esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
A Visco-Elasto-Perfectly Plastic (VEPP) model was proposed for deformation prediction of high CFRD dams during design stage. The model uses the following: (1) information from laboratory tests in the visco-elastic part; (2) a correction in the elastic parameters that accounts for differences between porosity in laboratory tests and the target porosity at the dam; (3) a plastic yield surface based on actual rockfill parameters, including non-downscaled and actual rockfill grain size; and (4) a simplified creep model. This approach helps to partially remove the scale effect problem linked to the limited capability of laboratory tests to simulate rockfill specimens with large sizes. The model also allows to account for porosity changes. As porosity after compaction is an explicit parameter of the model, it is possible to adjust the elastic parameters when porosity in the dam is targeted different from porosity used in the laboratory tests.  
The results obtained with the application of the model to Shuibuya CFRD dam, 233 m high, support the predictive capability of the proposed VEPP model. It allows for deformation prediction of high CFRD dams using information on rockfill material and from laboratory tests, avoiding the use of parameters taken from back analyses of similar dams, as their application is subjected to uncertainty.  
The model can be improved as further refinement can be achieved related to the evolution of the particle size distribution during construction and impoundment. Also, the creep part of the model can be improved, given that results of laboratory tests are available at the time of design. The possibility to use the model at design stage to simulate different construction sequences and different reservoir impounding strategies seems promising to help in the optimization of the construction process of this type of dam in practical engineering applications.  
Conceptualization, F.R. and L.A.-G.; Methodology, F.R.; Software, F.R.; Validation, F.R. and L.A.-G.; Formal analysis, F.R.; Investigation, F.R. and L.A.-G.; Resources, F.R. and L.A.-G.; Data curation, F.R.; Writing--original draft, F.R.; Writing--review & editing, F.R. and L.A.-G.; Visualization, F.R.; Supervision, L.A.-G.; Project administration, L.A.-G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Department of Mining and Civil Engineering of the Technical University of Cartagena grant number 300518A505.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors wish to express thank the Department of Mining and Civil Engineering of the Technical University of Cartagena for the support to this research.  
Author [PERSON] was employed by the company ELC-Electroconsult. The remaining author declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks
## 4 Experiments and Analysis
### 3D ConvNets and Non-Local Neural Networks
#### 4.3.3 Non-Local Blocks and Multi-Scale  
It is known that the convolutional neural network is mainly concerned with local information. However, the vehicle speed estimation problem requires consideration of the vehicle trajectory change throughout the entire video footage. In order to focus on more global information, we add non-local blocks to the second and third residual blocks of the network. Besides, we add a convolutional layer whose kernel is (3, 5, 5), the stride is (1, 2, 2) after the first convolutional layer to reduce video memory. Moreover, we also change the first layer of the ConvNet to the multi-scale convolution detailed in Section 3.3.  
Comparative experimental results across model configurations are presented in Table 5. To facilitate understanding, we adopt straightforward naming conventions, e.g., 'conv_add' indicates the convolutional layer with the convolution kernel size of \((3,5,5)\) and the stride size of \((1,2,2)\), which are added after the first layer. The numerical results are shown in Table 6.  
From Table 6, it can be seen that the additional layer of convolution in the network does not reduce the useful information and model performance does not degrade with the addition of the convolutional layer. The incorporation of non-local blocks into the IR(2+1)D model leads to improved model results, suggesting that non-local and 3D ConvNets are complementary, which can be understood that 3D ConvNets capture local features and the additional non-local blocks shed light on global information. Additionally, the model performance further improves with multi-scale convolution being used. The reason is that multi-scale convolution can increase the richness and diversity of features, thus better capture different sizes of vehicle motion information in the video.
## 2 Materials and Methods
### Experimental Setup  
In order to further verify the correctness and accuracy of the proposed method, an experimental system was established, as shown in Figure 6a. The internal structure of the cavity is shown in Figure 6b. The proposal is validated by comparing the experimental results with the computation results. The microwave is fed into the waveguide through a coaxial line from a microwave solid-state source with an output power of 100 W at 2.45 GHz. The circulator and water load absorb the reflected microwave, protecting the microwave source. A microwave power meter (AV2433) is connected to a dual-directional coupler to measure the effective input and reflected power. The turntable and support rod in the cavity are controlled by the motor to rotate and lift, and a slice of potato is placed on the turntable. There is a cut-off waveguide on the upper surface of the microwave cavity. This setup ensures the prevention of microwave leakage, allowing the use of an optical fiber thermometer to measure the point temperature of the potato slice. The surface temperature image of the potato slice is recorded using a thermal imaging camera (VarioCAM hr inspect 500, InfraTec, Dresden, Germany) with an accuracy of \(\pm\)0.03 K. The total time of the microwave heating process is 20 s, and during the heating process, the lifting speed and the rotation angular speed are set to 3 mm/s and \(\pi\)/10 rad/s, respectively. Two groups of experiments were carried out. One group used potato with size 25 mm \(\times\) 25 mm \(\times\) 10 mm, and the center of the potato was placed 2.5 cm away from the center of the turntable. The other group used a potato with a size of 35 mm \(\times\) 35 mm \(\times\) 10 mm and placed the potato in the turntable's center. During the heating process, a fiber optic thermometer was used to record the temperature at the point we set in the potato. After heating, remove the potato from the cavity and measure its upper surface with the thermal imaging camera.  
Figure 6: (**a**) Device of the experimental system. (**b**) Internal structure of the cavity.
## 4 Data Quality Control in TasMAN Using Fuzzy Rules
### Combination of Fuzzy Sets for Quality Assessment  
For a given measurement, \"small\", \"medium\" and \"large\" uncertainty values were assigned for each of the parameters in Table 1. Empirically determined weightings were provided to the different parameters and they were combined according to:  
\[\begin{split}\text{Overall}_{i}=0.2\times\text{TM}_{i}+0.1\times \text{TC}_{i}+0.25\times\text{RoC}_{i}+0.25\times\text{cRoC}_{i}+0.2\times\text {Ndiff}_{i}\\ i=\text{small, medium, large}\end{split} \tag{2}\]  
where TM is the \"time since last maintenance\" membership function, TC is the \"time since last calibration\" membership function and Ndiff is the \"node difference\" membership function.  
Once combined, the overall \"small\" value was referred to as the QC green fraction, the overall \"medium\" value as the QC yellow fraction and the overall \"large\" value as the QC red fraction. Finally the data was compared against thresholds (Table 2) based on the range of historical measurements in the region. Where data fell outside the expected range, the small and medium error fractions were overwritten with 0 and the large error fraction overwritten with 1. It should be emphasized that the data samples are not being altered according to this assessment, quality assessments of the samples are only being provided.
# Analysis of Load-Settlement Curve Based on Load Transfer at Pile-Soil Interface
## 4 Analysis of Pile Settlement Mechanism
### Displacement Analysis of Pile-Soil Interface  
Under the same load, as shown in Figure 14a,b, the pile-soil relative displacement is negatively connected with pile length, which is a process that occurs gradually from the top to the bottom of the pile. Under a pile top load of 2400 KN, the pile-soil relative displacement increases considerably for test piles S1#, S2#, and S3#, rising 16.0 mm for test pile S1#, and 4.75 mm for the pile bottom, thereby resulting in less and less deformation and hardening in the slag. The relative displacement rises 12.3 mm at the top and 12.2 mm at the bottom for test piles S2# or S3#. Therefore, as the pile top weight grows, the thick layer of silty soil beneath the pile end compresses and deforms, causing the relative displacement of pile-soil to increase proportionally.  
The field observation data is fitted by the Boltzmann curve model, and the analytical formula of the model is obtained:  
\[S_{t}=21.035-\frac{10.854}{1+e^{\frac{5-2.92}{0.306}}} \tag{7}\]  
R2 = 0.9985 is the correlation coefficient. As shown in Figure 15, the relative pile displacement increases with increasing pile lateral resistance, which is positively linked, and its slope is the correlation coefficient, which is directly connected to soil depth and soil parameters. The correlation coefficient in silt soil is visibly and considerably higher than in silt-clay soil, and it gradually falls as depth increases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**The Layer of Soil**} & \multicolumn{4}{c}{**Maximum (Ultimate) Lateral Friction Resistance/kPa**} \\ \cline{2-6}  & **Measured Values** & **Measured Values** & **Measured Values** & **Geological** & **Average of Scale** \\  & **for Test Pile S1\#** & **for Test Pile S2\#** & **for Test Pile S3\#** & **Reports Provide** & **Factor** \\ \hline Clay & 10 & 10.2 & 10.7 & 11 & 0.936 \\ Silty clay & 11.5 & 11.76 & 11.83 & 12 & 0.975 \\ Pelitic silty-sand & 23 & 24 & 24.8 & 25 & 0.957 \\ Silt with clay & 12.8 & 13 & 13.2 & 12 & 1.083 \\ Silt & 20.5 & 21.3 & 22 & 20 & 1.063 \\ Silty sand & 26.4 & 27.5 & 28.6 & 26 & 1.057 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strengthening or weakening coefficient of lateral friction resistance of different soil layers.  
Figure 14: (**a**) Pile-soil relative displacement curve for test pile S1#; (**b**) Pile-soil relative displacement for test pile S2# and S2#.
## 3 Results and Analysis
### Identified Domain of Tone Ratios  
#### 3.2.1 Case of Unique Tone Ratio \(\beta_{m}\)  
Figure 3 plots the maximal and the minimal model trunk stiffnesses as functions of tone ratio for the five directions of perturbation.  
As expected, the upper and lower estimates of trunk stiffness increase with muscle tone. For each perturbation direction, the circled (squared) tone value, for which the maximum (minimum) model stiffness matches the experimental one, sets a lower (upper) bound on the RMTT (Table 3). The maximum (minimum) of these values, among the five directions, is a net lower (upper) bound for the RMTT.  
The net range D of tone ratio found, common to all directions, is the interval [9.6%,10.2%]. The lowest and largest ratios are those relative to directions 5 and 3, respectively.
# Water Use Efficiency Spatiotemporal Change and Its Driving Analysis on the Mongolian Plateau
## 2 Materials and Research Methods
### Research Methods
#### 2.3.3 WUE Change Point Detection  
The change point detection tool in the ArcGIS pro3.0 software was used to detect the change points of the annual WUE. This tool enables us to check changes in the slope (linear trend) of discrete variables. Change point detection is relatively similar to time-series anomaly detection but differs in several important aspects. When a model is changed to a new model (such as an average value change), change point detection identifies the time step, whereas outlier detection identifies time steps that significantly deviate from a single model. The former highlights continuous changes, whereas the latter highlights short-term abnormalities. In this study, we used the slope change to detect interannual WUE trend change points from 1982 to 2018. The optimal change point detection with the linear computing cost (PELT) option uses the pruning precision linear time [68; 69] algorithm to estimate the number of changes and time steps. The reduction in the subdivision cost must be greater than the additional penalty value determined by the sensitivity parameter. Based on the characteristics of the constraints, a penalty function was constructed and added to the objective function to establish an unconstrained problem using an empirical formula. If the cost reduction is less than the increased penalty value, the penalty cost will improve, and the time step is not defined as a change point. If the sensitivity value is between 0 and 1, more detection points are obtained with high sensitivity. The penalty formula is as follows:  
\[Penalty\,Value=6^{2-2 sensitivity} \tag{6}\]  
In this study, we used the PELT to detect change points. Because change point detection based on the slope (step 2) requires conservative control sensitivity, we defined the WUE sensitivity as 0.3, with which only one change point is detected at most.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 3 Decomposition Method Based on HHT-AMD  
### Hilbert-Huang Transform (HHT)  
The main concept of the Hilbert-Huang transform is to decompose the original signal into several intrinsic mode functions (IMFs) via EMD, and then the Hilbert spectrum is obtained by applying Hilbert transform on IMFs [26]. The HHT process includes two parts: EMD and Hilbert spectral analysis (HSA). The combination of EMD and HT has led to definitions of instantaneous frequency and amplitude, as well as a more physically meaningful description of the dataset.  
Generally, EMD is a sifting process to obtain the IMFs from a given dataset [27]. For a given dataset \(s(t)\), the first step is to identify the local extrema (maxima and minima) and connect them to produce the upper and lower envelopes, which cover the whole data. Their mean is \(M_{1}\), and \(h_{1}\) is given by the difference between \(M_{1}\) and original data  
\[h_{1}=s(t)-M_{1} \tag{3}\]  
Figure 1: Boulic model of a human.  
Since \(h_{1}\) is not an IMF all the time, the sifting process should be performed iteratively. \(h_{1}\) is regarded as the original data in the subsequent steps,  
\[h_{11}=h_{1}-M_{11} \tag{4}\]  
The \(h_{1j}\) turns into an IMF until the fixed constrains are satisfied. Thus, \(h_{1j}\) is the first IMF component extracted from the data  
\[imf_{1}=h_{1j} \tag{5}\]  
We separate \(imf_{1}\) from the data, and the residual part is  
\[r_{1}=s(t)-imf_{1} \tag{6}\]  
The residue \(r_{1}\) may contain longer-period components, and it should be subjected to the above sifting process as the new data. The procedure should be repeated for all subsequent \(r_{m}\)  
\[r_{m}=r_{m-1}-imf_{m} \tag{7}\]  
The process stops when no more IMF can be extracted and the residue \(r_{m}\) is a monotonic function or a function with single extremum. Now, the original data is decomposed into \(M\) IMFs and a residue. In summary, \(s(t)\) can be reconstructed by the summation of all extracted IMFs and a final residue  
\[s(t)=\sum_{j=1}^{M}imf_{j}+r_{M} \tag{8}\]  
For IMFs, the Hilbert spectrum can be obtained by Hilbert transform. The instantaneous frequency of IMF is physical significant [17].  
\[H(\omega,t)=\text{Re}\sum_{j=1}^{M}a_{j}\exp(i\int\omega_{j}dt) \tag{9}\]  
By integrating with respect to time, we get the marginal spectrum [28]  
\[h(\omega)=\int_{o}^{T}H(\omega,t)dt \tag{10}\]
## 4 Discussion  
We proposed an interferometric method to investigate the pad surface in CMP, in which a common-path optical interferometer with phase-shifting interferometry was implemented to reconstruct the surface structure of the pad. The proposed system was designed for immersion metrology, which can be easily applied to a submerged sample, and its common-path interferometry is beneficial because the interference is insensitive to external noises. Moreover, the phase-shifting approach does not require mechanical beam scanning, thus allowing the rapid reconstruction of the surface topology. The results showed that our approach was able to provide an areal topology map of a wet or immersed CMP pad, subject to external vibrations that were used to simulate a working environment. This demonstrated an application of the proposed method for in situ inspection of the pad surface roughness during CMP.  
A small number of technical issues in the present system remain to be addressed. (1) Although the PZT used was driven by a piezo controller with a positional feedback system, the phase-shifting approach using PZT motion may be unstable, thus preventing the phase gain from being obtained. One method for achieving non-mechanical phase-shifting is to use polarization-base phase shifters consisting of quarter-wave plates and polarizers [21]. Therefore, the common-path interferometer equipped with the polarization optics would allow surface mapping of vibrating pads free of the errors associated with the mechanical motion. (2) The current measurement area, which is limited by the magnification of the objective lens, may not be sufficient to characterize the large surface area of the pad in a working environment. Wide illumination using low N.A. optics may be an appropriate choice for large area measurement, although resulting in a compromise regarding lateral resolution.  
Although this preliminary work demonstrated precise and stabilized surface mapping using the proposed technique, significant progress must be made to allow this approach to be applied to commercial CMP tools. Considerable effort will be necessary to integrate the system into the polishers used in production lines, and to improve the system's capabilities of rapidly acquiring and automatically analyzing data to effectively monitor key pad characteristics. Monitoring these characteristics of the surface roughness, including material removal rate, pad thickness non-uniformities, platen run-out, and groove occlusion, is a long-term but achievable goal.
## 5 Results
### GPS Parameters  
On comparing the data from the GPS data (Figure 4), there were significant differences between the GPS recorded distances at high intensities (F (3,27) = 4.67, \(p\) = 0.009, \(\eta^{2}\) = 0.087) and distances covered at sprint speeds (F (3,27) = 2.98, \(p\) = 0.049, \(\eta^{2}\) = 0.108) but no significant differences for distances covered at very high intensities (F (3,27) = 0.699, \(p\) = 0.561, \(\eta^{2}\) = 0.010). Post-hoc comparisons showed a significant decrease between PRE-1 and RTP 1 in distances covered at high intensities (\(p\) = 0.007). All other post-hoc comparisons between PRE and RTP data showed no significant differences between data registered at PRE and POST. A comparison of the total distance run showed significant differences between the matches, with the distances run during RTP 2 being significantly higher than PRE-2 (\(p\) < 0.001) and PRE-1 (\(p\) < 0.001); and the distance run at RTP 1 was higher than that at PRE-2 (\(p\) = 0.007).  
The variation in peak speed (Figure 5) was not significant between the different instances (F (3,27) = 2.49, \(p\) =0.136, \(\eta^{2}\) = 0.126). However, a significant difference was observed for the peak acceleration registered (F (3,27) = 4.74, \(p\) = 0.009, \(\eta^{2}\) = 0.257), although post-hoc comparisons showed no significant differences between the different instances. The explosive distance showed significant differences between the instances (F (3,27) = 13.4, \(p\) < 0.001, \(\eta^{2}\) = 0.481), and post-hoc comparisons showed significantly higher values for RTP 2 compared to PRE-2 (\(p\) < 0.001) and PRE-1 compared to RTP 1 (\(p\) < 0.001).  
Figure 3: Transverse B-Mode ultrasound images of the evolution of the injured muscle. (**A**) Initial. 48 h post-injury. Ultrasound imaging shows muscle fiber discontinuity at the level of tear (arrowhead), with surrounding echogenic muscle edema (asterisk). (**B**) Ultrasound imaging at seven days post-PNE. (**C**) Ultrasound imaging at 14 days post-PNE.
# Indoor Vehicles Geolocalization Using LoRaWAN
## 4 The Proposed Architecture
### The Geolocalization Algorithm
The approach we followed was to use the mean value for the obtained RSSI at 50 m SF12 and determine \(n\) using a derived equation from Equation (2), namely: \(n=\frac{|\overline{RSSI}|}{10*log_{10}(50m)}\), which gives an estimated value \(n=4.66\). This procedure is purely empirical but this is well known in the literature since this simple parameter aggregates various factors that strongly depend on the actual characteristics of the scenario being modelled. Values in the range of [4; 6] would fit almost any indoor scenario. We then proceeded to apply the trilateration algorithm described above. Figure 11 shows the distribution of the obtained position estimation; we could place the client in a circle of diameter 24 m. The localisation values got a standard deviation of \(\sigma_{l}=4.23\). Recalling what is known as GDOP (geometric dilution of precision) or PDOP (position dilution of precision) that describe the error caused by the relative position of the positioning devices; basically, the more signals a client can see (spread apart versus close together), the more precise it can be. From the observer's point of view, if the gateways are spread apart in the locations, then the clients will have a good GDOP. If the gateways are physically close together, then we will have a GDOP, lowering the quality of the positioning. Figure 8: Variation and stability of the Received Signal Strength Indicator (RSSI) with two different spreading factors and three distances. Figure 10: Q-Q plot of the RSSI with two different spreading factors at 50 m.  
Figure 9: Cumulative distribution of the RSSI with two different spreading factors at 50 m.
## 4 The Proposed Key Distribution Framework  
### A Spatiotemporal Diversification-Based Multipath-Multi-Key Distribution  
The proposed framework operates in three primary stages: _selection_ and _execution_ phases for multi-key distribution on different routes, and the _testing_ phase to detect compromised nodes. The basic representation of the proposed model is shown in Figure 4.  
Figure 4: The process of MTD-based QKD distribution using a multipath routing algorithm.