esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A series of studies have been carried out to measure the tilt of the alidade using an inclinometer and indirectly infer the antenna pointing errors [16; 17; 18; 19; 20; 21]. Still, the accuracy of the data obtained from these experiments is  
Figure 2: The antenna pointing errors of a radio telescope. not sufficient for pointing error correction. One possible reason is that the unstable thermal environment compromises the zero-point stability of inclinometers. One study has shown that an inclinometer can be calibrated with empirical formulas using data measured over several nights [22], but there is still a lack of evidence that the calibrated inclinometer is capable of maintaining stability when experiencing seasonal temperature variation. Our goal is to take full advantage of the high precision of the inclination sensor. An inclinometer measurement system with an environmental control box (ECB) is designed and tested based on the 25 m Nanshan Radio Telescope (NSRT). The NSRT (as shown in Figure 3) is located at the Nanshan Observatory in Urumqi, Xinjiang, China, and can operate at frequencies up to 43 GHz [23; 24]. At present, the average pointing error of NSRT is greater than 15\({}^{\prime\prime}\). A stationary test was performed, which proves that the zero-point stability and repeatable accuracy with the application of the ECB are at the same level as those measured in an indoor environment. Multiple tests were conducted to demonstrate that the inclinometer measurement system is able to infer the pointing errors caused by the tilt of the alidade due to track unevenness and thermal gradients with satisfactory accuracy.
## 4 Recommendation on Improving the Efficiency of Intensity Measures
### EDP-IM Relationship
#### 4.2.1 Period Dependence
_Sa(T)_ shows strong correlation for short-period frames and remains constant for longer periods. Among the energy measures, _A_RMS and _I_A decrease with periods, while _CAV_ remains approximately constant. thus causing significant impact on structural response [99, 100]. However, the selected frames do not account for effects of structural fatigue; therefore, the responses do not correlate with duration. Despite high correlations and low variability, peak and spectral measures (except _Sa(T)_) are not entirely structurally independent. Therefore, it is important that the selection of these IMs must be made based on the fundamental vibration mode of the structure. Our analysis shows that for very small periods (<0.3 s), _PGA_ is a good indicator, while for periods (0.3-0.6 s), _ASI_ is an efficient indicator. For moderate (0.6-1.2 s) and long periods (>1.2 s), _PGV_ and _VSI_ better capture the response of structure, respectively. In the next section, we examine the effect of magnitude \(M_{w}\) and epicentral distance \(R_{qpi}\) on the correlations between IM and EDP.
# Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality
## 3 Methodology
### Numerical Simulation of Modulus of Vibration Compaction \(E_{vc}\)  
Because the action depth of the vibration wheel is greater than the thickness of the filling layer, vibration feedback to the vibration wheel actually reflects the overall compaction quality in a certain range below the roller surface, as shown in Figure 2a. The continuous compaction control index obtained is a function of the modulus and thickness of each filling layer, and \(E_{vib}\) is calculated as Equation (1).  
\[E_{vib}=f(E_{1},E_{2},E_{3}...h_{1},h_{2},h_{3}...) \tag{1}\]  
where \(E_{i}\) = modulus of filling layer; \(h_{i}\) = thickness of filling layer.  
However, for some special conditions (cut to fill subgrade), the strength of the underlying layer is higher, and the vibration response to the upper layer is greater. In addition, the upper layer is relatively weak, and then, more vibration feedback is provided by the underlying layer, as shown in Figure 2b. The existence of a high-strength underlying layer makes the monitoring value of the continuous compaction control index too large to reflect the actual compaction state of the current filling layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Liu Gong CLG1622E** \\ \hline Total Static Mass (kg) & 22,000 \\ \hline Rated Power (kw/rpm) & 132/1800 \\ \hline Eccentric Force (kN) & 290 \\ \hline Excitation Frequency (Hz) & 32 \\ \hline Excitation Amplitude (mm) & 1.0 \\ \hline Working Width (mm) & 2130 \\ \hline Total sizes (Length \(\times\) Wide \(\times\) Height) (mm) & 6450 \(\times\) 2340 \(\times\) 3050 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the roller.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental** & **Thickness of Filling** & \
\begin{tabular}{c} **Strength of** \\ **Underlying Layer** \\ **\(E_{vib}\) (MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Roller Velocity** \\ **(km/h)** \\ \end{tabular} \\ \hline
1 & 40 & 93 & 3.0 \\
2 & 40 & 80 & 3.0 \\
3 & 40 & 60 & 3.0 \\
4 & 50 & 60 & 3.0 \\
5 & 60 & 60 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compaction parameters of test strips.  
In view of the above defects, this paper establishes an elastic multi-layer model of subgrade, and proposes a new continuous compaction control index, modulus of vibration compaction \(E_{exc}\), which is suitable for controlling the actual compaction quality of the filling layer on special conditions such as when the strength of the underlying layer is high or there is an uneven distribution.
# Assessing the Compaction Quality of Dolomitic Asphalt Pavements Using Ground Penetrating Radar
## 3 Data and Methods
### Compaction and \(\varepsilon\) Relationship Measurement  
To explore the relationship between the degree of compaction and the change in the dielectric constant, stationary GPR data were collected at the same location at the construction site after each pass of a vibratory roller during the laying of each asphalt layer (Figure 4). For the two test sites, seven short GPR profiles were measured after each roller pass and after laying each asphalt layer. The measurements were carried out in time mode. Immediately after the compactor completed one pass, the GPR vehicle moved toward the lane to place the antenna above the pavement. The data collection was made in time mode for approximately 10 s. The GPR vehicle moved back to its original position, as shown in Figure 4. Following the GPR measurement, a density reading was taken by a CPN MC-3 PORTAPROBE-type nuclear gauge [29]. The backscatter mode was chosen for the measurements [30]. The device was placed on the compacted layer after warming up and determining the GPS position of the measurement point.  
Figure 4: The procedure of repeated stationary GPR and nuclear gauge measurements. From left to right: vibratory roller pass, GPR horn antenna measurements, nuclear gauge measurements.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 6 Conclusions  
This paper studies the problem of solenoid valve fault diagnosis on the basis of the current detection method at the driving end. The research group analyzed the characteristics of the drive-end current under four conditions: normal solenoid valve spool, spring broken, spool stuck and slightly stuck spool. The proposed method selects the solenoid valve drive end current value and its second-order rate of change curve as the characteristic curve and constructs a multi-feature fusion solenoid valve state eigenvector on the basis of a three-layer wavelet packet decomposition and PCA. Then, the research group reconstructed the original signal using the energy-fault method to calculate the energy statistics on the reconstructed frequency bands and employed the statistically obtained energy value of each frequency band as a feature vector. Finally, the research group designed C-SVC to perform pattern recognition on the state of the solenoid valve and to compare the performance of the method against the BP neural network and C-SVC on the basis of time-frequency analysis. The significant findings of the research are:  
1. Using the solenoid valve drive end current value and its second-order rate of change curve as the signal's characteristic curve, it is possible to extract characteristic vectors with apparent differences.
2. The C-SVC pattern recognition method on the basis of RBF has an accuracy rate of 91.3% and a cross-validation accuracy rate of 50%.
3. The BP neural network used to perform pattern recognition and to exploit the multi-feature fusion on the four states of the solenoid valve manages an accuracy rate of 84.78%.
4. The proposed C-SVC method on the basis of RBF exploiting multi-feature fusion on the four states of the solenoid valve manages an accuracy rate of 100% and a cross-validation accuracy rate of 92.11%. Specifically, both the training and testing accuracy rates are 100%, highlighting the method's capability to correctly classify all four states of solenoid directional valve spools, i.e., normal, spring broken, spool stuck and spool stuck slightly.  
There are still some shortcomings in this paper: the support vector machine belongs to machine learning and is a method on the basis of mathematical statistics, which lacks the physical interpretation of experimental results. We plan to analyze the interpretability of this method in the future from the perspective of the failure mechanism.  
Writing--original draft preparation, D.M.; writing--review and editing, Z.L. and T.H.; methodology, Q.G. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China: 51905541; Shaanxi Province Natural Science Basic Research Program: 2020 JQ487; Shaanxi University Association for Science and Technology Young Talent Support Program: 20190412.  
The authors declare no conflict of interest.  
Figure 14: Comparison of test results of different methods.
## 2 Materials and Methods
### Experimental Methods
#### 2.2.3 Ceramsite Performance Test  
The physical properties (porosity, cylinder compressive strength) of the ceramics before and after calcining were tested according to _\"Light Aggregates and Test Methods_ (GB/T 17431.2-2010, Beijing, China)\", with some modifications. The porosity of the ceramics was measured by vacuum water absorption meter (ZK-16, Tianjin Jianyi, Tianjin, China), and the cylinder compressive strength was measured by microcomputer controlled universal material testing machine (TFW-50S, Jinan Xinguang, Jinan, China). The leaching toxicity of ceramsite before and after calcining was acquired according to _\"Leading Method of the Solid Waste Leaching Toxicity-Horizontal Oscillation Method_ (HJ 557-2010, Beijing, China)\", although slightly modified, and the leaching agent was 1% nitric acid. The specific surface area of the ceramsite was measured by specific surface area meter (ASAP2460, Micromerities, Georgia, GA, USA) at 77K of N\({}_{2}\).  
The zero electric point (pH\({}_{\text{PZC}}\)) of the sintered ceramsite was determined by solid addition method [20; 21]. Adding 25 mL 0.01 mol/L KNO\({}_{3}\) solution into 10 beakers, and adjust the pH of the solutions between 3-12 with 0.1 M HNO\({}_{3}\) solution and NaOH solution, respectively. The initial pH of each beaker was recorded (recorded as pH\({}_{0}\)), then 1.0 g of crushed ceramsite were added to each beaker. The beakers were sealed immediately and placed on the oscillator to oscillat for 24 h. After that, the pH of supernatant was measured and recorded as pH\({}_{\text{e}}\). The difference between the initial pH and the equilibrium pH (\(\Delta\)pH = pH\({}_{0}-\) pH\({}_{\text{e}}\)), the point at which the \(\Delta\)pH = 0, is the zero electric point.
## 4 Stability Analysis of VSC-HVDC with Improved Virtual Impedance Control Strategy
### Impact of Virtual Impedance Parameters by Nichols Plots  
Figure 10 shows the Nichols plots of \(T_{\rm m}\) with different virtual impedance parameters. The DC cable length is 10 km and the transmission power is \(-500\) MW. The proposed strategy in this paper is adopted to the DC-voltage-controlled converter, and the controller parameters are set as \(k_{potc}=15\), \(k_{intdc}=100\), and \(k_{i}=10\). The blue, red, and yellow lines in Figure 10a show the Nichols plots of \(T_{\rm m}\) when the virtual impedance parameters \(R_{eq}\) are \(0.5\ \Omega\), \(2\ \Omega\), and \(5\ \Omega\), respectively. Figure 10a shows that \(T_{\rm m}\) encircles (\(-180^{\circ}\), \(0\)), and the system is predicted to be unstable when \(R_{eq}=0.5\ \Omega\). The system is predicted to be stable when \(R_{eq}=2\) and \(5\ \Omega\). Increasing the \(R_{eq}\) value in a certain range helps improve the stability of the system. The phase margin of the system is insufficient to suppress DC-side oscillation when \(R_{eq}=0.5\ \Omega\).  
Figure 9: Impedance frequency responses of \(Z_{dc1}\) and \(Z_{dc2}\) under different equivalent virtual impedance values (**a**) \(R_{eq}\) and (**b**) \(L_{eq}\).  
The blue, red, and yellow lines in Figure 10b show the Nichols plots of \(T_{\rm m}\) when virtual impedance parameters \(L_{eq}\) are 0.002 \(\Omega\), 0.02 \(\Omega\), and 0.05 \(\Omega\), respectively. Figure 10b shows that \(T_{\rm m}\) encircles (\(-180^{\circ}\), 0), and the system is predicted to be unstable when \(L_{eq}\) = 0.002 \(\Omega\). The system is predicted to be stable when \(L_{eq}\) = 0.02 and 0.05 \(\Omega\). Increasing the \(L_{eq}\) value in a certain range helps improve the stability of the system. The phase margin of the system is insufficient to suppress DC-side oscillation when \(L_{eq}\) = 0.002 \(\Omega\).
# Path Optimization Using Metaheuristic Techniques for a Surveillance Robot
## 2 Related Works
The main aspects that this work deals with in relation to those previously mentioned can be presented in three groups: the monitored environment, the surveillance method, and the optimization of path planning. Regarding the first, the different places that are monitored are for indoor or outdoor spaces. In our case, given the characteristics of the robot and the sensor, the proposed methodology can be applied in both environments. Regarding the method used to carry out the surveillance task, in this work a mobile ground robot with a built-in 3D LiDAR ([[https://www.sick.com/es/es/sensores-lidar/sensores-3d-lidar/multiscan100/c/g574914](https://www.sick.com/es/es/sensores-lidar/sensores-3d-lidar/multiscan100/c/g574914)]([https://www.sick.com/es/es/sensores-lidar/sensores-3d-lidar/multiscan100/c/g574914](https://www.sick.com/es/es/sensores-lidar/sensores-3d-lidar/multiscan100/c/g574914)) (accessed on 27 September 2023)) is proposed, but as it has been shown in other papers, this task could be carried out with aerial vehicles, or with a swarm of robots, and also using different sensors. Finally, there are many optimization methods that have been proven effective and each of them has advantages and disadvantages, as has been shown in this work in which three of them, GA, PSO and PS, that are well-known and easily implemented are compared.
# Transient Analysis of a Selective Partial-Update LMS Algorithm
###### Abstract
Concisely, the SPU-NLMS groups a set of adaptive coefficients in a single or multiple blocks, and selects the significant excitation data blocks according to their squared Euclidean norms. By a proper selection of input data blocks, the reduction of the computational burden can be attained. In this paper, a new deterministic optimization approach is proposed, which avoids the normalization steps commonly required by SPU-based schemes. Based on it, a novel SPU-based LMS algorithm, referred to as SPU-LMS-M-min, is introduced. Furthermore, stochastic models are advanced to predict its mean weight dynamics and mean square error performance. In order to obtain a comprehensive analytic characterization of the learning abilities of the SPU-LMS-M-min algorithm, tracking and deficient-length analyses were also carried out. Figure 1: Block diagram of an adaptive filtering algorithm applied to systems identification. The proposed coefficient selection method can be easily adapted to mitigate the computational cost of other strategies. For instance, Variable Filter-Length (VFL) algorithms [11; 12; 13; 14; 15; 16] dynamically update the filter size throughout iterations, thereby reducing computational cost during transients. However, such algorithms update all adaptive coefficients, leading to an increased computational cost, especially in steady-state conditions. Additionally, these algorithms commonly asymptotically demand a longer filter length than optimal [17], implying unnecessary computational overhead. Both drawbacks can be alleviated through coefficient selection techniques. Thus, it is possible to reduce the computational cost of these algorithms without compromising their asymptotic performance, albeit with a controllable loss (to be judiciously chosen based on the application requirements) in convergence rate. This paper is organized as follows. In Section 2, the proposed framework is derived, covering the single-block case for didactic purposes. A generalized algorithm that engenders a selection of several blocks in each update is the focus of Section 2.2. Section 3.1 introduces concepts for first-order analysis, which precedes the second-order analysis described at Section 3.2. Generalizations of the advanced stochastic model for both time-variant and deficient-length scenario approaches are presented on Sections 3.3 and 3.4, respectively. Section 4 presents extensions of the proposed selection methodology to variable tap length and active noise cancellation (ANC) algorithms. Results and discussion are the focus of Section 5. At last, Section 6 contains the final considerations of the paper.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 4 Results  
### Experimental Setup  
In this study, experiments are carried out on a workstation featuring an Intel i5-13400F processor (Intel, Santa Clara, CA, USA), 32 GB of Random Access Memory (RAM) (Dell, Round Rock, TX, USA), and an NVIDIA RTX 4080 graphics processor (NVIDIA, Santa Clara, CA, USA) equipped with 16 GB of video memory. The experiments are conducted using the PyTorch 3.8 deep learning framework and implemented in the Python programming language.  
The Stochastic Gradient Descent (SGD) optimizer is employed for all training procedures, with an initial learning rate of 0.001 and a batch size of 32. The training consists of 200 epochs, incorporating a weight decay factor of 0.005 and a momentum factor of 0.937. The prediction box threshold is set at 0.7. Within the optimizer, the learning rate decay is managed through the cosine annealing scheduler. During the training phase, the input images are normalized within the range of (0, 1).
## 4 Results and Discussion  
This paper carried out static field tests and semi-physical simulations based on a telemetered missile trajectory to verify the validity of the proposed methods. In the static field test, the integration of GPS and INS is not involved, and only GPS is utilized to compare the performance of the baseband signal preprocessing models based on signal-filter and dual-filter structures. In the simulation, the navigation results of the ultra-tight GPS/INS integration are analyzed, and special attention is given to the further improvement of carrier frequency tracking accuracy.
## 5 Experimental Results
### Wavelet Packet-Based Structural Damage Indices
The damage indices in this period, as shown in Figures 10 and 11, do not continue to present the similar increasing and decreasing trends as before. As the load cycled, the values of damage indices of all the SA sensors maintained a constant value near one, which indicates that the major crack at the bottom of the concrete column cannot be \"recovered\" even when the RC column was pushed back to its original position. As shown in Figure 11, the peaks and valleys between the damage indices and displacement history match well until approximately load Step 3250, which corresponds to the maximum load and severe damage in the column. Beyond this point, the damage indices maintain a value near one. Each peak corresponds to cracks opening and each valley corresponds to a cracks closing (or opening on the opposite side of the column). In addition, the results using wavelet packet-based damage index are compared with both the strain energy of the RC column and the recorded key events presented by [PERSON] et al. [45]. The cycles of the structural strain curve shown in [PERSON] et al. [45] also agree with the cycles of the damage indices. After around Step 3200, the strain value of the RC column at the cracking area approaches to its maximum values and the values in our computed damage indices corresponding approach one, which implies that the RC column is under severe damage at this stage. On the other hand, from the observed key events shown in [PERSON] et al. [45], we can see that the first reported event of concrete cracking occurred at Step 1243. These results also agree with our damage indices, where we can see that the first obvious increase happens between Step 1000 and Step 1250. When the recorded concrete spelling event happens at Step 3261, the values of our damage indices correspondingly approach one. The agreement between our results and others' show that the smart aggregate-based active sensing approach is capable of monitoring the crack opening and closing condition in real-time. Figure 11: _Cont._
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 6 Conclusions  
This study presented an FDIA localization method based on GA-CNN-LSTM. The proposed approach regards FDIA localization as a multi-label classification problem, utilizing a CNN for local feature extraction and LSTM for integrating time series information to capture global features. By leveraging the strengths of CNN and LSTM, it could accurately differentiate between attacked and normal measurement values, facilitating the precise localization of the attack location. Furthermore, we incorporated genetic algorithms to optimize the neural network's hyperparameters, thereby enhancing the detection performance of the model.  
The method proposed in this paper identifies compromised data by analyzing historical measurement data characteristics, independent of the specific power system topology. Consequently, the proposed approach remains effective even when the power system topology undergoes changes. Simulation experiments conducted on the IEEE 14-bus and IEEE 118-bus test systems validated the efficacy of our proposed method. Comparative experiments against other detection methods demonstrated superior detection results achieved by the method presented in this paper. Future research will focus on further optimizing the model structure to reduce the dependence on computational resources, striving to maintain or even enhance the detection performance.  
Conceptualization, Z.L. and Y.X.; methodology, Z.L. and Y.X.; software, Z.L. and Y.X.; validation, Z.L. and Z.W.; formal analysis, Y.X. and R.M.; investigation, Z.L. and Z.W.; resources, Z.L. and Y.X.; data curation, Z.L. and Y.X.; writing--original draft preparation, Z.L.; writing--review and editing, Y.X. and R.M.; visualization, Z.W.; supervision, Y.X.; project administration, R.M.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NSFC Young Scientist Fund, grant number 62372465.  
\begin{table}
\begin{tabular}{c c c} \hline  & **IEEE 14** & **IEEE 118** \\ \hline GA-CNN-LSTM & 14 ms & 31 ms \\ GA-CNN-MLP & 13 ms & 29 ms \\ GA-CNN & 11 ms & 26 ms \\ CNN & 12 ms & 27 ms \\ DNN & 6 ms & 14 ms \\ MLP & 2 ms & 5 ms \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of detection time.