esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents an innovative solution to address agricultural irrigation needs through a hybrid renewable energy system (HRES) that was specifically designed for a farm located in the Skikda region of Algeria. This system is tailored to irrigate 830 fruit trees spread across 3 hectares with a total perimeter of 770 m. The proposed approach integrates two main renewable energy sources (while eliminating the use of traditional batteries for electrical energy storage): solar and wind. Instead, a large water reservoir is employed as an energy storage medium in the form of potential energy. Utilizing gravity, this reservoir directly powers the irrigation system for the fruit trees, thereby reducing the costs and environmental impacts associated with conventional batteries. This innovative design not only enhances sustainability, but also improves the system's energy efficiency. To ensure precise and customized sizing of the system for the irrigation area, a detailed mathematical modeling of the key system components (solar panels, wind turbines, and reservoir) was conducted. This modeling identifies the critical design variables required to meet technical specifications and irrigation needs. A multi-objective optimization approach was then developed to determine the optimal configuration of the HRES, and this was achieved by considering both technical and economic constraints. The optimization algorithm used was tailored to the formulated problem, ensuring reliable and applicable results. The robustness of the optimization approach was shown by the precise match between energy production (24 kWt at 16,119.40 $) and the minimum demand. This alignment prevents over- or under-designing the system, which increases costs and reduces energy use. The findings highlight the relevance and effectiveness of the proposed methodology, demonstrating its practical utility and significant potential for generalization and adaptation to different agricultural zones with varying conditions. This work paves the way for sustainable and innovative solutions for agricultural irrigation, particularly in remote areas or regions lacking traditional energy infrastructure.  
h +
Footnote †: journal: Applied sciences  
Article  
Article
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 3 Results and Analysis  
### The Process of Compression and Deformation of the Seal  
The forces acting on the upper spherical shell of the model at different water depths were calculated using Equation (1) and simulated in ANSYS Workbench to obtain the resulting downward displacement of the upper spherical shell at varying water depths. The obtained results are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Depth** & **Force** & **Downward Displacement of the Upper** \\
**(m)** & **(kN)** & **Spherical Shell (mm)** \\ \hline
0 & 20 & 3.3097 \\
1 & 26.998 & 3.5753 \\
2 & 33.997 & 3.8074 \\
3 & 40.996 & 3.9097 \\
6 & 61.993 & 4.3431 \\
10 & 89.989 & 4.6835 \\
12 & 103.987 & 4.8618 \\
14 & 117.985 & 5.0009 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Displacement of the upper spherical shell in the downward direction.  
Figure 5: Finite element model of the seal.  
Figure 6: Water pressure path.  
Table 1 shows that when a pre-tension force of 20 kN is applied to the fastening strap, the downward movement of the upper shell is 3.3097 mm underwater at a depth of 0 m, which represents the vertical compression of the sealing ring. At this stage, the upper and lower shells are not completely joined, and further compression of the sealing ring is still feasible. Ultimately, at a water depth of 14 m, the upper and lower shells are fully joined, and the sealing ring is entirely compressed into the groove. Subsequently, when subjected to high water pressure, the vertical compression of the sealing ring exhibits constancy and remains unchanged.  
During the course of utilizing this particular seal, the state of vertical compression may experience deformation and changes. This is in stark contrast to the typical utilization of bolted connections or other such methods for firmly fastening pressure vessel seals in their operational state. It should be noted that when the water depth is relatively shallow, the level of vertical compression in the seal is decreased, necessitating the careful consideration of corresponding sealing performance requirements.
# Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness
## 4 Channel Estimation Method Based on Deep Learning  
In this section, firstly, the signal generation, processing and the traditional OMP algorithm are described, and then the network super-resolution denoising network (SRCNN) DNCNN network, SDNet) after model fusion is introduced in detail, and the optimized super-resolution denoising residual network (SDRNet) on this basis is introduced. Finally, the generation of the channel-aware adversarial sample algorithm based on frequency-domain transformation was elaborated in detail, verifying the importance and accuracy of SDRNet channel estimation. As shown in Figure 2, it represents the training process of SDRNet, and Figure 3 represents the generation process of adversarial samples.  
Figure 3: Flowchart of adversarial sample production method based on frequency-domain algorithm and channel awareness.  
Figure 2: SDRNet channel estimation framework.
## 5 Conclusions and Future Work
However, the open issues concerning message replication for OppNets in Smart City scenarios arise only when the node densities and encounter frequencies resemble the ones in city-wide scenarios. For instance, the scenario needs to portray the high number of encounters experienced due to high node population (e.g., during rush hours), high encounter frequency in popular places (e.g., bus stops and shopping malls), the large number of encounters that extremely popular devices (e.g., those carried by bus and taxi drivers) experience, and sufficient encounters between road side sensors and user devices. Distance and geographic area also play an important role in validating LARep since it exploits concepts of spatial locality inherent to user movement in urban environments. Hence, it is crucial to achieve a balance between node population and geographic area. A major condition is for the network to consist of different geographical districts, in which user movement is restricted (e.g., the 4 major districts in the Helsinki scenario). At the moment, publicly available datasets are unable to meet these conditions. Take the Dartmouth mobility trace [75] for instance, which is currently one of the largest publicly available contact datasets in terms of scale and number of monitored nodes [76]. The contact trace was collected in the Dartmouth College campus on 200 acres of land. Covering an area less than 1% of the Skudai scenario, it is fair to say that it does not portray spatial locality in a city. Moreover, the movement depicts that of a compact campus rather than a city. Hence, we have utilized WDM since it offers a scaled down version of a city (i.e., the node population can be adjusted in accordance with the geographical area to achieve a realistic density that portrays all the desirable movement properties in an urban environment) and have left evaluations in real-world traces for future work. [PERSON] conceived the original idea, designed the figures, implemented the proposals in the simulator and wrote the manuscript with input from all authors. [PERSON] and [PERSON] contributed to the problem background the evaluation methodology. [PERSON] assisted with the simulation set up and helped carry out the experiments. [PERSON] and [PERSON] were involved in processing the experimental data. [PERSON] and [PERSON] aided in interpreting the results and supervised the findings of this work. All authors provided critical feedback and helped shape the research, analysis and manuscript. The authors declare no conflict of interest.
## 4 AdaBoost Regressor-Based Seismic Vulnerability Analysis Method  
### The Basic Theory of the Seismic Vulnerability Analysis of Electrical Equipment  
Seismic vulnerability and risk analysis have been constantly developed in the area of engineering structures including substation equipment [22; 23; 24; 25; 26]. The seismic vulnerability of a structure is commonly expressed by a probability-based failure index. This failure index represents the probability that the structural response exceeds the material strength, which brings in structural damage. Generally, it can be expressed as Equation (2) [54; 55].  
\[P(x)=P(S>R|IM=x) \tag{2}\]  
in which \(P\) represents the failure probability, \(S\) represents the structural responses under an earthquake citation, and \(R\) represents the performance level. \(IM\) is the intensity measure. For electrical equipment in substations, \(S\) usually adopts the peak stress responses of porcelain or composite insulators due to the low material strength, so \(R\) is determined as the material strength. \(IM\) is generally specified as the PGA or spectral acceleration at the fundamental period. Nowadays, the related investigations indicate that the seismic failure probability \(P\) of electrical equipment can statistically obey the log-normal distribution, as shown in Equation (3) [54; 55]:  
\[P(x)=\Phi(\frac{\ln x-\mu}{\sigma}) \tag{3}\]  
in which \(\mu\) and \(\sigma\) are the logarithmic mean and logarithmic standard deviation, respectively, and \(x\) is the intensity measure. Obviously, they are the two critical parameters that can determine the overall function. For electrical equipment, the use of PGA as the IM of the fragility curves is based on the following reasons: (1) PGA is a straightforward and commonly utilized index for quantifying ground motion intensity in seismic evaluations of substation equipment, as its value is readily accessible. (2) The Chinese seismic design regulations for structures and electrical installations utilize PGA as the vital intensity measure for delineating ground motion intensity and seismic hazard correlations. (3) Extensive literature about the seismic fragility of substation equipment primarily uses PGA as the IM, hence encouraging its selection in this work for the fragility analysis of power transformers. Therefore, this study adopts PGA as the IM in the fragility analysis [7; 20; 21; 55]. Given a PGA range, a fitted vulnerability curve can be obtained via Equation (3) and then is used to estimate the structural failure risk based on the PGA.
###### Abstract  
This research aims to investigate the development of muscle fatigue and the recovery process revealed by tissue oxygenation. The tissue hemodynamics were measured by near-infrared spectroscopy (NIRS) during a 30-min pre-exercise rest, a 40-cycle heel-lift exercise and a 30-min post-exercise recovery. Wavelet transform was used to obtain the normalized wavelet energy in six frequency intervals (I-VI) and inverse wavelet transform was applied to extract exercise-induced oscillations from the hemodynamic signals. During the exercise phase, the contraction-related oscillations in the total hemoglobin signal (\(\Delta\)tHb) showed a decreasing trend while the fluctuations in the tissue oxygenation index (TOI) displayed an increasing tendency. The mean TOI value was significantly higher (\(p<0.001\)) under recovery (65.04% \(\pm\) 2.90%) than that under rest (62.35% \(\pm\) 3.05%). The normalized wavelet energy of the \(\Delta\)tHb signal in frequency intervals I (\(p<0.001\)), II (\(p<0.05\)), III (\(p<0.05\)) and IV (\(p<0.01\)) significantly increased by 43.4%, 23.6%, 18.4% and 21.6% during the recovery than that during the pre-exercise rest, while the value in interval VI (\(p<0.05\)) significantly decreased by 16.6%. It could be concluded that NIRS-derived hemodynamic signals can provide valuable information related to muscle fatigue and recovery.  
Keywords:near-infrared spectroscopy; wavelet transform; peripheral muscle fatigue; hemodynamic responses; frequency characteristic +
Footnote †: journal: Applied
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 4 Methodology
### Spatiotemporal Trajectory Embedding  
The whole embedding of spatiotemporal trajectory contains these parts: a position encoder to obtain the embedding of latitude and longitude features, and a time encoder, which is used to deal with uneven time intervals, to obtain time features in each coarse trajectory record.  
**(1) Position Encoder:** This part was designed to obtain the embedding of the latitude and longitude of the trajectories. Following the definitions in the preliminary part, the formula of the representations in the position encoder is as follows.  
\[\vec{x}_{u,i}^{n},\vec{y}_{u,i}^{n}=\text{Normalized}(lat_{u,i}^{n},lat_{u,i} ^{n}), \tag{2}\]  
\[\vec{x}_{u,i}^{pos,n}=\text{MLP}(\vec{x}_{u,i}^{n},\vec{y}_{u,i}^{n}), \tag{3}\]  
where \(\vec{x}_{u,i}^{n},\vec{y}_{u,i}^{n}\) is the normalized latitude and longitude information for each location in the trajectory sequence, \(\vec{x}_{u,i}^{pos,n}\) is the positional embedding of this location. The embedding of the \(n\)th trajectory for user \(u\) is defined as \(\vec{x}_{u}^{pos,n}\), and the position representation of the model is defined as \(\vec{X}_{loc}\).  
**(2) Time Encoder:** Since the time interval between two consecutive signaling records can vary, we cannot ignore this problem. To tackle this problem, we design a time encoder  
Figure 2: An overview of the model.  
to deal with the timestamps and the uneven time interval information in signaling records. For each timestamp in the trajectory segment \(\mathbf{p}_{u,i}^{u}\), the formulas are as follows.  
\[\mathbf{t}_{i}^{u}=\text{Extract}(t_{u,i}^{u}(in),t_{u,i}^{u}(out)), \tag{4}\]  
\[\mathbf{x}_{time,i}^{u}=\cos(\mathbf{W}_{t}\mathbf{t}_{i}^{u}+\mathbf{b}_{t}), \tag{5}\]  
where Extract refers to extracting the features of the timestamps (hour, minute, and second values). We use the _cos_ function and learnable parameters \(\mathbf{W}_{t}\) and \(\mathbf{b}_{t}\) in each record to get periodic time features. We use \(\mathbf{X}_{time}^{u}=(\mathbf{x}_{time,1}^{u},\mathbf{x}_{time,2}^{u},\ldots,\mathbf{x}_{time,k}^ {u})\) as the time features of the trajectory for an individual \(u\). The time features of the whole model is defined as \(\mathbf{X}_{time}\).
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 3 Building Blocks
### Laser Sources
Using the hybrid platform, a 1D beam steering OPA with an FOV of 12\({}^{\circ}\), a beamwidth of 1.8\({}^{\circ}\)\(\times\) 0.6\({}^{\circ}\), and a background noise suppression of 7 dB was achieved [81]. The first fully integrated 2D beam scanner using the hybrid Si platform contained 164 optical components and exhibited an FOV of 23\({}^{\circ}\)\(\times\) 3.6\({}^{\circ}\) with a beamwidth of 1\({}^{\circ}\)\(\times\) 0.6\({}^{\circ}\)[82]. Compared with hybrid III-V/Si laser integration approaches, monolithically integrated rare-earth-based lasers offer various highly demanded performance characteristics to the on-chip sources, such as high output power, good thermal stability, and a narrow linewidth [77; 78]. In general, on-chip integration of tunable laser sources results in complicated device structures [64], and their performances are limited in output power, linewidth, and tunable range [75]. The tunable lasers on the Si platform typically show tens of mW output power, kHz-level Lorentzian linewidths, and over 40 nm wavelength tuning in the C+L bands [83]. Note that a WGA can only provide a limited FOV over such a tuning range [84], and thus  
Figure 5: OPAs with on-chip laser sources based on the hybrid III–V/Si platform. **(a)** Schematics of an OPA with an on-chip laser on the hybrid III–V/Si platform. Reprinted with permission from [81] © The Optical Society. **(b)** The layout of a fully integrated OPA. Reprinted with permission from [82] © The Optical Society. arbitrary beam forming and steering cannot be achieved by this structure. Additionally, for the laser sources integrated with OPAs, the influence of temperature drift on the emission wavelengths should be considered [73]. The ability to actively switch among lasers [82] or emitters as in Figure 3c [37,62] expands the wavelength-tuning-enabled FOV of WGA-based OPAs. For the fully integrated hybrid Si OPAs, two tunable Vernier ring lasers with different tuning ranges were used to create a much wider wavelength-tuning range [82]. The limitation in FOV with the wavelength-tuning approach can also be expanded through the polarization-division and spatial-division multiplexed nanoantenna arrays, based on which over 40\({}^{\circ}\) tuning within a 100-nm wavelength range was achieved [37].
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 3 Materials and Methods
### Data Analysis  
Data were collected using the Max patch, which stored pairs of data points showing the original virtual sound source positions and the perceived position for each participant. The task was divided into two rounds, resulting in a comprehensive dataset for each participant that included responses from both the horizontal plane localization test and the elevated plane localization test. The resulting data statistical analysis was performed using Python 3.12, with libraries such as NumPy, Pandas, Seaborn, and Matplotlib. Circular statistics were applied to assess the angular characteristics of the data [29; 30; 31; 32; 33; 34].
## 3 Results  
### Study Characteristics  
Figure 1 presents the PRISMA flow chart of the included studies. A total of 800 articles were found in the initial search. To ensure the accuracy of the systematic search process, two reviewers conducted a multi-step search process. We screened the titles, abstracts, and full-length texts to make an initial assessment independently. If the two reviewers had any disagreements, a third reviewer was included. After screening the titles and abstracts, 22 articles were selected to conduct full-text screening.  
Furthermore, 12 articles passed the inclusion criteria. Additionally, one manually searched article agreed on by the two reviewers met the inclusion criteria. Finally, 13 articles were selected for the systematic review.  
Tables 1 and 2 lists the detailed information of the 13 studies, which included 9 horizontal studies [17; 18; 19; 20; 21; 22; 23; 24] (VGP: 153; NVGP:168) and 4 interventional studies [25; 26; 27; 28] (EG:124; CG:75).  
Figure 1: Flow diagram showing the study selection process.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Study** & **Number** & **Age** & **Imaging Modality** & **Game Type** & **During** & **Task/Sismuli** \\ \hline \multicolumn{9}{c}{Horizontal study} \\ \hline
1 & Gong (2019) & TRVC-20 (6:0) & 25.35 \(\pm\) 2.39 & Resting-state fMRI & Action real-time strategy game & - & - \\  & LRVGP-34 (6:0) & 24.39 \(\pm\) 213 & Resting-state fMRI & Action real-time strategy game & - & - \\ \hline
2 & [PERSON] (2019) & VGG 15 (5:9) & 55.74 & Resting-state fMRI & - & - & - \\ \hline
3 & Rohlan (2018) & VGG-14 (6:7) & 22.50 \(\pm\) 2.96 & \multirow{2}{*}{Task-state fMRI} & First and third person & \multirow{2}{*}{
\begin{tabular}{c} Visoustitial task and a verbal \\ shorter detection task \\ (tent
## 3 Test Results and Analyses
### Strain Field Evolution Characteristics of the Free Surface  
#### 3.3.1 Without Static Load  
Figure 3 presents the magnitude and direction of the maximum principal strain on the free surface in the case without static load. t = 0 corresponds to the distribution of the maximum static principal strain \(\varepsilon_{10}\) immediately after the static load was applied and before the blasting was started. The static strain on the free surface was zero when static load was not applied. The direction of the principal strain was randomly distributed, consistent with the actual situation. During the blasting, owing to expansion of the impact waves induced by the copper wire explosion, a circular tensile strain zone was formed near the blasthole center. The direction of the maximum tensile strain \(\varepsilon_{1}\) was perpendicular to the radius of the blasthole. When its magnitude satisfied Equation (5), radial tensile cracks emerged. The monitoring results show that the first expansion-induced tensile crack emerged on the free surface at 36 \(\upmu\)s after the blasting; the corresponding maximum tensile strain was 0.00325. Hence, the dynamic tensile strength of the sandstone \(\varepsilon_{td}\) was 0.00325 under such blasting conditions.  
The coordinate origin \(o\) in Figure 2 is the intersection point of the blasthole axis and free surface. The points on the axes were traced to quantitatively analyze the evolution law of the strain field on the free surface. Figure 4 depicts the variations in maximum principal strains \(\varepsilon_{1}\) at various points on the \(x\) and \(y\) axes at different moments. The distributions of the maximum principal strains \(\varepsilon_{1}\) for  
Figure 3: Magnitude and direction of maximum principal strain on the free surface in case of no static load: (**a–f**) images of maximum principal strain after 0 \(\upmu\)s, 30 \(\upmu\)s, 40 \(\upmu\)s, 50 \(\upmu\)s, 60 \(\upmu\)s and 70 \(\upmu\)s of explosion.  
various points on the axes at the same moment were similar. The maximum principal strain was observed at the coordinate origin \(o\) and decreased gradually to the initial static strain \(\varepsilon_{10}\) with the increase in distance from the blashhole center. The strains exhibited almost equal magnitudes at points that were at the same distance from the blashhole center. This indicates that the maximum principal strain \(\varepsilon_{1}\) was distributed approximately concentrically and uniformly on the free surface, exhibiting axisymmetric characteristics. The tensile strain distribution led to a random emergence of cracks on the free surface; multiple cracks might simultaneously appear (Figure 5). This can well explain the phenomenon reported by [PERSON] et al. [17; 20], i.e., the random emergence of 3-5 radial cracks on the free surface simultaneously, and the radially outward propagation of cracks from the center.