esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Settlement Data and Discussion
### MDD-GNSS Total Settlement Analysis  
The total settlement for all MDD positions was recomputed using Equation (5), which added the calibrated data obtained from the MDD to the absolute GNSS measurements. The comparison of recalculated MDD measurements for each laser module position presented in Figure 18 reveals that the total deformation experienced at Position P1 was larger than those of the other positions. As mentioned previously, this difference can be attributed to  
Figure 16: Original deformation monitoring results and estimated displacement using the trend model at Laser module #4.  
Figure 17: Estimated displacement of MDD positions using the trend model and resampling method.  
the location of P1, which was placed between the concrete slab and compacted soil surface. We postulated in the previous chapter that Position P1 is known to experience the cavity expansion resulting from cement grout pressure and the unexpected void space between the slab and the compacted soil surface (Figure 19).  
Before grouting pressure was applied to the weak foundation soil and the void space for restoration of the slab track as shown in Figure 19, the slab might have endured deformation for a while by its strong flexural stiffness even though there was the void space below it. However, some cracks were observed on the surface of the slab externally. This phenomenon was pointed out by some observers [31].  
After the restoration of the slab track, it was presumably predicted that a smaller void space might have remained below the track slab as illustrated in Figure 19 since it was observed that a relatively large following settlement was measured by the GNSS rover as described previously (Figure 18). An approximately 2.0 mm difference in total settlement  
Figure 19: MDD displacement recalculated using the absolute GNSS settlement.  
Figure 18: MDD displacements recalculated using the absolute GNSS settlement.  
between Position 1 and other positions (2, 3, 4) in the MDD/GNSS system is attributed to one main reason: The bearing capacity in the track foundation soil near the slab was not still sufficient to resist the slab weight and the train load fully after restoration. This lack of bearing capacity came from the existing small void space below the slab even though its size was reduced somewhat by grouting restoration.  
To analyze the contribution of the remaining void space to the total settlement difference between Position 1 and other positions, curve fitting was prepared first to construct the most effective model for predicting settlement curves measured by the MDD with the GNSS. This is explained in detail in the following chapter.
## Appendix A \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & **No Risk** & **Risk** & **No PCI** & **PCI** & **No CABG** & **CABG** \\  & **(3717)** & **(2822)** & **(3640)** & **(1734)** & **(3609)** & **(526)** \\ \hline Gender (male) & 1771 & 994 & 1759 & 626 & 1734 & 150 \\ Race & & & & & & \\ Missing & 98 & 64 & 97 & 42 & 96 & 11 \\ Caucasian & 2419 & 2128 & 2360 & 1267 & 2341 & 407 \\ African American & 1034 & 482 & 1019 & 325 & 1010 & 81 \\ Other & 166 & 148 & 164 & 100 & 162 & 27 \\ Age & & & & & & \\
18-24 & 6 & 1 & 6 & 0 & 6 & 1 \\
25-29 & 27 & 3 & 27 & 2 & 27 & 0 \\
30-34 & 80 & 14 & 80 & 7 & 78 & 2 \\
35-39 & 173 & 56 & 166 & 35 & 165 & 8 \\
40-44 & 330 & 130 & 326 & 85 & 323 & 18 \\
45-49 & 469 & 259 & 455 & 164 & 455 & 33 \\
50-54 & 583 & 355 & 571 & 220 & 560 & 67 \\
55-59 & 563 & 413 & 546 & 257 & 537 & 76 \\
60-64 & 499 & 426 & 488 & 265 & 492 & 83 \\
65-69 & 420 & 415 & 412 & 242 & 408 & 70 \\
70-74 & 247 & 340 & 243 & 199 & 237 & 83 \\
75-79 & 199 & 267 & 197 & 159 & 199 & 65 \\ \(\geq\)80 & 121 & 143 & 123 & 99 & 122 & 20 \\ \hline History of peptic & & & & & & \\ ulcer disease & & & & & & \\ History of diabetes & 740 & 698 & 719 & 400 & 704 & 144 \\ History of angina & 2552 & 2500 & 2488 & 1607 & 2460 & 459 \\ History of & & & & & & \\ Hypertension & 2015 & 1769 & 1966 & 1086 & 1947 & 326 \\ History of & & & & & & \\ hyperlipidemia & & & & & & \\ History of & & & & & & \\ smoking & & & & & & \\ \multicolumn{6}{c}{Acute coronary syndrome status upon presentation (ACS)} \\ No ACS & 2545 & 1292 & 2492 & 715 & 2478 & 280 \\ STEMI & 17 & 75 & 17 & 51 & 15 & 6 \\ Non-STEMI & 42 & 111 & 42 & 76 & 41 & 20 \\ MI Unspecified & 2 & 2 & 3 & 1 & 3 & 0 \\ Unstable Angina & 1111 & 1342 & 1086 & 891 & 1072 & 220 \\ \hline Third heart sound & & & & & & \\ (S3) & & & & & & \\ Carotid bruits & 47 & 105 & 47 & 42 & 47 & 32 \\ Height & 170.64 & 171.95 & 170.53 & 171.88 & 170.58 & 172.64 \\ (cm) & (10.8, 0) & (10.34, 0) & (10.76, 0) & (10.39, 0) & (10.79, 0) & (10.45, 0) \\ Weight & 86.29 & 86.54 & 86.03 & 87.19 & 86.13 & 86.37 \\ (kg) & (24.09, 0) & (20.18,0) & (24.12, 0) & (20.42, 0) & (24.11, 0) & (19.92, 0) \\ Body surface area & 1.97 & 1.99 & 1.97 & 1.99 & 1.97 & 1.99 \\ (m\({}^{2}\)) & (0.27, 0) & (0.24, 0) & (0.27, 0) & (0.24, 0) & (0.27, 0) & (0.24, 0) \\ Body mass index & 29.63 & 29.3 & 29.58 & 29.58 & 29.6 & 28.95 \\ (kg/m\({}^{2}\)) & (8.44, 0) & (7.03, 0) & (8.48, 0) & (7.47, 0) & (8.48, 0) & (6.13, 0) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Univariate analysis of patient information.
## 4 Case Study
### Negotiation Framework
#### 4.2.1 Reward  
The PG algorithm optimizes weights \(\theta\) of the deep neural network \(\zeta(s_{t};\theta)\) used as an approximation function of the stochastic policy \(\pi_{\theta}(s_{t},a_{t})\) in order to maximize the discounted future reward \(G_{t}\) (Equation (2)). The _reward_ function is critical for the proper working of the RL, and it is defined heuristically as:  
\[r_{t}(s_{t},a_{t})=\left\{\begin{array}{cl}z_{t}(a_{t}),&\text{if }\alpha_{2}^{1} \text{ or }\alpha_{3}^{1}\
otin[0,1]\\ 2000,&\text{if }e_{s}(t)<e_{b_{t}}(t)\forall s,\\ -1000,&\text{Otherwise}\end{array}\right.,\text{ if }\alpha_{2}^{1}\text{ and }\alpha_{3}^{1}\in[0,1] \tag{22}\]  
Figure 4: PG-DMPC negotiation algorithm implemented.  
where \(e_{s}(t)=\left(h_{s}^{\mathcal{T}}(t)-h_{s}^{c}(t)\right)^{2}\) is the tracking square error, and \(e_{s}^{b}\) is the error threshold for each subsystem. Note that a positive reward corresponds to a desired situation and negative rewards penalties for the RL.  
The maximum reward is given when the tracking error does not exceed the threshold value for any subsystem and the weighting coefficient provided by the deep neural network belongs to \([0,1]\). A small penalty is given when any error exceeds the threshold, but still, \(\alpha_{2}^{1}\) and \(\alpha_{3}^{1}\in[0,1]\). Finally, a large penalty defined by \(z_{t}\) is added if \(\alpha_{2}^{1}\) or \(\alpha_{3}^{1}\
otin[0,1]\) in order to obtain normalized values for the coefficients:  
\[z_{t}(a_{t})=-3000-\Big{(}\Big{(}\Big{|}\alpha_{2}^{1}\Big{|}+\Big{|}\alpha_{3 }^{1}\Big{|}\Big{)}\times 1000\Big{)}. \tag{23}\]
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 5 Discussion
### Typical Region Analysis
#### 5.3.2 Comparison of Interface Areas of Different Land Covers  
As shown in Figure 14, the results reflect the differences between the reservoir shoreline and the reservoir itself, demonstrating the authenticity and accuracy of the findings. That is to say, landslides may occur at the intersection of water and land, but never in the middle of the water body (only the geographical surface is studied here, and submarine landslides are not considered in this study).
# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 2 System Model  
For this work, we consider that the sensory data generated by SNs are gathered by a single UAV used as a sink node [25], as it flies over all wireless SNs. Our objective is to deliver all the collected data to the Base Station (BS), which is the final destination and point of concentration of all sensory data for future analysis. First, we introduce the main elements of this system, detailing the main functionalities of the UAV, Sensor Nodes, and Base Station. After that, we discuss an optimized path for the UAV to fly over the SNs, minimizing the total flight.
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 2 Numerical Analysis
### Results of Numerical Analysis
#### 2.4.1 Effect of Cavitation on Oil Film Velocity  
To study the flow field characteristics with texture, non-texture oil film was selected as the control group, with the same condition parameters. Firstly, the solution is performed for the control group. Figure 7 shows the velocity distribution of the non-texture oil film. The radial velocity decreases along the radius direction, when \(r=r_{1}\), \(u=0.8489\); the angular velocity increases along the radius direction, \(v=12.71\) m/s for \(r=r_{1}\) and \(v=15.13\) m/s for \(r=r_{2}\).  
Figure 6: Flowchart of finite difference algorithm for Reynolds equation.  
Figure 8 shows the velocity distribution of uniform hexahedral textured oil film. Compared with the oil film without texture in Figure 7, the velocity distribution is no longer uniform, the velocity distribution in the non-texture region is basically the same as the velocity in the oil film without textures, and the flow fluctuates more when the oil enters the texture dimples. The minimum radial velocity is \(-1.89\) m/s and the maximum value is 2.71 m/s. The reason is that the oil suddenly increases at the texture entrance and the oil film thickness suddenly decreases at the exit, and the change in film thickness causes the velocity to change drastically. Circumferentially, when the oil enters the texture dimples, the speed decreases rapidly, and the minimum value is 6.1 m/s. It may be that the oil enters into micro-dimples from radial lower boundary, and the vortex causes negative speed and then flows rapidly from the radial upper boundary.
# VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application
## 5 Evaluation
### Experimental Setups
** We used the confusion matrix, as shown in Table 6, during the evaluation procedure. TP (True Positive) is the number of samples where both predicted and actual labels are positive. FP (False Positive) is the number of examples where the actual label is negative, but the predicted label is positive. TN (True Negative) is the number of samples where both the forecast and actual labels are negative. FN (False Negative) is the number of examples where the true label is positive, but the forecast label is negative. To fully use the value of data sets and objectively verify the performance of different methods and models, we choose the following evaluation indicators: accuracy, precision, recall, and F1 score. The specific definitions are as follows:  
Accuracy: Reflects the correct proportion of prediction in all samples. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{18}\]  
Precision: Reflects the proportion of correctly predicted vulnerabilities in the samples judged as vulnerabilities by the system. \[Precision=\frac{TP}{TP+FP} \tag{19}\]  
Recall: Reflects the proportion of vulnerability samples determined by the detection system in the actual vulnerability samples. \[Recall=\frac{TP}{TP+FN} \tag{20}\]  
F1 score: Comprehensively considers the precision and recall and more evenly reflects the vulnerability detection performance of the system. \[F1 score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{21}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Config** \\ \hline CPU & 12 th Gen Intel(R) Core(TM) i9-12900 KF(24 cores) \\ \hline RAM & 128 GB \\ \hline GPU & NVIDIA RTX 3090 \\ \hline OS & Ubuntu 22.04.1 LTS \\ \hline \multirow{2}{*}{Soft} & Python 3.9.12, Torch 1.12, CUDA 11.6, \\  & TorchGeometer 2.0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental Environment Configure.
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 2 Structure of a PMSM  
The structure of a PMSM used in this paper [29] is shown in Figure 1. The PMSM consists of two parts: A spherical rotor and a spherical-shell stator. The radius of the rotor is 65 mm, and the length of the rotor shaft is 40 mm. There are 40 NdFeB permanent magnets on the spherical rotor, which are divided into four layers symmetrically distributed around the equatorial plane of a rotor. 24 air-core coils are assembled on the spherical-shell stator, which are divided into two layers and evenly distributed on both sides of the equator.  
Figure 2 shows three-DOF motion of a PMSM. Figure 2a shows the motion range of three-DOF PMSM's rotor shaft. A stator coordinate frame (SCF) and a rotor coordinate frame (RCF) are used in a PMSM to describe the motion of a spherical rotor. The SCF is stationary relative to the earth, and the center of sphere is defined as the origin (O). The RCF also defines the center of sphere as the origin (o). The centers of the SCF and RCF are completely coincided at the initial position. Figure 2b,c shows the spinning motion and tilting motion of a PMSM, respectively.
# Phononic Coupled-Resonator Waveguide Micro-Cavities
## 4 Conclusions  
As a summary, we have experimentally investigated how a set of three phononic coupled-resonator elastic waveguide cavities form collective vibrations. The CREW cavities are fabricated in a fused silica plate and resonante at a few Megahertz. They are weakly and evanescently coupled for frequencies within the wide complete bandgap, as they are separated by four lattice spacings. Overall, the resonance modes of the individual phononic cavities are essentially preserved and can be excited at once. The observed collective vibrations can generally be explained in the light of a dynamical matrix model with one degree of freedom per defect and within the nearest-neighbor approximation. Beyond this simplified model, the details of the vibrations at resonance are observed by a scanning laser vibrometer. In particular, the vibration modal shapes are found to be consistant with a full finite element model of the experiment.  
**Author Contributions:** conceptualization, T.-T.W. and V.L.; funding acquisition, V.L. and Y.-S.W.; technology, S.B. and T.-T.W.; metrology, F.L.-V. and T.-T.W.; project administration, V.L., Y.-F.W. and Y.-S.W.; software, T.-T.W.; validation, Y.-F.W.; writing--original draft, T.-T.W. and V.L.; writing--review and editing, S.B., F.L.-V., Y.-F.W. and Y.-S.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the EIPHI Graduate School (contract ANR-17-EURE-0002), the French Investissements d'Avenir program, project ISITE-BFC (contract ANR-15-IDEX-03), and the National Natural Science Foundation of China (11702017 and 11532001). This work was supported by the French RENATECH network and its FEMTO-ST technological facility.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 5: **(a)** Phononic band structure around 2.1 MHz. **(b)** the modal shape at the point marked with a red cross in **(a)** is depicted. Experimental **(c)** and numerical **(d)** out-of-plane displacement maps are shown at frequency 2.09 MHz.
## 3 Results  
The filtering process applied to the raw input data of 595,355 ha revealed significant reductions in the filtered area over the years from 2015 to 2021 (Table 1). Initially, after filtering for minimum data, the total filtered area ranged from 440,327 ha (73.96% of the total area) in 2015 to 463,443 ha (77.84%) in 2021, indicating an overall increase in the percentage of data retained. However, following the crop type filtering, the filtered area sharply declined, with the values falling from 4060 ha in 2015 to 2659.6 ha in 2016, stabilizing around 3500 ha for the subsequent years, thereby constituting only 0.45% to 0.68% of the total area. Further filtering based on shape index and valid crop rotation systems produced similar results, consistently maintaining the filtered area near 3900 ha, approximately 0.36% to 0.66% of the total area. The most stringent filtering criterion, which required at least 50% coverage, resulted in an extremely reduced filtered area ranging from 2268 ha (0.38%) in 2015 to 990 ha (0.17%) in 2016 but fluctuated in subsequent years. Notably, while all years except 2016 met the threshold for this filtering criterion, it highlighted challenges in maintaining crop coverage consistently across the dataset. The performance evaluation of various algorithms for LAI and FAPAR estimation over the years 2015 to 2021 based on cross-validation is summarized in the results in Table 2, while the accuracy assessment metrics from final model fit, which were based on all used training and test data, are presented in Table 3. The RF algorithm generally exhibited the highest R\({}^{2}\) across most years for both the LAI and FAPAR, with values peaking at 0.560 and 0.590 in 2015 and 2020, respectively. The RF algorithm consistently outperformed other methods, including XGB, SVM, and their combination, although a noticeable decline in performance was observed in 2021, with R\({}^{2}\) dropping to 0.250 for LAI and 0.358 for FAPAR. XGB showed comparable results, particularly in earlier years, but similarly exhibited a downward trend over time. The RMSE and MAE metrics complemented the R\({}^{2}\) values,indicating a decrease in accuracy, especially in 2021, where most algorithms reported relatively high error rates.
## 3 Methodology
**Data Extraction and Synthesis:** Data extraction and synthesis focused on identifying categories and methods related to the use of LLMs for generating fake news, creating fake profiles, and detecting these activities. Figure 4 depicts the most popular topics mentioned in the selected articles. We specifically considered survey works that illustrate different approaches to using LLMs, studies discussing aspects such as detection accuracy, robustness of fake news generation, and user identification techniques. Given the recent broad access to LLM technologies, the number of surveys and studies available was limited. This limitation was reflected in the literature, which mostly consists of recent publications. The limited literature on LLMs and their application in fake news and profile detection underscores the emerging nature of this field and the need for further research and exploration. **Results of the Literature Search:** The findings are organized in alignment with the document's structure, providing a detailed analysis of the use of large language models (LLMs) for generating fake news, creating fake profiles, and detecting such activities. This includes examining the existing literature that highlights the importance and various dimensions of LLMs in the context of fake news and profile detection. Given the limited literature due to the recent broad access to LLM technologies, our survey covers the most relevant and recent studies in this emerging field. We analyze the different methodologies employed, the effectiveness of various detection techniques, and the security and privacy considerations associated with using LLMs for these purposes. Figure 4: The most popular terms in the titles of the retrieved articles. Higher term count values correspond to a bigger size of the corresponding bubble and a lighter color.
## 3 Results
### Numerical Results: Point Scatterer
#### 3.1.3 Lateral Shift Variance  
In Figure 7d,e, point scatterers positioned at the depths of 10 and 20 mm, respectively, are laterally shifted from \(-6.5\) to 6.5 mm. This image is normalized to its maximum to fairly track the effects of grating lobes (see the green arrows) [41]. Figure 7d,e indicates that if a scatterer is positioned in the direction of the grating lobes, its effect is much lower than that of a scattering target in the middle of the array. This is mainly because of the AW technique in reception and the fact that the direct transmitted pressure field excites targets in the lateral center of the array with a higher power, compared to those in the grating lobes angles (see Figure 2a). The best resolution is also obtained when the scatterer is at the lateral center.
## 4 Results and Discussion
### Effect of PFHE Size Parameters  
#### 4.2.1 Number of Fin Layers  
Above all, it should be noted that there is one more fin layer on the cold side than the hot side. The effect of the number of fin layers on the heat transfer area and pressure drop are illustrated in Figure 5a. In general, the increasing of the number of fin layers causes an increasing in the heat transfer area and a decrease in the pressure drop. The decline in pressure is because of both the decline in Re and the increasing of the number of fin layers. As the number of fin layers increased from 2 to 10, there was a sharp drop in pressure drop; after that, the pressure drop continued down to around 0.1 kPa. There is an obvious change in pressure drop when the number of fin layers is from 10 to 13. From Figure 5b, it can be explained that the increase in the number of fin layers increases the flow area, affecting exhaust changes from turbulence to the laminar flow which makes the j factor increase, and the increase in the heat transfer coefficient on the hot side ultimately results in the reduction of the required heat exchange area. It seems that both heat transfer area and pressure drop are relatively small when the number of fin layers is about 30, which is consistent with the results of the optimization.
## 2 Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions
### Remote Sensing Technologies for Deriving Land-Use and Land-Cover (LULC) Changes
An alternative to using SAR backscattering to detect and map surface changes is exploiting interferometric SAR products, such as the coherence. Coherent change detection (CCD) techniques make use of the cross-correlation between the interfering pairs of complex SAR images [151]. A change of the scene can lead to a sensible variation of the coherence, and the coherence can be used as an additional way to discover and map such variations. A component of the coherence that is more difficult to model and relate to physical parameters is the temporal decorrelation [150; 152], resulting in a combination between natural random changes and those possibly associated with events that are desired to track and study over time. In coherent change detection (CCD), ambiguity remains when coherence changes are not causally related to the events are misinterpreted. Thus, poor change detection performances are obtained. Further efforts to model the different causes of decorrelation in a coherence map are still required to discriminate and isolate the temporal decorrelation signals not causally related to the events under investigation. Tracking coherence differences between couples of SAR images were used to study different phenomena. A non-exhaustive but nearly comprehensive discussion of applications using CCD technologies, with a specific focus on those related to the monitoring of coastal and deltaic regions, can be found in [153; 154; 155; 156; 157; 158].