esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, high-resolution seismic profiles and well data provided a good opportunity for better understanding the reefs and carbonate platforms in the Wan'an Basin, southwest of the South China Sea, and also provided valuable information for the oil-gas exploration in the reef reservoirs. Four evolutional phases, including the initial phase, the prosperous phase, the recession phase and the submerged phase, of the reefs and carbonate platforms are proposed according to our data. In the Early Miocene, a few small, isolated carbonate platforms initiated in the center of the basin. In the Middle Miocene, they flourished and mainly formed around the Northern Uplift and Central Uplift, with two belts of carbonate platforms in the western area and eastern area that were mainly platform-edge reefs, massive reefs and a few point reefs. In the Late Miocene, the carbonate platforms began to retreat towards the high topographic position because of the rising of sea level. Meanwhile, the numbers and styles of reefs increased to include platform-edge reefs, massive reefs, atoll reefs and point reefs. Since the Pliocene, most of the carbonate platforms have been covered by detrital materials from terrestrial sources. Crustal tectonic activity provides favorable topography for reef growth and the distribution of platforms, and eustasy controlled the vertical growth and lateral migration of reefs. Since the Late Miocene, the rapidly crustal tectonic subsidence and the rising of relative sea level may lead to the drowning of the carbonate platform.  
W 'an Basin; carbonate platform; controlling factors; evolution phases +
Footnote †: journal: Applied Sciences
# Space Weather Effects on Satellites
## 2 SW Drivers and Their Effects on Spacecraft Technology
### EM Emission  
The photons of different energies can interact with the hardware and software systems aboard the spacecraft. These sporadic bursts of additional EM radiation are due to the SF eruptions.  
Enhanced ultraviolet (UV), extreme ultraviolet (EUV), and X-ray radiation are known to affect the upper atmosphere, namely the ionosphere heats and expands. Ionospheric disturbances have several consequences on the satellites and their work performance, and the effects are known for many decades [47]. The perturbed ionosphere can cause delay, distortion, and absorption of radio signals during their propagation. The delay occurs since the ionosphere increases the propagation path for the signal, thus affecting the quality of satellite communication, navigation, and positioning. Ionospheric density inhomogeneities lead to the so-called scintillation patterns. SF emission can be also the reason of day-side radio blackouts due to plasma density increases known as sudden ionospheric disturbances. Less common are the direct interferences with the radar systems due to solar radio bursts (e.g., air traffic control problems in Sweden on 2015-11-04). The largest SFs can also cause a minor disturbance (magnetic crochet) of the magnetic field.  
The spacecraft-related effects of the EM emission can be summarized as follows:  
* Satellite signal degradation and loss;
* Radio blackouts;
* Increased atmospheric drag.
## 5 Results and Discussion  
The comparison of the HESS controlled by fuzzy logic with the battery storage system was carried out in the ADVISOR and Simcenter Flomaster programs. The NEDC driving cycle was used in the simulations, and the current values on the battery and battery SoC were compared. Although SCs lack a high energy density, they quickly store the instantaneous energy demands from regenerative braking and quickly meet the instantaneous energy demands during sudden acceleration. In this way, the consumption values on the battery were reduced and the battery's health was maintained by reducing the high current values in the energy stored and consumed from regenerative braking on the battery. Figure 25 shows the current values generated on the battery by the HESS in ADVISOR and Simcenter Flomaster programs.  
The ADVISOR program, on the other hand, starts the battery's initial SoC value with only 70%. For this reason, the tests of the battery at different SoC values were performed using the Simcenter Flomaster program. In addition, tests at different outdoor temperatures and heat generation values of the battery were performed using the Simcenter Flomaster program. In the EV with the HESS, the current values on the battery decreased by 29.1% in the ADVISOR program and 28.7% in the Simcenter Flomaster program. Similarly, the battery SoC values decreased by 7% in the ADVISOR program and 4.15% in the Simcenter Flomaster program.  
[PERSON] et al. [60] reduced the energy consumption by 5% by optimizing the power distribution between the SCs and batteries in composite energy storage systems. Furthermore, the energy efficiency was increased from 4.1% to 6.7% by optimizing the parameters of the flywheel device and the energy management strategy. As a result of  
Figure 25: Comparison of the current values generated by the HESS on the battery.  
their simulation study, [PERSON] et al. [61] argued that the total cost of the HESS during a 10-year operation, including capital costs, electricity costs, and battery replacement costs, is 25.9% less than current battery prices. [PERSON] et al. [62] developed a multi-objective optimization dual HESS and achieved a 19.57% range increase and 22.88% battery life extension compared to a single HESS-equipped EV. In the existing literature studies, a HESS system based on a regenerative braking system is quite scarce. In this study, simulations in the ADVISOR and Siemens Simcenter Tomaster programs show that the HESS is effective in maintaining the battery's health and optimizing energy consumption, especially in regenerative braking situations. The integration of SCs in the storage and utilization of the battery contributes to a significant reduction in average energy consumption, an increase in vehicle range, and the preservation of battery health. This study provides more comprehensive results than other studies by presenting a comprehensive analysis with tests performed over a wide temperature range and data obtained at different SoC values.
## 2 Materials and Methods
### Surface Electromyography  
EMG data were collected telemetrically during all MVC, CMJ, and landing tasks. The skin was carefully prepared by shaving and cleansing with alcohol. Dual Ag/AgCl surface electrodes (Noraxon, Scottsdale, AZ, USA) were positioned on vastus lateralis (VL) and medialis (VM), biceps femoris (BF), and gluteus medius (GM) muscles according to SENIAM recommendations (www.seniam.org; accessed on 1 May 2021), and were kept on their place throughout the performance tests. EMG signals were collected (Noraxon, Scottsdale, AZ, USA, sampling frequency: 2000 Hz), and the raw data were processed with the root mean square technique, using 50-ms moving window. After this, the peak EMG values were determined for every performance test trial. The peak EMG values for CMJ and landing task trials were considered in a pre-specified recording period (Figure 2). The onset of this period was manually marked by considering the time point, when legs were unweighted and muscles were seen relaxed on the EMG register. To determine the end point, the peak VL EMG activity was used as reference point, which was clearly identifiable in every recording. The end point of the measurement period was 500 ms after the peak of VL EMG activity in order to ensure that the entire movement (either CMJ or landing) is involved in the measurement. All CMJ and landing EMG data were normalized to those obtained during MVCs. The BF/VM activation ratio during the landing task was also determined.  
Figure 1: Landing task performed to the flat side of a TOGU® Jumper.
# Scheduling for Media Function Virtualization
## 4 System Model and Algorithm
### Problem Complexity
}x_{p}^{i,j}bw_{n_{u},n_{v},c}^{ f_{i}f_{j},p}\leq bw_{n_{u},n_{v},c},\quad\forall(n_{u},n_{v})\in E,c\in C.\]  
Here, the objective function is to maximize \(\sum\limits_{p\in\texttt{Pths}(G^{l},n(f_{i}),n(f_{j}),K)}x_{p}^{f_{i}f_{j}}\); where \(x_{p}^{f_{i}f_{j}}\) indicates if virtual link \((f_{i},f_{j})\) is mapped to physical path \(p\) and \(bw_{n_{u},n_{v},c}^{f_{i}f_{j},p}\) is the amount of bandwidth allocated to virtual link \((f_{i},f_{j})\) on physical link \((n_{u},n_{v})\) during cycle \(c\). The constraint ensures that the total allocation during \(c\) on physical link \((n_{u},n_{v})\) does not exceed the available bandwidth, i.e., \(bw_{n_{u},n_{v},c}\). The objective of the formulation is to map as many virtual links to physical paths as possible. This formulation can be thus expressed as a decision problem (yes or no) by setting the threshold for the objective function to \(|L|\). Therefore, a given VMF-FG is said to be scheduled if the objective value obtained after solving the ILP is \(|L|\). In order to prove that the VMF-FG scheduling problem is NP complete, we need to show that a known NP-complete problem can be reduced to it, similar to [16]. The \(k\)-disjoint paths (\(k\)DP) problem decides whether \(k\) discrete paths are possible between two nodes \(n_{s}\) and \(n_{d}\) of a given graph \(G\). \(k\)DP can be reduced in the above instance of VMF-FG scheduling problem by setting the total cycles in a hypercycle to \(|C|=1\), the bandwidth requirement of virtual link \(bw_{n_{u},n_{v},c}^{f_{i}f_{j}}=1,\forall(f_{i},f_{j})\in\mathcal{L}\), and the available bandwidth in each cycle to be \(bw_{n_{u},n_{v},c}=1,\forall(n_{u},n_{v})\in E,c\in C\). Assuming \(k=|L|\) and the above assumptions, the decision problem for VMF-FG scheduling problem returns _True_ if \(k\) discrete paths are possible for \(\mathcal{L}\). All the above reduction steps have polynomial complexity and any solution can be verified in polynomial time, thus the VMF-FG scheduling problem is NP-complete. Above, we considered an (simpler) instance of the VMF-FG problem. The hardness of the problem is further accentuated because of multiple choices to place the VMFs and route the virtual links of the given VMF-FG. Next, we present a scalable greedy heuristic to solve the problem.
# Enhancement of In-Plane Seismic Full Waveform Inversion with CPU and GPU Parallelization
## 1 Introduction  
The physical properties of the Earth's subsurface have always been of interest to humankind. Ranging from scientific development to industrial use, such as oil and gas industries and engineering applications, determining or approximating the physical properties is essential. Exploration geophysics is the branch of geophysics that uses physical methods such as seismic, acoustic, electromagnetic or electric measurements to extract information about the Earth's subsurface without physical disturbances. Full Waveform Inversion (FWI) is a wave inversion procedure where the high-quality estimate of the subsurface parameters is obtained by minimising the misfit between the observed and modelled data. Seismic and acoustic waveform inversion is widely in practice. Seismic or acoustic signals are applied at the surface, which gets propagated through the subsurface of interest, and the output signals are obtained at receiver points (see Figure 1). The wave phenomenon is modelled using an appropriate numerical technique, and the output is compared to that obtained from the field test. The model is updated with a mathematically calculated approximation function until we get a desired minimum value of the misfit to get a high-quality approximation of the subsurface properties [1]. This paper deals mainlywith time-domain seismic wave inversion in a two-dimensional system using the finite difference method (FDM).
## 3 Obstacle Avoidance Scenario
### Obstacle Detection Technologies
#### 3.3.3 Laser Ranging  
Laser imaging uses single- or multiple-laser sensors to generate an image or map of the surroundings. For laser-imaging techniques, the most used technology is laser imaging and ranging or lidar [230]. Lidar works the same as radar, except that it uses a laser for ranging. The single-point laser uses one-point distance measurements [231]. Creating a single line or 2D laser beam, using a single laser or multiple lasers, for 2D area or obstacle imaging creates an array of point clouds [232; 233]. Using a four-layer laser scanner, [PERSON] and [PERSON] [234] proposed an obstacle detection algorithm that can be used on autonomous land vehicles. The lidar technology uses laser point clouds and scans continuously to create a 3D map [235; 236]. [PERSON] et al. [237] presented a multi-scaling method to compute geometric structures on lidar point clouds to retrieve the optimal surrounding size for each point. [PERSON] et al. [238] designed bounding box encoding, which uses a fully convolutional network with lidar to detect vehicles. [PERSON] et al. [239] used 2D lidar scanning on an agricultural helicopter to scan obstacles. [PERSON] et al. [240] also used 2D lidar scanning to detect obstacles that can effectively filter noise from raw laser data with the ground robot. [PERSON] et al. [241] used clustering based on relative distance and density (CBRDD) and the point-cloud correction method to detect surrounding obstacles from airborne UAVs. Basically, the 2D lidar scanning method is used for avoidance operations [242; 243; 244]. Usage of 3D lidar scanning is frequent in airborne geographical monitoring, such as agricultural plant monitoring, forest canopy monitoring, urban area monitoring, etc. [230; 245; 246; 247; 248; 249]  
Imaging with lidar is very precise, but it is relatively costly. Laser-ranging system uses highly intensive light, which can be interrupted by fog, rain, or dust. However, because of the increasing precision engineering, the usage of laser imaging is very high.
## 1 Introduction  
Determining individual psychophysiological load during flight training has innovative potential for enhancing flight safety, preventing sudden cardiovascular diseases, and maintaining the mental health of flight personnel [1, 2, 3]. The mental health of a flight instructor is extremely important for ensuring the safety and effectiveness of the training process [4]. A high level of mental health allows the instructor to maintain concentration, make balanced decisions, and effectively respond to stressful situations [5]. It is important to understand that mental health is dynamic and can change under the influence of various factors [6]. Constant monitoring of and support for the mental health of flight instructorsnot only ensure their well-being but also increase the overall safety and effectiveness of aviation training [7].  
Thus, the current study aimed to acquire knowledge about the psychophysiological load of flight instructors. Understanding the psychophysiological state of instructors enables administrators to optimize work schedules, provide adequate support, and promptly address potential issues [8]. Instructors can leverage this knowledge for self-monitoring and decision-making regarding their health, which helps prevent burnout and sustain high performance levels [9]. This information is crucial for developing policies and procedures designed to enhance flight safety and ensure adherence to safety standards [10]. Consequently, the knowledge gained contributes to the overall well-being of flight instructors, which, in turn, elevates the quality of the training process and aviation safety standards.  
The purpose of this study was to develop an intellectual-analytical platform for assessing the psychophysiological load on flight instructors in a flight school.  
From the above, the following scientific research questions (RQ) were formulated:  
RQ1: Can the proposed intellectual-analytical platform effectively integrate predictive assessments of psychophysiological load and expert data on mental health to form knowledge about the overall level of workload of the flight instructor?  
RQ2: Can the developed intellectual-analytical platform automate the process of objective evaluation for predicting the overall level of psychophysiological load on flight instructors in the working environment?  
The structure of the intellectual and analytical platform is based on a combination of two key sources of information: first, objective data from sensors and devices that record physiological and acoustic indicators of the flight instructor's activity; second, subjective criteria formulated and evaluated by experts in the field of psychophysiology, which enable interpretation of this data in the context of mental state and overall level of workload.
## 2 Methods
### Docker and Docker Compose as Solutions  
To overcome these compatibility challenges, JAVIS utilizes **Docker**[18] and **Docker Compose**. Docker containerizes applications by bundling them with their own operating systems, CUDA libraries, and all required dependencies. This encapsulation ensures that each component of JAVIS operates within a controlled and consistent environment independent of the host system's configuration.
## 3 Methodology
### Convolutional Long Short-Term Memory (ConvLSTM)
In application to futures spread prediction, the ConvLSTM network method considers the correlation of influencing factors. Samples can be perceived as \"T images (n, K)\" where T corresponds to the time step, n to the predicted step, and K to the number of selected influencing factors. This design allows ConvLSTM not only to handle time series autocorrelation effectively but also to consider the correlation between various factors. This holistic approach facilitates addressing multicollinearity problems in regression models, yielding more accurate futures spread predictions. Thus, through the application of ConvLSTM, incorporating both time series autocorrelation and inter-factor correlation into account, the accuracy of predictions is enhanced.
## 2 Problem Formulation  
### Topology Optimization Approach  
The continuum structural topology optimization can be seen as finding the optimal material distribution within a given design domain. As the most widely used topology optimization approach, the density method regards elements for analysis as basic design units, with which the design domain is discretized. The existence of material in the \(e\)th element is measured by its density \(x_{e}\) ranging from 0 to 1, and \(x_{e}=1\) or 0 means that the \(e\)th element is the solid or void one. In order to suppress the occurrence of elements with intermediate densities (i.e., \(0<x_{e}<1\)) in the final design, the following modified SIMP interpolation scheme is adopted:  
\[E_{e}=E(x_{e})=E_{\text{min}}+x_{e}^{p}(E_{0}-E_{\text{min}}),\ \ x_{e}=[0,1] \tag{1}\]  
where \(E_{e}\) denotes [PERSON]'s modulus of the \(e\)th element material, \(E_{0}\) and \(E_{\text{min}}\) denote [PERSON]'s moduli of solid and void materials, respectively, and \(E_{\text{min}}\) takes a minimal positive number to avoid singularity of the stiffness matrix; \(p\) is a penalization factor introduced to ensure black-and-white solutions and it is typically set to 3.  
According to the SIMP based density method, the global stiffness matrix \(K\) is calculated by  
\[K=\sum_{e=1}^{N}E_{e}\mathbf{k} \tag{2}\]  
where \(\mathbf{k}\) is the element stiffness matrix for an element with unit [PERSON]'s modulus, and \(N\) is the total number of elements used to discretize the design domain.
# Exploring ISAC: Information-Theoretic Insights
## 5 Network ISAC with Sensing Distortion
### Multi-Access ISAC: Collaborative Sensing and Suboptimality of Symbolwise Estimators
#### 5.2.3 Example  
The following example shows the improvement in Theorem 9 over the previous scheme in [65]. As mentioned, a further improvement is achieved by the scheme in [66].  
**Example 2**.: _Consider binary noise, states, and channel inputs \(B_{0},B_{k},S_{k},X_{k}\in\{0,1\}\). The noise to the Rx \(B_{0}\) is Bernoulli-\(t_{0}\), and \(B_{k}\), the noise on the feedback link to Tx \(k\) is Bernoulli-\(t_{k}\). All noise sources are independent and also independent of the states \(S_{1},S_{2}\), which are i.i.d. Bernoulli-\(p_{s}\). We can then describe the channel as_  
\[Y^{\prime}\ =\ S_{1}X_{1}+S_{2}X_{2}+B_{0},\qquad\ Y=(Y^{\prime},S_{1},S_{2}), \tag{52}\]  
\[Z_{1}\ =\ S_{1}X_{1}+S_{2}X_{2}+B_{1},\qquad\ Z_{2}=S_{1}X_{1}+S_{2}X_{2}+B_{2}. \tag{53}\]  
_In this example, the Rx has perfect channel state information and the Hamming distance is considered as a distortion measure: \(d(s,\hat{s})=s\oplus s\)._  
_Figure 14 shows the maximum sum-rate \(R_{1}+R_{2}\) as a function of distortion \(\mathsf{D}_{2}\) achieved by Theorem 9 (with collaborative sensing) and the region in [65] without collaborative sensing where \(V_{1}=V_{2}=constants\). Both curves are strictly concave and thus are improvements over classic time- and resource-sharing strategies. The minimum distortions achieved are \(D_{2,\min}=0.035\) with collaborative sensing and \(\mathsf{D}_{2,\min}=0.04\) without._  
Figure 13: Block Markov strategy of the improved ISAC multi-access scheme in [66].  
Similar ISAC coding ideas were also proposed for the interference channel (IC) where two Txs communicate to two Rxs [108]. The idea is to use block Markov coding as for the MAC and that the two Txs compress and convey sensing information in addition to the cooperative data communication in previous blocks. The corresponding set of achievable rate-distortion tuples can be found in [108].
## 5 Mechanism Analysis
### Hydration Mechanism of Cement Paste under Mechanical Agitation
#### 5.1.2 Cement Paste after CO2 Absorption under Mechanical Agitation  
The hydration process of cement paste after CO2 absorption under mechanical agitation is shown in Figure 16. It can be seen that under mechanical agitation, CO2 gases scatter uniformly in the cement paste and firstly dissolve into H2 CO3. H2 CO3 reacts with Ca(OH)2 which is precipitated from the initial hydration of cement to produce flocculent  
Figure 15: Hydration mechanism of fresh cement paste under mechanical agitation: (**a**) Initial state of fresh cement paste; (**b**) early hydration stage of cement paste; (**c**) late hydration stage of cement paste. (Note: cement particles; hydration products of flocculating gel).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{3}{c}{**Molar Percentage**} \\ \cline{2-4}  & **Figure 14a** & **Figure 14b** & **Figure 14c** \\ \hline C-K & 11.38 & 14.23 & 16.09 \\ O-K & 68.92 & 63.22 & 71.35 \\ Si-K & 2.47 & 2.81 & 2.62 \\ Ca-K & 11.66 & 12.01 & 13.11 \\ Al-K & 2.18 & 1.56 & 1.24 \\ Mg-K & 1.33 & 0.72 & 1.52 \\ K-K & 1.24 & 0.83 & 0.98 \\ Fe-K & 0.33 & 0.44 & 0.27 \\ S-K & 0.50 & 0.38 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: EDS analysis results.  
CaCO\({}_{3}\) crystals which adhere to the surface of cement particles (Figure 16a). With the increase of CO\({}_{2}\) AA, the CaCO\({}_{3}\) gel layer on the cement particle surface thickens and the cement paste viscosities gradually, decreasing the fluidity accordingly [37].  
The CaCO\({}_{3}\) gel layer on the cement particle surface has a loosened structure and then the cement particles begin to hydrate gradually. Calcium metasilicate (C-S-H) gel and CaCO\({}_{3}\) gel which are the hydration products of cement combine on the cement particle surface into a gel layer. Some flocculent CaCO\({}_{3}\) gel crystals penetrate the C-S-H and flocculent CaCO\({}_{3}\) gels as needle-like whiskers, forming a skeletal network (Figure 16b).  
In the late hydration stage of cement, hydration products on the cement particle surface increase significantly and the wrapper thickens accordingly. Moreover, CaCO\({}_{3}\) crystals are wrapped in the hydration products. Meanwhile, the CaCO\({}_{3}\) needle-like crystal whiskers are still in the paste to form an effective network structure (Figure 16c), which improves the gelling performance among cement particles.