esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Reliability  
No significant differences were found between mean values of either EMGT1 or EMGT2 obtained in the first and second tests (\(p<0.05\)). Intraclass correlation coefficients (ICC) between repeated measurements were significant (\(p<0.05\)) and high (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{EMGT1} \\ \hline  & **Vastus Lateralis** & **Vastus Medialis** & **Biceps Femoris** & **Gluteus Maximus** \\ \hline
**VT1** & 0.75 \({}^{*}\) & 0.69 \({}^{*}\) & 0.72 \({}^{*}\) & 0.88 \({}^{*}\) \\ \hline  & & \multicolumn{4}{c}{EMGT2} \\ \hline  & **Vastus Lateralis** & **Vastus Medialis** & **Biceps Femoris** & **Gluteus Maximus** \\ \hline
**VT2** & 0.90 \({}^{*}\) & 0.82 \({}^{*}\) & 0.82 \({}^{*}\) & 0.83 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\) Significant correlation (\(p<0.05\)).  
\end{table}
Table 4: Correlations between the power output (W) obtained at the first electromyographic threshold (EMGT1) and at the first ventilatory threshold (VT1), and between the power output obtained at the second electromyographic threshold (EMGT2) and at the second ventilatory threshold (VT2).  
Figure 3: Graphic analysis of power output (W) data corresponding to (**a**) the first electromyographic threshold (EMGT1) and ventilatory threshold (VT1), and (**b**) the second electromyographic threshold (EMGT2) and ventilatory threshold (VT2), for the vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), and gluteus maximus (GLM).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{EMGT2} \\ \hline
**Variable** & **VT2** & **Vastus Lateralis** & **Vastus Medialis** & **Biceps Femoris** & **Gluteus Maximus** \\ \hline W & \(383.5\pm 44.4\) & \(378.6\pm 24.5\) & \(372.2\pm 27.4\) & \(374.4\pm 31.6\) & \(379.8\pm 30.2\) \\ VO2 (mL/kg/min) & \(65.6\pm 4.5\) & \(66.9\pm 3.5\) & \(65.8\pm 3.8\) & \(66.9\pm 4.8\) & \(67.2\pm 4.7\) \\ \%VO2 MAX & \(91.6\pm 8.3\) & \(93.5\pm 6.0\) & \(91.9\pm 6.0\) & \(93.4\pm 5.2\) & \(93.8\pm 4.9\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean values of the power output (W), VO2, and %V obtained at the second electromyographic threshold (EMGT2) and at the second ventilatory threshold (VT2). All values are expressed as mean \(\pm\) SD.
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 3 Results and Discussion
### Plant Height Estimation in Maize Populations
#### 3.1.3. Normal Vector Analysis and Column Space Approximate Segmentation Algorithm  
The column space approximate segmentation algorithm was employed to segment maize stalks from the point cloud and improve height estimation accuracy. By analyzing the normal vectors, points with direction angles between one and two radians were identified as stalk points, effectively excluding leaf points and irrelevant data.  
Figure 9 presents a histogram of the direction angle distribution, where the horizontal coordinate represents the angle in radians and shows a near-normal distribution. Based on these thresholds, Figure 10 illustrates the segmented point cloud with the stalk points preserved. Figure 9(a) provides an overall view, while Figure 9(b),c show detailed close-ups.  
Figure 10: Normal vector point cloud map.  
Figure 9: Histogram of direction angle.  
Using the column space approximation approach combined with AABB segmentation, single-plant point clouds were extracted for subsequent height measurements. Figure 11 demonstrates this segmentation process, with Figure 11c showing the single-plant regions after segmentation. This segmentation algorithm was particularly effective in dense canopies with overlapping plants, achieving high precision in isolating individual maize stalks.
## 3 Experiments
### Data Preparation
#### 3.1.2 Experimental  
The experimental datasets were generated by performing the fan-beam PCCT experiments on the mouse paw, which was provided by Institute of High Energy Physics, Chinese Academy of Sciences. The used mouse was kept in a pathogen-free environment and was fed ad lib. The procedures for care and use of this mouse were conducted in accordance with the \"Guiding Principles in the Care and Use of Animals\" [44] and were approved by the Ethics Committee of the Institute of High Energy Physics, Chinese Academy of Sciences. While scanning, 720 views were acquired within 360 degrees using the laboratory fan-beam X-ray source to obtain complete projections. Four phase steppings occurred at each sampling view. Then, sparse sampling was carried out on the complete projections to obtain sparse-view projections with 180, 120, 90, and 60 views. The distances between the source and detector and that between the source and object were 22,400 and 20,200 pixels. The offset was four pixels. The acquired projection images had a size of \(512\times 512\) pixels, and the corresponding sinograms and reconstructed images had sizes of \(720\times 512\) and \(512\times 512\) pixels, respectively. In the experiments, 600 tomographic images were obtained, where the first 400 images from top to bottom were chosen for training and the remaining 200 images for testing.
# HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition
## 2 Material and Methods
### HYDRUS-ID Model  
#### 2.2.1 Model Description  
The one-dimensional numerical model HYDRUS-1D [13] was used to simulate soil water dynamics in the sweet corn field. As the water flows from surface to groundwater is in a vertical direction under rainfall, and horizontal flow such as runoff can be covered through boundary conditions, there was no need to simulate multi-dimensional movement. The HYDRUS-1D solves the Richards equation for  
Figure 3: View of the sub-surface water collection system in the field experiment.  
saturated and unsaturated water flow. Following is the governing equation for water flow, which is the modified form of Richards equation assuming water flow due to thermal gradient is neglected.  
\[\frac{\partial\theta}{\partial t}\ =\ \frac{\partial}{\partial z}\bigg{[}K\bigg{(} \frac{\partial h}{\partial z}\ +\ 1\bigg{)}\bigg{]}\ -\ S, \tag{2}\]  
where \(\theta\) is the volumetric water content (cm\({}^{3}\)cm\({}^{-3}\)), h is the water pressure head (cm), t is time (day), z is the spatial coordinate (cm), K is the unsaturated hydraulic conductivity function (cm day\({}^{-1}\)), and S is the sink term in the flow equation (cm\({}^{3}\)cm\({}^{-3}\)day\({}^{-1}\)).  
The soil hydraulic properties were modeled using [24] as follows:  
\[\theta(h)\ =\ \left\{\begin{array}{ll}\theta_{r}\ +\ \frac{\theta_{s}-\theta_{r}}{ \left(1\ +\ \left|\Delta h\right|^{2}\right)^{m}}&h\ <\ 0\\ \theta_{s}&h\ \geq\ 0\end{array}\right., \tag{3}\]  
\[K(h)\ =\ K_{s}S_{e}^{l}\Big{[}1\ -\ \left(1\ -\ S_{e}^{1/m}\right)^{m}\Big{]}^{2}. \tag{4}\]  
Here  
\[S_{e}\ =\ \frac{\theta\ -\ \theta_{r}}{\theta_{s}\ -\ \theta_{r}},\ m\ =\ 1\ -\ \frac{1}{n}. \tag{5}\]  
Here, \(\theta s\) is the saturated water content (cm\({}^{3}\)cm\({}^{-3}\)), \(\theta_{r}\) is the residual water content (cm\({}^{3}\)cm\({}^{-3}\)), \(K_{s}\) is the saturated hydraulic conductivity (cm day\({}^{-1}\)), \(S_{e}\) is the relative saturation, and \(a\) (cm\({}^{-1}\)), and \(n\) (-) and \(l\) (-) are shape parameters.  
The details of simulation process and model description have been presented in the flow chart (Figure 4).
## 2 Materials and Methods
### Machine-Learning Approaches
#### 2.4.1 Support Vector Machine (SVM)  
Support Vector Machine (SVM) is a machine-learning technique that analyzes data for classification and regression analysis using supervised learning models with associated learning algorithms. Being a nonprobabilistic binary linear classifier, SVM algorithm settles a given set of training examples into one of two categories. Consequently, SVM creates a gap between the two categories, in order to maximize the space between them. The new examples are then mapped into that space and predicted to a category, based on the gap side they fall in. SVM is a standard and suitable method for audio classification [39].  
Figure 4 describes a schematic manner of the general SVM algorithm. There are multiple SVMs, using different mathematical functions (or kernels), as follows: linear, nonlinear, radial basis function (RBF), polynomial, and sigmoid. They were tested and evaluated to conclude which is the best kernel to use to determine heart-failure severity [40; 41] (Figure 4).
# OneMap Drought Monitoring Analysis Based on Statistical Models
## 2 Materials and Methods
### OneMap Drought Index Combining Meteorological, Agricultural, and Hydrological Drought Indices
#### 2.3.2. Accuracy Evaluation of Drought Monitoring by the Drought Index  
A receiver operating characteristic (ROC) analysis based on a confusion matrix was conducted to review the applicability of the drought index. If a drought-affected area was identified as being affected by drought, it was classified as a Hit (H), and if the drought-affected area was not identified as being affected by drought, it was classified as False (F). If a drought area was not identified as exhibiting drought, it was classified as missing (M),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SDI** & **SRSI** & **WBDI** & **Classification of** \\  & & & **Drought Conditions** \\ \hline \(>\)0 & \(>\) 0 & \(>\)0 & No drought \\ \(-\)1.0\(-\)0 & \(-\)1.0\(-\)0 & \(-\)0.5\(-\)0 & Mild drought \\ \(-\)1.5\(--\)1.0 & \(-\)1.0\(--\)1.5 & \(-\)0.5\(--\)1.0 & Moderate drought \\ \(-\)2.0\(--\)1.5 & \(-\)2.0\(--\)1.5 & \(-\)1.5\(--\)1.0 & Severe drought \\ \(<\)\(-\)2.0 & \(<\)\(-\)2.0 & \(<\)\(-\)1.5 & Extreme drought \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification of drought conditions according to SDI, SRSI, and WBDI ranges.  
and if an area without drought was identified as not being affected by drought, it was classified as a negative hit (N). ROC values between 0 and 1 were calculated, with perfect predictions represented by a hit rate (HR) = 1 and a false alarm rate (FAR) = 0, implying that the closer the ROC is to 1, the better the prediction is (Table 5, [29]).  
The drought index, the drought forecast and the warning information did not match exactly. Therefore, the calculated drought index was therefore corrected and used to yield consistent forecast and warning information. ROC scores were utilized to evaluate the classification model and correct the drought index. Drought specificity and accuracy were considered in the performance evaluation. The self-organizing migrating algorithm (SOMA, [30]) optimization technique was used to determine the weight for drought index correction. The objective function estimated values with maximum drought specificity and overall accuracy. The application range of the weights was \(-20\%\) to \(+20\%\), adjusted and verified in increments of 1%.
## 3 Closed-Loop Supply Chain Decision
### Decentralized Decision
#### 3.2.1 M-R Decision
Conclusion 2: Under the decentralized M-R decision, the optimal sales price of new products, remanufactured products and refurbished products under CLSC decisions \(p_{i}^{cs}\) and the optimal service level \(s^{c}*_{i}\) are:  
1. \(p_{n},\omega_{n},s_{n}\) decrease with the increase in \(\theta\);
2. \(p_{r},\omega_{r},s_{r}\) increase with the increase in \(\theta\);
3. \(p_{s},\omega_{s},s_{s}\) increase with the increase in \(\theta\);  
Conclusion 3: Under the decentralized M-R decision, the optimal sales price of new products, remanufactured products and refurbished products under CLSC decisions \(p_{i}^{cs}\) and the optimal service level \(s^{c}*_{i}\) are:1. \(p_{n}^{c*}<p_{n}^{d1*}\);\(p_{r}^{c*}<p_{r}^{d1*}\);\(p_{s}^{c*}<p_{s}^{d1*}\);
2. \(s_{n}^{c*}>s_{n}^{d1*}\);\(s_{r}^{c*}>s_{r}^{d1*}\);
3. \(\Pi_{M}^{d1*}+\Pi_{R}^{d1*}<\Pi_{S}^{c*}\). Inference 2:  
1. With the increase in consumer acceptance of remanufactured products and new products, the best selling price, the best wholesale price and the optimal service level should be increased, and new products should be reduced accordingly;
2. In the manufacturer-dominated Stackelberg game model, the manufacturer's gain \(\Pi_{M}^{d1*}\) and the optimal sales price for new products, remanufactured products and refurbished products are greater than those under centralized decision making. Further, the optimal service level is less than that under centralized decision making. The CLSC presents a double marginal effect, and the supply chain is non-optimal.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 5 Discussion
### Strength, Limitations and Future Directions  
The S1 satellite with the 6 days revisit time allows more precise temporal follow-up of agricultural crops. S1 is now the only operational satellite system providing dense time series in free and open access mode with global coverage. Thus, any method based on the use of S1 time series could interest a wide range of end users. Moreover, the advantage of using S1 temporal series rather than the optical time series is that radar sensors are not limited to good weather conditions. The cloud cover, that may be present in the optical images of certain geographical regions, could be a drawback when working with optical satellites. Even though the combined use of optical and radar data did not highly enhance the classification results, the synergic use of microwave and optical data could increase the possibility of transferring the model across different regions.  
The six days temporal resolution of the S1 satellite allows the detection of irrigation events especially when the irrigation frequency is high. As shown by [PERSON] et al. (2018), irrigation could be still detected even if the irrigation took place three days before the radar acquisition. On the other hand, the detection of irrigation becomes difficult if the irrigation event takes place five or six days before the SAR acquisition. However, the effect of irrigation on the radar backscattering can change depending on the irrigation technique used (inundation or sprinkler) and the frequency of irrigation (weekly or daily).  
Although the RF classifier was less accurate than the deep learning model, the RF classifier remains quite precise and competitive. With the dense time series now available, the comparison between classical and advanced machine learning techniques allows us to study the importance of both techniques to intelligently exploit the temporal behavior of SAR signal in the multi-temporal remote sensing data.  
The proposed model was trained on Catalonia region of Spain which is a Mediterranean climatic region. Other regions may have different climatic and soil conditions which could lead to unsatisfactory results when applying the trained model. Hence, for future studies, including other climatic regions in the training process, we would make the proposed model more generic. However, with an accuracy exceeding 90%, the trained model could be applied across regions with close climatic conditions while maintaining good classification results. Currently, studies are concentrating on region adaptation and transfer learning which allow the use of one model on several regions. Therefore, future work should concentrate on adapting the proposed model to use it over other study sites. Moreover, future work should also concentrate on building effective unsupervised approaches that do not require any training procedure. In this case, the transferability of the approach would be easier.
## 8 Commercial Applications  
As previously mentioned, numerous studies suggest the use of MAAs as UV screen substances [23; 33; 41; 146]. However, at present, few products based on MAAs and/or red algae extracts are commercially available.  
HelioguardTM365 is a natural UV-screening active principle based on MAAs from _Porphyna umbilicalis_, commercialized by the Mibelle group. This product has been demon strated to exhibit photoprotective properties against UV-A radiation and presents other beneficial effects for the skin, such as inhibition of lipid peroxidation, and improvement of the firmness and smoothness. Helionori(r) is another active ingredient based on a MAA-enriched extract from _Porphira umbilicalis_ commercialized by the Gelyma company. It can act as a natural UV-A photoprotector, prevents the formation of sunburn cells, protects cell metabolism against UVA irradiation, and protects DNA and cell membranes against UV-A. Ronacare(r) RenouMer is also a commercial active principle based on _Polysipohonia elongate_ extracts, which can reduce skin roughness, present anti-wrinkle effects, and improve skin hydration.  
The sunscreen commercialized with the name ALGA MARIS(r) (Laboratoires Bierritz, Bierritz, France) contains _Gelidium convenum_ extracts (Alga-Gorria(r) enriched in oligoelements and antioxidants such as flavonoids or carotenoids. This sunscreen is a combination of physical filters that protect against UV-B and UV-A radiation, and Alga-Gorria(r), an algal extract that neutralizes the free radicals and protects against premature skin aging.
## 2 Results
### Targeting the CSNK1D-GLI Axis Inhibits CSC-Like Characteristics In Vitro and In Vivo
(**C**) A673 cells were retrovirally transduced with _GLI1_, active _GLI2_ (GLI2 act) or control overexpression constructs, cultured under anchorage-independent conditions and treated with CK1D008 at non-toxic concentration [0.3 \(\mu\)M]. The number of 3D spheroid colonies was counted (\(n=4\) experiments). (**D**) A673 cells were lentivirusally transduced with shCSNK1D or control shRNA (shCtrl) and engrafted in the flanks of NSG mice (\(n=6\) mice each). Tumor volume was measured every 3–4 days. Student’s \(t\) test was used for statistical analysis (* \(p<0.05\); ** \(p<0.01\); *** \(p<0.001\)). acting downstream of CSNK1D, GLI1 and GLI2 overexpression conferred at least partial resistance to CSNK1D inhibitor treatment compared to control cells (Figure 5C). To investigate whether CSNK1D is required for GLI-driven tumor initiation in vivo, we performed xenograft experiments using GLI1-dependent A673 and GLI1-dependent PANC1 pancreatic cancer cells [60] with concomitant inhibition of CSNK1D. As shown in Figure 5D, CSNK1D knockdown in A673 cells severely impaired their engraftment ability compared to control cells transduced with non-targeting shRNA. Similarly, pretreatment of GLI1-dependent PANC1 cells with a low concentration of CK1D008 [0.3 \(\upmu\)M] effectively abrogated the engraftment capacity of PANC1 cells (Figure S5H). These data strongly support a model where CSNK1D regulates the oncogenic activity of activator GLIs in TIC independent of SMO function.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 4 User's Demands Awareness
### Video Request Probability  
A certain video requested by users is mainly related to the user's interest and the video's popularity. Therefore, based on the user's interests and video's popularity, the calculation of video request probabilities can take into account the commonness of the video and the user's personality simultaneously.
## 4 Design of the POM-PSASIDEA Algorithm
### Progressive Optimization Mechanism
#### 4.1.2 Time Window Optimization Phase  
The time window optimization phase (TOP) aims to maximize the task completion rate as the optimization goal through the time window matching rules, which allocate a suitable time window for each target task based on the task planning scheme generated in the allocation optimization phase. The satellite can give priority to the target mission with a high task benefit and early observation start time. Figure 5 illustrates the time window optimization phase in the form of an example.  
Firstly, set the assignment task queue to store the observation tasks required by each satellite in descending order of task priority, match the time window for the observation target tasks required by each satellite in the initial task planning scheme one by one according to the time window matching rule, and store the visible time window scheme obtained at this time in \(Pool_{1}\). The target tasks that fail to be allocated and meet the constraints of the mesostructure of the MSIMPLTS-MLOO model are stored in the candidate task queue \(candidate_{Task}\) in order of priority from large to small. The time window matching rule considers the priority rule of high-priority tasks and the priority rule of the early start time. In other words, priority is given to assigning visible time windows to target tasks with higher priority and selecting visible time windows with earlier start times to match with target tasks.  
Secondly, all free time windows are obtained by traversing the set of satellite visible time windows and the allocated visible time windows, the tasks in the satellite free time windows are randomly assigned to the candidate task queue \(candidate_{Task}\), and the POM-PSASIDEA algorithm is used to optimize the search to maximize the task completion degree and store it in \(Pool_{2}\); finally, the visible time window allocation scheme for the satellites and the target task is obtained by considering \(Pool_{1}\) and \(Pool_{2}\) together. In the time window allocation scheme, the idle time window is defined as the remaining visible time window in the satellite visible time window set that has not been allocated. The detailed description is shown in Algorithm 3.  
Figure 5: Time window optimization phase.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 3 Case 3
#### 5.3.4 Further Verification  
To further verify the correctness and validity of the model, the weather type of a certain day in the winter of December 2020 in Wuhan City, Hubei Province, China was used as the input of the model. The results of the economic dispatch of the inner-loop model are shown in Table 8, and the results of the network loss optimization of the outer-loop model are shown in Table 9. The network loss cost and energy loss cost in four cases are shown in Table 10. The final economic cost of MMGS in the four cases on another day is shown in Table 11.  
power output by DC/AC converters. Dynamic economic dispatch optimizes the active power output of RESs, EVs, and DC/AC converters in MMGS to obtain the optimal daily operating cost of MMGS. Reactive power optimization reduces the daily operating network loss of the distribution network by optimizing the reactive power output of the DC/AC converters to the distribution network. By comparing the results of the four cases, the following conclusions are drawn:  
1. According to the simulation results, the economic dispatch model of the inner-loop in the cooperative multi-objective optimization can reduce the operating cost of MMGS, which makes full use of the ATSET of EVs. Additionally, the optimization of the output reactive power output of the DC/AC converters of the outer-loop can reduce network loss cost and carbon emissions cost of the distribution network. The two cooperate to realize the improvement of the economy of MMGS and the efficient operation of the distribution network.
2. The cooperative multi-objective optimization model not only realizes the optimization of the economic cost of MMGS and the network loss of the distribution network, but also reduces the total carbon emissions of MMGS and the distribution network, which greatly responds to the calls for national carbon neutrality and carbon peak.  
Conceptualization, Z.X. and C.C.; methodology, Z.X.; software, Z.X.; validation, Z.X., M.D. and J.Z.; formal analysis, Z.X.; investigation, D.H.; resources, H.C.; data curation, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, Z.X. and C.C.; visualization, Z.X.; supervision, C.C.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51977086.  
Not applicable.  
The authors declare no conflict of interest.