esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
###### Abstract  
The resistivity method is widely used to address long-term monitoring challenges in fields such as environmental protection, ecological restoration, seawater intrusion, and geological hazard assessment. However, external environmental changes can influence monitoring data, resulting in inversion results that fail to accurately reflect subsurface variations. Furthermore, the data volume required for such monitoring is several times larger than that for conventional single-point observations, leading to excessively long inversion times and low computational efficiency. To address these issues, we develop a three-dimensional inversion algorithm for the resistivity method that incorporates time-lapse constraints. Additionally, MPI parallelization is integrated into the program to increase computational efficiency. Through the design of theoretical models and the synthesis of data to test the algorithm, the results show that, compared with those of separate inversion, the shapes and values of time-lapse inversion results at different time points are more consistent, maintaining temporal continuity, and the computational efficiency of MPI parallel inversion is greatly improved. Particularly in high-noise environments, time-lapse inversion effectively suppresses background noise interference, reduces false anomalies, and produces results that closely align with the true model, thus confirming the algorithm's effectiveness and superiority.  
resistivity method; time-lapse; MPI; three-dimensional Article
## 1 Introduction
On the other hand, multiple scattering occurs around the caverns as the disturbing stress waves pass through the multi-cavern structure [21]. Therefore, the dynamic response around the caverns becomes more complex compared to single-cavern structures, and the stability of the multi-cavern structure under dynamic disturbance has attracted the attention of many scholars. [PERSON] et al. [22] and [PERSON] et al. [23] calculated the steady-state response around double circular caverns under the S waves and P waves by the wave function expansion method, and pointed out that the space between caverns and frequency of the incident wave have considerable influence on the dynamic responses around the caverns. [PERSON] et al. [24] investigated the effects of incident angle and wave number on the twin circular subjected to P-wave. However, the existing research mainly focused on the dynamic response of double circular caverns, and the studies of the non-circular caverns were confined to a single structure. Further investigation of the dynamic responses and failure characteristics of double non-circular caverns is needed, especially for unequal caverns. This paper aims to understand the dynamic responses around the deep double U-shaped caverns under disturbing load. A two-dimensional numerical simulation model with double U-shaped caverns under coupled static-dynamic loading was established by PFC2D, and the dynamic stress concentration and energy evolution around the caverns were investigated. Moreover, the influences of cavern clearance, cavern height ratio, and disturbing direction on the dynamic response and failure modes of surrounding rock were investigated systematically. This will provide a theoretical basis for disaster prevention and control of deep double-cavern structures.
# Backstepping Based Formation Control of Quadrotors with the State Transformation Technique
## 3 Backstepping-Based Quadrotor Formation Control
### Altitude Control of a Quadrotor
**Assumption 2**: _The attitude angles of the \(i\)-th follower \(\phi_{fi}\), \(\theta_{fi}\) and \(\psi_{fi}\) satisfy the following conditions:_  
\[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]0  
Therefore, based on the definition of \(v_{i,1}\) in (8) and (9), we can derive the actual altitude control input of the \(i\)-th follower as follows:  
\[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]1  
**Theorem 1**: _Consider the altitude system of the \(i\)-th follower in (8) controlled by the virtual and pseudo altitude control inputs of the \(i\)-th follower in (12) and (14), respectively. Then, there exist the design parameters \(k_{i,1}\) and \(k_{i,2}\) such that the actual altitude control input of the \(i\)-th follower in (15) asymptotically stabilizes the formation error system related to the altitude of the \(i\)-th follower in (11) and (13)._  
Let us consider the following Lyapunov function candidate:  
\[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]2Differentiating \(V_{zi}(e_{zi,1},e_{zi,2})\) with respect to the time and substituting (11) and (13), we obtain:  
\[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]3 \[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]4 \[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]5  
Using (12), (14) and (17) can be rewritten as follows:  
\[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]6 \[T_{i,1}(\phi_{fi},\theta_{fi})=\frac{\cos\phi_{fi}\cos\theta_{fi}}{m_{i}}. \tag{10}\]7  
Therefore, by the Lyapunov stability theorem [31], the formation error system related to the altitude of the \(i\)-th follower in (11) and (13) is asymptotically stable with the actual altitude control input of the \(i\)-th follower in (15). Finally, the follower maintains the formation that is separated by the relative distance on the \(z\)-axis \(d_{i,z}\) from the leader.
## 4 Summary and Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{**SD**} & \multicolumn{4}{c}{**Convergence Area 1**} & \multicolumn{4}{c}{**Convergence Area 2**} \\ \cline{3-8}  & & **Forecast** & **Model** & **Actual** & **Forecast** & **Model** & **Actual** \\ \hline \multirow{3}{*}{0101} & 100 m & 68 & 75 & 95 & 484 & 450 & 440 \\  & 300 m & 402 & 465 & 435 & 733 & 710 & 700 \\  & 500 m & 739 & 755 & 500 & 943 & 925 & 800 \\ \hline \multirow{3}{*}{0201} & 100 m & 332 & 380 & 290 & 499 & 500 & 495 \\  & 300 m & 326 & 375 & 320 & 676 & 690 & 620 \\  & 500 m & none & none & none & none & none & none \\ \hline \multirow{3}{*}{0701} & 100 m & 255 & 335 & 200 & 564 & 580 & 490 \\  & 300 m & 698 & 670 & 545 & 790 & 795 & 755 \\  & 500 m & 959 & 945 & none & 996 & 965 & none \\ \hline \multirow{3}{*}{0801} & 100 m & 332 & 295 & 225 & 484 & 440 & 430 \\  & 300 m & 637 & 635 & 580 & 691 & 680 & 670 \\ \cline{1-1}  & 500 m & none & none & none & none & none & none \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the effectiveness of forecasting the depth changes of the convergence area at different sound source depths at different times (f = 50 Hz). area and can also provide us with relevant help in the acoustic detection of the relevant sea areas in the ocean front. For example, the decline of the depth of the convergence area is unfavorable to the acoustic detection under certain conditions, and this method can capture this disadvantage in advance. It should be pointed out that although we have proved the applicability of this method to KEF in this paper, we have not proved whether this method is applicable in other ocean front environments. This is also one of the key points of future research. In addition, we also need to further study the interference of other ocean phenomena such as internal waves on the two-dimensional sound speed field modeling of ocean front. Conceptualization, W.C. ([PERSON]), Y.L. and L.M. ; methodology, Y.L. and W.C. ([PERSON]); software, Y.C. ; validation, Y.L. ; formal analysis, Y.C. and Z.M. ; investigation, W.C. ([PERSON]) and Y.L. ; writing--original draft preparation, Y.L. and W.C. ([PERSON]); writing--review and editing, W.C. ([PERSON]) and L.M. ; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under contract No. 61901488. Not applicable. The authors declare no conflict of interest.
## 1 Introduction
The existing criteria for complex mode shapes reveal weaknesses and tend to fail in several situations;
* The evaluation of the performance of the developed mode-pairing criterion based on a case study involving a highly complex FE model of a railway vehicle and experimental modal parameters. In the existing mode pairing criteria, the validation is usually performed based on simple numerical or analytical examples. Additionally, the experimental restrictions associated with the positioning and number of sensors, noise and environmental interference create more challenging conditions to evaluate the performance of the pairing criteria. This article presents the main existing mode-pairing criteria for complex modes, with a special emphasis on the criteria relying on the Energy-based Modal Assurance Criteria (EMAC). Regarding the EMAC, an innovative mathematical formulation based on a state-space model is detailed. Then, this criterion was applied to a case study involving the automatic mode pairing between the experimental and numerical modes of a passenger railway vehicle. In railway vehicles, the localized damping introduced by the suspension systems is responsible for the existence of complex mode shapes. In addition, the numerical model considers the flexibility of the carbody's elements, and consequently, the local modes of the panels that form the floor, walls and roof are present. These two aspects make the application of a criterion such as the EMAC particularly relevant to obtain a stable and robust automatic mode pairing.
## 7 Security Considerations and Proposed Zero-Trust Security Model
### Security Considerations to Support Zero-Trust Model
#### 7.1.4 OT Endpoint Compliance Management  
Endpoint management applications to be implemented that continuously scans network endpoints for vulnerable misconfigurations and compliance violations. The agent must be installed on all eligible OT endpoints and discoveries are made on the local OT network to identify other potential and manageable endpoints. The agent will collect data for OT assets, software, and patching inventory in real-time and then they will be classified as managed or unmanaged. Figure 12 shows how this will work and at the end of the process, administrators will be able to identify the endpoints that are out of compliance. Automation can be set up to take measures on those endpoints immediately to comply with organization standards and also automatically update the dynamic access control policies for these endpoints to be more restrictive until the time they are out of compliance.  
The benefit of implementing a compliance management process is to have each site more secure through real-time insight into OT asset configurations, installed software, and missing patches. It also provides the detection of unmanaged OT assets and extensive insight into managed OT assets with reports for vulnerability assessment.
## 2 Experimental Setup
### Principle of Numerical Correction Method for Perfect Circularly Polarized Light
According to the theory given above, sample reflectivity R and phase retardation \(\delta\) can be extracted from the complex function using the following expressions:  
\[R(z)=A_{1}(z)^{2}+A_{2}(z)^{2} \tag{13}\]  
\[\delta(z)=\arctan\frac{A_{2}(z)}{A_{1}(z)} \tag{14}\]  
Before reaching the sample, the beam in the sample arm undergoes QWP modulation to achieve circular polarization. As depicted in Figure 2b, in the context of observation within a fixed plane perpendicular to the optical axis, the circularly polarized light vector exhibits a constant magnitude of rotation, forming a circular trajectory. When the polarization state of light passing through the QWP is oriented at a 45\({}^{\circ}\) angle relative to the plate, circular polarization is achieved. However, in practical optical systems, the propagation direction of light along the optical axis is difficult to ascertain, necessitating a numerical calibration process to compensate for such uncertainties.
## 3 Results
### Analysis of the Constraining of the Clock Synchronization Biases  
In different scenarios regarding OTWL observations, the clock synchronization biases are either hardly (at the level of the ranging precision of 1 mm) or loosely (five times looser constraints) constrained by introducing pseudo-observations. Scenarios without OTWL observations are not affected by the constraints' variations. In the following sections, we perform a separate detailed analysis on the mean error (see Section 3.2.1) as well as the standard deviation of the estimated clocks (see Section 3.2.2), but overall the different constraining types do not affect the standard deviation of the estimated clocks. Therefore, to analyze the effect of the constraining of the clock synchronization biases, we compute the mean error of the estimated clocks per satellite and per day of the simulation period. This leads to a total of 240 realizations for the MEO-only constellation and up to 280 realizations for the MEO+GSO constellation. These realizations are arranged in ascending order and the minimum, the 5%, the mean (average), the 95% and the maximum absolute values are extracted per scenario. Figure 2 shows the mean error of the estimated clocks as a function of the L-band+OTWL and L-band+OTWL+OISL scenarios as well as hard (left plot) or loose (right plot) constraining.  
For the L-band+OTWL scenario, a large discrepancy between the minimum and maximum value is generally noticeable. The L-band+OTWL scenario with hard constraints gives a factor of about 10 lower mean errors than we obtain from the scenario with five times looser constraints. For the L-band+OTWL+OISL scenario, much lower maximum values as well as a much smaller discrepancy between the minimum and maximum values can be  
Figure 2: Analysis of the constraining of the clock synchronization biases in the case of OTWL observations. The plots show the mean error of the estimated clocks as a function of the type of observables used, and hard (**left plot**) or loose (**right plot**) constraining. The minimum, mean (average) and maximum values as well as the values bounding the 5% or 95% of the results are reported.  
achieved. This is due to the additional use of OISL observations. With the hard constraints, the mean error of the estimated clocks is on average at a factor of about 7.5 smaller than in the scenario with looser constraints. The same behavior can be found for results regarding the MEO+IGSO and MEO+GEO constellations. For our specific simulations, this leads us to use the hard constraints for all the further results presented in this work. Our goal is to realize a time and ranging reference. Therefore, hard constraining is needed, and for the optical link it is assumed that it is calibrated as good as possible.
Both physical and psychological RTS should be used to ensure a safe return, not just in training, but also in competition. The Psychological Readiness of Injured Athlete to Return to Sport (PRIA-RS) questionnaire was developed by [PERSON] et al. [26] to assist clinicians in making decisions regarding a soccer player's psychological readiness to return to training after injury. Previous psychological scales, such as the Injury Psychological Readiness to Return to Sport (I-PRRS) [27], Re-Injury Anxiety Inventory (RIAI) [28], and the Anterior Crucate Ligament-Return to Sport after Injury (ACL-RSI) [29], have certain limitations as they assess only isolated aspects of psychological readiness to return to sport. Specifically, the I-PRRS consists of 6 items focused solely on self-confidence, the RIAI is composed of 28 items related to re-injury anxiety, and the ACL-RSI is injury-specific, as it was developed for anterior cruciate ligament injuries. [PERSON] et al. [26] highlighted that rehabilitation professionals can use the PRIA-RS questionnaire to comprehensively assess an athlete's psychological readiness for a return to sport (RTS). This tool evaluates factors such as confidence, personal perception, insecurity, and fear of re-injury at the end of the recovery process. The psychometric reliability and effectiveness of the PRIA-RS in measuring the psychological readiness for the RTS were further validated by [PERSON] et al. [9]. They concluded that (a) the tool is considered valid for determining the psychological readiness of a professional soccer player on the day before returning to practice after an injury, and (b) using the PRIA-RS questionnaire with soccer players could help prevent re-injury. However, none of the aforementioned instruments--i.e., I-PRRS, RIAI, and ACL-RSI--have been psychometrically evaluated to assess the psychological readiness to return to sport in Greek athletes. Only three valid questionnaires are available for assessing specific aspects of psychological readiness in Greek athletes: the \"Causes of Re-Injury Worry Questionnaire\" (CR-IWO) [30], the \"Sport Confidence Questionnaire of Rehabilitated Athletes Returning to Competition\" (SCQ-RARC) [31], and the \"Attention Questionnaire of Rehabilitated Athletes Returning to Competition\" (AQ-RARC) [32]. The primary difference between the PRIA-RS and the other three questionnaires in Greek is that the PRIA-RS evaluates the psychological readiness for the RTS through a holistic approach, while the CR-IWO focuses on re-injury worry, the SCQ-RARC assesses self-confidence, and the AQ-RARC measures competitive attention and its disruption. A valid and reliable instrument in Greek, such as the PRIA-RS, would further contribute to assessing an athlete's psychological readiness to return to competitive activity after an injury. By using this questionnaire, rehabilitation professionals can also help reduce the likelihood of re-injury. Thus, the aim of the present study was to examine the psychometric properties of the PRIA-RS questionnaire in Greek football athletes.
# Structural Reliability Analysis Using Stochastic Finite Element Method Based on Krylov Subspace
## 3 Numerical Verification
### A Plate Structure
In conjunction with the aforementioned fact that the computation time of the SFE-KS method is roughly half of the NSFE method, it is reiterated that the proposed SFE-KS method outperforms the NSFE and complete analysis approaches. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Complete Analysis** & **NSFE (with \(m\) = 4)** & **SFE-KS (with \(m\) = 2)** \\ \hline Vertical displacement range, mm & [2.5461, 2.6669] & [2.5461, 2.6668] & [2.5461, 2.6670] \\ \hline Mean, mm & 2.6109 & 2.6109 (0.00\%) * & 2.6109 (0.00\%) \\ \hline Standard deviation & 0.0175 & 0.0175 (0.00\%) & 0.0175 (0.00\%) \\ \hline Failure probability & 0 & 0 & 0 \\  & (0.00\%) & (0.00\%) \\ \hline \hline \end{tabular}  
* The data in brackets indicate the relative error between the calculated value and the exact solution.  
\end{table} 
Table 12: Statistical characteristics of horizontal displacement for example 3 when the standard deviation is 0.05 times deviation is 0.05 times the mean. Figure 10: Vertical displacement of 3000 samples for example 3 when the standard deviation is 0.05 times the mean.
# Automated Design of the Deep Neural Network Pipeline
## 2 Background
### Text Processing
#### 2.5.1 Sentiment Analysis  
Sentiment analysis extracts features within user-originating texts with the aim of determining the opinion that the author holds of a specific entity. The aforementioned entity could be anything from a political candidate to a restaurant. This research will make use of five datasets: ACL IMDB movie reviews dataset [28], Amazon product reviews dataset [29], Coronavirus tweets dataset [30], Sentiment 140 dataset [31] and the Yelp reviews dataset [32].  
The work in [33] predicts movie ratings by using a deep neural network pipeline. The preprocessing stage consists of case conversion, tokenisation, stop word removal, spelling correction, stemming and lemmatization. The feature engineering stage using the Word2 Vec technique in a skipgram configuration and the resulting feature representation is then given to a Long Short-Term Memory (LSTM). The deep neural network pipeline described was applied to the ACL IMDB movie reviews dataset [28] and is the state of the art for that dataset.  
The work in [34] showed that a deep neural network pipeline using an LSTM with no preprocessing techniques and a basic word embedding as the feature engineering technique provided optimal performance for the Amazon product reviews dataset [29]. The work in [35] developed a deep neural network pipeline to predict the sentiment of a tweet pertaining to the 2020 Coronavirus pandemic. The deep neural network pipeline contained a preprocessing stage with unspecified techniques and a feature engineering stage that used one-hot encoding. The neural network component was a Convolutional Neural Network (CNN). The deep neural network pipeline as a whole was applied to the Coronavirus tweets dataset [30].  
The comparative study in [36] sought to determine whether an LSTM or CNN would perform most effectively in a deep neural network pipeline for sentiment analysis, specifically for tweets. The preprocessing stage made use of stop word removal, lemmatization and stemming. The feature engineering stage was not specified. The best-performing deep neural network was a LSTM as applied to the Sentiment 140 dataset [31]. The last study [37] constructed a deep neural network pipeline where the preprocessing stage consisted of case conversion, punctuation removal, tokenisation, lemmatisation and stop word removal. The feature engineering technique chosen was GloVe, and the resulting feature representationwas given to a CNN for classification. The Yelp reviews dataset [32] was used, and the resulting deep neural network pipeline is now the state of the art for this dataset.