esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Community Detection on Induced Metric Space
### Convergence  
Convergence of the network community detection algorithms is the least studied research area of network science. However, the rate of convergence is an important issue, and a low rate of convergence is the major pitfall of most of the existing algorithms. Due to the transformation into the metric space, our algorithm is equipped with the quick convergence facility of the k-partitioning on the metric space by providing a good set of initial points. Another crucial pitfall suffered by the majority of the existingalgorithms is the validation of the objective function in each iteration during convergence. Our algorithm converges automatically to the optimal partition, thus reducing the cost of validation during convergence.  
**Theorem 2**.: _During the course of the \(k\) center partitioning algorithm, the cost monotonically decreases._  
Proof.: Let \(Z^{t}=\{z_{1}^{t},\ldots,z_{k}^{t}\}\), \(T^{t}=\{C_{1}^{t},\ldots,C_{k}^{t}\}\) denote the centers and clusters at the start of the \(t\)-th iteration of the \(k\) partitioning algorithm. The first step of the iteration assigns each data point to its closest center; therefore, \(cost(T^{t+1},Z^{t})\leq cost(T^{t},Z^{t})\).  
In the second step, each cluster is re-centered at its mean; therefore, \(cost(T^{t+1},Z^{t+1})\leq cost(T^{t+1},Z^{t})\).  
**Theorem 3**.: _If \(T\) is the solution returned by farthest-first traversal and \(T^{o}\) is the optimal solution, then \(cost(T^{o})\leq cost(T)\leq 2 cost(T^{o})\)._  
Proof.: The proof of the theorem can be obtained in [48].
# Silica-Coated Magnetic Nanocomposites for Pb\({}^{2+}\) Removal from Aqueous Solution
## 2 Materials and Methods
### Modeling of Sorption Isotherms, Kinetics and Thermodynamics  
In the adsorption process, dissolved Pb\({}^{2+}\) ions are binding to the adsorbent surface through physical or physico-chemical interactions until the equilibrium is reached. The equilibrium adsorption capacity (q\({}_{\rm e}\)) was calculated using Equation (1):  
\[\mathrm{q_{e}}=\frac{(C_{0}-C_{e})V}{m} \tag{1}\]  
where: q\({}_{\rm e}\)--equilibrium adsorption capacity (mg\(\cdot\)g\({}^{-1}\)); C\({}_{0}\)--initial concentration of metal ions (mg\(\cdot\)L\({}^{-1}\)), C\({}_{\rm e}\)--equilibrium concentration of metal ions (mg\(\cdot\)L\({}^{-1}\)); V--volume of aqueous solution containing metal ions content (L); m--mass of adsorbent (g).
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 3 The Novel Dihedral CR and SAR Data Set
### The Multi-Resource SAR Data  
Figure 3c shows the TSX and Sentinel dataset on the Huangtupo slope, as well as the installation time and on-site photos of various CR types. The imaging parameters of multi-satellite SAR data are presented in Table 1. The local incident angles for the ascending and descending TSX-spotlight data are 38\({}^{\circ}\) and 29\({}^{\circ}\), respectively. For the Sentinel data on the Huangtupo slope, there is an overlap between the third swath in the 084 track and the first swath in the 011 track. The local incident angles of these two tracks are 44\({}^{\circ}\) and 32\({}^{\circ}\), respectively.  
Figure 5 illustrates the average intensity of the image of the CR array on the Huangtupo slope with the Sentinel and TSX data. Since most CRs are installed in vegetation-covered areas with low reflection energy, the CR targets can be visually identified. In general, CRs  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SAR Sensor** & **TSX** & **Sentinel** \\ \hline band/wave length & \(\text{X}/3.1\) cm & \multicolumn{2}{c}{C/5.6 cm} \\ \hline orbit & Ascending & Descending & Ascending (084) & Ascending (011) \\ \hline local incident angle & 38\({}^{\circ}\) & 29\({}^{\circ}\) & 44\({}^{\circ}\) & 32\({}^{\circ}\) \\ \hline range resolution & 0.9 & 2.3 & \\ \hline azimuth resolution & 1.3 & 13.9 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main parameters of the SAR data.  
Figure 4: The structure of the novel dihedral CR; (**a**,**c**) are the dihedral CR with semicircular and square panels; (**b**,**d**) are the corresponding trapezoidal structure.  
appear in radar images as approximately 2 \(\times\) 2 or 3 \(\times\) 3 pixels in the Sentinel data. In the overlapping Sentinel data or the ascending and descending TSX data, the maximum difference between the deviation angles under two tracks is approximately 12\({}^{\circ}\), resulting in nearly 3 dB pixel intensity difference.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike-Encoding Methods  
There are two commonly used spike-encoding methods: rate encoding and temporal encoding. In image processing, these methods involve SNNs and generate spike sequences that contain varying numbers, intervals, and levels of information. They involve SNNs that can be applied to a wide variety of problems.
## 3 Materials and Methods
### Data Generation
#### 3.2.1 Flow-Based Approach  
The flow-based approach looks at high-level descriptions of communication between devices and can be more efficient, reducing the amount of information that needs to be analyzed [24]. In this research, packet-based data from the network were converted into a flow-based format using the Argus tool in order to facilitate machine learning-based approaches at a convenient scale [25].  
Figure 3: Block diagram of the IoT device.  
While IoT devices generally communicate with the required server over the Internet, normal and attack traffic, in particular, need to be executed simultaneously. This requires a particular Wireshark tool to capture raw packet data and store them physically in the Ubuntu Server. As the testbed environment is experimental and it is clear which IP address belongs to the attack traffic, it is more convenient for the job to observe the data once generated.
## 6 Case Analysis
### Verification of Photovoltaic Active Power Additional Control Method  
To verify the effectiveness of the ULFO suppression capacity of the PV additional controller proposed in this section, the controller parameter \(K\) was set to 250. The capacity of the PV station was 10 MW. In order to verify the effectiveness of the controller, two adjustment effects, one-way and two-way, were compared and analyzed, and the reservation of photovoltaic capacity w modified as no reservation, 10% reservation, and 30% reservation. The comparison of the system frequency response before and after adding the controller is shown in Figure 14.  
Figure 14: Comparison of system frequency response before and after adding controller in different PV reservation capacities.  
Figure 13: Comparison of system frequency response under different control methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(K_{P}\) & \(K_{I}\) & \(K_{D}\) & \(T_{G}\) & \(T_{W}\) \\ \hline
3.46 & 0.57 & 0 & 0.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of hydro turbine governor optimized by the traditional method.  
It can be seen in the above figure that after adding the PV additional controller, ULFO was better suppressed. With an increase in PV reservation, the oscillation suppression effect was better, but the utilization rate of PV generators was reduced. Therefore, taking into account the reserved capacity of PV generators, 10% was the optimal selection.
## 3 Results and Discussion
### Application of Artificial Intelligence, Predictive Analytics, and Digital Twins
#### 3.4.2 Anomaly Detection and Fault Diagnosis Using Machine Learning  
ML techniques have proven effective in anomaly detection and fault diagnosis within microgrid environments, supporting preventive maintenance and reducing operational costs [19; 20; 21]. Supervised learning models are frequently applied for fault classification, while unsupervised learning approaches help detect unknown anomalies based on operational patterns [54; 55; 56].  
Recent studies highlight the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyze time-series data and identify critical failures in real time [16; 17; 57]. Ensemble learning techniques, including Random Forest and Gradient Boosting, further enhance the accuracy and robustness of fault diagnosis frameworks [36; 83]. However, the effectiveness of these models depends heavily on access to large, representative datasets, which remains a significant limitation [58; 59; 60]. Addressing this challenge requires the development of data-sharing frameworks and federated learning models that enable distributed data utilization while preserving privacy [22; 31; 87]. Recent studies highlight the use of convolutional neural networks (CNNs) and deep reinforcement learning models to identify optimal energy management strategies in smart microgrids operating under uncertainty [88].
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 4 Implementation of the Wireless Node
### Hardware Node: UCODALI Board
#### 4.1.4 NodeID Selector  
This block consists of two 8 encapsulated microswitches and two 8-to-1 multiplexers. A 16-bit node identifier is set by means of the microswitches. In this way, if an action event on a streetlight takes place, this event will be easily identified by the technician. Due to the limited number of I/O pins on the CC2530 GEM module, the address is read through two 8-to-1 multiplexers, one for each microswitch. In this way, just three signals are needed to control both multiplexers at the same time, plus two to read the data. Once the data have been read, the application will be able to process them correctly and obtain the 16-bit identifier (nodeID).
## 3 Experimental Part
### Photogrammetry  
Photo recording was carried out with UAV (Unmanned Aerial Vehicle), i.e., DJI Mavic Pro Platinum (SZ DJI Technology Co. Ltd., Shenzhen DJI Sciences, Technologies Ltd., Nanshan, Shenzhen, China), equipped with a standard 3-channel RGB camera, and hand-held digital cameras (Sony alpha a6000 and alpha 7 mk I, Sony, Minato, Tokyo, Japan) for capturing the parts of the sculptures that were inaccessible for drone recording. For certain wooden sculptures (exhibited outdoors), the placement of the sculpture (proximity to other objects such as trees, walls, etc.) also makes it necessary to combine (image) captures from different devices. The correct import and coordination of the photographs in photogrammetry programs was required further on (for the program to treat them correctly in terms of focal length, lens, angle, spherical aberrations). The number of drone photographs averaged 380, and we used between 50 and 150 additional photographs, depending on the sculpture. Photogrammetry was performed in Agisoft Metashape (Agisoft LLC, Sankt Petersburg, Russian Federation). Some sculptures required mesh correction, model, and texture optimization for real-time web presentation after the polygonal mesh was created(missing parts of the sculptures). This was carried out in Blender (Blender Foundation, Amsterdam, Netherlands).  
The Tower of Babel was photographed three times from 209 to 350 photos and photogrammetrically processed in Meshroom (AliceVision framework, Paris, France) and Agisoft (Metashape). The photogrammetry process in Agisoft included the steps Align photos, Build dense cloud, Build mesh and Build texture. In Meshroom, the process consisted of Cameralnit, Depth Map, Depth Map Filter, Feature Ex-traction, Feature Matching, Image Matching, Mesh Filtering, Meshing, Prepare Dense Scene, Structure from Motion, and Texturing. Figure 2c shows the texture of MeshRoom and Agisoft. The results in both cases were satisfactory for further work; however, we used models and textures from Agisoft. Figure 2d shows the presentation of the sculpture on the Sketchfab web 3D viewer.  
Figure 2: Cont.
## 4 Results
### Pattern of Arriving Ship's Trajectory
#### 4.1.2 Waterway Phase  
The waterway phase is the process in which ships enter the berth from the waterway, as shown in Figure 11a, for Leg 2. As the SOG changes gradually, the average is 9.47 knots, but it features various distributions as displayed in Figure 14a. Among these, 10-11 knots is the most frequent, with 144 counts. According to Figure 14b, the COG is the most common at 335 to 340 degrees, with 580 counts, similar to the direction of the waterway, with an overall average of 335.11 degrees.  
The time series analysis of the waterway phase was divided into 11 stages, from 0 to 10, and is represented as a boxplot, which is shown in Figure 15. According to Figure 15a, ships entering at the average of 7.78 knots at the starting point gradually increase the SOG to a maximum average of 10.88 knots, and then, at the final point, the average again decreases to 10.58 knots as the ship enters the next turning section. The COG is shown in Figure 15b and is kept constant between about 330 and 340 degrees overall, but at the last point, it is determined at an average of 331.49 degrees. The summarized descriptive statistics are shown in Table 6.  
Figure 14: Frequency analysis of the waterway phase: (**a**) SOG; (**b**) COG.  
Figure 15: Boxplot time analysis of the arrival waterway phase: (**a**) SOG; (**b**) COG; * the time series analysis for ship trajectories in this phase is expressed in 11 stages, from 0 to 10, by multiplying the result scaled in Equation (1) by 10. In addition, the boxplot was used to visually express the descriptive statistics for 11 stages.
# Practical Approach for Assessing Wetting-Induced Slope Failure
## 1 Introduction
\(\psi\) is soil suction, \(s_{L}\) is the suction at the lower limit of integration (corresponding to gravimetric water content \(w(s_{L})\)) and is commonly taken as the air-entry value (AEV), \(s_{U}\) is the soil suction at the upper limit of integration (corresponding to gravimetric water content \(w_{U}\)) and is commonly taken as residual soil suction [1] or 1 GPa [14]. The coefficient of permeability for unsaturated soil (\(k\)) can then be defined as:  
\[k=k_{s}k_{r}(s) \tag{10}\]  
The shear strength (\(\tau\)) of unsaturated soil is commonly represented by using extended Mohr-Coulomb [2], which can be written as:  
\[S_{a}^{*}=1-\left\langle 1-S_{a}\right\rangle \tag{2}\]0  
where \(c^{\prime}\) is effective cohesion, (\(\sigma-u_{a}\)) is net normal stress, \(\phi^{\prime}\) is the effective friction angle, \(u_{a}\) is pore-air pressure, \(u_{w}\) is pore-water pressure, and \(\phi^{b}\) is the angle representing change in shear strength due to change in soil suction. Based on some experimental values, \(\phi^{b}\) ranged from 1/3 to 2/3 of the \(\phi\). Estimating unsaturated soil properties is a way to make unsaturated soil mechanics become practically applicable for routine industrial practice. However, it is important to verify whether the estimated parameter is able to capture the mechanism of slope failure. Back analysis of slope failure is one of the tools used to verify whether such a routine can be adopted. Based on the issue which has been raised in this paper, the objective of this paper is to verify whether estimating unsaturated soil properties based on an empirical approach is sufficient to capture the failure mechanism of rainfall-induced slope failure. Cimanggung slope failure will be used as a case study in this paper.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 4 Experimental Results and Discussions  
### Environment  
We implemented and tested our proposed idea configuration using two computers. One computer is Raspberry Pi 4 model B, and another computer is a personal computer that uses Windows OS, as seen in Table 2.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 1 Introduction
### Contributions  
This study introduces a regression model for the likelihood probability in time-varying MIMO systems using one-bit ADCs. To successfully achieve this, two types of likelihood probabilities are considered: the channel-based likelihood probability, which captures the variations in the channel over time, and the sample-based likelihood probability, which represents the likelihood probability of decoded data affected by the time-varying channels. The proposed method formulates a regression problem that aims to optimize the difference between the true likelihood probability and the regression of the channel- and sample-based likelihood probabilities. Key contributions include the following:  
* Two probabilities influenced by time-varying channels are derived. Channel-based likelihood probability, which tracks the change in the likelihood probability based on the channel model, is simplified to reduce computational complexity compared to [38]. In addition, a sample-based likelihood probability is introduced to exploit the received data that contain the likelihood information. Unlike the conventional sample-based likelihood probability in [37] which relies on a detected symbol, the proposed approach leverages the output of the decoder. These two probabilities are then used to estimate the true probability.
* An optimization problem is formulated to minimize the MSE between the true and estimated likelihood probabilities derived from the channel- and sample-based likelihood probabilities. Unlike previous work [19], which requires complex algorithms such as RL algorithms, the proposed method solves this problem using a regression model, eliminating the need for such intensive computation. Instead of heavy computation, the proposed method exploits the precomputed optimal weights stored in memory to calculate the regression-based likelihood probability efficiently. To further optimize the calculation, LASSO regression is introduced, allowing the model to adapt to gradual channel changes under the assumption of slow time-varying channels.
* Simulations demonstrate that the proposed method outperforms conventional methods on both slow and fast time-varying channels. This improvement results from the optimal weights, which enhance the reliability of the channel- and sample-based likelihood probabilities by fully utilizing the channel statistics and output of the decoder.
## 1 Introduction
Better results are most frequently obtained using supervised classification techniques [34]. However, in the case of muddy coastal zones, to achieve good results, these methods may require extensive and impracticable ground truth verifications. Even with the current advances in remote sensing products and techniques, the differentiation of individual intertidal species, an important aspect in the assessment of biological invasions, is still a difficult task [10] and usually comprises large errors and confusion with other features. In this study, we aim to assess the potential of multispectral images, acquired by the Pleiades-1 satellite constellation, in order to discriminate the invasive cordgrass, _Spartina anglica_, from the remaining marsh vegetation in the Bay of Arcachon (SW Atlantic coast of France). The evaluation exercise is based on NDVImaps and the use of pixel classification methods--either supervised (Random Forests) or optimized unsupervised (simulated annealing) approaches--applied to Pleiades images, which were acquired in different seasons. Any advances in the accurate identification of the different salt marsh structures will enable a better understanding of the mechanisms and extent of colonization by invasive species and allow for the monitoring of its evolution, facilitating an early intervention if needed.
## 2 Materials and Methods
### Data Pre-Processing  
The system input data are CXR in the JPG format. The source of the images is uncertain. They might come in different formats, usually DICOM or JPG. They also may have different resolutions, sizes, and qualities. To establish a standardization process for the input data, facilitate the model feature extraction and learning, and reduce training time, we perform a pre-processing routine [31]. Three preprocess routines correct the edges of the images, cut a bounding box with the lung area, resize it to 299 \(\times\) 299 pixels, normalize the data between 0 and 1, and execute a histogram equalization to improve the contrast.  
We decided to maintain the standard 299 \(\times\) 299 pixels image input size of the Inception V3 architecture. A study on the effect of image resolution on DL in radiography by [32], identified that maximum AUCs were achieved at image resolutions between 256 \(\times\) 256 and 448 \(\times\) 448 pixels for binary decision networks targeting emphysema, cardiomegaly, hernias, edema, effusions, atelectasis, masses, and nodules. Although the impact of resizing the image in this work is not completely clear, we assumed this resolution had low interference in the feature detection ability of the models.  
There are many images with a concentration of pixels in a reduced number of colors, which makes it difficult for the model to identify the inner region of the lung. Therefore, we apply a color histogram equalization to standardize and improve the images, as observed in Figure 5.  
To expand the assertiveness of the classification models and their ability for generalization and noise tolerance, we used a technique known as data augmentation. This technique aims to expand the training database of the deep learning models by generating new images from the original dataset, with the intentional introduction of variations in color, brightness contrast, flips, rotations, or spatial distortions. After trying multiple options, we encountered better results when introducing variations in the gamma contrast. In this way, two new images were created from each original image, tripling the training and validation datasets, which generated, in total, 132,093 images.  
Figure 4: Dataset class distribution.