esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 4 Smart EMS Control  
The charging station operates under the control of a Smart EMS. Upon an EV's arrival at the station, the EV owner is prompted to set the departure time and target state of charge (SOC). The dynamic energy management strategies will prioritize the energy storage system for electric vehicle charging during high-priced peak hours (refer to Table 1). During low-priced off-peak hours, if it is daytime, solar energy will be directly utilized for EV charging; if it is nighttime, grid electricity will be employed for either the ESS or EV charging.
## 3 The Main Sources of Bacterial Stress in the Intestinal Microbiota
### Stress by Short- and Long-Chain Fatty Acids  
Intestinal short-chain fatty acids (2-6 carbons in length) are mostly produced by microorganisms acting on carbohydrates and polyphenols, and these compounds have significant effects in terms of reducing optimal bacterial fitness. Bacillota are mainly butyrate producers, whereas Bacteroidetes excrete acetate and propionate [39; 40]. Although these effects are in part due to the reduction in pH (previously treated), they also have a pH-independent antibacterial mode of action. The long-chain fatty acids (12-20 carbons in length) present in the intestinal lumen originate from the host cells, the microbiota, and from dietary sources. The most abundant of these are unsaturated fatty acids, such as oleic and linoleic acids, and saturated fatty acids, such as stearic or palmitic acid. Free fatty acids are bound and further enzymatically released from other compounds, such as glycerol, sugars, or phosphate headgroups, to form lipids [41]. Their lipophilic nature allows them to invade and damage microbial membranes, ultimately leading to a lethal effect, particularly in Bacillota. Gram-negative bacteria such as Enterobacterales are protected in part due to their lipopolysaccharide-saccharide layer in the outer membrane. However, Enterobacterates might be able to sense extracellular long-chain fatty acids by using a 2-component system that influences gene regulation. Consequently, general metabolism, type 3 secretion systems, or the gene network involved in motility, fimbiae synthesis, and biofilm formation, can be modified, influencing global bacterial fitness [42]. Such effects on gene expression are in part linked to the fact that fatty acids might mimic diffusible signal factors [43].
## 4 Experiment Results and Discussion
### Dataset and Metrics  
ED\({}^{2}\)IF\({}^{2}\)-Net was trained and tested on a subset of ShapeNet [57], which comprises 13 classes and approximately 44,000 3D models. These models underwent pre-processing using the method proposed by DISN [24] to generate point coordinate-SDF pairs, as well as RGB images and normal maps from 36 random views at a resolution of \(224\times 224\). For the experiments, we adhered to the official training/validation/testing split.  
For the overall quality of the reconstruction, intersection of union (IoU), Chamfer distance (CD) and earth mover distance [58] (EMD) are computed. Moreover, the edge Chamfer distance [59] of the reconstructed shape (ECD-3D) and the edge Chamfer distance in the image [29] (ECD-2D) are used to measure the recovered detail information. The specific definitions of all the above evaluation metrics are as follows:  
IoU is used to measure the similarity between the reconstructed object and the ground truth, defined as  
\[IoU(PC_{P},PC_{Q})=\frac{intersection(\Gamma(PC_{P}),\Gamma(PC_{Q}))}{union( \Gamma(PC_{P}),\Gamma(PC_{Q}))}, \tag{20}\]  
where \(PC_{P}\) and \(PC_{Q}\) denote two point clouds, and \(\Gamma\) denotes the operation that converts a point cloud into a voxel grid.  
CD is a commonly used metric for measuring the distance between two point clouds, denoted as \(PC_{P}\), \(PC_{Q}\), defined as  
\[CD(PC_{P},PC_{Q})=\sum_{p_{1}\in PC_{P}}\min_{p_{2}\in PC_{Q}}||p_{1}-p_{2}||_{ 2}^{2}+\sum_{p_{2}\in PC_{Q}}\min_{p_{1}\in PC_{P}}||p_{1}-p_{2}||_{2}^{2}. \tag{21}\]  
EMD is a metric frequently used to measure the distance between two point clouds, denoted as \(PC_{P}\), \(PC_{Q}\), by considering the distribution problem. It can be defined as  
\[EMD(PC_{P},PC_{Q})=\min_{\phi:PC_{P}\to PC_{Q}}\sum_{p\in PC_{P}}||p-\phi(p)||_ {2}, \tag{22}\]  
where \(\phi:PC_{P}\to PC_{Q}\) represents a bijection between the two point clouds.  
ECD-3D is a metric calculated as the Chamfer distance (CD) between the edge points on the ground-truth object and the reconstructed object. The \"edgeness\" property of each sampled point from a 3D object is defined as  
\[\psi(p_{j})=\min_{p_{k}\in\Omega_{j}}|n_{j}\cdot n_{k}|, \tag{23}\]  
where \(\Omega_{j}\) represents the set of neighboring points of \(p_{j}\), and \(n_{j}\) and \(n_{k}\) denote the unit normal vectors at points \(p_{j}\) and \(p_{k}\), respectively.  
In our implementation, we consider a set of 10 neighbouring points (\(\Omega\)) for each point and we evaluate the edge feature recovery using points with an \"edgeness\" property (\(\psi(p_{j})\)) value below 0.8.  
ECD-2D represents the Chamfer distance (CD) between edge pixels on the rendered images. In our implementation, we utilize the Canny operator to extract the edges from the rendered normal map of the reconstructed object, which has a resolution of \(224\times 224\), in order to obtain the edge pixels.
## 4 Discussion
### Impact of Orthodontic Treatment on TMDs Development in Adolescents
Despite these recognized benefits, the relationship between orthodontic treatment and TMDs development remains controversial. Some studies suggest that orthodontic forces might elevate TMDs risk by altering occlusion or stressing the TMJ, particularly in cases where elastic bands are used or where significant occlusal adjustments are made [25; 27]. This risk may arise from changes in bite dynamics or excessive force application on the jaw, potentially increasing stress on the TMJ. Conversely, other research indicates that correcting malocclusions may reduce TMDs risk by enhancing occlusal stability and decreasing functional strain on the TMJ [70; 71]. [PERSON] et al. [71] discussed the multifactorial etiology of TMDs, noting that while occlusal instability and certain malocclusions may contribute to TMDs susceptibility, their correction alone does not consistently prevent TMDs due to the significant influence of psychological and hormonal factors. Similarly, [PERSON] et al. [70] conducted a systematic review revealing that untreated malocclusions, such as crossbites and large overjets, are mildly associated with TMDs symptoms. While occlusal correction might reduce TMJ strain in select cases, it does not provide a definitive reduction in TMDs risk due to the complex interplay of other contributing factors. By improving occlusal balance, orthodontic treatment could alleviate TMDs symptoms in patients with underlying occlusal discrepancies [61]. However, these opposing findings underscore the complex interaction between orthodontic forces and TMJ health, suggesting that factors such as individual growth patterns, appliance type, and force application influence the outcomes of orthodontic treatment on TMDs. This systematic review thus examines the interplay between adolescent orthodontic treatment and TMDs development, aiming to clarify whether orthodontic treatment serves as a risk factor or mitigator for TMDs.
## 3 Problem Statement and Results
### Final Lateral Distance Maximization  
In this first subsection, we compare the performance of functionals \(J_{D1}\) and \(J_{D2}\) for the configuration of parameters in Table 1, where the instantaneous lateral distance of the vehicle along the trajectory is not maximized, but the final (at \(t_{f}\)) lateral distance to the lateral protections is maximized. If we obtain the mobility evolution (acceleration, speed and position evolution along the trajectory) for two different values of \(t_{f}\) (10 and 2 seconds), we can notice from Figures 2 and 3 that for longer time intervals to execute the maneuver, the vehicle does not need to reach the maximum acceleration stated by the model's restrictions. On the other hand, for lower values of \(t_{f}\), the steering maneuver needs to use the maximum allowable values of the lateral acceleration to maximize the functional at the end of the trajectory. The most illustrative differences between both functionals can be read from Figures 2 and 3, where we see that \(J_{D1}\) (blue) reaches a higher lateral peak speed than \(J_{D2}\) just at the middle of the time period. The explanation for this fact is that by flipping between two opposite values of the acceleration (see Figures 2 and 3) until the final position is reached, the vehicle can get there with null lateral speed. However, for \(J_{D2}\), the speed will increase more smoothly from the first instant, but at the cost of not having null lateral speed at the end of the trajectory, which could lead to a further potential risk because of the inherent inertial dynamics.  
Figures 4 and 5 show the trajectories traced by the two functionals for different values of the final time (\(t_{f}\)). Analyzing the results in these figures, we can conclude that functional \(J_{D1}\) can reach the optimum lateral position at a later time but with a null lateral speed, whereas through functional \(J_{D2}\) it is possible to reach the optimum lateral position earlier, but at the expense of having a non-zero speed. The first one will not have problems to follow the trajectory, but for the second one, safety relies on how will the inertial mobility be from the instant \(t_{f}\) onwards.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluation parameter** & **Meaning** & **Value** \\ \hline \(N\) & Discretization factor & 20 \\ \(X_{0}\) & Initial lateral position & 1 m \\ \(V_{0}\) & Initial lateral speed & 0 m/s \\ \(a_{0}\) & Initial lateral acceleration & 0 m/s\({}^{2}\) \\ \(W\) & Road width & 20 m \\ \(v_{i}\) & Longitudinal speed & 120 km/h \\ \(c(v_{i})\) & Maximum absolute lateral acceleration & 3 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration parameters and values for all evaluations.  
Figure 3: Position, speed and acceleration \(J_{D1}\) (blue) and \(J_{D2}\) (red) (\(t_{f}=2\) s).  
Figure 2: Position, speed and acceleration \(J_{D1}\) (blue) and \(J_{D2}\) (red) (\(t_{f}=10\) s).
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 5 Experimental Results
### Expected Concurrency of Two Senders  
To further analyze the concurrency of the wake-up packets, we took the time delays between two concurrent packet transmissions. As the receiver and transmitter have different internal tasks to perform after packet transmission and to prepare to send a wake-up packet, we inserted several _nop()_ cycles to adjust the wake-up packets in time. Figure 18 plots the occurrences over delta time \(t_{d}\) in \(\,\mathrm{\SIUnitSymbolMicro s}\). We used a Gaussian fit of the form \(y=a\exp(-(x-x_{0})^{2}/(2s^{2}))\) to evaluate mean \(t_{d}=-1.4\,\mathrm{\SIUnitSymbolMicro s}\) and standard deviation \(s=-3.1\,\mathrm{\SIUnitSymbolMicro s}\). The results of Figure 18 show that this method achieves a timing that is sufficient to send concurrent wake-up packets using the beat-frequency at a data rate of 8192 kbps as required by the wake-up receiver that we used in this work.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### IPT Batteries Charging in EVs in SCs
#### 5.2.2 Wireless IPT Sustainable Design and Control  
The RA approach in IPT sustainable design and control involves the featured and topology construction of the coils and ferrites of ICT, compensations, static converters, and filters. Thus, this affects better performance [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84] through enhanced coupling and dropped stray fields [42; 85; 86] that control the OH approach. This illustrates the junction of  
Figure 5: Schematics of IPT involving ICT, (**a**) compensated ICT, (**b**) IPT components, (**c**) 3D structure of ICT including ground transmitter, EV bottom receiver coils, with their magnetic ferrites, and a steel chassis plate [42].  
the two approaches. In addition, these two approaches are intimately connected, in addition to the IPT design, to the battery, state of charge estimation, and management [87]. Such management involves load-planning (battery charging) control that enables sustainability connected to clean (carbon-free) energy use and economical pricing. See, for example, [43]. Actually, EM energy, as mentioned earlier, is thought to be rigorously clean if it originates from a clean energy conversion process [88; 89]. Figure 6 illustrates the two situations of unsustainable (a) and sustainable (b) designs of an IPT in the urban context of SCs [42].  
Figure 6 demonstrates the implication of sustainable design of an IPT in the urban context of SCs through the use of a clean energy source and eco-design, allowing for high performance and reduced adverse side effects.  
In the design and optimization of the ICT discussed in the last section (see Figure 5), the involved coupling coefficient \(\mathrm{k}\) and the resonant frequency \(\omega_{\mathrm{o}}\) (for coils SS compensation) are given by:  
\[\mathrm{k}=\mathrm{M}_{12}\;(\mathrm{L}_{1}\;\mathrm{L}_{2})^{-1/2} \tag{13}\]  
\[\omega_{\mathrm{o}}=(\mathrm{L}_{1}\;\mathrm{C}_{1})^{-1/2}=(\mathrm{L}_{2}\; \mathrm{C}_{2})^{-1/2} \tag{14}\]  
Figure 6: Causal approach to design and energy spent in EV-IPT in relation to RA and OH approaches, (**a**) unsustainable design case, (**b**) sustainable design and use of clean energy case counting all SC biodiversity [42].  
The ICT structure, outlined in Figure 5a, is presented in Figure 5c. It includes the coils, transmitter (on the ground), receiver (on the EV bottom), and the two magnetic ferrite plates covering the coils. Moreover, Figure 5c comprises a steel plate acting for the EV chassis. The coils of the ICT coupler with ferrites (pads) are parted by an air gap of (d) distance and (sh) coil axes shift. This structure will be considered in the design through Equations (6)-(9), accounting for (13) and (14) via (12) in strongly coupled EM and electric circuit domains. The BEs in living tissues relative to induced fields and temperature rise could be determined by solving (6)-(9) with a source corresponding to a stray field and (10) with a heat source \(\mathrm{P_{d}}\) relative to (11). These EM and BH domains will be coupled in a weak manner.
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
## 5 System Implementation
### System Architecture  
The architecture of the ECT system consists of two main compounds: an ECT controller (ECT-C) and an Image Reconstruction (IR) unit as shown in Figure 4. The ECT-C unit is connected to two parts outside the FPGA platform, a Wireless Base Station (WBS) and an LCD peripheral. In addition, the IR unit itself has three elements, which are a matrix processing block, memory and buffering blocks, as well as an Image RAM (Im_RAM) block. The matrix processing block realizes the implementation of the three steps of Equation (3), while the memory blocks work as a storage for the capacitance measurements in a C_Buffer, and the constant sensitivity matrix in a S_ROM block. The last Im_RAM block stores the pixels values of the reconstructed image.
###### Abstract  
The effects of different solar radiation intensities, heat flow density of indoor heat sources, outdoor wind speed, and the relative location of indoor heat sources on the natural ventilation performance of solar chimneys are investigated through three-dimensional numerical simulations. The mechanism of the mutual coupling of the solar chimney effect with the outdoor wind and indoor heat source heat plume is explored. The results of the study show that when the structural parameters of the solar chimney are the same, the heat flow density on the surface of the indoor heat source, the outdoor wind speed and the solar radiation intensity all have a gaining effect on the ventilation performance of the solar chimney and the effects of the three on the ventilation of the solar chimney promote each other, when the solar radiation intensity is 200 W/m\({}^{2}\), the outdoor wind speed is 1.0 m/s, and the indoor heat source heat flow density increases from 0 to 1 500 W/m\({}^{2}\), the solar chimney ventilation volume increases from 0.393 m\({}^{3}\)/s to 0.519 m\({}^{3}\)/s, the maximum value of the increase is 32.1%. In the other two cases, the maximum increase in solar chimney ventilation is 176.7% and 33.1%, respectively. Under the same conditions, solar chimney ventilation is optimal when the heat source is in the middle of the room. The presence of outdoor wind, however, affects the optimum design parameters of the solar chimney. Compared to the case where no outdoor wind is taken into account, the optimum inlet width of 0.2-0.3 m for the solar chimney no longer applies with outdoor wind, with the optimum value rising to 0.5 m.  
solar chimney; indoor heat source; natural ventilation; ventilation volume; solar radiation 2023
## 2 Materials and Methods
### Sliding Time Window Selection  
The sliding time window is a common method for handling time series data. The sliding time window mainly selects a fixed-size subsequence (window) on the time series data and then lets this window gradually slide over time. By inputting past data to predict one or more data points in the future, it captures the data characteristics of different time periods.  
ACF refers to the correlation between a time series and itself at different lags, and its range is usually between \(-1\) and \(1\). ACF can be used to determine whether a time series has autocorrelation, that is, whether there is a relationship with one or more previous observations within a lag period. PACF refers to the correlation between a time series and its own observations after a specific time lag period. It can be used to determine the lag order in the AR (autoregressive) model of the time series. PACF can directly measure the influence of this lag period by eliminating the influence of other lag periods. In time series analysis, ACF (the autocorrelation function) and PACF (the partial autocorrelation function) plots are used to assist in determining the parameters of the ARIMA model (autoregressive integrated moving average model), including the order p of the autoregressive term (AR) and the order q of the moving average term (MA). p and q represent how far past historical information should be considered when predicting the current value, which is what we often call the time window. If the ACF starts to decrease at a certain point and is close to 0 or becomes insignificant in subsequent lags, this point may be a suitable time window size. This may also be a window size to consider if the PACF suddenly decreases at some point and approaches 0 or becomes insignificant in subsequent lags.  
The ACF of the data in this study is shown in Figure 2 and the PACF is shown in Figure 3 as follows.  
It can be seen from the ACF and PACF graphs that in the ACF 38 is a significant value and in the PACF 5 and 38 are significant values. In order to further determine the time window value, the time window values 4-54 were experimentally compared in this study by using the SRU model training prediction to obtain the lowest time window value of the corresponding Mean Absolute Error (MAE). The final experimental results are shown in Figure 4.  
Figure 1: Weather_Relative_Humidity, Active_Power, Global_Horizontal_Radiation, Radiation_Global_Tilted, and Weather_Temperature_Celsius data information with the data spans from 1 January 2020 to 31 December 2022, with a time interval of 5 min and divided into a training set (104,752 sets of data) and a test set (26,179 sets of data) at a ratio of 8:2.  
Figure 4: Validation results for each time window value.  
Figure 3: PACF of Active_Power.  
Figure 2: ACF of Active_Power.  
The experimental results indicate that a time window value of 38 has the lowest MAE value of 0.16. Consequently, this time window value of 38 is selected in subsequent experiments in this study.
# Effect of Topography Truncation on Experimental Simulation of Flow over Complex Terrain
## 4 Turbulence Characteristics
### Velocity Spectra  
The spectra of streamwise velocity, normalized by \(nS_{u}(n)/\sigma^{2}\), where \(S_{u}\) is the streamwise velocity spectra, \(n\) is the frequency, and \(\sigma\) is the standard deviation of velocity, are shown in Figure 8. The abscissa axis is the normalized frequency, expressed as \(f=nZ/\)U(\(Z\)), where \(Z\) is the height above ground and U(\(Z\)) is the mean velocity at height \(Z\). The height of the spectrum shown in Figure 9 is 104 m, which is the hub height of most wind turbines.  
At location A1 in the windward region, the spectra of cases with transition sections are higher than those of the WTS case, which may be attributed to flow separation suppression at the leading edge of the terrain model. Thus, large-scale vortices representing the energy of the low-frequency range are preserved. As shown in Figure 5, the side mountain range at the front of location A1 in the case of OT generated flow separation and broke up large-scale eddies into small-scale eddies, leading to lower spectra in the low-frequency range, similar to the case of WTS.  
For the leeward region, spectra of four cases show little differences at location A2, but at location A3, the spectra of OT are lower than the other three in the low-frequency range. Despite the similarity in profiles of mean velocity and turbulence intensity obtained between OT and RTC, the discrepancy remains, owing to the truncation of the mountain peak ahead of location A3 in the RTC case.  
Figure 8: Normalized streamwise spectra for measurement locations at a height of 104 m: (A1) location A1; (A2) location A2; (A3) location A3; (A4) location A4; (A5) location A5.  
According to spectra of measurement locations in the valley region, one of the distinct differences is the shift of the spectra peak toward the high-frequency range and a reduction in the low-frequency range in all cases. Strong flow separation in the valley region may account for this difference, and another peak at high-frequency might support this explanation, which is associated with a vortex-shedding process that was also measured in the wake region of an escarpment by [PERSON] et al. [37]. In the case of OT, owing to the strong perturbation from complete terrain, spectra at high frequency are higher than those in the other three cases.  
As indicated in Figure 8, the inertial subrange of all cases corresponds roughly with the asymptotic relation of the \(-2/3\) slope derived by [PERSON] et al. [38] in windward and leeward regions, while the slopes of spectra for measurement locations in the valley region are mismatched with less negative values, which is more significant in the case of OT. This is similar to the research of [PERSON] and [PERSON] [26] in which the slope of spectra is less negative in the flow separation region. In addition, experiments with the truncated terrain model may underestimate spectra in the high-frequency range due to the incompleteness of topography ahead, and the slope of spectra is also dramatically influenced with a less negative value.