esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preparation and Input Selection  
Because the water quality parameters of the test come from different Internet of Things (IOT) collection devices and different time data intervals, and a large amount of data was input manually as well, the original data is not satisfactory. In addition, water parameters usually have different dimensions and orders of magnitude, and the data can be converted to the same order of magnitude without dimension by a normalized processing method (Equation (2)). At the beginning, the standard deviation was calculated using Equation (1). After the models have been successfully executed, the outputs of the models in the form of normalized values are converted to original values by inverse transformation using Equation (3). The priority is to preprocess the original data. We eliminate invalid and discrete data; the remaining data were normalized and grouped into training samples and test samples. These procedures were done with SPSS.18.  
\[\text{SD}=\sqrt{\frac{\sum\left(X_{i}-\overline{X_{i}}\right)^{2}}{N-1}} \tag{1}\]  
\[X^{\prime}=\frac{X_{max}-X}{X_{max}-X_{min}} \tag{2}\]  
\[X=X^{\prime}*\left(X^{\prime}_{max}-X^{\prime}_{min}\right)-X^{\prime}_{min} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Unit** & **Minimum** & **Maximum** & **Mean** & **SD** & **CDO** \\ \hline PH & - & 7.2000 & 8.2000 & 7.6735 & 0.2538 & 0.8818 \\ CHLA & mg/L & 0.0176 & 0.0283 & 0.0230 & 0.0021 & \(-\)0.0839 \\ NH4H & mg/L & 0.2900 & 0.3900 & 0.3387 & 0.0154 & \(-\)0.0266 \\ DO & mg/L & 2.6000 & 6.5000 & 4.1800 & 0.8654 & 1 \\ BOD & mg/L & 2.6 & 5.4 & 3.8 & 0.509 & 0.675 \\ EC & us/cm & 457 & 474 & 465.1440 & 3.6654 & \(-\)0.1370 \\ \hline \hline \end{tabular} SD: standard deviation; CDO: correlation with DO.  
\end{table}
Table 1: Basic statistics of the measured water quality variables of Beihai Lake.  
Figure 1: Location map of Beihai Lake in Beijing.  
where SD = standard deviation of \(X_{i}\); \(X_{i}\) = input data; \(\overline{X}\) = arithmetic average of \(X\); \(X^{\prime}\) = values after normalized method and also as input to models; \(X_{max}\) = maximum value of \(X\) and \(X_{min}\) = minimum value of \(X\). After normalization of linear functions, the experimental data values will be mapped to [0,1].
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 1 Introduction
### GPU vs. CPU: Similarities and Differences in Architecture and Fundamentals
Modern data-center GPUs like the Nvidia A100 illustrate these principles in practice: they pack 6912 cores, 40 GB or 80 GB of VRAM, and 54.2 billion transistors, delivering up to 624 TFLOPS in half-precision or 312 TFLOPS in single-precision workloads [42; 43; 44]. As machine learning (ML) demands continue to grow, architectures such as the A100 are specifically engineered to optimize both scalability and efficiency for large-scale AI training and inference [1; 50].
## 3 Simulation Results and Analysis
### Energy Characteristics of Rock-like Samples
With the increase of flaw inclination angle, the peak boundary energy, strain energy and dissipation energy increase approximately linearly. According to Figure 8 and Table 4, when the flaw inclination angle increases from 15\({}^{\circ}\) to 75\({}^{\circ}\), the boundary energy increases by 69.22%, the strain energy increases by 68.68%, and the dissipated energy increases by 80.49%. It can be seen that the dissipative energy increases the most, followed by the boundary energy, and finally the strain energy. This indicates that the energy used for crack initiation, crack propagation and friction between particles increases with the increase of flaw inclination angle. At the same time, the strain energy value increases with the increase of flaw inclination angle, and the proportion of the total energy is relatively stable; the average value is about 95%. It indicates that the ability of storing strain energy between particles increases, but most of the energy in the model is still stored in the form of strain energy under peak stress. In addition, the change of dissipated energy is closely related to the generation of micro-cracks. Figure 9 describes the change of the number of micro-cracks with the flaw inclination angle under peak stress. It can be seen that the cracks are mainly tensile  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flaw Inclination Angle/\({}^{\circ}\)** & _E/J_ & _Ee/J_ & _Ed/J_ & _Ee/E_ & _Ed/E_ \\ \hline
15 & 887 & 846 & 41 & 95.38\% & 4.62\% \\
30 & 1090 & 1034 & 51 & 94.86\% & 5.14\% \\
45 & 1  
dominating cracks, and the larger the flaw inclination angle, the more cracks are generated. This is consistent with the variation law of dissipated energy. Therefore, the larger the flaw inclination angle, the more energy consumed for crack generation and propagation, the more serious the internal damage, and the higher the damage degree.
# Effects of Diagonal Friction Dampers on Behavior of a Building
## 2 Experimental Studies
### Experimental Model Free Vibration Data  
In order to determine the modal behavior of the experimental models, first of all, their free vibration behavior was examined. As a result of free vibration, roof displacement-time graphs were obtained from the models (Figure 5).  
Figure 4: Positions of friction dampers in the experimental study.  
Figure 5: Free vibration graphs of Reference model and models with friction dampers.  
By evaluating the free vibration data, the period values of the models were determined. According to the Reference model, the highest period was obtained in the building model with a damper on the fifth floor (5-FD), while the lowest period was obtained in the building model with a damper on the first floor (1-FD) (Table 4).  
When the period values are examined, the models with dampers had a reduction of 23% in 1-FD, 14% in 2-FD, 9% in 3-FD, and the same as the Reference in 4-FD, and a 0.3% increase in 5-FD. It was determined that the dampers used on the lower floors provided a large percentage change in the period of the building, so the dampers used on the lower floors had a greater effect on the behavior of the building.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 1 Introduction
This paper proposes architecture for a Self-Organized Underwater wireless sensor NETwork (SOUNET), which addresses issues of design complexity and network recovering delay. In the proposed scheme, after constructing an initial tree-topological network through the flooding of a control packet as in S\({}_{13}\) and H2-DAB, the sensor nodes consistently update a neighbor table, which contains information about neighbors such as their identification, hierarchical level or hop count, and the dead-end status indicator based on the packets heard from the neighbors. When a link hole occurs due to a lost or dead node, the child node reconfigures the link based on the local information acquired from the neighbor table. Such a scheme without additional signaling for finding new links not only saves energy consumption but also reduces delays for repairing networks. Moreover, unlike the previous works where network joining takes place only at the beginning of the network reorganization, in the proposed scheme newly added sensor nodes can immediately join the network by selecting a parent node whose packet is heard first. Additionally, SOUNET addresses other important issues such as _node-isolation_ and _closed loops_[22; 23; 24]. SOUNET can resolve these issues by letting the isolated nodes sleep and by eliminating the loops through simple packet exchanges and local knowledge. Computer simulation results show that SOUNET outperforms other conventional schemes in terms of network connectivity, packet delivery ratio (PDR), and energy consumption over the whole network. In addition to computer simulation, we performed a lake experiment to verify some basic functions of SOUNET at the Gyeongcheon Lake in Korea. This paper is organized as follows: in Section 2, we provide the system description. The subsequent section, Section 3, describes the problem statement. In Section 4, we present the detailed description of SOUNET. Sections 5 and 6 discuss the simulation and experimental results, respectively. Finally, we conclude the paper in Section 7.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 3 Result and Discussion
### QoS Validation and Analysis
Furthermore, in the case of the vehicle density increases, the connection possibility increases, the latency falls, although delivering the ratio of the packets will fall. The findings of the study suggest three QoS metrics to be combined, in order to attain a complete QoS routing performance.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 2 Data  
In this study, 477 strong-motion records with complete information were selected from the strong-motion dataset compiled in the Loess Plateau region, including 159 vertical component records. These records, sampled at a frequency of 200 Hz, were collected from 113 stations across the region, covering 39 seismic events recorded between 2007 and 2019. The recorded events had magnitudes ranging from \(M_{\rm S}\)3.2 to a maximum of \(M_{\rm S}\)8.0. The distribution of earthquake magnitudes, epicenter locations, and station positions is illustrated in Figure 1.  
For magnitude-based analysis, the seismic moment magnitude scale (\(M_{\rm wg}\)) was adopted due to its advantages over the traditional \(M_{\rm S}\) scale [32]. The \(M_{\rm wg}\) values were calculated using the GOR1 method, as described in Equation (1). This method incorporates factors such as the earthquake's energy release and fault characteristics, providing a more accurate assessment of earthquake size and a comprehensive representation of the event's overall strength [33]. Using this approach, the converted \(M_{\rm wg}\) magnitudes ranged from 3.9  
Figure 1: Schematic map of the earthquake magnitude and epicenter and the locations of the strongest stations in the Loess Plateau region of China.  
to 7.9. The relationships between magnitude and epicentral distance, as well as PGA and epicentral distance, are shown in Figures 2 and 3, respectively.  
\[M_{\text{wg}}=\begin{cases}0.688M_{\text{S}}+1.672&3.1\leq M_{\text{S}}\leq 6.1 \\ 1.073M_{\text{S}}-0.646&6.2\leq M_{\text{S}}\leq 8.4\end{cases} \tag{1}\]  
To analyze the characteristics of HVSR and VRSR curves from strong-motion records in the Loess Plateau region, this study selected records based on the following three principles: (1) strong-motion stations with well-documented site investigation data were chosen to ensure availability of loess thickness information; (2) seismic events with a magnitude of 3.0 or higher were included; and (3) records with a peak ground acceleration (PGA) exceeding 10 gal and complete metadata were selected.
# Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework +
## 2 Problem Definition
### Interest Region within a Single Simulation Scenario  
A simulation scenario places concepts, attributes, and relations of simulated objects into a dynamic context [29]. The discrete event dynamic system needs a time-based simulation during a specified period suited for satisfactory simulation objectives. The motivated point for multi-fidelity modeling is that the outputs of the low-fidelity model might have little influence on the overall simulation result during a particular simulation period.  
Figure 4 shows an example scenario for tracking and attacking a true target of a UUV. The objective of this simulation is to evaluate a rate of success to attack the target. In this case, the scenario includes a course of action with sequential tasks. First, the UUV is launched and searches for a true target. When the UUV comes closer to a detectable area, it detects the true target. Then, the UUV approaches the true target and attacks it (this scenario will be concretely explained in Section 5). In this scenario, the UUV includes five major tasks: Launch, Search, Detect, Approach, and Attack. Among them, the simulation objective is considered, and the critical tasks are Detect, Approach, and Attack. That is, the low-fidelity outputs of the UUV model in Launch and Search have little effect on the overall simulation accuracy, whereas the outputs in Detect, Approach, and Attack are likely to have a serious impact on the overall accuracy.  
This paper proposes an interest region that is a portion of the whole scenario where the model output has a serious effect on the overall simulation analysis. In the example of Figure 4, the interest region is an interval when Detect, Approach, and Attack tasks are conducted. Thus, only the high-fidelity model is employed in this region and lower-fidelity models are used in the remaining parts. The fidelity of the target model is changed based on the interest region during the simulation. With this concept, the simulation result will be very similar to using the high-fidelity model for all intervals; furthermore, simulation speedup will be achieved.
# Anomalous Origin of the Right Vertebral Artery
## 3 Anomalous Origin of the Right Vertebral Artery
### Right VA Originating from the Aortic Arch (Aortic Origin)  
There are some points where the RVA can arise directly from the aorta [1; 2; 3]. The most common site of the aortic origin of the RVA is the distal portion of the aortic arch beyond the origin of the LSCA, which accounts for 85.7% of aortic-origin RVAs (Figure 3a,b). Other sites of aortic origin of the RVA are rare and include the site between the left common carotid artery (LCCA) and LSCA, at the ascending aorta, and between the RSCA and the right common carotid artery (RCCA) with a missing brachiocephalic artery [1; 2; 8; 9; 10]. [PERSON] et al. presumed that the embryonic mechanism of an aortic origin of the RVA was from the distal portion to the LSCA. According to their hypothesis, it is caused by the RVA originating from the persistent right 8 th CSA [8] and the physiologic obliteration of the right dorsal aorta distal from the 7 th CSA (Figure 2a). This type of aortic origin of the RVA is occasionally associated with the diverticulum of Kommerell, which is thought to be a remnant of the distal part of the right dorsal aorta [11] (Figure 4a,b). The embryologic mechanisms of the other types of aortic origin of the right VA may be more complicated  
Figure 2: Drawing of the variations of the anomalous origin of the RVA. (**a**) Aortic origin of the RVA originating at the distal segment of the aortic arch beyond the origin of the LSCA. (**b**) Common carotid or brachiocephalic arterial origin of the RVA. (**c**) Common carotid or brachiocephalic arterial origin of the RVA with aberrant RSCA. (**d**) Double origin of the RVA.  
processes involving fusion and division of the dorsal aorta, regression of the primary subclavian arteries and cervical segmental arteries and their anastomoses.
# On the Optimal Calculation of the Rice Coding Parameter
## 2 Related Work
### Parameter Calculation for Data Following Laplacian Distributions  
[PERSON] et al. in [PERSON] et al. (2015) analysed the optimality of Rice codes to Huffman codes for Laplacian distributions. Following this study, [PERSON] in [PERSON] (2018) proposed that the optimal Rice parameter for data following a Laplace distribution \(r_{[LAP]}\) be given by:  
\[r_{[LAP]}=\log_{2}\left(\ln(2)E(|x|)\right)\text{, where} \tag{3}\]  
\(|x|\) represents the absolute value of \(x\), \(E(x)\) is the expected value of \(x\), \(\ln(x)\) is the natural logarithm function and \(\log_{2}(x)\) is the binary logarithm function. The value of \(E(x)\) is the weighted sum taken over all possible symbols \(x\), viz., \(\sum|x|\cdot p(x)\), where the weight \(p(x)\) corresponds to the probability of occurrence of the symbol \(x\) in a sequence.  
[PERSON] et al. extended the study to Two-Sided Geometric Distributions (TSGD) in [PERSON] et al. (2018).
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 5 Results and Analysis
### Encryption Quality Analysis
#### 5.1.5 Correlation Analysis and Chi-Square Test  
The security of an image against statistical attacks is often dependent on the correlation between adjacent pixels. A robust encryption scheme should aim to break this correlation in the (V) vertical, (H) horizontal, and (D) diagonal directions. To quantify this, we calculated the correlation coefficient between pairs of adjacent pixels, denoted by \(r_{xy}\). This was done by selecting S random pairs of adjacent pixels and substituting them into Equation (13) to Equation (18):  
\[D(x) =\frac{1}{S}\sum_{i=1}^{S}(x_{i}-E(x))^{2} \tag{13}\] \[D(y) =\frac{1}{S}\sum_{i=1}^{S}(y_{i}-E(y))^{2}\] (14) \[E(x) =\frac{1}{S}\sum_{i=1}^{S}x_{i}\] (15) \[E(y) =\frac{1}{S}\sum_{i=1}^{S}y_{i}\] (16) \[cov(x,y) =\frac{1}{S}\sum_{i=1}^{S}(x_{i}-E(x))(y_{i}-E(y))\] (17) \[r_{xy} =\frac{cov(x,y)}{\sqrt{D(x)D(y)}} \tag{18}\]  
where \(x_{i}\) and \(y_{i}\) are the values of the selected adjacent pixels. The results of this calculation are as shown in Figure 9, which demonstrate that the correlation was strong and compact in the original image, but scattered and weak in the encrypted image. Further results of this  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Changed Parameter** & **MSE** & **PSNR (dB)** \\ \hline \(k_{1}\) & \(10.872\times 10^{3}\) & 7.767 \\ \(k_{2}\) & \(10.921\times 10^{3}\) & 7.748 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean square error (MSE) and peak signal-to-noise ratio (PSNR) of two encrypted images.  
analysis for different images are as listed in Table 2, which show that all of the encrypted images had a weak correlation and, hence, showed resilience against statistical attacks.  
Figure 8: Comparison of encrypted images with original images, their histograms, encrypted versions, and histograms of encrypted images (displayed vertically) is shown, with each subfigure labeled: (**a**–**d**) original image; (**e**–**h**) histogram; (**i**–**l**) encrypted image; (**m**–**p**) histogram of encrypted image.  
Another statistical test that can be employed to test the randomness of the generated encrypted image is the nonparametric chi-square test as follows:  
\[\chi^{2}=\sum_{i=0}^{255}\frac{(v_{i}-e_{i})^{2}}{e_{i}}\]  
\[e_{i}=\frac{M\times N}{256}\]  
where \(\chi^{2}\) represents the statistical chi-square test statistic, \(v_{i}\) and \(e_{i}\) denote the observed and expected occurrence frequencies of each gray level (0-255), respectively. The lower the resulting value, the more statistically uniform the distribution of pixel values.  
The test applies a comparison between the pixel value distribution of the encrypted image and the expected distribution of a random image. In the Table, all calculated chi-square values for the sample encrypted image fell below the theoretical chi-square value of 293 (at a significance level of 0.05). These results confirm that the encryption process altered the image's statistical properties, making it distinct from the original one.
### Structure of the Article  
The structure of the article follows the logical realization route of the several distinct steps involved in the executed study. Specifically:  
* Section 2:  
Section 2 presents the current conditions met in the under examination infrastructure, namely its location, the prevailing climate and the existing energy resources annual consumption. The section is integrated with a synopsis of the most feasible energy performance upgrade passive and active measures to be introduced in the examined Stadium.  
* Section 3:  
The energy performance upgrade study starts with the evaluation of the existing condition of the Stadium's indoor space envelope and the definition of the proposed passive measures. The section is integrated with the calculation of the indoor space heating and cooling loads in the envelope's existing condition and with the realization of the proposed passive measures.  
* Section 4:  
Once the indoor space heating and cooling loads potential reduction, anticipated through the proposed passive measures, has been estimated in the previous section, Section 4 investigates the most technically feasible active energy saving systems, given the existing forms of final energy consumption in the Stadium and the available R.E.S. potential. The section starts with the investigation of active systems for indoor space conditioning, moves to the thermal energy production for the hot water needs coverage and the swimming pools heating and finally examines the possible energy savings on indoor and outdoor space lighting systems. Finally, the possibility for electricity production from R.E.S. technologies is investigated. Finally, a Building Energy Management System (BEMS) is proposed for the automatic and integrated operation of the under consideration active systems.  
* Section 5:  
Section 5 summarizes the energy saving and production results from the passive and active systems investigated in Sections 3 and 4, taking also into account the reactive electricity consumption saving. The primary energy annual total saving and the new energy resources annual consumption are calculated and, eventually, the new energy performance rank is determined.  
The realization order of the involved steps in the present study are graphically depicted in the flow chart shown in Figure 1.  
* Section 6:  
Section 6 gives a short economic evaluation of the examined project with the estimation of the total set-up cost and the annual monetary saving due to the introduced energy saving and production systems.  
* Section 7:  
Finally, Section 7 presents some very characteristic KPIs, such as the annual achieved R.E.S. penetration percentage, the investment's payback period, the annual achieved primary energy saving percentage and the CO\({}_{2}\) emission annual reduction.
## 3 Results
### Effects of W/C Ratios on the Compressive Strength of SLMKPC  
Figure 5 expresses the effects of W/C ratios on the compressive strength of SLMKPC. The pre-compressive strength (\(\leq\)1 d) of SLMKPC decreased with the increase of W/C, whereas the long-term strength (\(\geq\)3 d) first increased and then decreased with the increase of W/C. When W/C = 0.25, the compressive strength of SLMKPC after each aging period was high. When W/C \(\leq\) 0.25, the compressive strength of the hardening body was the highest, whereas the compressive strength after 1 h of aging was 70% higher than that after 28 d of aging. The pre-compressive strength of SLMKPC was relatively low when W/C was greater than 0.30, whereas the long-term strength was significantly high.  
The compressive strength differences of different W/C samples after 3 d and 28 d of curing were \(\Delta_{\text{W/C = 0.2}}\) = 1.7 MPa, \(\Delta_{\text{W/C = 0.25}}\) = 3.1 MPa, \(\Delta_{\text{W/C = 0.35}}\) = 20.4 MPa, and \(\Delta_{\text{W/C = 0.4}}\) = 10.6 MPa. Therefore, higher W/C ratios significantly affected the compressive strength of SLMKPC. When W/C = 0.35-0.4, the compressive strength of SLMKPC was noticeably lower than that at W/C = 0.2-0.3.