esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed WPT System with the MMs
### Theoretical Analysis of the Four-Coil WPT System
According to the two-coil system optimal load analysis, the optimal reflected resistance, _R1 eqt_, from the load loop to the _Rx_ coil in the four-coil WPT system can be derived as:  
\[R_{Leqt}=\frac{\left(\omega M_{34}\right)^{2}}{R_{4}+R_{L}}=\sqrt{1+k_{23}{}^{ 2}Q_{2}Q_{3}}\cdot R_{3} \tag{5}\]  
From the above formula, to adjust the coupling coefficient between the load loop and the _Rx_ coil, namely, to change the distance between the load loop and the _Rx_ coil, an optimal load and improvement of the transfer efficiency of the system can be achieved. In the four-coil WPT system, the system transfer efficiency can be denoted by the power distribution among the equivalent reflected resistances in each coil. The total efficiency is equal to the product of the equivalent resistance ratios. Thus, when four coils are tuning to the resonance frequency, the efficiency is calculated as:  
\[\eta=\frac{\frac{\left(wM_{34}\right)^{2}}{R_{L}}}{\frac{\left(wM_{34}\right) ^{2}}{R_{L}}+R_{3}}\frac{\frac{\left(wM_{23}\right)^{2}}{\frac{\left(wM_{34} \right)^{2}}{R_{L}}+R_{3}}}{\frac{\left(wM_{34}\right)^{2}}{\frac{\left(wM_{3 4}\right)^{2}}{R_{L}}+R_{3}}}+R_{2}}\frac{\frac{\frac{\left(wM_{34}\right)^{2 }}{\left(wM_{34}\right)^{2}}+R_{2}}{\frac{\left(wM_{34}\right)^{2}}{\frac{ \left(wM_{34}\right)^{2}}{R_{L}}+R_{3}}}}{\frac{\left(wM_{34}\right)^{2}}{ \frac{\left(wM_{34}\right)^{2}}{R_{L}}+R_{3}}+R_{3}} \tag{6}\]  
The efficiency can be expressed in another form [24]:  
\[\eta=\frac{I_{4}^{2}R_{L}}{V_{S}I_{1}}=\frac{\left(k_{12}^{2}Q_{1}Q_{2}\right) \left(k_{23}^{2}Q_{2}Q_{3}\right)\left(k_{34}^{2}Q_{3}Q_{4}\right)}{\left[ \left(1+k_{12}^{2}Q_{1}Q_{2}\right)\left(1+k_{34}^{2}Q_{3}Q_{4}\right)+k_{23}^ {2}Q_{2}Q_{3}\right]\left[\left(1+k_{23}^{2}Q_{2}Q_{3}+k_{34}^{2}Q_{3}Q_{4} \right)\right]} \tag{7}\]  
Figure 5 shows the system efficiency, \(\eta\), as a function of \(k_{12}\) and \(k_{34}\). It can be seen that \(k_{34}\) has a greater effect on the efficiency than \(k_{12}\). Generally, when the transfer distance between the _Tx_ coil and _Rx_ coil is fixed, the maximum efficiency can be achieved by adjusting the distance between the _Rx_ coil and the load loop. In a practical experiment, the distance between the _Tx_ coil and the _Rx_ coil is fixed at some value (\(k_{23}\) is fixed value), and \(k_{12}\) and \(k_{34}\) are varied by adjusting the distance between relevant coils to change the \(Z_{in}\) and system efficiency in the meantime. From the optimal load analysis of the four-coil WPT system, the \(k_{34}\) is an important factor to the optimal load. In addition, the \(k_{34}\) has a greater influence than the \(k_{12}\) on the \(Z_{in}\) and system efficiency. In general, we fixed the distance between the _Tx_ coil and the _Rx_ coil and varied the distance between the load loop and the _Rx_ coil to achieve the optimal load, and then changed the distance between the drive loop and the _Tx_ coil to attain the needed \(Z_{in}\) of the Class-E amplifier. Figure 4: Equivalent simplified circuit diagram of the four-coil WPT system.
## 4 Results and Discussion  
Model 2 had to be verified to confirm the simulation results. For this purpose, model 2 was manufactured as shown in Figure 8 and was measured using an RF Vector Network Analyzer (E5062A, ENA-L, Agilent) in an anechoic chamber. Figure 9a highlights that the impedance bandwidth of the fabricated antenna was in good agreement with the simulation model. The simulated results produced an impedance bandwidth of 862.5 MHz from 1.85 to 2.71 GHz, whereas the measured result recorded an impedance bandwidth of 1.04 GHz from 1.77 to 2.81 GHz.  
Figure 8: Fabricated antenna: (**a**) the antenna ground on the front side and (**b**) the feedline on the back side.  
Figure 9: Simulated and measured (**a**) S\({}_{11}\), (**b**) axial ratio, and (**c**) antenna gain.  
The CP performance of the manufactured antenna conformed to the simulated model as illustrated in Figure 9b. The fabricated antenna produced a 3 dB ARBW of 787.5 MHz from 2.1625-2.95 GHz, which was narrower than the simulated 1.13 GHz range of 2.13-3.26 GHz. The measured ARBW was located entirely in the measured impedance bandwidth coverage. Figure 9c shows the antenna gain. The measured average gain generated a peak gain of 5 dBic at the lower frequency of 2.2 GHz and a lower gain of 3 dBic at the higher frequency of 2.8 GHz.  
The radiation patterns of the fabricated antenna were also measured in an anechoic chamber to confirm the simulation result. Figure 10 shows the measured and simulated far-field radiation patterns of the CSA when it is radiated by left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP) waves sequentially. The antenna produced bidirectional radiation with both LHCP and RHCP operations radiated toward the +\(z\) direction (\(\theta=0^{\circ}\)) and \(-z\) direction (\(\theta=180^{\circ}\)), respectively. The measured E-plane far-field radiation patterns for 1.8 GHz, 2 GHz, 2.2 GHz, 2.4 GHz, and 2.6 GHz in Figure 10a-e, respectively, showed good agreement with the simulated results at the corresponding frequencies.  
The performance and dimensions of the measured antenna are compared with those of some related wideband CP printed-slot antennas reported in the literature in Table 3. The proposed CSA with two asymmetrical rectangular slots and a parasitic patch had a higher peak gain compared to those reported in previous studies [6; 7; 8] and a broader 3 dB ARBW compared to those reported by [PERSON] et al. [8] and [PERSON] et al. [10].  
Figure 10: Measured and simulated E-plane radiation patterns of the antenna at (**a**) 1.8 GHz, (**b**) 2 GHz, (**c**) 2.2 GHz, (**d**) 2.4 GHz, and (**e**) 2.6 GHz.
###### Abstract  
The ISO 14687-2 standard sets requirements for the purity of the hydrogen that is delivered at refuelling stations. These specifications cover a wide range of impurities and include challenging measurements, mainly due to the very low levels of the required detection limits and the need for \"total\" measurements (total hydrocarbons, total sulphur compounds, halogenated compounds). Most of the compounds belonging to the species are organic. Thermal desorption often coupled with gas chromatography is a common speciation method used to determine the content of organic impurities. However, no existing sorbent tubes are sufficiently universal to trap all possible impurities; depending on the sorbents and the sampling volume, some compounds may irreversibly adsorb or may break through. It is therefore necessary to evaluate sorbents for the compounds targeted at the level required. In this study, the suitability of sorbent tubes for trapping organic impurities in hydrogen was investigated. Suitable sorbents were selected based on a literature review of suitable sorbent materials. Short-term stability studies for compounds among hydrocarbons, halogenated compounds and sulphur compounds on the selected sorbents have then been performed for storage periods of two weeks since this is the period typically required to complete the collection, transport and analysis of hydrogen samples. The study clearly shows that the method is promising for total species, even through the results show that not all of the compounds belonging to the three total species to be analysed when performing hydrogen purity analysis can be quantified on one unique sorbent. A multibed sorbent consisting of Tenax TA (weak), Carboxen 1003 (medium), Carbograph 1 (strong) is shown to be a versatile sorbent suitable for the three \"total species\"; only a few compounds from each family would need to be analysed using other analytical methods. This method proposed here for total species will not only provide a sum of concentrations, but also an identification of which compound(s) is/are actually present in the hydrogen.  
hydrogen; fuel cells; hydrogen vehicle; sorbent; thermal desorption; hydrogen quality Article
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 3 Experiments
### Quantitative Evaluation Metrics
#### 3.2.2 Structural Similarity Index Measure (SSIM)  
SSIM is a commonly used perceptual metric to quantify image quality by comparing the structure of generated and real images. The formula for SSIM is defined as follows:  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_{ y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{15}\]  
where \(\mu_{x}\) and \(\mu_{y}\) represent the average of all pixel values in the real HR image \(x\) and the generated HR image \(y\). Among them, \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) represent the variance of \(x\) and \(y\), and \(\sigma_{xy}\) represents the covariance between \(x\) and \(y\). \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are two constants, the default value is \(k_{1}=0.01\), \(k_{2}=0.03\), and \(L\) is the range of pixel values.  
The value of SSIM is between \(-1\) and \(1\). When the value of SSIM is closer to \(1\), it means that the two images are about the same and the quality of the generated image is better.
## 3 Results
### Performance Metrics
#### 3.6.3 Root Mean Squared Error (RMSE)  
The Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread-out these residuals are [48]. In other words, it says that the concentrated data is around the line of best fit. Root mean square error is commonly used in many sectors for experimental results verifications [44]. A framework capable of solar power forecasting in a smart grid is proposed [49]. It is achieved through Autoregression (univariate) framework and the optimization is achieved through RMSE. They are yet to explore multivariate that will help support better smart grit management by incorporating information from smart meters and other electronic devices. In this proposed work, the incorporation of multivariate data analysis was done for better result accuracy.  
If \(\hat{y}_{i}\) is the predicted value of the \(i\)th sample and \(y_{i}\) is the corresponding true value for the total \(n\) sample, then the root mean squared error (RMSE) estimated over \(n\) samples is defined as follows.  
\[RMSE(y,\hat{y})=\sqrt{\frac{1}{n}\sum_{i=0}^{n-1}(y_{i}-\hat{y}_{i})^{2}} \tag{3}\]
# Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
###### Abstract  
This paper is proposing and analyzing an electric energy storage system fully integrated with a photovoltaic PV module, composed by a set of lithium-iron-phosphate (LiFePO\({}_{4}\)) flat batteries, which constitutes a generation-storage PV unit. The batteries were surface-mounted on the back side of the PV module, distant from the PV backsheet, without exceeding the PV frame size. An additional low-emissivity sheet was introduced to shield the batteries from the backsheet thermal irradiance. The challenge addressed in this paper is to evaluate the PV cell temperature increase, due to the reduced thermal exchanges on the back of the module, and to estimate the temperature of the batteries, verifying their thermal constraints. Two one-dimensional (1D) thermal models, numerically implemented by using the thermal library of Simulink-Matlab accounting for all the heat exchanges, are here proposed: one related to the original PV module, the other related to the portion of the area of the PV module in correspondence of the proposed energy-storage system. Convective and radiative coefficients were then calculated in relation to different configurations and ambient conditions. The model validation has been carried out considering the PV module to be at the nominal operating cell temperature (NOCT), and by specific experimental measurements with a thermographic camera. Finally, appropriate models were used to evaluate the increasing cell batteries temperature in different environmental conditions.  
Keywords: photovoltaic; battery; integrated storage; PV cell temperature; thermal model; thermal image +
Footnote †: journal: Applied Sciences
## 3 Results
### Optimization of Protein Extraction Buffer  
In this study, we evaluated the efficiency of exosome isolation from urine under various elution conditions (elutions 1-5) using the PEG method and RIPA buffer as controls. To assess the characteristics of exosomes isolated under each condition, we examined the levels of key exosomal marker proteins through Western blot analysis (Figure 7).  
Figure 6: Optimization of exosome isolation conditions for biological samples. Western blot analysis of exosomal markers from (**A**) urine, (**B**) plasma, and (**C**) serum samples isolated using PEG precipitation (PEG) and EGCG-modified magnetic beads under varying conditions as indicated.  
The Western blot results showed that THP exhibited significantly higher levels with all the bead-based extraction conditions (elutions 1-5) compared to the PEG precipitation method and RIPA buffer control. This indicates that bead-based extraction methods can effectively concentrate THP from urine samples. For TSG101, it was barely detectable in the PEG precipitation method and RIPA buffer samples, but showed the highest levels in the elution 2 and 3 samples for the bead-based extraction method. This suggests that the bead-based approach, particularly the elution 2 and 3 conditions, is effective for isolating TSG101-positive exosomes. CD81 showed a clear presence in the PEG precipitation method samples, RIPA buffer samples, and Sample 1, but was barely detectable in the elution 2 samples. In contrast, strong expression was observed in the elution 3, 4, and 5 samples. CD63 exhibited low levels under most conditions, but a uniquely strong band was observed in the elution 2 samples for the bead-based extraction method. CD9 showed high levels in the PEG precipitation method samples that were used as a control, while among the bead-based extraction method samples, it showed moderate levels in the elution 1 samples and minimal levels in the elution 2 samples. However, high levels were observed in the elution 3, 4, and 5 samples.  
Collectively, these results demonstrate that bead-based extraction methods and various elution conditions can affect the selective isolation of exosomes from urine. In particular, TSG101 and CD63 were effectively isolated using the elution 2 conditions, while CD81 and CD9 were effectively isolated using the elution 3, 4, and 5 conditions. This suggests that the optimal elution conditions can be selected for specific target exosomal markers.  
A Western blot analysis of exosomal markers (THP, TSG101, CD81, CD63, and CD9) isolated from urine samples using PEG precipitation (control), RIPA buffer, and five different elution conditions with varying RIPA/lysis buffer ratios (elution 1--RIPA only; elution 2--lysis buffer only; elution 3--1:1; elution 4--2:1; and elution 5--3:1) was performed. The different elution conditions showed distinct enrichment patterns of exosomal markers suggesting that the buffer composition affects the isolation of specific exosome subpopulations.
# Resource Indexing and Querying in Large Connected Environments
## 4 Preliminaries & Assumptions
### Sensing Devices and Observations  
Concepts related to sensing devices and observations are presented here (cf. Definitions 4 and 5 respectively):  
**Definition 4** (Sensing Device).: _A sensing device \(d\) is defined as a 4-tuple:_  
\[d:\langle id,l,c,S\rangle\quad\text{where}: \tag{4}\]  
* \(id\)_: is the device identifier in the network (e.g., URI)_
* \(l\)_: is the location stamp of the device_
* \(c\)_: is the device's indexing capacity (i.e., the number of index entries_ \(d\) _is capable of storing locally_
* \(S=\bigcup_{i=0}^{n}s\)_: is the set of sensors_ \(s\) _embedded on_ \(d\)_. Each sensor is defined as_ \(s:\langle o,cz\rangle\) _where_
* _: is an observation (i.e., sensed data) (cf. Definition_ 5_)_
* _cz: is its coverage zone._  
In this study, we simplify the set S to one singleton, assuming that a sensing device has only one embedded sensor that provides observations. We also consider that all sensing devices are static, i.e., immobile, and cannot change locations over time  
**Example 4**.: _The mono-sensing temperature device \(d_{1}\) in our motivating scenario can be defined as follows:_  
\(d_{1}:\langle\mathit{small}\,\mathit{fieldsensor}_{1},l_{1},5,\{s_{1}\}\rangle\) _where_ \(:s_{1}:\langle\mathit{temperature},z_{1}\rangle\)_._  
**Definition 5** (Observation).: _We formally define an observation \(o\) as a 5-tuple:_  
\[o:\langle a,v,l,t,es\rangle\quad\text{where:} \tag{5}\]  
* \(a\) _is the data attribute_
* \(v\) _is the data value_
* \(l\) _is the creation location stamp of_ \(o\) _(cf. Definition_ 2_)_
* \(t\) _is the creation temporal stamp of_ \(o\) _(cf. Definition_ 3_)_
* \(es\) _is the embedded sensor that produced/created_ \(o\)_._  
**Example 5**.: _A temperature observation \(o_{1}\), such as the ones sensed in the forest (cf. Section 2), can be defined as follows:_  
\(o_{1}:\langle\mathit{temperature},18\ ^{\circ}\mathrm{C},(2,4,8),10-11-2020\ 15:34:23,s_{1}\rangle\)_._
## 3 Results and Discussion
### Analysis of Probability Distribution Functions  
The parameters of the six distribution functions, estimated using the seasonal and overall wind speed data, are presented in Table 11. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Season** & **N** & **R** & \(\overline{v}_{obs}\) & \(\sigma^{2}\)\({}_{obs}\) & \(\sigma_{obs}\) & **CoV** & **S** & **K** & **Min** & **Max** \\ \hline Summer & 6480 & 10.2 & 2.641 & 3.22 & 1.79 & 0.68 & 0.68 & 0.14 & 0 & 10.2 \\ Autumn & 6624 & 11.5 & 2.270 & 2.64 & 1.62 & 0.72 & 1.15 & 1.77 & 0 & 11.5 \\ Winter & 6624 & 13.8 & 2.738 & 4.14 & 2.03 & 0.74 & 1.48 & 2.47 & 0 & 13.8 \\ Spring & 6552 & 10.9 & 2.768 & 3.54 & 1.88 & 0.68 & 0.86 & 0.74 & 0 & 10.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The seasonal statistical wind data for University of Fort Hare at 10 m AGL. Figure 5: Diurnal variation in mean wind speed at the University of Fort Hare. The corresponding seasonal and overall wind speed distribution graphs illustrate how well the fitted probability density functions represent the observed data, as shown in Figures 6 and 7. As indicated in Figure 6, the GEV distribution provided the best fit for the entire period, aligning closely with the histogram's peak between 1 and 2 m/s and the tails, effectively modeling infrequent higher wind speeds beyond 8 m/s. GUM, ranked second, followed closely, while RAY, ranked last, failed to capture the central tendency and distribution spread. Seasonally, as shown in Figure 7, the GEV distribution consistently performed the best across all seasons, excelling in capturing peaks and tails. GUM ranked second in all seasons. In summer, IGA ranked last. RAY struggles in autumn and winter, ranking last in both seasons, while GAM consistently ranked fifth in all seasons except for spring, where it ranked fourth. GAM failed to effectively represent the variability and extreme wind patterns. These results confirm the GEV distribution's robustness in modeling wind speed patterns across different periods. However, it is important to note that the GEV distribution, having three parameters compared to the other distributions with one or two parameters, involves more complex parameter estimation methods. This complexity often leads to a preference for distributions that offer simpler estimation methods, particularly the widely used Weibull distribution [28].
# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
## 5 Experiments
### Comparison to the State of the Art on LaSSoV-Video
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **DR** & **FAR** (\(\downarrow\)) & **Precision** & **F1** & **Time/ms** \\ \hline YOLOv5s(0.2) & 0.9368 & 0.1349 & 0.4064 & 0.5580 & 134.5 \\ YOLOv5s(0.5) & 0.8471 & 0.0453 & 0.6379 & 0.7206 & 134.5 \\ YOLOv5 tiny(0.2) & 0.9087 & 0.1436 & 0.3967 & 0.5370 & 48.9 \\ YOLOv5 tiny(0.5) & 0.7168 & 0.0287 & 0.7000 & 0.6949 & 48.9 \\ Ours((ST)2 Net-prefix) & 0.7381 & 0.0297 & 0.7045 & 0.7200 & 68.2 + 61.5 \\ Ours((ST)2 Net-suffix) & 0.7713 & 0.0192 & 0.7936 & 0.7801 & 72.2 + 61.5 \\ R-VLBC [7] & 0.8453 & 0.1893 & 0.3171 & 0.4623 & 160.56 \\ \hline R-VLBC [7] & 0.9253 & 0.0893 & – & – & \\ LBP [3] & 0.8674 & 0.3103 & – & – & – \\ NR-RLBP [3] & 0.9125 & 0.1610 & – & – & 140.34 \\ RF-TOP [48] & 0.8757 & 0.1236 & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between our method and state-of-the-art methods with varied metrics. The first four methods are evaluated on our LaSSoV-video, and the others are reported in existing papers on the private SEU-SmokeVeh (Dataset 5) [7], which is similar to ours in data scale. The values in parentheses denotes the detection thresholds of YOLOv5. The time is reported on CPU for one frame. Figure 9: The F1 scores on four scenes. To clarify, we illustrate a sample frame for each scene at the top of the figure. either classified as the non-smoke category in our (ST)2 Net-suffix model or filtered out by our vehicle matching module (the top-middle one). In Table 3, we also report the computational cost on the CPU (Intel Xeon E5-2678 v3) for processing one frame. Specifically, the cost of our method depends on the YOLOv5 tiny, the (ST)2 Net, and the extra YOLOv5n for vehicle detection (61.5 ms). It is worth noting that all other methods did not provide vehicle regions yet it is necessary for practical application. In general, our method is efficient on the CPU platform and obtains a 78.01% F1 score with a speed of around 8 FPS (frames per second).
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 3 Methodology  
In this section, the proposed edge-to-cloud system for surveillance video analytics applications is presented in detail. Then, the method for moving object detection in the compressed-domain is analyzed. In addition, the performance evaluation model for video analytics platform is introduced.