esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### HVSR and VRSR Mean Curves Versus Loess Thickness  
Loess thickness (\(H\)) was determined from borehole logs collected during site surveys at strong-motion observation stations. Among the stations selected, 53 were found to have loess coverage, with thicknesses ranging from 0.4 m to 50.1 m. The loess thicknesses were grouped into four categories: 0-5 m, 5-10 m, 10-20 m, and \(\geq\)20 m. The mean HVSR and VRSR curves for each group plotted against the loess thickness are presented in Figure 15a,b. The \(A_{0}\) and \(T_{0}\) values derived from these mean curves for various loess thicknesses are provided in Table 8.  
Figure 14: (**a**) Curve of mean HVSR versus focal depth in the Loess Plateau region. (**b**) Curve of mean VRSR versus focal depth in the Loess Plateau region.  
Figure 15: (**a**) Curve of mean HVSR versus loess thickness in the Loess Plateau region. (**b**) Curve of mean VRSR versus loess thickness in the Loess Plateau region.  
As seen in Figure 15a,b and Table 8, HVSR and VRSR are significantly influenced by \(H\). As \(H\) increases, \(A_{0}\) generally increases, while \(T_{0}\) tends to decrease. In contrast, when \(H\) exceeds 20 m, a decrease in \(A_{0}\) and an increase in \(T_{0}\) are observed.
## 4 Conclusions  
For dry-type transformer temperature monitors causing erroneous responses due to sensor failure, in this study, a set of temperature monitors was designed. Then, a fault diagnosis and recovery algorithm based on the principal component analysis (PCA), long short-term memory neural network (LSTM), and decision tree was proposed. Finally, the feasibility and scientific experiments of the method were verified by the simulation experiments and field experiments. The research indicated the following:  
1. The fault diagnosis function based on PCA could accurately diagnose the impact fault, open circuit fault, power failure fault, drift fault, and deviation fault of single or multiple sensors. The diagnosis rate was above 96%, and the diagnosis time was less than 1 ms.
2. Fault localization could diagnose the faulty sensor through a decision tree and isolated the fault data.
3. The LSTM-based data recovery function could accurately track the temperature changes under dynamic processes. The error of the predicted value was less than or equal to 0.1 \({}^{\circ}\)C and the generalization performance was good. Compared with the BP and SVM, it has obvious advantages.
4. The field experiments verified that the algorithm could significantly improve the stability of the monitor. Even if the sensor fails, the dry-type transformer was guaranteed to work within the normal temperature range.  
Conceptualization, K.Y. and Y.L.; Formal analysis, M.Z. and Y.L.; Software, M.Z.; Validation, M.Z.; Writing--original draft, M.Z.; Writing--review & editing, M.Z., K.Y., C.S. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC): [PERSON] 41761084, and the Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province: [PERSON] YNWR-QNBJ-2019-200.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors would like to thank all of the authors for their hard work in this research.  
The founding sponsors had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 3 Simulation and Results
### Analysis of UAV Node Trust Evaluation Results
We analyze metrics such as data transfer latency, throughput, and energy consumption to evaluate the system's efficiency and feasibility. Through the analysis of the experimental data, we were able to derive a quantitative assessment of the performance of the system and compare it with the design goals. The AODV routing protocol performs well in UAV swarms and can provide stable communication services. Second, we evaluate the verdict accuracy against malicious nodes. By using the judgment method we designed, we can judge the malicious nodes in the UAV cluster in most cases. By analyzing the experimental data, we calculated the decision accuracy, that is, the proportion of correctly classified malicious nodes to the total number of malicious nodes. We observed that the judgment accuracy rate varies under different experimental conditions, and the comparison with other experimental schemes can illustrate the feasibility of the experimental scheme in this paper. Then, in terms of the change rate of the trust value, we observed the change in the trust value of the drone. Through the analysis of the experimental data, we found that the trust value of the drone fluctuated and changed in different periods. We noticed that the rate of change of the trust value can represent an indication of the change in the behavior of the UAV, which is of great significance for verifying the feasibility of the scheme designed in this paper. In addition, we also evaluate the performance of the blockchain in drone swarms. By analyzing the experimental data, we observed that the blockchain works effectively in data transmission between drones. We pay attention to the performance of blockchain delay, data update, and stability, and compare it with the design requirements.
## 3 Experiment Results
### Comparison Experiment  
Restricted by the experimental conditions, only the angular positioning error and the tilt error around the \(y\) axis measured by our device were compared to the results measured by a standard instrument. An autocollimator AC300 (the standard instrument was produced by AcroBeam) measured the target axis simultaneously with the measuring device. The max error of the standard instrument  
Figure 6: The repeatability of six DOF motion errors: the (**a**) angular positioning error, (**b**) tilt motion error around the \(y\) axis, (**c**) tilt motion error around the \(x\) axis, (**d**) radial motion error along the \(y\) axis, (**e**) axial motion error, and (**f**) radial motion error along the \(x\) axis.  
is \(\pm\)0.05\" in the range of \(\pm\)300\" and the uncertainty of indication error is 0.05\". The instrument was calibrated by the National Institute of Metrology in China. At the same position of measurement, the difference between our device and the standard instrument was considered the comparison error at this measurement position\".  
There are six DOF installation errors of the autocollimator and six installation errors of the mirror, which are introduced in the comparison experiment. Because of the measurement principle of the autocollimator, the translation errors and the tilt errors around the \(x\) axis have little effect on the measurement and can be ignored. Only four angular installation errors have an influence on the measurement results. The influences are constant and can be compensated for by subtracting the value of the first measurement point. As shown in Figure 7, the comparison error of the angular positioning error was 2.3\", and the comparison error of tilt motion error around the \(y\) axis was 2.9\".  
The experiment results show that the repeatability of the device for measuring all motion errors except for the angular positioning error is good, as shown in Figure 6. We believe that the large repeatability value of the angular positioning error is due to the low positioning accuracy of the target rotary axis, which was verified by the comparison experiments. As shown in Figure 7, the comparison errors of both the angular positioning error and the tilt motion error around the \(y\) axis were small. Moreover, the validity of the model compensation was also verified by the experiment. The three repeatability values of the measuring device were significantly improved after compensation, as shown in Table 3.
## 3 Results
### Surgical Planning and Intraoperative Decision Making  
Thirteen articles (59.1%) reported that 3 DPLMs enhanced surgical planning for LCR [11; 13; 14; 15; 16; 17; 19; 22; 29; 31; 32; 33; 40]. Seven of these articles were original research studies [11; 15; 16; 17; 19; 29; 31]; however, only two examined actual LCR cases [17; 19]. The other five studied how patient-specific 3 DPLMs improved resident/intern/medical student surgical planning and anatomical understanding [11; 15; 16; 29; 31], and their findings were less transferable to surgeons' preoperative decision making.  
Six articles (27.3%) described the utility of 3 DPLMs for intraoperative guidance and navigation of the surgical field during LCR [9; 13; 19; 22; 33; 40]. Only one original research article supported these findings [19] (the remaining five were case reports), limiting their generalisability.
## 7 Simulation Results  
This section is devoted to the simulation results, illustrating the theoretical background presented in Sections 2-6. In this sense, the subsections given below correspond to the ordered sequence of the above-presented theoretical results. The main intention here is to illustrate the practical applicability of the analyzed distributed calibration algorithms under the presence of nonlinearities in sensor models. In order to emphasize the main effects, the adopted values of some variables and parameters are slightly out of the realistic range. The main focus here is to delineate, within all the presented types of algorithms, the distinction between full-consensus-based and reference-based recursions. The main practical idea has been to emphasize the important fact that only one additional local sensor micro-calibration allows us to obtain both theoretical stability and practical effectiveness.  
All the results are obtained by simulating a ten-node network. In each experiment, a network digraph (together with its weights) is chosen at random from a large set of digraphs, satisfying Assumption A3. Sensor parameters \(\alpha^{i}\) and \(\beta^{i}\) have been selected at random to be about one and zero, respectively, with standard deviations equal to 0.1. The measured signal, \(x_{t}\), has been generated by a simple second-order AR process, with a standard deviation of 1. As far as the nonlinearities are concerned, they should remain bounded according to A2. All the presented simulations are obtained by using nonlinearities composed of pieces of polynomials and exponential and trigonometric functions, chosen at random.
## 3 Control System for Work Vehicle
### Control of the Work Vehicle's Walking System  
#### 3.2.1 Device Component  
The walking drive system is powered by 220 V AC. In the electrical control design, it is first necessary to invert the 72 V battery pack into 220 V, 50 Hz sine wave AC to achieve a stable voltage output. The main function of the inverter is to convert DC power into fixed frequency and voltage or variable frequency and voltage AC power, outputting a sine wave  
Figure 11: Vehicle electrical control cabinet. 1—220 V voltage zone. 2—24 V voltage zone.  
Figure 10: Flowchart of the electric control system of the work vehicle.  
(220 V, 50 Hz). It consists of units such as the inverter bridge, control logic, and filtering circuit. In this electrical control design, the parameters of the required inverter are an input voltage of 72 V and a power of 10 kW. During the process of converting 72 V direct current into 220 V alternating current, there will be an efficiency loss of 5-10%. This comes with a built-in LCD screen that can display information such as the input voltage, output voltage, power, and load. A physical image is shown in Figure 12.  
In order to achieve constant-speed walking, two AC synchronous servo drive motors were selected for the work vehicle. The installation diagram is shown in Figure 13. The rotation speed of the motors can be adjusted through the speed rotary switch and the control switch pedal, thus enabling the work vehicle to travel at the preset speed. Compared with DC servo motors, the permanent magnet AC servo drive motors have the main advantages of reliable operation, low requirements for maintenance and repair, and adaptability to high-speed and high-torque working conditions. The 130 ST-M15015 AZ-N AC servo drive motor of Longmen in China was chosen in this study. The specific parameters are shown in Table 2. This motor has a wide speed adjustment range of up to 1:1000, which can meet the speed requirements in different operating conditions. Moreover, it has a strong overload capacity and can withstand 150% of the rated torque in a short time, adapting to the overload conditions that the vehicle may encounter in complex field conditions. Meanwhile, the walking motors are used in combination with right-angle-turning planetary reducers to reduce the rotation speed, increase the torque, and match the inertia. In actual work, the torque output of the reducer is stable and the transmission efficiency is maintained above 95%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Rated voltage/(V) & 220 \\ Rated current/(A) & 9.5 \\ Rated power P/(kW) & 2 \\ Rated rotational speed/(r/min) & 1500 \\ Rated torque/(NM) & 15 \\ Length of the motor/(mm) & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main structural parameters of the drive motor.  
Figure 12: Installation drawing of the inverter.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 3 Preliminary Design and Simulation Verification  
### Design Indicators  
Research on the electrical parameters of several antennas commonly used on the market in the required frequency band (around 403 MHz) revealed that most of the antenna gains are in the range of 2-8 dB, and the VSWR is less than 1.5, so the following design indices were chosen:  
Antenna structure: four-armed spiral structure;  
Signal frequency: the specified frequency of the Typhoon Project (403 MHz);  
Maximum gain: \(\geq\)2.5 dB;  
Polarization mode: circular polarization;  
Antenna voltage VSWR: \(\leq\)1.5;  
Horizontal coverage angle: 360\({}^{\circ}\);  
Return loss: \(\leq\)\(-\)10 dB;  
The structural design of the antenna is based on the frequency band (403 MHz) specified by the Typhoon Project. One of the important indicators for measuring the performance of the four-armed helical antenna is to observe whether its radiation direction graph is heart-shaped or not [17]. Considering the limitation of Equation (1), the performance of the antenna, and the need to carry a cylindrical sounder with a height of about 20 cm and a radius of about 3 cm inside the antenna, the final dimensions of the antenna are calculated as follows. The height of the helical arm in the vertical direction is \(L_{z}\) = 34.21 cm, the radius of the helix is \(r\) = 5.323 cm, and the number of helical turns of the helical arm is \(m\) = 1.1.  
In the selection of the model material, the outer surface material of the cylindrical carrier wrapping the four-armed helical antenna should have good dielectric properties and the ability to adapt to temperature changes without a change in performance. Consideringthe requirement of the dielectric constant, polyvinyl chloride plastic with moderate cost was chosen as the material for the outer surface of the cylinder.
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Number** & **CNN Architecture** & **TLS\({}_{\text{ef}}\)** & **TLS\({}_{\text{if}}\)** & **Ground Truth** \\ \hline Case1 & VGG16 & 0.94414753 & 0.41299862 & Well-watered \\ Case2 & VGG16 & 0.49225223 & 0.93656325 & Slight stress \\ Case3 & VGG16 & 0.51609260 & 0.94980104 & Slight stress \\ Case4 & VGG16 & 0.00144600 & 0.95988095 & Mild stress \\ Case5 & ResNet34 & 0.81816900 & 0.28788370 & Well-watered \\ Case6 & ResNet34 & 0.74497569 & 0.48903641 & Well-watered \\ Case7 & ResNet34 & 0.48903641 & 0.22066300 & Slight stress \\ Case8 & ResNet34 & 0.45508286 & 0.28079394 & Slight stress \\ Case9 & DenseNet121 & 0.975930377 & 0.14972794 & Well-watered \\ Case10 & DenseNet121 & 0.48703855 & 0.29539412 & Slight stress \\ Case11 & DenseNet121 & 0.54188645 & 0.18642481 & Slight stress \\ Case12 & DenseNet121 & 0.16764741 & 0.44175833 & Mild stress \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Correct case sample TLS\({}_{\text{ef}}\) and TLS\({}_{\text{if}}\) for 3 CNN architectures. Figure 8: Accuracy of TLS\({}_{\text{ef}}\) + TLS\({}_{\text{if}}\), with softmax features, and without MLP. In the MLP, we expected that the output layer uses softmax, while all other layers use ReLU. We also tested whether this setting is reasonable through our experiments. In Table 9, we replaced all the activation functions of the MLP with tanh and sigmoid, except for the output layer. Since the values were all positive after ReLU conversion, and since the two scores (\(\text{TLS}_{\text{ef}}\) and \(\text{TLS}_{\text{lf}}\)) were all positive, we predicted that ReLU should have a better performance than tanh and sigmoid.However, there was no significant difference in the results of the replaced activation functions in Table 9, which shows that our MLP's number of layers and neuron settings provide enough capacity for this application. Compared with previous research, the method of this study mainly lies in the improvement of two points. First, we used two scores, \(\text{TLS}_{\text{ef}}\) and \(\text{TLS}_{\text{lf}}\), to retain degree information, explained by comparisons between Tables 3, 5, and 6. Second, we demonstrated through experiments that using thermal images is better than using RGB images. As shown in Table 10, we used the same architecture as a MLP with \(\text{TLS}_{\text{ef}}\) and \(\text{TLS}_{\text{lf}}\) in Table 3, and using RGB images as the input. These results show that it is difficult to directly use RGB images to identify the water content of plant leaves. A more suitable and easier solution is to use the response of water to leaf temperature and obtain temperature information through thermal imagery.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 6 Datasets and Tools
### Datasets
Traffic demand generators can be implemented as generic software or general-purpose programming languages such as MATLAB and Python. For example, TMGEN ([[https://github.com/progwriter/TMgen](https://github.com/progwriter/TMgen)]([https://github.com/progwriter/TMgen](https://github.com/progwriter/TMgen)), accessed on 22 September 2024) is a Python tool used for generating traffic matrices. With the popularity of applying GNNs to routing problems, open global competitions with relevant research topics have been held to attract a broad audience from both academia and industry. For example, the \"Graph Neural Networking Challenge 2020\" ([[https://www.itu.int/en/ITU-T/AI/challenge/2020/Pages/default_aspx](https://www.itu.int/en/ITU-T/AI/challenge/2020/Pages/default_aspx)]([https://www.itu.int/en/ITU-T/AI/challenge/2020/Pages/default_aspx](https://www.itu.int/en/ITU-T/AI/challenge/2020/Pages/default_aspx)), accessed on 22 September 2024) attracted more than 1300 participants from 60+ countries [94], with the purpose of predicting the per-path mean delay given a network snapshot based on the network topology, traffic matrix and routing configuration. RouteNet [61] is provided as the baseline and more neural network-based solutions are proposed by the participants. A simulated dataset is generated from a packet-level simulator called OMNeT++ [133] to evaluate different solutions, which can also be used in future research.
## 2 Methodology  
The aim of this study was to develop a virtual sensor for detecting faults in a SCWT sensor and evaluate its fault-detection performance and correction effects. Accordingly, this study was conducted in five steps, as shown in Figure 1.  
The first step involved developing a simulation model. For the case study, an office building, cooling system, and faults in the SCWT sensor were modeled using a building energy simulation tool, EnergyPlus. The second step involved the development of a virtual sensor based on a long short-term memory (LSTM)-autoencoder. Detailed information about the LSTM-Autoencoder is presented in Section 3. The third step involved the classification of the simulation cases according to the fault type. Fault scenarios were assumed and categorized into simulation cases, and datasets were created based on the simulation results for each case. Various types of fault data were input into the virtual sensor to evaluate the fault-detection performance and correction effect of the virtual sensor. The fourth step involved evaluating the fault-detection performance of the virtual sensor. The results of fault detection using the virtual sensor were analyzed, and the fault-detection performance was evaluated using four performance metrics. Finally, the fifth step involved evaluating the fault correction effect of the virtual sensor. This step constituted evaluating the impact on the indoor set-point temperature unmet hours, energy consumption resulting from the correction of SCWT sensor faults by the virtual sensor, sensor calibration, and replacement.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Year** & **Method** & **Sensor Fault Type** & **Quantitative Result** \\ \hline \multirow{3}{*}{[9]} & \multirow{3}{*}{2004} & \multirow{3}{*}{PCA} & Fixed bias & \multirow{3}{*}{Detection ratio: almost 100\%} \\  & & & Drifting & \\  & & & Precision degradation & \\  & & & Complete failure & \\
[10] & 2010 & PCA & Fixed bias & Detection ratio: 96.4–100\% \\
[11] & 2022 & Boltzmann-machine & Fixed bias & F-1 score: 0.92–1.00 \\
[12] & 2004 & GRNN & Fixed bias & Fault classification: O \\
[13] & 2014 & Combined ANN & Fixed bias & False alarm ratio: 0.6–7.5\% \\  & & & Missing alarm ratio: 0–8.3\% \\ \hline \end{tabular}
\end{table}
Table 1: Literature review of sensor fault detection.
## 2 Materials and Methods
### Antibacterial Properties of AgNPs  
Gram-positive _Staphylococcus aureus_ #6538 and gram-negative _Pseudomonas aeruginosa_ #13338 were purchased from American Type Culture Collection (ATCC) and grown in Luria Bertani media (LB). The antibacterial activity (AA) of AgNPs-S and AgNPs-C was evaluated by the microdilution method. Different concentrations of AgNPs were used (1000, 500, 250, 125, and 62.5 \(\mu\)g/mL) suspended in a phosphate buffered saline (PBS) media at 1 wt.% as a dispersant agent. The PBS buffer (200 mL) was prepared with 159.24 mg of KH\({}_{2}\)PO\({}_{4}\), 258.6 mg of K\({}_{2}\)HPO\({}_{4}\), and 1.753 g of NaCl dissolved in distilled water. The interaction of bacteria and AgNPs were evaluated after 1 and 24 h of contact, in three independent, duplicated experiments. First, inoculums of each microorganism were grown in LB media at 37 \({}^{\circ}\)C for 16 h and adjusted by optical density to obtain a final concentration of 2 \(\times\) 10\({}^{5}\) CFU/mL. A 1:1 mix of bacteria and nanoparticles was performed at 1 and 24 h and incubated at 37 \({}^{\circ}\)C. An aliquot (50 \(\mu\)L) of each sample was plated in LB dishes and incubated at 37 \({}^{\circ}\)C for 16 h. AA was calculated following the methodology of previous work [21]. Data were subjected to statistical analysis using one-way ANOVA with Minitab 19.2 \({}^{\text{\textregistered}}\), followed by [PERSON]'s comparison test.