esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 1 Introduction
To the best of our knowledge, this is the first DRL work for cooperative downloading in satellite networks; (2) by means of the SAC algorithm for the discretized action space and the neural network design including GAT and parallel FC layers, the proposed DRL-based cooperative downloading framework can effectively learn the dynamics of LEO satellite networks and the optimal policy; and (3) the presented evaluation results provide meaningful insight for future mega-constellations. The rest of the paper is organized as follows. The system model of LEO satellite networks is described in Section 2. The MDP problem is formulated and the proposed cooperative download scheme is presented in Section 3. The evaluation results and the concluding remarks are given in Sections 4 and 5, respectively.
## 4 Analysis Example and Methods
### Results from the BHP Vibration Experiment
#### 5.3.4 Vibration Experiment Results for the BHP with Different Lengths of Pipeline  
The radial acceleration amplitude for the pipe wall at point P for each BHP is shown in Figure 21.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Thickness (mm)**} & **RTV** & **RTV-SWCNTs Composite Materials** & \multirow{2}{*}{**Vibration Reduction(\%)**} \\ \cline{3-3} \cline{5-5}  & & **The Standard Deviation for Radial Acceleration** & \\ \hline
1 & 5 & 0.179 & 0.162 & 9.5 \\
2 & 10 & 0.143 & 0.106 & 25.9 \\
3 & 15 & 0.1202 & 0.0758 & 36.9 \\
4 & 20 & 0.098 & 0.0689 & 29.7 \\
5 & 25 & 0.0892 & 0.0689 & 22.8 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Suppression effect in BHPs with different thicknesses of the elastic layer on pipe vibration.  
Figure 20: Effect of elastic layer thickness on the radial acceleration at point P.(**a**) BHP with RTV as the elastic layer; (**b**) BHP with RTV-SWCNTs composite materials as the elastic layer.  
The radial acceleration amplitude in the double-layer BHP with RTV as the elastic layer and the three-layer BHP with RTV-SWCNTs composite material as the elastic layer both decreased with the increase in the pipeline length. Moreover, as the length increases, the vibration suppression effect in the BHP on the pipeline system increases.  
In reference to Section 5.2.1, Figure 15 (in Appendix C) compares the radial acceleration amplitude in the BHP with the same pipeline length and different elastic layers.  
As shown in Figure 15, the acceleration amplitude in the three-layer BHP with RTV-SWCNTs composite as the elastic layer is significantly lower than that of the double-layer BHP with RTV as the elastic layer.  
The vibration suppression effect of the BHPs with different lengths of pipeline is shown in Table 11.  
The standard deviation for the radial acceleration in the three-layer BHP with SWCNTs added is significantly lower than that of the double-layer BHP with RTV as the elastic layer. As the length increases, the standard deviation for the radial acceleration decreases, and the effect of the pulsation absorption increases. Moreover, when the length is 330 mm, the percentage of the pulsation reduction is the largest, and the vibration suppression effect in the BHP is the best. This lays a certain experimental foundation for further optimization of BHPs.
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
### Remote Sensing of Vegetation in Drought  
Vegetation, Earth's thermometer: the imprints of drought can be read from the vegetation condition [58; 59]. As the vegetation is directly connected with drought [60], its health, and changes in distribution pattern are primarily analyzed [61; 62]. The vegetation indices are generated from the different band combinations, especially the red and near-infrared, and the NDVI is a failed index applied extensively in analyzing the response of vegetation to drought. The Harmonized Landsat-8 and Sentinel-2 (HLS) is a NASA project which aimed to provide daily surface reflectance data generated from the Landsat-8 and Sentinel-2. HLS is widely used for vegetation monitoring, phenology, crop monitoring, and drought-related analysis [63; 64]. A combined radar-optical approach was applied to monitor the drought condition in the savanna region. In this approach, Sentinel-2 and Landsat-8 derived phenological information and integrated it with the Sentinel-1 derived soil moisture data. The derived normalized difference vegetation condition and canopy height model was used to measure the vegetation type structure and dynamics concerning the surface soil moisture index [65]. It is essential to monitor the phenological characteristics of vegetation to address the changes in the ecosystem due to climate change. Using a multi-sensor land surface phenology (MS-LSP) algorithm, a thirty-meter continental-scale land surface phenology data was produced from the HLS data [66]. Along with data continuity, the fusion of Sentinel-2 and Landsat-8 provides a greater possibility for monitoring the land surface and cover changes, particularly in drought analysis. The extended super-resolution convolutional neural network (ESRCNN), a deep learning data fusion algorithm used for fusing Sentinel-2 and Landsat-8, can generate 10 m data [67]. In terms of quantitative assessment, data derived from the ESRCNN method outperform the existing area-to-point regression kriging (ATKPK) method. Apart from the normal two data fusion method, [PERSON] et al. [68] fused Sentinel-2 along with Landsat-7 and 8 in the Google Earth engine cloud platform and produced a surface reflectance output of 30 m image pre-processing techniques including bidirectional reflectance distribution function correction, topographic correction, and band adjustments. The resultant information efficiently measured the seasonal vegetation characteristics in the drought-prone areas of Vietnam and Lebanon. However, the level of accuracy may be subject to varying topographical conditions. As the susceptibility ratio of vegetation to drought varies, hence it is essential to classify the vegetation to evaluate the impacts of drought. The random forest method was applied to resampled Sentinel-2 data to estimate the Mediterranean forest types and their major photocenosis and seasonal phenological characteristics [69]. Similarly, the potential of the Sentinel-2 derived vegetation indices namely NDVI, Carotenoid Reflectance
## 5 Fine-Grained Network Congestion Detection Algorithm Based on Flow Watermarking
### Fine-Grained Network Congestion Detection Algorithm
Let \(\overline{n_{j}}(j=0,\ 1,\ 2,\ 3)\) be the mean of multiple \(n_{j}\). For the encoded flow \(f\), the watermark decoding module performs the corresponding operation to obtain \(n_{j}\) according to the four decoded results. Comparing the decoded value of watermark with the encoded value of watermark, the DRW is obtained. According to the DRW, the network status can be first divided into four levels: normal, slightly congested, moderately congested and heavily congested. When the network status are classified as normal, the \(\overline{n_{j}}\) corresponding to the correctly decoded watermark are calculated, respectively. The Crps is used to detect micro-burst traffic. The changes in network status are divided into more fine-grained levels to realize network congestion detection on a small time scale.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Classification Based on Mode-Based Method  
According to the working mode of tracking sensors, tracking algorithms can be categorized into passive tracking and activity tracking. In the former, the tracker can only estimate the state of the target by the signal directly emitted from the target. In general, the obtained measurement is the relative bearing of the target to the tracker. While in the latter, the extra range information about the target can be acquired by the time of arrival (TOA) or time difference of arrival (TDOA) because these echoes reflected from the target are produced by the tracker. In other words, active tracking contains both transmitters and receivers while the passive tracking only includes receivers. In this section, algorithms based on different tracking mode are introduce and compared in tracking precision. The technology and measurements adopted in every algorithm are also summarized.
## 2 Designing Nominal SAR Operational Parameters at Nominal Altitude
### PRF Selection Considering Ambiguity
Such independency of AAR on the swath number is clear from (2) because the Doppler bandwidth, \(B_{\text{D}}\), is almost constant for all the swaths. The RAR and AAR performances within access region are shown in Figure 6, in which the RAR and AAR performances of Figures 5 and 6 are overlapped in a single figure. PRF should be selected from Figure 6 to satisfy \(-20\)-dB ASR for both RAR and AAR performances. In Figure 6, the dotted line in red represents PRF around 4.6 kHz that is optimum value for PRF because it satisfies the required RAR and AAR performances for both range and azimuth directions as well as for the all the access regions from 20 to 55\({}^{\circ}\). In this way, the optimum PRF can be effectively selected while saving time required for the optimization procedure because the reasonable PRF is intuitively selected among many swaths. Such PRF selection is verified by checking the PRF diamond diagram, which is described in the following section. Figure 4: RAR performances for PRF. Figure 5: AAR performances for PRF. Figure 6: PRF selection from RAR and AAR performances.
## 4 Discussion
### Deep Learning and Model Limitations  
We chose to use deep learning for our algorithm because of its potential to revolutionize medical imaging for disease diagnosis [35; 41; 42]. Furthermore, we needed our algorithm to be rigorous and dynamic, so that, regardless of the section of the TM that was being imaged, the TM could be automatically segmented. We used TensorFlow to create most of the neural network, but the key to obtaining accurate results was the use of two sequential classification steps, each with distinct object detection algorithms.  
As shown, our algorithm does a good job of removing most artifacts within the image. However, sometimes, the artifacts overlap or are very close to the TM, creating a conjunction of pixels that appears as one single object. Thus, most but not all artifact is removed. In the future, we may consider adding a third neural network with a separate image recognition algorithm specifically to identify and remove these types of artifacts, which tend to be vertical lines that originate from strong tissue reflections. While this may improve the image somewhat, it would also increase the processing time. Furthermore, it is unlikely to improve the image substantially enough to add further value to the clinician.  
Most of the time creating this algorithm was spent manually classifying the objects. The object-detection algorithms created thousands of images from each 3D image. However, the first step was to optimize the large-object-detection algorithm so that it best recognized the TM as one single object but did not include nearby artifacts. This tradeoff required lowering the threshold value of the object detection algorithm so that the entire TM was recognized as one object instead of being broken up into several smaller objects. However, doing this led to more artifacts within the TM image. As described in the methods section, we handled this by adding the second stage to the algorithm, using more accurate sorting methods for the smaller object area of the TM. Hardware improvements to reduce the number and intensity of the artifacts within the original image would improve the accuracy of this algorithm and speed training of future iterations of this algorithm.
# Production of Sustainable Postbiotics from Sugarcane Straw for Potential Food Applications
## 3 Results and Discussion
### Organic Acids and Sugars Profiles  
The analysis of the main sugars and organic acids was carried out by high-performance liquid chromatography with a refractive index detection (HPLC-RID) (Table 1).  
The only monosaccharide detected was glucose with an amount of 41.5 mg/g dw, which disappeared after _S. cerevisiae_ fermentation. The same happened with the disaccharide cellobiose, with amounts of 12.5 mg/g dw detected in the non-fermented extract. The presence of these sugars results from their accumulation at the end of saccharification process, and then the yeast uses the glucose as carbon source, resulting in a decrease in its content [39]. As opposed to that, _S. cerevisiae_ is not capable of hydrolyzing cellobiose [40]. The decrease in cellobiose content observed in this study after yeast fermentation may be related to the fact that the extract was sonicated after fermentation, a which process may have promoted the hydrolysis of oligomers released in saccharification leading to a breakdown of disaccharides [41].  
The acetic acid was the main organic acid detected in all samples, and increases after yeast fermentation were observed from 80.7 to 194.17 mg/g of dry extract. Regarding the other organic acids, lactic acid and propionate were only detected in extracts obtained after yeast fermentation. The lactic acid reached amounts of 9.83 mg/g of dry extract, respectively. It was shown that the wild-type strains of _Saccharomyces cerevisiae_ only create trace amounts of D-lactate, most likely because of the action of the methylglyoxal bypass. _Saccharomyces cerevisiae_ does not produce significant amounts of organic acids spontaneously [42; 43].  
Due to the overexpression of aldehyde dehydrogenase-coding genes, the increase in acetic acid level seen following yeast fermentation may be connected to the yeast's reaction to osmotic stress [44].
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 5 Experimental Results and Analysis
### Evaluation of the ELi and IDSER Detector
#### 5.2.2 Evaluation of IDSER Detection  
We analyze the IDSER detection effect of the ELi and IDSER detector using five-fold cross-validation. The results are summarized in Table 4, and the ROC curves are depicted in Figure 9. The TN and FP of the naive Bayes model are the best, achieving 78 and 0, respectively. Additionally, its precision is also the highest, at 1. Decision tree and random forest excel in TP and FN, resulting in recall and F1 score of 0.84 and 0.9, respectively. XGBoost also performs well in detecting IDSER, with TP, FN, and recall being the same as those of random forest and decision tree. Further analysis of the ROC curves shows that random forest, SVM, and XGBoost exhibit superior effects, achieving an AUC of 0.88. The above results show that decision tree and random forest are the most suitable models for IDSER detection.
# Synthesis of High-Precision Sub-Micron CaCO\({}_{3}\) Anticancer Drug Carriers from Coral Remains
## 3 Result and Discussion
### Drug Loading Simulation Experiment  
The co-precipitation method was employed to synthesize calcium carbonate (CaCO\({}_{3}\)) drug carriers, incorporating the fluorescently labeled drug TRITC-Dextran directly into the CaCO\({}_{3}\) synthesis process. The drug loading results of TRITC-Dextran (Figure 9b) shows that nearly all CaCO\({}_{3}\) particles emitted fluorescence signals, indicating effective drug incorporation into the carrier matrix. The results of the loading capacity (LC), which is calculated by Equation (4), reveal that the LC for the CaCO\({}_{3}\) drug carrier was 9.6 wt.%, compared with the LC of 10.3 wt.% achieved by [PERSON] et al. [48] using liposomes for Gemcitabine (Gem) delivery. Typically, the LCs for liposome-based drug delivery systems range between 5 and 10 wt.%. In contrast, [PERSON] et al. [49], who utilized an adsorption method for loading TRITC-Dextran onto CaCO\({}_{3}\), reported an LC of less than 1 wt.%;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Parameters** \\ \hline Adding Order & CaCl\({}_{2}\)-first \\ Stirring Speed (rpm) & 1000 \\ Concentration of EG & 85\% \\ Reaction Time (min) & 60 \\ CaCl\({}_{2}\)/Na\({}_{2}\)CO\({}_{3}\) (M) & 1 M/0.1 M \\ Reaction Volume (mL) & 4, 40, 120, 400 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters for total reaction volume experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Parameters** \\ \hline Adding Order & CaCl\({}_{2}\)-first \\ Stirring Speed (rpm) & 1150 \\ Concentration of EG & 85\% \\ Reaction Time (min) & 60 \\ CaCl\({}_{2}\)/Na\({}_{2}\)CO\({}_{3}\) (M) & 1 M/0.05 M \\ Reaction Volume (mL) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal parameters for CaCO\({}_{3}\) synthesis.  
similarly, [PERSON] et al. (2020), employing the adsorption method, achieved a maximum LC of only 0.01 wt.%. These comparisons highlight the efficiency of the co-precipitation method and its promising capability for drug carrier fabrication using CaCO\({}_{3}\).  
Figure 8: SEM images of CaCO\({}_{3}\) synthesized with varying total reaction volumes: (**a**) 4 mL; (**b**) 40 mL; (**c**) 120 mL; (**d**) 400 mL; (**e**) average particle size as a function of different reaction volumes.
## 6 Implementation
### The HealthTracker Admin Server  
This server, as the HealthTracker Upload server (see Section 6.2), is also implemented using _Node.js_, but it has been developed as a separate component, deployable on the same machine or a different one. Its core functionality was to manage the authentication of some \"admin\" accounts, allowing them to access the patient's recordings from a specific dashboard (which will be presented in Section 6.5).  
As anticipated before, user authentication is managed by the _Firebase Authenticator_, but an extension of its features was nevertheless needed to allow the definition and the management of subsets of the user accounts that can access sensitive information. This is the main reason that drove us to include enhanced security functionalities within the HealthTracker Admin server API, whereby any operation requested by a user is checked to verify whether that user has the rights to perform the above operation, after having verified first the legitimacy of the user's token (by means of a remote call to the _Firebase Authenticator_).  
In addition to verifying the user identity and checking that (s)he is allowed to access the data, we implemented a specific endpoint for retrieving the _ranges of time series_ for a specific user. By \"ranges\", we mean the contiguous portions of data delimited by a start and an end time. This endpoint allows the user to choose what time duration to consider as a splitter between a range and the next (which we will refer to now on as _Cutting Threshold_ or _CT_) and what time granularity to use for its interpretation.  
Referring to Figure 9 to better understand the ranges generation algorithm, we can observe the time series during different phases of the procedure (from 1 to 4).  
An index (represented by a green arrow) scans through the different time points (modeled as orange circles), and for each step, except for the first one where only the range starting point is defined, it calculates the temporal distance between the current point and the previous one (the considered gap is highlighted by a dotted red double arrow for each step).  
If the CT is greater than or equal to the current time gap, the algorithm advances to the next step, while if the CT is smaller than the current time gap, the algorithm closes the current unfinished range (defined as \(R1\) in step 4) and starts a new one. After we obtain this \"ranges\" representation, it is possible to request the time logs directly using the start and end times of the single range. This operation returns all the data points of the requested time series in the specified interval.  
In our prototype, the HealthTracker Upload server, the HealthTracker Admin server, and the time-series database have all been deployed on different virtual machines managed by _Chameleon Cloud_[36].
# Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
## 3 Bearing Capacity and Failure Modes
### Failures of Cables  
A given CSPS fails when the wind load continues to increase. Generally, the new CSPS fails owing to the failure of columns, beams, cables, triangle brackets, fasteners, or modules. Because columns, beams, fasteners, and modules are traditional components in normal arrangements, and their failure modes have been studied in detail, this subsection primarily focuses on the failures of cables and triangle brackets. A cable breaks when its internal force exceeds its resistance force. For the stainless-steel strand used in the proposed CSPS support system, the resistance force of the cables, \(F_{\rm{tk-C}}\), was calculated using Equation (2), as recommended by [38].  
\[R_{\rm{tk-C}}=KS\sigma_{\rm{b}}/1000 \tag{2}\]  
Figure 5: Midspan vertical deformation of the structure as the wind load increases.  
where \(K=0.86\) is the strength reduction factor, \(S\) is the sectional area (m\({}^{2}\)), and \(\sigma_{\rm b}=1570\) is the tensile strength (MPa).  
Based on Equation (2), the required resistance force is transformed into the required sectional area \(S\) when only the control cases are considered. Figure 6 shows the required cross-sectional areas of cables 1 and 2 (\(S_{1,2}\)), and cable 3 (\(S_{3}\)) as the wind load increases. The results show that \(S_{1,2}\) and \(S_{3}\) increase with increasing wind load. When the wind load \(w_{\rm k}\) is between 0 and 2.0 kN/m\({}^{2}\), the curves of \(S_{1,2}\) and \(S_{3}\) separate the required cross-sectional area space into two areas. Under a given wind load, if the cross-sectional area is smaller than the required cross-sectional area, the structure fails under the corresponding control condition. Consequently, the failure modes of the CSPS are summarized as follows: (1) when the cross-sectional areas of cables 1 and 2 are smaller than \(S_{1,2}\) and the cross-sectional area of cable 3 is larger than \(S_{3}\), the structure is prone to failure in Case 180\({}^{\circ}\); (2) when the cross-sectional areas of cables 1 and 2 are larger than \(S_{1,2}\) and that of cable 3 is smaller than \(S_{3}\), the structure is prone to failure in Case 0\({}^{\circ}\); and (3) when the cross-sectional areas of cables 1, 2, and 3 are smaller than the required cross-sectional area, the structure is prone to failure in both cases 180\({}^{\circ}\) and 0\({}^{\circ}\).
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 2 Study Area and Data
### Spatial Datasets
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Data** & **Variables** & **Data Type** & **Scale** \\ \hline Aerial photograph & Landslide location & Point & 1:1000 \\ \hline \multirow{6}{*}{Topographical map a} & Slope gradient [1  & & \\ \cline{2-3}  & Slope aspect & & \\ \cline{2-3}  & Curvature & & \\ \cline{2-3}  & Topographic wetness index (TWI) & & \\ \cline{2-3}  & Stream power index (SPI) & Grid & 1:1000 \\ \cline{2-3}  & Slope length factor (SLF) & & \\ \cline{2-3}  & Standardized height (m) & & \\ \cline{2-3}  & Valley depth (m) & & \\ \cline{2-3}  & Downslope distance gradient (DDG) (rad) & & \\ \hline \multirow{3}{*}{Forest map b} & Timber diameter & & \\ \cline{2-3}  & Timber type & Polygon & 1:25,000 \\ \cline{2-3}  & Timber density & & \\ \cline{2-3}  & Timber age & & \\ \hline \multirow{3}{*}{Soil map c} & Soil depth & & \\ \cline{2-3}  & Soil drainage & Polygon & 1:25,000 \\ \cline{1-1} \cline{2-3}  & Soil topography & & \\ \cline{1-1} \cline{2-3}  & Soil texture & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Landslide influential variables used in this study. Figure 4: Landslide-influencing variables related to vegetation and soil: (**a**) timber type, (**b**) timber diameter, (**c**) timber density, (**d**) timber age, (**e**) soil depth, (**f**) soil drainage, (**g**) soil topography, and (**h**) soil texture.