esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Synthetic Aperture Radar Interferometric Processing
### Acquisition of SAR Images
#### 2.1.3 Acquiring SAR Images by Along-track  
The along-track means to acquire SAR images by two SAR antennae, \(s_{1}\) and \(s_{2}\). The two antennae follow each other at a short distance on the same orbit and possibly on the same platform for SAR data acquisition. The baseline, \(B\), is parallel to the flight direction. Its imaging geometry is illustrated in Figure 4. At present, the along-track approach is only applicable to airborne SAR systems, as it requires two antennae on the same platform. The along-track approach is sensitive to movement of the scatters in the range direction. It can be used to measure Earth-surface velocities. As such, this technique holds promise for the detection of slowly moving ground targets. It was developed in mapping ocean currents. Above sea, it produces a map of the current velocity component parallel to  
Figure 4: Imaging geometry of along-track SAR interferometry.  
Figure 3: Imaging geometry of across-track SAR interferometry.  
the radar look direction. Now the master image and the slave image are recorded from the same position but at slightly different times. Therefore, the phase differences between the corresponding signals are caused by the movement of the measured object, e.g. water current. The moving surface leads to a Doppler shift according to the phase velocity of the water waves. All stationary targets are not visible whereas the moving ones can be seen in the radar imagery. The along-track mode can be used to measure the velocity of ocean currents and on-ground objects [51, 52].  
The quality of interferometric measurements depends on the relative distance of the antenna(s), the so-called base line. Across-track and along-track are only possible using airborne systems with two antennae. The yaw and pitch cause baseline components in y- and z-direction produce additional phase differences. The base line can, therefore, be readily found to the required precision. In principle, repeat-pass way is also possible for aircraft, but the flight track and the aircraft attitude should be known with sufficient accuracy.  
Figure 5: SAR image processing procedures.
## 1 Introduction
While the number of transfer zones generated limits the size of the Pareto set of journey profiles that can be generated, this is justified by considering that, in theory, there can be an inf  
Figure 1: Overview of ML-TZSA. non-dominated solutions. For instance, consider the case of two objectives, cost and time minimisation, and two modes, walking and taxi. In this cas, an infinitely sized Pareto set can be created by generating the set of journeys, including those where the commuter gets out of the taxi at all points along the taxi route and walks the remainder of the journey. This is because, in such a case, journey cost and travel time are conflicting objectives. Considering this, limiting the set of possible transfer points in some way makes practical sense as a solution space pruning method. Also, it is more practical to present only a limited number of journey alternatives to commuters. The contributions of this work are as follows: A transfer-zone sample approach is proposed and developed for greatly reducing the size of a very large journey-planning solution space without adversely effecting solution quality. A novel pedestrian transport network-based hybrid Dijkstra's algorithm and k-means clustering algorithm is introduced for selecting the sample of transfer zones--an approach which guarantees the feasibility of transfers of journeys planned via the sampled transfer zones. Existing shortest-path algorithms for open and public transport networks are adapted to the many-to-many case with numerous speed-up techniques and are used in conjunction with a machine-learning algorithm to optimise the transfer points and paths between the transfer zones of journey profiles. The new method has the beneficial feature that larger transfer-zone samples lead to large and better-quality Pareto sets of journeys without increasing query response times. Our experiment results show that our approach can generate large diverse sets of efficient journey options within seconds. Such large sets of journeys include cheap, fast, green, convenient and low effort ones which, when presented within a MaaS app, may encourage more people to opt for non-private car journeys and thereby reduce pollution and congestion. The remainder of this paper is structured as follows. Section 2 provides a review of the relevant literature. Section 3 defines the problem addressed in this work. Sections 4 and 5 detail the proposed solution methodology. Section 6 provides experiment results evaluating the effectiveness of the proposed approach. Section 7 summarises the main findings and promising directions for future work.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sl. No.**} & \multirow{2}{*}{**Material**} & \multicolumn{8}{c}{**Contribution**} \\ \cline{2-11}  & & **Regression** & **A** & **T\({}_{\text{on}}\)** & **T\({}_{\text{eff}}\)** & **A\({}^{2}\)** & **T\({}^{2}_{\text{on}}\)** & **T\({}^{2}_{\text{off}}\)** & **A\(\times\)T\({}_{\text{on}}\)** & **A\(\times\)T\({}_{\text{off}}\)** \\ \hline
1 & Al6061-15\% SiC & 98.13\% & 18.68\% & 67.91\% & 7.16\% & 0.38\% & 1.47\% & 0\% & 2.52\% & 0\% \\ \hline
2 & Al6061-20\% Al\({}_{2}\)O\({}_{3}\) & 73.17\% & 49.76\% & 10.83\% & 0.01\% & 0.07\% & 1.01\% & 9.98\% & 0.59\% & 0.93\% \\ \hline
3 & Al6061-5\% Al\({}_{2}\)O\({}_{3}\)-3\% SiC-2\% E-glass & 97.13\% & 84.5\% & 9.14\% & 2.05\% & 0.39\% & 0.48\% & 0.31\% & 0.05\% & 0.2\% \\ \hline
4 & Al6061-7.5\% SiC & 94.21\% & 54.25\% & 14.21\% & 12.57\% & 0.22\% & 3.59\% & 7.35\% & 0\% & 2.02\% \\ \hline
5 & Al6061-3\% SiC & 100\% & 52.17\% & 43.28\% & 0.47\% & 0.67\% & 3.07\% & 0.09\% & 0.01\% & 0.23\% \\ \hline
6 & Al6061-5\% SiC & 100\% & 71.44\% & 23.79\% & 2.93\% & 0.14\% & 0.1\% & 1.14\% & 0.42\% & 0.04\% \\ \hline
7 & Al6061-9\% SiC & 100\% & 81.07\% & 5.33\% & 1.83\% & 5.33\% & 2.56\% & 3.52\% & 0.04\% & 0.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of ANOVA for surface roughness. The work consisted of 15 experimental values. The input values of A were 3, 6 and 9; the values of T\({}_{\rm on}\) were 10, 20 and 30 \(\upmu\)s; and the values of T\({}_{\rm off}\) were calculated based on the duty cycle of 0.7, 0.8 and 0.9. The equations obtained are provided in Equations (7) and (8). The work pertaining to the Al6061 composites with the considered parameters was much smaller. Most of the works of Al6061 were identified to be in the area of wire EDM. Therefore, for the regression model, three composites were developed and EDM machining was carried out for these composites to identify a proper regression model. The composites developed were Al6061 with 3%, 5% and 9% SiC added to it with the average hardness value of 65, 69 and 75 HRB, respectively. Nine experiments each were carried out, with the values of current taken as 6, 9 and 12 A; the pulse-on times were taken as 36, 48 and 56 \(\upmu\)s; and the pulse-off times were taken as 7, 8 and 9 \(\upmu\)s. The characteristic equations for Al6061-3% SiC are provided as Equations (9) and (10), those of Al6061-5% SiC is provided as Equations (11) and (12) and for the Al6061-9% SiC composite are Equations (13) and (14).
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 4 Results and Discussion
### UAV Optimal Location  
As shown in Figures 3 and 4, we simulate three different buoy distribution situations which are random distribution, square distribution, and zigzag distribution to illustrate the optimal UAV's location obtained by our proposed algorithm. Since the numerical results obtained from the UAV's positions on the PF do not dominate each other, we plot all of the optimal positions of the UAV on a single figure. The location of each buoy is marked as a blue square, and the red dot denotes the optimal location obtained by the CRMOP algorithm. The UAV location obtained by the CRBC baseline is marked as a green triangle. As can be seen, different buoy distributions influence the final UAV's optimal location since both performances of communication and radar are considered. Specifically, according to Figure 3a, when the buoys are randomly distributed on the sea surface, the UAV will choose the location near the second or the fourth buoy as the best reception location. However, when the distribution of buoys has a dedicated pattern, such as square and zigzag distribution, as shown in Figure 3b,c, the locations of the UAV will present different distributions to achieve the optimal performance on radar and data collection at the same time. Moreover, the target location also plays the important role in the UAV's location optimization process. For example, when the target is located in Location 2 as in Figure 4a shows, the UAV chooses the location near the first or third buoy as the optimal location instead of the second or the fourth in Figure 3a. Due to the optimization process of the UAV's location being affected by the constraint of the minimum distance between the UAV and the target, the optimal location of the UAV is closely associated with the target location to reach the optimal result. The corresponding object values associated with the parameter are presented in Table 3. In each case, we have chosen two examples for different location areas. From Table 3 and its associated Figures  
For the achieved CRB for localization, it is clear that all cases are below the green dotted line which indicates that both algorithms are better than the preset threshold. The flexibility of the buoy's power for radar functions increases as the \(P_{\max}\) is incremented, resulting in a lower achieved CRB for system localization. Another interesting phenomenon is that the CRMOP algorithm exhibits superior throughput compared to the CRBC baseline while the latter demonstrates better radar accuracy, despite both meeting the threshold requirements for radar performance. Moreover, the change of \(P_{\text{SIC}}\) has little effect on both the system throughput and achieved CRB for localization of the CRBC baseline. This is because when other parameters are fixed and \(P_{\text{SIC}}\) meets the requirements of NOMA, the distribution of the UAV's position and power can converge to a certain range to reach the optimal value of the objective function.
## 5 Initial Alignment Error Compensation for Shearer's SINS  
As the initial alignment error of SINS will gradually accumulate in the subsequent navigation solution, it is necessary to reduce the initial alignment error as much as possible, especially to strictly restrain the initial attitude error. In addition, the alignment time should be as short as possible. The system can complete alignment in a short time after start-up, reducing the response waiting time, which can effectively improve the shearer operating rate and facilitate the personnel and various devices at the working face and the process coordination among the personnel and devices. In the following section, the initial alignment error of the shearer will be suppressed based on its attitude sensing requirements and the onsite characteristics of the working face.
## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Study** & **Aim** & **Data Used** & **Method** & **Result** \\ \hline \multirow{4}{*}{[PERSON], 2006 [51]} & \multirow{4}{*}{Determine between} & \multirow{4}{*}{Accelerometer} & Unsupervised method & Sensitivity: 97\% \\  & & & using & Specificity: 97\% \\ \cline{1-1}  & & based on daily activities & & frequency-based & \\ \cline{1-1}  & & using wearable data & & method & \\ \hline \multirow{2}{*}{[PERSON], 2019 [52]} & \multirow{2}{*}{Detect “On” and“Off” states} & \multirow{2}{*}{Gyroscope} & \multirow{2}{*}{SVM with fuzzy labeling} & Accuracy: 90.5\% \\  & & & & Sensitivity: 94.2\% \\ \cline{1-1}  & & & & Specificity: 85.4\% \\ \hline \multirow{2}{*}{[PERSON], 2020 [12]} & \multirow{2}{*}{Detect “On” and “Off” using gait signals} & \multirow{2}{*}{Accelerometer} & Random forest, & Accuracy: 96.72\% \\  & & & SVM, kNN, Naive Bayes & Sensitivity: 97.35\% \\ \hline \multirow{4}{*}{Current study} & \multirow{4}{*}{Predict “wearing-off” on individual-level} & Heart rate & Random Forest, & Accuracy \\  & & Stress score & Gradient Boosting, & P1: 70.0–71.7\% \\ \cline{1-1}  & & Sleep features & Logistic Regression, & P2: 76.1–76.9\% \\ \cline{1-1}  & & Step count & Linear SVM, & \\ \cline{1-1}  & & Decision Tree & & \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison of previous studies on predicting wearing-off and the current study. The prediction models could also be improved by reducing false positive and false negative predictions, as shown in Tables 18 and 19. It was possible that when the prediction model learned and discovered the wearing-off event, the participant has not reported the wearing-off. We learned this possibility from our short interview with the participants. Participant 2 often experienced mild to severe symptoms which resulted in fewer self-reports. Moreover, the participants forgot to replace their fitness tracker after doing household chores. Then, their symptoms began to manifest again. These events were backed up by the known information about the participants, such as fitness tracker usage (Table 8), PDQ-8, and H&Y profile (Table 3). In future work, these information will be helpful before data collection and would improve the study overall.
# A Study on the Remaining Reserves of the Weiyuan Shale Gas Block in Three-Dimensional Development
## 2 Regional Geological Overview  
The thickness of the Wufeng-Longyi 1 subsection in the Weiyuan block roughly increases gradually from northwest to southeast, ranging from about 35.6 to 53.6 m, with an average of 46.4 m. The overall relatively stable distribution of the thickness is characterized by a relatively stable distribution of thickness, which has been found in the stratigraphic thickness statistics of various evaluation wells [13; 14]. The pore size distribution of the Wufeng-Longyi 1 subsection is complex, with strong non-homogeneous characteristics between the various layers, porosity between 2.0 and 7.4%, with an average of 5.4%; the organic matter abundance is generally high, TOC distribution between 0.1 and 8.2%, with an average of 2.9%; the gas content ranges from 0.9 to 11.4 m\({}^{3}\)/t, with an average of 4.3 m\({}^{3}\)/t; the distribution of the brittle mineral content in the horizontal direction is generally stable,  
Figure 1: Three-dimensional development recovery ratio comparison histogram.  
with a brittle mineral content of 50.0-95.6% and an average of 72.1%, generally stable in plane, the compressibility of the reservoir is generally good, and the vertical direction is characterized by the gradual increase of clay mineral content from bottom to top.  
Based on the differences in lithologic assemblages, and paleontological and electrical characteristics, combined with the paleotopographic features of the Weiyuan area, the Wufeng-Longyi 1 subsection is divided from bottom to top into the Wufeng Formation, the Longyi\({}_{1}\)1 layer, the Longyi\({}_{1}\)2 layer, the Longyi\({}_{1}\)3 layer, and the Longyi\({}_{1}\)4 layer (Figure 2). A comprehensive evaluation of TOC, porosity, gas content, brittle mineral content, and other key parameters of shale reservoirs found that the reservoir quality of the Longyi\({}_{1}\)1 layer is the best, the Longyi\({}_{1}\)3 layer is the next best, and the Longyi\({}_{1}\)2 layer, Longyi\({}_{1}\)4 layer, and Wufeng Formation are relatively poor. According to the reservoir classification standard (Table 1), the Longyi\({}_{1}\)1 layer is a Class I reservoir, whereas the Wufeng Formation and Longyi\({}_{1}\)2-Longyi\({}_{1}\)4 layers are Class II reservoirs. The current intended layer for development of the Weiyuan block is the Longyi\({}_{1}\)1 layer. Due to the strong vertical inhomogeneity between the layers, the degree of mobilization of each layer is different when the Longyi\({}_{1}\)1 layer is used as the target layer for development. Thus, the three-dimensional development target layer should be preferred according to the reservoir characteristics of each layer [15; 16; 17].
## 3 Modeling the Temporary Facility Layout Problem
### ANP-Based Modeling for Layout Planning
#### 3.1.2 Step 2: Pairwise Comparisons  
Based on the generated temporary facility layout decision network structure, the relative importance of clusters and elements are required for final evaluation. In order to derive priorities, pairwise comparisons are conducted between clusters and elements according to the relationships and interdependencies.  
The experienced engineer or construction planner can make a judgement based on the fundamental AHP scale developed by [PERSON] [62], which is a ratio scale of 1-9. In this scale method, given a control criterion, a score of 1 indicates equal importance of two elements while a score of 9 indicates heightened importance of the \(i\)th element (row element in the matrix) versus \(j\)th element (column element in the matrix) [63]. The dominance between clusters and elements are reflected through the judgement. Considering all influences with respect to the same criterion, comparisons are made to derive the overall priorities. For each relevant relation between clusters and/or elements in the network structure, a pairwise comparison is required to be conducted.  
For an n \(\times\) n pairwise comparison matrix, the total number of pairwise comparisons that should be performed is \(\mathrm{n}\times(\mathrm{n}-1)/2\), where n is the total number of elements required to be compared. Moreover, within the matrix, a reciprocal value can be automatically calculated and assigned for reverse comparison. The pairwise comparison number \(\mathrm{a_{ij}}\) should meet the following equation:  
\[\mathrm{a_{ij}}\times\mathrm{a_{ji}}=1 \tag{1}\]  
where \(\mathrm{a_{ij}}\) is the pairwise comparison number, which is calculated by the value of fundamental scale [64].  
Through the pairwise comparisons, the final matrix is generated, which is a near consistent pairwise comparison matrix. The considered dependence between clusters and elements in the layout planning network structure includes several kinds of dependence: (1) outer dependence with respect to the objective criterion, (2) inner dependence, and (3) outer dependence on the same management level. As illustrated in Table 2, the pairwise comparisons of objectives with respect to the objective criterion are conducted. The relation between the objective criteria is defined as the outer dependence with respect to the objective criterion. Table 3 shows the pairwise comparison matrix between the elements of productivity clusters on the criterion of control of hazard zones, which is one of the cases  
Figure 2: ANP network structure for temporary facility layout planning.  
of outer dependence on the same management level. Another category of pairwise comparisons is the inner dependence, which is illustrated in Table 4. The elements in Table 4 belong to the safety cluster, and their relationships are inner dependent.
## 3 Benefits of Biochar Amendment in Green Roofs
### Air Quality Improvement  
Preliminary results have demonstrated that GRs are a promising solution to capture several air pollutants, including CO\({}_{2}\), O\({}_{3}\), NO\({}_{2}\), PM10, and SO\({}_{2}\). CO\({}_{2}\), a greenhouse gas, is the most extensively studied pollutant [117; 118; 119; 120]. CO\({}_{2}\) is either captured directly through the photosynthesis process of GR plants or indirectly through the decreased CO\({}_{2}\) emissions due to lower energy consumption. Therefore, the substrate and vegetation play a major role in determining the carbon sequestration capacity of GRs. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [67] noted that a limited number of studies have attempted to quantify the carbon storage in biochar-amended GRs. They identified two primary mechanisms through which biochar enhances carbon sequestration, namely by altering the physical properties of substrates and by promoting plant growth. Specifically, the addition of biochar increased the total carbon (TC) content of substrates, the total organic carbon (TOC) content in plants, and the overall carbon storage of green roofs, thereby enhancing their CO\({}_{2}\) sequestration capacity. Carbon storage of a biochar-amended GR was estimated to be 9.3 kg C/m\({}^{2}\)/year, which was higher than 6.47 kg C/m\({}^{2}\)/year of a non-biochar GR. [PERSON] and [PERSON] [68] tested biochar in both simulation box (micro-climate) and outdoor experiments. Biochar in all test plots significantly reduced the concentrations of CO\({}_{2}\), CO, SO\({}_{2}\), and NO\({}_{2}\) in the artificial environment, and reduced PM2.5, PM1.0, and PM10 in the natural environment. This capability of biochar was explained by a lower surface temperature and higher plant water availability promoting plant performance [68]. In the study of [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [98], all biochar-GR test plots were observed to effectively reduce CO\({}_{2}\) emission in both outdoor and indoor experiments during a 36 h period. The utilization of biochar towards carbon sequestration has also been studied in other research areas. For example, results from a systematic review of 64 studies by [PERSON], et al. [121] confirmed the significant potential of biochar applied in agricultural soils to sequester CO\({}_{2}\). A global meta-analysis from [PERSON], et al. [122] illustrated that applying biochar in agriculture had huge benefits relating to crop yield, soil carbon sequestration, and global warming.