esa-sceva/satcom-chunk-collection huggingface dataset:
# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
## 3 Faulty-Path Recovery Scheme Based on DDPG-LBBP
### DDPG-LBBP Agent Interacts with the Environment
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## 4 Experimental Results and Analysis
### Experimental Results  
The comparison results of the average precision value curve of the improved network model in this paper and the YOLOv5 model are as follows:  
In Figure 14, [EMAIL_ADDRESS] represents the average precision when the IoU value is 0.5, and [EMAIL_ADDRESS]:0.95 represents the average mAP when the IoU value is 0.5 to 0.95. As can be seen from the figure, the average accuracy of this algorithm is significantly improved compared to YOLOv5. When the algorithm in this paper iterates to the 40 th round, [EMAIL_ADDRESS] rises to about 0.8, and finally increases steadily to about 0.898, while the YOLOv5 algorithm iterates to the 60 th round. The [EMAIL_ADDRESS] only rose to 0.8 in the round, and finally stabilizes at around 0.842.  
As can be seen in Table 2, the algorithm performed well in the classroom behavior detection dataset, with [EMAIL_ADDRESS] reaching 89.8 and the average recall rate reaching 90.4. The detection accuracy of standing position is higher because the standing position occupied a more obvious image area, so the detection accuracy of standing position reached 93.3. The detection accuracy of listening and lying down behaviors is slightly lower than other behaviors because the area occupied by head is less.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **[EMAIL_ADDRESS] (\%)** & **Recall (\%)** \\ \hline Listening & 88.7 & 89.4 \\ Looking down & 86.9 & 85.8 \\ Lying down & 90.4 & 91.7 \\ Standing & 93.3 & 94.8 \\ Average for all categories & 89.8 & 90.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average precision and recall for each behavior.  
Figure 14: Comparison of the average precision value curve of the algorithm: (**a**) [EMAIL_ADDRESS]; (b) [EMAIL_ADDRESS]:0.95.  
As can be seen from Figure 17, the algorithm in this paper can still accurately detect a variety of classroom behaviors after adding Gaussian noise. As can be seen from Table 5, [EMAIL_ADDRESS] and Recall were still high even though the accuracy of the algorithm decreases slightly with the addition of noise, proving the robustness of our algorithm.
## Appendix A Subsurface Exploration by Various Design Codes  
Subsurface explorations shall provide the information needed for the design and construction of structures. However, in this respect, the current design codes are limited to some general recommendations, not indicating specific sampling locations that may effectively reduce statistical uncertainty.  
For example, for excavations in normal geological conditions protected by retaining structures, EN 1997-2:2007 [39] recommends that where the piezometric surface and the groundwater tables are below the excavation base, the investigation depth, \(z_{a}\), be greater than or equal to \(\max\{0.4h,\ (t+2.0)\ \mathrm{m}\}\) and where the piezometric surface and the groundwater tables are below the excavation base, \(z_{a}\), the recommendation is that it be greater than or equal to \(\max\{(H+2.0)\ \mathrm{m},\ (t+2.0)\ \mathrm{m}\}\); if no stratum of low permeability is encountered down to these depths, then \(z_{a}\geq t+5\ \mathrm{m}\).  
In turn, AASHTO [4] recommends a minimum of one exploration point per retaining wall; for retaining walls more than 30 m in length, investigation points spaced every 30 to 60 m with locations alternating from in front of the wall to behind the wall are recommended. For the minimum depth of exploration, AASHTO suggests that the investigation be extended at least to a depth below the bottom of the wall where the stress increases due to the estimated foundation load being less than 10% of the existing effective overburden stress at the depth and between 1 and 2 times the wall height.  
Appendix B Stability of Numerical Results (Number of Realizations Considered in the RFEM Models) and Effect of Element Size  
As shown in Figure 11, 3000 realizations can be deemed sufficient for the problem studied herein. Although the \(p_{f}\) versus number of realizations curves of the figure in question refer to \(d_{p}/H=0.667\) and (when not mentioned) to \(\mathrm{x}/H=0\), the same results are taken for other sampling locations.  
Regarding the element size, it is recommended that this be less than the half of the spatial correlation length, although an element size equal to the \(\theta\) does not introduce great error in the analysis (e.g., see the comparison chart of Figure A2 where the reference wall has been used).  
Figure A2: Effect of element size on the failure probability.
# Nascent Rice Husk as an Adsorbent for Removing Cationic Dyes from Textile Wastewater
## 3 Results and Discussion  
### Impact of Thermal Treatment on the Adsorption Capacity of Rice Husk  
Figure 1 depicts the adsorption capacity of RH under different temperatures from RT-700 \({}^{\circ}\)C with two initial MB concentrations (300 and 500 mg/L). At 300 mg/L, the MB adsorption by untreated rice was similar to that of RH treated at 75 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, respectively, but was larger than that obtained under high temperatures (300-700 \({}^{\circ}\)C). The low adsorption of RH treated under the high temperatures was also observed when the initial MB concentration was 500 mg/L. These outcomes revealed that pyrolysis of the RH under high temperatures did not improve the adsorption of MB. The RH treated at 75 \({}^{\circ}\)C showed higher MB adsorption than untreated RH and that treated at 100 \({}^{\circ}\)C. MB adsorption capacity slightly increased from 25.64 mg/g at RT to 27.58 mg/g at 75 \({}^{\circ}\)C.  
Lower temperatures resulted in higher adsorption efficiency. The decrease in adsorption capacity was ascribed to the certainty that, at higher temperatures, the RH surface melts; and due to high silica content, it is easily transformed to crystabolites, which cause partial blockage of pores [53; 54]. The specific surface area and pore and pore diameter were determined by Brunauer-Emmett-Teller analysis and those showed that the RH without thermal treatment had the highest surface-area (35.14 m\({}^{2}\)/g) and smallest pore diameter (5.11 nm). However, with increasing temperature, a smaller specific surface-area and bigger pore-diameter were obtained--as for RH-100 (24.63 m\({}^{2}\)/g and 6.34 nm) and RH-300 (18.38 m\({}^{2}\)/g and 7.56 nm)--due to high silica content, which contributes to agglomeration, reducing the porosity of RH and is consistent with other literature [55; 56]. Surface morphology of the RH treated at different temperatures was studied with FE-SEM; the results are depicted in Figure 2.  
All thermally treated RH was observed to have nodule-like surfaces structures; increased temperature caused their destruction, resulting in the reduction of specific surface-area.
# Design of a Rotary Transformer for Installations on Large Shafts
## 4 Design of the Transformer
### Grid-Search Optimization  
This first process aimed to study the behaviour for a large number of feasible geometrical configurations and to get as close as possible to the optimal result. Indeed, many combinations of the variables reported in Figure 8 should be analysed.  
To keep the execution time of the algorithm reasonably low, some constraints were introduced as follows:  
* the air gap between the two cores was set to \(g=1\,\)mm;
* the radius of the shaft was set to \(r_{\rm{s}}=90\,\)mm;
* the height of the core was set to \(h_{\rm{c}}=20\,\)mm.  
Moreover, the two winding slots were considered identical, i.e., \(w_{\rm{w1}}=w_{\rm{w2}}=w_{\rm{w}}\). Regarding the electrical part of the system, the numbers of turns of the coils \(N_{1}\) and \(N_{2}\) were set to be equal, \(N_{1}=N_{2}=N\), as well, whereas the transferred power on the secondary side of the transformer was constrained to be at least \(2\,\)kW. In light of these indications, the degrees of freedom of this study were the number of turns \(N\), the width of the winding slot \(w_{\rm{w}}\) and the thicknesses of the core in different points of the structure \(w_{\rm{c}}\), \(w_{\rm{c1}}\) and \(w_{\rm{c2}}\). In addition, Litz wires were considered in the very first stages of the design of the rotary transformer, assuming a cross section of \(0.127\,\)mm\({}^{2}\) for each conductor and a pessimistic filling factor equal to \(0.4\); thus, this optimization included the same variable \(N_{\rm{Litz}}\) for both windings. It can be noted that these widths can be constrained based on the geometrical structure of the device and the number of conductors in each slot so that only variations in \(w_{\rm{c}}\), \(N\) and \(N_{\rm{Litz}}\) are considered. Therefore, the following ranges of variation were exploited for this first optimization:  
* \(3\,\)mm \(\leq w_{\rm{c}}\leq 8\,\)mm, step between consecutive samples equal to \(0.2\,\)mm;
* \(100\leq N_{\rm{Litz}}\leq 250\), step between consecutive samples equal to \(5\);
* \(6\leq N\leq 13\), step between consecutive samples equal to \(1\).  
Figure 8: Geometrical parameters of the magnetic core.  
In detail, the grid-search foresaw the variation of the geometrical parameters in the aforementioned pre-determined samples at the beginning of each optimization step. Then, a first analysis in FEMM allowed us to evaluate the inductance matrix of each configuration. Based on these data, the equivalent circuit reported in Figure 3 was exploited to compute the current that should flow in each winding and the power transfer, as well (allowing us to check the corresponding limits on \(j\) and \(P_{2}\), as well). Finally, a new analysis in FEMM was run to obtain the information regarding the possible saturation of the ferrite core. Hence, the best candidate showed the parameters reported in Table 1. The optimization procedure ran in about 9287 s on a desktop PC with Intel(r) Core(tm) i7-8700 processor at 3.20 GHz and with 16 GB RAM.
First, in MG the rolling/sliding contact, the possibility of having multiple pairs of meshing teeth leads to the contact force to vary in both direction and amplitude [26; 27]. This leads to have, in the fillet region, a multiaxial and non-proportional stress history [35]. Conversely, in STBF tests the applied forces have a constant direction and vary in amplitude (sinusoidally). Therefore, such amplitude trend fails to reproduce the uneven force sharing due to the engagement or disengagement of additional pair of meshing teeth [26; 27]. In addition, in STBF tests, the angle between the loaded tooth axis (\(\alpha_{Fen}\)) and the applied forces can be considerably different from the one in the Outer Point of Single pair tooth Contact (OPSC) that, in turn, according to the standard, it is exploited to calculate \(\sigma_{F}\) on MG. Second, in STBF tests, to avoid undesired movement of the specimen, a compressive load is usually present. A common ratio between the minimum and maximum applied load is R = 0.1, e.g., the STBF tests described in [29; 36; 37; 38; 39]. It modifies the average stress in fillet region since, conversely, in MG the above-mentioned ratio in usually equal to zero [19; 20; 40; 41]. Based on the theoretical differences before mentioned and experimental results aimed at estimating the difference between \(\sigma_{FlimMG}\) and \(\sigma_{FlimSTBF}\), it is possible to state that a correction coefficient is needed since \(\sigma_{FlimSTBF}\
eq\sigma_{FlimMG}\)[42; 43]. To consider this effect, [PERSON] in [42] and [PERSON] in [43] suggested to exploit a constant correction coefficient (\(f_{korr}=0.9\)) defined as \(f_{korr}=\sigma_{FlimMG}/\sigma_{FlimSTBF}\). However, experimentally, \(f_{korr}\) has been estimated without investigating the effect of gears geometry and material [26; 42; 43]. Numerically, in [26; 44] the scholars highlighted that considering this coefficient constant is inaccurate. Indeed, preliminary results show that varying materials, load ratio (R), and some gear design parameters, \(f_{korr}\) varies from 0.65 to 1.1. However, in [26; 44] the relationship between the gear design parameters and \(f_{korr}\) has not been systematically studied. Therefore, in the present paper, a specific Design of Experiment (DOE) was created to study the effect of specific gear design parameters on \(f_{korr}\) using the numerical approach proposed in [26; 44]. In Section 2, a background related to the implementation of multiaxial fatigue criteria on gear and to the mathematical model for implementing the Findley criterion on Finite Element Models (FEM) results is presented. In Section 3, the method used to establish the gear geometries to be simulated and the settings of the FEM simulations are shown. The statistical elaboration of the results and the related discussion are presented in Section 4. Conclusions can be found in Section 5.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 3 GNSS Performance Characterization
### Error Modelling
#### 3.1.6 Building Facade Reflection  
For the case of reflection from building facades, the receiver-image theory used in modelling the ground reflection is applied here as well. The employed ray-tracer is used to model multipath and NLOS conditions and quantify the corresponding delays and amplitudes. In order to verify if a multipath/ NLOS condition exists for a given satellite, reflector and receiver configuration, and to compute the corresponding reflectance and path-length difference, the following steps are applied:  
* Compute the position of image of the receiver antenna in the reflector;
* Compute the vector from the satellite to the receiver image;
* Inspect whether the vector intersects the reflector. If such an intersection exists, a reflected signal exists in addition to the direct-path signal;
* If a direct ray exists between the receiver and satellite, the reflected ray is a multipath ray; If there is no direct line of sight, the reflected ray is an NLOS ray.  
A similar approach has been employed in [32; 33]. The building facades are discretised into triangular elements, and the Moller-Trumbore [34] algorithm commonly used in computer graphics applications is used to check for the intersection of rays with triangular elements. This type of ray-tracing model has previously been experimentally verified in [35]. More generally, these asymptotic methods are valid when the size of the obstacle is larger than the wavelength of the impinging wave [18]. A priori knowledge of the urban environment is required, and can be obtained from aerial views using stereo-photogrammetry, from digital cadastre data or by digitising analogue maps (as is the case for most radio channel modelling applications). The attainable accuracy of urban databases is typically on the order of 0.5 m (1\(\sigma\)) for the horizontal dimension, and is usually larger for vertical dimensions. It is also a common practice to prune unnecessary geometrical features from the database and to simplify the urban geometry into canonical shapes to minimize required computational resources. Accurately assigning electromagnetic parameters for each individual reflector surface is impractical and the commonly employed solution is to adopt effective electromagnetic parameters, i.e., parameters representative of the overall behaviour of the channel. The following parameters (\(\epsilon_{\tau}=5,\ \sigma=10^{-2}\)) are usually employed for typical building walls in European cities [36]. A more conservative approach is to assume reflection from a metal surface in all instances as these are more common in urban environments (bridges and metal-clad buildings) and represent the worst-case scenario. This assumption means that virtually no attenuation occurs at the signal-reflector interface.
# Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension
## 4 Conclusions  
According to the different SFD characteristics of the signal and noise, a double-threshold CSS algorithm based on SFD is proposed in this paper. Theoretical analysis and simulation show that the proposed scheme can sense some modulation signals that cannot be sensed by the box dimension; meanwhile, it is insensitive to noise uncertainty. Considering the characteristic of the noise SFD fluctuating within a certain range, the double-threshold is adopted in the multi-user cooperation sensing. Simulation experiments are under AWGN environment without channel fading effects. The results show that the SFD algorithm can achieve high detection probability at low SNR region and the double-threshold cooperative sensing effectively improves the accuracy of CSS. The proposed algorithm achieves a stable detection result in the case of noise uncertainty. In conclusion, the  
Figure 8: The influence of noise uncertainty on detection probability.  
Figure 7: Detection performance comparison of the proposed algorithm with the single threshold detection.  
double-threshold CSS algorithm based on SFD is suitable for the fast blind spectrum sensing with low SNR and noise uncertainty, at the same time obtaining better detection results.  
This research was supported by the National Natural Science Foundation of China (Grant No. 61701134), the National Natural Science Foundation of China (Grant No. 51509049), the Natural Science Foundation of Heilongjiang Province, China (Grant No. F2017004), National key research and development program of China (Grant No. 2016 YFF0102806).  
The idea of this work was proposed by [PERSON] and [PERSON]. [PERSON] and [PERSON] performed the experiments and analyzed the simulation results. [PERSON] assisted in the research work and wrote the paper.  
The authors declare no conflict of interest.
###### Abstract  
The estimation of robot pose and pipe diameter is an essential task for reliable in-line inspection (ILI) operations and the accurate assessment of pipeline attributes. This paper addresses the problem of robot pose and pipe diameter estimation for natural gas pipelines based on 3D time-of-flight (ToF) sensors. To tackle this challenge, we model the problem as a non-linear least-squares optimization that fits 3D ToF sensor measurements in its local coordinates to an elliptic cylindrical model of the pipe inner surface. We identify and prove that the canonical ellipse-based estimation method (C-EPD), which uses a canonical residual function, suffers from bias in diameter estimation due to its asymmetry to depth errors. To overcome this limitation, we propose the robust and unbiased estimation of pose and diameter (RU-EPD) approach, which employs a novel error-based residual function. The proposed function is symmetric to depth errors, effectively reducing estimation bias. Extensive numerical simulations and prototype pipeline experiments demonstrate that RU-EPD outperforms C-EPD, achieving an at least six times lower estimation bias and a 2.5 times smaller estimation error range in pipe diameter and about a 2 times smaller estimation error range in pose estimation.  
Keywords:in-line inspection; in-pipe robot; pose estimation; pipe diameter estimation; estimation bias; depth error
# Fourier Singular Values-Based False Data Injection Attack Detection in AC Smart-Grids
## 3 FDIA Detection
### Fourier Singular Values  
#### 3.1.1 Theory of FFT  
Fourier transform is a way to convert signal amplitude that is signal detail based on spectrum or frequency domain. The periodic signal is able to analyze based on Fourier series, whereas the aperiodic signal is converted according to Fourier transform [44].  
The FFT presents an efficient approach to convert information from the domain of time to the domain of frequency. Generally, the required data to transform includes N uniformly spaced spots \(x_{j}=x\left(\mathfrak{f}_{j}\right)\), where \(N=2^{n}\) (n is an integer value), and \(\mathfrak{f}_{j}=j\cdot\Delta t\) where \(j\) has a range from zero to \(N-1\).  
The discrete Fourier transform can be expressed in several ways. FFT is a fast and efficient calculation approach, using a computer to calculate the Fourier discrete conversion. Based on the FFT definition, the sequence of finite length is able to transform into a frequency domain. At time of applying computer spectrum assessment, it is frequently necessary to discrete the signal processing, in the discretization process, the spectral leakage is caused by the processing of the signal with finite length, time domain and also frequency domain. All cycle sampling procedures must be performed to avoid leakage, integer sampling, or power for 2; therefore, this paper chooses the eight-cycle signal spectrum analysis length.  
According to the References [45; 46], the common form of this approach is given as follows:  
\[X_{k}=\sum_{j=0}^{N-1}x_{j}\exp\left(-2\pi i\frac{j}{N}k\right);\ i=\sqrt{-1} \tag{6}\]  
where in the Equation (6), \(k=-\frac{N}{2},\ldots,-1,\ 0,\ 1,\ \ldots,\frac{N}{2}-1\), and \(x_{j}\) gives the time domain data, \(X_{k}\) defines their frequency domain representation. The FFT conversion process scheme, or other comparable approaches, makes FFT extensively applicatory because the number of calculations is reduced from \(n^{2}\) to \(n\) log(n), which greatly reduces the time of computation.  
Frequency data is usually shown from one of these two types: a power spectrum or an amplitude range of spectrum. The amplitude spectrum defines usually as Equation (7):  
\[A_{k}=\frac{2}{N}\cdot|X_{k}| \tag{7}\]  
While the power spectrum is usually stated using Equation (8):  
\[P_{k}=\frac{1}{N}\cdot|X_{k}|^{2} \tag{8}\]where k = 0, 1,..., N/2.