esa-sceva/satcom-chunk-collection huggingface dataset:
# Reporting Key Features in Cold-Adapted Bacteria
## 3 Compounds and Mechanisms Involved in Stress Resistance and Cold Adaptation
### Polyhydroxyalkanoates Metabolism
Regarding the PHA function in the cellular redox balance and the protection against oxidative stress derived from cold, some studies have reported that antioxidative enzymes (e.g., catalase, superoxide dismutase and glutathione peroxidase) are induced under cold conditions [46; 47; 48]. Some of these enzymes depend on nicotinamide dinucleotides as cofactors [49], giving an explanation regarding the PHA mechanism to cope with oxidative stress derived from cold exposure [32]. However, proteomic analysis performed in _P. haloplanktis_ showed a repression of catalase, glutathione reductase and peroxideroxin at low temperatures [15] and similar results were obtained in RNAseq experiments performed in _P. extraneustralis_, in which genes encoding the alkyl hydroperoxidase, the glutathione peroxidase, the oxyR sensor and the superoxide dismutase were repressed at low temperatures in the early exponential growth phase [16]. Nevertheless, the adjustment of other pathways related to oxidative stress protection was found in these bacteria. The upregulation of _algZ_ involved in alginate production and putrescine and spermidine accumulation genes was observed in concordance with the downregulation of iron acquisition, an important metabolic modification contributing to the alleviation of oxidative stress since iron is involved in the Fenton reaction [13; 14; 17]. In line with this, in _Psychrobacter_ sp. PAMC 21119, putrescine synthesis was also found to be upregulated while heme protein synthesis was downregulated [20]. Putrescine and spermidine have been implicated in oxidative and cold stress responses [50; 51]. In addition, screening for PHA producers was performed in different extreme cold environments. PHA producers belonging to _Pseudomonas_ and _Janthinobacterium_ genera, isolated from Antarctic soils, showed higher polymer accumulation between 5 \({}^{\circ}\)C and 20 \({}^{\circ}\)C in comparison with higher temperatures. The presence of highly unsaturated mclPHA in the isolate _Pseudomonas_ sp. UMAB-40 was associated with its ability to survive in the cold [52]. PHA synthesis capability of bacterial isolates from Antarctic freshwater belonging to Alpha, Beta and Gammaproteobacteria was regarded as a common feature at pioneer sites [53]. Culture independent studies performed in extremely cold environments, such as the Baltic Sea and Greenland Sea ice, showed the presence of genes encoding PHA synthases in the bacterial community [54; 55], suggesting the relevance of this metabolism for the whole community. All these reports constitute evidence that points out the synthesis and degradation of PHA as an important trait for thriving under cold conditions.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
## 2 Materials and Methods
### Electrical Analysis of an SC Working in a Fast-Charging System
#### 2.1.4 Efficiency of the Charging Process  
The efficiency of the energy transference between the charger and the vehicle accumulator, \(\varepsilon(\%)\), can be defined as follows:  
\[\varepsilon(\%)=\frac{\text{W}_{\text{vehicle}(\text{absorbed})}}{\text{W}_{ \text{charger}(\text{supplied})}}\cdot 100=\frac{0.5\text{-C}_{2}\cdot(\text{U}_{ \text{final}}^{2}-\text{U}_{02}^{2})}{0.5\text{-C}_{1}\cdot(\text{U}_{01}^{2} -\text{U}_{\text{final}}^{2})}\cdot 100. \tag{22}\]  
By using (11), the efficiency simplifies as follows:  
\[\varepsilon(\%)=\frac{(\text{U}_{\text{final}}+\text{U}_{02})}{(\text{U}_{01 }+\text{U}_{\text{final}})}\cdot 100=\frac{\left(1+\frac{\text{U}_{02}}{\text{U}_{ \text{final}}}\right)}{\left(1+\frac{\text{U}_{01}}{\text{U}_{\text{final}}} \right)}\cdot 100. \tag{23}\]  
As it can be deduced from (23), the efficiency only depends on the initial voltages of the charger, vehicle SCs bank, and the final voltage of both. The higher the initial voltage of the charger, U\({}_{01}\), the lower the efficiency; therefore, it is desirable that the charger presents an initial voltage that is as low as possible. Similarly, the higher the initial voltage of the vehicle SC bank, U\({}_{02}\), the higher the efficiency.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 7 Hydrological Application
### Runoff Simulation and Prediction  
Rainfall is the key input of many hydrological models, and the errors and uncertainties of rainfall data sets will propagate through the hydrological system [107; 108]. [PERSON] et al. [109] used CML-derived rainfall data as inputs of a distributed hydrological water balance model, named as WaSiM-ETH, to predict the runoff in the Ammer River Basin in Germany. As compared to RG and radar, the Nash-Sutcliffe efficiency (NSE) based on the CML data model is much higher, which effectively improved the initial state of the flow simulation and forecasting system. [PERSON] et al. [85] used a lumped hydrological model, i.e., MISDC, to simulate the runoff of Andunshui River basin in Huizhou, Guangdong Province, China, with the CML rainfall data as the inputs, and the model also generates a high NSE.
## 3 Signal-to-Noise Ratio (SNR) and Noise-Equivalent Sigma Zero (NESZ)
### NESZ Algorithm for System Design  
As we mentioned above, the NESZ is considered during early design. In ensuring all critical aspects for designing and evaluating the X-band SAR payload for a microsatellite, the NESZ equation is important. In this paper, the NESZ equation has been analyzed. The following Algorithm 2 shows the NESZ consideration for system design.  
The NESZ equation is given in Equation (1), where L\({}_{\text{s}}\) is the system loss, B\({}_{\text{r}}\) is the transmitted bandwidth, \(\tau_{\text{p}}\) is the pulse width, f\({}_{\text{p}}\) is the PRF, \(\lambda\) is the wavelength, \(\theta\) is the incidence angle, k is the Boltzmann constant, T is the system temperature, c is the speed of light, R is the target range, and P\({}_{\text{t}}\) is the transmitted power.  
\[\text{NESZ}=\frac{4\left(4\pi\right)^{3}\!\!\!\text{R}^{3}\!\!\!\text{k}\text{TB }_{\text{r}}\text{V}_{\text{s}}\text{L}_{\text{s}}\text{Sin}\theta}{\text{P}_ {\text{t}}\text{G}^{2}\lambda^{3}\tau_{\text{p}}\text{f}_{\text{p}}\text{c}} \tag{1}\]
###### Abstract  
The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Pennam-Monteith-Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.  
ET; TSEB-SM; TCH; model; Heihe River Basin 2024
## 3 Experiment
### Results
#### 3.6.3 Results from the Data Classification  
The final result for the training accuracy is calculated by applying deep learning using Bayesian optimization on our optimal model. The results are given in Table 4. For better understanding, the histogram of results is shown in Figure 5 as below. The result shows that by selecting all the features, we got the highest accuracy which is 75.5%. The approximate 95% confidence interval (written as \"testError95 CI\") of the generalization error rate are also given in the table. \"testError95 CI\" is the interval resulting from Equation (18):  
\[testError-1.96\cdot testErrorSE\leqslant testError95 CI\leqslant testError+1.9 6\cdot testErrorSE \tag{18}\]  
where \(testErrorSE\) is the standard error.  
The standard error is calculated in Equation (19):  
\[testErrorSE=\sqrt{\frac{testError\cdot(1-testError)}{NTest}} \tag{19}\]  
where \(Ntest\) is the number of elements in the array of label of testing data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Feature Type** & \begin{tabular}{l} **Feature** \\ **Amount** \\ \end{tabular} & \
\begin{tabular}{l} **Random Forest** \\ \end{tabular} &
\begin{tabular}{l} **SVM** \\ \end{tabular} \\ \hline Statistic-based features & 5 & 0.51 (+/ – 0.25) & 0.54 (+/ – 0.20) \\ Wavelet-based features & 18 & 0.46 (+/ – 0.24) & 0.49 (+/ – 0.17) \\ HOC-based features & 50 & 0.64 (+/ – 0.30) & 0.58 (+/ – 0.09) \\ Statistic-based + Wavelet-based features & 23 & 0.54 (+/ – 0.17) & 0.53 (+/ – 0.17) \\ Wavelet-based + HOC-based features & 68 & 0.57 (+/ – 0.39) & 0.59 (+/ – 0.06) \\ Statistic-based + HOC-based features & 55 & 0.61 (+/ – 0.40) & 0.59 (+/ – 0.21) \\ Statistic-based + Wavelet-based + HOC-based features & 73 & 0.62 (+/ – 0.21) & 0.61 (+/ – 0.09) \\ + HOC-based features & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of accuracy by using the random forest and SVM (kernel = ’linear’) for different amount of features selection.  
By increasing the number of features by applying CNN Bayesian Optimization, we obtained the highest accuracy. When adding the 50 HOC-based features, the prediction accuracy increased significantly. When using Wavelet-based features, the accuracy was poorer than using other features. Therefore, wavelet-based features do not affect much the results when combined with other features. However, it still gives the best results when adding all of the 73 features. The result presents that feature selection improves the classification accuracy. Nonetheless, we do not claim that it can apply to all types of data, since classification accuracy achieved with different feature reduction strategies is highly sensitive to the type of data [37].
## 2 Methods
### Evaluation Methods  
Algorithm performance was evaluated for LV segmentation and function measurements. LV segmentation accuracy was evaluated using Dice coefficient (DSC) and average surface distance in a symmetric manner (ASSD) by comparing algorithm and manual segmentation masks [24; 31]. We denote \(R_{a}\) and \(R_{m}\) the algorithm and manual segmentation, respectively. DSC measures the overlap of \(R_{a}\) and \(R_{m}\) and is calculated as: \(\frac{2|R_{a}\cap R_{m}|}{|R_{a}|+|R_{m}|}\), where \(|.|\) represents the size of a mask. ASSD evaluates the closeness between the algorithm and manual segmentation boundaries and is given as: \(\frac{1}{2}\{\frac{1}{|\partial R_{a}|}\sum_{p\in\partial R_{a}}d(p,\partial R _{m})+\frac{1}{|\partial R_{m}|}\sum_{p\in\partial R_{m}}d(p,\partial R_{a})\}\), where \(\partial R_{a}\) represents the algorithm segmentation boundary and \(d(p,\partial R_{a})\) is the shortest Euclidean distance from a vertex \(p\) (e.g., a vertex from the manual segmentation boundary \(\partial R_{m}\)) to \(\partial R_{a}\). \(\partial R_{m}\) and \(d(p,\partial R_{m})\) are defined the same way. Please note that traditional classification accuracy metrics, including true/false positives, true/false negatives and their combinations, can also be used to evaluate image segmentation accuracies [32] and DSC can be derived based on the four basic cardinalities when evaluating Boolean data. In fact, DSC, ASSD, and volume errors are widely used overlap, volume, and distance-based metrics for comprehensive evaluation of segmentation algorithms [33], and here we adopted the same or similar metrics consistent with most image segmentation studies.  
In addition, the derived algorithm segmentation masks were used to determine LVMM, LVEDV, LVESV, LVSV, and LVEF For LVMM calculation, a density of 1.05 g/mL for myocardium [34] was used.
## 3 Multi-Objective Dynamic Mission Scheduling Algorithm (MODMSA)
### Two-Stage Individual Representation  
Individual representation directly affects the crossover and mutation in evolutionary algorithms, which has a significant impact on the search efficiency and performance of evolutionary algorithms. In the MODMSA, each individual is represented in a two-stage manner, which are the VTW allocation stage and the single-orbit missionvscheduling stage. Specifically, each individual is represented by a VTW chromosome \(V\) and a single-orbit observation plan \(\Pi\).  
(1) Encoding: As shown in Figure 3, the encoding of the individual is carried out in two stages. In the first stage, each individual is encoded as a VTW chromosome \(V=\{v_{1},\cdots,v_{N}\}\), and the gene \(v_{i}\) in the VTW chromosome \(V\) indicates that VTW \(w_{i\bar{v}_{i}}\) is allocated to mission \(t_{i}\) for observation. Thus, we can obtain the VTW allocation result \(W_{M}\) based on the VTW chromosome \(V\). In the second stage, each individual is further represented as the single-orbit observation plan set \(\Pi=\left\{\Pi_{11},\cdots,\Pi_{Mm_{M}}\right\}\) based on the VTW allocation result \(W_{M}\). \(\Pi_{ij}=\left\{\Pi_{ij}^{O},\Pi_{ij}^{U}\right\}\) is the observation plan on the orbit \(o_{ij}\), which  
Figure 2: The framework of the proposed MODMSA.  
consists of a single-orbit observation sequence \(\Pi_{ij}^{O}\) and an unscheduled mission set \(\Pi_{ij}^{U}\). The single-orbit observation sequence \(\Pi_{ij}^{O}\) includes all scheduled missions and observation times on the orbit \(o_{ij}\), and the unscheduled mission set \(\Pi_{ij}^{U}\) consists of missions assigned to orbit \(o_{ij}\) but not executed.  
(2) Decoding: The decoding of each individual is also performed in two sages. Firstly, each mission is assigned a VTW based on VTW chromosomes \(V\) and generate the VTW allocation result \(W_{M}\). In the single-orbit mission-scheduling stage, if the mission is allocated to the same VTW as the previous iteration and is observed at the previous iteration, the mission will be added to the single-orbit observation sequence and is allocated the same observation time window as the previous iterations. Otherwise, the missions are added to the corresponding unscheduled mission set. Afterwards, we attempt insert the missions in the unscheduled mission set into the current observation sequence in ascending order of  
Figure 3: Illustration of two-stage individual representation.  
the mission arrival time. The earliest feasible observation time window (OTW) is selected when inserting the unscheduled mission.
# Pulsed Thermography Dataset for Training Deep Learning Models
## 2 Related Work  
### Pulsed Thermography  
Pulsed thermography is an active thermographic method that involves the use of a heat source to stimulate an object with a heat pulse. An infrared camera then records a video of the object to measure the cooling process on the object's surface, which can be described, using a 1D formulation, by the following Equation [21; 22; 23]:  
\[y(z,t)=T_{0}+\frac{Q}{e\sqrt{\pi t}}exp(-\frac{z^{2}}{4\alpha t}) \tag{1}\]  
\[e=\sqrt{kpc_{p}} \tag{2}\]  
where \(\alpha\) is the thermal diffusivity of the sample. \(k\) is the sample's thermal conductivity. \(c_{p}\) is the heat capacity of the sample. \(e\) denotes the material's thermal effusivity. It describes the materials' capability of exchanging thermal energy with their surroundings. \(z\) is the depth. At the surface of the sample where \(z\) is zero, the equation can be simplified to:  
\[y(t)=T_{0}+\frac{Q}{e\sqrt{\pi t}} \tag{3}\]  
Since the sound region has homogeneous materials, its thermal diffusivity and conductivity are similar. However, the material of the defective area has different parameters, resulting in a distinct thermal behavior. This difference forms the theoretical basis for PT as a method for non-destructive testing. PT has been applied to a range of materials, including CFRP and glass fiber reinforced materials, as well as used for the inspection of spot welds and in the protection of cultural heritage [19; 24; 25; 26].
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 7 Conclusions  
In this study, we build a novel method for benefiting from the MLF LSTM model from the 3D geometric transformation and displacement features in hand skeleton data, as well as the hand shape features in depth data from the hand component segmentation model. For the hand skeleton feature approach, we improve the handcrafted features in the motion features by adding the major axes and the skeleton shape through the displacement and rotation of the hand joints with respect to their neighbors. We propose using PointNet in the joint point-cloud model to exploit the 3D geometric transformation on the skeletal data. Our skeleton features improve the performance of our model over the state-of-the-art accuracy with 93.69% and 90.11% on DHG 14 and 28.  
For the hand depth feature approach, we also propose using the hand component segmentation features from the depth shape model to recognize the hand shape. Our pre-trained depth shape model was based on U-Net with the Seresnext backbone. Our model using depth shape features gives improved performance with accuracies of 92.26% and 88.33% on DHG 14 and 28.  
To learn from the two features, we propose MLF LSTM using Conv1D, the Conv2D pyramid block, and the LSTM block to exploit the hand features. Our model, using depth and skeleton data, gave the best performance with an accuracy of 96.07% and 94.4%. Upon comparison of our model with related works, our model achieves the best results.  
In the future, we need to exploit the point-cloud features in the whole hand and enhance the LSTM model to natively integrate the diversity of features from the handcrafted features in the time-series the feature vector from the visual deep learning model and the point-cloud model.  
Conceptualization, N.-T.D. and S.-H.K.; Funding acquisition, S.-H.K., G.-S.L. and H.-J.Y.; Investigation, N.-T.D.; Methodology, N.-T.D.; Project administration, S.-H.K., G.-S.L. and H.-J.Y.; Supervision, S.-H.K.; Validation, N.-T.D.; Writing--original draft, N.-T.D.; Writing--review & editing, N.-T.D. and S.-H.K. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Confusion matrix of the best prediction results using depth + skeleton data: MLF LSTM (accuracy of 96.07%) (**a**) on DHG-14 and (accuracy 92.38%) (**b**) on DHG-28.  
**Funding:** This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2018R1D1A3A03000947 and Grant NRF-2020R1A4A1019191.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experimental Results and Analysis
### Absorb Energy  
The fracture of coal can be understood as the result of the work performed by the force acting on the coal, which meets the criteria for crack instability. The deformation and failure of rocks occur alongside an energy evolution process characterized by energy absorption, accumulation, and dissipation [40]. By studying energy conversion during rock failure, the mechanical response patterns of rocks can be more accurately described [41; 42; 43; 44]. During the loading process of the specimen, the absorbed energy is associated with the area under the load-displacement curve, delineated by the horizontal and vertical axes [40]. The instability of the main crack at peak load leads to the overall fracture of the coal sample. Therefore, the calculation formula is expressed in Equation (6).  
\[U_{T}=\int_{0}^{Sj}P_{j}d_{S} \tag{6}\]  
Among these variables, \(U_{T}\) represents the absorbed energy (mJ); \(P_{j}\) denotes the load (N) at step \(j\); \(d_{s}\) is the displacement difference (mm) between steps \(j\) and \(j+1\); and \(S_{j}\) refers to the displacement value at step \(i\). Figure 9 presents the energy calculation results for coal samples during the fracture process influenced by mine water immersion and varying loading rates.  
The absorbed energy during the fracture of coal samples is influenced by closure stress, peak stress, compaction stage displacement, and elastic stage displacement. As illustrated in Figure 9a, the absorbed energy gradually decreases with increasing immersion time in mine water. This reduction results from the physical and chemical reactions of mine water, which damage the coal, making it require less energy for crack expansion. Immersion for 30 days, 60 days, and 120 days resulted in decreases of 45.71%, 57.92%, and 63.21%, respectively, compared to the non-immersion condition. Notably, as the immersion time exceeds 60 days, although the energy absorbed by the coal sample during fracture continues to decrease, the rate of decrease slows. This observation serves as a reference point for measuring the energy conditions required for the residual fracture of coal samples. From Figure 9b, it is apparent that as the loading rate increases, the energy absorbed by the coal sample also increases. This increase is attributed to the reduced duration of the applied load, which causes the main crack to propagate increasingly toward the direction of the  
Figure 9: Absorbed energy curve of coal sample ((**a**) depicts the absorbed energy versus immersion time (loading rate is 0.002 mm/s); (**b**) depicts the absorbed energy versus loading rate).  
applied load, thereby overcoming the coal matrix and bedding planes along this path. This process shortens the activation and expansion time of primary fractures, thereby decreasing the probability of connection between the main fracture and weaker surfaces. It can be inferred that one effective measure to control crack propagation may relate to the rate of applied load.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 4 Methodology
### Plant Distribution Study
#### 4.1.1 Case I  
In this case, to attain the initial restrictions (Equations (33), (34) and (35)), the horizontal FoV, \(FoV_{H}\), of the camera must completely cover the entire shelf's length (Equation (37)).  
The other restriction is that the camera's vertical FoV, \(FoV_{V}\), must be lower than or equal to the shelf's height (Equation (38)). Otherwise, the floor and the ceiling will be visible within the image, reducing the photobioreactors' vertical size.  
\[FoV_{H} =2\cdot\tan\left(\frac{\text{A}\text{o}V_{H}}{2}\right)\cdot d_{ \text{cam},1}\geq l_{\text{shelf}} \tag{37}\] \[FoV_{V} =2\cdot\tan\left(\frac{\text{A}\text{o}V_{V}}{2}\right)\cdot d_{ \text{cam},1}\leq l_{\text{shelf}} \tag{38}\]  
To resolve the stated equations, \(AoV_{V}\) must be set to its maximum possible value. As design criteria, it was established as 70\({}^{\circ}\). In Section 5, the reason behind not selecting a higher \(AoV_{V}\)is discussed.  
Using Equations (37) and (38), the camera distance, \(d_{\text{camera}}\), is obtained, and consequently the SUR. The selected worst-case viewing angle corresponds to the container located further to the right.