) \\ \end{tabular} & \(-21.4\) (9.7) & \(-20.8\) (10.0) & \(-20.7\) (9.0) & \(-27.7\) (10.9) \\  & \
\begin{tabular}{c} Maximum joint angular acceleration ** \\ (deg/s) \\ \end{tabular} & 231.0 (117.2) & 221.9 (94.0) & 258.8 (117.0) & 347.6 (131.8) \\  &
\begin{tabular}{c} Minimum joint angular acceleration ** \\ (deg/s) \\ \end{tabular} & \(-236.5\) (104.8) & \(-221.9\) (73.7) & \(-254.8\) (106.5) & \(-342.0\) (117.4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The significant results of analysis of variance and the mean values (\(\pm\)standard deviation) according to walking speed (\({}^{*}\)_p_ \(<\) 0.05, **_p_ \(<\) 0.01).
## 4 Iterative Trust and Reputation Computation Model
### Reputation Computation Model  
As the proposed reputation system for B2C e-commerce is based on the trust review records of other service clients or buyers, the trust value computation process for different types of service clients is discussed first. Thereafter, the aggregated reputation computation model is proposed which integrates all different trust values. From Section 3, the dynamical direct trust computation function \(f_{\mathit{dir}}\) can be further shown as\[f_{dr}(t)=T_{e}*T_{c}(t)\frac{e^{u}-e^{-u}}{e^{u}+e^{-u}} \tag{7}\]  
where values of \(T_{e}(t)\) is assumed constant \(C\), and \(T_{e}(t)\) is relative with service clients. Let \(T_{e}(t)\) denote three classical types of service clients, that is risk taking, cautious, and conservative, which can be formulated as  
\[T_{c}(t)=T_{e}=\begin{cases}H,\;\;\text{if}\;\;\text{service client is risk taking}\\ M,\;\;\text{if}\;\;\text{service client is cautious}\\ L,\;\;\text{if}\;\;\text{service client is conservative}\end{cases} \tag{8}\]  
Concrete values of \(H,M,\text{and}L\) will be discussed in Section 5.  
Lifetime of trust value is also considered in the proposed model. Weight function associated with lifetime is used to construct the time sensitive trust valuation. The more recent trust value has a high weight. Weight function \(W(t)\) can be defined as the following exponential function [21]  
\[W(t)=\lambda^{t-t_{0}} \tag{9}\]  
where \(\lambda\in(0,1)\) is factor of weight, \(t\geq t_{0}\), and \(t_{0}\) is reference destination time. The weighted trust value computation function \(f_{dr}\) is as  
\[f_{dr}(t)=C*T_{c}*\lambda^{(t-t_{0})}\frac{e^{u}-e^{-u}}{e^{u}+e^{-u}} \tag{10}\]  
Parameters of Equation (10) are the same as those in Equations (4) and (7). Consulting with Figure 1, there are total \(N\) service-clients, and the reputation evaluation computation formulate \(r(t)\) can be shown as  
\[r(t)=\frac{1}{N}\sum_{i=1}^{N}f_{dr}^{(i)}(t)=\frac{1}{N}\sum_{i=1}^{N}C^{(i) }*T_{c}^{(i)}*\lambda_{(i)}^{(t-t_{0})}\frac{e^{e^{(t)_{i}}t}-e^{e^{(t)_{i}}t }}{e^{e^{(t)_{i}}t}+e^{e^{(t)_{i}}t}} \tag{11}\]  
The decrement process of the reputation computation model is similarly shown as  
\[r(t)=\frac{1}{N}\sum_{i=1}^{N}f_{dr}^{(i)}(t)=\frac{1}{N}\sum_{i=1}^{N}C^{(i) }*T_{c}^{(i)}*\lambda_{(i)}^{(t-t_{0})}\frac{e^{e^{-(t)_{i}}t}-e^{e^{(t)_{i}}t }}{e^{-e^{(t)_{i}}t}+e^{e^{(t)_{i}}t}} \tag{12}\]  
Figure 1: Reputation system in service-oriented networks environments based on trust reviews.  
We see the values of reputations contain dynamical direct trust values from all \(N\) service clients. The relationship of direct trust values and the values of reputations will be simulated in Section 5.
## 5 Conclusions  
1. In this study, a thermal error prediction model has been developed based on self-organizing DNN with the aim of improving feature extraction capabilities, reducing test errors, and improving convergence speed. The dropout mechanism was used to train the self-organizing capability of the hidden layer during unsupervised training of the neural network, thereby preventing synergy between neurons in the same layer and improved the feature extraction capability. Furthermore, a regularization enhancement factor was introduced into the training objective function to prevent overfitting and reduce training times.
2. The effects of ambient temperature changes on thermal error of the machine tool-foundation system was analyzed within a 9-h timeframe based on experimental data. A comparison of the prediction results of self-organizing DNN and traditional DNN revealed that the self-organizing DNN network predictive model proposed in this paper has better generalization ability and higher convergence speeds, moreover, adjusting the drop rate improves the overall predictive capability of the network. Moreover, because the machine's environment and construction are different, if we want to transfer the trained model on other machines (or new testing environments), new training data must be collected, and the model must be re-trained or fine-tuned. So, the trained model is not universal, because it is only trained for the particular experiment. However, the methodology that we proposed in the paper is totally original and universal, we believe if more data could be collected in various conditions, a model with universal power could be trained.  
**Author Contributions:** Conceptualization, Y.T.; methodology, Y.T. and G.P.; validation, Y.T.; formal analysis, G.P.; data curation, Y.T.; writing--original draft preparation, Y.T. and G.P.; supervision, G.P.; project administration, Y.T.; funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Scientific Research Fund of Department of Education of Liaoning Province, China (LG201926), Shenyang Young and Middle-aged Science and Technology Innovation Talents Support Fund (RC180185).  
**Acknowledgments:** The authors gratefully acknowledge the anonymous reviewers for their careful reading and useful comments that allowed to improve the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.