esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### REE Concentrations in the Roots of Scutellaria bicalensis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Habitats** & **Profile** & **Layer** & \begin{tabular}{c} **Depth** \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} **EREE** \\ **(mg\(\cdot\)g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **ELREE** \\ **(mg\(\cdot\)g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **EHRee** \\ **(mg\(\cdot\)g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **LREE/HREE** \\ **(mg\(\cdot\)g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Lav/Yb\({}_{N}\)** \\ \end{tabular} & \begin{tabular}{c} **\(\delta\)Eu** \\ \end{tabular} & \
\begin{tabular}{c} **\(\delta\)Ce** \\ \end{tabular} \\ \hline \multirow{8}{*}{
\begin{tabular}{c} NGHs \\ (hornblende \\ genesis) \\ \end{tabular} } & Soil & 0–20 & 169 & 150 & 18.2 & 8.28 & 9.52 & 0.734 & 0.872 \\  & regolith & 20–30 & 259 & 235 & 23.8 & 9.88 & 11.6 & 0.596 & 0.895 \\  & regolith & 50–70 & 259 & 237 & 21.2 & 11.2 & 13.8 & 0.572 & 0.889 \\  & regolith & 260–270 & 255 & 231 & 24.6 & 9.37 & 9.9 & 0.650 & 0.902 \\  & regolith & 280–300 & 243 & 220 & 23.2 & 9.47 & 10.5 & 0.587 & 0.842 \\  & regolith & 310–320 & 289 & 259 & 30.3 & 8.54 & 9.24 & 0.538 & 0.888 \\  & regolith & 340–360 & 242 & 221 & 21 & 10.5 & 12.9 & 0.612 & 0.883 \\  & bedrock & 540–560 & 233 & 208 & 24.4 & 8.56 & 9.48 & 0.563 & 0.875 \\ \cline{2-10}  & \multirow{4}{*}{Profile 2} & soil & 0–10 & 238 & 210 & 28.3 & 7.4 & 8.19 & 0.729 & 0.852 \\  & soil & 20–40 & 134 & 117 & 17.7 & 6.6 & 6.31 & 0.879 & 0.901 \\  & regolith & 60–80 & 238 & 218 & 20.4 & 10.6 & 14.3 & 0.619 & 0.827 \\  & regolith & 110–120 & 146 & 126 & 20.7 & 6.08 & 5.83 & 0.756 & 0.883 \\  & regolith & 210–220 & 151 & 132 & 18.6 & 7.11 & 7.36 & 0.693 & 0.867 \\  & regolith & 230–240 & 172 & 149 & 23.5 & 6.32 & 6.11 & 0.700 & 0.918 \\  & regolith & 290–300 & 142 & 126 & 16.6 & 7.55 & 8.01 & 0.621 & 0.890 \\  & regolith & 510–520 & 180 & 163 & 16.9 & 9.67 & 12.4 & 0.660 & 0.783 \\  & regolith & 560–580 & 223 & 202 & 20.6 & 9.81 & 10.6 & 0.620 & 0.889 \\  & bedrock & 600 & 264 & 252 & 12.2 & 20.6 & 35.8 & 0.724 & 0.921 \\ \hline APHs & \multirow{2}{*}{profile 3} & soil & 0–20 & 119 & 107 & 12.6 & 8.43 & 9.87 & 0.841 & 0.990 \\ (loess) & & loess & 20–40 & 139 & 124 & 15.6 & 7.90 & 9.32 & 0.835 & 0.982 \\ \hline BCHs & \multirow{2}{*}{profile 4} & soil & 0–20 & 136 & 121 & 14.8 & 8.17 & 9.23 & 0.775 & 0.981 \\ (loess) & & loess & 20–40 & 133 & 118 & 14.6 & 8.11 & 9.26 & 0.799 & 0.968 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters on REEs distribution characteristics along profiles in various habitats, Hebei Province.
# Energy Consumption Model for Drilling Processes Based on Cutting Force
## 1 Introduction
Whether these existing models can accurately describe the energy consumption of the drilling process is yet to be verified. Besides, as the name suggests, in an MRR-based model, the only considered variable is MRR. However, the same MRR can be combined from many different processing parameters; cutting experiments have shown that energy consumption is not always the same at the same MRR, as many other affecting factors need to be considered. In addition, DPT-based models, such as [PERSON] et al. [19], only consider the relationship between cutting power and total power; the coefficients in their model are purely obtained by mathematical regression thus lacking a clear theoretical basis. In this paper, an energy consumption model for drilling processes was proposed. Idle power, cutting power, and auxiliary power were included in the proposed energy consumption model. The performance of the machine determines the idle power which can be calculated from the spindle speed. The cutting power was calculated from the cutting forces on the cutting edge. The auxiliary power was calculated from the cutting power by establishing its relationship to the cutting power. The model was validated in experiments and achieved high prediction accuracy.
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 2 Related Work  
Multiple beams forming with a single carrier are utilized in NOMA systems to accommodate numerous users as a two-stage beam forming solution for modular beam forming vectors, according to the author in [32]. A reduced total transmission packet shaping issue is built to identify both users' packet-shaping vectors and power.  
The author in [33], established successful precoding and detecting procedures to produce a considerable difference between users' effective channel gains, allowing NOMA's potential to be achieved even when the users' initial channel conditions are comparable. The author investigated the performance of MIMO-NOMA when numerous users are aggregated into a group, finding that MIMO-NOMA outperforms MIMO-OMA in terms of total channel capacity and total practical capacity [34].  
Using statistical channel state information at the transmitter, the ergodic capacity maximization problem for selective Rayleigh fading MIMO-NOMA systems was investigated in [30]. The MIMO-NOMA schemes greatly outperform the conventional OMA scheme, according to numerical results.  
Following a review of the concept of integrating NOMA downlink with MIMO, an experiment was carried out to assess the performance of NOMA downlink combined with MIMO under realistic settings in [35]. In UL, the user connection. The author investigated NOMA in [36], considering numerous specified power allocation techniques. It has been demonstrated that NOMA with the suggested user pair technique outperforms NOMA with the previously described signal realignment.  
The author looked in [37] at many NOMA DL and UL user power field-based communication systems with different fading bindings for all users who can follow one of the many conceivable distributions. At high SNRs, analytical expressions of the OP for the NOMA DL and UL systems were derived.  
An unmanned aerial vehicle-assisted NOMA network with UL and DL transmissions was explored, and analytical expressions of OP as the major measure were derived in [38]. The author explores a novel UL/DL NOMA system with a uniform relay and set decode order that involves the use of statistical channel state information, resulting in enhanced fairness and applicability [39]. The author evaluated the potential of UL and DL resource utilization, adaptive control, and power control for wireless communications systems under the assumptions of in-band full-duplex BSs, NOMA operation, and queue stability limitations.  
A method is proposed for solving by finding a correlation similarity in [40]. The efficacy of different NOMA plots over the tapping delay line channel in both normal and fast UE speed and correlation-level modeling was explored by the author in [40; 41; 42]. With UE's normal and fast speed, NOMA methods work differently.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 5 Discussion
For the hybrid architecture, broadcasting nodes broadcast data and metadata while the remaining agent nodes broadcast metadata at a given time step such that the combined effect is equivalent to if all nodes transmit data. This is the reason for getting overlapping DSRC channel utilization for the data collection using all 3 methods. The significance of this research is that the proposed optimization framework minimizes communication cost and latency when selecting broadcasting nodes and agents. The two problems--data loss due to node isolation and redundant data transmission by agent nodes which can occur if a new solution to the optimization is not found when the topology of the vehicular network changes--could be overcome with frequent optimization (at an optimization frequency greater than or equal to 4 Hz) and by setting a low (0.005) entropy threshold value, as evident from the results. As data is the basis for obtaining global network decisions such as routing optimization, network security, etc., at the centralized controller, data can be collected by the proposed optimization framework satisfying the ultra-reliability benchmark at the same time with lower cost and latency, in order to make accurate decisions at the controller without violating minimum latency requirements in vehicular communication.
# A Review on Vibration Control Using Piezoelectric Shunt Circuits
###### Abstract  
Vibration control is a critical aspect of engineering, particularly in structures and mechanical systems where excessive oscillations can lead to fatigue, noise, or failure. Vibration suppression is essential in aerospace, automotive, civil, and industrial applications to enhance performance and longevity of systems. Piezoelectric shunt circuits (PSCs) offer a passive or semi-active approach to damping vibrations by leveraging the electromechanical properties of piezoelectric materials. Traditional passive damping methods, such as viscoelastic materials, are effective but lack adaptability. Active control systems, while tunable, require external power and complex electronics, increasing cost and weight. Piezoelectric shunt circuits provide a middle ground, utilizing piezoelectric transducers bonded to a structure and connected to an electrical circuit to dissipate vibrational energy as heat or store it electrically. This review synthesizes the fundamental mechanisms, circuit designs, and practical applications of this technology. It also presents the modeling of lumped and distributed parameter systems coupled with PSCs. It complements the recent reviews and primarily focuses on the period from 2019 to date in addition to the earlier seminal works on the subject. It explores the principles, configurations, advantages, and limitations of piezoelectric shunt circuits for vibration control, alongside recent advancements and potential future developments. It sheds light on the research gaps in the literature that future work may tackle.  
vibration control; piezoelectric shunt circuits; nonlinear; saturation; review; modelling +
Footnote †: journal: Applied Sciences  
1
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 2 Earthquake and Scour Combination in the Failure Probability of Bridge
### Application for a Practical Project
#### 2.2.1 The Introduction of Bridge Model and the Determination of Scour Depth Distribution
The value of probability density and the scour depth were nonlinearly fitted in accordance with Gaussian distribution. The correlation coefficient was 0.99991, which is close to 1, indicating that the nonlinear fitting has satisfactory reliability. The specific data are shown in Figure 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Period of Vibration/s** & **Frequency/Hz** & **Vibration Mode** \\ \hline
1 & 0.79288 & 1.26122 & Longitudinal drift of main beam and longitudinal vibration of main pier \\
2 & 0.76576 & 1.30589 & Lateral bending of main beam and transverse bending of main pier \\
3 & 0.632 & 1.58227 & Lateral bending of main beam and transverse positive symmetrical \\  & & & bending of main pier \\
4 & 0.62516 & 1.59958 & Lateral bending of main beam and transverse antisymmetric bending of \\  & & & main pier \\
5 & 0.58152 & 1.71962 & Lateral bending of main beam and longitudinal antisymmetric vibration \\  & & & of main pier \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dynamic characteristics of the bridge model. Figure 3: Function of probability density fitting for scour depth. The probability density function of the scour depth after fitting is  
\[f_{\rm H}(H)=5.99841E-4+\frac{1.04516}{3.06402\sqrt{\frac{\pi}{2}}}e^{-2(\frac{x- 2.53031}{3.06402})^{2}} \tag{7}\]
## 2 SAR Data, Field Data and Study Sites
### Field Observations for Calibration of the Rice Detection Algorithm and Map Validation  
Field observations were performed throughout the season in up to 20 paddy fields within each footprint. These fields were selected, with the farmers' consent, prior to the start of the rice season and the image acquisition schedule. Observations were made on or as close to the image acquisition date as possible, depending on national holidays or other events that prevented easy access to locations. Observations included latitude and longitude from handheld GPS receivers, descriptions and photos of the status of the field, plant height, water depth, weather conditions, crop stage and leaf area index (LAI). The same field data collection protocols were used at all sites. LAI measurements were taken only during visits between seedling and flowering stages, and these were recorded non-destructively using the same equipment at each site: an AccuPAR LP-80 Ceptometer (Decagon Devices, Inc., Pullman, WA, USA). The specific model of the GPS and digital cameras varied across sites. At the end of the season, the farmer was interviewed to collect information on the rice variety, water source, crop management and establishment practices, as well as inputs, such as pesticide and fertilizer. In total, 228 locations were regularly monitored across the 13 footprints, with 1922 separate visits made to these locations to collect in-season information on the status of the rice crop, an average of 8 visits per location and 18 locations per footprint (Table 2). A validation exercise was conducted for each footprint to assess the accuracy of the rice classification. We first considered the regular sampling grid and random selection of locations as appropriate methods to select representative and spatially well-distributed samples. However, these approaches were quickly deemed too time consuming and too expensive to apply over 13 sites covering 46,500 km\({}^{2}\). For these reasons, a rapid land cover appraisal method was adopted to collect land cover information at approximately 100 locations throughout each footprint with these points split 50/50 between non-rice points and rice points. This conforms to the minimum number of samples per land cover class accounting for both statistical sampling requirements [27] and practical considerations, given the available resources and accessibility.
## 5 Evaluation Results
### Detection Accuracy of ML Classifier  
As described in Section 5.1, training data was created using three feature-attributes, i.e., 1 XDR create time, 2 Bearer create time, and 3 summation of XDR create time and Bearer create time. With these identifiers, the accuracy and _F1 score_ by individual ML classifiers were measured.  
From the measurements, it was found that, for two ML classifiers (i.e., SVM and random forest), the detection accuracy and F1 of the algorithm score were the highest when training data corresponding to the third attribute was used, and that, for the Naive Bayes classifier, they revealed the best result when based on the Bearer create time, all as shown in Figure 18. Figure 18 compares the detection accuracy for each machine learning algorithm according to the method of classifying learning data. In the case of the NB algorithm, the yellow bar is its accuracy and the solid yellow line is its _F1 score_. In the case of the SVM algorithm, the blue bar is its accuracy, and the solid blue line is its _F1 score_. In the case of the RF algorithm, the green bar is its accuracy, and the green solid line is its _F1 score_.  
When it comes to the Naive Bayes classifier, it can be known that the classifier is more dependent than other features for signaling DDoS detection. This is attributable to the Naive Bayes classifier assuming that all features are independent of each other in data analysis.
# Effect of Surfactant on Water Content of Phosphogypsum
## 3 Results and Discussion
### Effect of Surfactant  
The results shown in Table 3 display the effects of the species of surfactant on the surface tension of the leaching solution of phosphate rock. The concentration of surfactant was kept as 10 mg/L\({}^{-}\). The surface tension was measured as 75.70 mN/m (without surfactant), 60.99 mN/m (with SDBS), 64.75 mN/m (with SDS), 62.54 mN/m (with SR), 64.36 mN/m (with PDMS), 53.21 mN/m (with SDBS+SDS), 40.26 mN/m (with SDBS+SR) and 59.77 mN/m (with SDBS+PDMS), respectively. The contact angle of the leaching solution of phosphate rock was also affected by the species of surfactant and, asshown in Table 4. The addition of SDBS could decrease the surface tension, which was more effective than SDS, SR and PDMS. However, adding more species was more effective than single species, and the performance of SDBS+SR was better than SDBS+SDS or SDBS+PDMS.  
Figure 2: Process of surface-active agent in the leaching system of phosphate rock.  
The addition of surfactant could decrease the interface energy and block the hydrogen bond to rearrange the water molecule, and then increased the bond-angle of water [17] (Figure 3), thus decreasing the surface tension and enlarged the contact area of leaching solvent and phosphate rock, which favored the mass transfer rate and improved the leaching efficiency.
# A Novel Approach of Unit Conversion in the Lattice Boltzmann Method
## Appendix A  
Table 11 shows the conversion factors to go from the physical-scale to the dimensionless-scale and from the dimensionless-scale to the lattice-scale. To go in the other direction (i.e., from the dimensionless-scale to the physical-scale or from the lattice-scale to the dimensionless-scale) the relations in Table 11 need to be inverted (i.e., raised to power \(-1\)). For example, the conversion factor to scale velocity from the physical-scale to the dimensionless-scale can be obtained from Table 11 as:  
\[u^{*}=u_{(PS)}\frac{t_{o(PS)}}{x_{o(PS)}}. \tag{11}\]  
Then, the conversion factor to scale this velocity to the Lattice-scale (\(\frac{x_{o(LS)}}{t_{o(LS)}}\)) is achieved from Table 11 as:  
\[u_{(LS)}=u^{*}\frac{x_{o(LS)}}{t_{o(LS)}}=u_{(PS)}\frac{t_{o(PS)}}{x_{o(PS)}} \frac{x_{o(LS)}}{t_{o(LS)}}. \tag{12}\]  
To go back from the lattice-scale to the dimensionless-scale, the conversion factor is inverted (\(\left[\frac{x_{o(LS)}}{t_{o(LS)}}\right]^{-1}\)):  
\[u^{*}=u_{(LS)}\frac{t_{o(LS)}}{x_{o(LS)}}. \tag{13}\]  
Likewise, this velocity can be scaled to the physical-scale as:  
\[u_{(PS)}=u^{*}\frac{x_{o(PS)}}{t_{o(PS)}}=u_{(LS)}\frac{t_{o(LS)}}{x_{o(LS)}} \frac{x_{o(PS)}}{t_{o(PS)}}, \tag{14}\]  
where the conversion factor is inverted (\(\left[\frac{t_{o(PS)}}{x_{o(PS)}}\right]^{-1}\)).  
The same procedure can be applied to scale the unit of any variable from/to the physical-scale to/from the lattice-scale using the listed data in Table 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Quantity**} & \multirow{2}{*}{**SI Unit**} & \multirow{2}{*}{**Dimensions**} & **Converting Factor from Physical-Scale to Dimensionless-Scale to Lattice-Scale** \\  & & & **Dimensionless-Scale to Dimensionless-Scale** & **Lattice-Scale** \\ \hline Length (\(L\)) & m & \(L\) & \(\frac{1}{x_{o(PS)}}\) & \(x_{o(LS)}\) \\ \hline Mass (\(m\)) & kg & \(M\) & \(\frac{1}{m_{o(PS)}}\) & \(m_{o(LS)}\) \\ \hline Temperature (\(T\)) & K & \(\Theta\) & \(\frac{1}{\Theta_{o(PS)}}\) & \(\Theta_{o(LS)}\) \\ \hline Time (\(t\)) & s & \(t\) & \(\frac{1}{\omega_{(PS)}}\) & \(t_{o(LS)}\) \\ \hline Acceleration (\(a,g\)) & m/s\({}^{2}\) & \(Lt^{-2}\) & \(\frac{t_{o(PS)}^{2}}{x_{o(PS)}}\) & \(\frac{x_{o(LS)}}{t_{o(LS)}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Factors to transform some selected quantities from/to the physical-scale to/from the dimensionless-scale to/from the lattice-scale.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Quantity**} & \multirow{2}{*}{**SI Unit**} & \multirow{2}{*}{**Dimensions**} & \multicolumn{2}{c}{**Converting Factor from Physical-Scale to Dimensionalless-Scale to Lattice-Scale**} \\  & & & & \\  & & & & \\ \hline Area (\(A\)) & m\({}^{2}\) & \(L^{2}\) & \(\frac{1}{x_{o(PS)}^{2}
## 3 Proposed Dataset
### Image Annotation  
As mentioned regarding ImageNet [28], there are two basic requirements in a bounding box annotation system: quality and coverage. Quality means that each bounding box needs to be tight, i.e., all visible parts of the object must be contained by a minimal bounding box. Coverage means that every instance of an object needs to have a bounding box to tell the algorithm which parts of the image are to be focused on. For common objects, individual instances such as people, chairs, and cars are easy to annotate using a properly scaled bounding box, and the annotated results are generally not ambiguous. However, cracks are different because they do not have a particular structure. For example, Chinese standards for evaluating the performance of highways classify cracks as alligator, block, transverse, or longitudinal cracks. Using only a single tight bounding box to label a transverse or longitudinal crack means that this bounding box will have an incongruuous aspect ratio, as shown in Figure 4a. In addition, the study of ImageNet [28] showed that objects with thin structures have the worst localization accuracy. A huge bounding box will appear in an image when the trend of a crack is sloping, and in which the crack occupies only a tiny portion, while most of the pixels represent the background, as shown in Figure 4b. This type of annotation can lead to mismatched labels when training the network [52]. For common items, an object is one entity; however, a whole crack can be considered to be composed of many sub-cracks. Therefore, in this study, we propose a dense and redundant crack labeling method, in which a crack is densely contained by multiple tight small bounding boxes instead of a very long or large bounding box, and the adjacent bounding boxes need to overlap (see Figure 4). We propose this for the following reasons:  
* Dense annotations, although still blocky, can show more accurate crack localization and structures relative to patch-level annotations;
* Redundant annotations increase the number of instances in the dataset.
## 4 Numerical Results and Discussion
### In-Plane Simulation
#### 4.2.3 Effect of Strain Rate and Size  
As the simulation results show in Figure 48a, with the strain rate increasing, the maximum stress increased as well. This trend was the same as the experimental trend. As shown in Figure 49a, in both simulation and experimental results, before a certain point, the slope of the stress-strain curve decreased with increasing strain rate. After this point, the slope of the stress-strain curve increased with increasing strain rate. However, the range is not great. As shown in Figures 48b and 49b, both simulation and experiment results showed that, when the thickness and area became smaller, the slope values of stress-strain became lower. Moreover, the influence of thickness was greater than the influence of cross-section area. The specific maximum stress values are provided in Table 11. The biggest and smallest relative errors between simulation and experiment were 3.4% and 0.8%, respectively. Therefore, the simulation results had good agreement with the experimental results.  
Figure 47: The contours of the specimen at 55 μs (unit: cm- μs-g): (**a**) effective stress of sub-laminates; (**b**) effective strain of sub-laminates; (**c**) damage degree of cohesive elements.  
Figure 48: The experimental and simulated stress–strain curves of different specimens under same impact velocity: (**a**) effect of strain rate (\(8\times 8\times 8\)); (**b**) effect of different size.