esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Overview
### FESS Operating Modes  
Different FESS operating modes have been considered depending on user's preferences and rotor speed value, as resumed in Table 1. In particular, when the on/off signal equals zero, _shut down_ always occurs, meaning that the FESS has to decelerate down to zero regardless of its actual speed value. At this operating mode, the EMS is in FESS-based configuration, namely it prioritizes the achievement of zero speed by delivering all the stored energy to the grid, thus ignoring the desired reference grid power.  
Differently, when the on/off signal is equal to 1, two different operating modes can occur depending on the rotor speed value. Particularly, _pre-charging_ occurs when the rotor speed is below the rated value, so FESS has to be accelerated up to the rated speed in order to be able to exchange energy at rated power without any torque overloading. Consequently, also at this operating mode, FESS draws energy from the grid to achieve the target speed regardless of the reference grid power profile synthesized in accordance with PV generation and load demand. This reference power profile is instead prioritized at _ready_, namely when the rotor speed is greater than or equal to the rated speed; in this operating condition, the EMS imposes the achievement of the reference grid power profile by driving FESS accordingly. In this regard, it is worth noting that FESS power is limited appropriately to preserve its speed operating range (from \(\omega_{m,nom}\) to \(\omega_{m,max}\)); this means that no energy is delivered by FESS at \(\omega_{m,nom}\), while no energy is drawn at \(\omega_{m,max}\).  
Regardless of the specific operating mode, the EMS has to always ensure DC-link voltage regulation, by synthesizing reference power profiles accordingly. Once these have  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**On/Off** & \(\omega_{m}>\omega_{m,nom}\) & **Operating Mode** & **EMS** & \(\omega_{m}\)\({}^{*}\) \\ \hline
0 & 1 & shut down & & 0 \\ \hline
1 & 0 & pre-charging & & 0 \\ \hline
1 & 1 & ready & grid-based & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: FESS operating modes.  
Figure 2: Some views of the IRLA-SPM designed for the proposed FESS: (**a**) 3D and (**b**) 2D (dimensions in mm).  
been defined, their tracking is demanded to the M/G-side and grid-side converter control systems, whose structure and operating principle are detailed extensively in Section 3.
## 3 Test Results
### Hardened Properties of RPC
#### 3.2.4 Drying Shrinkage of RPC  
The drying shrinkage characteristics of a plain concrete mixture and RPC mixture containing 20% SF are illustrated in Figure 11. As expected, for one-day air curing (Figure 11a), plain concrete has less drying shrinkage than RPC. It is well known that the drying shrinkage of concrete relies on cementitious material content, water content, paste content, sand content, water permeable porosity, and strength level [39]. The higher the paste content of concrete, the higher the drying shrinkage. It was calculated that the volumetric paste contents of plain concrete and an RPC mixture 0.22 WB-20 SF from Table 1 are 0.672 m\({}^{3}\) and 0.647 m\({}^{3}\), respectively. Thus, the higher value of the drying shrinkage of RPC is due to the higher paste content.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Mixture** & **Density (g/cm\({}^{3}\))** & \multicolumn{3}{c}{**Porosity *} & \multicolumn{3}{c}{**Porosity/Density**} \\ \hline  & & **Normal** & **Warm** & **Normal** & **Warm** & **Normal** & **Warm** \\  & & **Curing** & **Curing** & **Curing** & **Curing** & **Curing** & **Curing** \\ \hline
1 & 0.18 WB-20 SF & 2.23 & 2.19 & 4.14 & 5.75 & 1.85 & 2.62 \\
2 & 0.22 WB-0 SF & 2.26 & 2.24 & 13.47 & 13.10 & 5.95 & 5.84 \\
3 & 0.22 WB-15 SF & 2.21 & 2.24 & 8.65 & 8.16 & 3.92 & 3.65 \\
4 & 0.22B-20 SF & 2.17 & 2.20 & 5.27 & 11.35 & 2.43 & 5.15 \\
5 & 0.22 WB-25 SF & 2.19 & 2.18 & 6.66 & 10.73 & 3.03 & 4.92 \\
6 & 0.26 WB-20 SF & 2.18 & 2.20 & 15.05 & 13.30 & 6.89 & 6.04 \\ \hline \hline \end{tabular} Note. * Porosity of RPC was measured according to ASTM C 642-13 Standard Test Method for Density, Absorption, and Voids in Hardened Concrete.  
\end{table}
Table 4: Density and porosity test results.  
Figure 10: Effect of SF and curing temperature on flexural strength.  
However, for seven-day moist curing (Figure 11b), RPC has lower drying shrinkage than plain concrete, despite its higher paste content. This result may be attributed to the strength and water permeable porosity of the concrete. Regarding strength, the 7-day average strength of the RPC is about 84.563 MPa in comparison to 65.5 MPa for plain concrete, which is a difference of approximately 20 MPa. SF could refine the pore structure and improve the early strength. This enhanced microstructure of RPC produces less water permeable capillary voids, which play a critical role in drying shrinkage. The less the capillary void content of the concrete, the lower the drying shrinkage. This is confirmed by the porosity result shown in Table 4.
# An Intelligent Parking Management System for Urban Areas
## 3 Description of the System
### System Architecture
#### 3.1.2 Gateway  
The gateway node receives the packets sent by the sensor nodes and sends them on to the data centre. It also links communications in the opposite direction. From the hardware point of view, this element is composed of a mainboard similar to that used for the sensor nodes. Therefore, the gateway software will have characteristics and functionalities similar to that used for the sensor nodes without having to perform data packing or unpacking.  
The gateway will communicate with the sensor nodes using the 6 LoWPAN communication protocol. If there is a node in the data centre acting as a border router, communication between the base station and the gateway will be done by 6 LoWPAN. However, if the gateway is located very close to the data centre, a RS-232 serial connection will be used.
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 4 Model Construction
### TPE-CatBoost Model Performance Evaluation  
The model was trained using a specified training dataset and its predictive accuracy was evaluated using a separate test dataset. Figure 5 displays the computed R\({}^{2}\) values of the model. The R\({}^{2}\) value for the training dataset is as high as 0.998. Similarly, the model achieves an impressive R\({}^{2}\) value of 0.906 on the test dataset, indicating a satisfactory level of predictive accuracy.  
As shown in Figure 6, the initial R\({}^{2}\) value of the unoptimized CatBoost model on the testing set was 0.831. The difference in accuracy before and after feature selection highlights the effectiveness of the proposed feature selection method in accurately identifying the essential variables that influence fault prediction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Space Range** & **Optimum** \\ \hline \multirow{3}{*}{iterations} & The number of iterations & min = 40 & 150 \\  & during the training process. & max = 200 & \\  & How fast does the algorithm & min = 0.01 & \\  & move in one step (\(>\)0) & max = 0.5 & \\ \multirow{2}{*}{depth} & Maximum height allowed for & min = 2 & \\  & each tree (\(\geq\)1) & max = 10 & \\  & L2\_leaf\_reg & L2 regularization term & min = 0.01 & \\  & & max = 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter selection for CatBoost.  
Figure 5: A comparison of predicted and measured values for TPE-CatBoost (with 6 input variables) in the (**a**) training set and (**b**) testing set.
## 3 Results and Discussion
### Nutrient Composition  
Forage grasses are considered to be the most remunerative form of animal feed in semi-arid regions. The nourishing value of forages mainly depends on their nutritional composition such as crude protein (CP) and fiber and ash contents [2]. Crude protein is an important element of the animal diet that enhances their milk-producing capacity and maintains meat quality [16]. Both CP and ash contents of three _Chloris gayana_ genotypes exhibited a decreasing trend with increasing soil salinity. The highest CP (6.1%) and ash (15.5%) contents were obtained in ILRI-6633 and ILRI-7384 genotypes at low salinity level (5 dS m\({}^{-1}\)), whereas CV-massaba reported the lowest crude protein (3.8%) and ash content (12.4) at the same salinity level (Figure 5). However, CV-massaba genotype produced a higher CP content at salinity levels of 10-15 dS m\({}^{-1}\). Generally, low dry matter-producing genotypes are known to have higher CP content whereas high dry matter producing accessions are low in nutrition at low salinity levels [30; 39]. All three genotypes produced high CP and ash content at low to medium salinity (0-10 dS m\({}^{-1}\)) levels while a falling trend was observed at higher salinity levels (15-20 dS m\({}^{-1}\)). This shows that these varieties are more suitable for low to moderate salinity conditions.  
Neutral detergent fiber (NDF) mainly consists of hemicellulose, cellulose, and lignin. From the feeding point of view, lower NDF values are desirable for fodder as well as grains [40; 41]. The NDF  
Figure 5: Effect of different salinity levels on (**a**) Ash content (LSD = 1.71; CV = 9.75) and (**b**) Crude protein (LSD = 1.05; CV = 13.65) on three _Chloris gayana_ genotypes.  
values for all three genotypes showed an increasing trend with the growing salt stress. Differences in NDF among the _Chloris gayana_ genotypes show a similar trend as of CP. ILRI-6633 reported the highest NDF (70.9-73.9%) value whereas the lowest NDF values (67.5-72.9%) were observed in CV-massaba for all salinity levels (Figure 6). The average Invitro dry matter digestibility content (IvDMDC) was 42.9% for control, 41% at 5 dS m\({}^{-1}\), 41.6% at 10 dS m\({}^{-1}\), 40.2% at 15 dS m\({}^{-1}\), and 36.1% at 20 dS m\({}^{-1}\) (Figure 6). The highest IvDMDC was obtained in ILRI-6633 at control while the lowest was observed in CV-massaba at 20 dS m\({}^{-1}\).
## 3 Results and Discussions
### Water Levels  
During the first period (Figure 3), the water level was slightly raised until the end of January and then increased sharply to reach the maximum storage level in the last week of February (filling period). This level was kept constant until the end of May (storage period). During the third period, the reservoir was managed to satisfy the water demand, leading to a progressive almost linear decrease in the water level reaching its minimum in the middle of November (emptying period). During the fourth period, at the end of 2015, the rainy season allowed the stored water to reach a higher level than that observed at the beginning of the time series.  
The water surface classification approach allowed the estimation of the water levels from remote sensing images (\(H_{\rm RS}\)) which show strong agreement with in situ (\(H_{\rm IS}\)) measurements (Figure 4). The accuracy of the procedure is confirmed by a very high coefficient of determination (\(r^{2}\) = 0.96, although imposing a null intercept) and a low Root-Mean-Square Error (\(RMSE\)\(\approx\) 1.06 m). The standard deviation, \(\sigma_{\rm XY}\), between measured \(H_{\rm IS}\) and estimated \(H_{\rm RS}\), is 3.32 m.
## 2 The Method for Self-Healing Efficiency Estimation
### Measured Data Processing
#### 2.2.3 Transformation of Data Back to the Space-Space-Time domain  
Filtered data is converted back from the wavenumber-wavenumber-time domain to the space-space-time domain using two-dimensional inverse Fourier transform. Its discrete implementation may be noted as:  
\[\widetilde{w}(x,y,t)=\frac{1}{\sqrt{XY}}\sum_{k_{x}}\sum_{k_{y}}W\big{(}k_{x},k _{y},t\big{)}e^{j2\pi(\frac{b_{x}\cdot x}{X}+\frac{k_{y}\cdot y}{Y})}. \tag{7}\]  
Figure 5: Smoothed adaptive filter mask in the wavenumber-wavenumber domain.  
Figure 6: Filtered wavefield image captured at \(t_{1}\) in the wavenumber-wavenumber domain.  
The comparison between original and filtered wavefield images captured at time moment \(t_{1}\) is given in Figure 7. It may be noted that the amplitude of propagating Lamb waves was decreased dramatically, leaving high amplitudes in the bottom part of the specimen-at the damaged region.
# Remediation of Copper Contaminated Soils Using Water Containing Hydrogen Nanobubbles
## 2 Experimental Methods and Condition
### Electrokinetic Test  
A laboratory test was carried out to apply an electrokinetic remediation process to soils contaminated with copper. The same soil specimens from the batch test, sand and clay, were used to construct contaminated and uncontaminated soil. The copper-contaminated solution was prepared, using copper nitrate powder (Cu(NO\({}_{3}\))\({}_{2}\)) in DW. Then, 500 ppm of copper was contaminated to soil, at a water content of 40%. Clay was tested immediately after contamination. Sand was used as a simulation of the ground condition, and it was contaminated with 500 ppm of copper solution and dried in the shade. Dry sand contaminated with copper was installed with the maximum consolidation and tested. The contamination concentration of copper was referred to as the Korea Standard. To identify the remediation characteristics, different conditions of soils with Jumunjin standard sand and clay were used. In addition, nanobubbles identified as an appropriate enhancer  
Figure 3: Average particle concentration of nanobubbles: (**a**) concentration of nanobubbles in gasoline [15]; (**b**) concentration of nanobubbles [16].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Units** & **Conditions** \\ \hline Soil & - & Sand, Clay \\ Contaminant & - & Copper \\ Contaminant Concentration & ppm & 500 \\ Agents & - & Distilled Water (DW) \\  & & H\({}_{2}\)-Nanobubble Water (NBW) \\ Solid–Liquid Ratio & g:mL & 1:1, 1:2, 1:3, 1:5, 1:10, 1:20, 1:50 \\ Contact Time & Hr & 0.5, 1, 2, 3, 6, 12, 24 \\ Contact Speed & rpm & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Batch-test conditions.  
from the batch test were placed into a Mariotte bottle and injected constantly. The experimental device designed for electrokinetic remediation is shown in Figure 4. The cell and Mariotte bottle are made of acrylic, and the electrode plate used graphite. The nanobubbles were injected into the cell, using a hydraulic gradient. An electrode water tank was installed at each end of the cell, to provide unrestricted outflow of electro-osmosis during the experiments. The testers were designed to conduct five simultaneous tests. The experimental conditions are summarized in Table 4.
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 3 Experimental Verification
### Evaluation Index  
To validate the correctness and precision of the proposed methods, the predicted response must be compared with the real response; the 3 dB relative error is widely used for this in engineering practice. Given predicted response \(\hat{s}_{f_{n_{1}+h/n_{1}+h}}(l\Delta f)\) and real response \(s_{f_{n_{1}+h/n_{1}+h}}(\Delta f)\), to satisfy the 3 dB relative error, the condition is  
\[10\log_{10}\!\left|\frac{\hat{s}_{f_{n_{1}+h/n_{1}+h}}(l\Delta f)}{s_{f_{n_{1}+ h/n_{1}+h}}(l\Delta f)}\right|\!\leq 3\;and\;10\log_{10}\!\left|\frac{s_{f_{n_{1}+h/n_{1}+h }}(l\Delta f)}{s_{f_{n_{1}+h/n_{1}+h}}(l\Delta f)}\right|\!\leq 3 \tag{11}\]  
For each frequency \(\omega\) (\(\omega=\Delta f,2\Delta f,\cdots,l\Delta f,\cdots,\Omega\Delta f\), where \(\Delta f\) is the frequency resolution and \(\Omega\) is the total number of frequencies), the predicted response and true response did not satisfy Equation (7), which was recorded as a frequency 3 dB error. The percentage of the frequency 3 dB error was taken as the percentage of 3 dB.  
In order to evaluate the prediction performance of the whole model, the mean absolute percentage error (MAPE) and root mean square error (RMSE) were also used to evaluate the model. The formulae are as follows:  
\[MAPE=\frac{1}{\Omega}\sum_{l=0}^{\Omega}\frac{\left|\left(s_{f_{n_{1}+h/n_{1}+ h}}(l\Delta f)-\hat{s}_{f_{n_{1}+h/n_{1}+h}}(l\Delta f)\right)\right|}{s_{f_{n_{1}+h/n_{1}+ h}}(l\Delta f)} \tag{12}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{l=1}^{\Omega}\left(s_{f_{n_{1}+h/n_{1}+h}}(l \Delta f)-\hat{s}_{f_{n_{1}+h/n_{1}+h}}(l\Delta f)\right)^{2}}{\Omega}} \tag{13}\]  
The smaller the RMSE and MAPE, the closer the predicted value is to the true value.
## 2 Methodology
The detection outcomes of two different NDT methods are represented by circles and crosses, for four cases (a)–(d). For each case, the corresponding spatial density per sensor along the \(x\)-axis is plotted below. The likelihood of observing a true defect (gray area) depends on both sensors yielding significant hit densities. hit locations after registration are not jointly gridded in the common coordinate system, but appear scattered instead. This is visualized in Figure 2c. After establishing relationships between the individual coordinate systems, the next question is how to implement the density-based fusion concept already introduced before. One central challenge in the decision-level fusion of non-gridded locations is the uncertainty in localization. Two main factors contribute to this uncertainty. First, each sensor's localization ability is limited by the physical resolution as well as the spatial sampling rate. Second, the coordinate transformations computed during spatial registration might be inaccurate to some degree. To be robust, a fusion approach must adequately cope with the inherent uncertainties about hit positions and must associate nearby hits for the purpose of density quantification. This loose concept of \"proximity\" should therefore be mathematically formalized. To that end, various non-parametric techniques have been developed such as Mean shift [8], DBSCAN [9], OPTICS [10], Spectral clustering [11], and Kernel density estimation (KDE) [12; 13]. We select the framework of kernel density estimation. This choice was motivated by considering that our data space typically has only two or three spatial dimensions, independent from the number of sensors. Therefore, the density can be directly modeled without being affected by the curse of dimensionality. Furthermore, it allows us to evaluate the density at arbitrary positions, not only at the hits. Returning to the flow chart, KDE is applied to estimate the spatial hit densities from each individual sensor in Figure 2d,e to associate nearby hits. Finally, Figure 2f consists of a fusion rule that combines the individual densities and produces a fused image in which higher intensity corresponds to increased estimated likelihood of defect presence. Next, we will formally introduce our method using ideas from KDE, and propose different fusion rules that implement the behavior of the gray shaded area in Figure 1 to recognize conflicts and agreement among the densities of different sensors.
## 3 Case Study  
### Case Background  
Wenbishan Tunnel is a two-lane separated extra-long tunnel, located in Sanming, Fujian, with a left tunnel length of 4786 m and a right tunnel length of 4760 m. In this paper, the right-hand tunnel (YK223 + 375\(\sim\)YK228 + 135) is taken as the object of study, with a 360-m-long section of V-grade surrounding rock and a 1567-m-long section of IV-grade surrounding rock. The geological longitudinal section of the tunnel in the right panel is shown in Figure 2. The geological conditions in the tunnel are complex, and most of the surrounding rocks are residual clay and granite with different degrees of weathering. In the section from YK223 + 473 to YK223 + 728, the joints and fissures are developed, the rock is more broken and the rock is a mosaic fracture structure. During the construction process, it is very easy to cause tunnel collapse. Therefore, there is an urgent need to conduct a collapse risk assessment of this tunnel section to reduce the damage caused by the collapse.
## 4 Results and Discussion
### Influence Factors
#### 4.2.1 Climate Factors
In the June-October period, the minimum area occurred in October 2001, and the monthly precipitation and the difference in the monthly precipitation and evaporation in that month were at the lowest as well, thus suggesting that other factors had little influence on the water-surface area during this period. For the November-March period, the water-surface area and the aforementioned climate factors (curve abbreviated) indicate no significant correlation. Although this phase has little precipitation, the precipitation accumulated from the June-October period may maintain the stability of the water-surface area. Meanwhile, it is known that the water demand for agriculture in this area is non-existent during this period. In contrast, the aquaculture, consuming most water in this area, improves the bottom substrate and the elimination of toxic materials through several means, such as draining water, freezing and desiccating the bottom of fishponds in the winter and early spring [39]. It is very important to the success of aquaculture production.
## 3 Environmental Applications of the Tool and Results  
As mentioned earlier, neural network models are now widely used in the environmental sciences, and our tool in particular has been mainly applied in the climate field. Only three examples are given here to illustrate some of the application methods. These make it possible to identify the importance/role (linear, non-linear or threshold) of the causal variables in determining the behaviour of the target variable and to obtain predictions of the value of the target variable for future scenarios of the causal variables.
## 3 Source Rocks Geochemical Characteristics and Distribution
### Distribution of Source Rocks  
The source rocks revealed by drilling are mainly distributed around sag, and distribution of source rocks in sag is mainly predicted based on seismic due to lack of drilling data. The quality of seismic data has been poor for a long time and it isn't easy to characterize the distribution of source rocks in the Junggar Basin. Several basin-level 2D seismic grid lines were acquired in the Junggar Basin from 2018 to 2020. The imaging quality of deep seismic data was significantly improved through the combined processing of 2D survey lines and 3D seismic. The distribution characteristics of mudstone can be defined through the description of formation thickness. We consider the mudstone thickness to characterize the hydrocarbon source rock distribution because most of the mudstone samples of the Fengcheng Formation have TOC's greater than 0.5%.  
The distribution of the Permian source rocks in the western part of the basin is described, as shown in Figure 5. The source rocks of the Permian Fengcheng Formation are widely distributed in the three sags of the basin's western region, with a thickness more than 100 m.  
In summary, the Permian source rocks in the Junggar Basin have high organic matter abundance and are mainly medium-good hydrocarbon source rocks, with organic matter type II, and are developed from low to high maturity. They are primarily distributed in the Mahu, Penyijingxi, and Shawan sags in the western part of the basin.  
Figure 5: Source rock thickness isoline map of the Permian Fengcheng Formation in the Junggar Basin.