esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 6 Limitations and Challenges
### Using RS Products to Predict Soil Properties  
The use of RS measurements as a substitute for soil analyses is a way to increase the density of soil information at a low cost. However, this surrogate often remains difficult to obtain with satisfactory accuracy and broad spatial coverage. With respect to accuracy, work remains to be conducted to better distinguish the effects of different factors on the spectral response of a soil property. A typical example can be taken for mapping SOC using the spectral signature of bare soil. A large number of limitations include, for example, the following:  
* How do we discriminate bare soil from vegetated soil, e.g., what NDVI threshold should we use? Which unmixing method should we use for partly vegetated pixels?
* How do we distinguish bare soil from soil covered with dead vegetation residues? Additionally, what threshold should we use?
* How do we take into account the complex effects of different soil properties at a given time/location (e.g., vegetation, residues, moisture, roughness, SOC, and clay or lime content)?
* How do we extend the coverage of bare soils? Mosaicking is now well developed, but what time period should we take? What are the limitations related to mosaicking? How can we take into account the fact that different dates are usually associated with different situational conditions (e.g., moisture and roughness)?
* In many cropped regions, more and more soil will no longer be bare due to the implementation of cover crops and/or seeding under a vegetative cover.
* How do we take advantage of various RS products and sensors, depending on their resolution (in space, time, and spectral domain), remoteness and related perturbing factors?
* At the field or farm-scale, responding to these questions may imply using several RS sensors. For example, using UAV RS enables the selection of the most adapted dates to obtain bare soil imagery together with minimizing the perturbing factors of soil spectral data. This is, however, not feasible for broad-scale monitoring. The effect of some perturbing factors may also be studied using airborne imagery. Thus, studies comparing laboratory spectra, proximal sensing, UAV, or airborne data to satellite imagery, should be conducted in order to estimate/reduce the errors due to changes in spectral resolution/remeteness and to improve the criteria for selecting the relevant satellites data (e.g., [324; 325; 326]).
## 6 Result and Discuss
### Transmission Efficiency Analysis
#### 6.3.3 Energy Consumption Analysis  
RL-ADTA (0.1, 0.2, 0.01) and RCAR are used for the comparison of energy consumption. The total energy consumption of all the nodes in the underwater network during the simulation are considered. The average energy consumption is shown in Figure 14.  
Figure 14 shows that the trend in energy consumption of the four algorithms is that, as the number of nodes increases, the energy consumption also increases synchronously. As the node density increases, more nodes participate in the process of data forwarding, causing the energy consumption to gradually increase. The RL-ADTA uses the design of the 'virtual pipeline', like that of RCAR, which reduces the training space of reinforcement learning, shortens the convergence time, and reduces the energy consumption. In the selection of relay nodes, it pays more attention to the location of relay nodes and the delay  
Figure 14: Comparison of energy consumption between RCAR and RL-ADTA (0.1, 0.2, 0.01). The comprehensive energy consumption of RCAR and RL-ADTA includes transmission, reception, calculation, and idle energy consumption.  
of the transmission channel, making the selection range of relay nodes more accurate and further reducing the energy consumption. RL-ADTA makes use of all kinds of information sent by the neighbor nodes to form the state information of the underwater network, instead of using ACK packets; thus, it further reduces the communication energy consumption. The most important application is edge prediction, which greatly reduces the amount of data transmitted in RL-ADTA. Therefore, the energy consumption of RL-ADTA is greatly reduced compared with that of RCAR. Compared with RCAR, when the number of nodes is 100, RL-ADTA (0.1) reduces the energy consumption by 44.4%; when the number of nodes is 200, RL-ADTA (0.1) reduces the energy consumption by 44.7%; and when the number of nodes is 300, RL-ADTA (0.1) reduces the energy consumption by 45.2%.  
Due to the difference in the accuracy\({}_{low}\) setting of the RL-ADTA algorithm, RL-ADTA (0.01) sends the most data, RL-ADTA (0.1) takes second place, and RL-ADTA (0.2) sends the least data. This is why RL-ADTA (0.2) consumes the least energy, followed by RL-ADTA (0.1) and RL-ADTA (0.01). The RL-ADTA consumes less computational energy, but saves more communication energy.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 6 Conclusive Observations
### Road Conditions  
The shortest route for transportation has been applied in theory and practice for many years to minimise the travel time and, hence, the response time in the disaster relief operation. However, after any disaster scenario, the road condition changes, which directly affects the travel time. Therefore, in practice, the distribution model must consider the road conditions after the disaster to make the estimation of travel time between the supply point and disaster regions more realistic. One of the practical approaches to overcoming this is to use a fuzzy variable that reflects different road conditions while finding the best feasible travel route. The dynamic model applied in this paper covered the varying road conditions, one of the major driving components in a distribution plan, to reflect a post-disaster roads condition. Fuzzified distance matrix is calculated based on road conditions to reflect the effective travel time. This inclusion of fuzzified effective distance reflects a more realistic and practical approach to finding the effective shortest distance between supply points and disaster regions.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 2 Need of Sensors in Transmission and Distribution Systems
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Characteristics of Current Structure of The Power Grid** & **Detrimental Impacts** \\ \hline
1. Aging infrastructure. & 1. Increased probability of equipment failure with high-cost replacement and repairment options. Concerns for power system efficiency. \\ \hline
2. Exposure to extreme weather conditions. & 2. Makes equipment susceptible to faults and degradation over time. Concerns for power system efficiency. \\ \hline
3. Limited reach of existing monitoring and protection systems. & 3. Incomplete monitoring of faults with improper protective device coordination and incorrect operations of protection. Concerns for power system reliability and efficiency. \\ \hline
4. Increase in energy demand. & 4. Increase in power flow leading to congestion. Concerns for power system reliability. \\ \hline
5. Penetration of distributed energy sources. & 5. Intermittent power flow with voltage, current and frequency profile deviations, and grid instability. Concerns for power system security. \\ \hline
6. Increased use of control systems. & 6. Increased unpredictability of cascading events. Concerns for power system stability. \\ \hline
7. Transformation into smart grids and DC-based grids. & 7. Unpredictable faults and disturbances. The inability of traditional monitoring equipment to handle bidirectional power flow. Concerns for power system reliability and security. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of Current Structure of The Power Grid. Sensors have been used in some of the key areas of the transmission and distribution systems (e.g., overhead transmission lines, underground cables, transmission line equipment, insulators and lightning arrestors, substations equipment, power transformers, and circuit breakers, transmission and distribution line poles, battery systems). Sensors form an important input facing part for the remote terminal units (RTUs) and phase measurement units (PMUs) in a Supervisory control and data acquisition (SCADA) system [27; 42]. Discrete and precise sensors are required to serve various purposes on the power grid such as the sensing of the working temperature of equipment, the measurement of rated and leakage currents, and acoustic sensing for better monitoring of the transmission and distribution lines. Various sensors such as magnetic sensors, vibration sensors, acoustic sensors, fiber optic sensors, infrared ray sensors, etc., have been deployed on insulators, power conductors, power transformers, breakers, and other components of the power system, as presented in Figure 1[2; 43].
# Spatial Variability of Grouting Layer of Shield Tunnel and Its Effect on Ground Settlement
## 3 Modeling Aspects
### Random Model
summarized several previously published articles and recommended that the horizontal scale of fluctuation ranges between 10 m and 40 m, and the vertical scale of fluctuation ranges between 0.5 and 3.0 m [31]. The recommended range can be adopted in this work. The focus of this study was to explore the influence of randomness of grouting-layer thickness on the ground-surface settlement. Under the assumption that the \(g\) is fixed, the randomness of grouting thickness would lead to the randomness of excavation boundary convergence. That is, the random distribution of radial displacement of each node reflected the spatial variability of grouting-layer thickness. The randomness of the grouting-layer thickness was simulated by controlling the displacement of each node on the excavation boundary. Regarding the randomness of grouting-layer thickness, the COV of the grouting layer ranges from 0.1 to 0.4 (see Section 2 for details). The SOF of the grouting layer is difficult to determine statistically due to the limited collected data. However, it is recognized the spatial fluctuation of the grouting layer in practice mainly relies on the grouting pipe layout. Thus, the SOF of the grouting layer is assumed as the circumferential spacing of the grouting pipes. In this study, a one-dimensional random field is employed and the SOF of the grouting layer is taken as 4.71 m. This SOF corresponds to a four grouting pipes distribution, which is the most commonly adopted in tunnel projects. In short, parametric studies were divided into two categories. In (a) traditional soil parameters, the COV of Young's modulus, COV of cohesion, COV of friction angle, SOF of the horizontal direction and SOF of the vertical direction, and (b) grouting-layer thickness: the mean value of grouting-layer thickness \(t\) (Corresponding to the gap parameter \(\delta\)), COV of grouting-layer thickness. In the random analysis of each parameter, Monte Carlo simulations with 300 realizations were conducted to obtain a statistically stable result, with an error of less than 0.2% (Figure 6). All random variables were considered as truncated normal distribution in order to eliminate some unrealistic data using statistical intervals of the input parameters. The details of the random parameters are shown in Table 3.  
Figure 6: Validation of mean and standard deviation.
## 4 Experiments  
### Dataset and Metric  
To verify the effectiveness of the proposed LFASM method, we conducted experiments on three important datasets, namely VehicleID, VeRi-776, and PKU-VD, and compared our results with those of the state-of-the-art vehicle methods for re-ID.  
VeRi-776 [3] contains roughly 50,000 images of 776 vehicles captured by 2-18 cameras from different view angles. Every image in the query set contained 678 images of 200 vehicles, in which the images were captured by all the cameras in the cars.  
VehicleID [11] comprises 221,763 images of 26,267 vehicles captured by different cameras and provides three test subsets of different sizes, with 800, 1600, and 2400 gallery images, respectively, such that we can evaluate our model on different data scales. The dataset contains images captured from two view angles: front and back.  
PKU-VD [12] contains a large number of images with rich annotations (vehicle model and color). So far, it is the largest dataset for vehicle re-ID and is divided into two subsets: VD1 and VD2. The images in VD1 and VD2 were captured from surveillance videos and traffic cameras, respectively. They comprise approximately 1,098,649 and 807,260 images, respectively.  
We computed the mean average precision (mAP) to evaluate the performance of our model. Average precision (AP) is a measure that considers both recall and precision. The AP for image \(q\) can be expressed as  
\[AP(q)=\frac{\sum_{k}P(k)\times rel(k)}{N_{gt}(q)},\]  
where \(N_{gt}(q)\) is the number of ground truths, P(k) is the precision at rank \(k\), and \(rel(k)=1\) when the matching of query image \(q\) to a test image is satisfied at rank \(k\).  
The mAP is the mean value of APs of all queries and can be expressed as  
\[mAP=\frac{\sum_{q}AP(q)}{Q},\]  
where \(Q\) is the number of query images. The mAP combines both precision and recall and is a comprehensive evaluation criterion.  
Figure 8: When the query is at the edge of the space in its category (**left sample**), it is more easily recalled as a false-positive. One of the methods to avoid this is to enlarge the inter class distance (**right sample**).
# Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers
## 4 Experimental Methodology
### Validation Methods Selection  
There are various methods or techniques to compare the results obtained from classification algorithms. One of these techniques is cross validation. The pioneer in using cross-validation was [PERSON] [66] in 1931. In his research work, he divided the data into two parts, a sample was used for regression and a second sample was used for prediction. In the 70's, [PERSON] [67] and [PERSON] [68] formally developed the concept of cross-validation, which is a statistical method to divide a set of data into different subsets. Its application is very useful when the number of data is relatively small due to the complexity or even impossibility of obtaining it. There are different variants of validation, among which are:  
Hold-Out: the complete set of data is taken and divided into two subsets, the first one dedicated to the training phase and the second to the test phase. The partition of the data is done by taking random elements.  
K-fold cross validation: the data set is divided into K partitions that give K mutually exclusive subsets. That is, one of the K subsets is used as a test set and the remaining sets are grouped to form the training set. The procedure is repeated K times by exchanging the test set to ensure that the K subsets have been used in the test phase.  
\(5\times 2\) cross validation: It is important to highlight the work of [PERSON] [69], who compares various evaluation methods. He proposed to apply \(5\times 2\) cross validation, which consists of repeating a cross validation five times with K = 2. In each of the five executions, the data is randomly divided into two subsets of data, one for training and the other for testing. Each of the training partitions are taken as input of the classification algorithm and the test partitions are used to make a test of the final solution.  
Considering the high imbalance of some of the datasets used (for example, Polish_1, with an IR = 24.9), it was decided to use the \(5\times 2\) cross validation method, to compare the results obtained in the investigation.  
Figure 1: Stages of the proposed methodology.  
For this, the datasets were divided using the KEEL software [70], which allows to store, in each case, the partitions obtained. This is a clear advantage, since the different classification algorithms are compared on the same test sets, in each case.
## 2 Materials and Methods
### Extraction, Isolation Procedure and Characterization of Compounds  
The leaves of _Plragmantltera capitata_ were air-dried, and after grinding, the powder obtained (2.3 kg) was macerated with methanol (MeOH), followed by filtration with Whatman N\({}^{\circ}\)1 and evaporation of the solvent in vacuo to yield 210 g of a greenish extract (MLPC). Ten grams of this extract was kept for biological assays while the remaining 200 g was dissolved in ethyl acetate (EtOAc), and after filtration and evaporation, 23 g of ethyl acetate extract was obtained. This extract was then subjected to silica gel open column chromatography eluted with a gradient of _n_-Hexane-EtOAc. Two hundred fractions of 150 mL each were collected and grouped into 10 fractions (F\({}_{1}\)-F\({}_{10}\)) based on their thin-layer chromatography (TLC) profiles after evaporation. Fraction F\({}_{3}\) (0.6 g) was submitted to column chromatography (CC) eluted with _n_-Hexane/EtOAc (80:20, _v_/_v_) to afford 78,15\(\alpha\)-dihydroxy-3\(\beta\)-palmitatelup-20(29)-ene (120 mg) (**1**) [40]; fraction F\({}_{6}\) (0.8 g) was submitted to column chromatography eluted with 100% EtOAc to afford 3-O-\(\beta\)-D-glucopyranosyl-28-hydroxy-\(\alpha\)-amyrin (18 mg) (**2**) [41]. Fraction F\({}_{10}\) (2.5 g) was further purified by column chromatography and eluted with 100% EtOAc to yield apigenin- 8-C-\(\beta\)-D-glucopyranoside (35 mg) (**3**) [42]. The structure of these isolated compounds was determined using spectroscopic techniques, that is, mass spectrometry (MS) and one- and two-dimensional nuclear magnetic resonance (1D and 2D NMR) and by comparison with data from the literature.  
Two kilograms of ground entire plants of _Globimetula dinklagei_ and crushed powder from seeds of _Tapinanthus bangwensis_ were separately extracted with a mixture of methylene chloride and methanol (CH\({}_{2}\)Cl\({}_{2}\): MeOH, 1:1, _v_/_v_) at room temperature for 48 h to yield 120 g of a greenish oily extract (MEGD) and 170 g of an oily crude extract (MSTB), respectively. The three lupan-type triterpenadrivatives [globimetulin A (**4**), B (**5**) and C (**6**)] were isolated from the entire plant extract of _G. dinklagei_ [43] while the compounds bangwaeleanene A (**7**), bangwausrene B (**8**) [44] and 3\(\beta\)-acetoxy-11,12-epoxytaraxerod (**9**) [45] were isolated from the seed extract of _T. bangwensis_. The isolation procedure and structure elucidation of these compounds were previously described [17]. The chemical structures of all compounds are presented in Figure 1.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 4 Experimental Results
### Security Analysis
#### 4.1.1 Histogram of Images  
In order to test the pixel distribution of the images generated in different phases, we give the corresponding experimental results and histograms for Lena as an example, as shown in Figures 14 and 15.  
As can be seen in Figure 15, the original image (a) and the recovered image (d) in Figure 14 are the same, indicating that the proposed scheme can recover the original image losslessly. In addition, the pixel distribution of the encrypted image (b) and the embebded encrypted image (c) is even and chaotic that is different from the pixel distribution of the original image. It can be concluded that it is almost impossible to obtain the information of the original image from the encrypted image.  
Figure 14: The different phases of EPBTL on Lena image: (**a**) Original image, (**b**) Encrypted image, (**c**) Emebded encrypted image, (**d**) Recovered image.  
Figure 15: The histograms of images at different phases of EPBTL on Lena: (**a**) Histogram of the original image, (**b**) Histogram of the encrypted image, (**c**) Histogram of the embebded encrypted image, (**d**) Histogram of the recovered image.
## 6 Influence Analysis of Construction Parameters and Field Verification
### Impact Analysis of Friction between Shield Skin and Soil  
The calculation condition (\(P_{s}^{*}=0.90,V_{g}^{*}=1.00,F_{f}^{*}=0.13\ \sim 0.68\)) was selected to analyse the influence of the friction between the shield skin and soil. Figure 18 depicts the vertical settlement curve at point A for various \(F_{f}^{*}\). It can be concluded that with the increase in \(F_{f}^{*}\), the uplift value of An increased when the cutterhead passed through, and the settlement value of A also increased when the shield tail passed through. This was because the ground around the shield produced horizontal displacement under the friction between the shield skin and the soil, which indicated the direction of tunneling and compressed the front layer, causing elevation, and collapsed the layer after the shield passed. The vertical displacement of the soil above the shield is given in Figure 19. In addition, the horizontal displacements along the axial and transverse directions at the hance of the tunnel are presented in Figures 20 and 21, respectively. The radial displacement of the segment is shown in Figure 22. According to the results, it can be seen from Figures 19-22 that with the increase of \(F_{f}^{*}\), the vertical displacement of the stratum above the shield gradually increased; the horizontal displacement of the stratum on the shield's side increased gradually; the ovality of the segment increased from 16.53 mm to 18.30 mm, and a notable change was not observed.  
Hence, increasing the friction between the shield skin and the soil was extremely detrimental to the deformation control of the water-rich soft stratum. Consequently, the jacking pressure should be closely monitored in practice, and bentonite slurry should be injected around the shield shell in time to reduce friction between the shield skin and the soil.  
Figure 21: Horizontal deformation of the stratum along the transverse direction under different friction coefficients \(F_{f}^{*}\).  
Figure 22: Lining ovality under different friction coefficients \(F_{f}^{*}\).  
Figure 20: Horizontal deformation of the stratum along the axis direction under different friction coefficients \(F_{f}^{*}\).