esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Scenarios
### Case Study 2: Interactions in a Narrow Area
#### 5.2.1 Simulations Using Random Walk/SLAW  
In this scenario, nodes adhering to a Random Walk or SLAW model have shown an almost identical energy consumption and network behavior. This happens because the working area is too long and therefore the node encounter rate is really low. This means that most of the time the nodes are isolated, therefore they do not have to disseminate TC messages and thus they save energy. Evidence of this situation is shown in Figure 12, where we can see that the nodes have low energy consumption, regardless of the network node density.  
The network degree shown in Figure 13 also supports this finding. Therefore we can say that the shape of the working area definitively affects the node encounter rate, and the node energy consumption (RQ5). This consumption and the benefits of using the predictor tend to increase with the network degree. Contrary to the results obtained in the square area, the consumption dispersion does not depend on the node speed and the energy saved is not related to the dispersion of the node energy consumption.  
The simulations in this scenario allow us to realize that the key aspect to be considered when analyzing the energy consumption (or the node meeting rate) is the network degree instead of the node density. In narrow areas, a higher number of nodes will be close to a border, therefore part of their communication threshold will be outside the interaction area. This means that the potential influence of these nodes would be different depending on where they are located. Therefore, the metric to be  
Figure 11: Energy consumption _versus_ network degree when nodes follow a RPGM model.  
considered when determining node meeting rates and energy consumption (during the TC process) should be the _network degree_ or the _density of node communication areas_. Each of these areas is defined as the physical space covered by the communication threshold of a node.  
In order to determine how narrow a working area is, it is important to consider the relationship between the node communication threshold and the shortest distance between the borders of the considered area. In case of irregular areas, they must be split in regular sub-areas to analyze the node energy consumption.
# A New Device to Test the Bending Resistance of Mechanical Endodontic Instruments
## 3 Results  
The results of the present study are shown in Table 1 for the instruments with a traditional NiTi alloy, ET, and PTU; Table 2 shows the results for the heat-treated instruments, ETP, and PTG.  
Statistical analysis showed significant differences among:  
* Each instrument alone (S1, S2, F1, F2) at each specific point (3, 6, and 9 mm);
* Each instrument of the series (S1, S2, F1, F2) comparing ET with PTU;
* Each instrument of the series (S1, S2, F1, F2) comparing ETP with PTG.  
The means and standard deviations were calculated and the data analyzed using the Mann-Whitney U-test for bending resistance comparisons at 3, 6, and 9 mm for each PTU and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{PTU} & \multicolumn{4}{c}{ET} \\ \cline{2-10}  & **S1** & **S2** & **F1** & **F2** & **S1** & **S2** & **F1** & **F2** \\ \hline
3 mm & 101.33 \(\pm\) 3.80 & 151.33 \(\pm\) 579 & 226.17 \(\pm\) 6.84 & 256.33 \(\pm\) 5.24 & 125.00 \(\pm\) 5.7 & 228.83 \(\pm\) 7.3 & 317.50 \(\pm\) 4.0 & 329.17 \(\pm\) 3.9 \\
6 mm & 249.00 \(\pm\) 10.28 & 381.50 \(\pm\) 7.15 & 432.50 \(\pm\) 9.98 & 434.17 \(\pm\) 8.35 & 528.67 \(\pm\) 8.0 & 554.33 \(\pm\) 9.1 & 556.17 \(\pm\) 5.8 & 566.33 \(\pm\) 7.8 \\
9 mm & 764.33 \(\pm\) 24.48 & 764.33 \(\pm\) 15.94 & 820.33 \(\pm\) 13.30 & 825.67 \(\pm\) 21.92 & 1048.00 \(\pm\) 23.3 & 980.00 \(\pm\) 4.1 & 967.17 \(\pm\) 12.1 & 974.67 \(\pm\) 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Traditional alloy bending resistance test: PTU and ET mean values (g).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{PTG} & \multicolumn{4}{c}{ETP} \\ \cline{2-10}  & **S1** & **S2** & **F1** & **F2** & **S1** & **S2** & **F1** & **F2** \\ \hline
3 mm & 54.33 \(\pm\) 2.94 & 103.67 \(\pm\) 2.40 & 116.17 \(\pm\) 2.33 & 154.50 \(\pm\) 6.21 & 42.83 \(\pm\) 2.56 & 86.33 \(\pm\) 3.50 & 113.67 \(\pm\) 3.52 & 116.83 \(\pm\) 5.49 \\
6 mm & 230.00 \(\pm\) 9.16 & 294.00 \(\pm\) 9.20 & 269.00 \(\pm\) 9.12 & 281.67 \(\pm\) 5.39 & 209.17 \(\pm\) 4.60 & 233.00 \(\pm\) 5.46 & 218.33 \(\pm\) 4.24 & 205.67 \(\pm\) 4.55 \\
9 mm & 556.67 \(\pm\) 7.77 & 558.00 \(\pm\) 11.49 & 562.17 \(\pm\) 8.56 & 564.33 \(\pm\) 8.85 & 472.00 \(\pm\) 8.77 & 445.33 \(\pm\) 4.98 & 443.00 \(\pm\) 7.06 & 457.00 \(\pm\) 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Heat-treated alloy bending resistance test: PTG and ETP mean values (g).  
Figure 1: Mobile bending resistance device: overview.  
Figure 2: Mobile device with protractor for calculation of the bending angle.  
ET instrument and for each PTG and ETP instrument and for 3, 6, and 9 mm bending resistance in the same instrument. Significance was set to the 95% confidence level.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 5 Results and Discussion  
### 1 st Experiment--Single Look Approach  
The initial experiment was implemented directly on SLC dataset, thus exploiting the full resolution of the images. In the following section, results of LDA classification and PS detection are presented in the case of both datasets, for single-look and multi-look analysis. In the scenes classification map, each category of evolution is represented using a different color. The detected PS candidates are represented over the amplitude of the scenes, with a prominent color which differentiates them from the amplitude's grayscale.  
In the case of ERS dataset, targets which present a mean per sigma ratio of amplitude above 1.3 and spectral coherence above 0.3 were classified as PS. A number of 30,209 PS candidates were detected, distributed on a surface which covers 3.32% of the test scene (Figure 10C). For the Sentinel-1 dataset, same statistics were employed for stable target detection. This resulted in a total of 408,128 PS candidates representing 7.77% of the analyzed scene surface (Figure 11C). As PS are usually corresponding to man-made structures, we expect them to be located inside urban areas.  
Combining the results of the PS detection algorithm with the LDA classification, it was observed that 99.68% of detected PSs are included in the same class (the red one) for the analysis of the Buzau area (Figure 10B) and 96.21% of the detected PSs are included in the same class (the yellow one) for the Sentinel-1 SITS analysis (Figure 11B). Considering that PSs are found in areas characterized by high temporal correlation, it is assumed that the evolution class in which those candidates included denote the stable structures from the urban area.
## 5 Conclusions  
In this paper, a novel optical cone and pendulum scanning imaging mode is proposed, where in-motion imaging is adopted to break the restrictive relationship between the high resolution, wide width, and compact size of remote sensing satellites. This imaging mode uses an optical camera composed of a single CMOS sensor, allowing the camera to have an initial tilt angle to obtain ultra-wide coverage through the three-dimensional motion of the satellite and the camera. Two 3D motion imaging modes are designed, the single-camera constant speed OCPSI mode and the dual-camera variable speed OCPSI mode. Compared with each other, the control strategy of the single-camera constant speed OCPSI mode is simpler and covers a wider range, which is suitable for use in situations where too high resolution is not required. For tasks that require higher image quality, the dual-camera variable speed OCPSI mode is a better choice. Finally, numerical simulations and ground verification experiments are conducted to verify its effectiveness. The results show that, under the same conditions, the ground coverage width of the OCPSI mode is seven times wider than that of the push-broom imaging mode and three times wider than that of the whisk-broom imaging mode.  
The OCPSI mode breaks the routine and applies the high-resolution camera imaging method in motion to reasonably plan the image position of each frame. Then, it adopts the frame-type splicing method, which effectively improves the single-track ground coverage width of the high-resolution optical remote sensing satellite. At the same time, due to its unique motion imaging mode, the exposure time is very short in order to ensure that the imaging quality does not suffer from severe image shift. Therefore, this imaging mode requires a large sun elevation angle, which limits its application scenarios. In addition, the too-fast the camera pendulum velocity and the attitude control ability of the micro-nanosatellite will increase the difficulty of maintaining the stability of the OCPSI satellite. As a new imaging method, the OCPSI mode still has many related topics to be studied to improve its theoretical system, such as the rational selection of camera detectors, the design optimization of camera structure and motion structure, the design of large-angle moving image processing algorithms, the research to the promotion of the exposure time mode, and the research on the stability of the satellite platform ability.  
Conceptualization, H.Q. and Z.Z.; data curation, Z.Z.; funding acquisition, S.T.; Investigation, Z.Z., H.Z. and S.Y.; methodology, Z.Z.; Project administration, H.Q. and Z.Z.; resources, H.Q. and L.Z.; software, Z.Z.; supervision, H.Q.; validation, Z.Z.; writing--original draft, Z.Z.; and writing--review and editing, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62075219 and in part by the Key Technological Research Projects of Jilin Province, China, under Grant 20190303094 SF.  
The authors declare no conflict of interest.
## 2 Wellbore Attitude Measurement Method and Error Analysis
### Near-Vertical Wellbore Attitude Errors Characteristics
Utilizing the auxiliary equipment to achieve combined attitude measurement is the other solution such as the estimation of sensor errors through a combined navigation algorithm [18; 19]. Unfortunately, the harsh conditions of the downhole make it difficult to combine the other sensors. A redundant configuration of inertial sensors has the potential to improve the measurement performance of the wellbore attitude with low developing costs. In addition, the miniature MEMS sensors have perfect size adaptability on the cylindrical logging instrument. In the follow  
Figure 4: Wellbore attitudes characteristics in near-vertical state. ing sections, we concentrate on studying the near-vertical attitude measurement methods based on the redundant MEMS accelerometers for improving the azimuth and roll accuracy.
## 7 Conclusions  
A universal expression of the outage probability has been derived for a dual-hop DF relay system. Analyses were carried out considering generalized fading scenarios, i.e., no restrictions were placed on the PDF of the SNR of the transmission link. This paper demonstrates a convenient way to analyze the outage behavior of a relay-aided system by utilizing the CDF of the per-hop SNR. Simulation results were used to verify the validity of the derived analytical expressions. Illustrations have been provided, choosing the PDF values of channels between S-R and R-D links to parameterize the commonly encountered practical fading scenarios faced by current and future wireless communication systems. The present work can be extended further in the future to include sophisticated system models such as multiple relay modes, complex forwarding schemes, and multiple antenna trans-receivers.  
Conceptualization, D.S.; M.A.O.; software, D.S.; validation, M.S.A.M. and A.A.A.E.-L.; formal analysis, M.A.O.; data curation, D.S.; writing--original draft preparation, D.S. and M.A.O.; writing--review and editing, A.B.M.A.; visualization, A.M.; A.K.; project administration, D.S.; funding acquisition, A.M., A.K. and A.A.A.E.-L. All authors have read and agreed to the published version of the manuscript.  
The studies at St. Petersburg State University of Telecommunications. Prof. [PERSON] were supported by the Ministry of Science and High Education of the Russian Federation by the grant 075-12-2022-1137.  
Not applicable.  
The authors declare no conflict of interest.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 2 Preliminaries
### Channel Combination  
When \(n\) sub-channels \(W\) are combined into one \(W_{n}\) channel, the channel capacity of \(W_{n}\) is  
\[C(W_{n})=I(U;Y)=I(X;Y), \tag{4}\]  
where \(U=[u_{1},u_{2},\cdots,u_{n}]\), \(Y=[y_{1},y_{2},\cdots,y_{n}]\), and \(X=[x_{1},x_{2},\cdots,x_{n}]\). Because sub-channels are exactly the same, \(u_{1},u_{2},\cdots,u_{n}\in\{0,1\}\) are independent and identically distributed when sub-channels are B-DMC channels, and thus  
\[C(W_{n})=n\cdot I(X;Y)=n\cdot C(W). \tag{5}\]  
After the channels are merged, the total capacity remains unchanged. At this time, the channel transition probability is  
\[W_{n}(y_{1}^{n}|u_{1}^{n})=W^{n}(y_{1}^{n}|u_{1}^{n}G_{n}), \tag{6}\]  
Figure 1: Diagram of channel.  
Figure 2: Diagram of channel combination and separation.  
where \(y_{1}^{n}\in Y^{n}\), \(u_{1}^{n}\in U^{n}\), \(\mathbf{G}_{n}=B_{n}\mathbf{F}^{\otimes n}\), \(B_{n}\) is the bit flip sequence, \(\mathbf{F}=\begin{bmatrix}1&0\\ 1&1\end{bmatrix}\), and \(\otimes\) is the Kronecker product, which can be calculated as in [18].  
The polar code, which completes channel combination and splitting through the corresponding coding and decoding processes, uses the channel polarization effect to realize reliable transmission. The process of channel combining is realized by polar code coding, and the index corresponding to the excellent sub-channels used to transmit information bits is called the information-bit position. Usually, \(k\) information bits transmit through the first \(k\) sub-channels with the largest channel capacity (that is, the smallest Bhattacharyya parameter). The index of the first \(k\) sub-channels is written as \(A{=}\left\{\pi(1),\pi(2),\cdots,\pi(k)\right\}\in\left\{0,1,\cdots,n\right\}\), and its complementary set is denoted as \(A^{c}{=}\left\{0,1,\cdots,n\right\}\backslash A\), so the codes with length \(n\) can be written as  
\[x_{1}^{n}=u_{A}\mathbf{G}_{n}(A)\oplus u_{A^{c}}\mathbf{G}_{n}(A^{c}), \tag{7}\]  
where \(\mathbf{G}_{n}(A)\) is a sub-matrix of \(\mathbf{G}_{n}\) with index \(A\), \(u_{A}\) denoting information bits, and \(u_{A^{c}}\) denoting frozen bits, which always takes 0, so (7) can be simplified as \(x_{1}^{n}=u_{A}G_{n}(A)\).  
The goal of blind polar recognition is to obtain the right \(G_{n}(A)\), which is needed to obtain the code length \(n\), information-bit positions \(A\), and number of information bits \(k\). Note that when \(n\) and \(k\) are determined, the rate code \(R\) can be calculated as \(R=k/n\).
## 4 Results
### Community Disaster Resilience in Lembang Fault Area
However, the survey did not collect specific information concerning the vulnerability of the houses to disaster, although 24.07% of the respondents agreed that there should be structural reinforcement of their houses. While the survey did not collect further information on the condition of natural and built environment, the extent of residential and agricultural areas between 2019 and 2021 increased by 34,476 square km (or 13.41%) and 46,528 square km (or 12.40%), respectively. On the other hand, the extent of the forest between 2019 and 2021 decreased from 546,007 square km to 411,644 square km (or 24.61%) of the total extent of the forest. See Table 3 for details. Furthermore, according to the survey, 50% of the respondents stated \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Exent (km\({}^{2}\))**} & \multicolumn{6}{c}{**Land Use/Land Cover Change**} \\ \cline{3-10} LULC & \multirow{2}{*}{**2019**} & \multirow{2}{*}{**2020**} & \multirow{2}{*}{**2021**} & \multirow{2}{*}{**2020**} & \multirow{2}{*}{**2020–2021**} & \multirow{2}{*}{**2019–2021**} & \multirow{2}{*}{**2021**} \\ \cline{2-2} \cline{6-9}  & & & & & & & & **Extent** & **(km\({}^{2}\))** & **\%** \\ \hline Body of & & & & & & & & & \\ Water & 25,111 & 27,468 & 25,844 & 2357 & 9.39\% & \(-\)1624 & \(-\)5.91\% & 733 & 2.92\% \\ Settlement & 279,443 & 298,080 & 316,919 & 18,637 & 6.67\% & 18,839 & 6.32\% & 37,476 & 13.41\% \\ Forest & 546,007 & 528,249 & 411,644 & \(-\)17,758 & \(-\)3.25\% & \(-\)116,605 & \(-\)22.07\% & \(-\)134,363 & \(-\)24.61\% \\ Agriculture & 375,118 & 385,788 & 421,646 & 10,670 & 2.84\% & 35,858 & 9.29\% & 46,528 & 12.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Land use/land cover change between 2019 and 2021.  
that their neighborhood is prone to earthquake, specifically due to Lembang Fault, while 19.44% of the respondents mentioned that their neighborhood is also prone to landslide and flood. As mentioned in the Introduction, the level of hazard, particularly concerning earthquake, in the Lembang Fault area is considerably high. On land administration domain, it is argued that the social connection between the people and their land was high. This is because most respondents lived in the Lembang Fault area for more than 10 years. The economic connection between the people and their land was also high, as 63.89% of the respondents' place of works were less than 5 km. As the land use/land cover of the Lembang Fault area between 2019 and 2021 is dominated by agriculture land and forest, the dependency of the people living in the Lembang Fault area to their environment was also high. Furthermore, there are also policy and regulations concerning DRM-related land administration, although, based on the survey, such policy and regulations provided no impact to the people. Furthermore, the respondents agreed that there were less activities on DRM-related land administration.
## 4 Results
### Experiment 5 Results: Three Devices, Distance between 255-1245 m  
In this experiment, only 17 settings were used, discarding some of the settings that did not have good results in Experiment 4.  
Some settings such as SF = 9, BW = 250 kHz, CR = 4/5 (S1), and SF = 9, BW = 250 kHz, CR = 4/8 (S2) performed well both in Experiment 4 and in Experiment 5. The first setting was one of the three best settings in each of the experiments, while the second setting was not within that group. The results obtained in these settings in both experiments (4 and 5) are presented in Table 3.  
There were also some settings that had a very good percentage of received packets in Experiment 4 and showed a considerable reduction of this indicator in Experiment 5. For example, the setting SF = 9, BW = 125 kHz, CR = 4/5 (going from 94% in Experiment 4 to 65% in Experiment 5) and setting SF = 8, BW = 125 kHz, CR = 4/8, (going from 94% in Experiment 4 to 60% in Experiment 5).  
Although, there were cases of inverse behavior (better in Experiment 5 than in Experiment 4) the differences were not too significant. For example, the setting with SF = 7, BW = 500 kHz, CR = 4/8 of Experiment 4 presented bad indicators (percentage of packets received 44%, maximum distance 424 m and average RSSI -94 dBm), while in Experiment 5, two of these indicators improved a little (percentage of packets received 58.3%, maximum distance 630 m and average RSSI -98 dBm).  
For identifying the possible causes of these results, and also discarding the possibility that these results have been obtained with the influence of some type of event or arbitrary conditions, this experiment was repeated under the same conditions (configured parameters, number of devices, and distances).  
The baseline setting obtained an average RSSI value of -103 dBm, 64.6% of received packets, and 720 m of maximum distance. These results were close to the best setting, except for the percentage of packets received (SF = 9, BW = 250 kHz, CR = 4/5), with an average RSSI value of -100 dBm, 81% packets received, and a maximum distance of 848 m.  
Regarding the RSSI, Figure 15 shows an example for one of the settings with the best percentage of packets received and distance reached.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measured Parameters**} & \multicolumn{2}{c}{**S1 (SF = 9, BW = 250 kHz, CR = 4/5)**} & \multicolumn{2}{c}{**S2 (SF = 9, BW = 250 kHz, CR = 4/8)**} \\ \cline{2-5}  & **Experiment 4** & **Experiment 5** & **Experiment 4** & **Experiment 5** \\ \hline Packets received at the gateway (\%) & 87 & 81 & 81 & 73 \\ Max. distance for packet reception (meters) & 848 & 848 & 676 & 807 \\ RSSI level (dBm) & –94 & –100 & –96 & –99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Better settings in both Experiments 4 and 5.  
Figure 14: RSSI versus distance with BW = 125 kHz and CR = 4/7, in Experiment 4.
# Sand Rubber Mixtures under Oedometric Loading: Sand-like vs. Rubber-like Behavior
## 2 Materials and Methods  
### Materials, Sample Fabrication, and Experimental Setup  
The oedometer tests on the sand and sand-rubber mixtures were conducted on cylindrical, rigidly laterally confined samples of 74 mm diameter and 40 mm height. While Leighton Buzzard fraction A sand [44] and rubber particles obtained from the shredding of used lorry tires [17; 37; 45] were used as base materials, the sand-rubber mixtures contained specific sand and rubber fraction dimensions. Pretest sample preparation procedures, therefore, included sieving both sand and rubber particles and particle size separation into the following grading sizes: 0.6-0.8 mm (\(D_{50}\) = 0.7 mm), 1.25-1.6 mm (\(D_{50}\) = 1.4 mm), 1.6-2.0 mm (\(D_{50}\) = 1.8 mm), and 2.0-2.24 mm (\(D_{50}\) = 2.1 mm) for LB sand, and 1.25-1.6 mm (\(D_{50}\) = 1.4 mm) and 1.6-2.0 mm (\(D_{50}\) = 1.8 mm) for the rubber, where \(D_{50}\) represents the mean grain size of the selected fraction.  
All the samples were fabricated in two layers, which were 20 mm each in height, and followed the moist damping procedure [45; 46; 47] (see Figure 1a). The required quantities of dry sand or sand and rubber for each sample layer were, first, manually mixed in a container together with 10% water (by dry weight of solids). Then, the mixture was gently placed in the rigid oedometer cylinder using a spoon and compacted by a rigid steel cylindrical tamper, which had a diameter half that of the sample's diameter [48], until the required target sample void ratio was achieved. Following the completion of fabrication, the sample was placed on the testing configuration and saturated by flushing water through the porous stone cylinder base. Once the saturation was completed, a relatively light steel cylinder top cap was placed, the sample settlement was recorded, and the fabrication void ratio was updated.  
The one-dimensional testing was conducted using a displacement-controlled electromechanical loading frame (refer to Figure 1b). The lower platen supporting the sample moves upwards with a constant speed set for all tests at 0.5 mm/min, while the sample top cap remains fixed and in contact with the loading ram, incorporating a linear variable differential transformer (LVDT) for vertical displacement measurements, and a 10 kN-load cell (with a linear response throughout the whole measurement range). The one-dimensional compression was stopped once the force approached the load cell limit capacity (corresponding to approximately 2.3 MPa of vertical stress) and the sample was, then, fully unloaded by reversing the lower platen movement at a similar speed. However, at the unloading point, the sample experienced some relaxation due to the backlash in the electromechanical loading system.