esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Various islanding detection methods are comprehensively reviewed and presented in this paper. Islanding detection methods are classified into conventional and modern methods. The conventional methods include local (passive, active, and hybrid) and remote methods, while the modern methods include signal processing and computational intelligent based-methods. The main principle of passive methods is based on observing the network parameter variation, such as voltage or frequency changes at PCC. Passive methods have a high preference to be used in the system because passive methods are cheap and easy for practical implementation. However, when passive methods are implemented, the non-detected zone can be very large. Active methods are based on perturbation injection and analyze the influence of injection on system parameters. Hybrid methods are a combination of both passive and active methods. Both active and hybrid techniques need additional devices for perturbation, which might raise the cost and complexity of installation. Remote methods depend on data collection and communication between the utility side and the DGs side. Remote methods have a free non-detected zone, but remote methods are more complex than local methods. Signal processing-based methods are based on extracting the feature. Signal processing-based methods have the highest preference in terms of cost, reliability, non-detected zone, and accuracy in comparison to other islanding detection methods which can be generally recommended for industrial applications. Computational intelligent-based methods are based on pattern recognition and data training. Computational intelligent-based methods have a reduced non-detected zone and high reliability. However, these methods have high costs and long detection times. Moreover, based on the investigated studies, it has been shown that each method has some advantages and disadvantages, and the selection of the method to be implemented in the network can be achieved by considering one criterion or even more, such as NDZ, implementation cost, quality factor, and so on. Therefore, it is difficult to determine the most appropriate method among others to provide a complete solution for all circumstances. However, for future work, it is strongly advised to employ a multi-criteria decision-making strategy, such as a group decision-making tool, to weigh the criteria, which can provide a precise comparison to choose the ideal islanding detection technique depending on the existing applications, cases, or circumstances that the system faces.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
###### Abstract  
To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36-56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions.  
image defogging; dark channel prior; Retinex; two-stage optimization framework; depth-map-guided transmittance correction; atmospheric light intensity Article
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.4 Emergency Medicine Telemedicine Development  
Research on telemedicine in Poland has focused on collaborative efforts, technology-enhanced training, telemonitoring, and pandemic reviews to enhance emergency care delivery. Early initiatives have improved cross-border cooperation in emergency medical services (EMS) through regional coordination [111]. Telemedicine interventions have been piloted to improve the quality of cardiopulmonary resuscitation (CPR) among healthcare providers. A randomized trial showed promising results in augmenting resuscitation skills [112], whereas a pilot study evaluated telemedicine tools in simulated cardiac arrest scenarios [113]. Research on prehospital electrocardiogram (ECG) teletransmission has improved transport logistics and hospital admissions [114]. The COVID-19 pandemic prompted a review of telemedicine in emergency medicine, covering prehospital, in-hospital, and remote consultations [115]. Emergency medicine telemedicine initially focused on harmonizing cross-border EMS systems. Telemedicine platforms enhance emergency skill training and clinical competencies. ECG teletransmission expedited patient triage and cardiac care. This pandemic has accelerated the adoption of telemedicine in emergency medicine. Polish emergency telemedicine research emphasizes clinical applications rather than large trials or technological development.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 4 Adaptive Channel Allocation Algorithm
### The Adaptive Channel Allocation  
In this phase, we introduce a new decision function to decide whether to change the channel allocation dynamically or to use the same allocated channel. The concept behind the decision function originates from the limited number of links adjacent to the GW, in which the maximum traffic must pass through those links to explore the internet. The congestion around the GW's links may cause bottlenecks; therefore, the traffic over these links should be distributed evenly to avoid such a situation. The channel re-allocation should occur exactly when the uniformity of the traffic is not fulfilled by the last channel allocation.
## 4 Discussion  
The model presented in this paper can help understand the leakage development process from a micro-scale. It provides a prediction method of leakage using physics and mathematics, which helps to clearly understand the underlying mechanisms of leakage. For the shield engineering cases mentioned  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Joint Gap** & **Time** & **Roughness h** & **Groundwater** & **Leakage** \\  & **(mm)** & **(Year)** & **(nm)** & **Pressure (Mpa)** & **(L/m\({}^{2}\)/day)** \\ \hline  
\hline  & A & B & C & D & \\ \hline R\({}_{\rm j}\) & 0.123722 & 0.233642 & 0.423584 & 0.154520 & \\ \hline Rank & 4 & 2 & 1 & 3 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orthogonal array for calculations.  
above, it can reflect the influential law of factors, including the joint gap, the joint offset, roughness of the contact surface, and service time, of the EPDM gasket on leakage. It can be known that the calculated results of each influencing factor are consistent with its qualitative analysis; the slope of the self-sealing stress is similar to that in the literature, and the seepage evolution during one year is in good agreement with the observed leakage of the second Dapu Road Tunnel. These indicate that the model is suitable for analyzing the leakage of shield lining joints. However, due to the complexity of the actual engineering situation, it is hard to calculate it accurately by mathematical methods only. As a result, there are some defects in this research, especially the differences between the hypothesis and the actual situation, which are explained as follows:  
1. The measurement error of the contact surface height profile of the EPDM gasket is large, which has a great influence on the accuracy of the calculation results.
2. Consider that the groove is filled with a fully compressed gasket and it is bonded by the sealant, the model assumes that the leakage mainly occurs at the contact interface of the gaskets and ignores the effects of water pressure (lateral pressure) on the gasket to the gasket contact pressure.
3. When the groundwater pressure is large enough, the contact region will be split into several leakage channels, which is not reflected in the model. As a result, the calculation results may be relatively smaller than that in practice.
4. The model cannot consider the effects of water--swelling sealing materials.
5. The calculation results of self-sealing stress and leakage need more observed results and experiments for further verification.
# 3D Point Cloud Dataset of Heavy Construction Equipment
### Research Scope and Methods  
This research has delineated its scope to encompass the process of creating 3D laser scans and constructing point cloud datasets for five distinct types of heavy construction equipment: dump trucks, rollers, graders, excavators, and dozens. The five types of heavy construction equipment selected in this research are commonly used in earthwork construction sites. Diverse manufacturers and models were utilized for each equipment type, to ensure a varied collection of point cloud data. In this research, various diecast models for each construction equipment type were 3D scanned to form a diverse dataset. This included two models of caterpillar-type graders, five models of caterpillar-type excavators, three models of dump trucks, five models of caterpillar and wheeled-type dozens, and two models of rollers.  
Furthermore, the research was conducted in a structured sequence, as depicted in Figure 1: planning dataset construction, generating and collecting point cloud data, preprocessing point cloud data, and constructing and reviewing the dataset. To establish the point cloud dataset for heavy construction equipment, the research was initiated by reviewing prior studies on classification and segmentation datasets in the construction domain, with a focus on large 3D point cloud-based datasets for object recognition. Subsequently, the research assessed efficient data collection methods to build the heavy construction equipment point cloud dataset and evaluated its practicality. This led to the adoption of diecast models for 3D laser scanning of heavy construction equipment. The diecast models were selected for their ability to faithfully replicate the movements of actual heavy construction equipment. This method enabled the research to efficiently gather 3D point cloud data for heavy construction equipment. The next phase involved preprocessing the 3D-scanned point cloud data and organizing them into datasets in Mesh files and PCD files. Additionally, the scales of the scanned data for diecast models with varying scales were adjusted to match the actual scale of heavy construction equipment in the point cloud dataset. Lastly, the research categorized and adjusted the point density of the scanned data for heavy construction equipment into three levels, resulting in the 3D-ConHE dataset for the five types of heavy construction equipment. The research concluded with a comprehensive review and modification process for the constructed 3D-ConHE dataset.  
Figure 1: 3D-ConHE construction method and process.
## 4 Simulation Results  
To study the security of PE-based power systems, a modified version of the IEEE 39-bus test system is considered as a benchmark test system [72]. Assume all generators are synchronous machines, as is described in Table 3. All moment of inertia values are rated based on their generators' rated power. The inertia constant is calculated as \(H=\frac{1}{2}\frac{\mu\omega_{0}^{2}}{S_{b}}\), where \(J.\omega_{0}.2\pi\) and \(S_{b}\) are the inertia gain of the machine and the rated power of it, respectively. For instance, the moment of inertia for G7 equals 3.771 based on 700 MVA. G2 is the reference generator, and G1 presents the equivalent of the interconnection to the rest of the USA and Canada.  
The power grid should be able to withstand a loss of a major component in a generation trip [10]. Therefore, in this section, the loss of generator 5 (G5) is considered as the contingency. To assess the impact of the PE-based units on the security of the power system, three case studies are presented. Case study 1 presents a conventional power system without any PE-based energy generation source. Case study 2 includes a wind farm, in which a wind farm is modeled as an aggregation of wind turbines connected in parallel at the same bus. For instance, in order to substitute a 100 MVA synchronous generator with a wind farm, one hundred 1 MVA wind turbines connected at the same bus can be used. In this case study, it is shown that the conventional power system security analysis is still credible. Case study 3 presents a power system with high penetration of wind turbines. In this case study, it is shown that although the system is stable from the point of view of power system operation, due to the low center of inertia of the system, the system may enter into an unstable mode for a generation trip. All simulations are done in DigSILENT PowerFactory.  
Figure 3: PE-based power system security assessment. (**a**) Static security assessment, (**b**) dynamic security assessment, (**c**) and transient security assessment.  
It should be noticed that, in this study, wind turbines are controlled as the grid-feeding converters [73]. Based on that, it could be replaced with other PE-based energy sources, that are controlled in the same mode, such as photovoltaics. In this way, this study can be extended to the penetration of other types of PE-based units. However, other control modes of converters, such as grid-forming converters, can have different impacts on the system-level stability.
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 6 Discussions
### Generating Block Identifiers  
As discussed in Sections 4 and 5, a main contribution of SDV is that documents are broken into many blocks, and the encrypted blocks are submitted to the cloud storage servers. In order to be able to identify each single block, an identifier (or a filename) must be assigned to the block. Different blocks may contain identical content and we must avoid name collision and ensure uniqueness of each identifier. Hence we derive the name of the block by hashing the first few bytes together with a timestamp using SHA256. The reason we do not hash the entire block is due to computation efficiency, especially if a block is configured to be of large size (e.g., 50 MB).
## 4 Matrixes for Enzyme Immobilization
### Synthetic Organic Carriers  
Synthetic polymers, unlike natural biopolymers, are engineered for high purity, defined composition, and homogeneity [62]. They are ideal for enzyme immobilization, especially due to their tunable fiber synthesis via molecular weight control [63]. Common choices include polyvinyl alcohol (PVA), polyethylene oxide (PEO), polylactic acid (PLA), polyethylene glycol (PGA), polyacrylamide (PAN), polymethyl methacrylate (PMMA), polycaprolactone (PCL), polystyrene (PS), polysulfone (PSF), and polyurethane (PU) [64]. While most require organic solvents for electrospinning, PEO and PVA stand out for their water solubility and enzyme compatibility.  
PVA is particularly favored for its low cost, hydrophilicity, and reactive hydroxyl groups, enabling efficient enzyme and cell immobilization [65]. When functionalized, PVA enhances the continuous processing of flexible carrier-enzyme composites and improves enzyme hydrolysis performance, and its fibers are effective in enzyme hydrolysis [63]. Similarly, P4 VP (derived from pyridine), leverages vinyl groups and nitrogen atoms to form strong hydrogen bonds, enhancing enzyme attachment and stability [66]. PAM further excels with its high-water absorption, gel-forming, cross-linked structure, promoting enzyme stability and recyclability [67].  
Polystyrene and polypropylene exhibit robust mechanical and chemical properties, making them ideal for industrial and bioreactor applications due to their high enzyme activity, stability, and reusability [68; 69]. In tissue engineering, synthetic polymers such as PCL, PLA, and PLGA are widely used for their favorable mechanical properties, and cost-effectiveness [70]. However, their environmental persistence and potential to form microplastics raise significant concerns.  
Studies have highlighted the environmental impacts of polyacrylates, PVP, PEG, and PAA on aquatic life [71; 72], while under-researched polymers like polyethylene and polypropylene warrant further scrutiny [73]. These materials can also pose human health risks, including oxidative stress; apoptosis; and neurological, cardiovascular, and gastrointestinal effects [74; 75]. To address these concerns, researchers are developing biodegradable alternatives, including hybrid systems employing biopolymers (e.g., starch or chitosan), although some composites may still generate microplastics [49].
## 3 Problem Definition and Formulation
### Constraints
#### 3.2.5 Delay Constraint  
The delay constraint is critical in preventing excessive delays that could affect user experience and system reliability.  
**Assumption 3**.: _Network and processing delays may vary depending on the availability of resources such as bandwidth and storage at the selected node._  
The delay constraint is given as  
\[\sum_{i=1}^{N}\sum_{j=1}^{M}\delta_{ik}^{j}X_{ij}\leq L_{\max},\ \forall k \tag{10}\]  
This constraint requires that the total delay for tasks received by each user terminal \(k\) does not exceed the maximum allowable delay \(L_{\max}\). Here, \(\delta_{ik}^{j}\) represents the delay for task \(j\) from satellite node \(i\) to user terminal \(k\); \(X_{ij}\) indicates whether task \(j\) is assigned to satellite node \(i\); and \(L_{\max}\) is the maximum delay acceptable for user terminal \(k\).
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Experimental Design  
Results are obtained via 7-fold cross-validation on the full dataset. For each dataset fold, separated as described in the previous section, one run consists of a training phase on all the other 6 folds. Among them, one is kept as validation set, while 5 folds compose the actual training set. Given a test fold, the validating one is selected in such a way as to have a minimum cardinality equal to 20% of the remaining dataset. For each model configuration, cross-validations are repeated multiple times, averaging the results, to make performance independent from the starting conditions.  
Motivated by the limited dataset size and the class imbalance, data augmentation techniques have been applied on the training set to increase the variability of the data at each epoch, performing random rotations, horizontal/vertical flips, and random shears. The two backbones of the DSF are trained separately, freezing one network's weights when the other is being trained. After applying data augmentation, the binary-class backbone is trained on the set images organized in batches of 16. Adam optimizer is used with a fixed learning rate of \(1\times 10^{-4}\). Then, the regression backbone training is performed with the same parameters. We chose such parameters after applying a grid search on possible values within specified ranges. We observed no relevant changes in model performance when moving to learning rates of the same order of magnitude as the proposed one. Results instead rapidly decrease when moving to higher or lower values. Concerning batch size, we chose the highest one allowing our hardware settings to train all the models, although slightly different values are observed to make no significant variations in the results. Each of the processes is terminated either after 50 epochs or by means of an early-stopping mechanism with a patience of 5 epochs and a tolerance of \(1\times 10^{-3}\) evaluated on the validation loss. We observe that most of the models training phases of the models get stopped before the 30 th epoch. Within this range, no signals of overfitting were found in the loss function trend curves, suggesting that our configuration effectively prevents related issues.  
The evaluation metrics we adopted are Root Mean Squared Error (rMSE) for the severity prediction and IoU on the intermediate step. Due to dataset class imbalance, the first metric is computed separately for each severity level for a proper evaluation. Specifically, we compute the rMSE between all the ground-truth pixels with the target level and the corresponding pixel on the neural network predictions.
## 5 Conclusions  
In this paper, two sector beam scanning approaches based on the EPP technique, denoted as the EPP-BSA and the SLL/EPP-BSA, are introduced. Based on prior simulations, we can conclude that the proposed EPP-BSA provides wider scanning ranges than the proposed SLL/EPP-BSA but with much higher SLLs at the endpoints of the scanning range. In this regard, the proposed SLL/EPP-BSA may be useful in interference-sensitive applications that require small scanning sectors. The proposed techniques are based on modifying the EPP pattern rather than using phase shifters while maintaining the elements excitations. This simplifies the production process and lowers the cost of the feeding network. Furthermore, the proposed technique eliminates interference and coupling between the main beam and the back lobe at any scanning angle by maintaining a 180 spacing angle between them. Furthermore, a proposed PAA thinning approach is proposed to realize the proposed EPP-BSA approach. It allows for precise array thinning to select the desired active \"ON\" antenna elements and turn off the other elements to configure the designed EPP pattern to achieve a specific scan angle with the desired radiation properties. The results are realized using the computer simulation technology (CST) microwave studio software package, which is highly matched to the MATLAB simulations.  
**Author Contributions:** Methodology, A.M.E.; Software, S.I.A.E.; Writing--review & editing, A.H.H.; Supervision, A.E.A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.