esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method for Physical Layer Performance Estimation
### Reference Scenario  
We consider the scenario of an operating WSN of which we are interested in estimating the channel status. We assume that we cannot perform any measurement at these nodes and that the positions of these nodes are unknown; for this reason these are called _blind_ nodes. Such a set of nodes is defined as the set \(\mathbf{B}=\{1,...,B_{k},...,B_{N_{B}}\}\). The nodes are operating and are connected in a mesh structure according to the specific communication technologies adopted. We may or may not know the graph of the connections among the nodes, but this is not an issue for our problem.  
Our objective is to estimate the power of the transmission signal of node \(B_{k}\) as seen by node \(B_{j}\), which we call \(T_{jk}^{B}\). To this we make use of anchor nodes with known position. The set of anchor nodes is defined as the set \(\mathbf{A}=\{1,...,A_{i},...,A_{N_{A}}\}\). These are the sniffers used in our measurements. Indeed, we may have only one sniffer that changes its position over time and makes several measurement sessions or many sniffers that make measurements in parallel. Note that each sniffer \(A_{i}\) performs measurements of the signal coming from every blind node. The signal transmission measurements performed by the anchors are represented by \(T_{jk}^{A}\). For every \(i\), we expect to have a measure for any blind node \(k\), with \(k=1,...,N_{B}\); however, due to the distance and the presence of occluding objects, some of these measures can be set to zero, meaning that the anchor has not been able to detect any signal from blind node \(k\). The measurement \(T_{ik}^{A}\) is usually obtained as the average power measured for a set of packets received during the sniffing time, and its accuracy clearly depends on the length of the observation time and the transmission frequency of blind node \(k\). Specifically, herein without loss of generality, we refer to the RSSI (Received Signal Strength Indication) measures.  
A key part in the proposed approach is the estimation of the distances between the blind nodes and between the blind nodes and the anchor nodes. In line with the notation used for the signal power, we refer to these two entities as \(D_{ik}^{B}\) and \(D_{ik}^{A}\), respectively.  
Figure 2 shows an exemplary scenario where the number of anchor and blind nodes is 5 and 6, respectively. In this figure, we represent the power and distance only for some couples of nodes for the sake of clarity, and the same applies to all other figures.
# The Design and Implementation of a Novel Open Source Massive Deployment System
## 3 Design and Implementation
### Software Implementation
Any output files that are larger than 4096 Mega Byte (MB) will be split into smaller 4069 MB pieces. Each piece is output to a file, entitled \"sda1.ntfs-ptcl-img.gz\" in the /home/partimag/vmware-windows-7 directory. The following command will be executed inside the main Clonezilla program:  
partclone.ntfs -z 10485760 -N -L /var/log/partclone.log -C -s /dev/sda1 -output -! pigz -C -fast -b 1024 -p 16 -rsyncable! split -a 2 -b 4096 MB - /home/partimag/vmware-windows-7/sda1.ntfs-ptcl-img.gz. In this example, partclone.ntfs is the program that was developed in this research, while the programs pigz and split are the existing programs from the Linux system. The procedures for saving an image from a drive and restoring the image to another drive are shown in Figure 2a,b, respectively. Figure 2a shows the imaging engines for a different file system, which includes Partclone, Partimg, ntfsclone, and dd. Among them, Partclone is the default engine for Clonezilla. In addition, the sequential compressing programs from the Linux system, which include gzip, bzip2, lzma, lzip, or xz, could be used to compress the image. By default, gzip is chosen. If the CPU on the system has multiple cores, the corresponding parallel program, like pigz, pbzip2, plzip, or pixz, can be used when such a program is available from the Linux system. However, if the corresponding program is unavailable, then Clonezilla will roll back to use the sequential program. As an image is restored, the image should be decompressed by the corresponding program and then written to a partition, according to the format of the image, as described in Figure 2b. A screenshot about the image directory from Clonezilla is presented in Figure 3. One can easily identify the file(s) by reading its name. For example, sda-mbr is the MBR data of disk sda, where sda is the device name under Linux for the first hard drive in the system. Meanwhile, sda1.ext4-ptcl-img.gz.aa is the image of sda1 being saved by the imaging program Partclone. Here, sda1 is the first partition on the first drive. After being compressed by the gzip or pigz program, the image file is split into several volumes. The default limit of each volume is 4 Giga Byte (GB). Because the image size of the example is only 2.5 GB, and there is only one volume, then the suffix is aa. If more volumes exist in the image, the suffixes ab, ac, etc., will be used, respectively. The complete list of the file formats supported is provided in Table 1. The names denoted in brackets are assigned according to the name of the device (disk, partition, or Logical Volume Manager (LVM)), file system, compression format, or suffix. The other names without brackets are fixed styles. Figure 3: Screenshot of the files in an image directory that was saved by Clonezilla. Figure 2: Flowchart for (**a**) saving and (**b**) restoring an image.
# Comparative Analysis of Discrete Subtraction and Cross-Correlation for Subpixel Object Tracking
###### Abstract  
Many applications in physics and engineering require non-invasive, precise object tracking, which can be achieved with image processing methods at very good cost-efficiency ratios. The traditional method for measuring displacement with subpixel resolution involves cross-correlation between images and interpolation of the correlation peak. While this method enables target tracking with a resolution of thousandths of a pixel, it is computationally intensive and susceptible to peak-locking errors. Recently, a new method based on discrete subtraction between images has been presented as an alternative to cross-correlation to improve computational efficiency, which also results in being free of peak-locking errors. This manuscript presents an experimental evaluation of the performance of the discrete subtraction method (DSM) and compares it with the cross-correlation method in terms of subpixel accuracy and deviation errors. Four different targets were used with apparent displacements as small as 0.002 px, which approaches the theoretical digital resolution limit. The results show that the discrete subtraction method is more sensitive to noise but does not suffer from peak-locking error, thus being a reliable alternative to the correlation method, mainly for calibration processes.  
subpixel tracking; cross-correlation; discrete subtraction; image processing Article
# Analysing Near-Miss Incidents in Construction: A Systematic Literature Review
## 5 Discussion
### Methods Used to Analyse the Information and Data Sets (Question 3)  
The most common techniques used for the analysis of numerical data in near-miss investigations are quantitative statistical methods, which employ statistical inference techniques. Qualitative methods are based on the analysis of existing near-miss reports. The increasing volume of near-miss data presents a challenge for conducting precise analyses using conventional techniques. In recent years, there has been a notable emergence of artificial intelligence (AI) methods designed to facilitate ongoing research and analysis.  
The principal aim of utilising artificial intelligence methodologies is to derive valuable insights from near-miss data and transform them into knowledge that can be leveraged to enhance the overall safety standards within the organisation. A review of the methods employed to analyse near-miss data has revealed that there is a tendency to prioritiise the identification and analysis of events, with less emphasis placed on the dissemination of knowledge about near-miss occurrences. It may therefore be proposed that a potential avenue for future research would be the development of procedures to optimise the efficacy of near-miss event management systems within an organisational context, whilst simultaneously enhancing their dissemination beyond the organisation.
## 4 Summary of the Project and Preprocessing of Data
### Significance Analysis  
One indicator is selected for variable determination of its weight, with other indicators at a fixed value. However, when the weight of this indicator changes greatly, it is an important indicator; on the contrary, if the change is small, it is a less important indicator. The value of other indicators is controlled to 0.125, and the variable indicator is recorded on a scale from 0 to 1 by increments of 0.1. The result is shown in Figure 7, below.  
In Figure 7, it can be seen that the weight values of each evaluation index are proportional to the actual measured values. This phenomenon occurs because the model takes into account the influence of the objective fluctuation of the index values on the index weight in the process of model establishment. The fluctuation of the index values in the figure can reflect the importance of the index, thus showing the influence of different indices on the tunnel risk. By sorting the changes in the weight values of each index in Figure 7 and ranking them in order of magnitude, the order is \(I_{7}>I_{5}>I_{1}>I_{3}>I_{2}>I_{4}>I_{6}>I_{11}>I_{8}>I_{10}\)\(>I_{9}>I_{14}>I_{13}>I_{15}>I_{12}\), with the magnitude of the changes in weight values increasing in the order of \(0.449>0.406>0.388>0.381>0.365>0.356>0.317>0.247>0.245>0.228>0.203>0.116>0.093>0.090>0.077\). According to the theory of variable weight, the weight  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tunnel**} & \multicolumn{3}{c}{**Accessible Degree**} & \multicolumn{1}{c}{\multirow{2}{*}{**Model in This**}} & **Standard** & **Fuzzy Synthetic** \\ \cline{2-2} \cline{4-7}  & \(\mathbf{V_{1}}\) & \(\mathbf{V_{2}}\) & \(\mathbf{V_{3}}\) & \(\mathbf{V_{4}}\) & **Model** & **Model** \\ \hline \(K_{1}\text{\#}\) & 1.000 & 0.831 & 0.407 & 0.000 & \(V_{1}\) & \(V_{2}\) & \(V_{1}\) \\ \(K_{2}\text{\#}  
values of each evaluation index will change with the change in actual measured values, and the magnitude of the change in the weight values of different indices is also different, because the model takes into account the objective fluctuation of the index values on the index weight.  
Specific analysis of the data shows that the weight changes in the surrounding rock category \(I_{7}\) and cutter head torque \(I_{5}\) are obviously large, which indicates that the changes of these two indices have a great impact on the tunnel risk assessment results. The monitoring data \(I_{12}\) of the guidance system has the smallest variation, which indicates that the change in \(I_{12}\) has relatively little impact on the evaluation results in this model. The fluctuation range of the protective layer thickness \(I_{11}\) is relatively obvious, although the importance is not as high as the surrounding rock category; it is still obvious that the protective layer plays a role in reducing the risk.
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 3 Results
### Water Quality Assessment
It was noticed that most of the contaminated sampling points with heavy metals were A7, A9, A12 and A13. The water samples sites were connected to Rosia Montana and Rosia Poieni mining areas, indicating a significant impact on the chemical composition of the Aries River catchment over time. Previous studies conducted in the investigated area highlighted a high degree of pollution of the Abrud River (A9), emissary river of the Sesii Valley (A7) and of the Aries River, in the proximity of Baia de Aries (A13, A14) [10,47]. Relying on previous studies, it can be concluded that, even if the mining activity reduced or stopped operating in the studied area, the water quality did not present major improvements over time. Some studies indicated a long period of time until major improvements in the quality of the environment and surface waters in areas affected by mining activities are reached without any human intervention [48,49]. Thus, before overflowing into the effluent, wastewaters are highly recommended to be pretreated before being released into the environment in order to prevent the destruction of the natural water system and the imminent negative effects on human health. The nitrification, biochemical removal and thiocyanate processes are three successful wastewater treatment methods for addressing the sources of water contaminated by mining effluents [50]. [PERSON] et al. [51] proposed an efficient treatment to neutralize the pH with wet Fe(VI) synthesized from Fe(NO\({}_{3}\))\({}_{3}\), FeCl\({}_{3}\), or Fe\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) and removing the toxic elements resulted from mining activities (gold extractions). Figure 3: The variation of elements in the sampling sites.
# Vibroacoustic Assessment of an Innovative Composite Material for the Roof of a Coupe Car
## 3 Damping Coefficient Assessment for the New Biocomposite Material  
### Procedure  
The mechanical properties of the bioccomposite material, reinforced with vegetable fibers, were measured at CRF in Turin, obtaining a Young modulus and a density lower than traditional composite materials.  
For damping measurements of different types of materials, in particular polymers and composite materials, the CRF Composite Materials Laboratory is equipped with a machine (Figure 2) that provides the damping loss factor, \(\eta\), and the damping ratio, \(\zeta\).  
These parameters are obtained according to ASTM E756 \"Standard Test Method for Measuring Vibration-Damping Properties of Materials\". The starting data are acquired in the time domain as an oscillating response of the specimen. The frequency analysis returns the values of loss factor, \(\eta\), and damping ratio, \(\zeta\), vs. frequency for the material under examination.  
The aforementioned procedure is based on the following steps:  
* the specimen is clamped at one end in a fixed-free configuration,
* an electromagnetic coil actuator applies an excitation force that causes the specimen to vibrate,
* two measuring sensors provide displacement and acceleration of a specific point.  
The analysis software enables the control of the whole measurement process, driving the forcing signal and analyzing the output signal in the time/frequency domain. Typically, the vibrating modes of the bar are sufficiently separated in frequency to allow an easy estimate of the damping parameters, in accordance with ASTM E756.  
The damping parameters are determined through the half power method, in which an intermediate parameter is evaluated by:  
\[Q^{-1}=\frac{\Delta f_{n(-3 db)}}{f_{n}} \tag{1}\]  
where \(f_{n}\) is the resonant frequency and \(\Delta f_{n(-3 db)}\). is the half-power bandwidth of the n-th mode estimated at \(-3\) dB with respect to the peak at \(f_{n}\).  
According to the Complex-Spring model, the loss factor \(\eta\) is determined from the relationship:  
\[\eta=\sqrt{1-\frac{1}{4}\bigg{[}2-\bigg{(}\frac{1}{Q}\bigg{)}^{2}\bigg{]}^{2}}. \tag{2}\]  
According to the Kelvin-Voigt model, the damping ratio \(\zeta\) is determined by the relationship:  
\[\zeta=\frac{1}{2}\sqrt{\sqrt{4+\frac{8}{Q^{2}}}-2-(\frac{1}{Q^{2}})} \tag{3}\]  
The software uses the two previous exact formulas for the calculation of \(\eta\) and \(\zeta\).  
The input signal is of simple harmonic type, with fixed amplitude and frequency, and the FRF (Frequency Response Function) is measured point by point for each frequency value considered.  
Figure 2: Machine for damping measurement.  
The measurement with this type of input proceeds in two phases: A first exploration, carried out with a large frequency range, (0-2000 Hz), with the purpose of identifying the resonance peaks (Figure 3), followed by a detailed analysis carried out around the resonance peaks with very small frequency increases in order to accurately collect the most significant data relating to the individual modes.
## 6 Baseline Statement and AAMO Approach Results
### Figure of Merits  
The performance of approaches is assessed regarding the following figures of merit. The first three are the most popular and represent the strongest quality indicators [20]. The fourth has been taken from [47] and sometimes can be useful to explain the results concerning the first two. The fifth was inspired by [48] in order to measure the connectivity quality. Besides, a sixth indicator is proposed here in order to have a better qualitative analysis of the connectivity aspects. Moreover, the connected components of the topology along the exploration are also plotted. The indicators are defined as follows:  
* _Total exploration Time (TT)_: time elapsed from the beginning until the end of exploration measured in seconds.
* _Path Length (PL)_: sum of the distance travelled by each robot measured in meters.
* _Coverage Ratio (CR)_: percentage of the accessible terrain covered by the team. Calculated as: \(\frac{\text{explored cells}\cdot 100}{\text{accessible cells}}\).
* _Oper-Sensing cell Ratio (OSR)_: percentage of cells sensed as new by more than one robot. Calculated as: \(\frac{\text{over-sensed cells}\cdot 100}{\text{explored cells}}\).
* _Disconnection Last Ratio (DLR)_: percentage of TT where at least one robot is totally unconnected. Calculated from the Fiedler number corresponding to the network connectivity graph (see Section 2.6.3).
* _Maximum Disconnection Last Ratio (MDLR)_: calculated as: \(\frac{\text{longest disconnection period}\cdot 100}{TT}\).
## 3 SPCA-Based Iterative Solution
### Multiuser  
Here, we consider the problem for multi-users. We denote \(\mathbf{s}=[s_{1},s_{2},s_{3}...,s_{k}]\), \(\mathbf{r}=[r_{1},r_{2},r_{3},...,r_{k}]\) and \(\mathbf{q}=[q_{1},q_{2},q_{3}...,q_{k}]\) as slack variables and rewrite Equation (7) as a non-convex sub-problem that can be solved by using a SPCA based iterative algorithm.  
\[\min_{P_{A},\tau,\overline{t},\ s,r,d,t} \quad{P_{A}}^{2}+\sum_{k=1}^{K}s_{k}+\sum_{k=1}^{K}r_{k}+\sum_{k =1}^{K}q_{k}\] (10a) s.t. \[0\geq\frac{2\frac{\frac{R_{blue}}{h}-1}{\alpha_{k}}-\ 2\frac{P_{A}^{(n)}P_{A}}{t^{(n)}}-\ \frac{{P_{A}^{(n)}}^{2}t}{t^{(n)} ^{2}}-s_{k}\ \,\ \forall k\] (10b) \[0\geq\frac{E_{blue}}{1-\sum_{k=1}^{K}(\tau_{k})}-\ 2\varsigma_{k}|h_{k}|^{2}P_{A}^{(n)}P_{A}- \varsigma_{k}|h_{k}|^{2}{P_{A}^{(n)}}^{2}-r_{k}\ \ \,\forall k\] (10c) \[0\geq\frac{1}{t}\ -\ \frac{2}{t^{(n)}}\sum_{k=1}^{K}\left( \widetilde{\tau}_{k}^{(n)}\widetilde{\tau}_{k}\right)+\frac{t}{t^{(n)^{2}}} \sum_{k=1}^{K}\left(\widetilde{\tau}_{k}^{(n)^{2}}\right)-q_{k}\ \ \,\forall k\] (10d) \[0\leq P_{A}\leq\sqrt{P_{max}}\] (10e) ```
**Algorithm 2** Sequential parametric convex approximation (SPCA)-based iterative algorithm to generate data for a single user.  
### Multiuser  
Here, we consider the problem for multi-users. We denote \(\mathbf{s}=[s_{1},s_{2},s_{3}...,s_{k}]\), \(\mathbf{r}=[r_{1},r_{2},r_{3},...,r_{k}]\) and \(\mathbf{q}=[q_{1},q_{2},q_{3}...,q_{k}]\) as slack variables and rewrite Equation (7) as a non-convex sub-problem that can be solved by using a SPCA based iterative algorithm.  
\[\min_{P_{A},\tau,\overline{t},\ s,r,d,t} \quad{P_{A}}^{2}+\sum_{k=1}^{K}s_{k}+\sum_{k=1}^{K}r_{k}+\sum_{k=1 }^{K}q_{k}\] (10a) s.t. \[0\geq\frac{2\frac{R_{blue}}{h}-1}{\alpha_{k}}-\ 2\frac{P_{A}^{(n)}P_{A}}{t^{(n)}}-\ \frac{{P_{A}^{(n)}}^{2}t}{t^{(n) 2}}-s_{k}\ \ \,\forall k\] (10b) \[0\geq\frac{E_{blue}}{1-\sum_{k=1}^{K}(\tau_{k})}-\ 2\varsigma_{k}|h_{k}|^{2}PThis problem is very complex to solve in MATLAB CVX with an increasing number of users, and it becomes difficult to solve with the traditional method, whereas the DNN can solve it with less complexity and less time, which will be discussed in a later section. We solve this problem using Algorithm 2.
```
1:initialize: Find a feasible point for Equation (10) as an initial point \(P^{(0)},\ \tau^{(0)},\widetilde{\tau}^{(0)},\ t^{(0)}\) and find \(k^{(0)}=\ {P^{(0)}_{A}}^{2}+\sum_{k=1}^{K}s^{(0)}_{k}+\sum_{k=1}^{K}r^{(0)}_{k}+ \sum_{k=1}^{K}q^{(0)}_{k}\)
2:// initial loop:
3:repeat
4: Solve Equation (10) using CVX and find \(P^{*}_{A},\ \tau^{*},\widetilde{\tau}^{*},t^{*},k^{*}\)
5: Assign \(k^{(n)}\ \leftarrow\ k^{*}\), \(P^{(n)}_{A}\gets P^{*}_{A}\), \(\tau^{(n)}\leftarrow\ \tau^{*}\), \(\widetilde{\tau}^{(n)}\leftarrow\ \widetilde{\tau}^{*}\), \(t^{(n)}\gets\ t^{*}\)
6:until
7:\(\frac{\left|k^{(n)}-k^{(n-1)}\right|}{k^{(n-1)}}\leq\ tolerance\) and \(\sum_{k=1}^{K}s_{k}+\sum_{k=1}^{K}r_{k}+\sum_{k=1}^{K}q_{k}\ \leq\ \epsilon\)
8:// main loop:
9: Generate 10,000 samples for \({P^{(n)}}^{*},\ {\tau^{(n)}}^{*}\) that satisfy Step 7
```  
**Algorithm 2** SPCA-based iterative algorithm to generate data for multiple users.
# Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems
## 1 Introduction
Notably, the set of constraints in real-world problems is more complex, such as multiple objective scheduling challenges in a job shop, processing times can be either deterministic (constant) or probabilistic (variable), limit idle time requests to no more than two consecutive machines, or no idle time. Any change to the problem's limitations can create a new variation of the problem. Consequently, JSSP solving approaches evolve with each research development phase [10]. Metaheuristic optimization is one of the practical approaches to JSSP with the ability to provide a satisfactory optimization solution in a reasonable time [11]. Metaheuristic algorithms employ innovative search strategies to explore the solution space and avoid getting stuck in local optima by steering the feasible solution with a bias, enabling the rapid generation of high-quality solutions. Contemporary metaheuristic algorithms also combine with different mathematical models [12] and analytical operating procedures [13] to enhance performance. The coral reef optimization approach (CRO) is one of the complex bio-inspired computation methods used to solve engineering and science problems by simulating the \"formation\" and \"reproduction\" of corals in coral reefs. The approach was first envisioned by [PERSON] et al. in 2014 [14]. Since then, it has been utilized in various relevant topics, including optimal mobile network deployment [15], enhanced battery scheduling of microgrids [16], and wind speed prediction systems with success in renewable energy in \"Offshore Wind Farm Design\" [17]. This article contributes by proposing modified coral reef optimization methods with local search techniques for the JSSP feature. Two hybrid algorithms have been developed and presented based on the original coral reef optimization (CRO) method. CROLS1 integrates CRO with the Simulated Annealing (SA) strategy, whereas CROLS2 combines CRO with the Variable Neighborhood Search (VNS) technique. This article focuses on the optimizing effect of local search techniques on the CRO algorithm. The experiments demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms. The remaining of this article is structured into several parts: Section 2 summarizes prior work and CRO. Section 3 outlines the basis for the suggested strategy. Then, in Section 4, the experimental results are reported. Finally, Section 5 concludes this study.