esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response of Container Crane under Near-Field and Far-Field Ground Motions
## 3 Input Ground Motions
### Matched Input Ground Motion  
To characterize the selected ground motions according to the local condition of the prototype container crane and to normalize the effects of the intensity of the real earthquakes on the seismic response of the container crane when comparing the near-field and far-field ground motions, the three ground motions were matched to a Korean elastic response spectrum developed for the site based on the Korean Design Standard KDS 17 10 00 [30] by  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Earthquake** & **Year** & **Magnitude** & \begin{tabular}{c} **EpiD** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **PGA** \\ **(g)** \\ \end{tabular} & \begin{tabular}{c} **PGV** \\ **(cm/s)** \\ \end{tabular} & \begin{tabular}{c} **PGD** \\ **(cm)** \\ \end{tabular} & \
\begin{tabular}{c} **PGV/PGA** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **PGD/PGV** \\ **(s)** \\ \end{tabular} \\ \hline Nahanni Canada & 1985 & 6.76 & 22.36 & 0.14 & 3.34 & 0.98 & 0.025 & 0.296 \\ Mineral Virginia & 2011 & 5.8 & 53.8 & 0.12 & 1.67 & 0.73 & 0.014 & 0.438 \\ Gazli USSR Iran & 1976 & 6.8 & 12.82 & 0.61 & 65.38 & 25.38 & 0.110 & 0.388 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Original input ground motions.  
Figure 3: Peak values of ground acceleration, velocity, and displacement for three earthquakes: (**a**) Original earthquakes; (**b**) Matched earthquakes.  
using SeismoMatch software [31; 32; 33]. The elastic response spectrum Z1S4_2400 was developed with the following parameters: seismic zone 1, soil type S4 (deep and hard ground), and a return period of 2400 years. The comparison of the peak values of ground acceleration, velocity, and displacement of matched ground motions are shown in Figure 3b. For the matched near-field Gazli USSR Iran, PGA was 0.335 (g), PGV was 0.410 (m/s), and PGD was 0.335 (m). The PGA of the matched Mineral Virginia and Nahanni Canada earthquakes was 53% and 29% higher than that of the matched Gazli USSR Iran earthquake, respectively, but the PGD of the matched Mineral Virginia and Nahanni Canada earthquakes was 53% and 59% lower than the PGA of the matched Gazli USSR Iran earthquake. The PGV of the matched Mineral Virginia earthquake was 12% higher than the value of the matched Gazli USSR Iran earthquake, while the PGV of the matched Nahanni Canada earthquake was 20% lower than the PGV of the matched Gazli USSR Iran earthquake. The PGV/PGA and PGD/PGV ratio of the matched Gazli USSR Iran earthquake was 0.12 (s) and 0.82 (s), respectively. Therefore, the matched Gazli USSR Iran earthquake had both the widest acceleration and displacement-sensitive regions. Figure 4 shows the original and matched response spectra of near-field and far-field earthquakes, alongside the target response spectrum (Z1S4_2400).
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating the Water Environment Impact on Energy Production
#### 3.1.1 Variable Module Positioning  
The variation in the position of FPV modules over time due to the effect of water waves results in energy losses, which are currently measured and investigated by various models [31]. These measurements are also crucial for ensuring the safety of the anchoring mechanisms of the floating photovoltaic system itself.  
Unlike land-based systems, floating systems may experience pitch, yaw, and roll movements similar to those of a properly anchored vessel, as shown in Figure 2, where the conventions used for ship movements are adopted.  
The various rotations can be identified through direct measurements using accelerometers; see [33]. For instance, an inertial measurement unit (IMU) Ellipse-E from SBG was attached to a panel to measure its roll, pitch, and angular velocities, as has been reported in [34]. For offshore plants, indirect measurements related to wave motion are highly complex [35]. Wind speed and direction measurements, when combined with wave motion data, can be converted into the motion of the floating platform using algorithms based on artificial intelligence.  
Another indirect measurement approach involves comparing global radiation measurements taken on the ground on the theoretical plane of the modules (assuming a perfectly flat water surface) with those on the floating platform. The discrepancies between these measurements can be translated into misalignment angles between a ground-based module with a fixed orientation and a floating module. In this context, the difference in albedo between terrestrial and aquatic environments should be considered as a bias when evaluating the actual losses due to the movement of the floating platform. To mitigate this issue, installing two pyranometers (one on the ground and the other on the floating system) horizontally could minimize the albedo problem as much as possible. Additionally, a GPS receiver and a geomagnetic sensor solution, as used in [36], could provide valuable data for monitoring the movements of FPV systems.
# Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings
###### Abstract  
In this paper, a non-permutation variant of the Flow Shop Scheduling Problem with Time Couplings and makespan minimization is considered. Time couplings are defined as machine minimum and maximum idle time allowed. The problem is inspired by the concreting process encountered in industry. The mathematical model of the problem and solution graph representation are presented. Several problem properties are formulated, including the time complexity of the goal function computation and block elimination property. Three solving methods, an exact Branch and Bound algorithm, the Tabu Search metaheuristic, and a baseline Genetic Algorithm metaheuristic, are proposed. Experiments using Taillard-based problem instances are performed. Results show that, for the Tabu Search method, the neighborhood based on the proposed block property outperforms other neighborhoods and the Genetic Algorithm under the same time limit. Moreover, the Tabu Search method provided high quality solutions, with the gap to the optimal solution for the smaller instances not exceeding 2.3%.  
flow shop; scheduling; time couplings; limited-idle; elimination properties; metaheuristics; tabu search
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 2 Evacuation Wayfinding Systems
### Static and Mobile Wireless Sensor Network-Based Systems  
Compared with static WSNs, mixed WSNs, which contain mobile nodes, can monitor uncovered areas of static sensors and are less prone to failures in harsh hazardous environments. Hence, some research has employed mixed WSNs to build emergency response systems. For instance, the studies in [22; 23; 24] proposed a resilient emergency support system (ESS) with the aid of opportunistic communications [25]. This system consisted of sensor nodes (SNs) and communication nodes (CNs). SNs can detect the hazard in their vicinity and inform the evacuees passing by of the location, while CNs are portable devices that are taken by occupants. The work in [26] presented an indoor autonomous wayfinding system composed of an intelligent evacuation sub-system (IES) for primary use and an opportunistic communication-based evacuation sub-system (OCES) for backup purposes; both sub-systems were supported by pre-installed sensors in the building; the IES utilised static decision nodes to guide evacuees in proximity, while the OCES employed mobile decision nodes carried by civilians to disseminate emergency messages and direct evacuees when the IDEmalfunctioned. The experimental results showed that the use of the OCES can considerably reduce the number of fatally-injured civilians during an evacuation process. With the increasing ubiquitousness of smart phones, which provide powerful sensing ability and suffer less from battery power limitations, many studies have integrated evacuees' portable devices in evacuation wayfinding systems. For instance, the study in [27] proposed an emergency support framework that integrated a pre-deployed WSN with the existing mobile network infrastructure to guide evacuees out of a built environment; the framework, namely \"CoWiSMoN\", employed both fixed sensors pre-installed in the building and mobile phones carried by evacuees to collect information and send to a quick rescue response centre via short-range wireless communication links or the mobile cellular network; moreover, a cognitive communication protocol that was optimised both of the network layer and data link layer was designed to ease the network congestion caused by the transmission of large volumes of sensory data and the degradation of communication quality during disasters. Similarly, the research in [28] presented an indoor emergency evacuation system composed of a sensor-data management sub-system and an indoor navigation sub-system; the sensor-data management sub-system gathered sensory information and could alert users and the building manager via mobile phones; the indoor navigation sub-systemutilised radio beacon devices to estimate users' position; each user carried a beacon receiver that could receive signals from beacons and transmit to the mobile phone via Bluetooth.
## 3 Results and Discussion  
### Extraction and Evaluation of Antioxidant Power  
Only scarce information is known about the antioxidant capacity and other therapeutic properties of the species analysed in this work, as most of the currently published research concerns _C. officinalis_;the only species for which some data are available is the widespread CA [10; 46], whereas the results here presented for CM and CF are the first available for those taxa, as far as we know. Including CA (for which other data are available), the (up to now) un-investigated CM and CF, and their hybrid CI makes it possible a useful comparison also with previously published data. Figure 1 shows the yield of extraction obtained from the matrix of dried leaves of different species of Calendula, comparing different solvents. It has been observed that the solvent with the highest extraction efficiency is ethanol 80% for all species with values ranging from 21.27 \(\pm\) 0.19 (g/100 g) (CM) to 28.16 \(\pm\) 3.24 (g/100 g) (CI). Significantly lower yields were obtained with SFE, SFE with co-solvent (SFE CS) and with n-Hexane. The extraction yield and antioxidant activity of plant extracts strongly depend on the polarity of the solvent, which determines both qualitatively and quantitatively the antioxidant compounds extracted [9; 32].  
In the literature, it has been observed that the highest yields are usually obtained with ethanol, methanol, and their mixtures with water [32]; similar results have been observed on _C. officinalis_, where the methanolic and ethanolic extracts for the antioxidant analysis have shown the highest efficiency for the selective isolation of these compounds from this species [21; 46; 47]. [PERSON] et al. [48] reported similar efficiency for the extracts of leaves and flower for CA.  
The most commonly used solvents are water and ethanol due to their low toxicity and high extraction efficiency. The main disadvantage of aqueous extraction is the low yield of low-polarity antioxidants or fat-soluble antioxidants such as carotenoids [32]. In our work, it has been observed that the yield of the aqueous extract has been significantly lower than the hydroalcoholic extract (Figure 1); for this reason, it is appropriate and advantageous to modulate the polarity of the solvent using ethanol/water mixtures in different ratios [32].  
Even if the yield with SFE was lower than the yield obtained with other solvents, the use of this technique allows to obtain of a solvent-free extract, since CO\({}_{2}\) is used as a supercritical fluid [9; 49]. CO\({}_{2}\) is a gas at room temperature, so once the extraction is completed and the system decompressed, a substantial elimination of CO\({}_{2}\) is achieved without residues, yielding a solvent-free extract [33; 50].
# Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
## 1 Introduction
[15] proposed Super-Resolution Convolutional Neural Network (SRCNN) model which first applied Convolutional Neural Networks (CNN) to ISRR, various network architecture designs and training strategies based on CNN [16; 17; 18; 19] have been developed. However, these methods tend to output over-smoothed results without sufficient high-frequency details. In response to the above problem, [PERSON] et al. [20] have presented to calculate the super-resolution model's perceptual loss in feature space instead of pixel space. [17; 21] have introduced Generative Adversarial Network (GAN) [22] to encourage network to generate more realistic and natural images. [PERSON] et al. [23] have enhanced the deep residual network by removing the Batch Normalization (BN) layers in SRGAN (Generative Adversarial Network for Image Super-Resolution) model. [PERSON] et al. [24] have used Residual Dense Block (RDB) to constitute the main body of generator network. Although the effect of reconstructed images has been improved, unfortunately, these methods still existed unpleasant artifacts in generated images. In order to further improve the quality of reconstructed images, this paper presents ISRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which is based on GAN. Firstly, we use RDB as the basic unit of generator network and adopt Visual Geometry Group(VGG)19 network as the basic framework of discriminator network. This measure can strengthen the reuse of forward features, reduce the amount of training parameters and control the training direction of reconstruction images. Secondly, four losses are introduced to constitute the total objective function of generator. We propose texture loss to encourage local information matching, enhance perceptual loss by employing the features before activation layer to calculate, optimize adversarial loss based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory and use content loss to ensure the accuracy of low-frequency information. Experimental results show that the model in this paper has achieved good results, which can generate images with more realistic textures.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 5 Proposed Secure and Efficient Authentication Protocol for GLOMONET  
Many biometric-based user authentication protocols [23,24] have been presented to improve the security flaws associated with mobile device authentication. Biometric-based schemes are difficult to guess, duplicate, and forge and cannot be stolen or lost. Therefore, biometric-based three-factor authentication mechanisms are more secure than mobile device and password based two-factor authentication mechanisms. Therefore, we present a secure and efficient authentication protocol using biometrics to overcome the security problems of the existing protocol [16].
## 3 Results and Discussion
### Results of Numerical Simulation of Comparison with the Conventional Method
Figure 12: Results of the proposed method as: (**a**) Time history of the Euler angle; (**b**) Time history of the torque by SGCMGs; (**c**) Time history of the satellite angular velocity; (**d**) Time history of the gimbal angle; (**e**) Time history of the angular momentum of SGCMGs; (**f**) Time history of the gimbal angular velocity; (**g**) Time history of the BSDW; (**h**) Time history of singularity parameter. As shown in Figure 11b, torque is outputted to the roll axis and the other axis. Therefore, since it is not taken into account in the attitude controller, there is a possibility that the stability might be adversely affected. On the other hand, as shown in Figure 12b the proposed method also outputs the torque to the other axes, but because it is realized by optimal input from the attitude controller, there is no adverse effect on the stability. Moreover, as shown in Table 3, the gimbal angle guidance of both methods is performed well. This is because an optimal distribution of gimbal steering for the attitude maneuver and gimbal angle guidance is performed in both methods. From the above, it is shown that the proposed method is effective for agile attitude maneuvers in comparison with the conventional method.
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 2 Theoretical Background of the Models  
### Ensemble Empirical Mode Decomposition  
EEMD (ensemble empirical mode decomposition) is one of the most common EMD improved methods, and its advantage is that it can solve the modal blending phenomenon in EMD methods. The mode overlap phenomenon (i.e., the signals of different modes are mixed), generally has two cases: one is that the signals of different feature scales appear in one IMF component; the other is that the signals of the same feature scale are dispersed into different IMF components. The article's detailed methodology of the EMD method can be found in [23].  
The main improved idea of EEMD is to use the property that the mean value of the white noise is 0. By introducing uniformly distributed white noise several times in the decomposition process, the noise in the signal is masked by the noise added artificially several times to obtain more accurate upper and lower envelopes. At the same time, the decomposition results are averaged, and the more times the averaging process is performed, the less the noise is brought into the decomposition.  
The main steps in EEMD decomposition can be found in Figure 1.  
1. Setting the original signals' processing times to be equal, m;
2. Adding random white noise of different amplitudes to each of these m original signals to form a new series of signals;
3. Performing EMD decomposition on each of these new signals to obtain a series of IMF components;
4. The EEMD decomposition results are obtained by averaging the IMF components of the corresponding modes separately.  
EEMD can decompose the original signal data into a linear combination of a finite number of intrinsic mode functions (IMFs), with high to low frequencies, and each IMF component of the decomposition contains local feature signals of the original signal at different time scales. Therefore, the EEMD algorithm can be used to decompose more time-dimensional features of the original signal data. The decomposed time-dimensional features are input into the LSTM network model to extract time-dimensional features to predict the missing parts of the original signal data. Among them, the decomposed IMFs do not need to satisfy other conditions to be input into the subsequent LSTM network model.
# Low Power QC-LDPC Decoder Based on Token Ring Architecture
## LDPC Codes and Their Decoding--Preliminaries  
LDPC codes are an ECC scheme belonging to the class of linear block coding methods. The encoding process of an \((N,K)\) code of rate \(R=K/N\) adds \(M=N-K\) parity check elements to the vector \(\mathbf{u}=\{u_{1},u_{2},\ldots,u_{K}\}\), which is an information vector to be ECC protected. As a result, the code vector (codeword) \(\mathbf{c}=\{c_{1},c_{2},\ldots,c_{N}\}\) is obtained. Vectors \(\mathbf{u}\) and \(\mathbf{c}\) are over Galois field \(\mathrm{GF}(2)\) for the binary codes considered in this paper.  
A binary \((N,K)\) LDPC code is specified by a low density parity check matrix \(\mathbf{H}_{M\times N}\) with \(\mathrm{GF}(2)\) elements. In the decoder, a vector \(\mathbf{X}\) of length \(N\) is approved to be a correct codeword if and only if it satisfies the parity check equation \(\mathbf{H}X^{T}=\mathbf{0}_{M\times 1}\) over \(\mathrm{GF}(2)\) field arithmetic. Otherwise, a specific error correction decoding algorithm can be used. The LDPC systems employ iterative belief propagation decoding algorithms [5].  
A parity check matrix that contains the same number of \"1\" values in every row and the same in every column is called a regular matrix. A matrix that does not satisfy the above condition is called an irregular matrix and a corresponding code is called irregular code. Irregular codes, with properly selected weight distributions of ones in columns of \(\mathbf{H}\) are known to outperform their regular counterparts.
# Moving Object Tracking and Its Application to an Indoor Dual-Robot Path
## 2 Image Processing  
### Color Space Transformation  
The webcam captures images in Red, Green, Blue (RGB) color space. Then the RGB image is transformed to HSL color space by code transform processing [26]. RGB color space uses additive color by primary color to produce many colors, but colors produced by the primary color component are not so intuitive. HSL color space includes the hue, saturation, and lightness. This space is similar to human vision. RGB color space values are transformed to HSL color space values by  
\[H = \begin{cases}0^{\circ}&if\ \text{max}=\text{min}\\ 60^{\circ}\times\frac{g-b}{\text{max-min}}+0^{\circ}&if\ \text{max}=r\ and\ g\geq b\\ 60^{\circ}\times\frac{g-b}{\text{max-min}}+360^{\circ}&if\ \text{max}=r\ and\ g<b\\ 60^{\circ}\times\frac{b-r}{\text{max-min}}+120^{\circ}&if\ \text{max}=g\\ 60^{\circ}\times\frac{r-g}{\text{max-min}}+240^{\circ}&if\ \text{max}=b\\ \end{cases} \tag{1}\] \[L=\frac{1}{2}(\text{max}+\text{min})\] (2) \[S = \begin{cases}0&if\ =0\ or\ \text{max}=\text{min}\\ \frac{\text{max-min}}{\text{max+min}}&if\ 0<l\leq\frac{1}{2}\\ \frac{\text{max-min}}{2-(\text{max+min})}&if\ l>\frac{1}{2}\\ \end{cases}. \tag{3}\]  
In the process of catching visual image, we used a Microsoft LifeCam studio webcam, and put it on the photographer's left shoulder. The camera is used for target tracking and pattern recognition. Figure 1 shows the images in the RGB and HSL color spaces. In target pattern processing, we use RGB color space to decide the R, G, and B values for getting the color that is needed. This method can help us to filter out the colors that are not needed.
## 2 Experimental Section
### General Procedure for the Synthesis of 5-pyrazol-4,5-dihydropyrazoles Derivatives 3
#### 2.2.9 Compound **3i**  
4-(1,3-_bis_(4-Chlorophenyl)-4,5-dihydro-1\(H\)-pyrazol-5-yl)-5-chloro-3-methyl-1-phenyl-1\(H\)-pyrazole. Yellow solid, 70%. mp 150-152 \({}^{\circ}\)C. \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\) RT) \(\delta\) (ppm): 2.14 (CH\({}_{3}\)), 3.21 (m, 1H, CH\({}_{2}\)), 3.80 (m, 1H, CH\({}_{2}\)), 5.36 (q, 1H, CH), 7.03 (d, 2H, Ho, N1-aryl, \(J\) = 8.78 Hz), 7.19 (d, 2H, Hom, N1-aryl, \(J\) = 9.04 Hz), 7.40 (m, 3H, Hp N-Ph, Hom C3-aryl, \(J\) = 8.53 Hz), 7.49 (t, 2H, Hom N-Ph), 7.54 (d, 2H, Ho Ph, \(J\) = 8.28 Hz), 7.68 (d, 2H, Ho C3-aryl, \(J\) = 8.54 Hz). \({}^{13}\)C NMR \(\delta\) (ppm): 12.9 (CH\({}_{3}\)), 40.6 (CH\({}_{2}\)), 54.9 (CH), 114.1 (Cm, N1-aryl), 116.8 (C4 pyrazole), 124.2 (Ci N1-aryl), 124.5 (Co N1-aryl), 124.7 (C5 pyrazole), 126.6 (Cm C3-aryl), 127.9 (Cp Ph), 128.6 (Co C3-aryl), 130.5 (Ci C3-aryl), 134.4 (Cp C3-aryl), 137.6 (Ci-Ph), 142.5 (Ci N1-aryl), 145.9 (C3), 147.3 (C3 pyrazole). MS (70 eV) \(m/z\) (%) = 485/483 (M\({}^{+5}\)/M\({}^{+3}\), 3/9), 484/482 (M\({}^{+4}\)/M\({}^{+2}\), 11/31), 480 (M\({}^{+}\), 33),321/320/319 (7/4/16), 139/137 (4/11), 127/125 (26/73), 113/111 (5/15), 99/97 (5/20), 87/85 (3/12), 83/81 (23/52), 79/77 (6/20), 71/69 (21/100), 57 (33), 55 (29). HR-MS Calc. For C\({}_{25}\)H\({}_{19}\)Cl\({}_{3}\)N\({}_{4}\) 480.0675, found 480.0663. FT-IR (KBr, v en cm\({}^{-1}\)), 1584 (C=N, _st_), 1488 (C=C, _st_).