esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management Scheme for an EV Smart
## 3 Component Modeling and Control
### Bidirectional DC-DC Converter  
EV-side DC-DC converter models are based on the half-bridge topology. Such DC-DC converters have the ability to perform in both directions. The topology is simple with fewer components, low cost, higher efficiency and one input for pulse width modulation (PWM) [20]. The design consists of an inductor \(L_{b}\) and a capacitor \(C_{b}\) for the battery side. Two transistor switches \(G_{1}\) and \(G_{2}\) with anti-parallel diodes are operated by complementary signals from the controller and provide the bi-directional ability. \(G_{1}\) switch operation is for charging where it bucks the right-side \(V_{dc}\) to \(V_{b}\), whereas \(G_{2}\) switch operation is for discharging, which is active during the boost operation [32]. The circuitry is shown in Figure 4. Values for \(L_{b}\) and \(C_{b}\) for the converter are calculated as follows [42]:  
\[L_{b}=\frac{V_{dc}-V_{b}}{2\Delta i_{L}f_{s(DC)}}D_{buck}=\frac{V_{b}}{2\Delta i _{L}f_{s(DC)}}D_{boost} \tag{7}\]  
\[C_{b}=\frac{\Delta i_{L}}{8\Delta v_{C}f_{s(DC)}} \tag{8}\]  
Figure 3: Battery discharge curves. (**a**) Discharge voltage characteristics of the Nissan LEAF battery. (**b**) Discharge voltage characteristics of the ENVYON battery. (**c**) SoC characteristics of the Nissan LEAF battery. (**d**) SoC characteristics of the ENVYON battery.  
where \(V_{dc}\) is the DC-link voltage, \(V_{b}\) is the nominal battery voltage, \(\Delta i_{L}=\gamma^{DC}\times i_{b}^{IC}\) is the current ripple in \(L_{b}\) for a 1C current, \(\gamma^{DC}\) is the current ripple percentage, \(\Delta v_{C}=\gamma^{v}\times V_{b}\) is the voltage ripple in \(C_{b}\), \(\gamma^{v}\) is the voltage ripple percentage, \(f_{s(DC)}\) is the switching frequency, \(D_{buck}=V_{b}/V_{dc}\) and \(D_{boost}=1-(V_{b}/V_{dc})\).  
The DC-DC converter controller tends to operate the battery as a current source in the CC strategy [18]. The battery current loop is cascaded with a PI control block, which gives the duty ratio \(d_{DC(i)}\) of the \(i\)-th EV (Equation (9)). The duty ratio \(d_{DC(i)}\) is fed into the PWM, which operates the two switches \(G_{1}\) and \(G_{2}\).  
\[d_{DC(i)}=(i_{b(i)}^{*}-i_{b(i)})\left(K_{p(b)}+\frac{K_{i(b)}}{s}\right) \tag{9}\]  
where \(i_{b(i)}^{*}=P_{EV(i)}^{*}/V_{b(i)}\) is the reference battery current, \(P_{EV(i)}^{*}\) is the active power reference of the \(i\)-th EV and \(K_{p(b)}\) and \(K_{i(b)}\) are the proportional and integral gains of the PI controller. When \(P_{EV(i)}=0\), the signal to both switches \(G_{1}\) and \(G_{2}\) becomes zero, and the switches are in halt mode. The control strategy is shown in Figure 5.
###### Abstract  
Synthetic Aperture Radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with various geophysical processes. To investigate the land-surface deformation in Karamay, a typical oil-producing city in the Xinjiang Uyghur Autonomous Region, China, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired for the period from 2007 to 2009, and a two-pass differential SAR interferometry (D-InSAR) process was applied. The experimental results showed that two sites in the north-eastern part of the city exhibit a clear indication of land deformation. For a further evaluation of the D-InSAR result, the Persistent Scatterer (PS) and Small Baseline Subset (SBAS)-InSAR techniques were applied for 21 time series Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) data from 2003 to 2010. The comparison between the D-InSAR and SBAS-InSAR measurements had better agreement than that from the PS-InSAR measurement. The maximum deformation rate attributed to subsurface water injection for the period from 2003 to 2010 was up to approximately 33 mm/year in the line of sight (LOS) direction. The interferometric phase change from November 2007 to June 2010 showed a clear deformation pattern, and the rebound center has been expanding in scale and increasing in quantity.  
ALOS-PALSAR; ENVISAT-ASAR; land-surface deformation; subsurface water injection; D-InSAR; PS-InSAR; SBAS-InSAR Article
# Webcam Eye Tracking for Monitoring Visual Attention in Hypothetical Online Shopping Tasks
## 3 Materials and Methods
### Stimuli and Task  
Two screenshots, each taken from a website of well-known online retailers, served as stimuli. The product category was narrowed down to the single product category T-shirts in order to exclude effects that could arise from searching for specific items of clothing (e.g., trousers, blowes). The product category T-shirts was chosen because T-shirts are a ubiquitous clothing item worn by a large number of people of many age groups.  
When selecting the online shops for the screenshots, care was taken that they would allow analysing the factors expected to influence visual attention, i.e., spatial location and the inclusion of human images. In both online shops, product depictions were arranged in a table-like manner with three columns and multiple rows (online shop A 16 rows; onlineshop B 28 rows), and both online shops included human models in some of their product depictions (cf. Figure 1). In online shop A, 75.0% (36 out of 48) of the product depictions involved a human model wearing the T-shirts, online shop B included human models in 58.3% (49 out of 84) of the product depictions. To enable quantitative analysis, each product depiction was defined as an area of interest (AOI). Each AOI was uniquely labelled, with the label containing the name of the website, row and column number and whether a human model was depicted. Within each stimulus, all AOIs were set exactly to the same size, with all AOIs of online shop A having a width of 274 px and a height of 560 px, and all AOIs of website B having a width of 308 px and a height of 560 px.  
The online eye tracking experiment started with a 9-point calibration procedure provided by the EYEVIDO Lab software. After successfully completing the calibration process, participants were asked to imagine that they were buying a T-shirt for a friend at an online store. They were instructed to behave as they normally would when shopping online. By clicking on the corresponding product, they should mark their choice of a T-shirt (which was not recorded), and then proceed with the study. After seeing the screenshot of the first online store and selecting a T-shirt, participants responded to the four items of the short version of the Visual Aesthetics of Websites Inventory (VisAWI-S) described by [PERSON] and [PERSON] [32]. Each question captured one of the four facets of visual aesthetics, which are Simplicity, Diversity, Colourfulness, and Craftsmanship [32]. Following the work of [PERSON] and [PERSON] [32] and [PERSON] and [PERSON] [33], participants were asked to indicate their level of agreement to each item on a 7-point Likert scale (ranging from 1' strongly disagree' to 7' strongly agree'). Participants' assessment of the visual aesthetics of the online shop was surveyed, since different perceptions of visual aesthetics might be associated with different gaze patterns [34]. The same procedure was applied the second online shop. Eye tracking data was recorded only while the participants were looking at the online shop screenshots.
# Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader
## 3 Numerical Simulations and Experiments
### Simulation Model and Boundary Conditions
#### 3.1.2 Simulation Boundary Condition Setting  
After dividing the grid, the next step was to set the specific conditions for the simulation. For this purpose, the following assumptions were made.  
(1) Heat exchange between the air outside the battery case and the battery shell was not considered (i.e., the battery shell is an adiabatic boundary);  
(2) During the heating process of the battery, thermal parameter changes of the battery due to heating are not considered, and the battery will not be deformed by the heat;  
(3) The cooling air is an incompressible fluid.  
After the assumptions of the simulation model were made, the boundary conditions, solver, and turbulence model of the simulation were set.  
Figure 3: (a) Histogram of grid determinant. (b) Histogram of grid aspect ratio.  
Figure 2: Schematic diagram of the battery pack grid.  
The boundary conditions mainly include the cell pack material parameters, air parameters, inlet boundary conditions, outlet boundary conditions, cell pack shell boundary conditions, air and cell wall boundary conditions, and the initial temperature.  
The battery pack shell and separator are made of steel, and the thermal physical parameters of air were required. The air parameters and the thermal physical parameters of the battery are given in Table 2.  
The entrance boundary condition was set as a velocity entrance boundary condition, and the exit boundary condition was set as a free exit boundary condition; the air and battery wall surface were set as a coupled wall surface; the initial temperature was set as 35 \({}^{\circ}\)C, as the battery-type downhole loader operates in a well where the ambient temperature range is 25-45 \({}^{\circ}\)C. The boundary conditions for the battery pack shell wall were set as follows:  
(1) The wall of the housing was set as a slip-free wall (i.e., u = v = w = 0);  
(2) To reduce the interference of structural changes of the wall of the housing on the subsequent heat dissipation optimization, the heat dissipation effect at the wall of the housing on the interior of the battery pack was not considered, and the wall was set as an adiabatic environment (i.e., the heat flow on the wall was zero);  
(3) The normal gradient of the wall surface is zero.  
2. Solvers  
The considered solver is considered a semi-implicit solver based on a system of coupled pressure equations (i.e., the SIMPLE algorithm).
## 3 Results and Discussion
### The Relationship between Compressive Strength and Entrapped Air Content  
At the same water-to-binder ratio, the relationship between compressive strength and air content of UHPC specimens, containing different dosages of steel fiber and superplasticizer, are plotted in Figure 12. It can be found that the compressive strength of UHPC increased linearly with the decrease of entrapped air content, regardless of the variable fiber content, ranging from 0% to 3%. The regression equations are given in Figure 12a-d, where Fc is the compressive strength (MPa), and Ac is the air content (%).  
For normal-strength concrete, every 1% decrease in air content increases compressive strength of the concrete by 4%-6% [27], which is much lower than that found in UHPC specimens in this study. It was also mentioned that the air-void characteristics, such as size distribution, spacing (dispersion), and shape of air voids, have substantial effects on the mechanical properties of concrete [28]. Unfortunately, there is little information and experimental results available in the literature about this topic. Therefore, the variation in entrapped air structure may be another aspect for influencing the compressive strength of UHPC containing different dosages of steel fiber. Furthermore, the fact that the entrapped air content has a much higher influence on the compressive strength of UHPC than observed for normal concrete should be further investigated.  
Figure 12: The relationship between the compressive strength and entrapped air content. (**a**) \(w/b=0.18\); (**b**) \(w/b=0.20\); (**c**) \(w/b=0.22\); (**d**) \(w/b=0.24\).
# Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites
## 1 Introduction
### Related Work  
Virtual reality (VR) applications offer a powerful tool for the preservation and exploration of cultural heritage, allowing users to experience historically significant sites in immersive, interactive ways [5; 6]. By recreating ancient monuments, museums, or inaccessible cultural sites in virtual environments, VR allows users to engage with these spaces as if they were physically present, regardless of geographical limitations or site fragility [7; 8]. Digital heritage utilizes augmented reality (AR) to transform cultural preservation by making digital content accessible to a broader audience [9]. AR applications enhance traditional conservation methods by overlaying historical data, imagery, and narratives onto real-world environments, thus creating a hybrid space where users can explore cultural heritage interactively [10; 11; 12]. As extended reality (XR) continues to evolve, it offers new methods to document, visualize, and share cultural knowledge, promoting both the preservation and dissemination of digital heritage [13].  
The integration of 3D reconstruction technologies with AR has significantly impacted the preservation and visualization of cultural heritage artifacts [14]. Techniques like photogrammetry, laser scanning, and lidar are widely used to capture detailed 3D models of fragile or inaccessible artifacts and buildings, offering immersive experiences to users without requiring physical handling [15]. These 3D reconstructions enable museums and cultural institutions to digitally preserve and display items with a high level of realism. Through MR, users can interact with these reconstructions, exploring historical artifacts in their original context or virtually visiting archaeological sites [16; 17].  
In the context of tourism, MR enhances cultural tourism by enabling visitors to access historical details and reconstructions through mobile devices during site visits [18]. With AR, tourists can visualize missing or damaged parts of historical landmarks, bringing the past to life in real time as they explore [19]. This technology also offers personalized, immersive storytelling experiences and educational opportunities [20], blending digital content with the physical surroundings to enrich tourists' understanding of historical locations [21]. By offering interactive and informative content, MR boosts the appeal of cultural heritage sites, transforming them into engaging destinations that combine education with exploration.
## 2 Materials and Methods
### Color Occurrence in Polysaccharides  
In order to test the earlier hypothesis [17] that uronic acid is the reason why the flexible polysaccharides xylan and pectin change in color while others do not, carboxymethyl cellulose (CMC) powder was used as a test material. CMC is a cellulose derivate with carboxymethyl groups (-CH\({}_{2}\)COOH) bound to some of the hydroxyl groups, and xylan is characterized by \(\beta\)-1,4-linked D-xylopyranosyl units that carries a variable number of neutral or uronic monosaccharide substituents or short oligosaccharidesacharide as side chains. The CMC was pelletized in the same equipment as described in Section 2.1.1 and pretreated as described in Section 2.1. Two pellets were produced at each test series, resulting in a total of 24 pellets produced, see Table 2.  
Figure 3: Test equipment; (**a**) a cross section of the steel cube and (**b**) a picture of the die disassembled with a pellet ready to be picked out for further measurements.  
The nature of the emergent colors of these samples were investigated by dissolving the pellets in alkali and measuring the absorbance of the resulting solutions using a UV-2550 UV-VIS spectrophotometer (Shimadzu, Kyoto, Japan). Specifically, tests were carried out on one xylan and one CMC pellet, each of which were produced with 5%, 10% or 15% moisture content, with a pressure of 5 or 14 kN. Additionally, control tests were performed on each of the respective untreated original powders, as well as the original powder that was heat-treated at 225 \({}^{\circ}\)C for 30 min in a sealed container at 5% and 15% moisture content. For each experiment, 10 mg of sample was dissolved in 4 mL of 1M NaOH. In the case of the pellets, this required that the samples were mortared. Furthermore, in the case of both pellets and powders, this also required thermomixing at 60 \({}^{\circ}\)C for a few minutes for each sample, with the exception of the 5 kN 15% moisture content xylan sample, which was particularly difficult to dissolve and which was instead thermomixed at 40 \({}^{\circ}\)C over 60 min. Then, each sample was analyzed between 230-700 nm in a Shimadzu UV-2550 UV-VIS spectrophotometer with quartz cuvettes from Hellma GmbH & Co. KG (Mullheim, Germany) being used for the analysis. A baseline was taken on a pure 1M alkali reference. The slit width was 2 nm. Cuvettes with a 10 mm path length were used for most samples, while 1 mm cuvettes were used for the heat-treated powders.  
Because the molecular weights of the samples were unknown, absorption coefficients could not be calculated. Instead, all results have been normalized to the equivalence of a 10 mg sample and a 10 mm path length. Thus, for instance, the samples analyzed in 1 mm cuvettes had their absorbances multiplied by 10.
# Global Time-Varying Path Planning Method Based on Tunable Bezier Curves
## 3 Transient Path under the Single-Obstacle Environment
### Center Point of Obstacle  
Extract unchanged initial data in Step 1 and the initial data that need to be updated in real time are extracted in Step 2. In Step 3, the center point \(O_{0}\) is obtained through the two endpoints of the trajectory and the obstacle, which lays the groundwork for the subsequent amplification. The start point \(x_{\text{start}}\) and the end point \(x_{\text{end}}\) are connected to generate a straight line. If the line does not intersect the obstacle, there is no need to consider obstacle avoidance and the moving object can move along the line from \(x_{\text{start}}\) to \(x_{\text{end}}\). If part of the line segment is inside the obstacle, the two ends of the line segment are represented by \(x_{\text{left}}\) and \(x_{\text{right}}\), respectively and the midpoint of the line segment is the center point, which is marked \(O_{0}\). The pseudo-code corresponding to the Mid function that determines the center point \(O_{0}\) is shown in Algorithm 2.  
After inputting the characteristic information of the obstacle \"Obs_Information\", \(x_{\text{start}}\) and \(x_{\text{end}}\), let \(x_{\text{left}}\) move step by step from \(x_{\text{start}}\) to \(x_{\text{end}}\), according to the specified step \(\delta\). The calculation equation is as follows.  
\[x_{\text{left}}=x_{\text{start}}+n\cdot\delta \tag{2}\]  
where \(n\) = 1,2,3,......  
Each step determines whether the \(x_{\text{left}}\) in the step is in the obstacle space. If it is not in the obstacle space, it analyzes whether \(x_{\text{left}}\) reaches or exceeds the \(x_{\text{end}}\) point. If so, there is no need to consider obstacle avoidance. If \(x_{\text{left}}\) does not reach or does not exceed \(x_{\text{end}}\), let \(x_{\text{left}}\) continue moving from \(x_{\text{start}}\) to \(x_{\text{end}}\) and cycle again.  
If \(x_{\text{left}}\) is in the obstacle space, let \(x_{\text{right}}\) move step by step from \(x_{\text{end}}\) to \(x_{\text{start}}\), according to the specified step \(\delta\) and the calculation equation is as follows.  
\[x_{\text{right}}=x_{\text{end}}-n\cdot\delta \tag{3}\]  
where \(n\) = 1, 2, 3,......  
Each step determines whether the \(x_{\text{right}}\) in the obstacle space. If not \(x_{\text{right}}\) continues to move from \(x_{\text{end}}\) to \(x_{\text{start}}\) and cycle again. If \(x_{\text{right}}\) is in the obstacle space, the center point \(O_{0}\), is calculated as follows.  
\[O_{0}=\Big{(}x_{\text{right}}+x_{\text{left}}\Big{)}\Big{/}2 \tag{4}\]
## 3 Evaluation Approach  
### System Model  
As in [16], our contribution consists of a handover decision phase; decision makers must choose the best network from available networks. The goal of this contribution is to analyze the weights given to the metrics, so that, as reported in the Algorithm block diagram shown in Figure 2, simulation provides the system with the metrics in realtime and a pairwise comparison is applied according to each QoS class: Conversational, Streaming, Interactive and Background. Then a weighting method (AHP in this work) is used to calculate the weight vectors in order to obtain the relative importance of each metric or attribute. Consequently, SAW, TOPSIS and MEW are applied to the weighted matrices to obtain the final decision for each method. The handover decisions are made in real-time and repeatedly, in our case the process is repeated every 5 s.  
In [17], authors explain that a determined set of weights produces certain quality or merit degree for each network; these merit values change if we consider another set of weights. The goal is to obtain the best merit value, which will correspond to the selected network for the VH decision phase. Accordingly, the more combinations of weights, the more possibilities to get b  
Figure 2: Algorithm Block.  
have. In this work, and to compare SAW, TOPSIS and MEW algorithms, we have been concentrated on the weighting phase with AHP, we have reconfigured the weights done previously in the literature, considering Throughput, Delay, Jitter and Bit Error Rate ( Ber) of the participating access networks to make the handover decisions. The vertical handoff decision problem can be formulated as a matrix form \(Q\), where each row \(i\) corresponds to the candidate network and each column \(j\) corresponds to an attribute. The matrix of alternative networks is settled conforming to the attributes.  
\[Q_{N,M}=\begin{array}{cccc}&Attribute_{1}&Attribute_{2}&\ldots&Attribute_{M}\\ &Network_{1}\\ &Network_{2}\\ &\vdots\\ &Network_{N}\\ \end{array}\left(\begin{array}{cccc}&q_{11}&q_{12}&\ldots&q_{1M}\\ &q_{21}&q_{22}&\ldots&q_{2M}\\ &\vdots&\vdots&\ddots&\vdots\\ &q_{N1}&q_{N2}&\ldots&q_{NM}\\ \end{array}\right)\]  
As all three methods grant the evaluated criteria to be expressed in different measurement units, it is necessary to convert them into normalized values.  
\[n_{ij}=\frac{q_{ij}}{\sqrt{\sum_{i=1}^{i=N}q_{ij}^{2}}},j=1,...,M \tag{1}\]  
In addition, weight factor is assigned conveniently to each criterion to report its importance which is determined by (AHP).
## 1 Introduction
In this previous research, characteristics of the leaky radiation beam radiated from the plate strip were acoustically analyzed on two median and lateral planes; however, this research assumed that the velocity distribution of leaky Lamb waves on the aperture was uniform in the width direction. In other words, the effect of the plate width on the leaky Lamb wave radiation was partially studied without full consideration of the propagation characteristics of the leaky Lamb waves in the plate strip. This paper investigates leaky Lamb wave radiation from a waveguide plate with a finite width to gain a basic understanding of the radiation characteristics of the plate-type waveguide sensor. First, the propagation characteristics of the Lamb wave in the plate strip was evaluated using the semi-analytical finite element (SAFE) method. Then, leaky Lamb wave radiation was computationally modeled on the basis of the analyzed propagation characteristics, and was also measured experimentally for comparison. From this analysis and measurement, the formation and characteristics of the leaky radiation beam radiated from a waveguide plate are three-dimensionally revealed and the design direction for performance improvement of the waveguide sensor is briefly proposed.
## 4 Ground Track Repeat Period of GPS, GLONASS, and BDS Satellites  
In order to accurately analyze the influence of floods on the GNSS signal, the quantity used to compare and analyze the non-flood period should be determined first. Considering that the multipath is related to the surrounding environment of the observation station, the elevation and azimuth of the non-flood observation should be the same as that of the flood observation. This requires the same ground track repeat period as the satellite.