esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Stage 1 and 3--X-Ray Diffraction  
The mineralogical composition of powdered proppants was characterized via XRD before and after static exposure. Analyses were conducted using a Bruker D8 Advance X-ray Diffractometer equipped with a Lynxeye detector. Scans were performed over a 2\(\theta\) range of 3-80\({}^{\circ}\), with a step size of 0.002\({}^{\circ}\) and a dwell time of 0.10 s per step. Diffraction patterns were processed and analyzed using Bruker's AXS Diffrac.EVA software Ver 5.2.0.5 (2-25-2020) for semi-quantitative mineral phase evaluation. To quantify and identify peak changes and the emergence of new peaks, Equation (1) and a condition given in Equation (2) were used, respectively.  
\[\Delta I(2\theta_{i})=I_{after}(2\theta_{i})-I_{before}(2\theta_{i}), \tag{1}\]  
where, \(\Delta I\) is the intensity change at each angle \(2\theta_{i}\), \(I_{after}(2\theta_{i})\) is the peak intensity after reaction, and \(I_{before}(2\theta_{i})\) is the peak intensity before interactions.  
\[New\ peak\ at\ 2\theta_{i},\ \ \ \ \ if\ I_{before}(2\theta_{i})<I_{threshold},\ I_{after}(2 \theta_{i})\geq I_{new} \tag{2}\]  
where, \(I_{threshold}\) is a baseline noise threshold and \(I_{new}\) is the minimum required intensity to qualify as a new peak (commonly > counts above noise peaks).
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 2 Methodology  
There are some differences between a new leveee and an HTL. For an HTL, the compressibility of the old leveee and old foundation is very low. The deformation mainly arises owing to the backfill and new foundation. Hence, the settlement prediction method and settlement control criterion are different from those of the new levee.  
This section focuses on four aspects. They are a generalized model, a generalized settlement prediction model, settlement monitoring, and a finite element method. These four aspects have a certain logical relationship because settlement prediction needs a settlement criterion to clarify the position that needs to be controlled. Hence, settlement prediction and the settlement control criterion are interdependent.
## 6 Conclusions  
Due to the dynamically singular factor of robotic manipulator and the strong coupling between the UAV base and the robotic manipulator, the inverse kinematics of AEROM system cannot be directly employed for trajectory planning. A novel trajectory planning method for an AEROM system is proposed in this paper, where the AEROM system consists of a UAV base and a robotic manipulator with single arm. The following factors are considered: (i) a specific case, where the AEROM is non-holonomic, (ii) each joint trajectory is depicted with a Bezier curve due to its characteristics of simplicity, smoothness and normalization, (iii) the AEROM maneuver time is derived through boundaries of joint velocities and accelerations, (iv) HPSO with self-adaptive ability is proposed to search through the global space for the construction of each Bezier curve. (v) multiple objectives can be simultaneously optimized utilizing the proposed method. Numerical simulations show that the minimum disturbance acting on the UAV base is realized. The optimization of multiple objectives, i.e., various cost functions, is also well performed.  
Figure 16: Pose variation of UAV base, where (a) UAV attitude, and (b) UAV position.  
Figure 14: Angular variation curves of \(i\)th joint, where (a) \(i=1\), 2, 3, and (b) \(i=4\), 5, 6.  
Figure 15: Velocity variation curves of \(i\)th joint, where (a) \(i=1\), 2, 3, and (b) \(i=4\), 5, 6.  
Variations of physical parameters such as rotational inertia are out of consideration, which will be studied in future work. In addition, the collision avoidance issue is also one of the subjects of future work. Since the trajectory planning is studied through AEROM kinematics, AEROM dynamics are not further considered and analyzed. The AEROM control, based on both kinematics and dynamics, will be studied in a follow-up study.  
Conceptualization, S.Z. and C.C.; methodology, S.Z. and J.L.; writing--original-draft preparation, S.Z. and J.L.; writing--review and editing, C.C., S.G. and X.G.; supervision, C.C. and S.G.; funding acquisition, S.Z., S.G. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the technological innovation Guidance Fund of Shaanxi Province (Grant No. 2022 QFY01-16), by the Science and Technology foundation of Shaanxi Province (Grant No. 2022 GY-236), by National Natural Science Foundation of China (Grant No. 62103182) and by the China Postdoctoral Science Foundation (Grant No. 2021M701548).  
Not applicable.  
The authors sincerely acknowledge the technological innovation Guidance Fund of Shaanxi Province (Grant No. 2022 QFY01-16), the Science and Technology foundation of Shaanxi Province (Grant No. 2022 GY-236), the National Natural Science Foundation of China (Grant No. 62103182) and the China Postdoctoral Science Foundation (Grant No. 2021M701548). The authors would also thank the members of the Autonomous Unmanned Systems Research Laboratory for their useful and valuable suggestions.  
The authors declare no conflict of interest.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 5 Conclusions  
This paper presents a review of cybersecurity vulnerabilities in smart grids. It discusses how information technologies are integrated with power systems, creating novel issues that were previously unknown. Then, mitigation requirements are documented as discussed in different standards and research outputs. It also includes an overview of possible cyberattacks in smart grids, focusing on false data injection attacks. These attacks are handled separately as their possible impact on the power system operation is much larger. A thorough review of the literature is given on research dedicated to detecting false data injection in the smart grid domain.  
When using synthetic datasets, SVM-based methods (e.g., KF, EKF, DKF, RBF kernel, and Gaussian and linear kernels) were used dominantly and performed better than the classical attacks detection methods (PCA, BDD, etc.) that employ the state estimation (SE) approach for the FDIA. The studies also showed that the semi-supervised learning approaches (supervised learning over labelled data and trained SVM) are stronger to deal with the different data sparsity degrees than the fully supervised learning approaches. PCA does not require to train data to detect the deviation of the measurements. However, in real-time data, another method of detecting FDIA against a complex system using deep autoencoders offers better detection performance than SVM-based methods. Besides, deep autoencoders are more comfortable to train since they do not require labelled data for training and can detect different attacks because they can learn hidden, complicated correlation structures in the data.  
In this study, cyber-attacks that can be encountered in grids have been examined, with a particular focus on false data injection attacks. Future deep learning and deep autoencoders approach such as SARSA and POMDP can be investigated as it can work on different systems.  
1. Machine learning/AI integrated cybersecurity systems are required since hackers are getting smarter, and attacks are getting diverse.
2. More holistic cybersecurity designs are required instead of solutions that only focus on 1 aspect of security such as access control or encryption.  
Conceptualization, D.B.U.; T.S.U.; S.M.S.H., and A.O.; methodology, T.S.U. and D.B.U.; software, D.B.U. and T.S.U.; validation, T.S.U.; S.M.S.H. and A.O.; investigation, D.B.U.; T.S.U. and S.M.S.H.; writing--original draft preparation, D.B.U. and T.S.U.; writing--review and editing, D.B.U., T.S.U., S.M.S.H., and A.O.; visualization, D.B.U., T.S.U., S.M.S.H., and A.O.; supervision, T.S.U. and A.O.; funding acquisition, T.S.U. and S.M.S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Variable Selection in Time Series Forecasting Using Random Forests
## 1 Introduction  
### Time Series Forecasting and Random Forests  
The use of machine learning algorithms in predictive modelling (see for the definition [1]) and in the specific case of time series forecasting has increased considerably recently [2]. Relevant applications can be found in the review papers [3; 4; 5; 6].  
Time series forecasting can be classified into two categories according to the forecast horizon, i.e., one-step and multi-step forecasting [2; 7]. The one-step forecasting (examined here) is useful in single applications, e.g., in assessing forecasting methods using simulations [8], engineering forecasting [9; 10], environmental forecasting [11; 12], and financial forecasting [13; 14; 15; 16; 17]. Furthermore, the recursive and the DiRec multi-step forecasting strategies depend on the one-step forecasting. In particular, one-step ahead forecasts are used in the recursive strategy, while in each step the forecasted value of the previous step is used as additional input, without changing the forecasting model. The DiRec strategy combines the recursive strategy, but the forecasting model changes in every step [7].  
The random forest (RF) is a machine learning algorithm introduced in [18], which can be used for classification and regression. It is popular because it can be applied to a wide range of prediction problems, it has a few parameters to tune, it is simple to use, it has been successfully applied to many practical problems and it can deal with small sample sizes, high-dimensional feature spaces, and complex data structures [19; 20]. A review and a simple presentation of the RF algorithm can be found in [20; 21; 22]. Regression using RF can be implemented for time series forecasting purposes. Representative applications can be found in many scientific fields including these of engineering [23; 24], environmental and geophysical sciences [25; 26; 27], financial studies [28; 29], and medicine [30], withvarying performance. Furthermore, small datasets are used in these applications; therefore, the results cannot be generalized.  
The performance of the RF algorithm depends on the tuning of its parameters and the variable selection (also known as feature selection). Procedures for estimating the optimal set of parameters are proposed in several studies. These procedures consider the performance of the algorithm depending on the selected variables [22; 31], the number of trees [32; 33], the number of possible directions for splitting at each node of each tree [20; 34] (p. 199), and the number of examples in each cell, below which the cell is not split [20; 35]. There are also some studies examining the RF properties in a theoretical context [19].  
The performance of machine learning algorithms is usually assessed using single case studies. Sometimes large datasets composed of real-world studies are used for benchmarking and the comparison of several algorithms [7; 36; 37; 38; 39; 40]. Moreover, [41] proposes the use of simulations as a considerable alternative. Respective applications can be found in [42; 43].
## Appendix A \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Code** & **Sitename** & **Lat** & **Lon** &
\begin{tabular}{c} **Elevation** \\ **(masl)** \\ \end{tabular} & **IGBP Code** & **Metabolism** & **Homogeneity** \\ \hline BE-Bra & Brasschaat & 51.308 & 4.520 & 30 & MF & C3 & Homo \\ \hline BE-Dor & Dorinne & 50.312 & 4.968 & 248 & GRA & C3 & Hetero \\ \hline BE-Lon & Lonzee & 50.552 & 4.746 & 169 & CRO & C3 & Homo \\ \hline BE-Maa & Maasmechelen & 50.980 & 5.632 & 86 & CSH & C3 & Homo \\ \hline BE-Vie & Vielsalm & 50.305 & 5.998 & 495 & MF & C3 & Homo \\ \hline CH-Aws & Alp Weissenstein & 46.583 & 9.790 & 1969 & GRA & C3 & Hetero \\ \hline CH-Cha & Chamau & 47.210 & 8.410 & 391 & GRA & C3 & Hetero \\ \hline CH-Dav & Davos & 46.815 & 9.856 & 1652 & ENF & C3 & Hetero \\ \hline CH-Fru & Früebüel & 47.116 & 8.538 & 980 & GRA & C3 & Hetero \\ \hline CH-Lae & Laegern & 47.478 & 8.364 & 685 & MF & C3 & Homo \\ \hline CH-Oe2 & Oensingen crop & 47.286 & 7.734 & 452 & CRO & C3 & Hetero \\ \hline CZ-BK1 & Billy Kriz forest & 49.502 & 18.537 & 876 & ENF & C3 & Homo \\ \hline CZ-Lnz & Lanzhot & 48.682 & 16.946 & 181 & MF & C3 & Homo \\ \hline CZ-RAJ & Rajec & 49.444 & 16.697 & 653 & ENF & C3 & Homo \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Site details: longitude (negative values are west of the prime meridian); latitude (north as positive); site elevation (m above sea level); vegetation categories follow the IGBP land cover classification: evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrublands (CSH), open shrublands (OSH), savannahs (SAV), grasslands (GRA), permanent wetlands (WET) and croplands (CRO). spatial heterogeneity assessment (homogeneous versus heterogeneous).
## 3 Study Area and Data
### Landslide Predisposing Factors
#### 3.3.2 Geological Factors  
The lithology is one of the basic geological conditions for landslide occurrence. The weathering process of the slope, the development of joints and fractures, and the rock strength often vary dramatically due to different rock types [44]. This can eventually affect the slope stability in terms of the slope morphology and rock mass strength [5]. Based on a 1:250,000-scale geological map and referring to the National Standard of China called the Standard for Engineering Classification of Rock Mass (GB/T50218-2014) [49], we classified the lithology of the study area into 6 rock groups (Figure 5e): very hard, hard, less hard, soft, very soft, and water [20]. Fault zones often control the development of landslides, and we adopted the factor of the fault density (Figure 5f) to reflect its effect on landslide occurrence. On the one hand, the structural planes are very developed near the fault zone, determining the spatial position of the failure surface of the slope and the boundary of the landslide [50]. On the other hand, due to the internal dynamics, fault zones often form landforms of fault scarps and rift valleys [51]. They are characterized by large topographic relief and provide favorable geomorphological conditions for landslide occurrence. Furthermore, Table 3 indicates that areas with a higher fault density tend to be more prone to landslides. In  
Figure 5: Landslide predisposing factors: (**a**) elevation; (**b**) aspect; (**c**) slope; (**d**) topographic relief; (**e**) rock group; (**f**) fault density; (**g**) PGA; (**h**) river density; (**i**) NDVI; (**j**) annual precipitation.  
addition, the burst of an earthquake often leads to the occurrence of a large number of landslides, and they are distributed in clusters [52]. In this study, PGA (Figure 5g) refers to the maximum absolute value of the acceleration of surface particle motion during the propagation of seismic waves [53]. It can reflect the instantaneous intensity of the energy released by an earthquake [54].
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 5 Results
### Correlation Between Attributes in the Historical Weather Dataset (2020-2024) and CLMS Portfolio  
Figure 8 indicates the correlation between each attribute. The sampling period is monthly, the total data points are 51 raw points for the water river height and 51 monthly average points for each weather attribute. The attribute that has the highest correlation with the river water height is the rain precipitation at 0.56, and there is a correlation of 0.88 between the temperature and the evaporation.  
Figure 8: Heat map of the correlation matrix for all variables.  
Figure 9 shows the scatter plot of water river height and the rain at the same year and month, along with the regression line calculated. The correlation between the two variables is stronger in low rain precipitation and initial water height. The divergence and spread of values are higher after the value of 0.15 mm of monthly average rain. Some outstanding points may be caused by water management at a dam that is located upstream in the river (Aguieira dam). This dam manages water retention according to the needs for electricity production along the year.  
Table 3 shows the results and errors obtained for the regression model. The intercept value is linked to the initial water river height, compared to the sea level reference. The coefficient is high due to the limitation of using the monthly average rain precipitation. The errors between data points and prediction using the regression line are on average less than 2%, which shows that the regression is a good approximation for estimating water level in the function of precipitation level.
## 4 \(m\)-Principal-\(u\)-Assistant Scenario
### Branch-and-Price Algorithm
#### 4.2.1 Linear Relaxation and Decomposition  
In the branch-and-price algorithm, the master problem is converted from a 0-1 integer linear problem into a general linear problem, as follows:  
\[\max\sum_{r\in\Omega}c_{r}\cdot w_{r} \tag{41}\] \[\text{s.t.}\quad\eqref{eq:constraint}-\eqref{eq:constraint}\] \[\quad 0\leq w_{r}\leq 1,\forall r\in\Omega \tag{42}\]  
After the linear relaxation, the difficulty of solving the objective (37) has been decreased slightly, but the set \(\Omega\) is still too large to enumerate completely. However, a fact should be exploited, i.e., that the ratio of the number of actually used routes to the number of all valid routes is exceedingly small, so the final solution can be constructed from a smaller route set \(\Omega^{\prime}\subseteq\Omega\). The column generation formulates a restricted master problem by replacing the set \(\Omega\) with the set \(\Omega^{\prime}\) as follows:  
\[\max\sum_{r\in\Omega}c_{r}\cdot w_{r} \tag{43}\] \[\text{s.t.}\quad\sum_{r\in\Omega}a_{i,r}\cdot w_{r}\leq 1\quad \forall v_{i}\in V\backslash\{v_{0}\}\] (44) \[\quad\sum_{r\in\Omega}w_{r}\leq m\] (45) \[\quad 0\leq w_{r}\leq 1,\forall r\in\Omega \tag{46}\]  
The initial set \(\Omega^{\prime}\) can be enlarged by adding new routes. According to the duality theory, a better route \(r\) satisfies the following condition:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(r\) & A principal–assistant route. \\ \(\Omega\) & The set of all available routes. \\ \(c_{r}\) & Parameter that indicates the number of tasks served in the route r. \\ \(a_{i,r}\) & Parameter that equals one when one task \(v_{i}\) is served in route r, and zero otherwise. \\ \(w_{r}\) & Binary decision variable equal to one if route \(r\) is used, and zero otherwise. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and variables for the \(m\)-principal-\(u\)-assistant scenario.  
\[\widehat{c_{r}}=c_{r}-\sum_{v_{i}\in V\setminus\{v_{0}\}}a_{i,r}\cdot\delta_{i}- \gamma>0 \tag{47}\]  
where \(\delta_{i}\) is the dual variable of the current optimal solution for each constraint in Constraint (44), and \(\gamma\) is the dual variable for Constraint (45). Furthermore, if no route with \(\widehat{c_{r}}>0\) exists, the global optimum is found. The following process is to find and price an undiscovered route \(r^{\star}\) with \(\widehat{c_{r^{\star}}}=\max_{r\in\Omega-\Omega^{\prime}}\widehat{c_{r}}\), which is called the pricing subproblem and is detailed in the next section.
# Upgrading _In Vitro_ Digestion Protocols with Absorption Models
## 2 Technological Approaches of Intestinal Epithelium Models
### Cell Cultures Used in Epithelium Models
#### 2.3.2 Verification of Cell-Based Absorption Models  
It is very important that before and during absorption experiments the quality of the intestinal monolayer is checked. Cell viability can be determined by LDH (lactate dehydrogenase) assay or MTT (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazololium) assay. Membrane permeability can be checked with Trypan blue, cell proliferation with 5-bromo-2\({}^{\prime}\)-deoxyuridine (BrdU), while apoptosis with annexin or catalase [7]. Redox damage can be established by measuring reactive oxygen species or glutathione [7].  
It is essential to determine barrier integrity before and after the experiment, to be sure that the absorption measurements are accurate. [PERSON] et al. [58] describe transepithelial electrical resistance measurements (TEER), dextran permeability, and cellular junctional complex imaging as proper methods to measure barrier integrity. Out of these, TEER is the most commonly used. TEER measures \"the resistance of electrical current passing through a cellular monolayer\", thus representing the permeability of ions and barrier integrity (Figure 4) [58]. Phase contrast and fluorescence confocal microscopy can also be applied to evaluate the morphology of the cells [59].
## 2 Materials and Methods
### Design Procedure and Analysis of the FSS Element
In Figure 2b,c, real and imaginary parts of \(Z_{FSS}\) obtained from full wave simulations by varying the strip width are reported. The Q-factor is defined considering the resonance frequency \(f_{0}\) and the \(-3\) dB band \(\Delta f_{-3\text{dB}}\) (i.e., the bandwidth between the two frequencies at which the power is \(-3\) dB below the peak value) and is calculated as follows:  
\[Q=\frac{f_{0}}{\Delta f_{-3\text{dB}}} \tag{4}\]  
This definition is applied to the real part of \(Z_{FSS}\), as shown in Figure 2a, to provide the Q-factor values for varying strip widths, which are summarized in Table 2. As the strip width increases, the Q-factor decreases, making the configuration with \(w=0.2\) mm the one with the highest Q-factor. Thus, the impedance condition is satisfied over the widest frequency range possible, determining a design procedure that ensures compactness without sacrificing the wideband bandpass performance integrity.