esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Configuration Optimization of
## 1 Introduction
The rated capacity and power of each ESS clearly determine the power fluctuation compensation performance. Figure 1: Illustration of energy storage system (ESS) configurations. (**a**) Aggregated ESS; (**b**) Distributed ESS. Figure 2: Control diagram of wind-energy storage system. In order to investigate the compensation of power fluctuation of distributed generation with intermittent characteristics, and reasonably allocate HESS for balancing the compensation effect and investment economy, this study establishes a multi-objective model for optimally allocating HESS considering wind power generation, in which the objectives are to minimize the installation, operation, and maintenance costs and to maximize the satisfaction rate of power fluctuation compensation. Meanwhile, fuzzy control [8] is introduced to optimally schedule the power output of each ESS for avoiding destroy the SOC limits, lengthening the battery lifetime, and ensuring HESS with enough energy to compensate the fluctuation of the next time interval. To provide the decision-maker with alternatives and to analyze correlations between optimization objectives, a multi-objective particle swarm optimization with integration of bacteria quorum sensing and circular elimination (BC-MOPSO) presented in [12] has been applied to provide diverse alternative solutions. Numerical simulation along with discussion has been carried out to demonstrate advantages of the proposed model and effectiveness of the application of BC-MOPSO.
## 2 Related Works
### Obsidian Sources Discrimination by ED-XRF  
In general, rocks of the same type but different geographic provenance can be distinguished by means of elemental analyses as they contain typical trace elements [42]; actually, obsidian from different sources has distinct concentration patterns for the different elements. Available literature about source discrimination refers to some elements as markers of different origins, and different elements are considered depending on the aim. Referring only to the sources considered in the present work, Sr and Zr were used for the identification of the provenance of Corsican obsidian finds [32], while Rb, Nb, Zr, and rare earth contents allowed the identification of Palmarola obsidian [12]. Fe, Sr, Rb, and their ratios were the markers for Lipari sub-sources [27; 28; 31], while Rb and Zr can be used to discriminate sub-sources in Pantelleria [26]. Again, Rb/Nb and Sr/Nb ratios obtained via non-destructive portable XRF are able to distinguish Monte Arci (Sardinia) sub-sources, as well as Lipari and Pantelleria sub-sources [30]. Monte Arci sub-sources can also be discriminated using K\({}_{2}\)O/CaO ratio [29]. Our aim is instead to use as many elements as possible to obtain a discrimination without sorting out proper elements but instead exploiting MV potential. It is worth noting that the variability of single elements withing the same source is quite high due to the presence of sub-sources; for this reason, average data available in the literature sometimes have large relative errors.
## 1 Introduction  
In the 1960s an experimental research complex of the Tien Shan mountain cosmic ray station was developed by the Institute of Physics and Technology of the Ministry of Education and Science of the Republic of Kazakhstan and of the P. N. Lebedev Physical Institute of the Russian Academy of Sciences. The experimental site is located at a height of 3340 m above the sea level in the mountains of Zailiyskiy Alatau (Northern Tien Shan), in the vicinity of Almaty city [1]. The complex provides the means to solve many actual problems related to different areas of the cosmic ray particle physics, astrophysics, physics of atmospheric electricity, and experimental geophysics. The list of scientific research at the Tien Shan station includes the following topics:  
1. Investigation of the processes of high-energy particle interaction measuring extensive air showers (EAS) which are the large cascades consisting of (10\({}^{5}\)-10\({}^{8}\)) secondary particles, mostly electrons, created by the (1-1000) PeV cosmic rays in the atmosphere [2]. Investigation of the high energy hadronic and muonic EAS cores where the most energetic cascade products are concentrated that keep the movement direction of a primary cosmic ray particle [3, 4].
2. Study of the large-scale distribution of cosmic ray primaries' directions over the celestial sphere and the search for possible sources of high energy cosmic rays, in dependence on their energy, using the anisotropy effects of this distribution [5, 6].  
4. Investigation of the cosmic ray particles' interaction with the matter of the Earth and of the role which such interactions play in a number of the atmospheric electricity and seismology connected phenomena [7,8].  
Accordingly, the experimental complex of the Tien Shan mountain station nowadays includes a HORIZON-T system of charged particles detectors aimed at registering the widely scattered electron component of high-energy EAS; the ionization-neutron calorimeter INCA, the neutron super monitor NM64, and the separate neutron counters for the detection of the interaction products of collimated hadronic cores in the central region of EAS; the underground muon and acoustic detectors for the search of the effects which the passage of high-energy cosmic ray muons might cause in the deep seismically stressed regions of the Earth's core. The coordinated operation of the different independent subsystems of the Tien Shan research complex is ensured by means of the high-speed local computer network.  
This work studies the keystone components of the Tien Shan experimental complex and illustrates some scientific results which were recently obtained in the experiments currently active at the station.
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 6 Simulation Results
### Scenario 2  
In this scenario, the PV is assumed to be in the OFF mode. Table 6 shows the generator scheduling for Scenario 2. Similar to the first scenario, the best optimal power dispatch obtained by using the BAS algorithm along with other algorithms reported in the various studies is shown in Table 7. The maximum power generation is taken from the FC for 24 h due to the comparatively lower bid of the FC, and also because it holds the maximum power level. In the early morning (1-8 h), the demand load is met by the grid as the market price is comparatively low during these hours. During these 8 h, the battery is in a charging state while it is in a discharging state for the next 16 h.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multicolumn{3}{c}{**DG Sources (kW)**} & \multicolumn{2}{c}{**Battery**} & **Utility** \\  & **MT** & **FC** & **PV** & **WT** & **(kW)** & **(kW)** \\ \hline
1 & 6 & 30 & 0 & 1.5 & 15.5 & 30 \\
2 & 6 & 30 & 0 & 1.5 & \(-\)17.5 & 30 \\
3 & 6 & 30 & 0 & 1.5 & \(-\)16.5 & 30 \\
4 & 6 & 30 & 0 & 1.5 & \(-\)11.12 & 30 \\
5 & 6 & 30 & 0 & 1.5 & \(-\)12.5 & 30 \\
6 & 6 & 30 & 0 & 0.9142 & \(-\)6.9142 & 30 \\
7 & 6 & 30 & 0 & 1.785 & 0.15 & 30 \\
8 & 6 & 30 & 0 & 1.3 & 8.0947 & 29.605 \\
9 & 30 & 30 & 0 & 15 & 30 & \(-\)26.25 \\
10 & 30 & 30 & 0 & 15 & 30 & \(-\)25 \\
11 & 30 & 30 & 0 & 15 & 30 & \(-\)25 \\
12 & 30 & 30 & 0 & 15 & 30 & \(-\)30 \\
13 & 30 & 30 & 0 & 12 & 30 & \(-\)30 \\
14 & 30 & 30 & 0 & 12 & 30 & \(-\)30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Economic dispatch for Scenario 2.  
Figure 10: Comparison of convergence characteristics of the objective function with different algorithms.  
The scheduling of power generation for Scenario 2 is shown in Figure 11.  
The convergence characteristics of BAS are compared with other existing algorithms for this test system in the case of Scenario 2, as shown in Figure 12. It is clear from the figure that the BAS algorithm only takes 10 iterations to converge with the best solution. Moreover, BAS shows a quick and stable convergence with the global searching ability to find the optimal cost. The computational efficiency regarding the simulation time is not compared as the simulation time is not available for the other algorithms. The optimal DA scheduling strategy is developed for the VPP by considering the unavailability of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time (h)**} & \multicolumn{3}{c}{**DG Sources (kW)**} & \multicolumn{1}{c}{**Battery**} & **Utility** \\  & **MT** & **FC** & **PV** & **WT** & **(kW)** & **(kW)** \\ \hline
15 & 30 & 30 & 0 & 13 & 30 & \(-\)30 \\
16 & 30 & 30 & 0 & 15 & 30 & \(-\)25 \\
17 & 30 & 30 & 0 & 1.6 & 30  
solar generation. The bidding strategy with the grid is developed based on the power purchasing price from the grid. There are less literature studies with this strategy as it considers both the market strategy and the uncertainty of renewable generation.
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 3 Compression Scheme
### The CCSDS-123 Standard
#### 3.2.2 Encoder  
A compressed image comprises two distinct parts: a header containing all compression settings and a body that consists of mapped prediction residuals that have been encoded losslessly. There are two different methods through which the mapped prediction residuals can be encoded: the sample-adaptive entropy coding approach and the block-adaptive approach.  
The sample-adaptive entropy encoder utilizes a set of variable-length codes based on GPO2 codes to encode each mapped prediction residual, \(\delta_{z,y,x}\). The initial state of the coder is determined by the initial count exponent, \(\gamma_{0}\), and the accumulator initialization table, \(\{k_{z}^{\prime}\}_{z=0}^{N_{z}-1}\). The selection of the code is based on the ratio \(\sum_{z,y,x}/\Gamma_{z,y,x}\); it is used to estimate of the average value of the mapped prediction residuals in each spectral band. The accumulator, \(\sum_{z,y,x}\), and the counter, \(\Gamma_{z,y,x}\), are adaptively updated, and the interval is determined by the rescaling counter size parameter, \(\gamma^{*}\), during the encoding process. A smaller value of \(\gamma^{*}\) results in faster adaptation. The unary length limit parameter, \(U_{max}\), is employed to constrain the length of the unary part of the codeword such that if the bit  
Figure 6: Local differences used under the full and reduced prediction modes: (**a**) full mode; (**b**) reduced mode.  
depth of the input image is \(D\) bits, then the maximum codeword length is \(U_{\text{max}}+D\) bits. In this way, the hardware implementation is simplified, and the cost of encoding poorly predicted samples is reduced.  
Unlike the sample-adaptive approach, the block-adaptive entropy coder partitions the sequence of \(\delta_{z,y,x}\) into blocks and selects an encoding method for each block. The components of each block are related to the sample encoding order. As a consequence, the encoding order and the block size have a substantial impact on the compression ratio under this approach.
## 1 Introduction
Considering the significance of wind speed prediction of tropical storms for slender structures in the view of structural health monitoring, this paper presents a performance evaluation of linear and nonlinear models for short-term wind speed forecasting. Five commonly used models are taken into account, namely, ARIMA, BPNN, LSTM, GRU, and SVR models. The measured wind samples of the tropical storm Rumbia are employed for the analysis. One-step and multi-step ahead predictions are both conducted, and the performances are evaluated in light of the mean and maximum wind speeds in a given period. Finally, the advantages and disadvantages of these models in the short-term forecasting of tropical storms are summarized, and corresponding suggestions are made for the purposes of early warning.
## 2 Materials and Methods
### Analytical Measurements  
At all sampling sites, temperature (T), pH, electrical conductivity (EC), total dissolved solids (TDS), and salinity were determined monthly at approximately 10 cm below the water surface using a 556 MPS Handheld Multiparameter Instrument (YSI, Yellow Springs, OH, USA).  
The analysis of dissolved Na, Mg, K, Ca, Mn, Fe, Co, Ni, Zn, As, Sr and Cd in water was carried out using flame atomic absorption spectroscopy (F-AAS, AAnayst 200 by Perkin Elmer, Waltham, MA, USA), total x-ray reflection fluorescence spectroscopy (TXRF, S2 PicoFox, Bruker, Billerica, MA, USA) or graphite furnace atomic absorption spectroscopy (GF-AAS, PinAcle 900Z by Perkin Elmer, Waltham, MA, USA), depending on the concentration of the elements in the samples and the limits of detection of the instruments used. The determination of dissolved Ag, Pt, Au and U in the water samples was conducted in the Analytical Facility, University of Johannesburg, using inductively coupled plasma mass spectrometry (ICP-MS, NeXION 300x by Perkin Elmer, Waltham, MA, USA). For ICP-MS measurements, Ir and Rh functioned as internal standards.  
For the determination of the activity concentrations of \({}^{238}\)U and \({}^{234}\)U, total uranium was separated from the acidified water sample (8 M HCl solution) by ion exchange on Dowex 1 \(\times\) 2 (100-200 mesh), followed by elution with 0.1 M HCl. After microprecipitation of U with neodymium fluoride, this alpha-source was measured with a PIPS Si surface barrier detector (Canberra(tm), Atlanta, GA, USA).  
The trace elements Ti, V, Ni, Cu, Zn, As, Rb, Sr, Ag, Cd, Pt, Au, Hg, Pb and U in sediment leachates were measured using TXRF or GF-AAS, depending on the element and content. The total mercury (Hg) content of sediment samples was measured using a cold vapor atomic absorption spectrometer (CV-AAS; FIMS 400 by Perkin Elmer, Waltham, MA, USA). The trace element content in sediment was determined as mg/kg of dry weight (dw). Table S1 gives the limits of detection for elements using the corresponding method as well as the recovery rates of the reference material PACS-2 by NRCC for the respective methods under applied conditions.
## 3 Results
### Application in Heterogeneous Tumor Models
#### 3.3.1 Case 1  
Figure 12A illustrates an example of the electric-field distribution of the high-resolution model provided in Figure 5B, showing that the tumor is fully covered by an electric field larger than the electric-field threshold (~500 V\(\cdot\)cm\({}^{-1}\)). Note that this example was intended to highlight the impact of a well-defined form of heterogeneity, rather than to demonstrate how a therapeutically optimal field distribution can be achieved. The electric-field distributions of the high- and low-resolution models along O\({}_{1}\) and O\({}_{2}\) lines are presented in Figure 12B, with the associated relative mean deviation shown in Table 1. These results demonstrate a significant reduction in the relative mean deviations after the inclusion of EPE by 30.7% along O\({}_{1}\) and 25.6% along O\({}_{2}\). In addition, the inclusion of EPE significantly improved the electrical resistance by reducing the deviation relative to the high-resolution model from 28.4% to 18.6%. Furthermore, the simulation of the high-resolution model required a calculation time of 22,156 s (369 min), while the simulation of the low-resolution models only required 1.4 s (a gain factor of ~16,000).  
Figure 11: Relative error of the numerically calculated resistance values of the cylindrical electrode pair model with respect to the analytical resistance value as a function of voxel sizes for (**A**) centralized and (**B**) shifted grids.  
Figure 12: Electric-field distributions of Case 1 within the x,z-plane of the tumor model in Figure 5. (**A**) An example of high-resolution electric-field distributions excluding EPE. Filled contours were plotted to maintain the overview, since the electric-field strength varies within the contours. Note that the grey rectangles and the large, dashed circle illustrate electrodes and tumor, respectively. (**B**) Electric-field distribution of the high-resolution model (black line), and low-resolution models excluding EPE (+) and including EPE (*) along the lines O\({}_{1}\) (**B1**) and O\({}_{2}\) (**B2**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} \\ \hline  & **Figure 12B1** & **Figure 12B2** & **Figure 13A** & **Figure 13B** \\ \hline LRM excl. EPE & 44.7\% & 49.9\% & 43.1\% & 52.8\% \\ LRM incl. EPE & 14.0\% & 24.3\% & 13.0 & 22.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean of the deviations of the electric-field distributions of the low-resolution models (LRMs) ex-/including EPE relative to the electric-field values at the corresponding position within the high-resolution model within a radius of \(15\times 10^{-3}\) m from the center (a volume that includes the whole tumor and a part of the healthy tissue).
# Barrel Shifter Physical Unclonable Function Based Encryption
## 5 Circuit Implementation
### Shift Unit  
Shift units implement the path selection and form shift stages. A shift unit schematic is shown in Figure 8b. Either _inputA_ or _inputB_ is mapped to the output. The mapping is determined by the _key_. A _key_ value of 1 causes the upper transmission gate to open; output then becomes _inputA_. Otherwise, output is driven by _inputB_.  
A sequence of shift unit transmission gates composes a delay path. Each unit has a unique delay, making the delay of each path unique.  
BS-PUF uniqueness depends on how much delay variation is provided by the same path on different chips. Modifying the transistor area is the main method for increasing the inter-chip variation. Transistor delay variation is inversely proportional to transistor area [22]. Sizing transistors smaller results in increased delay variation. However, BS-PUF requires a plaintext independent path delay to maintain physical commutativity (Section 6.5). It is hard to balance 0 and 1 transmission delay with minimum sized transistors. The minimum transistor size that preserves the physical commutativity is obtained from Cadence Monte Carlo Simulation.  
Figure 8: **(a)** schematic of 1-bit input logic. Each input bit is controlled by an input logic unit; **(b)** shift unit of barrel shifter. If _KEY_ = 1, \(N1/P1\) is on (\(N2/P2\) is off), then output equals \(input_{A}\); otherwise, output equals \(input_{B}\).
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 3 Results
### Effect of Iterations on the Micro-Capsule Networks  
Since the iterations are an essential parameter of the micro-capsule network model, their value can be used to evaluate the model's performance. After routing iterative calculation, a set of routing coefficients can be obtained so that the output from the low-level capsule can best match the production of a high-level capsule. In the case of binary classification (such as AB vs. E), three classifications (for example, A vs. C vs. E) of training and testing epileptic detection, the training and testing phases are set to 1000 cycles. After extensive training, testing and operation on different types of epileptic EEG datasets, most of the model training and testing can achieve stable classification results after 400 cycles. The accuracy and loss value of the micro-capsule network model for seizure detection are shown in Figure 3.  
Figure 3: The accuracy and loss graphs for binary and three classifications of training and testing seizure detection.  
As the number of iterations increases, the epileptic recognition rate of the micro-capsule network model increases while the loss value decreases. When the number of iterations reaches 200\(\sim\)400, the recognition rate and loss in the training phase change slowly and gradually stabilize. At this time, the epileptic recognition rate maintains at about 99%, and the gradient of the loss rate converges smoothly, indicating that the model has a good fit. Considering that the network model is overfitted or takes too long, the number of routing iterations is set to 400 to meet the best match between the output of low-level capsules and that of high-level capsules.
## 2 Materials and Methods
### Feature Extraction and Multi-Feature Fusion Analysis
#### 2.4.3 Extraction of Phase Spectrum Features  
The frequency spectrum of a signal is comprised of the phase spectrum and amplitude spectrum, which offer valuable insights into the signal's frequency components, phase information, and facilitating signal reconstruction. However, during the analysis of the phase spectrum, the occurrence of phase jumps was a common issue. This phenomenon arose from the periodic nature of the phase spectrum and the utilization of the Fourier transform, which operated on periodic signals. Consequently, the phase spectrum may demonstrate discontinuous jumps, referred to as phase wrapping or phase discontinuity, which can present challenges in signal processing and analysis. To address this issue, a frequently employed approach was phase unwrapping [46], also referred to as phase jump removal, which aimed to transform the discontinuous points in the phase spectrum into corresponding continuous points, thereby achieving a smoother phase spectrum curve that was more amenable to analysis and processing.  
The specific processing process is as follows.  
Phase unwrapping was conducted on each dataset to extract the extremal points of first-order and second-order derivatives. The first-order derivative was calculated as the rate of change between adjacent sampling points, whereas the second-order derivative was calculated as the acceleration of change between three adjacent sampling points. The purpose was to capture the characteristics of phase transformations, specifically the prominence and depression features in the phase spectrum, and to quantify the peak and valley points in the phase spectrum. These measurements were utilized to assess the spatial distribution characteristics of pore spaces, specifically examining their uniformity.  
\[P\_parameters_{i,j}=\sum_{\text{pks}>\text{thre}}\text{pks}+\sum_{\text{ valleys}<-\text{thre}}\text{valleys} \tag{19}\]  
Here, the characteristic parameter matrix \(P\_parameters_{i,j}\) contains the \(j\)-th derivative extremum points of the \(i\)-th sample, \(pks\) and \(valleys\) are the points of the first-order and second-order derivative extremum points, and \(thre\) is the set threshold.
# IoT-Enabled Vehicle Speed Monitoring System
## 3 Proposed IoT-Enabled Speed Monitoring System
### Generating the Data and Getting the Required JSON File  
After configuring and obtaining the live traffic stream on the highway, various video and image-processing algorithms such as the open-source Library OpenCV, PyTesseract, Open ALPR, Background Subtraction Algorithms, Plate Recognizer API's, haar cascade classifier and PixelLib object detection, and JSON libraries are used to generate the required JSON file. This file contains all vehicle information, such as a still photo, the license plate, and the vehicle's roadway entry and exit timings, as shown in Figures 3 and 4.  
Figure 3: Data fetched by the system, including a vehicle photo, capture time, and the license plate.