esa-sceva/satcom-chunk-collection huggingface dataset:
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 3 Search and Rescue in Maze-like Environments
### Modeling the Problem
#### 3.1.4 Modeling the Drones  
At the present stage of the development and application of this hybrid model, we only account for a controlled discretized motion of UAVs, as specified, for example, in Table 4. While this obviously ignores virtually all aspects of actually deploying drones inside SAR  
Figure 3: Color-coded illustration of the beacon signal power received across the maze. The signal attenuates with distance and across the obstacles (walls), shown here in black color. The attenuation is a function of the distance. The signal undergoes added attenuation across walls due to shadowing.  
environments, it suffices here to provide a minimally adequate model for the operation of UAVs and allows us to use our algorithms to drive the movement of the drones, albeit restricted to the choices _hover (do not move)_, _turn clockwise_, _move forward_ or _move backward_.
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 3 Typical Engineering Case Study  
In this section, a fragility analysis of three typical prototypes of transmission towers is conducted to demonstrate the effectiveness of the proposed fragility analysis framework. Six parts, including the uncertain finite element model, downburst wind load, nonlinear buckling analysis, uncertain collapse analysis result, fragility analysis result, and failure probability analysis, will be presented in sequence.
# Edge-Nodes Representation Neural Machine for Link Prediction
## 3 Edge-Nodes Representation Neural Machine (ENRNN)
### Node Pattern Encoding  
In this section, we introduce the method of generating node's representation. Node2 vec works well in link prediction, therefore, we use Node2 vec to generate node embedding in the node pattern encoding part of our method. Indeed, other network embedding methods are suitable too.  
Node2 vec changes the way of the random walk compared with DeepWalk. It uses two parameters, Return parameter \(p\) and In-out parameter \(q\) to control the next step of the walk, and the parameters settings can lead to different node sentences so the node embeddings. The parameter \(p\) controls the probability of visiting the previous node, and parameter \(q\) controls that the next step will visit a node which is close to the previous node or far away to it. As shown in Figure 4, the current node is node \(v\), the previous node is node \(t\), now we need to determine the next walk to which node selected from node \(t\), node \(x_{1}\) and node \(x_{2}\). We choose the next node according to the distance between the node \(x\) and the previous node \(t\). The transition probability is shown in Equation (2) [18].  
The transition probability \(\alpha_{pq}(t,x)\) is as fol  
Figure 4: Illustration of the random walk procedure in node2 vec. The walk just transitioned from \(t\) to \(v\) and is now evaluating its next step out of node \(v\). Edge labels indicate search biases \(\alpha\).  
\[\alpha_{pq}(t,x)=\begin{cases}1/p,&\text{if }d_{tx}=0\\ 1,&\text{if }d_{tx}=1\\ 1/q,&\text{if }d_{tx}=2.\end{cases} \tag{2}\]  
After random walk getting a node sentence for each node, we feed these node sentences to the skip-gram model to learn the node embedding. We illustrate the node pattern encoding algorithm in Algorithm 3. In the end, we get the embedding of each node in the form of vector.  
```
0: network \(G\), dimension \(d\), walks per node \(r\), walk length \(l\), context size \(k\), return \(p\), in-out \(q\)
0: every node's representation _nodevec_
1: calculate transition probability \(\alpha_{pq}(t,x)\) according to \(p\), \(q\) and \(d_{tx}\)
2:\(\pi=PreprocessModifiedWeights(G,\alpha)\)
3:\(G^{{}^{\prime}}=(V,E,\pi)\)
4: initialize walks to empty
5:for\(i=1\) to \(r\)do
6:for all nodes \(u\in V\)do
7:\(walk=node2 vecWalk(G^{{}^{\prime}},u,l)\)
8: append walk to walks
9:endfor
10:endfor
11:\(nodevec=SGD(k,d,walks)\)
12:return nodevec
```  
**Algorithm 3** Node pattern encoding
# Data-Driven Analyses of Low Salinity Waterflooding in Carbonates
## 3 Results and Discussion
### Effect of LSW Governing Parameters on Oil Recovery  
Injection of LSW affects the interaction between rock and fluids and alters parameters such as wettability and surface charges. Although the dependence of LSW performance on various controlling parameters in carbonates has been proved in different experimental studies, there are some contradictions reported in the literature. The preliminary data analysis of the incremental oil recovery by LSW shows that the crucial oil/brine/rock parameters are permeability, salinity of brines, cations concentration, SO\({}_{4}\)\({}^{2-}\) concentration, AN, BN, and temperature. Table 5 summarizes the single-variable linear regression of each parameter and incremental oil recovery. Only coefficients of correlation obtained for temperature and BN showed weak linear relationships. Among the brine parameters, the linear regression of SO\({}_{4}\)\({}^{2-}\) concentration against RF showed better data-fitting than the others. However, it is not enough to explain the variance of LSW effect. The linear relationships between single parameters and the improved recovery factor are mostly negligible, so a single parameter cannot explain the LSW performance in carbonates. It is thus inferred that LSW effect is probably the synergistic result of several properties.  
Since linear regression between single variables and incremental RF failed in the interpretation of LSW performance, the combined effects of controlling parameters on the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{1}{c}{\begin{tabular}{c} **Number of** \\ **Data Points** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Correlation** \\ **Coefficient R** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Strength of** \\ **Relationship \({}^{*}\)** \\ \end{tabular} } \\ \hline Rock & Permeability & 118 & 0.1721 & Negligible \\  & Low salinity & 117 & 0.1059 & Negligible \\  & Change in salinity & 117 & 0.0460 & Negligible \\  & Cations & 110 & 0.0290 & Negligible \\  & SO\({}_{4}\)\({}^{2-}\) & 107 & 0.1593 & Negligible \\ Oil & AN & 80 & 0.1848 & Negligible \\  & BN & 60 & 0.2334 & Weak \\ Temperature & T & 98 & 0.2647 & Weak \\ \hline \hline \end{tabular}
\end{table}
Table 5: Single-variable linear regression results.  
LSW effect were investigated. For this purpose, we analyzed the oil/brine effect using multivariable regression and nonlinear regression techniques. The dimensionless numbers shown in Equations (1)-(5) were applied to preserve the physical significance of controlling parameters. The predicted RF values are calculated based on the estimated regression coefficients using independent variables.
## 4 Results
### Subjective Metrics Results and Analysis  
The raw distribution of responses for each survey question is shown in Figure 4. Due to the ordinal nature of the Likert scale variables, a non-parametric test was applied to test H3-H5. As a result, we chose to employ a single-tailed Wilcoxon signed-rank test on the paired differences between the AR and VR headsets. Statistical significance was found in most questions, with \(p\)-values shown in Table 4.  
The VR headset was found to be easier to learn, more responsive to user input, was perceived as being more efficient and participants perceived fewer errors when compared to the AR headset. Four out of five survey questions related to Intuitivelyeness showed a statistically significant improvement of the VR headset over the AR headset. For question I2, which asked about the comfort of use, VR again received better ratings but the difference was not statistically significant. Given these results, while we can claim that H3 is mostly supported, more user studies can be useful in testing whether the VR headset is also more comfortable to use compared to the AR headset in question.  
When examining users' preferences towards the different graphical interfaces between the AR and VR devices, the AR headset was found to provide a better view of real-world objects and was overall more visually comfortable. However, we did not observe statistical significance in the user responses to AR visualization of virtual objects over VR. Hence, H4 is only partially supported. No subjective metric used to evaluate H5 was found  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **AR** & **VR** \\ \hline IoU (\%) & 0.262 (\(\sigma\) = 0.112) & 0.407 (\(\sigma\) = 0.157) \\ \hline Task Time (s) & 466.6 (\(\sigma\) = 21.1) & 333.8 (\(\sigma\) = 83.5) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation for each objective metric for the two headsets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & H\({}_{\mathbf{a}}\) & \(\rho\) \\ \hline IoU (\%) & VR \textgreater{} AR & **0.001** \\ \hline Task Time (s) & VR \textless{} AR & **<0.001** \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(p\)-values using paired \(t\)-test for objective metrics used to evaluate hypotheses H1 and H2. Significant results are indicated in **bold**.  
to be statistically significant. Hence, H5, that participants will enjoy the VR headset more, cannot be affirmed.
# A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning
## 4 Experimental Results and Discussion
### Robustness-to-Dataset Scale  
In a real-world setting, this section analyzes the impact of dataset size on the robustness of DECC. Generating an easy-to-use, high-quality, and understandable dataset is extremely difficult, and will incur huge costs in terms of time, labor, and finances as the amount of data increases. At the same time, massive data will bring fatal problems to deep learning models, due to the cost of operation, maintenance, and training. Therefore, we verify the accuracy comparison of DECC in datasets of different sizes to verify the robustness of DECC to the number of datasets. DECC is trained on 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% of the original dataset, used as the experimental group, and DECCs trained on the full original dataset (14,448 CSI), which served as the control group for comparison experiments, as shown in Figure 11.  
Figure 10: The MeanError after 300 training epochs.  
Figure 9: Performances of different SNRs of the first 30 training epochs: (**a**) in terms of RMSE; (**b**) in terms of MeanError.  
From experimental group 1 to experimental group 9, the mean error increases by 138% and 5%, respectively. As the dataset size increases, the training results become closer and closer to the control group. The mean error for experimental group 9 was 0.19, which was only 0.01 away from the control group. Experimental group 1 only used less than 1500 images (randomly sampled from SNR10, 20, and 50, trained 200 times, but the average error was still 0.43 m, reaching sub-meter accuracy. This shows that DECC can still achieve ideal results, even when the amount of data is small and the number of training times is low, and the positioning accuracy can be further improved with the expansion of the dataset, which shows that DECC has high practical application value.
## 3 Methods
### Task 4--Interpolation of MODIS LST Data at the Geographic Coordinates of ARPA Monitoring Stations  
To compare in situ Tair measurements from ARPA Monitoring Stations with MODIS LST data, we used the coordinates of the Monitoring Stations within the study area, obtained from the data downloaded from Geoportale Lombardia.  
We wanted to obtain the time series of the daytime and nighttime LST at these locations, by interpolating values of the MODIS raster images of both Aqua and Terra satellites. The following Figure 11 shows the time series of MODIS temperatures recorded by the Aqua and Terra satellites, daytime and nighttime, from January 2018 to December 2022. In this specific case, the temperatures are interpolated at the coordinates of the Fortunago ARPA Monitoring Station-ID 8007. Daytime temperatures for Aqua (in blue) and Terra (in red) indicate that the Aqua sensor typically records higher values than the Terra sensor. For nighttime temperatures, the opposite occurs: Terra temperatures (in orange) are overall higher than Aqua temperatures (in light blue).  
For a comprehensive view of the MODIS LST time series at the coordinates of all the considered ARPA Monitoring Stations, please refer to Appendix A (Figures A1-A6).  
Task 5--Creation of a Table Containing MODIS LST Data and ARPA Monitoring Stations Tair Data Averaged over 8-Days  
The data downloaded from the ARPA website are available in csv format, one file per Monitoring Station. The data contain the following information: sensor ID, acquisition Date-Time, and the average temperature measured on an hourly basis.  
A function was used to load and harmonize all the Monitoring Station tables, setting the value \(-999\) to NaN.  
Subsequently, starting from the collected data, we compute the average over an 8-day period by sensor ID. The 8-day average has been chosen to align with the temporal resolution of the satellite data, to obtain a meaningful comparison between in-situ and satellite-derived measurements.  
In the end, we obtained a table, consisting of 1610 rows and 13 columns, containing both satellite and Monitoring Stations data. An illustrative excerpt of this table is shown in the following Table 4. Latitude (Lat) and longitude (Lon) represent the coordinates, in  
Figure 11: Comparison between daytime and nighttime temperatures detected from MODIS Aqua and Terra satellites, interpolated at the Fortunago ARPA Monitoring Station coordinates (ID 8007).  
decimal degrees, of the considered monitoring station (Sensor ID). MODIS date refers to the date of acquisition of raster data (8-day span). Today and Tnight correspond to the Land Surface Temperatures measured by MODIS Aqua and Terra satellites, measured twice a day. Tair ARPA is the air temperature recorded by the ARPA monitoring station, calculated on an 8-day average to ensure comparability with satellite data.
## 2 Measurements
### Electric Field Mill  
There are various methods for measuring the AEF; commonly used methods involve ground-based rotating electric field meters, roller electric field meters, sphere-borne double-ball electric field meters, and microrocket electric field meters. In the past, the most common methods were potential probes and burning fuses, and these methods are still used by some observatories today. The most widely used method today is the electric field mill (EFM), which provides good exposure to the AEF. It usually consists of one or more electrodes that are alternately shielded and exposed to the AEF. In addition, it provides the opportunity for faster measurements with multiple options for meteorological conditions. An electric field mill is mounted outdoors, where it is well exposed to the AEF.  
Figure 1: Location of the Antarctic Chinese Zhongshan Station (the yellow dot represents the location of Antarctic Chinese Zhongshan Station, and the colors [from blue to green] represent different altitudes [above mean sea level]).  
The AEF meters that were used for measurements in this paper are EFMs with a measurement range of \(\pm\)50 kV/m, measurement accuracy \(<\) 5%, response time \(<\) 1 s, linearity \(<\) 1%, and resolution of 10 V/m. When the meter operates, the stator is fixed and grounded while the rotor rotates (the stator and rotor are two sets of similarly fan-shaped metal conductive sheets) at a uniform speed, and then the stator is periodically and alternately exposed to the environmental electric field, which generates induced charges and induced current on its fan blades. Since the atmospheric electrostatic field is a DC component, the current induced in the induction blade is a very weak signal and needs to be amplified before the AEF measurement is performed. Therefore, the preamplifier will amplify the weak current signal generated by induction, and then, through current-to-voltage conversion, amplification, filtering, signal synchronization, and rectification, the voltage signal is output, and finally, the AEF data can be obtained after processing the data arithmetic. The meter can be powered by the mains power (175-275 V), UPS power, or solar power. During measurement, the data are transmitted through a 4G network to a cloud server. Then, the local PC can download and save the data from the cloud server in real time.
## 3 Experimental Results
### AE Response Characteristics of Damaged Samples during Loading  
According to the AE energy time-series characteristics and the three-dimensional positioning results of AE events in the rock sample loading process (as shown in Figure 4), the AE energy response characteristics of the loaded rock sample in the process of crack propagation were analyzed. It can be seen that, as the stress level increases, there is no linear relationship between the number of AE events inside the sample and the stress level of the sample. The key feature (the transition points that the sample loaded in three stages) of the internal crack propagation of the sample with different structures shows similarity at the stress level. It can be seen from the loading process of the intact rock sample that, in the previous compaction phase (0-866 s), there are many AE events and the local agglomeration phenomenon is obvious. At this time, the AE energy has a low value response. Then, the sample enters the elastic stage (866-2606 s) and the inside of the sample begins to produce micro-cracks. New fractures are mainly generated in the compacted region of the first stage, and the number of AE events is less than the number of AE events in the compaction stage. At this time, the overall increase of AE energy amplitude is not obvious with random energy jumps slightly. At the late loading stage (2606-3704 s), the internal cracks of the sample expanded and penetrated to form regional instability. According to the location of AE events, the AE events in this stage transferred from the geometric center of the sample to the outside. The overall amplitude of AE energy increased significantly and showed a significant jump when the sample was critically unstable.  
Compared with the intact sample, the number of AE localization events of the holey sample during loading is small, but the general law of energy fluctuations still exists when the sample is in the compaction stage (0-1000 s), the elastic stage (1000-2537 s), and the fracture instability stage (2537-3552 s). At the initial stage of loading (compaction stage), the AE localization event is randomly distributed around the geometric center of the sample and the hole effect is obvious, at this time, the AE energy generated by the sample damage is low overall and the phenomenon that energy suddenly jumps is not obvious. As the loading progresses, the AE energy of the sample is at a stable low value in the early stage of the elastic phase, and when the load level reaches 24 MPa, the AE energy suddenly jumps and drops and has a huge fluctuation partly. The less AE localization event corresponds to a  
Figure 4: Time-space response of AE energy during damage of granite samples. (**a**) intact sample; (**b**) holey sample.  
large AE energy value and the AE energy reaches the maximum value before the instability in the fracture instability stage (27-42 MPa) of the rock sample.
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 3 Adaptive Tracking of the Moving Targets Using Multiple Beams
### Target Locating with Phase Difference of Multiple Beams
To give a more specific demonstration of the target locating and tracking methods, the detailed procedures with reference to Figure 5 are listed in Table 1.  
Figure 4: Illustration of angle measurement with phase difference.
# Efficient Multi-UAV Path Planning for Collaborative Area Search Operations
###### Abstract
However, with increase in the scale of an MTSP, the solution space will increase exponentially. Due to a large amount of calculation involved in an exact algorithm, the efficiency of the algorithm is low. It is difficult to find the optimal solution in a limited time, so most scholars use a heuristic algorithm to solve the MTSP. These include the PSO (particle swarm optimization) algorithm [17]. the SA (simulated annealing) algorithm [18], the TS (Tabu search) algorithm [19], the ACO (ant colony optimization) algorithm [20], the GA (genetic algorithm) algorithm [21], etc. The PSO algorithm can make full use of group experience to adjust the running results of the algorithm and has a fast convergence speed, but its local optimization ability is poor. The SA algorithm and TS algorithm are based on individual independent running algorithms; the solution results will be affected by the initial solution. The GA algorithm performs crossover and mutation with a probability mechanism and the algorithm is random, but the convergence speed of the algorithm is slow, and it is difficult to maintain an ideal path between retaining excellent individuals and maintaining the diversity of the population. At the same time, the quality of the algorithm solution is also related to the selection of the initial population. How to effectively apply swarm intelligence algorithms to the path planning of UAVs is worth further study. As described in this paper, unmanned vehicles can carry multiple drones, and they can transport the drones to the working environment to avoid the power loss of drones flying long distances in non-working areas. When the unmanned vehicle arrives at the working environment, it can be used as a landing platform for the UAVs and assume the role of a power replenishment base station. The cooperative search problem in a work area environment is decomposed into region coverage and target search tasks. In the task environment, the target is static. It is assumed that each UAV has: (1) A target awareness device for target detection. (2) Communication equipment for exchanging information. UAV swarms need to perform two different types of target-related tasks: coverage and search. The term \"coverage\" refers to UAVs visiting as many locations as possible within a given area, and \"search\" refers to information collection or target search for all areas visited,while minimizing their overall mission execution time. During mission execution, UAVs need to take a series of actions to complete their tasks, such as moving to a specific location, taking pictures, or scanning for information collection. In our research, path planning and the way paths are distributed are considered simultaneously to ensure that UAVs use routes as efficiently as possible during mission execution. The purpose is to achieve a near-optimal distribution of multiple UAV routes within the mission area, i.e., to maximize the coverage area so that the UAV swarm can reach all locations and complete the search mission in the shortest possible time.