esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory  
### Multiscale Mathematical Morphological Operators  
[PERSON] and [PERSON] were among the first to employ mathematical morphology in image processing, and it is currently often used in rotating machinery fault diagnosis [37]. Multiscale morphological filtering is used in the fault diagnosis of bearings in order to gain more specific information about the fault characteristics. Assuming that \(\varepsilon\) (\(\varepsilon\) = 1, 2, 3,..., \(\lambda\)) is the scale of the structural element, the basic morphological operators for the four multiscale scales of dilation, erosion, opening and closing are described as follows:  
Multiscale dilation operators:  
\[(f\oplus\lambda g)(n)=\underbrace{f\oplus g\oplus g\oplus\cdots\oplus g}_{ \lambda-1} \tag{1}\]  
Multiscale erosion operators:  
\[(f\Theta\lambda g)(n)=\underbrace{(\Theta g\Theta g\Theta\cdots\Theta g}_{ \lambda-1} \tag{2}\]  
Multiscale opening, closing operators:  
\[(f\circ\lambda g)(n)=((f\Theta\lambda g)\oplus\lambda g)(n) \tag{3}\]  
\[(f\bullet\lambda g)(n)=((f\oplus\lambda g)\Theta\lambda g)(n) \tag{4}\]  
where denotes \(\oplus\) dilation operator, \(\Theta\) stands for erosion operator, denotes \(\circ\) opening operator and \(\bullet\) stands for closing operator.  
The above is based on the properties of the morphological data that the aforementioned basic operator extracted. Additionally, given that the top-hat operator can differentiate the positive from the negative pulse sequences, the 1D signal's detailed information is improved. A new multiscale enhanced morphological top-hat filter (MEMTF) has been demonstrated by [PERSON] et al. [38]. The expression is shown below:  
\[\text{MEMTF}(f(n)_{\lambda g})=2\bigg{[}f(n)-\frac{(f\oplus\lambda g)(n)+(f \Theta\lambda g)(n)}{2}\bigg{]}\cdot\bigg{[}f(n)-\frac{(f\bullet\lambda g\oplus \lambda g\Theta\lambda g)(n)+(f\circ\lambda g\Theta\lambda g\oplus\lambda g)(n )}{2}\bigg{]} \tag{5}\]  
In order to obtain more detailed information about the fault characteristics, the signals calculated by multiscale mathematical morphology are considered to contain richer fault information, and no artificially set parameters are required for the full-scale calculation. Therefore, the MEMTF multiscale morphology is chosen to process the data. First, the one-dimensional original vibration signal to be input is separated into small windows and repeated sampling is performed. The separated windows are reconstructed using the MEMTF method, the results are analyzed by multi-scale FFT spectral analysis, and finally, the two-dimensional image feature information at the frequency scale is obtained for the next step of the study.
# Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-\(\omega\)
## 1 Introduction  
### Background and Motivation  
Environmental sustainability, rapid depletion and constantly fluctuating prices of fossil fuels are currently forcing the incorporation of renewable energy sources (RESs) into power grids [1, 2]. The recent research predominantly focused on exploring energy generation using various renewable resources [3, 4, 5]. However, these RESs present their own limitations, both climatic and technical, as their resource availability is subject to variations caused by climate conditions. In addition, the extraction of bulk power from major renewable energy sources (such as solar and wind) is primarily feasible in locations that are situated far away from major load centers. Thus, there is a requirement for properlydesigned dispatch mechanisms to transmit the produced power from the source to the load in an efficient and economically effective manner [6; 7].  
RESs have issues; however, they still manage to produce good results in their power generation performance, such as wind; biofuel and solar systems produced approximately 700, 671, and 659 GWs of installed capacity till 2020 [8; 9]. However, the high variability of resource availability requires a well-designed metaheuristic optimization algorithm for the calculation of economic dispatch (ED) [10]. The developed ED algorithm should not only provide the efficient dispatch of power, but also account for the penalties imposed as a result of resource variability.  
Pakistan presents a significant potential for RESs and there is a growing trend towards utilizing these emission-free resources for power generation. The authors conducted an extensive survey of the availability of wind resources in the southern regions of Pakistan, as documented in [11; 12]. A detailed survey of the regions rich in solar-based power generation in Pakistan and the prospects of its hybridization with wind energy sources was presented in [13; 14]. In [15; 16; 17], the authors deliberated on the potential of extractable bio-fuel-based energy in the country and examined various scenarios for extracting energy from waste materials. The authors in [18; 19; 20] provided a comprehensive analysis of several renewable energy-based projects that were executed in various appropriate locations to generate environment friendly electricity.  
The ED problem (EDP) is a computational mechanism used to determine the optimal distribution of generated electric power in order to meet the load demand, while minimizing the production cost [21]. Obtaining the optimum solution for EDPs is a critical requirement for a sustainable power network, while considering various operational constraints. The developed algorithm should not only consider the operational limitations of RESs, but also the constraints of thermal systems, such as ramp rate limits (RRLs), and prohibited operating zones (POZs) should be taken into account [22; 23].
## 2 Acoustic Metasurfaces
### Sound Insulation Metasurfaces  
Sound insulation metasurfaces are innovative acoustic control structures that employ resonators, interference architectures, and multi-resonance mechanisms to achieve excellent low-frequency transmission loss even in ultra-thin, lightweight designs. These metasurfaces induce strong resonances at targeted frequencies to absorb or scatter sound energy for high attenuation, and form bandgaps through phase and amplitude interference between adjacent units to enable broadband sound insulation. The future integration of multi-material 4D printing and intelligent design libraries is expected to accelerate the commercialization of ultra-thin, lightweight metasurface barriers tailored for low-frequency noise sources [55; 56].  
[PERSON] et al. proposed a multi-film composite metasurface with enhanced sound insulation performance and magnetically adjustable acoustic properties [46]. This metasurface consists of a circular magnetron film of 1 mm thickness and 15 mm radius, an aluminum frame of 3 mm thickness, and a lead mass block of 6 mm radius and 2 mm thickness. For the composite structure, three films are separated by an aluminum spacer and laminated. Figure 9 illustrates the key components of the composite metasurface, including the laminated multi-film structure and the individual material layers. The experiment showed that the Young's modulus and adjustable frequency range of the magneton film are changed by the presence of a magnetic field. Utilizing the composite film, the structure exceeded  
Figure 8: Schematic of an adjustable acoustic impedance unit based on a Phase–Amplitude–Phase modulation (PAP). (**A**) PAP modulation. (**B**) Solid structure consisting of 2 phase modulators, 4 resonators, and 1 amplitude modulator. (**C**) Air domain considered in numerical simulations. Reproduced under a Creative Commons Attribution (CC BY) 4.0 license [45], Research, 2024.  
the bounds of previous single-film methods, generating several resonant frequencies and achieving sound insulation in an extended frequency range.
# Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core
## 5 Forensic Evidence in Windows 10 IoT Core
### Apps  
Similarly to smartphones, the programs installed in Windows 10 IoT Core are present in the form of apps. Since they are the ones that provide meaning to a system, their relevance in a forensic analysis is very high, firstly because of the useful data that they store, and, secondly, as they help to understand what the purpose of the device is.  
The apps installed on the system are stored in the Programs\WindowsApp route of the \"Data\" partition, in which the user configuration data is stored. The process involved in an app installation is the following:  
* A directory is created for the app in \Programs\WindowsApp, where it will be installed.
* The packages needed for the app are stored in \ProgramData\Microsoft\Windows\App\Repository\Packages.
* The user information of the app is saved in their local directory: \Users\DefaultAccount\AppData\Local\Packages\.  
Figure 9: Content of the user directory and “AppData”.  
Thus, in conclusion, apps behave as a program does in the desktop version: they are installed in a directory, then the general information is stored in a common folder so it can be accessed by any user that launches the app and, finally, each user has a folder created in their local directory where the configuration and program data is stored.  
In Figure 10, the \ Programs\WindowsApp directory is shown, where it can be seen that three applications that were not originally on the system have been installed.
## Materials and Methods
### Photocurrent Spectroscopy  
Photocurrent spectroscopy is a method used to measure photogenerated current depending on the wavelength of excitation light. When a reverse bias is applied to an LED, the carriers generated by light absorption in the MQW layer appear as a reverse current by the built-in electric field in the junction [13; 14].  
There are two conditions that must be satisfied for absorption to occur in the depletion region or in the MQW region. First, in order for the light to reach the active (depletion) region, the photon energy must be lower than that of the bandgap of the barrier, n-AlGaN, AlN, and sapphire substrates. Second, the photon energy of the incident light must be greater than the bandgap energy of the quantum wells (Al\({}_{0.53}\)Ga\({}_{0.47}\)N). Generally, no photocurrent is observed for the wavelength region longer than the absorption band of the QWs, and a photocurrent does not flow for wavelengths shorter than the absorption wavelength of the barriers (Al\({}_{0.7}\)Ga\({}_{0.3}\)N) since light is absorbed in the thick n-AlGaN layers. Therefore, the expected photocurrent spectrum will exhibit a peak.  
The photocurrent spectroscopy setup is shown in Figure 3. As seen in the figure, the measurement setup consists of a xenon lamp, a monochromator, and a source meter. Xenon lamps have an emission range of 200 nm-800 nm. This can be converted to a single wavelength from 200 to 800 nm through a monochromator. A reverse voltage is applied to the sample, and the current is measured through the source meter while irradiating the sample with monochromatic light. The photocurrent spectrum shows the intensity of the photocurrent while changing the wavelength of light from the monochromator. LEDs typically have recovery times of a few \(\upmu\)s after illumination [15]. The sweep rate of the monochromator was set to 1 nm in 1 s intervals, and the photocurrent value was obtained 1 s after each wavelength change in order to ensure a static value of the photocurrent.  
Figure 2: The electroluminescence (EL) spectrum of a 265 nm DUV LED in the range of 200 nm to 700 nm at a temperature of 300 K. The insets are the EL spectra at temperatures of 80 K, 100 K, 200 K, and 300 K.  
Additionally, with this setup, it is possible to measure the photocurrent over time while the wavelength of the excitation light is fixed. In order to analyze the time-dependent recovery of the photocurrent reversal shown in the results, we measured the recovery of photocurrent with a time interval of 1 min while we fixed the wavelength of the excitation light at 480 nm when applying back to zero bias after applying a forward bias of 4 V. In addition, in order to confirm the temperature dependence of the photocurrent recovery, the experiments were conducted at 26 \({}^{\circ}\)C (room temperature), 50 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C.
## 4 Experimental Validation
### Results
#### 4.3.2 Time-Varying Frequency Extract Based on TFR  
The TFR-based algorithm is implemented for the identification of cable tension based on measured acceleration responses. Firstly, the reassignment spectrograms of acceleration responses are shown in Figure 7. The window function length used in STFT is 256 based on the discussion in Section 4.3.1. It is obvious to see from Figure 7a that the first eight order frequencies are identified in the time-frequency plane. The energy of the 1 st and 2 nd order modal shapes is relatively low and almost unrecognizable. The zoom-in views of each sensor are shown in Figure 7b-f, respectively. The overall variation of frequencies is relatively consistent, corresponding to the loading process step by step.  
Figure 6: Time-frequency reassignment spectrogram with window function length of (**a**) 64, (**b**) 128, (c) 256, and (**d**) 512, respectively.  
For comparison, traditional STFT analysis is also applied to deal with the acceleration signals. The STFT result is shown in Figure 8. The window function with the length of 256 sampling points, i.e., a time interval of 12.8 s, was used to extract the time-frequency characteristics simultaneously in the STFT frame. However, as mentioned in the introduction, the frequency resolution is pretty low if the length of the window function is short. Obviously, the time-frequency resolution is not as clear as the TFR-based algorithm shown in Figure 7. There is strong interference on the spectrum in Figure 8, so it can only roughly find the range of frequency distribution.  
Figure 7: TFR spectrogram of measured acceleration response. (**a**) Sensor \(1\)-global view, (**b**) Sensor \(1\) (**c**) Sensor \(2\), (**d**) Sensor \(3\), (**e**) Sensor \(4\), (**f**) Sensor \(5\)-zoom-in view.
# Deploying Secure Distributed Systems: Comparative Analysis of GNS3 and SEED Internet Emulator
## 5 Network Performance Comparison
### Jitter  
Jitter is the variation in packet delay at the receiver end of the communication. It is computed by measuring \(n\) samples of ping time between two endpoints and computing the difference \(D_{i}\) between the individual pings and the average value. As every packet can be routed to different paths to reach the destination, jitter measures the consistency in transmission delay. Therefore, lower jitter values are preferred [45].  
\[Jitter\ (s)=\frac{\sum_{1}^{n}|D_{i}|}{n}\ (s) \tag{6}\]
## 2 Materials and Methods
### Analytical Methods and Calculated Parameters  
Algae growth was controlled by cell counting using an improved [PERSON] hemocytometer (Rohem Instruments, Nashik, Maharashtra, India) through an optical microscope (Biomed, Russia with a Biomed Lum 206070112209 nozzle and a Myscope 500 M digital camera for the microscope). Concurrently, the OD\({}_{540}\) of the cell suspensions was controlled with an Agilent UV-8453 spectroscopy system (Agilent Technology, Waldbronn, Germany) to investigate the kinetic curve of growth. The calibration graphs reflected correlation between the OD\({}_{540}\) and DCW of each culture of studied microorganisms used. The dry cell weight (DCW) of biomass was determined by a standard gravimetrical method using a sample drying at 105 \({}^{\circ}\)C to a constant weight.  
The initial concentrations of suspended and immobilized cells of phototrophic microorganisms were 1.2 g dry cell weight/L.  
For procedures of determination of the concentration of intracellular adenosine triphosphate (ATP) in microbial cells, methyl parathion and \(p\)-nitrophenol were used as published previously [42]. The pH value was determined as described previously [13].  
The average rate of biomass accumulation (V\({}_{\text{biomass}}\)) during the culture period was calculated from the equation: V\({}_{\text{biomass}}\) (g DCW/L/d) = \(\Delta\)C/\(\Delta\)t, where \(\Delta\)C is the variation of biomass concentration (g DCW/L) within a cultivation time of \(\Delta\)t (days).  
To control the presence of residual amounts of PFCs in culture media, the residual bioluminescence of immobilized photobacteria was determined when they were exposed to selected media samples. For this purpose, calibration dependences of the residual bioluminescence of photobacteria on certain concentrations of PFCs in the studied media were plotted and used. For this purpose the _Photobacterium phosphoreum_ B-1717 cells with natural bioluminescence were cultured in a standard Fargaly medium. The accumulated biomass was immobilized by a known method [46] through its inclusion into a cryogel of poly(vinyl alcohol) so that its final concentration in the polymer matrix was 0.1%. The formed samples of immobilized photobacteria were used as sensitive elements for biosensor examination of the toxicity of the studied media. The estimations of toxicity of various media were carried out in a similar way as described earlier [46].  
The data are shown as means of at least three independent experiments \(\pm\) standard deviations (\(\pm\)SD). The statistical analysis was realized using SigmaPlot 12.5 (ver. 12.5, Systat Software Inc., San Jose, CA, USA). The significant (\(p\leq 0.05\)) differences between the obtained results were estimated by a one-way analysis of variance (ANOVA).
## 3 DCS Scheme Families
### Practical Data-Centric Storage  
In **PathDCS**[32] messages are routed to locations using a tree-structure, and the path to a location is identified using landmarks, which are shared reference points. The interesting novelty of this approach is that an event type is mapped to a path instead of mapping the event type to a spatial location. The path is defined by an initial landmark and then followed by a set of procedural directions. The landmarks are also called beacon nodes, which are elected randomly or manually configured. Standard tree construction techniques are used to build trees rooted at each of these beacon nodes. This ensures that all nodes know how to reach the beacons. A path consists of a sequence of \(b_{i}\) pairs _(\(b_{i}\), \(l_{i}\))_, where \(b_{i}\) is a beacon and \(l_{i}\) is a length. A beacon node \(b_{i}\) is the beacon whose identifier is closest to the hash function _h(k, i)_. The packet is first routed to beacon \(b_{l}\), and then it is sent \(l_{2}\) hops toward beacon \(b_{2}\) using the tree rooted at \(b_{2}\), and so on, until it arrives at the previous \(i-1\) segment. The packet is then sent \(l_{i}\) hops toward the next beacon \(b_{i}\). In addition, the first segment length \(l_{i}\) is equal to the distance to the first beacon \(b_{l}\), whereas segment lengths for \(i>1\) are given by:  
\[l_{{}_{i}}=h(k,i)\bmod hops(n_{{}_{i}},b_{{}_{i}}) \tag{9}\]  
The PathDCS algorithm has two parameters: the total number of beacon nodes denoted by \(B\) and the number of path segments \(P\). The performance of PathDCS largely depends on these two parameters and by varying the number of \(B\), PathDCS trades off the control traffic overhead due to tree construction versus load on the beacons. Data is also been locally replicated by flooding within k-hops of the destination.
# Vulnerability Management Models Using a Common Vulnerability Scoring System
## 6 Results
### Summary of the Obtained Results  
The results obtained here show the possibility of using vulnerability management models that consider environmental factors (Figure 2) while using the VMC software developed in this paper. In the first phase of the experiment, for each investigated environment (environments A, B, and C), the use of the CVSS 2.0 environmental assessment was discussed. This discussion provided useful insights into the vulnerability management process with respect to vulnerabilities that affect a large numbers of resources. In the second phase of the experiment, for each environment, the use of the CVSS 3.x environmental assessment was discussed; despite receiving CIA flags only for 23% of the monitored infrastructure resources in environment A, this assessment changed the base CVSS 3.x scores for 51.85% of the detected vulnerabilities. Consequently, the criticality category changed for 37% of the detected vulnerabilities. The changes in the criticality categories when using the CVSS 3.x environmental assessment for environment A caused a reduction in the estimated work hours by 7%. For environment B, the administrator did not provide the CIA values; therefore, the CVSS 3.x environmental assessment did not show any changes in prioritization. This fact confirms the importance of information from an inventory database in the determination of the triad of confidentiality, integrity, and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{T_{FIX_{min}}}\) & \(\mathbf{T_{FIX_{average}}}\) & \(\mathbf{T_{FIX_{max}}}\) \\ \hline \(T_{Bas\omega 2}\) & 9846 h & 44,226.5 h & 88,430 h \\ \(T_{Ent\omega 2}\) & 23 h & 23 h & 23 h \\ percentage profit & 99.77\% & 99.94\% & 99.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: The estimated work hours required to improve the security of environment C by prioritizing CVSS 2.0.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{Low}\) & \(\mathbf{Medium}\) & \(\mathbf{High}\) & \(\mathbf{Critical}\) \\ \hline Base CVSS 3.x & 802 & 4453 & 2651 & 1824 \\ CVSS 3.x Environmental & 1725 & 4537 & 1981 & 1487 \\ \hline Change & 923 & 84 & \(-\)670 & \(-\)337 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Differences in the vulnerabilities’ severity according to the base CVSS 3.x scores and CVSS 3.x environmental scores in environment C.  
availability according to an organization's business requirements. For environment C, the administrator had only general information on the resources, which resulted in changes in the criticality categories in about 20% of the detected vulnerabilities. This translated into a reduction of the work hours required for environment C by more than 17%.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 4 Simulation Studies for a 6-DOF Manipulator
### Solving Kinematics Model
To obtain \(\theta_{3},\ \theta_{2},\ \text{and}\ \theta_{4}\), transform the matrix of both sides of Equation (3):  
\[{}_{1}^{0}T^{-1}\cdot{}_{6}^{0}T\cdot{}_{6}^{5}T^{-1}={}_{1}^{0}A^{-1}\cdot{}_ {6}^{0}T\cdot{}_{6}^{5}A^{-1}={}_{5}^{1}T={}_{2}^{1}A_{3}^{2}A_{4}^{3}A \tag{40}\]  
According to the left and right sides of row 1 of this equation, column 4 corresponds to the same thing; row 2, column 4 corresponds to the same thing; and \(\theta_{3}\), \(\theta_{2}\), and \(\theta_{4}\) are obtained:  
\[\theta_{3}=\pm\arccos\left(\frac{{m_{3}}^{2}+{n_{3}}^{2}-a_{2}^{2}-a_{2}^{2}}{ 2a_{2}a_{3}}\right) \tag{41}\]  
\[\theta_{2}=atan2(s_{2},c_{2}) \tag{42}\]  
\[\theta_{4}=atan2\big{(}-c_{6}\big{(}o_{x}c_{1}+o_{y}s_{1}\big{)}-s_{6}\big{(}n _{x}c_{1}+n_{y}s_{1}\big{)},-o_{z}c_{6}-n_{z}s_{6}\big{)}-\theta_{2}-\theta_{3} \tag{43}\]  
where \(m_{3}=a_{2}c_{2}+a_{3}c_{23}\)\(n_{3}=a_{2}s_{2}+a_{3}s_{23}\). Based on these equations, the corresponding inverse kinematics solutions of eight groups (\(\theta_{1}\sim\theta_{6}\)) can be obtained.