esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Analysis of Geologic Hazards
## 2 Contributions of the Special Issue  
Satellite and terrestrial remote sensing allow for the analysis of different geological processes and are of paramount importance in specific tasks of hazard analysis. In particular, this Special Issue collects contributions that are concerned with (i) detecting and mapping potential hazards, (ii) acquiring data for analysis of susceptibility and triggering factors, (iii) monitoring the evolution of the process, and (iv) developing strategies of warning and early warning. The considered geological processes are landslides, mining-induced subsidence and land deformations, volcanoes, glacier flows and dune migration. The adopted techniques are varied and include, but are not limited to, optical sensors, SAR, GNSS and multi-sensor approaches (Figure 2). Usually, multi-sensor-based studies use satellite optical images to detect the areas of interest and multitemporal SAR interferometry (MTInSAR) to measure ground deformation in such areas.  
Figure 1: Word cloud of the abstracts of the contributions of this Special Issue.  
Figure 2: Distributions of the topics dealt with in this Special Issues, according to different schemes of classification: (i) the investigated process, (ii) the scope of the study, (iii) the adopted technique (TLS is terrestrial laser scanner, while CHIRPS is the Climate Hazards group Infrared Precipitation with Stations dataset) and (iv) the platform adopted.  
In the following, we will describe the contributions present in this Special Issue. They are organized according to: first, the investigated geologic process, and second, the scope of the study (i.e., detection, monitoring, process analysis and warning).
## 3 Results and Discussion  
### Abnormal Data Processing  
Because of the limited accuracy of the sensors used for data acquisition in the field, abnormal points will be collected in the drilling process, as shown in Figure 7. These outliers will increase the difficulty of training the model. The quality of the data determines the upper limit of the accuracy of the model. Since we aim at the prediction of mechanical drilling rate and the optimization of drilling parameters in the normal drilling process, detecting and eliminating these abnormal data is more conducive to improving the training accuracy of the model.  
Figure 6: The flow chart of the NSGA-II.  
Figure 7: ROP data collected from the oil field.  
The machines that record data in the field take one piece of data every second. The ground engineering parameters remain in a relatively stable range in a short period of time (5 s, 10 s). In such a short time interval, it can be considered that the formation has not changed. When drilling into a uniform formation, the drilling parameters and formation should not change much in a short time, and the ROP should be maintained in a small range. If abnormal data points occur in a small time window, the data variance will increase.  
Based on this regulation, we select a data window with a fixed time step and calculate the variance of the engineering data in the window. When new data appear in the window, if the variance of the data in the window changes in a range exceeding the set value, the new data are judged as abnormal data and vice versa as normal data. This outlier detection process was programmed in python, and good detection results were achieved on the collected data, as shown in Figure 8.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
## 3 Computation and Comparison
### Improvement of Computational Efficiency of Parallel Approach
#### 3.2.3 Improvement of Efficiency with Different Number of Processes  
In order to further reflect the improvement of efficiency by the parallel POD approach, the influence of the number of processes on efficiency is analyzed. Multi-process is mainly used to construct normal equations in parallel. All schemes use the same number of threads, the same processing strategy, and select the number of processes as the only control variable to analyze the effect of increasing the number of processes on the computational time. The time consumption is shown in Table 6, and the speedup is shown in Figure 5.  
As can be seen from Figure 5 and Table 6, compared to a single-process, the speedup ratios of two-process, three-process, four-process, and five-process are 1.788, 2.525, 3.102, and 3.406, respectively. As the number of processes increases, the speedup ratio increases. When five processes are used, the speedup in POD reaches the maximum. The speedup shows a nonlinear trend probably because the cost of opening and closing processes and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Number of Process & 1 & 2 & 3 & 4 & 5 \\ Computational Time & 824.0s & 460.8s & 326.3s & 265.6s & 241.9s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time consumption of multi-process parallel computing.  
Figure 4: Computational time for different number of threads.  
inter-process communication increases as the number of processes increases, and depends on the proportion of parallel programs to the total program. In general, if the hardware can support more processes, the computational efficiency of POD can be further improved, which is similar to multithreading.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 1 Introduction
proposed a fixed set of seven quantization matrices (QMs), which were defined for effectively encoding the WZ frames while keeping a smooth quality transition between the decoded key and WZ frames. This work is extended in [15] to obtain an additional QM to be added to the fixed set and then applied for verifying the performance of the IST-Transform domain Wyner-Ziv codec (IST-TDWZ). However, regarding to the consistent video quality problem, these approaches are only effective for a limited number of test sequences and specific video coding standards that are applied for DVC codecs, such as H.263+ or H.264/AVC [12] and not so effective for the H.265/HEVC [16] and the new future video coding standard (FVC) [17]. As reported in the experimental results that were obtained for the HEVC based DVC [9], the performance of DVC codec in this case is much better than that of the H.264/AVC based DVC; however, the fluctuation of video quality on the decoded key and WZ frames are very strong and it thus degrades the user's quality of experience. We propose a novel DVC solution named Joint exploration model based DVC (JEM-DVC) in this paper to solve the problem, which can provide not only higher performance as compared to the traditional DVC solutions, but also an effective scheme for the quality consistency control. In the proposed JEM-DVC solution, several advanced techniques that were provided in the Joint exploration model (JEM) of FVC standard have been employed to effectively improve the performance of JEM-DVC codec. Subsequently, we propose two novel methods, named key frame quantization (KF-Q) and Wyner-Zip frame quantization (WZF-Q), which determine the optimal values of quantization parameter (QP) and quantization matrix (QM) applied for the key and WZ frame coding for consistent quality control, respectively. Unlike the conventional approaches, the optimal values of QP and QM are adaptively controlled and updated for every key and WZ frames to guarantee the consistent video quality for the proposed codec. Our proposed JEM-DVC is the first DVC codec in literature employing the JEM coding technique, and then all results that are presented in this paper are new. The experimental results show that the proposed JEM-DVC significantly outperforms the relevant DVC benchmarks, notably the DISCOVER DVC and the recent H.265/HEVC based DVC, in terms of both Peak signal-to-noise ratio (PSNR) performance and consistent visual quality. The rest of the paper is organized, as follows. Section 2 summarizes the background works on DVC. Section 3 describes the proposed JEM-DVC solution in detail. The experimental results are discussed in Section 4. Finally, Section 5 concludes the paper.
# Numerical Study of Mode Conversion between Fundamental Lamb and Rayleigh Waves
## 4 Discussion
_Limitations and future work:_ The generation length results suggest that the position of the receiver when carrying out a measurement is significant. This is especially critical where characterization of the plate thickness is dependent on the measured transmission coefficients. Therefore, understanding the mode conversion phenomenon becomes critical for such NDE and SHM applications. The above-described hypotheses are all frequency dependent and require a more comprehensive analysis of different mode types and the effect of frequency on the mode conversion phenomenon. However, this is out of the scope of the current paper and will be explored elsewhere. The case of a plate attached to a half space can also be generalized to a rib as shown earlier [24]. The analysis carried out here has to be repeated for several combinations of plate thickness and frequency to confirm that the mode conversion phenomenon is conserved but the efficiency changes. The mode type and frequency dependence also need to be validated with experimental results. In the case of Lamb to Rayleigh conversion, Figures 4 and 5 shows a Rayleigh wave propagating along the vertical edge. As mentioned earlier, this Rayleigh energy is included in a power coefficient analysis. From an NDE perspective, both waves are of interest. However, one application of the current problem is inspecting inaccessible structures, wherein the vertical edges may not be accessible from the outside. Additionally, the case  
Figure 9: Numerical Hilbert B-Scans for (**a**) interaction of the \(A_{0}\) mode with the discontinuity and \(A_{0}R\) generation and (**b**) incident R mode and mode conversion to \(RA_{0}\) and \(RS_{0}\). of a plate attached to a quarter space can be treated as an elastic wedge problem with the plate thickness \(h\to 0\). In this case, it is important to address both Rayleigh modes as shown earlier [25]. Since the current work is only interested in understanding the Lamb-Rayleigh mode conversion, we limited the focus to the Rayleigh wave propagating along the horizontal edge.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code  
### Cloud Storage System Model  
The cloud storage system can be represented by a directed graph \(G=(V,E)\), where \(V\) is the set of vertices and \(E\) is the set of edges connecting two points. All storage nodes and terminals in the cloud storage system are considered vertices, and the network connections between nodes are considered edges in the graph. As shown in Figure 1, the vertex set \(V\) is divided into three categories based on the different types of nodes. Server nodes (\(V_{S}=\{S_{1},S_{2},\ldots,S_{n}\}\)) possess the original copies of each file in the storage system; storage nodes (\(V_{N}=\{N_{1},N_{2},\ldots,N_{m}\}\)) receive and store \(R\) replicas generated for each file; and terminal nodes (\(V_{T}=\{T_{1},T_{2},\ldots,T_{s}\}\)) are the nodes or user ends that require access to data files. The edge set \(E\) can be divided into two major categories based on the type of data transmitted: \(ES\), which transfers data from server nodes to storage nodes, and \(ET\), which transfers data from storage nodes to terminal nodes. The direction of the directed graph edges represents the direction of data flow.  
The network composed of storage nodes and server nodes constitutes a simple cloud storage model, while the terminal nodes are the clients connected to the cloud. The server node stores \(n\) original data chunks of each data file \(F\). In contrast, the storage nodes store data chunks that have been encoded with redundancy. Terminal nodes can retrieve a certain number of required data chunks from different storage nodes to reconstruct the data file. Therefore, the storage nodes need to be connected to the server that contains the original data chunks. Each terminal node must be connected to a set of storage nodes that collectively provide the necessary data chunks to recover the original file.  
In order to simplify the cloud storage model without losing generality, we propose the following assumptions.  
The original copy of each file exists only in one server node; that is, the intersection between the server nodes \(S_{i}\) and \(S_{j}\) does not belong to the file \(F_{n}\) (i.e., \(S_{i}\cap S_{j}\
otin F_{n}\)).  
The type of node is single, and nodes of the same type do not communicate. In fact, a node can be used as the server node of file 1, the storage node of file 2, and the terminal node of file 3. Here, we divide the types and expand the multi-type nodes to multiple single-type nodes.  
Assume that there are \(R\) copies of each file in the cloud storage system to provide data redundancy, where \(R\) is the backup factor.  
The direction marked in Figure 1 is not the data link but the flow direction of the data flow in the cloud storage model. Therefore, the model does not involve the redistribution of the link. Because the target application is a storage system, the bandwidth of the link only affects the data transmission speed.
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 1 Introduction
In this regard, the use of green roofs [35] has also intensified in recent years. Such solutions are known to be able to improve the energy and environmental performance of urban environments [36; 37] by combining phytoremediation techniques in urban areas with other approaches aimed at mitigating climate change [38]. Phytoextraction is considered a non-invasive technique for removing potentially toxic metals from contaminated soil in an ecological and economical way by adsorption from the roots and accumulation by translocation in the different tissues of the plant [39; 40]. Several studies have explored the possibility of using fast-growing and highly tolerant species assisted by chelating/mobilizing agents to increase the uptake of metals from soil, to be used in place of hyper-accumulative species that generally suffer from low biomass production and slow growth rates [41; 42]. In fact, the chelating/mobilizing agents have the precise function of promoting the release in soil solutions of metal ions originally bound to soil particles, thus increasing their bioavailability for adsorption by plants [43], since plants can absorb only those species that are present in soluble form in soil solutions [44; 45]. Another possible strategy to aid phytoextraction is the use of plant-growth-promoting bacteria, such as those that populate the rhizosphere (PGPR) [46]. These rhizobacteria can act by facilitating the bioavailability of soil nutrients and/or by modulating the production and levels of phytohormes in plants [47]. In some cases, they can also promote the mobility and bioavailability of metals in the soil, increasing their absorption by plants [48; 49]. In the first part of this work, we showed how a treatment with KHC\({}_{2}\)O\({}_{4}\) allowed an increase in the bioavailability of arsenic and its bio-adsorption by plants, especially in the presence of PGPR [50]. As a continuation of the previous study, this work aimed to investigate the single and synergistic effect of PGPR and different chelating agents on the growth of two tolerant species, _Cannabis sativa_ (hemp) and _Zea mays_ (corn), and their uptake of As, in order to evaluate their potential use in the site from which the As-contaminated soils were removed. The selection of bacterial strains capable of improving the efficiency of phytoremediation is a fundamental step, made non-trivial by the specificity of the action of PGPR and their effectiveness, which depend on numerous factors related to the complex interactions between soil and plants [47]. In this study, the addition of a selected mixture of potassium hydrogen oxalate (KHC\({}_{2}\)O\({}_{4}\)) and ascorbic acid (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)) allowed an increase in the bioavailability of the target contaminant and the attainment of detectable levels of phytoextraction without the creation of percolation problems. The optimization of the process, achieved through microcosm and mesoccosm tests, made it possible to outline the application protocol for a field test.
## 4 Discussion
For example, deep learning-based segmentation could be applied to detect and classify vascular abnormalities such as aneurysms, stenoses, or arteriovenous malformations. By integrating automated segmentation with lesion detection models, a comprehensive AI-assisted workflow could be developed for cerebrovascular diagnosis. Such an approach could not only enhance diagnostic efficiency but also reduce inter-operator variability in image interpretation, leading to more standardized assessments across institutions.
## 3 Platform Design Criteria
### Sensor Node Design Criteria
#### 3.1.2 Sensor Node Communication Protocol and Radio Device  
Radio communication is widely recognized as one of the major energy drains on sensor nodes. Thus, it influences the selection of the energy supply (with the criteria presented in Section 3.1.1) and some other node costs that we will analyze in the following.  
The largest part of the communication energy is typically spent by the receiver while listening for incoming data packets. For this reason, most communication protocols operate the receiver with very low duty cycles (typically 1%-3% for random MACs [77,78] and much less for synchronous MACs [79]). Even more, the receiver can be eliminated altogether for random access unidirectional communication in star topologies.  
Multihop message forwarding typically uses less energy to cover the same distance and may be less affected by node faults or changing propagation conditions. However, mesh networks require more microcontroller resources and involve regular packet exchanges to maintain network connectivity. Thesemay increase both node energy consumption and cost. Moreover, message routing in large mesh networks may be affected by dynamic effects that are hard to detect, such as bottlenecks or instabilities [8, 80, 81], which may increase deployment complexity and cost. Synchronous protocols also need an accurate timer constantly running on the nodes, which further increases their cost and energy consumption.  
Transmit-only nodes in star topologies can use very small protocol stacks and have much more relaxed requirements for on-node timing accuracy. Thus, they need less microcontroller resources and energy, but they cannot create mesh topologies to improve robustness and reduce transmit power. Furthermore, these nodes cannot actively avoid message conflicts or receive confirmations, queries, configuration changes or in-field updates for node software.
# Applications of Magnesium and Its Alloys: A Review
## 1 Introduction  
As an alkaline earth metal, magnesium is shiny and silvery-white in appearance. It is also highly reactive and never found free in nature [1, 2] with terrestrial and cosmic abundance [3]. It exists as a chemical compound until [PERSON] (in 1754), with prior contributions from [PERSON] (in 1729), recognized it as an element [4]. Even though [PERSON] discovered the first magnesium metal in 1808 [1, 4, 5, 6, 7, 8], the first production of pure magnesium was made by [PERSON] in 1828 [4, 6].  
Since its discovery, magnesium has played an influential role in society. In its early days, military applications and was fueled its growth [8]. For example, magnesium was weaponized to construct incendiary bombs, flares, and ammunitions that were subsequently deployed in World War II, and it caused massive conflagrations and widespread devastations [9]. Post-War, magnesium's availability and unique blend of properties were explored and were found to be highly attractive for an extensive range of applications. Today, magnesium is used for engineering applications in automotive, aerospace, and consumer electronics. In addition, it has a role in organic chemistry and pharmaceuticals [10] and is used to construct several general-purpose applications, such as sporting goods, household products, and office equipment [8].  
Furthermore, with its superior biological properties, especially its ability to biodgrade in vivo, magnesium has received accelerated interest as a promising biomaterial by both the research and industry communities [11]. As a result, the global magnesium market is forecasted to grow at a compound annual growth rate of 4.9% and reach 1.6 million metric tons (Mt) by 2027, increasing from 1.1 Mt in 2020 [12]. China alone produced 0.9 Mt in the same year, accounting for more than 80% of global production [13].  
Henceforth, given its continued interest and developments, both a comprehensive state of knowledge and a primer for readers interested in the properties and applications of magnesium and its alloys are imperative. Therefore, this article aims to synthesis and present the recent progress and developments of this domain. Mainly, this article focuses on its engineering and biomedical applications. First, the article succinctly introducesthe main methods in producing magnesium. It also delves into the recent progress and developments in its production methods. Then, it presents the key considerations enabling magnesium and its alloys to be considered for engineering and biomedical purposes. Finally, an extensive discussion on the recent progress and developments surrounding its applications follows.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.4 Session Key Agreement  
\(SN\) authenticates \(TA\) by evaluating the authenticity of \(AUTH_{TA,n}\), \(TA\) authenticates \(S_{s}\) by evaluating the authenticity of \(AUTH_{TA,s}\), and \(TA\) authenticates the \(SN\) by evaluating the authenticity of \(AUTH_{n}\); thus, \(SN\), \(TA\), and \(S_{s}\) ensure they are entitled to random nonce \(RND_{3}\), \(RND_{4}\), \(RND_{5}\), \(RND_{6}\) and \(RND_{7}\). To generate the session key, \(SK=SK_{ns}=SK_{s,n}=H(RND_{6}||Z_{TA,s}\oplus V_{temp4}||T_{s})\) and employ the session key when communicating. The presented approach can thus offer a robust session key agreement, as shown in Table 5.
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 3 Vision-Based Line Marking Detection and Condition Assessment
### Feature Extraction  
This module aims to extract features of pavement markings using a hybrid detector. Both color information and gradient features [38] are used for detection, so as to cope with a range of environmental factors that can adversely affect the performance of feature extraction. Previous studies have also used color and shape features and achieved image retrieval tasks with improved efficiency [39]. Such factors include considerable variations in image illuminations, noises on the pavement, and occlusions caused by other vehicles. Pavement distress, repairs and shadows of road-side facilities and plants should all be excluded after this module. For this reason, color values in the HSL color space (i.e., Hue, Saturation, and Lightness) are extracted and pre-defined thresholds are applied to filter yellow and white pixels in the image. The Sobel operation [40] is used to compute both gradient magnitude and orientation. This hybrid detector has been tested on a succession  
Figure 2: Flowchart of vision-based lane line detection.  
of pavement images, and Figure 3 shows a few of the results. The road segment for preliminary tests is located in a metro area and the results indicate that shadows and rutting on pavement can be successfully eliminated.