esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Directional Sensing Model  
Compared to traditional omni-directional sensor node, which has a circle sensing range, the sensing area of a directional sensor node is a smaller sector-like area [16]. As shown in Figure 2, the sensing area of a directional sensor node is a sector determined by a 5-tuple \(\left(x_{s},y_{s},\overrightarrow{D},\alpha,R\right)\), where \(x_{s},y_{s}\) is the coordinate of the node, \(\overrightarrow{D}\) is the working direction of \(S\), \(\alpha\) is the angle of view (AoV), and \(R\) is the sensing radius. In addition, \(\rho\) is the angle value of working direction \(\overrightarrow{D}\) relative to the horizontal.  
Figure 2 also shows the coverage situation of a point in the monitoring area, it can be observed that point \(P\) can be covered by the directional sensor node \(S\) if and only if the following two conditions are satisfied.  
1. Let \(dis\left(S,P\right)\) be the distance between \(S\left(x_{s},y_{s}\right)\) and \(P\left(x,y\right)\), and it must be no more than the sensing radius \(R\), i.e., \(dis\left(S,P\right)\leq R\): \[dis\left(S,P\right)=\sqrt{\left(x-x_{s}\right)^{2}+\left(y-y_{s}\right)^{2}}\leq R\] (1)
2. The absolute included angle between \(\overrightarrow{SP}\) and working direction \(\overrightarrow{S}\) must be no more than the half of AoV, i.e., \(\alpha/2\) \[\delta{=}\arccos\frac{\overrightarrow{SP}\cdot\overrightarrow{D}}{ \left|\overrightarrow{SP}\right|}\leq\frac{\alpha}{2}\] (2)  
In brief, a point \(P\left(x,y\right)\) is said to be covered by the sensor node \(S\) if and only if (1) and (2) are satisfied with the sensor node coordinate \(\left(x_{s},y_{s}\right)\) and characteristic parameters \(R\) and \(\alpha\).
## 3 Mathematical Formulation
### Cooperative Game-Theory Profit Allocation Method  
In individual bidding, wind power producers (WPPs) and demand response (DR) aggregators use their own strategies to respond to regulation signals, which may lead to financial losses. However, when they coordinate, the coalition can optimize operations and submit joint bids to maximize profits. This collaborative approach lowers costs for frequency regulation and increases market participants' profits compared to individual strategies. The additional revenue is fairly distributed using cooperative game theory methods, such as the Nucleolus- and Shapley-value methods. However, both the Nucleolus- and Shapley-value-based methods have drawbacks. The Shapley-value method focuses on utilitarian distribution, disadvantaging entities with unstable contributions like wind and photovoltaic power, and lacks adaptability to special cases. The Nucleolus method fails to reflect marginal contributions, which may cause unfair incentives, reducing the enthusiasm of large-contribution entities and making small-contribution ones lack the motivation to improve.
## 4 Conclusions  
In this paper, a nondestructive inspection system was proposed to detect defects on the near-side and far-side of the boiler water-cooled wall tube, membrane, and weld and to quantitatively evaluate the size of the defects. A magnetizer manufactured in a curved shape according to the cross-sectional shape of the tube, membrane, and welding part magnetizes a portion of the water-cooled wall in the axial direction. In addition, the shape of the surface defect can be qualitatively determined from the magnetic flux density distribution measured by the magnetic sensor array deflected from the center of the magnetizer to one side. The minimum depths of surface defects that can be measured are 0.3 mm, 0.9 mm, and 1.2 mm in each case of the tube, membrane, and weld. The depth of defects located in the tube, membrane, and weld can be quantitatively evaluated with a standard deviation of 0.329, 0.269, and 0.523 mm. A method of scanning with a flexible ultrasonic probe (FUP) after applying an acoustic medium to the defect surface of the water-cooled wall, covering a thin film of PET (polyethylene terephthalate), and applying a separate acoustic medium was proposed. According to the FUP arranged in a direction perpendicular to each cross-section of the tube, membrane, and weld, the location and shape of the surface defect and the back defect can be distinguished. Furthermore, the depth of the defect can be quantitatively evaluated with a standard deviation of 0.089 mm.  
By combination of the magnetic flux leakage testing and ultrasonic testing, both the near-side and far-side defects could be detected and a quantitative evaluation of the depth could be made. Furthermore, the system is also expected to detect and evaluate the internal surface defects. For instance, if the defect is shallow in the near-surface, then the magnetic flux leakage testing is efficient for detection; otherwise, if the defect is deep to near the far-side, then the ultrasonic is more efficient. The further development of the proposed system should quantitatively evaluate different sizes of the defect such as length and width, or recognize the shape of the defects.  
Conceptualization, M.L. and J.L.; methodology, M.L.; software, M.L. and C.-T.P; validation, J.L., M.L. and E.C.; formal analysis, E.C.; investigation, J.L. and M.L.; resources, J.L. and M.L.; data curation, E.C. and M.L.; writing--original draft preparation, J.L.; writing--review and editing, M.L.; visualization, C.-T.P. and E.C.; supervision, J.L. and M.L.; project administration, M.L. and J.L.; funding acquisition, M.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 103.02-2019.342, and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.NRF-2019 RIA2C2006064).  
Figure 19: Estimation of damages’ depth using the FUP.
## 2 Materials and Methods
### Proposed Methodology
#### 2.3.2 Preliminary Identification and Ranking of Critical Constructions  
In this section, the third part of the methodology is described. The flowchart shown in Figure 4 describes the procedure for the preliminary structural monitoring and ranking of the critical constructions in a built environment.  
The first two boxes of the flowchart in Figure 4, bounded by solid blue lines, represent the initial steps of the proposed preliminary structural monitoring procedure. For each building, each cluster is sub-divided into two sub-groups, according to the fundamental features (_ASC_ and _DES_ orbits). The relative distances between all the _ASC_ and _DES_ PSs of each cluster are analyzed. Only the couples of points constituted by one _ASC_ and one _DES_ PS, having planimetric and altimetric distances less than the values of error reported in Section 2.1, at once, are considered for the next steps. For the selected couples, the values of the velocity components \(V_{V}\) and \(V_{E\text{-}W}\) are calculated according to Section 2.1 and plotted using a symbology with graduated colors. The values of \(V_{V}\) and \(V_{E\text{-}W}\) are associated to a new point, spatially located in a mean position with respect the original PSs forming the pair.  
Then, the second step regards the analysis of the deformation evolution in the monitored period by using the velocity components of the selected couples with the goal of  
Figure 4: Flowchart of the operations for the preliminary identification and ranking of critical constructions.  
providing a preliminary structural monitoring through the ranking of the criticalities (blue dashed box of the flowchart in Figure 4). In this phase, some statistical values of the velocity components are recognized in each cluster: the maximum (\(V_{V,max}\), \(V_{EW,max}\)), the minimum (\(V_{V,min}\), \(V_{EW,min}\)), and the mean velocity weighted on the values of the temporal coherence. Moreover, the maximum (\(V_{V,max}\), \(V_{EW,max}\)) and the minimum (\(V_{V,min}\), \(V_{EW,min}\)) values allow to understand if the building is interested by differential displacements, and to estimate the differential velocity amount in the considered direction. The resulting component maps are a very useful tool to easily identify the buildings most affected by displacements, and to identify critical situations, if existing. The definition of a critical building such as the actions to be taken when this condition is present will be discussed in Section 4, since they are out of the scope of this paper. Generally, once the most exposed buildings are identified, the third phase can be activated for them (blue dotted box of the flowchart in Figure 4). In this phase, it is important to combine on-site information for the single critical building or for the entire area (traditional measurements and geometrical and structural relief of the buildings) with more building-specific DInSAR measurements.
## 5 Global Spatial and Temporal Variability of Soil Salinity  
### Global  
According to [PERSON] et al. [132], the global area of soil salinization has exhibited an increasing trend over the past 30 years. The research team analyzed this phenomenon utilizing thermal infrared data from 1986 to 2016, alongside auxiliary variables and ML methods. During this period, the land area affected by soil salinization has significantly grown, cumulatively surpassing 1 billion hectares. The generated global soil salinity change maps indicate that the total area of salt-affected land increased by over 100 million hectares. Most soil salinity-affected regions experienced adjacent changes among salinity classes, such as transitions from non-salty to mildly saline and from extreme salinity to high salinity. The predominant category of increase was mildly salinized land, suggesting that an increasing number of previously unaffected areas are beginning to experience soil salinization. Concurrently, the area of moderately salinized land has declined, whereas the areas of heavily and extremely salinized land have exhibited more fluctuating changes. [PERSON] et al. [133] utilized the OCO-2 satellite's solar-induced chlorophyll fluorescence (SIF) to investigate the long-term dynamics of global soil salinity. During the study period from 2000 to 2020, a total of 54.98 million hectares of saline land were observed globally, with decreasing land areas recorded in the Americas, Asia, Europe, Oceania, and Africa, in that order. The eight countries with the largest areas of soil salinity are China, the United States, Mongolia, India, Australia, Ukraine, Mexico, and Iran. The annual rate of change in global soil salinity typically ranges from \(-\)0.05 to 0.05 dS\(\cdot\)m\({}^{-1}\)yr\({}^{-1}\), with Central and Eastern Europe, along with East Asia, showing a decreasing trend concentrated in the range of \(-\)0.05 to \(-\)0.1 dS\(\cdot\)m\({}^{-1}\) yr\({}^{-1}\). In contrast, West and Southeast Asia exhibit an increasing trend concentrated in the range of 0.1 to 0.3 dS\(\cdot\)m\({}^{-1}\) yr\({}^{-1}\). Numerous scholars have indicated that climate change contributes to soil salinization through more frequent drought events, seawater intrusion in coastal areas, and a general rise in temperature [134]. This increase may also be associated with factors such as inappropriate irrigation practices, alterations in precipitation patterns due to climate change, and inadequate land management. The absence of substantial areas with reduced salinity may be closely related to the environmental conditions and land management practices specific to a given region.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 3 Proposed Method
### Research Outline  
In this study, several approaches are conducted to validate the improved reliability of the proposed method for steel surface defects diagnostics. The approaches can be divided into two main strategies as shown in Figure 3. First of all, the machine learning (ML) based classification model is adopted for a comparative study with a deep neural network-based diagnostics model. Input images are preprocessed as statistical texture features, i.e., ASM, contrast, entropy and homogeneity, and gradient-based histograms from GLCM and HOG, respectively, where the extracted features are fed into to the ML-based model, i.e., SVM and logistic regression for categorizing into a particular class. Secondly, as a way of deep learning (DL) algorithm, the CNN model is constructed as an end-to-end framework of feature learning and classification process, utilizing the raw input image as it is. The performances of ML and DL based approaches are compared and evaluated in terms of accuracy and F1-score metric. In particular, the DL-based CNN model includes a global average pooling (GAP) operation for producing attention maps, which can enrich the interpretability of the model. Class activation maps are described to visualize the most salient regions for decision-making of the CNN diagnostics model.
# An LDPC-RS Concatenation and Decoding Scheme to Lower the Error Floor for FTN Signaling
## Appendix A Parallel RS Encoding Coefficient Calculation  
From deriving (22) to (23), we can conclude that each register in the parallel encoder can be determined linearly. Let \(p_{n}(0)\), \(p_{n}(1)\), \(p_{n}(2)\), \(p_{n}(3)\) be the constant multiplication factors for the parallel calculation of \(R_{n}(k+4)\). Accordingly, each register can be calculated as in (24). Since the register value at clock period \(k\) and parallel input data \(d_{k},d_{k+1},d_{k+2},d_{k+3}\) are all known, the problem is to compute constant multiplication factors in a concise and convenient way.  
We formed a data vector and input it into serial encoder, let it run for 4 clock periods, and stored each register's value at each clock. These values are \(p_{n}(0),p_{n}(1),p_{n}(2),p_{n}(3),\)\(n=0,1,2,\ldots,19\), respectively. Here are the steps:  
1. Initialize the serial encoder, reset all registers to zero. Input \([d_{0},d_{1},d_{2},d_{3}]=[1,0,0,0]\). Let the encoder run for 4 clocks. Store all registers' values and \(p_{n}(0)=R_{n}(4)\).
2. Initialize the serial encoder, reset all registers to zero. Input \([d_{0},d_{1},d_{2},d_{3}]=[0,1,0,0]\). Let the encoder run for 4 clocks. Store all registers' values and \(p_{n}(1)=R_{n}(4)\).
3. Initialize the serial encoder, reset all registers to zero. Input \([d_{0},d_{1},d_{2},d_{3}]=[0,0,1,0]\). Let the encoder run for 4 clocks. Store all registers' values and \(p_{n}(2)=R_{n}(4)\).
4. Initialize the serial encoder, reset all registers to zero. Input \([d_{0},d_{1},d_{2},d_{3}]=[0,0,0,1]\). Let the encoder run for 4 clocks. Store all registers' values and \(p_{n}(3)=R_{n}(4)\).  
The solution is to form zero and the unit input, then leave the calculations to the serial encoder (through MATLAB in this study). We give the following detailed explanations. The 4-parallel encoder runs for 1 clock, which is equivalent to the serial encoder running for 4 clocks. In step 1, with other terms set to zero, and applying (24), we obtained \(R_{n}(4)=0+1\cdot p_{n}(0)+0+0=p_{n}(0)\). In step 2, we obtained \(R_{n}(4)=0+0+1\cdot p_{n}(1)+0+0=p_{n}(1)\). In step 3, we obtained \(R_{n}(4)=0+0+0+1\cdot p_{n}(2)+0=p_{n}(2)\). In step 4, we obtained \(R_{n}(4)=0+0+0+1\cdot p_{n}(3)=p_{n}(3)\). Since \(p_{n}(0),p_{n}(1),p_{n}(2),p_{n}(3),n=0,1,2,\ldots,19\) will not change with \(k\), the calculation of parallel multiplication coefficients is complete. Thus, we propose a 4-parallel encoder whose structure is given in Figure 8. Furthermore, since the initial states of the registers are all zero, step 2 can be simplified as input \([1,0,0]\), letting the serial encoder run for 3 clocks, and step 4 can be simplified as input \([1]\), letting the serial encoder run for 1 clock (\(p_{n}(3)=g_{n}\)). We can accordingly propose Algorithm 2.  
Note that \(p_{n}(3)=g_{n}\) and the proposed algorithm use a serial encoder such as that of (22) to calculate \(R_{n}\) values. An example of a 4-parallel encoder structure is given in Figure 8. Other parallelism implementations are very similar to the 4-parallel scenario.
## 1 Introduction
Lightweight analysis ensures that the computational resources required for processing data remain minimal, which is crucial in resource-constrained environments like the IoT. Addressing lightweight analysis, trust, and privacy preservation in IoT research is crucial for optimizing resource usage, building trust among users, and protecting sensitive. Our research contributes to addressing the significant challenges facing current CIDSs in IoT environments. Recognizing the vulnerability of IoT devices to cyber threats and the limitations of existing security measures due to resource constraints, our study proposes a multiapproach method to enhance CIDS effectiveness. By implementing a lightweight architecture distributed across edge, fog, and cloud layers, we optimize resource usage, ensuring security tasks are performed efficiently without overburdening IoT devices. Additionally, trust management is bolstered through blockchain-based DLT, fostering decentralized and transparent communication among IoT devices to enhance reliability. Moreover, we address privacy concerns by employing FL, enabling collaborative data learning while safeguarding individual data confidentiality. Combining these methods, our adaptive CIDS is poised to effectively counter security threats in resource-constrained IoT environments. Validated using the realistic CICIoT2023 dataset, renowned for its diverse scenarios and attack vectors, our solution demonstrates efficacy in detecting and mitigating various security threats in complex IoT networks. Unlike previous research, which either lacks implementation of edge-fog-cloud architecture or focuses solely on trust and privacy, our study comprehensively addresses lightweight analysis, trust management, and privacy preservation, crucial aspects for optimizing resource usage, building trust, and protecting sensitive data in IoT environments.
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 4 Hardware/Software Partitioning Based on IBSO Algorithm
### IBSO Algorithm
#### 4.2.2 Improved Updating Strategy  
In terms of the updating strategy, in one iteration, different from the original algorithm which updates all individuals in the population, the improved algorithm only updates the optimal individual and a random selected individual in each cluster. In addition, in some SI algorithms such as PSO [7], ABC [8] and SFLA [11], an individual updates itself by moving towards other individuals which have better quality. This updating strategy is beneficial for obtaining high quality solutions. Therefore, we introduce this strategy to update the individuals in IBSO. Firstly, updating the optimal individual in the cluster. The optimal individual adds random information to generate a new individual. If the new individual is superior to the original one, replacing the original individual with the new individual. Then, randomly select an individual in the cluster and update it. The selected individual will generate a new individual by moving towards the cluster's best individual. If the new individual is superior to the original one, replacing the original individual with the new individual. Otherwise, generating an individual randomly in the solution space to replace the original one. The pseudo-code of the improved updating strategy is shown in Algorithm 3 and the calculation of \(\xi\) is the same as (4).  
It can be seen from Algorithm 3, in one iteration, the improved updating strategy does not update all individuals in the population, it only updates two individuals in each cluster(the best individual and a randomly selected individuals in the cluster), which will reduce the running time of one iteration. The updating of the optimal individual uses the method of adding random information to it. It is advantageous to the optimal individual to jump from the local region. The updating of the randomly selected individual uses the method of moving random length towards the optimal individual, which not only guarantees the difference between the randomly selected individual and the optimal individual, but also helps the randomly selected individual effectively search for better individuals. In addition, some individuals will be updated by the randomly generated individuals, which will better keep the population diversity. Based on the improved updating strategy, individuals in Figure 2a will be updated to new individuals as show in Figure 4. Where the 5 th individual is the optimal individual and the first individual is the randomly selected individual.  
Figure 3: Clustering result by the improved clustering method.  
It can be seen from Figure 4 that by using the improved updating strategy, the population diversity is better kept.  
With the improved clustering method and updating strategy, IBSO can successfully avoid the premature convergence and achieve a higher efficiency. The flow charts of BSO and IBSO are shown in Figure 5.  
Figure 4: Individuals updated by the new strategy.  
Figure 5: The flow charts of BSO and improved BSO (IBSO).
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
## 4 Applications and Accuracy Evaluation
### Data Sets  
The methodology is tested in a real case study: an academic building indoors surveyed with the Indoor Modelling System Viametris IMS3D. The dataset is provided by the ISPRS Scientific Initiative on Indoor Modelling [12] and the technical characteristics of the laser device are summarized in Table 1.  
The dataset corresponds to an acquisition carried out in a building of the Technische Universitat Braunschweig (Germany) and it includes both the point cloud and the trajectory followed by the system during data acquisition (Figure 10). The complete indoor scene comprises 10 rooms belonging to the same floor althought in this work just a set of seven rooms were processed.
## 5 Camera Placement based on HybVOR Approach  
The main purpose of this work is to propose a new approach for surveillance cameras deployment named \"HybVOR\" which belongs to the family of Landscape based methods. This approach is primarily developed for monitoring built environment. In fact, in such environment, the most important elements that should be taken into consideration in the deployment of a monitoring camera network are: Routes, building facades and entrances. It is also necessary to optimize the number of cameras while achieving the highest possible level of coverage. In our case study, which is related to the Port of Jeddah, these key elements have been confirmed by the costal guard officials (the authority responsible for protecting the security of this facility) through a questionnaire that was developed as part of this study. In addition the officials informed us that the roofs of buildings are not as important as facades or entrances. Therefore, the main objective of the HybVOR approach is to reach near 100% coverage while focusing on the following objects: (1) Routes, (2) Building facades and entrances, (3) minimum possible number of cameras.  
The HybVOR approach for deploying surveillance camera network requires a Digital Elevation Model (DEM), which represents the ground elevation of the target area. In addition, it requires a 3D vector data that models buildings and their characteristics as well as other objects that can be useful for visibility calculation (such as trees and street furniture). The required data for coverage assessment comes mainly from topographic survey of the site of interest.  
Figure 5: Voronoi Diagrams generated from lines and polygons. (**A**) Unweighted lines, (**B**) weighted lines, (**C**) unweighted polygons, and (**D**) weighted polygons (adapted from [33]).