esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### TOC and Tock-Eval Pyrolysis Evaluation  
The analysis of organic matter abundance in hydrocarbon source rocks is a crucial method that employs the results of the TOC and pyrolysis tests on rocks [42; 43]. This study utilizes the TOC and hydrocarbon generation potential of the S\({}_{1}\) + S\({}_{2}\) to evaluate the abundance of organic matter [44; 45; 46].  
The TOC contents of 117 mudstone samples from P\({}_{1}\)-\(q\) in Qimeigan section analyzed range from 0.27 to 2.56%, with an average value of 1.75%. The S\({}_{1}\) + S\({}_{2}\) values range from 0.63 to 4.96 mg/g, with an average value of 1.94 mg/g. The HI values range from 108 to 212 mg/g and the Tmax values range from 313 to 596 \({}^{\circ}\)C. The TOC contents of 50 mudstone samples from P\({}_{1}\)-\(q\) in the Qimeugan section analyzed range from 0.28 to 2.15%, with an average value of 1.05%. The S\({}_{1}\) + S\({}_{2}\) values range from 0.6 to 4.8 mg/g, with an average value of 2.17 mg/g. The HI values range from 110.5 to 330 mg/g and the Tmax values range from 333 to 593 \({}^{\circ}\)C. The TOC contents of 176 mudstone samples from P\({}_{1}\)-\(q\) in the Well Qiant-1 analyzed range from 0.1 to 2.77%, with an average value of 1.04%. The S\({}_{1}\) + S\({}_{2}\) values range from 0.11 to 1.3 mg/g, with an average value of 0.59 mg/g. The HI values range from 15 to 104 mg/g and the Tmax values range from 437 to 633 \({}^{\circ}\)C (Figure 9).  
Figure 8: Lithology, geochemical characteristics, and sedimentary facies in the Permian Qipan Formation of Akeqi outcrop section.  
Figure 9: Tmax distribution (**a**) and organic matter types (**b**) of Permian Qipan Formation hydrocarbon source rocks in the piedmont southwestern Tarim Basin.
## 3 Results and Analysis
### Influence of Different Forces on the Resonance Properties of Graphene with Vacancies
Actually, based on thin plate theory, only when the deflection extremum value is much less than the thickness of the thin plate can linear theory describe the mechanical response process of the thin plate. This result is in good agreement with the literature [32]. As for a single-layered graphene sheet, its thickness is only 0.335 nm, so the linear response range is narrow. In other words, the resonance behavior of nanoscale systems is different from the resonance behavior of macroscopic systems. When the external force is less than 15.91 nN, the resonance frequencies of both types of graphene structure show a consistent trend. The maximum frequency is 132.9 GHz. When the external force was less than 90 nN, the resonance frequencies of graphene with a single vacancy defect were greater than the resonance frequencies of graphene with edge defects. When the external force was less than 90 nN, the resonance frequencies of graphene with edge defects was greater and changed more rapidly. In this regard, a defect-driven resonator may be useful to identify such defects. In order to more accurately analyze the vibration process, the central atom of the graphene sheet was tracked. Absolute displacement of the tracking atom in the normal direction under different external loading is shown in Figure 8. It can be inferred that the graphene sheet demonstrated a sinusoidal periodic vibration process. Under the effects of different external forces, the amplitude and the vibration period of the graphene were different. As the stress increased, the amplitude of the graphene showed an increasing tendency.
## 6 Application Experiment
### Results and Discussion  
Figures 6(a), 6(a) and 6(a) respectively portray time series fitness changes of the applied filter sets obtained using GNP for Datasets 4, 5, and 6. In the first half of the transitions up to the tenth epoch, the fitness of our method was lower than the original recognition accuracy. Subsequently, fitness retained high scores with variation. For this experiment, the maximum generation is set to 50 epochs for the iteration of evolutionary learning. Fitness improved slightly in the last half of the transition. Figures 6(b), 6(b) and 6(b) portray the generated graph networks. Graph networks generated from Datasets 4, 5, and 6 respectively comprise 2, 4, and 4 branch nodes and 8, 9, and 12 processing nodes.  
Table 6 presents results obtained from comparison of recognition accuracies from \(f_{b}\) for original signals and filtered signals. As optimization results, the improved accuracies for Datasets 4, 5, and 6 are, respectively, 27.22%, 11.49% and 17.94%. The mean improved accuracy was 18.88% for the three datasets using our method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Before [\%]** & **After [\%]** & **Improvement [\%]** \\ \hline
4 & 39.64 & 50.43 & 27.22 \\
5 & 40.81 & 45.50 & 11.49 \\
6 & 40.35 & 47.59 & 17.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Improved recognition accuracies obtained from \(f_{b}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & \(O_{D}\) & \(O_{P}\) & \(O_{G}\) & \(O_{C}\) & \(O_{M}\) & \(O_{E}\) & \(P_{C}\) & \(P_{M}\) \\ \hline Setting value & 5 & 15 & 50 & 200 & 10 & 1 & 0.5 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Setting parameters.
## 7 Channel Estimation-Based DL Algorithm  
The transmitted frame involves data and pilot symbols. The applied channel model is assumed to be constant during one frame of pilot and data symbols and the channel coefficients are changing from one frame to another. In the proposed DL scheme, two major stages are implemented to achieve an effective DNN model for channel estimation. The first stage involves both training and testing, where the DNN model is trained and tested with the samples that are created based on variety of distinct Rayleigh channel coefficients [24,33]. In the second stage, the trained DNN model is utilized to explicitly predict the channel taps for each user and these estimated taps will be employed to recover the desired transmitted data symbols for each user.
## 4 Simulation and Experimentation
### Experiment  
The UR5E robotic arm test platform was set up as shown in Figure 11. The motion control commands were written on the host computer and passed to the robotic arm controller via Modbus-TCP. Then, the robotic arm controller converted the commands into electrical signals for the robotic arm body to finally realize the movement of the robotic arm according to the given time-displacement curve. In the upper computer, the robot arm 30003 interface signal was read by the script command to obtain the real-time joint torque of the robot arm. A comparison of the torque curves for each joint before and after optimization is shown in Figure 12.  
Figure 11: UR5E robotic arm test platform.  
Figure 10: Comparison of joint torque curves: (**a**) joint two; (**b**) joint three; (**c**) joint four; (**d**) joint five.  
As shown in Figure 8, DRCSO had higher search accuracy than CSO and could jump out of the optimal local solution. The ADAMS simulation showed that the moment continuously and smoothly changed throughout the motion of the robot, and there were no collisions in the robot arm body; therefore, it was stable and safe for a real machine test. Since the simulation process was in an ideal state, only the influence of weight was considered. The characteristics of the friction between the joints of the robot arm were not considered, and there were discrepancies between the modeling and the actual results. Therefore, the actual results were not exactly the same as the simulation results, but the trends for the whole torque were similar and showed smooth and stable characteristics. The reliability of the five-order B-spline function interpolation was proven. The running time was greatly reduced by the PDCSO algorithm, from 45 s before to 35.3 s after optimization (a reduction of 21.11%). The torque peak was smaller, the torque change was smoother and the unnecessary torque change was reduced. The torque change rates for joints two to five were reduced by 8.81%, 6.71%, 2.74% and 11.70%, respectively. The experiments made it clear that the optimized robot arm ran fast and smoother.
## 2 Materials and Methods
### Machine Learning Methodology
#### Pre-Processing Data  
A series of primary energy sources and needs were the dataset used to create different scenarios after the optimization step (Figure 6).  
Figure 5: ML methodology.  
(II) **Implementation of the Neural Network Time Series (NNTS)**  
For the ML analysis, an NNTS was applied in this research, considering a non-linear autoregressive exogenous model (NARX) which related the energy surplus/deficit (\(E_{s-d}\)) and the consumption energy (\(E_{c}\)). The NARX considered previous values of \(E_{s-d}\) for each time step and the current and past values of the function \(E_{c}\). This model can be explained considering the following formulation:  
\[E_{s-d,t}=F(E_{s-d,t-1},E_{s-d,t-2},E_{s-d,t-3},\ldots,E_{c,t},\ E_{c,t-1},\ E_{c,t-2},\ E_{c-3,\ldots})+ \varepsilon_{t} \tag{15}\]  
where \(\varepsilon_{t}=\) error term.  
Figure 5 illustrates the structure of the neural network time series (NNTS), which consists of a hidden layer and an output layer. Simulations were performed using MATLAB(r) R2024A.  
(III) **Evaluation of Statistical Measures**  
To assess the performance of the implemented NNTS, two statistical measures were considered in the analysis, as outlined below:  
* Mean square error \[MSE=\frac{1}{N}\sum\limits_{i=1}^{N}{(E_{s-d,t}-\widehat{E_{s-d,t}})^{2}}\] (16) where \(\widehat{E_{s-d,t}}\) is the predicted value of \(E_{s-d,t}\).
* Coefficient of determination (\(R^{2}\)) \[R^{2}=1-\frac{\sum\limits_{i=1}^{N}{(E_{s-d,t}-\widehat{E_{s-d,t}})^{2}}}{ \sum\limits_{i=1}^{N}{(E_{s-d,t}-\overline{E_{s-d,t}})^{2}}}\] (17) where \(\overline{E_{s-d,t}}\) is the mean of the \(E_{s-d,t}\).  
Figure 6: Shortcut of primary energy sources, battery storage solution, and excess and deficit energy.
## 2 Design Method, Study Case, Objectives and Assumptions  
### Design Steps  
In this section, the steps for setting up this work's two contributions are described and summarized in Figure 1, on which the main contributions are framed in blue. Firstly,measurements of the acceleration signals at the various sensor locations were carried out as in [7]; the measurements were carried out throughout January 2022. The accelerometer used for the measurements was an ACC 103 accelerometer marketed by Omega Engineering. These accelerometers can record vibrations of up to 500 g for frequencies up to 10 kHz [34]. The vibration data were taken every minute, and then spectral analyzes were be carried out in the MATLAB environment. According to [35], this analysis will define the piezoelectric transducer's resonant frequency. A mechanical-electric conversion model considering the system's dynamics was then set up to analyze the harvestable power levels by the various sensors deployed in the industrial process. The PHPV was set up to minimize the root mean square error. Comparing the performance of PHPV with previous linear predictors completed this first step of the design method.  
For the implementation of the HEBP, an energy cost per data bit was established, and a comparison between the available energy (predicted by the PHPV) and the expenditure was carried out to define the quantity of data that can be transmitted at the end of the measuring cycle.
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 2 Methods
### Study Case  
#### 2.3.1 Dam, Foundation, and Loads  
A real concrete arch dam with a height of 50.25 m and a crest length of 535.3 m was used as a model. The foundation was composed of igneous rocks, mainly grandiorites and dacites. Based on existing studies of this dam and due to the lack of more precise available data, the simplification that the foundation material is homogeneous was assumed and, consequently, the bedrock can be simplified to average values of the bedrock properties. As field borings do not analyze Poisson's modulus, an average value corresponding to that type of rock was assumed. The influence of variations in the Poisson's value on dam movements is demonstrated in the literature to be very low [17]. Likewise, in the absence of specific studies of the concrete of the dam, a concrete-concrete rough interface friction coefficient for dry conditions of 0.8 was assumed for concrete grades \(\leq\) C50/60. All other material properties were obtained from the dam reports (Table 1).  
Three reservoir levels were considered for the numerical model calibration: 49.24 m above foundation (Extreme Flood Level), 40 m, and 30 m. In addition, another two reservoir levels were considered to validate the performance of the method: 45 m (Maximum Normal Level) and 35 m. Lower levels were not simulated, taking into consideration that the upstream cracks do not open, and the behavior of the dam is almost identical to that of a non-cracked dam when the hydrostatic pressure is low.  
As this is an exploratory study aimed at evaluating the potential of the methodology, the objective is to see if the order of magnitude obtained falls within the range of what can be captured by the monitoring device. As this is the core objective, the study should not be made overly complex in order to achieve unnecessary precision. Therefore, it was assumed that the effect of a homogeneous thermal decrease on dam displacement is similar to the non-homogeneous decrease with the same mean value. Although the displacements of the dam will not be the real ones, they remain very close to the actual displacements. Therefore, the thermal stress condition consisted of a theoretical uniform temperature drop over the entire dam of 5 \({}^{\circ}\)C in relation to the temperature at the time of joint grouting. It was expected that an approximately linear relationship between the temperature increase and the crack opening (as verified in Section 3.1) would be found; therefore, the results for any temperature value can be estimated from the results obtained for a 5 \({}^{\circ}\)C drop.
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 5 Comparison of Online SOC Estimation before and after Model Parameter Optimization
### Comparison of the AUKF Estimation Accuracy before and after Optimization of the Model Parameters  
Substituting the optimized model parameters into the AUKF simulation model, the results are compared with the estimated values before the optimization of the model parameters. The simulation output is shown in Figure 14, and the error is shown in Figure 15.  
From the simulation results shown in Figures 14 and 15, the online estimation of the SOC is closer to the theoretical value after the reasonable optimization of the model parameters. The maximum SOC estimation error obtained after optimization of the model parameters is 1.22% and compared to the maximum error of 2.28% before parameter optimization, the error decreases significantly. It is verified  
Figure 14: SOC estimation before and after optimization of the model parameters.  
Figure 13: The model simulation errors.  
Figure 15: SOC estimation error before and after optimization of the model parameters.  
that the optimization process can significantly improve the online estimation accuracy of the AUKF based on the original data.
## 3 Numerical Experiments
### Robustness to Random Noise  
The signals in the numerical experiments were clear synthetic signals (Figure 22a). The SNR is defined as follows, where \(P_{signal}\) and \(P_{noise}\) are the power levels of the signal and noise, respectively, and \(A_{signal}\) and \(A_{noise}\) are the amplitudes of the signal and noise, respectively:  
\[SNR(\text{dB})=10\,\log_{10}\frac{P_{signal}}{P_{noise}}=20\,\log_{10}\frac{ A_{signal}^{2}}{A_{noise}^{2}} \tag{1}\]  
Figure 22: Imaging result using the clear signal (**a**) and noisy signal (**b**). (**c**) Kirchhoff migration imaging result using the clear signal. (**d**) DAS beamforming result using the clear signal. (**e**) MVSS beamforming result using the clear signal. (**f**) Kirchhoff migration imaging result using the noisy signal. (**g**) DAS beamforming result using the noisy signal. (**h**) MVSS beamforming result using the noisy signal.  
We added Gaussian noise to the signal (Figure 22b) to make the SNR 0 dB (the power of noise equals the power of the target reflection signals) and then generated images using Kirchhoff migration, DAS beamforming, and MVSS beamforming.  
In the Kirchhoff migration result with the noisy signal (Figure 22d), the interference caused by white noise can be observed as dots in the image. In the imaging result of DAS beamforming for the signal with Gaussian noise (Figure 22f), compared with the imaging result containing clear signals (Figure 22e), the white noise in the signal is detectable in the imaging result as well.  
Figure 22h shows the MVSS beamforming result for the signal with Gaussian noise. Compared with the Kirchhoff migration and DAS beamforming results, the influence of white noise is much less apparent for the MVSS beamforming results. However, relative to the imaging result with a clear signal (Figure 22h), although the noise is well suppressed, the brightness of the reflection interface decreases. The reason is that the energy of all signals increases when white noise is added, which narrows the energy gap separating the reflection signal and other signals from empty space. As a consequence, the energy of the reflective interface is relatively reduced. This phenomenon corresponds to the principle of keeping the effective signal gain unchanged while minimizing the energy of the whole image in the MVSS beamforming method.
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 4 Numerical Results  
To analyze the effect on single-lensed microlensing events in crowded star fields, we generate dense stellar fields with airy spread point sources. For a \(128\times 128\) pixel image, we generate star sources equal to 75% of the total number of pixels. Each star is chosen to have an airy disk radius between 1 and 5 in pixel units and amplitude ranging from 50 to 5000 units. The values are chosen randomly from a uniform random distribution. For an image of size \(n\times n\), where \(n=128\), we use \(m=0.25\times n\). Hence, instead of using 16,384 samples to obtain a 16,384 pixel image, we use 4096 samples. From Equation (1), \(y\) is of size \(m\times n\). For transient events, as portrayed by our modeling using single-lens gravitational microlensing, we want to reconstruct the time domain signal, representing the pixels experiencing a transient event. For CS, sparsity is essential; hence, in our simulations, we apply CS on differenced images, which are sparse. We difference the crowded star fields with a reference image. For understanding the effect of noise, the crowded stellar images have noise added. Thus, when differenced, noise as well as the microelensed star should be evident in the residual. For ideal comparison, we simulate the detector point spread function (PSF) to be the same for the observed and reference image. Noise is the only addition in the observed image. Hence, in the differenced image, the characteristics of the noise are preserved. In our simulations, we use orthogonal matching pursuit (OMP) to reconstruct CS measurements. Convex optimization algorithms provide better accuracy in reconstruction [5] than greedy algorithms. However, greedy algorithms are computationally less complex and can havefaster run times. Due to the latter advantage, we use greedy algorithms to run 100 Monte Carlo simulations for each set of parameters.  
Percent error as described in Equation (19) is used as a metric to quantify the accuracy in reconstruction of the image.  
\[\frac{|f_{diff}^{\prime}-f_{diff}|}{f_{diff}}\times 100\% \tag{19}\]  
where \(f_{diff}^{\prime}\) and \(f_{diff}\) are the total fluxes within the 3-pixel radius of the source positions of the reconstructed and original differenced images, respectively. The error at the source star is of critical importance as the photometric light curve is generated based on the magnification of the source star of interest.
## 5 Simulation Results  
Simulation results presented in this paper were obtained using the ns-2 simulator [40]. We modified the simulator to follow the upcoming WAVE standard closely (all these improvements and modifications of the simulator are publicly available at [[http://www.grc.upv.es/software/](http://www.grc.upv.es/software/)]([http://www.grc.upv.es/software/](http://www.grc.upv.es/software/))), extending it to implement IEEE 802.11p [5]. Mobility is performed with CityMob for Roadmaps (C4R) [41], a mobility generator that can import maps directly from OpenStreetMap.  
In our study, each simulation lasted for 120 s. In order to achieve a stable state before gathering data traffic, we only started to collect data after the first 60 s. All results represent an average over thirty executions with different random scenarios, presenting all of them a maximum error of 10% with a degree of confidence of 90%. We evaluated the following performance metrics: (i) the warning  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline roadmap size & 2,000 m \(\times\) 2,000 m \\ downtown size & 1,000 m \(\times\) 1,000 m \\ downtown probability & 0.5 \\ downtown attraction & 0.5 \\ warning packet size & 256B \\ normal packet size & 512B \\ warning messages priority & AC3 \\ MAC/PHY & 802.11p \\ maximum transmission range & 400 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used for the simulations.  
Figure 2: Scenarios used in our simulations as street graphs in SUMO: (**a**) fragment of the city of New York (USA); (**b**) fragment of the city of Rome (Italy); and (**c**) fragment of the city of San Francisco.  
notification time; (ii) the percentage of blind vehicles; and (iii) the number of packets received per vehicle. The warning notification time is the time required by normal vehicles to receive a warning message sent by a warning mode vehicle. The percentage of blind vehicles is the percentage of vehicles that does not receive the warning messages sent by the warning mode vehicles. These vehicles can remain blind because of their positions, due to collisions, or due to signal propagation limitations. Table 4 shows the parameters used for the simulations. The downtown probability and the downtown attraction are the probability that a vehicle is within downtown and the probability that a vehicle travels into downtown area, respectively.
# Research on Multi-Robot Formation Control Based on MATD3 Algorithm
## 5 Simulation Experiments
### Training Parameters' Setting  
This experiment used the MADDPG algorithm, the MATD3 algorithm, and the improved MATD3 algorithm for training. The network structure was the same for all three algorithms, and each network contained two hidden layers with 128 nodes in each layer. The activation function used was ReLU, and the optimizer was Adam. The maximum number of training episodes was 60,000; the maximum number of time steps per episode was 150; the learning rate was set to 0.001; the network parameters were updated every 100 steps; the discount factor \(\gamma\) was 0.95; the parameter \(\lambda\) was set to 0.2; the replay buffer size was \(10^{6}\); the training batch size was 1024; parameters \(\alpha\) and \(\beta\) were set to 0.6 and 0.5, respectively; the robot detection distance \(\rho_{d}\) was set to 0.2, and the collision safety distance \(\rho_{r}\), as well as the obstacle safety distance \(\rho_{o}\) were set to 0.1; the obstacle area parameters \(p_{o,\min}^{x}\), \(p_{o,\max}^{x}\), \(p_{o,\min}^{y}\), \(p_{o,\max}^{y}\) were set to \(-3,3,-2,2\), respectively; the locations of the target points were randomly generated within the range \((3,3.5)\).