esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
###### Abstract  
The hydrogeological conditions of coal mines in China are quite complex, and water inrush accidents occur frequently with disastrous consequences during coal extraction. Among them, the risk of coal mining under a river is the highest due to the high water transmissivity and lateral charge capacity of the unconfined aquifer under the river. The danger of mining under a river requires the accurate determination of the developmental mechanisms of the water flowing fractured zone (WFFZ) and the water flow mechanisms influenced by the specific geological conditions of a coal mine. This paper first used the transient electromagnetic (TEM) method to monitor the development of the WFFZ and the water flow mechanisms following the mining of a longwall face under a river. The TEM survey results showed that the middle Jurassic coarse sandstone aquifer and the K1 zhv unconfined aquifer were the main aquifers of the 8101 longwall panel, and the WFFZ reached the aquifers during the mining process. Due to the limited water reserves in the dry season, the downward flowing water mainly came from the lateral recharge in the aquifer. The water inrush mechanisms of the 8101 longwall panel in Selian No.1 Coal mine were analyzed based on the water flow mechanisms of the aquifer and the numerical simulation results. This provides theoretical and technical guidance to enact safety measures for mining beneath aquifers.  
monitoring; water flowing fractured zone; aquifer; water flow mechanisms; lateral recharge; transient electromagnetic method +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
We utilized the FFP model to evaluate the footprints of the 78 FLUXNET sites across ten vegetation types on the globe and calculated the Landsat-footprint NDVI and the MODIS-footprint NDVI. We optimized the parameters (\(\varepsilon_{\max}\), \(\theta\) and VPD\({}_{0}\)) of the revised EC-LUE model and compared the model performance using Landsat-footprint NDVI, incorporating spatial and temporal dynamics of the flux footprint at 30 m spatial resolution and MODIS-footprint NDVI with a fixed footprint at 500 m spatial resolution. Our results showed that Landsat-footprint NDVI, compared with MODIS NDVI, could improve GPP estimation by 18% for savannas, 14% for croplands, 9% for wetlands, and 6% for evergreen needleaf forest, with slight improvement or comparable performance as compared to other ecosystems. This indicated that using NDVI with high spatial resolution resulted in more detailed GPP estimates, particularly for areas with heterogeneous surfaces. Our study highlighted the significance of using data with a high spatial resolution to match the flux tower footprint more accurately and improve estimation of GPP.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14236062/s1](https://www.mdpi.com/article/10.3390/rs14236062/s1)]([https://www.mdpi.com/article/10.3390/rs14236062/s1](https://www.mdpi.com/article/10.3390/rs14236062/s1)), Table S1. List of 78 eddy covariance flux tower sites used in this study. Vegetation types includes the following. ENF: evergreen needleaf forest; EBF: evergreen broadleaf forest; DBF: deciduous broadleaf forest; MF: mixed forest; SHR: shrublands; SAV: savannas; WSA: woody savannas; GRA: grassland; CRO: cropland; WET: wetland.  
Conceptualization, W.Y.; methodology, X.H. and W.Y.; software, X.H.; validation, X.L. and S.L.; formal analysis, S.L.; investigation, X.H., M.M., C.W. and W.Y.; resources, S.L., X.H. and W.Y.; writing--original draft preparation, X.H.; writing--review and editing, X.H. and W.Y.; visualization, S.L. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the National Youth Top-Notch Talent Support Program (grant no. 2015-48), the China National Funds for Distinguished Young Scientists (grant no. 41925001), the Fundamental Research Funds for the Central Universities (grant no. 19 lgc02) and the Changjiang Young Scholars Program of China (grant no. Q2016161).  
None.  
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We thank the FLUXNET community for providing the covariance data and standard products.  
The authors declare no conflict of interest.
###### Abstract  
Early-season area estimation of the winter wheat crop as a strategic product is important for decision-makers. Multi-temporal images are the best tool to measure early-season winter wheat crops, but there are issues with classification. Classification of multi-temporal images is affected by factors such as training sample size, temporal resolution, vegetation index (VI) type, temporal gradient of spectral bands and VIs, classifiers, and values missed under cloudy conditions. This study addresses the effect of the temporal resolution and VIs, along with the spectral and VIs gradient on the random forest (RF) classifier when missing data occurs in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, a study area is selected on an overlapping area between two Landsat Data Continuity Mission (LDCM) paths. In the proposed method, the missing data from cloudy pixels are retrieved using the average of the k-nearest cloudless pixels in the feature space. Next, multi-temporal image analysis is performed by considering different scenarios provided by decision-makers for the desired crop types, which should be extracted early in the season in the study areas. The classification results obtained by RF improved by 2.2% when the temporally-missing data were retrieved using the proposed method. Moreover, the experimental results demonstrated that when the temporal resolution of Landsat-8 is increased to one week, the classification task can be conducted earlier with slightly better overall accuracy (OA) and kappa values. The effect of incorporating VIs along with the temporal gradients of spectral bands and VIs into the RF classifier improved the OA by 3.1% and the kappa value by 6.6%, on average. The results show that if only three optimum images from seasonal changes in crops are available, the temporal gradient of the VIs and spectral bands becomes the primary tool available for discriminating wheat from barley. The results also showed that if wheat and barley are considered as single class versus other classes, with the use of images associated with 162 and 163 paths, both crops can be classified in March (at the beginning of the growth stage) with an overall accuracy of 97.1% and kappa coefficient of 93.5%.  
Keywords:wheat classification; random forest; spectral gradient difference; vegetation indices +
Footnote †: journal: Applied Sciences
# A Scalable and Trust-Value-Based Consensus Algorithm for Internet of Vehicles
## 4 C-PBFT Consensus Algorithm
### Reputation Calculation Using Multi-Weighted Subjective Logic Model
#### 4.2.4 Combining Local Opinions with Recommended Opinions  
After obtaining a local opinion vector for the RSU from other vehicles, a particular vehicle has a subjective opinion for each RSU based on its interaction history. This partial opinion should still be taken into account when forming the final initial credit rating of a single vehicle to avoid deception.  
After \(V_{i}\) combines the local opinion vector of its own credit evaluation of \(RSU_{j}\) in a time period with the merged recommendation opinion, the resulting credit opinion can be expressed as \(\omega_{s\to j}^{\text{final}}:=\{b_{s\to j}^{\text{final}},d_{s \to j}^{\text{final}},u_{s\to j}^{\text{final}}\}\).  
\[\left\{\begin{array}{l}b_{i\to j}^{\text{final}}=\frac{b_{i \to j}u_{s\to j}^{\text{rec}}+y_{s\to j}^{\text{rec}}u_{i\to j}}{u_{i \to j}+u_{s\to j}^{\text{rec}}-u_{s\to j}^{\text{rec}}u_{i\to j}}\\ \\ d_{i\to j}^{\text{final}}=\frac{d_{i\to j}u_{s\to j}^{\text{rec}}+d_{s \to j}^{\text{rec}}u_{i\to j}}{u_{i\to j}+u_{s\to j}^{\text{rec}}-u_{s\to j }^{\text{rec}}u_{i\to j}}\\ u_{i\to j}^{\text{final}}=\frac{u_{s\to j}^{\text{rec}}u_{i\to j}}{u_{i \to j}+u_{s\to j}^{\text{rec}}-u_{s\to j}^{\text{rec}}u_{i\to j}} \end{array}\right. \tag{10}\]  
Similar to Equation (2), Vi's final reputation opinion on RSU is  
\[F_{i\to j}^{\text{final}}=\epsilon+\tanh\!\left(b_{i\to j}^{\text{final}}+ \gamma u_{i\to j}^{\text{final}}\right) \tag{11}\]  
where \(\epsilon\) is the initial creditworthiness of any predefined node in the system and is a negligible non-zero positive number.  
In a certain period, \(RSU_{j}\) receives all vehicles in the corresponding area to evaluate its credit value and gets the initial credit value, and then adds it to get its own node credit value \(R_{j}\).
# Computation of Implicit Representation of Volumetric Shells with Predefined Thickness
## 5 Examples
### Triply Periodic Minimal Surfaces (TPMSs)
#### 5.2.1 Error Measurement Procedure  
To measure the accuracy of the proposed method, we employ a sampling technique. We want to measure the distance between the surface of the TPMS and the zero contour of its offset function. The computer algebra software Mathematica is used to find a large sample of points \(N\) on one of the surfaces. The sampled points are uniformly distributed in \(x\) and \(y\) directions. The \(z\) coordinate is determined by solving for \(z\) given \(x\) and \(y\) on the surfaces. The procedure for collecting the points is described in Algorithm 1.  
```
input :implicit surface function \(F(x,y,z)\) output :list of 3-tuple \(SampledPoints\) // create two list of coordinates in \(x\) and \(y\) directions \(A:=\{\;a\;|\;a\in[0,0.05,0.1,\ldots,0.95,1]\;\}\) \(B:=\{\;b\;|\;b\in[0,0.05,0.1,\ldots,0.95,1]\;\}\) // substitute each \(a\) and \(b\) into \(F\) and solve for the \(C\) \(SampledPoints:=\{\;(a,b,\text{Solve}(F(a,b,c)=0,c))\;|\;a\in a\;\text{ and }\;b\in B\}\)
```  
**Algorithm 1**Points sampling from an implicit surface \(F(x,y,z)=0\)  
From each point, a ray is shot in the normal direction to the surface. Then, the intersection distance \(h_{m}^{(n)}\) with the other surface is measured and compared to the predefined distance \(h\). The error is measured in the following norm:  
\[Error=||h^{(n)}-h||_{2}=\frac{1}{N}\sum_{i=1}^{N}\sqrt{\left(\frac{(h_{m}^{(n) }(i)-h}{h}\right)^{2}}, \tag{28}\]where \(N\) is the number of points used and \(n\) is the order of the approximation used to represent the offset surface. The procedure for calculating the error is described in Algorithm 2. The error measurement procedure, including sampling of the points from the surface and finding the intersection distance to the next surface, is done on both surfaces, i.e., the original zero contour of the TPMS and the offset function.  
```
input :list of 3-tuple \(SampledPoints\) input :implicit surface functions \(F_{1}(x,y,z)\) and \(F_{2}(x,y,z)\) input :prescribed distance \(h\) output :real \(Error\) list \(pointError:=\{\}\) foreach 3-tuple \(\vec{p}\) in \(SampledPoints\)do // calculate the normal \(\hat{n}\) at \(\vec{p}\) \(\hat{n}:=\frac{\
abla F_{1}(\vec{p})}{|\
abla F_{1}(\vec{p})|}\) // shoot a ray from \(\vec{p}\) a distance \(h_{m}\) and calculate the position vector \(\vec{r}(h_{m}):=\vec{p}+h_{m}\hat{n}\) // solve for the distance \(h_{m}\) \(h_{m}:=\operatorname{Solve}(F_{2}(\vec{r}(h_{m}))=0,h_{m})\) // calculate the \(L^{2}\) error \(pointError:=\operatorname{Append}(pointError,\sqrt{\left(\frac{h_{m}-h}{h} \right)^{2}})\) end // calculate the error mean \(Error:=\operatorname{Mean}(pointError)\)
```  
**Algorithm 2**Calculation of the \(L^{2}\) error in distance \(h\) from points in the surface \(F_{1}(\vec{x})=0\) to a distance surface \(F_{2}(\vec{x})=0\)
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 4 Experiment
### Evaluation Metric  
We use the same evaluation criteria defined in [PERSON] et al. (2019) to test the proposed method's performance. First, a minimum area of connected components is set for each category. Then, using the method to be evaluated to predict anomaly score maps on a validation set containing only normal images. After that, binary decisions are made on these anomaly score maps by incremental thresholds. Until the area of the largest connected component in binary images is equal to the defined minimum area, this threshold is determined as the final binary threshold. Based on this threshold, we calculate the average of the percentage of correct images that are correctly classified as anomaly-free and anomaly for image-level  
Figure 4: Samples of the MVTec AD dataset. The first row of each category is normal and the second row is one of anomalies.  
detection. For pixel-level detection, evaluate per-region overlap (PRO) and the area under the ROC curve (AUROC). PRO is the relative per-region overlap of the predicted segmentation map \(S_{p}\) with the ground truth \(S_{g}\), and it gives greater weight to the connectivity component that contains fewer pixels:  
\[PRO=\frac{1}{n}\sum_{i}^{n}\frac{s_{p}^{(i)}}{s_{g}^{(i)}} \tag{7}\]  
where \(s_{p}^{(i)},s_{g}^{(i)},n\) are each connected component of \(S_{p}\) and its corresponding \(S_{g}\) and the number of connected components it contains, respectively. In addition, the image-level and pixel-level F1 Score are also evaluated, where F1 score is computed as:  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[F1\ Score=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{10}\]  
We define \(TP\) as the count of images or pixels correctly classified as anomalous, \(FP\) as the count of images or pixels incorrectly classified as anomalous, and FN as the count of images or pixels incorrectly classified as normal.
###### Abstract  
The importance of fat mass estimation in multiple areas related to health and sports has led to the emergence of a large number of methods and formulas for its estimation. The aim of the present study was to compare the agreement and differences between different formulas for estimating fat mass by anthropometry. Eighty-seven subjects underwent an anthropometric assessment following the protocol from the International Society for the Advancement of Kianthropometry (ISAK). The fat percentage was calculated with 14 different formulas for men and with 12 different formulas for women. In the case of men, they were proposed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. In the case of women, the equations used were those proposed by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Significant differences were found between the formulas in both men (8.90 \(\pm\) 2.17% to 17.91 \(\pm\) 2.84%; \(p\) < 0.001-0.016) and women (15.33 \(\pm\) 2.94% to 28.79 \(\pm\) 3.30%; \(p\) < 0.001-0.004). It was observed that in the case of men, the [PERSON] and [PERSON] formulas and the [PERSON] and Faulkner formulas showed moderate agreement with each other (CCC = 0.910-0.915). In the case of women, it was observed that the [PERSON] and Yuhasz formulas showed moderate agreement with each other (CCC = 0.974). In conclusion, the formulas used for the estimation of lipid mass in anthropometry reported significantly different results between them and were therefore not comparable.  
adipose tissue; body fat; body weight; fat tissue; sports; health; kinanthropometry; performance; skinfolds +
Footnote †: journal: Applied Sciences
###### Abstract  
A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed-spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment.  
connected vehicle; in-vehicle warning system; vehicle interaction; driving behavior; PVD +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Phosphate Recovery as Struvite  
The concentrations of phosphate, ammonium and magnesium in the precipitate are presented in Table 2. The values for these anions found in the precipitate from the desorbed solution collected during the first 30 min were 41.7%, 10.9%, and 12.6%, respectively. These values closely matched the theoretical composition of struvite, _i.e._, phosphate, ammonium and magnesium of 38.8%, 7.3%, and 9.8%, respectively [26]. The slightly lower values obtained for the precipitate may be due to other ions contributed by the desorbing agent NaCl and ions that were originally present in the MBR effluent. The respective concentrations were smaller in the struvite precipitate formed in the solution collected in the last 30 min. This may have probably been due to the smaller concentration of phosphate in the desorbed solution.
## 1 Introduction
reported a balance correction method with a non-unit correction matrix for the near-vertical position to reduce the correction errors and improve the accuracy of the attitude estimation [12]. [PERSON] et al. proposed the inclination and azimuth calculation method with a weight function that assigned separate weights to the established axial equation and all-axis equation for continuous inclination and azimuth in the near-vertical position [13]. The reported weight function requires a large volume of cases and data to estimate the weighting factors. The current technology is not capable of applying this method to actual borehole measurement projects. [PERSON] et al. established the IMU system based on four Euler rotations for vertical attitude measurement to enhance the amplitude and signal-noise ratio of the original signal from radial accelerometers [14]. However, the additional Euler rotation will not reduce sensor noise in practical engineering applications. The aforementioned methods study the near-vertical attitude calculation with the optimal algorithm that enhances inclination and azimuth accuracy; unfortunately, they missed the actual attitude error propagation mechanism and azimuth accuracy under the near-vertical position. The fundamental reason affecting near-vertical attitude accuracy is that the true physical magnitude of the horizontal accelerometer is much less than the noise. To increase the signal-to-noise ratio of the horizontal accelerometer, this paper will develop an optimal redundant MEMS-IMU/ magnetometer system to improve the accuracy of the horizontal attitude measured by the horizontal accelerometers. In addition, a near-vertical attitude accuracy improvement method based on the redundant system will be designed. The contributions of the proposed method are as follows:  
1. The wellbore attitude accuracy with the normal wellbore and near-vertical wellbore is demonstrated in combination with the error simulation. 2. A redundant MEMS-IMU/magnetometer system calculation method is reported to highlight compensation for non-orthogonal errors of redundant accelerometers. 3. A near-vertical attitude optimal algorithm with a redundant system is proposed to improve the near-vertical azimuth and roll angle accuracy. The structure of the main text contains four sections. In Section 2, typical wellbore attitude calculation methods are discussed, where we explain the attitude error specificities with simulation analysis in the near-vertical position. In Section 3, the redundant sensors configuration design and calibration method are reported, where we proposed a near-vertical attitude optimal algorithm-based redundant MEMS/magnetometer system. The experiments are designed to verify the proposed method in Section 4. In Section 5, we summarize the study.
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
## 3 Results and Discussion
### Analyses of Typical Cases  
#### 3.6.1 Application to Advanced Magnetism  
To demonstrate the practical usefulness of the WJ distribution in preference to the Gaussian function, we first analyze a typical case in extreme events and critical phenomena as an example [45]. A set of the experimental data are extracted from the literature and fitted with the WJ distribution and the Gaussian function, respectively. The assessment is conducted by interpreting the magnetization intensity \((M-<M>)/\sigma_{M}\) as the negative variable \(x\) in the WJ distribution (2) and the Gaussian function (1a) due to the positive definition of the parameters \(\alpha\) and \(\beta\), with the probability density scaled by the magnitude of \(\sigma_{M}\). The fitting results are shown in Figure 8, yielding the fit parameters of \(\alpha=1.542\), \(\beta=0.7467\) and \(\
u=0.5878\) (\(R^{2}=0.9983\)) for the WJ distribution and \(\mu_{GS}=0.8879\) and \(\sigma=1.28\) (\(R^{2}=0.8091\)) for the Gaussian function, separately. It is plain from the figure that the WJ function offers a well-defined matching to the experimental data with the correlation coefficient of 0.9983 (virtually one for a most optimized fit), in striking contrast to the unsatisfied performance of the Gaussian function with a rather poor correlation coefficient of 0.8091. This example strongly advocates that the WJ function has the advantage in such practical situations. In turn, we apply the WJ distribution to refit this Gaussian function of \(\mu_{GS}=0.8879\) and \(\sigma=1.28\) itself, and the outcome is given in the inset of Figure 8. As expected, both of them agree quite impressively with the parameter values of \(\alpha=8.482\), \(\beta=0.0764\), and \(\
u=62.36\) and give a remarkable correlation coefficient of 0.9969, substantiating the proposition once more that the WJ distribution can well represent the general Gaussian function.  
Figure 8: Comparative analysis of typical experimental data from the classical literature. Dots for experimental data, solid line for fitting of the WJ distribution and dashed line for fitting of the Gaussian function. Inset: Square solid points representing the Gaussian function obtained from the fit of the data and green solid line for the result of its refitting by the WJ distribution.