esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Sources
### Landslide Inventory  
The quality of landslide inventories significantly affects the results of LSP [26]. These inventories record historical landslide events [27], providing details that include landslide type, occurrence date, size, and slope movement features [28]. Field investigations, as the primary source of historical inventories, offer significant advantages in reliability, as direct on-site observations minimize misinterpretation. Furthermore, this approach allows for a focused analysis of landslide susceptibility in areas with vulnerable elements, such as human populations and infrastructure, enhancing the efficiency of disaster risk assessments. This landslide inventory was obtained from the China Geological Survey of the Ministry of Natural Resources. We set up a dataset that was updated in 2021 and included 589 landslides within the research area. Figure 1 illustrates the spatial distribution of landslides. These landslides mainly occur in high-altitude mountainous areas with distinct spatial clustering, particularly with significant topographic variation in the northwest and northeast valley area. Most landslides are small- to medium-sized, with slopes between 10\({}^{\circ}\) and 40\({}^{\circ}\).
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 4 Image Stitching
### Feature Point Matching  
Scale invariant feature transform (SIFT) [36] and speeded-up robust features (SURF) descriptor [37] are commonly used to detect common features in images and RANSAC [38] is used to match these features and calculate the homography. The images are then blended together to form a higher resolution image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline ground truth & 3.38 & 7.07 & 7.65 & 5.97 & 5.98 \\ \hline
3 rd order polynomial & 0.34 & 0.45 & 0.43 & 0.51 & 0.7 \\ \hline
4 th order polynomial & 0.24 & 0.31 & 0.39 & 0.32 & 0.53 \\ \hline
5 th order polynomial & 0.39 & 0.31 & 0.39 & 0.29 & 0.95 \\ \hline \end{tabular}
\end{table}
Table 2: Validation results on five calibration processes—reported in steps.  
\begin{table}
\begin{tabular}{c c c c c c} \hline PSO & 271.3 & 401.28 & 355.3 & 374.7 & 319.2 \\ \hline CMAES & 270.8 & 398.7 & 354.3 & 373.4 & 318.7 \\ \hline \end{tabular}
\end{table}
Table 3: Logged MSE values for PSO and CMAES.
# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 2 Materials and Methods
### Picking Pose Estimation
#### 2.4.3 Evaluator  
There are obstacles that surround the object. It is necessary to evaluate the feasibility of the picking candidates, which helps to select collision-free picking. We depict the 3D space occupied by obstacles as \(\mathcal{O}\) and the 3D space occupied by the gripper in the pose \(\frac{C}{E}\mathbf{T}_{i}\) as \(\mathbf{B}(\frac{C}{E}\mathbf{T}_{i})\). The definition of collision-free picking [31] is as follows:  
\[\mathbf{B}(\frac{C}{E}\mathbf{T}_{i})\cap\mathcal{O}=\oslash \tag{7}\]  
The procedure of the evaluator is shown in Algorithm 1. The evaluator detects whether each picking candidate collides with obstacles and outputs collision-free picking candidates. As shown in Figure 9b. \(\frac{C}{E}\mathbf{T}_{5}\) and \(\frac{C}{E}\mathbf{T}_{6}\) collide with obstacles. Thus, the collision-free picking candidates are \(G_{f}=\{\frac{C}{E}\mathbf{T}_{1},\frac{C}{E}\mathbf{T}_{2},\frac{C}{E}\mathbf{ T}_{3},\frac{C}{E}\mathbf{T}_{4}\}\), and one of them is arbitrarily selected as the picking pose during real operation.  
```
0: The 3D space occupied by obstacles, \(\mathcal{O}\); picking candidates, \(G_{c}\)
0: Collision-free picking candidates, \(G_{f}\)
1:\(G_{f}=\oslash\)
2:for i = 1 to 6 do
3: Calculate the 3D space occupied by the gripper, \(\mathbf{B}(\frac{C}{E}\mathbf{T}_{i})\)
4:if\(\mathbf{B}(\frac{C}{E}\mathbf{T}_{i})\cap\mathcal{O}=\oslash\)then
5:\(G_{f}=G_{f}\cap\frac{C}{E}\mathbf{T}_{i}\)
6:endif
7:endfor
```  
**Algorithm 1** The procedure of the evaluator
## 2 Thermal Energy Storage Systems in Buildings
### Passive Storage Systems Incorporating PCMs
#### 2.2.1 Building Envelopes  
Firstly, we will present the conventional building envelopes and the efforts put in place to limit the energy consumption of buildings. Then we will compare these systems with those integrating PCMs in order to discuss the interest of this integration in terms of energy performance of the building.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Storage Modes** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{Thermochemical} & - & Efficiency & - & Corrosion \\ \cline{2-4}  & - & Suitable for seasonal storage & - & Risk of exhaustion of \\ \cline{2-4}  & - & Large storage capacity & - & Complex and not mature \\ \cline{2-4}  & & & technology \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PCM Types** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{Organic} & - & High enthalpy of fusion & - & Incompatibility with some \\  & - & Long-term thermal and & & container materials, \\  & - & chemical stability & & e.g., plastic \\  & - & Compatibility with most & - & Flamability \\  & & building materials & - & Low thermal conductivity \\  & & & (about 0.2 W m\({}^{-1}\) K\({}^{-1}\)) \\ \hline \multirow{4}{*}{Inorganic} & - & High storage density & \\  & - & compared to organic PCMs & \\  & - & High thermal conductivity & \\  & & (almost double that of & - & Corrosion \\  & & organic PCMs) & - & Significant volume expansion \\  & - & Low cost availability & - & Phase segregation \\  & - & Compatibility with & \\  & & plastic containers & \\  & - & Non-flammability & \\ \hline \multirow{4}{*}{Eutectic} & - & Appropriate and adjustable & - & High cost \\  & - & transition temperature & - & Limited thermo-physical \\ \cline{1-1}  & - & High enthalpy of fusion & - & properties \\ \cline{1-1}  & - & Congruence of phase change & & \\ \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of the different types of PCMs [18; 19; 20; 9].
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces
### Parking Space Detection Stage
#### 3.2.5 Divider Line Recognition  
After determining the type of parking space, we obtained three samples along the \(x\)-axis starting from the virtual lane boundary line, as shown in Figure 12a. Each color in the figure indicates the results from one sampling. The intensities obtained from the three samplings were recorded. Next, we searched for white-to-black (W2B) and black-to-white (BZW) interface pairs along the markings. The line width, \(W_{line}\), was then used to select candidate points that may be located on a divider line. Finally, the parking space width, \(W_{slot}\), served as a criterion to eliminate erroneous candidates, as shown in Figure 12b. In this figure, the crosses and rhombi represent B2W and W2B interfaces, and the circles represent candidate points that may be located on a divider line. During the three samplings along the virtual lane boundary line, if a candidate point on a divider line is classified incorrectly or is accidentally omitted, a voting mechanism still allows for the choice of possible locations among the three samplings. Consequently, the least squares method [26] was adopted to fit the three points into a line, which is then identified as a divider line. The divider line recognition process is illustrated in Figure 13a, and Figure 13b shows the results of the process; yellow lines indicate divider lines.  
Figure 11: Parallel parking. (**a**) Scanned distribution; (**b**) Distribution after filtering.  
Figure 12: Sampling along the virtual lane boundary line: (**a**) virtual lane boundary line; (**b**) white-to-black (W2B) and black-to-white (B2W) interfaces.
## 3 Results and Discussion
### Modulus of Rupture  
Figure 7 depicts the modulus of rupture of fiber-cement flat sheets with the addition of YCF and MCF. In terms of YCF, the modulus of rupture of cement board increased when YCF content increased from 5% to 6.5% by weight of binder. However, the modulus of rupture tended to decline if the YCF exceeded 6.5% by weight of binder. The optimum modulus of rupture was approximately 12 MPa and the optimum YCF content 6.5%. For MCF, the optimum modulus of rupture was nearly 13 MPa and the optimum MCF content 7%. It was also observed that the strength decreased when the optimum MCF content was exceeded. [PERSON] et al. (2018) found that the addition of coconut fiber at 5% showed the best mechanical strength in concrete. [PERSON] and [PERSON] (2019) reported that incorporating coconut fiber into concrete, at proportions ranging from 5% to 10% by weight of the binder, resulted in optimal strength. It is worth noting that the fiber-cement flat sheets made with MCF had higher bulk density and modulus of rupture than the fiber-cement flat sheets made with YCF. This is due to the young coconut fiber increasing the porosity in the cement board.  
Figure 6: The bulk density of fiber–cement flat sheets.  
Figure 7: The modulus of rupture of fiber–cement flat sheets.
# Determination of Geometrical REVs Based on Volumetric Fracture Intensity and Statistical Tests
## 2 The Establishment of a 3D Fracture Network Model
### 3D Fracture Network Modeling
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Set**} & \multirow{2}{*}{\begin{tabular}{c} **Feature** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Mean Orientation** \\ **Dip Direction (\({}^{\circ}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**Measured Mean**} & \multicolumn{2}{c}{**Corrected Trace Length**} & \multicolumn{2}{c}{**Fracture Diameter**} & \multirow{2}{*}{
\begin{tabular}{c} **Fax (m)** \\ **Dip Angle (\({}^{\circ}\))** \\ \end{tabular} } \\  & & & **Dip Direction (\({}^{\circ}\))** & **Dip Angle (\({}^{\circ}\))** & **Mean (m)** & **Std. (m)** & **Distribution Type** & **Mean (m)** & **Std. (m)** & **Distribution Type** \\ \hline
1 & 49 & 219.37 & 85.15 & 1.38 & 1.94 & 0.73 & Gamma & 2.16 & 0.64 & Gamma & 0.462 \\
2 & 18 & 294.00 & 41.48 & 1.22 & 2.16 & 1.13 & Gamma & 2.26 & 1.31 & Gamma & 0.273 \\
3 & 79 & 144.53 & 83.49 & 1.23 & 1.79 & 0.74 & Gamma & 1.80 & 0.55 & Gamma & 2.179 \\
4 & 67 & 111.23 & 36.08 & 1.80 & 2.29 & 0.98 & Gamma & 2.38 & 0.88 & Gamma & 0.826 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for the 3D fracture network modeling. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Set**} & \multirow{2}{*}{\begin{tabular}{c} **Feature** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean Fracture Orientation** \\ **Dip Direction (\({}^{\circ}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**Trace Length**} & \multicolumn{2}{c}{**Trace Type**} & \multirow{2}{*}{\
\begin{tabular}{c} **Spherical** \\ **Variance** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **K** \\ **Variance** \\ \end{tabular} } \\ \cline{3-11} \multirow{2}{*}{1} & Measured & 49 & 219.37 & 85.15 & 1.38 & 1.94 & 0.18 & 0.55 & 0.27 & 0.058 & 16.35 \\  & Simulated & 49 & 224.90 & 87.10 & 1.31 & 1.79 & 0.09 & 0.63 & 0.28 & 0.059 & 16.62 \\ \hline \multirow{2}{*}{2} & Measured & 18 & 294.00 & 41.48 & 1.42 & 2.16 & 0.17 & 0.50 & 0.33 & 0.051 & 18.80 \\  & Simulated & 19 & 295.60 & 40.50 & 1.63 & 2.06 & 0.12 & 0.62 & 0.26 & 0.053 & 19.10 \\ \hline \multirow{2}{*}{3} & Measured & 79 & 144.53 & 83.49 & 1.23 & 1.79 & 0.19 & 0.54 & 0.27 & 0.025 & 38.44 \\  & Simulated & 79 & 141.90 & 83.30 & 1.31 & 1.63 & 0.13 & 0.65 & 0.22 & 0.025 & 38.40 \\ \hline \multirow{2}{*}{4} & Measured & 67 & 111.23 & 36.08 & 1.50 & 2.29 & 0.08 & 0.46 & 0.46 & 0.072 & 23.81 \\  & Simulated & 67 & 111.10 & 32.10 & 1.69 & 2.03 & 0.01 & 0.60 & 0.39 & 0.073 & 26.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter comparison between the measured data and simulated data obtained from the optimal model.
## 3 Multi-Modal Distribution of RSSI  
RSSI can be obtained in various approaches. Many wireless chipsets calculate RSSI for individual received packets to indicate the link quality between certain devices. As each vendor has its own method to compute RSSI, this makes RSSI values technology and/or device specific. In this paper, RSSI is calculated as the average of the squared magnitude of samples in logarithmic scale, as shown below:  
\[y[n]=10\times log_{10}(\frac{1}{N}\sum_{k=1}^{N}(I[k]^{2}+Q[k]^{2}) \tag{1}\]  
where \(y[n]\) denotes an RSSI value, \(I[k]\) and \(Q[k]\) denote the in-phase and Quadrature components of the received sample, respectively, and \(N\) indicates the amount of samples used to compute one RSSI. The unit of \(y[n]\) is dB, because it is uncalibrated. As the RSSI is obtained from a continuous range of samples from the analog-to-digital converter (ADC), without any filtering in favor of a specific technology, therefore it is generic.  
In practice, the probability density function (PDF) is approximated by the normalized histogram. Empirically, the bimodal or multi-modal-like irregular RSSI distribution is linked to the sudden change of signal strength. It could be caused by external factors such as shading or variation in the signal itself, which can be further divided into: (i) discontinuous transmission in the time domain; and (ii) the change of the amount of allocated carriers in the frequency domain.
## 5 Open Challenges and Research Perspectives
### Collaborative Caching  
With computational capabilities integrated into network nodes, caching extends beyond traditional content delivery to end-users, enabling cached data to also serve as input for in-network processing. This shift requires caching strategies that consider both storage and computation needs, ensuring that data are placed not only near users but also close to execution spots to minimize processing delays and maximize efficiency.  
As cached data may be transformed or modified during in-network computations, maintaining cache consistency becomes a critical challenge [80]. Inconsistent or outdated cache entries can lead to incorrect processing results or unnecessary re-computation, undermining the benefits of INC. Effective cache management requires mechanisms to propagate updates across the network efficiently, particularly in dynamic environments with frequent data changes. Techniques such as versioning, metadata tagging, and lightweight synchronization protocols can help ensure that all nodes access consistent and up-to-date data while minimizing overhead.  
In parallel, coordinating caching and computation across a large-scale network with diverse applications is non-trivial. Decentralized caching decisions may lead to suboptimal global performance, while centralized approaches may not scale efficiently. Collaborative protocols and distributed algorithms are needed to enable nodes to share information and make coordinated caching and computation decisions.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Ocean Oil Spill (OOS)  
The importance of petroleum for human life and industry is undeniable. Every day, numerous ships move oil and, consequently, this high amount of travel increases the risk of OOS pollution. Generally, OOS may occur due to two primary reasons [222]. The first reason is transportation accidents. For instance, a Tunisian cargo ship had a collision with an anchored Cypriot container ship in the Mediterranean on 7 October 2018 [223]. Although no one was injured in this accident, it caused a fuel leak of about 600 tones. The second and the most prominent reason is the oil waste illegally discharged by tankers [222; 224]. The tankers consider illegal waste discharging the most economical option because the chance of being caught is slight and the risk of fines is low [222; 225]. When the oil is spilled on the ocean surface, it tends to form a thin layer, called an oil spill or oil slick. Ocean wind and ocean currents spread the OOS over a wide area [226]. OOS can cause severe issues to the marine environment. It can also negatively impact the population of sea birds and fish. Moreover, phytoplankton and aquatic organisms that play an essential role in the food chain are severely affected by OOS. Finally, people with livelihoods based on fishing and tourism intensely suffer from this pollution [226; 227; 228]. Among various approaches for OOS study, RS is the most cost-effective technology. It helps to collect valuable information about the spills, such as the size and extent, which are critical for cleaning up the pollution before they cause widespread damage [222]. It is suggested that the combination of satellite and airborne RS datasets must be applied to create an effective OOS surveillance system [222]. In such a system, satellite data are first used to search a wide area and rapidly identify OOS candidates before they spread over the ocean. Then, aircraft images are applied for a detailed analysis and the collection of legal evidence to identify the possible polluter [222]. There are different types of RS systems that can be effectively used for OOS detection. Table 5 summarizes these systems and their advantages and disadvantages for OOS studies. Among these, SAR and optical RS systems have been widely used for OOS mapping and monitoring. Although optical sensors have been used for OOS detection [229; 230; 231], they only acquire images during the daytime. Furthermore, they cannot provide useful images in cloudy days/regions and inclement weather conditions [226; 232]. However, airborne optical systems (e.g., hyperspectral sensors), which do not have these limitations, have frequently been employed for OOS mapping. However, the high dimensionality of hyperspectral images causes challenges in the transmission, storage, and processing phases. Additionally, a well-known phenomenon, called the curse of dimensionality, decreases the accuracy of the classification of these datasets. More importantly, hyperspectral sensors are
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 3 Results and Discussion  
### Validation of Atmospheric Parameters (Precipitable Water and Air Temperature)  
Table 1 shows the average values of precipitable water estimated MOD05 and those obtained in situ in the DC area. We note that the observed values were obtained using Equation 4 together with field vapor pressure and atmospheric pressure data. The measured data in SC showed gaps. For this reason, the model was not evaluated using SC data.  
For the DC area, the mean observed value was 29.5 mm and estimated at 29.8 mm, with a minimum percentage error of 1.5% and a maximum of 35.5%, resulting in an average percentage error of 15.2%. Here, we emphasize the importance of obtaining an estimation based only on orbital data with relatively high precision. Moreover, when it comes to operability, an important feat with satellite-derived products is accurate precision in air temperature estimates. This is important, especially due to the fact that this variable is practically restricted to data from meteorological stations. Thus, it is valuable to validate estimates based on remote sensing data. We used data from the MOD07_L2 product as a substitute for the air temperature. This product provides the inference of the atmospheric profile at 20 pressure levels, from 5 to 1000 hPa. For further details, see [55].  
The air temperature values measured in the towers and estimated from MOD07_L2 for the two study areas are also shown in Table 1. For DC, the MPE was 9.4%, and for SC, it was 8.0%, which is relatively small, especially when taking into consideration the advantage of not using point measurements of temperature in place of areal measurements, which represent all the heterogeneity of an area. In this regard, an overestimation was observed in comparison to the measured data. The estimated values showed the same pattern as those measured, following the respective increases or decreases in values. There was a closeness between the air temperatures in the two areas for most of the study days. The average air temperature obtained from MODIS data was the same for the two areas (25.9 \({}^{\circ}\)C), while the field measurements resulted in an average temperature of 28.1 \({}^{\circ}\)C for DC and 27.2 \({}^{\circ}\)C for SC, respectively. These results are similar to those found by [56] comparing remote sensing air temperature estimates with eddy flux towers in the Brazilian Amazon. The authors found errors between 3.7 and 6.6%.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 3 Network Model and Aggregation Rules
### Network Model
The gathering node is responsible for collecting and aggregating all the sensor data in the gathering area and then sending the result toward the sink, while the member node transfers its data to the gathering node. A gathering area with node \(v_{i}\) as its gathering node and \(v_{j}\),..., \(v_{k}\) as its member nodes is expressed by \(GA_{i}=(v_{i},\{v_{j},\ldots,v_{k}\})\). One node belongs to at most one gathering area. If a node \(v_{k}\) does not find a gathering node, \(v_{k}\) turns into an independent node and forms a gathering area by itself as \(GA_{k}=(v_{k},\emptyset)\). If a node is not a gathering node, it is called non-gathering node; if a node does not join in a gathering area, it is called free node. Definition 4: Candidate parent nodes of \(v_{i}\), \(CP_{i}\): A set consists of the nodes at level \(L_{i}-1\) and in the communication range of \(v_{i}\). Definition 5: Neighbor nodes of \(v_{i}\), \(N_{i}\): A set includes the nodes at level \(L_{i}\) which can communicate with \(v_{i}\). Figure 2: Overlapping rate of sensing area. Definition 6: Candidate gathering nodes of \(v_{i}\), \(CG_{i}\): A set includes all the nodes which might be the gathering node of \(v_{i}\). In AggOR, \(CG_{i}\subseteq CP_{i}\cup N_{i}\). As an instance, Figure 3 shows a transmission hierarchy diagram having three gathering areas, i.e., \(GA_{4}=(v_{4},\{v_{3},v_{6},v_{7}\})\), \(GA_{2}=(v_{2},\{v_{5}\})\), and \(GA_{1}=(v_{1},\emptyset)\). The arrows indicate the directions of data transfers. Therefore, \(v_{3}\), \(v_{6}\) and \(v_{7}\) send their data to \(v_{4}\) which aggregates these data with its own data, and then sends the results to the sink; \(v_{5}\) transfers its data to \(v_{2}\), and \(v_{2}\) aggregates these data; \(v_{1}\) sends its data to the sink directly.