esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
## 5 Validation
### Fuzzy DEMATEL for Object-Oriented Metrics Results Discussion and Validation  
The first step in the validation process of the Fuzzy DEMATEL approach is to select the set of rules for each defect. In fact, we use the same rules generated in the reference study [12], based on the projects PMD 5.4.3 (with 433 classes) and Nutch 1.12 (with 247 classes). However, we introduce two formulae to compare the method of selecting the rules used in the reference study (i.e., based on the parameter N) and the new proposed one (i.e., based on Fuzzy DEMATEL). The first, Formula (8), represents the ratio of the number of rules in the new selected set divided by the number of rules in the original set. The second, Formula (9), represents the similarity between the two sets of rules: the set of rules identified using the parameter N and the set of rules identified using the Fuzzy DEMATEL approach. \[\text{Ratio of Rules}=\frac{\text{R1}}{\text{R0}} \tag{8}\]  
\[\text{Rules Similarity}=\frac{\text{R0}\cap\text{R1}}{\text{Min}(\text{R0}, \text{R1})} \tag{9}\]  
where:  
R0 represents the original set of rules selected based on the parameter N;  
R1 represents the set of rules selected based on metrics importance (Fuzzy DEMATEL). As presented in Table 11, based on Fuzzy DEMATEL, we reduced about a half the number of rules generated in [12]. The degree of similarity varies depending on the defects. For example, the DC defect detection is based on 0.462 rules that are similar to the rules used in [12], it means that we selected over than 50% of new rules comparing to [12]. For LC, the degree of similarity is 0.833, which means that we almost use the same rules as those selected based on the N parameter, but we reduced the number of rules, eliminating the non-useful rules. In Table 12, we present the detailed detection results by project, and of each defect. In fact, we significantly increased the precision and recall. The F1 score is higher when using rules selected based on the metrics cause and effects. We can identify clearly through Figure 3 that the accuracy of the detection is improved using rules selected based on metrics importance. However, we notice that, only for the LC defect, the improvement in the accuracy was due to the improvement in the precision. In fact, in Figure 3, there is no big difference in the recall curve for both selection methods. This is normal; as we can see in Table 11, the rules similarity is very high. It means that we almost used the same rule, which implicates a similar detection rate. However, the ratio R0/R1 shows that we used approximatively only 0.6% of the rules compared to the set of rules selected based on the N parameter. The selection of rules based on their importance reduced the set of rules by 0.4%. This has a direct impact on the precision; in fact, we use less rules, minimizing over-detection, and we decrease the number of false positive detections.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
## 3 System Models and Problem Statement
### Assumptions  
* The sink has knowledge about the complete network topology, including node positions and energy levels. It is also assumed that the sink has sufficient energy resources to perform computations to control the mobility and topology [30].
* The sink is equipped with GPS used for self-localization.
* After collecting the data from a given point, the sink moves to the next point within a specific amount of time defined by the network administrator.
* All nodes in the network are time synchronized, and various synchronization techniques such as; Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) are specifically designed for WSNs with virtually no overhead and provide reasonable synchronization performance [31].
* The proposed routing protocol is organized into multiple rounds. Each round has a predefined time T = 1 min.
* The sensor node transmits data in packets, and each packet has \(pl(\text{Packet Length})\) = 2048 bits (256 bytes).
* Energy consumption in communication is more than consumption in computation [32].
* Dissipated energy in the idle-mode of the sensor node can be negligible [33].
* To avoid interference between communications, a TDMA-based MAC (Media Access Control) protocol is assumed to be used in the communication phase.
* The overall energy dissipation over a single time period is divided into two categories, the actual communication energy cost and the network reconfiguration cost. As the actual communication includes all sensor nodes' transmission towards the mobile sink over a dwelling time, so it dissipates the major portion of the energy of the sensor nodes. While reconfiguration of the network also causes some energy dissipation, this is a minor energy dissipation (less than 1.0%), and we consider it as a negligible energy cost [34].  
Meanwhile, dense and larger sensor network areas demand a higher frequency of reconfiguration with the help of more complicated and sophisticated path-planning algorithms. These scenarios force designers to reconsider the reconfiguration's cost to track the energy resources' dissipation to ensure the performance of the implemented routing algorithm. This demands serious effort to keep routing algorithms simple to avoid complication and implement the calculated reconfigurations. Frequent reconfigurations can damage the cause of energy efficiency by rotating the communication responsibilities.  
In this paper, we focus on a balanced reconfiguration frequency for our proposed models, while avoiding the unnecessary complication the cluster-formation algorithm and the mobility pattern of the sink. In the current proposed model, we have managed the reconfiguration cost by managing manual settings for a single communication period and data gathering method, but in the advanced implementation case, we are committed to developing most the economical reconfiguration system, which can be altered through online reprogramming of network nodes.
# Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring Bay in Spring 2021
## 3 Methodology
### Google Earth Engine  
Launched by Google towards the end of 2010, Google Earth Engine (GEE) is an open-access cloud computing platform that primarily enables the management, processing, and analysis of publicly available big geospatial data. The GEE platform contains more than 40 years of remotely sensed raster data up to petabytes (e.g., Landsat, Sentinel, MODIS and ALOS) [34] as well as vector-based spatial data representing some social and demographic information [35]. The users take advantage of the high-speed parallel processing of Google system architecture without needing a high-performance supercomputer system. With an online code editor, which is one of the important components of the platform, users can write scripts, develop complex file commands, and debug them. In addition, the system API allows users to employ a series of operational tools such as machine learning algorithms, data visualization, and feature collection through client libraries within the Python and JavaScript programming languages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Band Number**} & \multirow{2}{*}{**Band Name**} & **Central Wavelength** & **Spatial Resolution** \\  & & **(nm)** & **(m)** \\ \hline
1 & Coastal Blue & 443 & 60 \\
2 & Blue & 490 & 10 \\
3 & Green & 560 & 10 \\
4 & Red & 665 & 10 \\
5 & Vegetation Red-Edge & 705 & 20 \\
6 & Vegetation Red-Edge & 740 & 20 \\
7 & Vegetation Red-Edge & 783 & 20 \\
8 & NIR & 842 & 10 \\
8A & NIR (narrow) & 865 & 20 \\
9 & Water Vapor & 945 & 60 \\
10 & SWIR-Cirrus & 1375 & 60 \\
11 & SWIR-1 & 1610 & 20 \\
12 & SWIR-2 & 2190 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of Sentinel-2 products.  
Until now, the GEE platform has been actively utilized in many geospatial data science and remote sensing practices, including wetland mapping and monitoring [36], albedo trend [37], land use land cover mapping [38], crop yield estimation [39], natural disaster identification and prediction [40], and drought monitoring [41], chiefly because of its promising prospects mentioned above. In this regard, Ref. [42] reported that the use of this cloud-based platform has significantly increased in recent years, and it is expected that this upward trend will continue in the future due to the potential engagement in different domains [43].
## 2 The Proposed Image Fusion Method
### SML and SMLF Energy
#### 2.2.2 SMLF Energy  
Side-scan sonar image is characterized by uneven brightness and severe speckle noise [29], which makes the SML energy unable to reflect the difference in image contrast and leads to false contours in the fused low frequency images. Therefore, SMLF energy is applied to improve the above problems. Its strategy can be described below. SML and the visual adjustment value \(\alpha\) are nonlinearly combined into the RLV value. RLV(\(x\),\(y\)) is defined as follows:  
\[RLV(x,y)=\begin{cases}\frac{SML(x,y)}{\bar{c}(x,y)(1+\alpha)}&\bar{c}(x,y)\
eq 0 \\ SML(x,y)&\bar{c}(x,y)=0\end{cases} \tag{2}\]  
where \(\bar{c}\) (\(x,y\)) is the coefficient mean of centered on the pixels (\(x,y\)). \(\alpha\) is the brightness value of the visual adjustment, and \(\alpha\in\) (0.6, 0.7) [30] and \(\alpha=0.65\) are used in this paper.  
Taking RLV as the guide map, the guided image coefficients \(G\!f_{A}\) and \(G\!f_{B}\) are obtained by the guided filter (GF) [31]. The decision map can be obtained by judging the size of \(G\!f_{A}\) and \(G\!f_{B}\) and filtering twice with multi-channel filters. \(p\times q\) is \(3\times 3\) window size.  
\[map=\begin{cases}1&G\!f_{A}\geq G\!f_{B}\\ 0&\!others\end{cases}\quad and,\quad map1=\begin{cases}1&\sum\limits_{p\
eq q} map(i,j)\geq 6\\ 0&\!others\end{cases} \tag{3}\]  
According to the value of \(dp\), the low frequency coefficient matrix of fused image \(c^{F}\) is finally generated. \(c^{A}\) and \(c^{B}\) are the low frequency sub-band coefficients, respectively.  
\[c^{F}=map1*c^{A}+(1-map1)*c^{B} \tag{4}\]
# Self-Organized PT-Symmetry of Exciton-Polariton Condensate in a Double-Well Potential
## Appendix A Polariton Condensate in a PT-Symmetric Double Well  
Consider a polariton condensate in a double-well potential described by the coupled-mode equations  
\[i\partial_{t}\psi_{L} = (\epsilon_{L}+i\gamma_{L})\psi_{L}+\eta|\psi_{L}|^{2}\psi_{L}-J \ \psi_{R}, \tag{11a}\] \[i\partial_{t}\psi_{R} = (\epsilon_{R}+i\gamma_{R})\psi_{R}+\eta|\psi_{R}|^{2}\psi_{R}-J^ {\ast}\psi_{L}, \tag{11b}\]  
where \(\epsilon_{L,R}\) are the single-particle energies, \(\gamma_{L,R}\) are the incoherent loss (\(\gamma<0\)) or gain (\(\gamma>0\)) rates at each well, \(\eta\) is the nonlinear interaction strength, and \(J\) is the Josephson coupling between the wells.  
Without loss of generality, we can set \(\epsilon_{L,R}=0\). If we assume negligibly weak nonlinearity, \(\eta|\psi|^{2}\ll J\), and set \(\gamma_{L}=-\gamma_{R}=\gamma\) so that the loss at the right well is exactly compensated by the gain at the left well, we obtain a PT-symmetric Hamiltonian matrix corresponding to Equation (A1) [29]:  
\[\mathcal{H}=\begin{pmatrix}i\gamma&-J\\ -J^{*}&-i\gamma\end{pmatrix}.\] (A2)  
Its eigenvalues and the corresponding eigenvectors are given by  
\[\lambda_{\pm}=\pm\sqrt{|J|^{2}-\gamma^{2}}\] (A3)  
and  
\[|\pm\rangle=\left[\left(\sqrt{|J|^{2}-\gamma^{2}}\pm i\gamma\right)|L\right) \mp J^{*}|R\rangle\right]/N_{\pm}\] (A4)  
with \(N_{\pm}\) the normalization factors. For \(\gamma<|J|\), the eigenvalue spectrum is real and the dynamics is Hermitian-like. For \(\gamma>|J|\), the eigenvalues become imaginary and the system enters the PT-broken phase. The case \(|J|=\gamma\) corresponds to the exceptional point of the system where the eigenvalues become degenerate and the eigenstates coalesce. Figure A1 illustrates the dependence of imaginary part of the eigenvalues on the loss/gain parameter \(\gamma\).
# Cybersecurity Requirements for Industrial Machine Control Systems
## 3 Machine Safety Requirements
### Safety Functions  
Risks from certain hazards are minimized with safety-related control systems. A given control system can then implement a single or multiple safety functions.  
The requirement specification for safety functions consists of functional requirements and safety integrity. Functional requirements are most often specified as a formulation, such as \"Stop the dangerous movement of the machine when the interlocking guard is opened and prevent it from starting until the guard is closed\".  
The ranking of system state parameters is based on their relative impact on the overall safety and security of the system. Parameters were evaluated using a weighted scoring system, considering criteria such as the frequency of occurrence, severity of potential consequences, and the likelihood of detection. The methodology follows established risk assessment frameworks, including ISO 12100 and ISO 13849-1 [18], which recommend prioritizing parameters based on their contribution to system vulnerability and resilience. This approach ensures that critical parameters are given higher weight in the analysis, reflecting their importance in mitigating cyber and operational risks.  
Typically, the safety function is realized with three elements, a sensor that detects the hazard (opening the guard), a logic unit that processes the signals from the sensor, and an actuator connected to the logic unit (e.g., a contactor that shuts down the drive under consideration or a valve that shuts off the operating medium). These elements form an SCS (safety-related control system) according to IEC 62061 [19] or SRP/CS (safety-related part of a control system) according to ISO 13849-1. Due to the greater popularity of ISO 13849 in the industry, we will focus on its requirements in the remainder of this article.
# L-Band Erbium Fiber Laser with Tunable and Narrow Sub-kHz Linewidth Output
###### Abstract  
In this paper, a wavelength-selectable erbium-doped fiber (EDF) laser in L-band bandwidth with a simple structure and low cost is described. To obtain stable single-longitudinal-mode (SLM) and narrow laser linewidth, the 2 m unpumped EDF-based saturable absorber (SA) and 100 m single-mode fiber (SMF)-induced Rayleigh backscattering (RB) injection effect are cascaded in the ring cavity of the fiber laser. The EDF SA and feedback RB injection can cause the auto-tracking ultra-narrow bandwidth filter effect to suppress the multiple longitudinal mode oscillations and narrow the laser linewidth. Thus, the tunable SLM wavelength output of the EDF laser with sub-kHz linewidth can be achieved. In the measurement, two EDF ring lasers can be executed when the placement of EDF-based SA and SMF-induced RB are switched to experiment with different output characteristics. The experimental results show that the wavelength bandwidths of the two lasers are between 1558.0 and 1618.0 nm. Additionally, the optimal optical signal-to-noise ratio (OSNR), wavelength linewidth, output power, and stability of the presented EDF laser configuration are also achieved and discussed.  
erbium-doped fiber (EDF) laser; single-longitudinal-mode (SLM); Rayleigh backscattering (RB); saturable absorber (SA) +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Adsorption and Photocatalytic Removal of MB
#### 3.2.4 Effect of the MB Dye Initial Concentration on its Photocatalytic Decay  
The effect of the initial dye concentration on the photocatalytic decay of MB is shown in Figure 7 for both SA/PVP/TiO\({}_{2}\)-1 and SA/PVP/TiO\({}_{2}\)-3 nanocomposite beads. In each experiment, the same quantity of nanocomposite beads (1 g L\({}^{-1}\)) was added to solutions containing different dye concentrations (10, 25, 50, and 100 mg L\({}^{-1}\)). The pH of the  
Figure 5: Effect of the illumination time in the degradation of methylene blue (%) in light using the SA/PVP/TiO\({}_{2}\) nanocomposites beads.  
Figure 6: Effect of the light intensity on MB photocatalytic degradation using SA/PVP/TiO\({}_{2}\) nanocomposite (pH 7; 120 min of illumination time; and 50 mg L\({}^{-1}\) initial MB concentration).  
- dye solution was kept at 7 and the contact time and light intensity were 120 min and 1200 lm, respectively. Figure 7 shows that dye decay tends to increase for higher initial MB concentration, the higher decay being attained at 50 mg L\({}^{-1}\). Further increasing the MB dye concentration negatively affected its decay. This can be justified by the dependence of the photodegradation rate of the dyes on the formation of OH\({}^{\bullet}\) radicals on the catalyst surface, followed by the reaction of such radicals with the MB dye molecules. The higher dye decay rate observed at lower initial dye concentrations can be related to a higher chance of the dye molecules to react with OH\({}^{\bullet}\)[27; 28]. The following steps describe the mechanism proposed for MB degradation via the nanocomposite beads. First, irradiation with visible light enables electrons in the valence band to move to the conduction band. The surface of the TiO\({}_{2}\) photocatalyst then forms holes and electrons. Electrons react with dissolved O\({}_{2}\) to produce O\({}_{2}\)\({}^{\bullet-}\), while holes react with OH\({}^{-}\) ions to produce OH\({}^{\bullet}\). The latter reacts with MB dye to degrade it into non-toxic products, such as CO\({}_{2}\), and water. Additional OH\({}^{\bullet}\) is produced in the reaction between H\({}_{2}\)O\({}_{2}\) and e\({}^{-}\), further enhancing MB dye degradation [29; 30; 31].  
For higher values of initial MB dye concentration, the photocatalyst activity is diminished. This effect is likely related to a hampering of the reaction between OH\({}^{\bullet}\) radicals and the dye molecules. As the initial dye concentration increases, the amount of MB molecules adsorbed onto the catalyst surface also increases, consequently hindering OH\({}^{\bullet}\) formation [32]. Additionally, with the increase of color intensity, the gaps related to the photons entry are inhibited in reaching the photocatalyst surface, restraining the photodegradation of the dye by OH\({}^{\bullet}\) radicals [31]. Herein, the optimum initial MB dye concentration was found to be 50 mg L\({}^{-1}\), with the SA/PVP/TiO\({}_{2}\)-3 nanocomposite beads revealing a photodegradation efficiency of 98.9 %.
# Monitoring System Enhancing the Potential of Urban Bekeeping
## 2 Methods
### Monitoring System Overview
#### 2.2.1 Multi-Sensor Device
Given the typically elevated PM levels in urban air, the shielding effect of the beehive against ambient conditions deserves examination. The schema of the multi-sensor module applied for beehive air measurements is shown in Figure 2. It was designed as a single PCB expansion card compatible with a Raspberry Pi GPIO connector. The expansion card contains connectors for sensors and a power management module. Except for one analogue sensor (Photo Ionisation Detector), all sensors used in the device were integrated with a digital I2C interface. High-precision ADS1256 analogue-to-digital converter was applied for handling the voltage signal from the PID sensor. All sensors were attached to PCB inside one housing. Only the temperature and humidity sensor STH25 was placed outside to avoid the systematic error resulting from heat emission by the electronics inside the housing. A miniature single-board computer (Raspberry Pi Zero W) with an operating system based on the Linux kernel was chosen as a processing unit for a multi-sensor module. It offers the easiness of remote supervision and software modification in comparison with solutions based on microcontrollers. The main drawbacks of this solution are the degradation of SD cards and power consumption. The first issue was handled by using  
Figure 2: The schema of the multi-sensor module. a read-only memory operating system with a separate partition for data and logs, and by using industrial class SD cards. The second problem was overcome by using a grid power supply. The measurement unit is powered by a 12 V DC cable with a DCDC converter on board, and Wi-Fi is used for data transmission. It can be expanded easily by connecting modules that use the UART interface, for example, additional sensors, GPS module, or GSM modem for data transmission. A multi-sensor device was dedicated to monitoring the indoor environment of a beehive. One device per beehive was deployed. One additional device was operated outside, in the vicinity of beehives, to provide the reference for the inside-beehive measurements.
## 4 Experiments and Results
### Training Strategies and Implementation Details  
We implemented the proposed CISPNet with PyTorch v0.4.1 on a PC with an NVIDIA GeForce GTX 1080 Ti graphics processing units (GPUs), CUDA8.0, and cuDNN 6.0.21. In this experiment, we perform and evaluate all experiments on our remote sensing image dataset benchmark. In order to obtain a more robust model for the size and shape of various remote sensing image targets, data enhancement operations should be carried out for each training image, such as random cropping and flipping of the input image. Considering the memory limitations of GPU, when the input size is 300, the batch size is set to 16. Following [27], we train a total of 120 k iterations, with a learning rate of \(10^{-3}\) for the first 80 k iterations, \(10^{-4}\) for the next 20 k iterations, then continue training for 20 k iterations with a \(10^{-5}\) learning rate. We fine-tune the entire model with a weight decay of 0.0005 and a momentum of 0.9. The optimizer chosen is the stochastic gradient descent.  
The aspect ratios of the images set the aspect ratios of the default boxes, as shown in Figure 4a, we set different aspect ratios for the default boxes according to the length-width ratio distribution of the image and denote them as \(a_{r}=\{1/3,1/2,2/3,3/2,1,2,3\}\), in addition, we use K-means to  
Figure 6: The histograms of the distribution of the area size and aspect ratio of the bounding boxes.  
calculate the three cluster centers of the area size as the scale of the default boxes. During the training process, we need to confirm which default boxes conform to ground truth box detection and train the corresponding network. For box matching, we match default boxes to any ground truth box with Jaccard overlap higher than a threshold of 0.5.