esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
The void hole problem has a long-lasting effect in the acoustic environment that attracts lots of attention from the research community. In order to address this problem for the sake of performance analysis and evaluation, we performed extensive simulation against the different number of dead nodes: packet transmission, energy tax, transmission loss, and path loss mechanism with state-of-the-art techniques like EBER\({}^{2}\), AEDG, ASEDG, WDFAD-DBR, AVH-AHH-VBF, and TANVEER. Simulated results showed that our proposed scheme performed better to increase high packet transmission and minimum transmission loss and path loss with affordable E2E delays. Therefore, we offered a novel avenue that has rotational orbits based on its mechanism of inter-node adjustment that has affordable E2E, which is 0.2 and remains constant. The EC and network lifetime also had good simulation results due to their path adjustment (that had orbital shapes) mechanisms. We also improved the packet transmission through this scheme around 68-92%, even in a dense network. Comparatively, all other schemes were not yet experimental when the network was dense (say 500 number of the node). Overall, we can conclude that the ROBINA outperformed against EBER\({}^{2}\) and WDFAD-DBR in terms of E2E and transmission loss, which was 60% higher than the abovementioned. Moreover, the AEDG and ASEDG were experimental against packet success ratio and path loss the proposed scheme, ROBINA, have similar results due to the dense avoidance of void hole problems compared with TANVEER and its variants. In the future, we have the intention to simulate ROBINA and propose a similar technique that enables blockchain technology for very large-scale networks with a testbed, large oceanic environment.  
[PERSON] and S.Y. proposed the research conceptualization and methodology. The technical and theoretical framework was prepared by [PERSON], T.A. and A.A. The technical review and improvement were performed by A.A. and T.A., N.Z. and D.-Y.S. performed the overall technical support, guidance, and project administration. The editing and, finally, proofreading was performed M.H.J. and Z.B.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2021-2015-0-00742) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).  
Figure 18: Analysis of energy tax compared with BIN-TANVEER and PL-ROBINA.
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Thermogravimetric (TGA) Analysis  
Figure 10 displays the TGA and DSC thermograms of the (a) UPSD and (b) cellulose fibres (CF) in the temperature range of 25-900 \({}^{\circ}\)C. Figure 9a shows an apparent weight loss that occurred at three different temperature ranges for both graphs. In the temperature range of 25-100 \({}^{\circ}\)C, the first phase of weight loss results in a decrease of nearly 6.82% for UPSD (Figure 10a) and 5.13% for cellulose fibres (CF), as demonstrated in Figure 10b through the TGA and DSC thermograms. This could be attributed to removing water molecules from the surface of the adsorbent, which is known to possess hygroscopic properties [7, 44]. The second stage of weight loss is observed to commence around 100 \({}^{\circ}\)C and is completed at 350 \({}^{\circ}\)C, resulting in a reduction of approximately 56.6% for UPSD (Figure 10a) and 68.95%, as shown in Figure 10b through the TGA and DSC thermograms, for both UPSD and cellulose fibres (CF) in the temperature range of 25 to 900 \({}^{\circ}\)C, utilising a heating rate of 10 C/min. The mass loss for UPSD may be attributed to the breakdown of hemicellulose and some portions of lignin at 300-450 \({}^{\circ}\)C. Additionally, the massive loss for the cellulose fibres (CF) (Figure 10b) thermogram may be due to the thermal decomposition of the carbonyl in the adsorbent surface [23].  
In the third phase, from 350 to 700 \({}^{\circ}\)C, a weight loss of 14.22% for UPSD (Figure 10a) corresponds to the decomposition of cellulose at about 450-700 \({}^{\circ}\)C. The weight reduction in cellulose fibres (CF) depicted in Figure 10b, which amounts to 17.49%, occurs at a temperature above 400 \({}^{\circ}\)C and is ascribed to the carbonisation mechanism of cellulose chains. Moreover, the morphology of untreated pine sawdust (Figure 10a) does not exhibit significant changes in contrast to cellulose fibres (CF) (Figure 10b) because of the existence of lignin, which comprises numerous aromatic rings with diverse branches [45].  
The DSC thermograms in Figure 10 demonstrate that UPSD and cellulose fibres (CF) remain in a consistent chemical and physical state throughout the temperature range of 30-350 \({}^{\circ}\)C. Additionally, the thermogram illustrated in Figure 10a displays a reduction in weight between 250 \({}^{\circ}\)C and 450 \({}^{\circ}\)C, which could be attributed to the breakdown of hemicellulose. Similarly, in Figure 10b, the DTA peak observed at approximately 350-450 \({}^{\circ}\)C shows a slight weight loss of about 20%, which may also be associated with the decomposition of cellulose in the sample. In the DTA graph (Figure 10b), cellulose fibres are stable up to 450 \({}^{\circ}\)C [46].  
Figure 9: Point zero charge of cellulose fibres (CF).
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 5 The Severity Assessment of the Corrugation
### Severity Assessment Based on Fuzzy Theory
#### 5.3.1 Image Binarization and Corrugation Area Localization  
In general, image binarization can reduce the amount of computation and can simplify the localizing process of each corrugation area. Therefore, the adaptive threshold algorithm, mentioned in Reference [42], is used to get the binarized image of the corrugation saliency map, in which the adaptive threshold \(T_{a}\) is computed as follows  
\[T_{a}=\frac{2}{w\times h}\sum_{x=0}^{w-1}\sum_{y=0}^{h-1}S(x,y) \tag{26}\]  
where \(w\) and \(h\) stand for the width and height of the corrugation saliency map, respectively. \(S(x,y)\) denote the saliency value at the coordinates \((x,y)\).  
After that, with the intention of localizing each corrugation area more accurately, the mathematical morphology algorithm is used. Firstly, the morphological closing operation is used to merge smaller regions without changing the overall region, that is  
\[S_{m}=(S_{b}\oplus b)\Theta b \tag{27}\]  
where \(S_{b}\) denotes the binarized saliency map. \(b\) is the structure element used in this paper. \(\oplus\) and \(\Theta\) represent dilation operation and erosion operation, respectively. Subsequently, the label matrix method is used to decide the position coordinates of each corrugation area, which is given by  
\[S_{c_{i}}=S_{m}(L_{s}==i),i=1,2,...,k \tag{28}\]  
where \(S_{c_{i}}\) represents the i-th corrugation area. \(L_{S}\) stands for the label matrix of \(S_{m}\). \(k\) is the number of corrugation area. The visual results of the used mathematical morphology algorithm are shown in Figure 13, where the scope of each corrugation area is accurately located without interference.  
Figure 12: The pipeline of corrugation severity assessment method.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 9 Tackling LoRaWAN Challenges and SWOT Analysis
### SWOT Analysis of LoRaWAN  
In this section, we do a SWOT (strengths, weaknesses, opportunities and threats) analysis of LoRaWAN.  
LoRaWAN main strengths are that it offers cheap end devices and possibilities of deploying private networks. The latter characteristics remove the need for subscription (such as in SigFox or NB-IoT), decreasing the operational costs for dense IoT application deployments. Other strengths include large coverage with single gateway and low power operation for end nodes. However, such characteristics can be achieved with other network solutions, e.g., SigFox.  
The main current weaknesses include security issues (especially for LoRaWAN v1.0), network scalability and malfunction of ADR mechanism in congested traffic. However, there are a number of opportunities to enhance and improve such issues. Many security vulnerabilities are already addressed with LoRaWAN v1.1, except bit flipping attack between NS and AS. Regarding network scalability, different opportunities can be exploited, such as low-power transmission synchronization,low-overhead transmission scheduling, listening-before-talk mechanisms for duty cycle exemption and other more intelligent MAC schemes.  
Not suitable for downlink traffic can threaten LoRaWAN's application in cases when closed-loop communication is needed. This includes many sensors and actuators network when it is crucial to support downlink too. The main threat will continue to be outsider interference as LoRaWAN operates in unlicensed bands. Different techniques to quantify and alleviate it should be considered in the future.  
Table 8 lists the SWOT analysis for LoRaWAN.
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 2 Dynamic Cable Rating
### Determination of Cable Parameters
#### 2.2.3 Static Temperature Calculation  
IEC 60287-1-1 describes the calculation of current rating and losses at 100% load. However, with the method proposed in this paper, more accurate results can be achieved. In the proposed method, it is assumed that the cable loading is known. IEC assumes a maximum conductor temperature of 90 \({}^{\circ}\)C; for the proposed method, this value is calculated by an iterative process with the steady-state reached temperature given by Equation (15) [10].  
\[\theta_{c}=\left(I_{r}^{2}\cdot R_{ac}+\frac{W_{d}}{2}\right) \cdot T_{1}+\left[I_{r}^{2}\cdot R_{ac}(1+\lambda_{1})+W_{d}\right]\cdot 3 \cdot T_{2}\\ +\left[I_{r}^{2}\cdot R_{ac}\cdot(1+\lambda_{1}+\lambda_{2})+W_{ d}\right]\cdot 3\cdot(T_{3}+T_{4})+\theta_{A}\quad[^{\circ}\text{C}] \tag{15}\]
# Three Dimensional CS-FEM Phase-Field Modeling Technique for Brittle Fracture in Elastic Solids
###### Abstract  
The cell based smoothed finite element method (CS-FEM) was integrated with the phase-field technique to model brittle fracture in 3D elastic solids. The CS-FEM was used to model the mechanics behavior and the phase-field method was used for diffuse fracture modeling technique where the damage in a system was quantified by a scalar variable. The integrated CS-FEM phase-field approach provides an efficient technique to model complex crack topologies in three dimensions. The detailed formulation of our combined method is provided. It was implemented in the commercial software ABAQUS using its user-element (UEL) and user-material (UMAT) subroutines. The coupled system of equations were solved in a staggered fashion using the in-built non-linear Newton-Raphson solver in ABAQUS. Eight node hexahedral (H8) elements with eight smoothing domains were coded in CS-FEM. Several representative numerical examples are presented to demonstrate the capability of the method. We also discuss some of its limitations.  
B 2018; 8, 2488; doi:10.3390/app8122488
## 2 Datasets
### Waves from Altimeters and ECMWF  
The SEASAT launched in 1978 was the first satellite to carry an altimeter [41], and it demonstrated the feasibility of sea surface height measurements. Since the 1980s, spaceborne radar altimeters have been rapidly developed worldwide, e.g., the Jason-2/3 belonging to the Ocean Surface Topography Mission (OSTM) and the HY-2 satellite constellation [42]. Altimeter-measured sea surface heights have spatial coverages of 10 km and follow the satellites' footprints. The SWHs are estimated by the sea surface heights and the large-scale current speeds are derived from the sea surface height anomalies. Therefore, altimeters create highly detailed measurements for sea surfaces, which allow researchers to  
Figure 1: The wind maps that were acquired on 7 September 2020 between (10\({}^{\circ}\)N, 100\({}^{\circ}\)E) and (60\({}^{\circ}\)N, 180\({}^{\circ}\)E): the advanced scatterometers (ASCATs) onboard the Metop-A (**a**) and Haiyang-2B (**HY**-2B) (**b**) satellites, and the spaceborne polarimetric microwave radiometer WindSAT (**c**).  
gain insights into ocean circulation and climate change. The cooperative satellite Jason-3 was launched in 2016 by international agencies (NOAA, CNES, and EUMETSAT), and the well-calibrated measurements from Jason-3 are popular and are used for the cross-validation of multiple altimeters [5; 43]. The monthly average SWHs from the HY-2B and Jason-3 products during the period November 2019 to October 2020 were used for conducting the error analysis of the hindcasting waves using two numerical models. As an example, the along-track SWH map from Jason-3 on 15 May 2020 between (10\({}^{\circ}\)N, 100\({}^{\circ}\)E) and (60\({}^{\circ}\)N, 180\({}^{\circ}\)E) is shown in Figure 2. The ECMWF has continuously provided atmospheric and oceanic data for scientific research since 1979. Typically, two types of waves are available in the ECMWF datasets, i.e., ERA-Interim and ECWWF reanalysis (ERA-5). Although the spatial resolution of ERA-5 is a 0.25\({}^{\circ}\) grid, which is relatively coarser than the 0.125\({}^{\circ}\) grid of ERA-Interim, the accuracy of ERA-5 is greater, and it was combined with the observations of the operational system. Therefore, the waves from ERA-5 were also used for the analysis of the modeling results. Figure 2 shows the along-track SWH map from the HY-2B and Jason-3 altimeters, which was taken on 15 May 2020 between (10\({}^{\circ}\)N, 100\({}^{\circ}\)E) and (60\({}^{\circ}\)N, 180\({}^{\circ}\)E).
## 2 Background and Related Work
### Related Work
#### 2.3.2 DAST Tools Comparisons Studies  
A study of current DAST tools [56] provides the background needed to evaluate and identify the potential value of future research in this area. It evaluates eight well-known DAST against a common set of sample applications. The study investigates what vulnerabilities are tested by the scanners and their effectiveness of. The study shows that DAST tools are adept at detecting straightforward historical vulnerabilities.  
In the work [57], a comprehensive evaluation is performed on a set of open source DAST tools. The results of this comparative evaluation highlighted variations in the effectiveness of security vulnerability detection and indicated that there are correlations between different performance properties of these tools (e.g., scanning speed, crawler coverage, and number of detected vulnerabilities). There was a considerable variance on both types and numbers of detected web vulnerabilities among the evaluated tools.  
Another study [58] evaluates five DAST tools against seven vulnerable web applications. The evaluation is based on different measures such as the vulnerabilities severity level and types, numbers of false positive and the accuracy of each DAST tool. The evaluation is conducted based on an extracted list of vulnerabilities from OWASP Top Ten project. The accuracy of each DAST tool was measured based on the identification of true and false positives. The results show that Acunetix and NetSparker have the best accuracy with the lowest rate of false positives.  
The study of [PERSON] el al. [59] proposes an automated framework to evaluate DAST tools vulnerability severity using an open-source tool called Zed Attach Proxy (ZAP) to detect vulnerabilities listed by National Vulnerability Database (NVD) in a Damn Vulnerability Web Application (DVWA). The findings show that the most prominent vulnerabilities, such as SQL injection and cross-site scripting found in modern Web applications are of medium severity.
## 2 Related Work  
### Data Reconstruction with Missing Values  
The reconstruction of missing data in the IoT is often accomplished using matrix completion algorithms. These algorithms rely on a low-rank matrix to recover missing values by leveraging available spatiotemporal correlations in the data [9; 18]. However, these methods have limitations in capturing the complex multivariate relationships inherent in WSN data. These restrictions prevent matrix completion algorithms from fully exploiting multivariate correlations. Utilizing multivariate data enhances the effectiveness of lost data recovery [19; 20]. For low-rank data structures, tensor decomposition techniques, particularly tensor singular value decomposition (t-SVD) [21], have been recognized for conserving and leveraging multiattribute correlations. The strength of t-SVD resides in its efficiency, which demands fewer iterations than other decomposition techniques such as CP, Tucker decomposition, and high-order SVD [22; 23]. Emerging studies across diverse fields have improved missing data imputation by adopting a unique approach in which the shrinkage of singular values is determined by their importance. This development has been driven by the realization that singular values within the core tensor carry varied importance and should not receive equal treatment during decomposition. Various methodologies have been used to achieve this, including the weighted tensor nuclear norm with tSVD for TC [24], which is used to restore missing pixels in video and image data. Similarly, the weighted nuclear norm has been applied to unfolded tensors for TC to recover lost data from remote sensing images [25; 26]. The weighted tensor nuclear norm for TC uses reconstruction loss data as image data [27]. However, the persistent presence of sparse noise and outliers in data continues to pose a challenge [11]. It is difficult to effectively handle tensor completion because of the effects of sparse noise and outliers. There remains a pressing need for a comprehensive analytical model that focuses on sparse noise and outlier modeling to improve the robustness of IoT data reconstruction address heterogeneously corrupted data complexities.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
## 4 Rain Detection
### Application of Dual-Pol Filter on Different Rain Types
As implemented here, the addition of a second channel of polarization as well as the optimization of the detection parameters  
Figure 13: (**a,b**) are VV- and VH-pol Sentinel-1 SAR images acquired over the Gulf of Mexico on 10 September 2017 at 23:27 UTC showing radar signatures of rain bands associated with the tropical cyclone IRMA. (**c**) base reflectivity from the NEXRAD KAMX station. (**d,e**) the masks generated only by single-pol VV/VH. (**f**) the dual-pol filter. The blue and purple contours in (**d–f**) mark the base reflectivity of 20 dBZ and 40 dBZ respectively. based on reflectivity measurements from high-resolution radar data is not sufficient for discriminating between various non-wind related phenomena.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 4 Simulation and Experiment
### Algorithm Comparison  
In Figure 16, the path planning results of four algorithms in a real-world environment are depicted. Notably, a curved narrow passage shortcut can be observed on the map. All four algorithms can generate feasible paths for the mobile robot, but only CERRT is able to discover and navigate through the curved passage shortcut in the actual environment. The other algorithms struggle to solve the narrow passage problem in the real environment, further confirming the practical value of the proposed algorithm. Additionally, the path quality is significantly improved after optimizing with BSOS compared to the initial paths.  
Since RRT-based algorithms have probabilistic completeness, the success rate of all four algorithms in planning paths in the actual environment is 100% when the sampling limit is not restricted. Table 7 displays the performance parameters of these algorithms. The average runtime of RRT is approximately five times that of CERRT, RRV is approximately 50 times that of CERRT, and Fast-RRT is approximately seven times that of CERRT. CERRT has the shortest average runtime with a standard deviation of only 4.68, which is significantly lower than the other three algorithms, indicating excellent performance of the proposed algorithm in the actual environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **Avg. Time (ms)** & **Std** & **Avg. Nodes** & **Path Cost** & **BSOS Cost** \\ \hline RRT & 278.31 & 139.61 & 3126 & 2499.48 & 2104.93 \\ RRV & 2782.19 & 435.89 & 3149 & 2446.17 & 2036.01 \\ Fast-RRT & 365.72 & 138.94 & 2674 & 2545.87 & 2124.81 \\ CERRT & 55.23 & 4.68 & 907 & 1990.01 & 1706.98 \\ \hline \end{tabular}
\end{table}
Table 7: Results for planning in actual map.  
Figure 16: Performance comparison of four algorithms in real environment. The blue lines represent the original paths and the red lines represent the paths optimized by bidirectional shrinking.  
Regarding path optimization, a horizontal comparison reveals that the path lengths after applying BSOS are all smaller than the initial paths, confirming the practicality of BSOS. A vertical comparison shows that the path length generated by CERRT is smaller than the other three algorithms. This is because CERRT is able to quickly discover and navigate through the curved passage shortcut in the actual environment, while the other algorithms struggle to pass through the curved passage efficiently.  
In conclusion, the CERRT algorithm outperforms the other three algorithms in a real-world environment. The main reason is that the expansion principle of CERRT is inspired by the hexagonal honeycomb structure found in nature. Reference [21] mentions that a hexagonal honeycomb provides the least-perimeter way to enclose and separate infinitely many regions of unit area, indicating that using a hexagonal expansion strategy can greatly improve the utilization of each tree node and thus enhance the algorithm's performance.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 6 Machine Learning for Enhancing PHY Layer Security
### Triplet Networks  
A triplet network is a type of neural network architecture designed to learn the similarity of fine-grained input data in an embedding space. The general network architecture is shown in Figure 10. The core idea behind a triplet network for RF fingerprinting is to take three input signals at a time, referred to as a _triplet_: an anchor signal \((x^{A})\), a positive signal \((x^{P})\)--similar to the anchor, and a negative signal \((x^{N})\)--dissimilar to the anchor. These signals are passed through a shared neural network, which generates embedding (vectors) for each image.  
The objective of the network is to learn embeddings so that the distance (often Euclidean or cosine similarity) between the anchor and the positive example is smaller than the distance between the anchor and the negative model by a margin. This is achieved  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
\begin{tabular}{c} CNN \\ threshold \\ approach \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Examples of CNN-based machine-learning methods for 5G physical layer security enhancement.  
Figure 9: Schematic of a simple CNN for processing 1D input data with depicted _Convolutional_, _Pooling_, _Flatten_, and _Fully-Connected_ layers. The input data are vectors \(X\) with length \(T\). Figure from [120] and extended.  
through a loss function known as the triplet loss. The triplet loss is formulated to ensure that, for a given anchor, a positive example of the same class is closer to the anchor than any negative example of a different class. The triplet loss is typically combined with the global loss function, which can be a standard classification loss function, such as categorical cross-entropy. The network can be built on any deep-learning architecture, \(F\), such as CNN, that typically share weights across the parallel architecture branches, as depicted in Figure 10[125; 126].  
The triplet function built upon CNN has been successfully implemented and demonstrated to achieve an accuracy of 99.86%, irrespective of the training/testing time gap for the over-the-air datasets [126].