esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Concentration**} & \multicolumn{3}{c}{**Exposure Time**} & **Median Lethal** \\ \cline{3-4}  & & **72 HAT** & **144 HAT** & **216 HAT** & **Time** \\ \hline  & 10\({}^{3}\) & 0.0 \(\pm\) 0.0 c & 13.0 \(\pm\) 5.8 c & 13.0 \(\pm\) 5.8 c & 208.0 \(\pm\) 0.6 a \\  & 10\({}^{4}\) & 0.0 \(\pm\) 0.0 c & 17.0 \(\pm\) 5.8 c & 23.0 \(\pm\) 5.8 c & 202.0 \(\pm\) 3.7 b \\ _F. longifundum_ & 10\({}^{5}\) & 3.4 \(\pm\) 5.8 b & 27.0 \(\pm\) 15.3 bc & 37.0 \(\pm\) 5.8 b & 193.0 \(\pm\) 3.3 c \\ _dum_ & 10\({}^{6}\) & 3.4 \(\pm\) 5.8 b & 33.0 \(\pm\) 10 b & 40.0 \(\pm\) 10.0 a & 187.0 \(\pm\) 2.2 d \\  & 10\({}^{7}\) & 6.7 \(\pm\) 10 a & 37.0 \(\pm\) 11.5 a & 46.0 \(\pm\) 11.5 a & 182.0 \(\pm\) 1.8 e \\  & 10\({}^{8}\) & 13.0 \(\pm\) 15.3 d & 40.0 \(\pm\) 15.3 a & 67.0 \(\pm\) 5.8 a & 161.0 \(\pm\) 2.9 f \\ \hline Control & H\({}_{2}\)O + Tergitol Np9 & 0.0 \(\pm\) 0.0 c & 0.0 \(\pm\) 0.0 d & 2.7 \(\pm\) 1.3 d & 215.0 \(\pm\) 0.2 g \\ \hline \end{tabular}
\end{table} 
Table 6: Mean mortality (\(\pm\)sd) and Median Lethal Time of _H. armigera_ adults exposed to _F. longifundum_ for 216 h of two season experiment. Means of the same column followed by the same letter are not significantly different (Tukey test, a = 0.05), Median Lethal Time (hours \(\pm\) sd) was estimated by the Kaplan–Meier method (F = 4.815, df = 6, \(p\) \(<\) 0.001), HAT: Hours After Treatment. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Concentration**} & \multicolumn{3}{c}{**Exposure Time**} & **Median Lethal** \\ \cline{3-4}  & & **72 HAT** & **144 HAT** & **216 HAT** & **Time** \\ \hline  & 10\({}^{3}\) & 0.0 \(\pm\) 0.0 c & 7.0 \(\pm\) 5.8 d & 13.0 \(\pm\) 5.8 e & 208.0 \(\pm\) 0.3 a \\  & 10\({}^{4}\) & 3.4 \(\pm\) 5.8 a & 17.0 \(\pm\) 11.5 c & 27.0 \(\pm\) 15.3 d & 189.0 \(\pm\) 2.1 b \\ _F. pseudoam-thophilum_ & 10\({}^{5}\) & 3.4 \(\pm\) 5.8 a & 23.0 \(\pm\) 15.3 c & 47.0 \(\pm\) 15.3 d & 157.0 \(\pm\) 2.3 c \\  & 10\({}^{6}\) & 3.4 \(\pm\) 5.8 a & 33.0 \(\pm\) 5.8 b & 57.0 \(\pm\) 5.8 c & 148.0 \(\pm\) 1.2 d \\  & 10\({}^{7}\) & 10.0 \(\pm\) 10.0 b & 37.0 \(\pm\) 11.5 ab & 67.0 \(\pm\) 5.8 b & 132.0 \(\pm\) 3.8 e \\  & 10\({}^{8}\) & 20.0 \(\pm\) 0.0 d & 40.0 \(\pm\) 10.0 a & 81.0 \(\pm\) 5.8 a & 106.0 \(\pm\) 1.9 f \\ \hline Control & H\({}_{2}\)O + Tergitol Np9 & 0.0 \(\pm\) 0.0 c & 0.0 \(\pm\) 0.0 e & 2.7 \(\pm\) 1.3 f & 215.0 \(\pm\) 0.2 g \\ \hline \end{tabular}
\end{table} 
Table 7: Mean mortality (\(\pm\)sd) and Median Lethal Time of _H. armigera_ adults exposed to _F. pseudoam-thophilum_ for 216 h of two season experiments. Means of the same column followed by the same letter are not significantly different (Tukey test, a = 0.05), Median Lethal Time (hours \(\pm\) sd) was estimated by the Kaplan–Meier method (F = 2.678, df = 6, \(p\) \(<\) 0.001), HAT: Hours After Treatment.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
###### Abstract  
Global dryland areas are vulnerable to climate change and anthropogenic activities, making it essential to understand the primary drivers and quantify their effects on vegetation growth. In this study, we used the Time Series Segmented Residual Trends (TSS-RESTEND) method to attribute changes in vegetation to CO\({}_{2}\), land use, climate change, and climate variability in Chinese and American dryland areas. Our analysis showed that both Chinese and American drylands have undergone a greening trend over the past four decades, with Chinese greening likely linked to climatic warming and humidification of Northwest China. Climate change was the dominant factor driving vegetation change in China, accounting for 48.3%, while CO\({}_{2}\) fertilization was the dominant factor in American drylands, accounting for 47.9%. However, land use was the primary factor resulting in desertification in both regions. Regional analysis revealed the importance of understanding the drivers of vegetation change and land degradation in Chinese and American drylands to prevent desertification. These findings highlight the need for sustainable management practices that consider the complex interplay of climate change, land use, and vegetation growth in dryland areas.  
d ryland areas; desertification; climatic warming; anthropogenic activities +
Footnote †: journal: Remote Sensing
# Survey on Videos Data Augmentation for Deep Learning Models
## 2 Methods Used and Overview of the Literature  
The focus of this review paper is to comprehensively cover the scientific publications on video data augmentation for DL models. We are going to center our attention specifically on data augmentation methods designed to handle the temporal dimension of video streams. Nowadays, several search engines for academic publications are available. We decided to use Scopus [24] to examine the scientific literature and select the best set of papers published in recent years on video data augmentation. Scopus has been chosen based on the size and the quality of its scholarly literature database. The final set of selected publications was obtained after few iterative refinement steps:  
1. An initial search was performed, resulting in a collection of 570 publications. The criteria used to select a paper were the following: 1. title, abstract or main text must contain the set of words (\"video\" \"data augmentation\") or (\"video\" \"synthetic\" \"data\" \"generation\") or (\"video\" \"simulation\" \"data\" \"generation\") 2. papers must be published from 2012 to 2022; 3. papers must be written in English; 4. book chapters were excluded. 2. Duplicated entries and papers with the titles and abstracts not relevant with the topic were removed, resulting in a pruned set of 76 papers. 3. The full text of the remaining 76 paper was evaluated. Several of the papers applied standard image data augmentation strategies without focusing specifically on the problem of video data augmentation. For this reason, only 33 papers were finally selected. 4. The set of 33 papers was extended with two more papers which we felt had an impact on the survey. The final number of paper selected for the review is 35.  
Analysing more in details the search results, we can point out some interesting findings. Figure 1 depicts a bar chart of the number of publication for each year. We have not found any relevant publication in the interval between 2012 and 2015; for this reason, only the years from 2016 to 2022 are shown. It is possible to notice an exponential growth in the number of papers that introduce data augmentation strategies tailored for video sequences. This is a clear sign on how this topic is significant and is attracting the interest of the research community. In addition, 77% of the papers selected for this review has been published after 2019, demonstrating the novelty of this research area.  
Some insight can be also drawn by the analysis of the most frequently occurring keywords (Figure 2). Besides the obvious high number of occurrences of keywords like \"Data Augmentation\", \"Deep Learning\" and \"Computer Vision\", it is worth noticing the presence in the plot of application areas highly dependent on time (e.g., \"Action Recognition\" and \"Human-action Recognition\"). Data augmentation techniques specifically tailored for video sequences are particularly interesting in applications dealing with subjects in motions and scenarios dynamically changing through time.
## 3 Imaging Method and Configuration
### Finite-Element Simulation  
In this section, the finite-element model is exposed--necessary for the implementation of the topological energy method--and developed with Comsol Multiphysics(r) and the postprocessing techniques in order to analyze the experimental signals.  
As illustrated in Figure 7, the plate is immersed in the water. Perfectly Matched Layers (PMLs) are set to simulate an infinite domain, especially at the right end of the plate to simulate the flawless infinite plate and around the domain to avoid reflections on the calculation boundaries. In the experimentation, the tank edges are far enough apart to avoid reflected waves in the studied time interval.  
Segments that represent each element of the transducer define the linear phased array. This simple definition leads to a surface wave in the water in the numerical model that is not observed in the experimental propagation. That is why a thin absorbent layer in the upper part of the water is introduced to cancel this numerical surface wave.  
The mesh is described by triangular elements. The maximum size of the elements is given by \(\lambda_{min}/8\) in the plate and the water, where \(\lambda_{min}\) represents the smallest wavelength that can exist in the considered bandwidth.
# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
## 3 Design of Distributed Unequal Error Protection Rateless Codes
### Optimizations of Rateless Coding Scheme and Network Coding Rule
\[\Omega_{1,1}+\Omega_{1,2}=1,\] \[\Omega_{2,1}+\Omega_{2,2}=1,\] \[p_{1}+p_{2}+p_{3}=1.\]  
Then, consider the primary links in our scheme, although the actual values of \(n_{1}\), \(n_{2}\) and \(n_{NC}\) are unknown, and the expected ratio of the numbers of the received coded symbols on different links, \(n_{1}\): \(n_{2}\) : \(n_{NC}\), can be derived by given the block lengths and erasure probabilities. If a desired overhead \(\gamma*\) and proportion of every importance level are given, the decoding performance of MIB and LIB only depend on the degree distribution and symbol selection weight. Thus, if we limit the decoding performance relation between LIB and MIB, the optimizations of rateless coding scheme of one source are finished when we finish optimizing the decoding performance of LIB. Furthermore, it is not difficult to show that \(y_{l,1,2}\) and \(y_{l,2,2}\) are two conflicting objective functions by investigating (2) and (3). Therefore, we have a multi-objective optimization (MOP) problem, MOP1, about the objective function \(y_{l,1,2}(Q)\) and \(y_{l,2,2}(Q)\) to minimize them concurrently, where \(Q\) is the set of decision variables shown above:  
\[\min_{Q} y(Q)=(y_{l,1,2}(Q),y_{l,2,2}(Q)),\] (13) s.t. \[I_{1,1}y_{l,1,1}\leq I_{1,2}y_{l,1,2},\;I_{2,1}y_{l,2,1}\leq I_{ 2,2}y_{l,2,2},\] \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]0 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]1 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]2 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]3  
Note that the inequalities on the second line of (13) are added to guarantee UEP property, which limits the decoding performance relation between LIB and MIB. Thus, it should be noted that when the original information in any source is divided into \(n(n>2)\) importance levels, optimization can also be in the same way due to the same degree distribution and network coding rule for every importance level. The MOP2 can be formulated as follows, in which it will have \(2n\) importance levels for the whole system:  
\[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]4 (14) s.t \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]5 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]6 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]7 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]8 \[\sum_{i~{}=~{}1}^{M_{1}}\Omega_{1,i}=1,\]9 \[y_{0,1}=1,l\rightarrow\infty,\]0A fast non-dominated sorting genetic algorithm (NSGA-II) [28] is one of the many algorithms that could give a Pareto front of MOP with an outstanding performance. Thus, we employ this algorithm to solve the set \(Q\).
## 3 Experimental Results
### Effects of Main Variables on Bearing Capacity
#### 3.3.2 Head Size  
As shown in Figure 14, when the value of \(A_{h}/A_{s}\) is equal to 3 to 6, the increase in head size improves the bearing capacity of the CHBC. The bigger heads bring the larger compression area of the diagonal strut between the heads, which improves the bearing capacity of the concrete strut as well as CHBC.  
When the value of \(A_{h}/A_{s}\) is larger than six, the improvement in the bearing capacity is less obvious with the increase in head size. The bearing capacity of the CHBC is actually the minimum of the headed-bar tensile strength and steel-bar connector-bearing capacity. Thus, the critical size of the head can be determined and the bearing capacity of CHBC with critical head size is just equal to the headed-bar tensile strength. Increasing the head size over the critical value cannot improve the bearing capacity of CHBC and those specimens with a head size equal or larger than the critical value should fail by the steel bar rupture. It should be noted that one of the three specimens of \(A_{h}/A_{s}\) equal to six is failed by the steel bar rupture and all the three specimens of \(A_{h}/A_{s}\) equal to eight are of steel-bar rupture failure mode. Then, the critical value of the head size should be varied from six to eight times the cross-sectional area of the headed bar when the lap length is equal to 7.5 times of the headed bar diameter.  
Figure 14: Relationship between head size and bearing capacity.  
Figure 13: Relationship between lap length and bearing capacity.  
To achieve the steel bar tensile strength, the lap length of CHBC with a head of critical size is only equal to about 0.24 times that of the straight bar slice specified in Chinese design specification GB 50010-2020 [37], a 76% reduction in lap length is achieved. In addition, by the confined stirrup, the lapping length of CHBC is about 0.36 times of the anchorage length of headed bar specified in Chinese design specification GB 50010-2020 [37], 64% reduction is achieved. It should be noted than the minimum \(A_{h}/A_{s}\) is equal to five in the determination of anchorage length according to the code. The superiority of CHBC in saving the lap length for the steel bar splice is proved. Moreover, the minimum connection length for grouted sleeve splice is eight times of the rebar diameter with the compression strength of grouting materials being higher than 85 MPa according to Chinese specifications JGJ 355-2015 [49]. When compared to the grouted sleeve splice connection, although the lap length in CHBC is not decreased, the requirements for grouting and high-strength grouting materials are eliminated, which can help save in construction costs and time.
## 4 Simulation Results and Discussion
#### 4.2.1 Effect of the Power Line Length  
By doubling the length of each line of the test system (feeder 4 connected to bus 6 of the IEEE RBTS), we can study the impact of the length of the power line on the optimal fault indicator configuration scheme and the influence of the total cost, which is shown in Table 4.  
As shown in Table 4, with the increase in the length of the power line, the installation number of acquisition units will increase from 14 to 17, and the installation number of collection units will increase from 1 to 3. As a consequence, the total cost will increase from 228.48 USD to 274.67 USD. It is because the time for patrolling the line will become longer with the increase in the length of power line when the speed of patrolling the line is constant. Consequently, if a fault occurs, the outage time will increase, so we need to install more acquisition units and collection units of fault indicators to ensure the reliability of the power supply. Therefore, for different power lines, the longer the length is, the higher the total cost will be.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Length of** \\ **Power Line** \\ **(km)** \\ \end{tabular} & \begin{tabular}{c} **Installation** \\ **Number of** \\ **Acquisition** \\ **Unit** \\ \end{tabular} & \begin{tabular}{c} **Installation** \\ **Position of** \\ **Acquisition** \\ **Unit** \\ \end{tabular} & \begin{tabular}{c} **Installation** \\ **Number of** \\ **Collection** \\ **Unit** \\ \end{tabular} & \begin{tabular}{c} **Installation** \\ **Position of** \\ **Collection** \\ **Unit** \\ \end{tabular} & \begin{tabular}{c} **Total Cost** \\ **(USD)** \\ \end{tabular} \\ \hline original & 14 & \
\begin{tabular}{c} 3, 5, 6, 8, 9, 10, \\ 11, 12, 13, 15, \\ 18, 19, 22, 23 \\ \end{tabular} & 1 & 7 & 228.48 \\ doubled & 17 &
\begin{tabular}{c} 2, 3, 4, 5, 6, 8, 9, \\ 10, 11, 12, 13, 15, \\ 18, 19, 20, 22, 23 \\ \end{tabular} & 3 & 3,15,18 & 274.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal placement of fault indicators under different power line lengths.  
Figure 10: The time required for any fault point to be accurately located under different outage time.
## 3 Results
Smooth values and eigenvectors can be appreciated for the reference method, indicating high tensile strains at the flow divider. Many outliers of the strain values and a chaotic distribution of eigenvectors can be observed for the zero-degree method, clearly indicating the disability in providing consistent and trustworthy results. Especially in the regions in which the reference principal components point in the horizontal direction, erroneous values manifest for the zero-degree method. For the compounding method, the errors in these regions are far less destructive. Although the compounding technique is not perfect, it does yield strain results with less noise compared with the zero-degree method for the complete cardiac cycle, which shows erroneous results primarily in the regions in which the direction of the eigenvectors coincides with the lateral direction for each probe, which for probe A corresponds to the horizontal direction depicted in Figure 10. For the compounding method, the errors in these regions are far less destructive. Figure 11 shows the RMSE of the minimal (left) and maximum (right) principal strain over time for a single cardiac cycle. The 5 th-95 th percentile strain ranges for both probes were depicted in the background to relate the RMSE to the overall strain development. In general, the RMSE for the compounding methods is smaller for the complete cycle in comparison with the zero-degree method. However, an up rise of the RMSE and the reference strain range occurs at \(t>\) -0.8 s, which indicates a progressive strain error in the process of strain accumulation for all methods. The compressive strain RMSE values for probe A and B using only the zero-degree angle at peak systole are 6.6% and 5.9% versus 2.7% and 3.1% for the compounding method. Also, the tensile strain RMSE shows elevated values for the zero-degree method of 14.3% and 12.6%, in comparison to 9.3% and 6.2% for the compounding method. Figure 11: The RMSE of the compressive and tensile strain components for the complete cardiac cycle for the zero-degree and compounding method for both probes. Please note the double axis for the RMSE minimal principal strain, in which the RMSE is always positive; the 5 th–95 th percentiles strain range is negative. Looking at the residual compressive strain RMSE, values of 5.4% and 4.1% versus 1.5% and 1.5% were found for the zero-degree method and the compounding method, respectively. The same trend holds for the residual tensile strain RMSE, which was 5.8% and 6.4% versus 1.8% and 2.0%. The mean RMSE values of the compressive principal strain over the complete cardiac cycle for the zero-degree method are 4.4% and 3.8%, respectively, versus 1.7% and 1.9% for the compounding method. For the tensile principal strain, 6.9% and 6.8% versus 3.3% and 4.0% were found. Therefore, the compounding method clearly outperforms the zero-degree method in terms of overall tensile and compressive principal strain over time.
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 3 Results and Discussion
### PHA Production via Batch Cultivation in Shake Flasks
A continuous increase in DCW and PHA content was noticed during the exponential phase, achieving a maximum of 6.84 \(\pm\) 0.15 g/L and 3.79 \(\pm\) 0.03 g/L (55.5% of DCW) respectively at 120 h of cultivation. Along with PHA accumulation, a maximum carotenoid content of 1.80 \(\pm\) 0.16 mg/L was obtained. The PHA production achieved by _H. mediterranei_ DSM 1411 in this work was significantly higher than that accumulated by the same strain cultivated in a minimal synthetic medium (MSM) supplemented with emulsified volatile fatty acids, for which a maximum PHA content of 0.93 g/L (37.4% of DCW) was recorded [23]. Likewise, the DCW and PHA levels recorded in this study were respectively 71% and 156% higher than those obtained by [PERSON] et al. [10]  
Figure 2: Time profile of OD at 600 nm, DCW, PHA accumulation, and substrate utilization by _Cupriavidus necator_ KCTC 2649 using 0.3 N HCl DCB hydrolysate as the carbon source in a flask culture. Data are shown as mean values \(\pm\) standard deviation (\(n=3\)). when using the same HCl DCB hydrolysate as a carbon source by _Paracoccus_ sp. LL1. Among hapliles, haloarchaeon _H. mediterranei_ has been reported to be the most efficient in accumulating PHA at high cell densities owing to its fast growth rate, genetic stability, and efficient metabolic capacity [24]. Several studies have reported the ability of this strain to produce high PHA levels through the conversion of different waste products, including cheese whey [25], rice-based ethanol stilage [26], and macroalgal biomass [7].
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 3 UWB Notch Antenna Design Approach
### Progression of UWB Notch Antenna  
This section describes the evolution stages of a UWB notch antenna. It shows a step-by-step iterative process to arrive at the final structure. The following Figure 5 shows a pictorial representation of the iterative process.  
A patch and a ground plane make up the proposed antenna. Both of these have undergone modification to arrive at the final structure. The S\({}_{11}\) characteristics of the UWB notch antenna at every stage are plotted together and depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Dimension (mm)** & **Parameter** & **Dimension (mm)** \\ \hline \(a\) & 24 & \(i\) & 1.25 \\ \(b\) & 32 & \(f\) & 2 \\ \(c\) & 9.5 & \(t\) & 0.5 \\ \(d\) & 6 & \(k\) & 16 \\ \(e\) & 11.6 & \(l\) & 8 \\ \(u\) & 11 & \(m\) & 0.75 \\ \(g\) & 11.25 & \(m\) & 0.75 \\ \(v\) & 4 & \(n\) & 12 \\ _l2_ & 4 & \(o\) & 3 \\ _l3_ & 2.47 & \(p\) & 3 \\ \(j\) & 1.9 & \(q\) & 3.5 \\ \(h\) & 1.5 & \(r\) & 0.5 \\ \(s\) & 1.5 & \(x\) & 0.5 \\ \(y\) & 6 & \(z\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimension of the proposed antenna.  
resonator is added to the ground plane. The iron-shaped lower resonator gives the second notch at 8.1 GHz. Thus, the upper resonator and lower resonator combination make an elliptical UWB notch antenna in step 4.  
These graphs in Figure 6 show that the notch antenna has S\({}_{11}<-10\) dB for the entire UWB range and has two notches at 5.3 GHz and 8.1 GHz due to the two resonators.