esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiment and Results  
When the acceleration excitation is \(4\sin(2\pi\times 5t)\) m/s\({}^{2}\), the excitation signal and acceleration response in the time domain are shown in Figure 9. It can be seen that the acceleration response is seriously distorted, due to the effects of nonlinearities in the hydraulic servo shaking table. Since higher harmonics are less dominant in all harmonics, the harmonic estimation is usually up to sixth harmonic in practical applications. The amplitude frequency diagram computed by FFT in Figure 10 indicates that the fundamental response is at 5 Hz, the second harmonic is at 10 Hz, the third harmonic is at 15 Hz, and so on. The total harmonic distortion (THD) is a very important criterion to evaluate its waveform performance and it is given by  
\[\mathrm{THD}=\frac{\sqrt{A_{2}^{2}+A_{3}^{2}+\ \cdots\ A_{N}^{2}}}{A_{1}} \times 100\% \tag{14}\]  
where \(A_{1}\) is the amplitude of the fundamental, \(A_{2}\) the amplitude of the second harmonic, \(A_{3}\) the amplitude of the third harmonic, and so on.  
Figure 8: Estimation of error.  
Figure 7: Estimation of phase.  
The result of THD based on FFT is calculated off line as shown in Table 3. It can be seen that the fifth harmonic plays a vital role in all harmonics, and the fourth harmonic is the least harmonic. The THD is 22.22%, which is a large value.  
Figures 11 and 12 represent the amplitude and phase estimation results respectively using simulated annealing and RLS algorithm of the given acceleration response. As can be seen from the figures, RLS based estimation method has large estimation error and oscillatory in the initial stage of estimation process, though it converges to the stable value. From the perspective of harmonic estimation, the estimated algorithm by simulated annealing is better than RLS in real time performance and convergence performance. In addition, each harmonic can be directly extracted from the distortion  
Figure 10: The response in frequency domain.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**THD** & \multicolumn{5}{c}{**Harmonic Amplitude (m/s\({}^{2}\))**} \\ \hline
22.22\% & \(A_{1}\) & \(A_{2}\) & \(A_{3}\) & \(A_{4}\) & \(A_{5}\) & \(A_{6}\) \\  & 3.9830 & 0.4162 & 0.3510 & 0.1971 & 0.5824 & 0.3306 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total harmonic distortion analysis result.  
Figure 9: The response in time domain.  
signal. Its results are shown in Figure 13, from which it can be seen that there are large fluctuations at the beginning of the estimation, but converge quickly to its steady states within 0.5 s.  
Figure 11: Estimation of amplitude.  
Figure 12: Estimation of phase.
## 3 Sampling and Methodology  
### Samples and Analytical Techniques  
A total of 753 sandstone samples from the Eocene sandstone reservoirs were analyzed in the laboratory of China University of Petroleum (East China). The samples were first cleaned with petroleum ether for removing the oil and then dried for 24 h under a temperature of 95 \({}^{\circ}\)C. Core plug samples (50 mm long and 25.4 mm in diameter) were drilled for helium porosity, air permeability, MICP, and NMR measurements. Core chips were used to make thin sections for optical microscopic analysis and SEM analyses. The sample preparation procedure conforms to the protocol of GB/T23561.1-2009 \"Methods for Determining the Physical and Mechanical Properties of Coal and Rock\". The experimental procedure and workflow for pore structure evaluation are shown in Figure 2.  
Figure 1: (**A–C**) Geological location of the study area in the Dongying Depression, Bohai Bay Basin, China.  
Helium porosity and air permeability measurements were conducted on 753 core plug samples at a net confining pressure of 400 psi. Optical petrographic analysis was conducted on 327 thin sections with 500 point-counting per sample to identify pore throats and petrological characteristics of the reservoir sandstones. SEM analysis was performed on 103 samples to identify pore throat types, sizes, and contact relationships between the pore throat and clay minerals. The pore structures and pore throat size distribution of 23 core plug samples were analyzed by MICP. The maximum mercury inlet pressure is 116 MPa, corresponding to a minimum throat radius of 0.0063 \(\upmu\)m.  
NMR \(T_{2}\) spectra of 21 core plug samples were measured under 100% water-saturated and centrifuged states. The samples were saturated with sodium chloride solution with a salinity of 12,000 mg/L. The experimental temperature and humidity used are 25 \({}^{\circ}\)C and 50%, respectively. Firstly, the samples were saturated for 24 h with sodium chloride solution under a net pressure of 30 MPa. Then, NMR \(T_{2}\) spectra under a water-saturated state were extracted. The echo interval, waiting time, and the number of echoes were set to 0.21 ms, 6000 ms, and 2048, respectively. The samples were centrifuged at 6000 r/min to remove free water in the core plugs, and then, they were measured again to obtain NMR \(T_{2}\) spectra.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 2 Related Work
### Traffic-Aware Routing
Finally, reinforcement learning is employed to establish a network among vehicle nodes within the road, thereby significantly enhancing the stability and reliability of message transmission.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 2 Thermal Modeling
### Radiation Modeling
As a general recap of the method workflow, the pipeline is summarized in Figure 4, where it is possible to see that the method starts from the geometry of the object, the thermal simulation then produces a mesh and a temperature field of the object, and they are both used to compute the view factors given the camera attitude and position. Once the view factors are computed, it is possible to compute the actual heat flux received by the camera using Equation (10) and generate the image. More details on the actual pipeline implementation and the respective results are presented in the next sections.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods
### Field Data  
Separately from agricultural and horticultural crops, the area under cultivation and the type of crop harvested were extracted from statistics from the Ministry of Jihad Agriculture and the Agricultural Jihad Organization of Zanjan region, as well as face-to-face interviews with Jihad Agricultural experts. More than 92,000 hectares of agrarian land will be covered by the agricultural sector with the cultivation of 68 types of crops and 21 types of horticultural crops in the region, and the volume of runoff produced by the region amounting to 2.2 billion cubic meters, which was constructed by several large and small dams or is under construction soon.
# Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder
## 2 Related Work
### Deep Learning-Based Approaches  
Recently, deep learning has been successfully applied to the task of anomaly detection. [PERSON] et al. [33] proposed two deep learning-based anomaly detection models based on an LSTM autoencoder and a 2-D convolutional autoencoder, respectively. First, the features are enriched by the statistical aspect. After that, the anomaly score is obtained by the difference between the enriched features and reconstructed features from the autoencoder. The feature vectors are ranked according to the anomaly score, and the top 5% of the feature vectors are considered to be outliers. [PERSON] et al. [34] proposed an unsupervised time-series anomaly detection method called DeepAnT, which consists of an anomaly detector and a time-series predictor. In the anomaly detector, the Euclidean distance is used for calculating the anomaly score from the predicted value and actual value. In the time-series predictor, a convolutional neural network (CNN) is used for the time-series regression problem. [PERSON] et al. [35] presented a model called DAGMM that uses autoencoder and Gaussian Mixture Model (GMM) simultaneously. First, an autoencoder is used to reduce the dimension of input data to obtain latent representations. GMM is then used to estimate the density of the representations. However, the drawback of this model is that it ignores the inherent temporal dependence of time series data. [PERSON] et al. [36] suggested that the performance of the recurrent neural network (RNN) can be improved by stochastic variables which can capture the probability distributions of time series. [PERSON] et al. [37] proposed a method that combines LSTM and Variational autoencoder (VAE), where the feed-forward layers in a VAE are replaced with LSTM layers. However, the basic assumption for this method is that the time series follows a particular statistical distribution and is linear, making it inapplicable to many practical problems. [PERSON] et al. [38] presented an LSTM-based EncoderDecoder model similar to seq2 seq models for anomaly detection in time series. The model aims to reconstruct normal time series data and uses reconstruction errors for detecting anomalies. [PERSON] et al. [39] presented a method to detect spacecraft anomalies. They use LSTM for multivariate time series prediction. The anomalies are detected based on prediction errors.  
In this work, we tackle the problem of how to automatically detect anomalies from a univariate time series dataset from the BOU data of the metro vehicle. In our anomaly detection framework, we adopt an LSTM autoencoder model similar to [39]. The difference is that our LSTM autoencoder model uses dropout as a stochastic regularization technique. As a result, our proposed model can deal with statistical noise which often occurs in the BOU data on the metro vehicle in the training data, which results in better performance compared to the model without dropout techniques.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Media Access Control Layer
#### 3.2.3 Coverage and Capacity  
NB-IoT support for extended coverage of up to 164 dB of maximum coupling loss is to enable the technology to be used for cellular IoT services, especially for applications that are in hard-to-reach areas. Its narrow bandwidth and support for repetition are the key features to enable the enhanced coverage.  
In [10], the authors simulated and analyzed the NB-IoT wide-area rural deployment and deep indoor urban deployment by using the network parameters of one metropolitan operator. Their work showed that NB-IoT devices could still transmit and receive data at an MCL of 167 dB, which is 3 dB higher than the 3 GPP's 164 dB of MCL limit set. Furthermore, in different indoor scenarios, even with an addition of 30 dB as penetration loss, NB-IoT had better outage probabilities as compare to another LTE LPWAN technology (eMTC). For outdoor and light indoor conditions with an additional 10 dB penetration loss and an average intersite distance of 2.8 km, NB-IoT had less than 0.1% of outage probability. However, despite the varying additional penetration losses of 10 dB, 20 dB and 30 dB, their simulation does not consider the impact of features such as mobility, CFO, lower power class on the achieved MCL.  
In [11], the authors showed that for the maximum number of repetitions (128 times), with 15 kHz and 3.75 kHz subcarrier spacing, coverage of up to 170.2 dB MCL and 174.2 dB MCL could be achieved, respectively. The work concluded that the evaluations show that NB-IoT could provide up to 20 dB coverage enhancement in various deployment scenarios as compared to legacy LTE. Similarly, the work did not study the impact of mobility and weak channel estimation quality to the achieved MCL.  
In [76], the authors proposed two less complex scheduling schemes (compared to brute-force) that can be used NB-IoT. The first proposed scheme is called the preconfigured access scheme and the second is the joint spatial and code domain scheme. Their simulation performance results (spectrum efficiency, number of active devices as well as low collision rate achieved) by the two low complex schemes were found better when compared to the ones that can be achieved by the brute-force scheme.  
In [78], the authors proposed a specific unidirectional system to study the coverage enhancement by using satellite network i.e., LEO constellation. Their proposed model with the mathematical derivations shows that NB-IoT could achieve the 20 dB more than LTE achieved MCL and could still operate according to Release 13 standards. From their results, it is seen that the packet error rate (PER) of the transmitted signal is distorted by Doppler spread. However, the model does not consider the clock synchronization between NB-IoT device and satellite, which can lead to performance degradation especially caused by the CFO or the sampling rate mismatch. Additionally, the work did not consider the maximum achievable throughput when their system is employed to comment on the effectiveness of the techniques as compared to terrestrial NB-IoT deployment.
## 3 Computational Methods
### Meshing  
The meshes used were created in ANSYS ICEM [20], where the pipe was divided into three sub-regions--the inlet region, the injection region, and the temperature collection region--as shown in Figure 6. To mesh the main flow, the meshing was gradually transitioned along the radial direction with three separate sections, as shown in the top-right corner of Figure 6. The radial center of the main pipe has a square cross-section that transitions to a circular mesh, which expands and then contracts as the wall is reached. At the injection pipe, vortex shedding and a wake likely form as the mean flow passes, resulting in careful consideration of the meshing in this region. A detailed image of the meshing near the injection pipe is shown in Figure 7. Since an inserted cylinder represents the injection pipe here, hexahedral meshing was difficult to apply in this irregular geometry. Instead, tetrahedral meshing was applied specifically in this region, as shown in Figure 7. The temperature collection region mesh was created to be sufficiently resolved to accurately represent the temperature response in the sensors by resolving the flow.  
Figure 5: One injection period for the injection pipe boundary condition in CFD simulations.  
Figure 6: One example of a meshing model for low-flow-rate simulation.
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
## 3 Results and Discussion
### The Water Absorption  
A water absorption study was performed for unmodified gelatin as well as for hybrid monoliths, however samples prepared from unmodified biopolymer disintegrated in water into small parts at the beginning of the test and a precise calculation was impossible in this case. On the contrary, it was observed that hybrids obtained with gelatin:siloxane mass ratios 1:1 and 2:1 did not fall into pieces during the performed procedure. Therefore, the results showed that the chemical modification of biopolymer with an appropriate amount of the siloxane resulted in an enhanced stability in aqueous solutions of the hybrid adsorbents. The water absorption of gelatin-siloxane monoliths was about 1800% for GS 1:1 and 1900% for GS 2:1. The obtained values are similar to the results described by [PERSON] at al. [34]. They observed 1700% of water absorption in case of porous chitosan-gelatin monoliths, as well as 2700% and 3100% when 1% and 4% of grafene oxide was incorporated into the monoliths, respectively. They assume that the increase in water absorption is due to the presence of hydroxyl and carboxyl groups on grafene oxide, which increase the hydrophilicity and water permeability of porous monoliths and participate in electrostatic interactions with chemical groups of chitosan. In case of the hybrid monoliths described in this study, the varied content of siloxane did not essentially affect water absorption values. This is probably due to the fact that functional groups of the siloxane have reacted with gelatin as well as the hydrophobic nature organomorphed silice. Interestingly, we also observed changes in the shape and size of the gelatin monoliths, which significantly increased their volume. Notably, the hybrid monoliths GS 1:1 started to disintegrate after two days of soaking in solution during shaking and after six days without shaking, which is similar to chitosan-gelatin monoliths' stability (five days) described by [PERSON] at al. [34]. It means that gelatin-siloxane monoliths show similar wet-strength and wet-state stability to chitosan-gelatin monoliths. Therefore, in order to investigate the resistance of hybrid adsorbents, further experiments were performed with various types of immersion liquid as well as various amount of the siloxane incorporated into gelatin matrix.
## 4 Control System Design
### The MPPT Controller  
The utilization of the system can be greatly increased using the MPPT controller. There are many techniques for applying the MPPT [27; 28; 29]. In this research, a simple approach is used, named \"Perturb and Observe\". The procedure of this approach is as follows:  
* An initial value for the duty ratio is suggested and applied to the boost converter.
* Then, the current and voltage of the PV are sensed.
* The change in the PV power is calculated [29].
* The output of the controller is the boost converter duty ratio to obtain the MPPT state.
# Immunohistochemical Expression of ABCB5 as a Potential Prognostic Factor in Uveal Melanoma
## 3 Results  
### Clinico-Pathological Characteristic of Uveal Melanomas  
The study was conducted on of 55 patients, 28 of whom were males and 27 women; median age was 67 years (range 29-85). 40 melanomas were localized only in the choroid, while 15 affected both choroid and ciliary body; extrascleral involvement was present and histologically confirmed in only one case. Regarding the histotypes, 15 cases were classified as epithelioid cells, 12 as spindle cells, while 28 cases were diagnosed as mixed type UM with both epithelioid and spindle cells. Considering the \"TNM classification of malignant tumours\", pathological T stage was: pT1a in 7 patients, pT1b in 1 patient, pT2a in 21 patients, pT2b in 8 patients, pT2d in 1 patient, pT3a in 8 patients, pT3b in 5 patients, pT4a in 1 patient and pT4b in 3 patients. Liver metastasis were present in 23 patients. Median follow-up period was 60 months (range 12-138 months). Out of 32 patients without metastatic disease, 17 were males and 15 females; the median age was 64 years (range 29-84). Considering 23 patients with metastatic localization of primary UM, 11 were males and 12 females; median age was 72 (range 50-85). 13 of 23 patients died during the follow-up period for disease progression (Tables 1 and 2). Comparing patients without metastasis and those with metastasis, no significant difference was seen in median age, location of the melanoma (choroid or choroid/ciliary body), tumor thickness, cell type, extrascleral extension, pathological pT stage; patients who developed metastasis had melanoma with greater median largest diameter (15.6 mm vs 11.9 mm, \(p\) = 0.007), and higher median ABCB5 expression (9 vs 3, \(p\) = 0.030); they had lower median disease free survival (25 months vs 81 months, \(p\) < 0.001). (Table 3) \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  
\end{table} 
Table 1: Demographics, tumor parameters, disease free time, follow-up and ABCB5 expression in primary uveal melanoma without metastasis (\(n=32\)).
# Development and Simulation of Motion Control System for Small Satellites Formation
## 8 Simulation Results
### Simulation the PD Laws and PDC Control Laws  
The coefficients for the PD laws and PDC control laws (in SI units) are \(K_{p_{x}}=K_{p_{y}}=K_{p_{z}}=0.025,\quad K_{d_{x}}=K_{d_{y}}=K_{d_{z}}=15\). They are obtained by the trial-and-error method. The criterion is the positioning accuracy after two orbits around the Earth. For PDC, the consensus algorithm coefficients \(\gamma_{0}=0.01\) and \(\gamma_{1}=0.04\) were additionally set for the proportional and differential components, respectively. These coefficients are the same for the \(x\), \(y\), \(z\) channels of all 8 satellites. The communication graph is plotted in Figure 2(a).  
The simulation results for the flight of a group of 8 satellites with PD control are depicted in Figures 4 and 5. The time histories of errors and control signals along \(x\), \(y\), \(z\) axes are shown in Figure 6. Figures 7 and 8 demonstrate the simulation results of the motion of a group of 8 satellites with PDC control. The time histories of errors and control signals along \(x\), \(y\), \(z\) axes are shown in Figure 9.  
Comparison of simulation results for PD and PDC laws is given in Table 2.
## 4 Results
### Comparison among Different Classifiers
#### 4.2.2 Support Vector Machine  
The SVM method [42] is used in this investigation as a term of comparison, though it is known to be more time consuming than kNN. In fact, it basically handles binary classes and, in case of multi-class problems, the method has to convert them to a combination of problems, each one characterized by binary classes [43].  
With this algorithm, the hyperparameters are: the kernel function related to the type of support vector classifier, which may be linear, quadratic, Gaussian, or cubic; the multi-class method, which may be one-vs-all or one-vs-one, the latter one expected to be more accurate but also more time consuming [43]; the kernel scale, to affect the input data by means of the distance between the minimum and the maximum values of a given feature before the application of the kernel function, which has been always heuristically selected. The outcomes of the SVM classifier are shown in Table 4. Training and testing accuracies respectively result to be in the ranges 86.1-95.4%, and 91.7-94.4%, while the computing time ranges in the interval between 134 s and 2030 s.  
Overall, the accuracy does not improve in comparison to the kNN one, while the computing time results to be far larger, independently of the use of any optimizer to enhance the performance, see models S1, S2 and S3. In model S4 the same feature exclusion discussed for the kNN algorithm is adopted: in comparison to model S5 the training accuracy remains the same, while the testing one slightly decreases down to 91.7%. If PCA is plugged in with 95% variance, the accuracies decrease to 86.1% and 91.7%, see model S5. If some features are also excluded, see model S6, the training accuracy becomes slightly larger than the model S5 one. As already mentioned, PCA tends to reduce the dimensionality of the problem to increase the efficiency of the algorithm, and thus it decreases in principle the accuracy, while speeding up the training process.