esa-sceva/satcom-chunk-collection huggingface dataset:
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 3 Methodology
### Performance Evaluation of the Models  
Model performance evaluation was performed to measure the predictive ability of the landslide model and whether it was suitable for the region. In this study, in order to evaluate the error of the prediction result, root mean square errors (RMSEs) were calculated for the prediction result of each model and the actual landslide information [85; 87]. RMSEs were calculated using the 20% landslide locations that were stored for testing in the previous step.  
Additionally, the accuracy of the model was calculated by calculating the area under the curve (AUC) of the precision-recall (PR) curve [88]. The PR curve is a curve with recall on the \(x\)-axis and precision on the \(y\)-axis, and is a line connecting recall and precision values for each data while varying the threshold value based on the susceptibility value of the model result. The AUC value created from the landslide modeling result depends on the recall and precision index of the \(x\)-axis and \(y\)-axis of the curve, respectively [88]. The AUC of the PR curve can be expressed as an average precision (AP) score, and it is the area of the graph when the PR curve is drawn. The AUC value approaches 1 when both recall and precision converge to the highest level, showing the model ability to efficiently predict the susceptibility of a landslide: the higher the value of the AUC of the PR, the higher the predictive ability of the model.
## 2 Study Area and Data
### Data Preparation
#### 2.2.3 Meteorological Data  
In this study, meteorological data as input parameters were used for the WOFOST model. Meteorological data were acquired from the China Meteorological Data Sharing Service System and include the temperature, solar radiation and sunshine duration.
# Verification of Control System Runtime Using an Executable Semantic Model +
## 3 Runtime Formal Model
### Model Semantics  
Presenting the application's operation in a formal manner can be achieved through semantics. Semantics is used to present the meaning of a program formally, independent of any specific programming language. The meaning of the program is defined by the expected result, or the outcome of executing the program.  
Three types of semantics can be distinguished:  
* Axiomatic semantics--defines the meaning of the program by specifying axioms and a set of rules, which is used to prove the program's properties.
* Operational semantics--precisely defines the meaning of the program by specifying the successive computational steps during the program's execution.
* Denotational semantics--defines the meaning of the program using abstract mathematical constructs [22; 23], not describing the details of the constructs (i.e., bit representation of numbers, carry propagation during calculations, or memory access method).  
Applying a semantic approach, which allows for a reliable presentation of the program's operation formally, has many advantages. These include the following:  
* Certainty of an accurate and understandable description of the program's operation;
* Independence from a specific implementation;
* Ease of implementing the program in any programming language;
* Ability to reliably infer the correctness of the program's operation.  
In this work, the formal description of the CPDev virtual machine is constructed using denotational semantics in the form of denotational equations, which depict in an understandable manner the successive steps taken during the execution of individual instructions to achieve the desired result. Based on the formal model of the VM operation, it is possible to prepare the implementation that operates in the same way as defined in the specification in any programming language and for various target platforms.
## 6 Evaluation
### Case Description and Simulation Configuration
However, we decided to consider this negative externality in our estimation), to do so we consider them to be implemented using a Raspberry PI Zero [75]. Figure 8: Protelis code for the case study. * We consider the Raspberry PIs to have an idle current drain of 65 mA ([[https://archive.is/Lk0](https://archive.is/Lk0) BI]([https://archive.is/Lk0](https://archive.is/Lk0) BI)) (hence consuming 0.325 W at 5 V), and a load power consumption of 2.7 W ([[https://www.phornix.com/scan.php?page=article&item=raspberry-pi-burst&num=3](https://www.phornix.com/scan.php?page=article&item=raspberry-pi-burst&num=3)]([https://www.phornix.com/scan.php?page=article&item=raspberry-pi-burst&num=3](https://www.phornix.com/scan.php?page=article&item=raspberry-pi-burst&num=3))). * We estimate the electricity price to be 0.28e/kwn ([[https://archive.is/N7](https://archive.is/N7) BPS]([https://archive.is/N7](https://archive.is/N7) BPS)), and we use a conversion factor of 1.25/e ([[https://archive.is/ID9](https://archive.is/ID9) ga]([https://archive.is/ID9](https://archive.is/ID9) ga)). We execute 10 runs of the experiment with a different seed for every combination of the variables in Table 3. Different seeds imply different household positions, users' desired temperature, and smart device power on instant. Data analysis has been performed using Xarray [76] and matplotlib [77]. The complete analysis, available on the aforementioned repository, counts over 350 charts, of which the most relevant are included here. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Name** & **Unit** & **Meaning** \\ \hline \(D_{15}\) & ms & Accumulated network delays in the last 15 min. \\ \(T_{i}\) & °C & Maximum allowed heating temperature for smart thermostat \(i\) \\ \(E[T]\) & °C & Mean of \(T\) across all devices \\ \(\sigma[T]\) & °C & Standard deviation of \(T\) across all devices \\ \(T_{max}\) & °C & Maximum of \(T\) across all devices \\ \(T_{min}\) & °C & Minimum of \(T\) across all devices \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics for the case. Figure 9: Sequence of snapshots of the simulator during an execution run of the experiment. Initially (**a**), \(PM_{10}\) levels are comparable in the whole city. After 25 h from the beginning of the experiment (**b**), intense rain enters from north, lowering the \(PM_{10}\) levels, and covers the whole city in six hours (**c**), thus lowering \(PM_{10}\) levels everywhere. Rain stops uniformly after 61 h from the beginning of the experiment (**d**), and the overall \(PM_{10}\) raises with time to the initial level (**e**).
## 5 Results and Discussion
### Analysis of Results among DE Variants
\begin{table}
\begin{tabular}{l l l l l} \hline
**Test** & **Case C1** & **Case C2** & **Case C3** & **Case C4** \\ \hline best/1/bin vs. best/1/exp & \((\approx)\) 5.4922 \(\times 10^{-2}\) & \((\approx)\) 1.0000 & \((+)\) 5.5879 \(\times\)\(10^{-6}\) & \((\approx)\) 2.5793 \(\times\)\(10^{-1}\) \\ best/1/bin vs. current-to-best/1 & \((+)\) 3.7253 \(\times\)\(10^{-6}\) & \((\approx)\) 8.0327 \(\times\)\(10^{-2}\) & \((+)\) 1.8626 \(\times\)\(10^{-6}\) & \((+)\) 1.4193 \(\times\)\(10^{-6}\) \\ best/1/bin vs. current-to-rand/1 & \((+)\) 6.9104 \(\times\)\(10^{-6}\) & \((\approx)\) 5.4488 \(\times\)\(10^{-1}\) & \((+)\) 3.7253 \(\times\)\(10^{-6}\) & \((+)\) 6.6395 \(\times\)\(10^{-3}\) \\ best/1/bin vs. current-to-rand/1/bin & \((+)\) 9.3132 \(\times\)\(10^{-6}\) & \((-)\) 2.7663 \(\times\)\(10^{-3}\) & \((+)\) 7.2960 \(\times\)\(10^{-4}\) & \((+)\) 1.8626 \(\times\)\(10^{-6}\) \\ best/1/bin vs. rand/1/bin & \((+)\) 1.6431 \(\times\)\(10^{-2}\) & \((\approx)\) 2.4075 \(\times\)\(10^{-1}\) & \((+)\) 1.3663 \(\times\)\(10^{-2}\) & \((+)\) 1.3039 \(\times\)\(10^{-6}\) \\ best/1/bin vs. rand/1/exp & \((-)\) 1.8626 \(\times\)\(10^{-6}\) & \((\approx)\) 5.9408 \(\times\)\(10^{-1}\) & \((-)\) 5.7183 \(\times\)\(10^{-6}\) & \((-)\) 1.0738 \(\times\)\(10^{-3}\) \\ best/1/bin vs. rand/2/dir & \((+)\) 3.7253 \(\times\)\(10^{-6}\) & \((\approx)\) 1.0000 & \((+)\) 1.8626 \(\times\)\(10^{-6}\) & \((\approx)\) 4.8985 \(\times\)\(10^{-1}\) \\ best/1/exp vs. current-to-best/1 & \((+)\) 4.7125 \(\times\)\(10^{-6}\) & \((\approx)\) 1.4028 \(\times\)\(10^{-1}\) & \((\approx)\) 2.9884 \(\times\)\(10^{-1}\) & \((\approx)\) 7.2412 \(\times\)\(10^{-1}\) \\ best/1/exp vs. current-to-rand/1 & \((+)\) 1.8626 \(\times\)\(10^{-6}\) & \((\approx)\) 8.3939 \(\times\)\(10^{-1}\) & \((-)\) 3.2230 \(\times\)\(10^{-3}\) & \((\approx)\) 3.7074 \(\times\)\(10^{-1}\) \\ best/1/exp vs. current-to-rand/1/bin & \((+)\) 1.8626 \(\times\)\(10^{-6}\) & \((-)\) 7.8221 \(\times\)\(10^{-3}\) & \((-)\) 3.7253 \(\times\)\(10^{-6}\) & \((\approx)\) 3.9305 \(\times\)\(10^{-1}\) \\ best/1/exp vs. rand/1/bin & \((+)\) 4.4076 \(\times\)\(10^{-5}\) & \((\approx)\) 1.0482 \(\times\)\(10^{-1}\) & \((-)\) 1.8626 \(\times\)\(10^{-6}\) & \((\approx)\) 3.9305 \(\times\)\(10^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 13: Wilcoxon test results for all case studies.
## 3 Experimental Results and Discussion
### Influence of Two-Phase Fluid Parameters on Sub-Droplet Length  
Figure 6 shows the effect of the two-phase flow parameters on the sub-droplet length. The definition formulas of the continuous phase capillary number \(Ca_{\text{c}}\) and the two-phase flow ratio \(q\) are as follows. \(W\) represents the width of the microchannel and \(H\) represents the depth of the microchannel.  
\[Ca_{\text{c}}=\frac{\left(\frac{q_{\text{c}}}{W\times H}\right)\times\mu_{ \text{c}}}{\sigma}, \tag{1}\]  
\[q=\frac{q_{\text{d}}}{q_{\text{c}}} \tag{2}\]  
Figure 7a,b shows the effect of the two-phase flow on the sub-droplet length. The sub-droplet length decreases with the increase of the continuous phase flow rate and increases with the increase of the dispersed phase flow rate. This is due to the fact that the inertial force of the dispersed phase increases with the flow rate and the head of the dispersed phase is more likely to move forward, thereby forming longer droplets. At a certain speed, as the flow rate of the continuous phase increases, the shear force between the two phases increases. As the shearing effect of the continuous phase is greater, the droplets are more easily broken up at higher continuous phase flow rates. The change in droplet length is inseparable from the interaction of the two phases. The size of the broken sub-droplets increases with the size of the generated droplets. Conversely, smaller droplets correspond to smaller sub-droplets. Figure 7c,d shows the influence of the dimensionless length \(L_{\text{d}}/W\) of the sub-droplet with the continuous phase capillary number \(Ca_{\text{c}}\) and the two-phase flow ratio \(q\) on the length of the sub-droplet. The dimensionless length of sub-droplets (\(L_{\text{d}}/W\)) increases with increasing \(q\) and decreases with increasing capillary number (\(Ca_{\text{c}}\)) of the continuous phase. This is because when the droplet \(q\) is relatively large, the \(q_{\text{d}}\) is relatively large and the \(q_{\text{c}}\) is relatively small; as such, the inertial force of the dispersed phase is relatively large and the shearing effect of the continuous phase is relatively small, making it is easier to generate longer droplets. \(Ca_{\text{c}}\) is proportional to the ratio of viscous force and surface tension. The larger the \(Ca_{\text{c}}\), the larger the viscous force relative to the surface tension. The length of the droplets becomes shorter as the shearing action of the continuous relative dispersed phase increases. Likewise, larger droplets correspond to larger sub-droplets.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Known-Key Security  
The session keys \({SK_{m}}=h(GID_{im}\|sk_{i,m})\), generated in various runs, are mutually independent, where \(sk_{i,m}=T_{r_{1}\cdot r_{2}\cdot r_{5}}(x)\cdot T_{r_{2}\cdot r_{3}\cdot r_{5 }}(x)\cdot\ldots\cdot T_{r_{n}\cdot r_{1}\cdot r_{5}}(x){\rm mod}p\), since \(r_{1}\),\(r_{2}\), \(\ldots\),\(r_{n}\) and \(r_{5}\) are randomly selected by \(SN_{1}\), \(SN_{2}\), \(\ldots\), \(SN_{n}\) and \(AS\), respectively, and are independent across protocol executions. Thus, the proposed group key agreement protocol exhibits known-key security.
# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 1 Introduction  
### Problem Statement  
Process mining is a scientific field at the intersection between Business Process Management (BPM) and data science. Process mining allows the ideal view of how the activities of a process should be performed (i.e., the _normative model_ of the business process), to be contrasted with the way they are carried out (i.e., the _descriptive model_) [1]. Predictive process monitoring (PPM) [2], as a fundamental use case of process mining, supports stakeholders by making predictions about the future of a running business process instance. A business process corresponds to a sequence of executed events affected by decision points and involving a number of actors to achieve one or more goals of the business process [3]. Predictions related to running process instances enable stakeholders to make preventive decisions in case of expected undesired outcomes, expected delays, or resource congestion.  
As stakeholder engagement is at the center of process mining tasks, performance and accuracy are not the only aspects that matter in the context of a PPM prediction task. When depending on an ML model for predicting the future of running businessprocess instances, it becomes necessary to persuade stakeholders about the validity of the reasoning mechanisms applied by the predictive model. In particular, explaining predictions to the various stakeholders fosters users' trust, engagement, and advocacy in PPM-employed mechanisms.  
In order to provide explanations of predictions generated by an ML model, eXplainable Artificial Intelligence (XAI) [4] methods and mechanisms [5; 6; 7; 8; 9; 10; 11] can be put in place. The explanations are obtained either in parallel to the prediction process or afterwards. Moreover, they are expected to reflect how a predictive model is influenced by the different choices made along the ML pipeline. The latter includes data analysis and cleaning, data preprocessing, model selection, parameter configuration, and evaluation of model predictions. Moreover, PPM tasks employ specific mechanisms for aligning process mining artefacts to ML model requirements. Despite increased awareness of the importance of explainability as a supportive mechanism for predictions in the context of PPM, to the best of our knowledge, no study of the effects of different choices made in the context of a PPM workflow has been carried out. Through such a study, it may be possible to understand the consequences of each choice in terms of its outputs, these being inputs in another step of the PPM pipeline. Through such a study, it might be possible to know beforehand which patterns are expected to affect an ML model, and hence which should be reflected in an explanation.
## 1 Introduction
However, because those processes are off-machine or manual, they take more time and incur greater costs due to the additional measurement and inspection instruments and work hours. With the use of the on-line diagnosis method, information and communication technology (ICT), and the empirical statistics method, an on-line monitoring and auto-compensation system for the wear of the turning cutter was developed in this study. The system predicts the machining quality inspection and status of the cutter based on the analysis of on-line turning vibration and machining information while turning is operational. Subsequently, the predicted tool wear will be auto-compensated to the computer numerical control (CNC) controller for next turning work. The method was implemented on an automotive component production line, and both of the original off-machine quality inspection and the manually tool wear compensation were able to be eliminated and replaced by the proposed method. In the study, preliminary experiments were first conducted to collect the data so that the correlations between the variation of cutting vibration signals, cutter wear, and machining errors could be obtained. The diagnosis algorithms were then developed based on the correlations. Lots of experimental data were collected to analyze the characteristics of tool wear and determine the compensation value for tool wear. Based on the statistical analysis and the developed algorithms, a monitoring system with an auto-compensation function was built. Furthermore, adopting FANUC Open CNC API (application programming interface) Specification provided by FANUC Co., Oshino-mura, Japan, a bilateral communication module for a CNC controller was developed with the ability for on-line communication with the CNC controller in order to extract cutting information (such as instantly executing NC (numerical control) codes, coordinates, and the number of cuts, _etc._) and send compensation commands to the CNC controller for the predicted tool wear. The module can also save all the monitoring results and information to a remote central computer for continuous engineering improvement in the future. An interface with functions of data acquisition, inspection, and error compensation was developed in C# language for easy operation. Finally, experiments on a CNC turning machine were conducted to verify the feasibility and effectiveness of the proposed system.
## 6 Conclusions  
This study aimed to quantify the relative importance of key input variables in leaf, canopy and atmosphere radiative transfer models (RTM) by using Gaussian process regression as emulator. Such models can be used to derive top-of-atmosphere radiance data that can be further used to estimate biophysical variables. To do so, the leaf RTM PROSPECT-4 was coupled with the canopy RTM SAIL and the atmosphere RTM MODTRAN. Because MODTRAN is computationally expensive, a bypass was sought by making use of emulation. Emulators are statistical constructs that enable to approximate the outputs of the original RTMs, but this is at low computation cost so that large LUTs can be produced almost instantly. The emulator subsequently allowed to calculate a global sensitivity analysis (GSA) and to identify the driving variables. The GSA total sensitivity index quantified that vegetation variables had a more dominant impact than atmosphere variables on TOA radiance for atmospheric windows. This finding provides support to develop retrieval strategies of biophysical variables such as leaf chlorophyll content (Cab), leaf water content (Cw) and leaf area index (LAI) directly from TOA radiance data, e.g., given Sentinel-2 band settings.  
Accordingly, the coupled leaf-canopy-atmosphere RTMs served to train hybrid retrieval models by using the machine learning algorithm Gaussian processes regression for the processing of Sentinel-2 TOA radiance data (L1C) and bottom-of-atmosphere reflectance data (L2A) given a cloud-free sky. Retrievals of Cab, Cw and LAI were consistent, although optimization is still required for operational processing. The maps demonstrate the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data by means of developing machine learning models, thus without the need of an atmospheric correction step, and this in a streamlined and largely automated environment.  
Summarizing, to the benefit of the community, the here developed toolboxes enable the coupling of leaf-canopy-atmosphere RTMs for any sensor band settings, so they can be used for the generation of TOA LUTs for multiple Earth observation applications, e.g., the retrieval of surface and atmospheric variables.  
[PERSON]. ([PERSON]) designed and ran the experiments, and wrote the paper. [PERSON]. ([PERSON]) designed the LUT experiments and was in charge of the ALG simulations. [PERSON] developed ARTMO and its toolboxes. [PERSON] conducted the GSA experiment. [PERSON] contributed with the implementation of the computer codes and supporting algorithms. [PERSON] advised on the study.  
This work was supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project (grant agreement 755617) and Ramon y Cajal Contract (Spanish Ministry of Science, Innovation and Universities).  
We thank the scientific editor and the reviewers for their constructive remarks.  
The authors declare no conflict of interest.
# Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm
## 4 Performance Evaluation
The chosen routes in the proposed scheme and reliable routing have at most four hops, whereas hop-based and energy-saver have \(\sim\)3 hop count. Figure 5a shows that the average distance between each hop in the proposed scheme and reliable routing is the lowest, which directly translates to better communication, lower propagation delay, and minimal transmission losses. The proposed scheme and energy-saver consider residual energy as a major route selection metric, thus resulting in as much as 87% for the minimum energy in chosen route. The metric in Figure 5b shows the residual energy of a vehicle in the route with the lowest battery. The hop-based and reliable routing chose routes regardless  
Figure 2: SUMO simulation trace (New York City). of energy consideration, thus achieving a minimum energy for the intermediate vehicles of as low as 75%. The proposed scheme outperforms three state-of-the-art routing methods on various fronts and achieves better packet delivery, longer connectivity, shorter transmission delay, shortest hop distance, and energy conservation. The EVs periodically share required information using BSM, whereas the UAVs need to include movement vectors with the route reply messages. The modified route reply messages require additional information delivery  
Figure 4: Comparative evaluation of the proposed with hops [9], reliability [7] and energy [6] routing. Figure 5: Comparative evaluation of the proposed with hops [9], reliability [7] and energy [6] routing. Figure 3: Comparative evaluation of the proposed with hops [9], reliability [7] and energy [6] routing. for at least four float values. Moreover, the proposed scheme requires analytical processing of the routing metrics (\(O^{1},O^{2},maxHV\), and \(\kappa\)). However, the lightweight calculation is trivial, and requires lesser efforts than or almost similar efforts to most existing schemes. The proposed multi-objective optimal routing reduces solution space for evolutionary algorithms and Pareto front calculations while achieving exceptional results. The evaluations suggest that the trade-off of additional processing for higher gains and reliable communication is reasonably acceptable.