esa-sceva/satcom-chunk-collection huggingface dataset:
# Distribution Grid Stability--Influence of Inertia Moment of Synchronous Machines
## 5 Conclusions  
Grid frequency variation causes a load angle to swing in the synchronous machines connected to the grid. The grid frequency variation spectrum shape grows the inertial moment on the shaft of the synchronous machine while also growing the swinging amplitude. This is visible from the shown equations. The maximum inertia moment value was thus determined for safe swinging amplitude. The evidence of conformity to the calculated value of the moment of inertia is shown in Figure 4 as a  
Figure 4: Random changes in grid frequency (top) and changes influenced by the oscillation of the load angle of a synchronous machine (bottom).  
recording of grid frequency and the corresponding response of the load angle. The average value of the load angle is present in the required interval. The instantaneous value of the load angle is mainly present in the required interval; however, the average value is more significant.  
The load angle in this safe interval enables the machine to be driven in a safe and reliable manner by external controllers. These controllers are not considered in the article. This limitation should be respected both in drives with tens and hundreds of kW as well as in drives with small synchronous machines and stepping motors, where large inertial masses have to be driven.  
As was written before, this paper did not consider external controllers. However, they are often a major part of frequency grid stability issues. Frequency grid fluctuations cause load angle fluctuations, which in turn cause frequency grid fluctuations. This interaction is a closed circle. In closing, it is necessary to mention trends and possibilities in frequency stabilization. A few studies last year discussed renewable sources of energy. These renewable sources can reduce the quality of grid services [9]. However, with the appropriate combination, along with batteries, they can be used to stabilize the properties of the grid [9]. A small battery source connected to photovoltaic panels was described in [10]. These battery sources, or even larger ones, would therefore be appropriate to include and use in the grid. This is especially true for intelligent buildings that combine many energy sources such as synchronous generators and photovoltaic panels; these batteries can be used as more than just backup sources.  
Conceptualization, T.P., I.U.; investigation, T.P., I.U., M.L.; methodology, M.D., V.P.; supervision, M.L.; validation, V.P., M.D.; visualization, V.P., M.L.; writing--original draft, I.U., T.P.; writing--review and editing, T.P., M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Results of Experiment 2: Precision and Alarm Rate
### Individual Performance  
Table 5 shows the number of alerts generated, the number of _TP_ and _FP_, the precision, and the average number of alerts per day (dividing the number of alerts by 30). The latter provides a hint on the amount of resources required for manual supervision of the alerts.  
Looking at the last two columns of Table 5, one can deduce that some configurations are not practical due to the large number of alerts generated and/or the rate of _FP_ (or poor precision). As a standalone system, we can conclude that the least sensitive configuration of InspectorLog (min) generates few alarms and no _FP_, but it fails to provide a minimal detection capability according to our previous experiment. On the contrary, the med. or max. configurations of InspectorLog provide some detection capability, but they scored a very poor precision (excessive _FP_) and excessive false alarms. Similarly, ModSecurity (low) does not provide an acceptable detection capability, and its med. configuration does not provide an acceptable precision. Only Nemesida rated a minimally usable detection rate (63.9%) and precision (0.83) generating an acceptable number of alerts. Therefore, we can conclude that none of the SIDS provided the expected level of protection against known attacks or else produced an excessive number of false alarms, which make them unpractical. This confirms the need of manual fine-tuning of the ruleset of these open-source detectors for the target operational environment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Union**} & \multicolumn{3}{c}{**Voting Majority**} & \multicolumn{3}{c}{**Intersection**} \\ \cline{2-10}  & **low** & **med.** & **max.** & **low** & **med.** & **max.** & **low** & **med.** & **max.** \\ \hline attacks-800 & 72.5 & 95.6 & 98.1 & 4.3 & 71.1 & 79.7 & 0.0 & 31.0 & 52.6 \\ \hline attacks-1100 & 74.7 & 95.8 & 98.4 & 4.0 & 75.1 & 82.0 & 0.0 & 42.7 & 60.7 \\ \hline fwaf-bad & 58.8 & 86.6 & 97.4 & 3.2 & 44.0 & 51.3 & 0.0 & 14.2 & 26.2 \\ \hline fwaf-2200 & 97.5 & 98.7 & 99.3 & 8.0 & 91.0 & 95.0 & 0.0 & 57.5 & 82.7 \\ \hline rdb & 68.8 & 93.7 & 95.8 & 6.1 & 69.8 & 76.6 & 0.0 & 28.7 & 49.2 \\ \hline osvdb & 58.3 & 86.8 & 92.5 & 7.0 & 64.4 & 71.4 & 0.0 & 33.3 & 44.6 \\ \hline a-420 & 83.8 & 92.9 & 95.0 & 2.9 & 76.9 & 81.9 & 0.0 & 18.8 & 22.9 \\ \hline AVERAGE & 73.5 & 92.8 & 96.7 & 5.1 & 70.3 & 76.8 & 0.0 & 32.3 & 48.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection rate or recall (percentage) with a cooperative decision.
## 4 Numerical Results  
Several simulations were run considering different mesh conditions and types of contact interaction (Figure 8). The first contact was performed on hexahedral mesh (Figure 8B), and the contact interaction was performed as a sticky interface. A sticky interface is similar to a tied interface, except that it allows an initial separation of the surfaces, and it breaks the \"tie\" condition after standard traction. The second contact was performed on a tetrahedral mesh (Figure 8B), and the contact interaction was performed as a sliding elastic interface. The sliding contact interface uses facet-on-facet contact but differs in the linearization of the contact forces. Thus, this contact definition considers a frictionless contact interface. The contact interfaces are shown in Figure 8A.  
The comparison of the number of elements versus the time elapsed is shown in Figure 9A. The comparative of the number of elements versus the Von Mises stresses is shown in Figure 9B. The comparative number of elements versus the displacement is shown in Figure 9C.  
The following table summarizes the computed results for hexahedral and tetrahedral elements with contact and no contact. The number of elements is modified through geometrical parameters, like the dimension, width, or depth of the element.  
Figure 8: Mesh conditions for experimental contact comparative; (**A**) Definition of region interactions. (**B**) Hexahedral 8 nodes linear mesh. (**C**) Tetrahedral 4 nodes linear mesh.  
Figure 9: Mesh convergence analysis; (**A**)Elements versus time. (**B**) Elements versus Von Mises. (**C**) Elements Versus deformation.  
By looking at the data shown in Table 1, it can be observed that values 7 and 11 are equal for several elements, but the computed data are quite different. This behavior is similar to reported values 8 and 12. The main reason for the duplicated number of elements is because they are parallel to the large axis. The results shows that the elements' numbers of depth is less significant than the number of radial elements of the simulation.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack
### Media Access Control Layer
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Feature** & **Article** & **Technique Used** & **Enhancement** & **Limitation** \\ \hline \hline \multirow{9}{*}{[76]} & \multirow{9}{*}{[11]} & & NB-IoT coverage comparisons in different scenarios for 15 kHz and 3.75 kHz spacing & The channel estimation impairments, carrier offset as well as mobility with respect to different configurations are not considered for the claimed 170 dB of achieved MCL of NB-IoT \\ \cline{2-5}  & & & & It can only be applicable in small cell configurations when NB-IoT is deployed in large scale, preconfiguring access for different require \\ \cline{2-5}  & & & & This scheme may results in NB-IoT signaling overhead due to Radio Resource Control (RRC) connection with small data \\ \cline{2-5}  & & & & Optimal repetition number for NB-IoT devices is not considered, with additional penetration loss, it does not explain the additional repetition requirement to enhance the coverage while guaranteeing the required performance \\ \hline \multirow{9}{*}{[78]} & \multirow{9}{*}{Low Earth Orbit (LEO) satellite to extend NB-IoT coverage} & \multirow{9}{*}{NB-IoT Coverage extension beyond LTE achieved link budget} & The work did not consider the impact of repetition number on extended coverage as well as time and frequency synchronization that can lead to sampling rate mismatch as well as carrier frequency offset for low-end NB-IoT modules \\ \cline{2-5}  & & & & Using two devices is not representative massive NB-IoT devices in the because different chips have different power consumption depending on the enabled features such as inter-RAT support that can affect the overall device consumption \\ \cline{2-5}  & & & & The solution is not optimal because it reduces scheduling request without considering the device requirement with respect to channel parameters \\ \cline{2-5}  & & & & The solution is not optimal because it reduces scheduling request without considering the device requirement with respect to channel parameters \\ \cline{2-5}  & & & & Energy consumption and delay requirement evaluation for NB-IoT systems by considering the four mentied modes and it does not include the space of repetition on the device power consumption \\ \cline{2-5}  & & & & The model does not include the energy consumption during transition between the four mentioned modes and it does not include the space of repetition on the device power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 2 Feature Extraction Method for Axis Motion Trajectories  
This method for extracting axis motion trajectory characteristics is primarily based on the distance sequence representation method, incorporating singular value decomposition. Using SVD, the axis trajectory signals are purified and denoised. Subsequently, the signals undergo centering and normalization. The distance sequence representation method is then employed to derive data sequences that characterize the geometric features of the axis motion trajectory. The methodology consists of three main components: the purification and denoising of axis trajectory signals, the preprocessing of axis trajectory signals, and the extraction of axis trajectory signal features, as illustrated in Figure 1.  
Figure 1: Feature extraction method for axis motion trajectories.
# Swarm Intelligence Algorithms for Feature Selection: A Review
## 6 Applications of Feature Selection with Swarm Intelligence
### Application Areas
With binary FSS, the researchers in [95] proposed the application which predicts the readmission of Intensive Care Unit (ICU) patients within the time window of 24-72 h after being discharged. Methodology IGWO-KELM for Medical Diagnosis was proposed in [92]. An Improved GWO algorithm (IGWO) is used to select the most informative subset of features, which are used in the second stage (KELM). In the latter, the prediction based on selected features in the previous stage is made. For predicting metabolic syndrome disease, the adaptive binary PSO learnable Bayesian classifier is proposed in [90]. Research [49] addresses the problem of selecting textural features for determining the water content of cultured Sunagoke moss. The proposed approach comprises the RIO algorithm, in which the feature subset is evaluated with BPNN. A machine vision-based system is also used in addition to the latter in [86]. To improve the leap reading recognition, the BPSO is used with Euclidean Distances of subsets as the fitness function. Researchers in [48] proposed the BA algorithm for improvement in a wood defect classification. The following studies have been done in the field of Electrical and Electronic Engineering. Authors in [56] used the FA algorithm in the near infrared (NIR) spectroscopy to find the most informative wavelengths. In the subarea of sensors, the researchers in [100] proposed a novel KH algorithm to improve gas recognition by electronic noses. Hyperspectral images are addressed in FS in [43; 87]. The first paper uses the GWO algorithm for band selection. For hyperspectral image classification, researchers in the second article proposed the PSO-based automatic relevance determination and feature selection system. In the Computer Engineering field, the intrusion detection is one of the most intriguing problems for researchers. It is addressed in [96] with the AFSA algorithm, in [47] with the BA* algorithm, and in [94] with the GWO algorithm. The dynamic subfield is also image steganalysis. The researchers in [33] presented a novel method named IAB based on an ABC algorithm for feature selection, and the authors in [53] used the FA algorithm for blind image steganalysis. Some other problems tackled in this field comprise fault diagnosis of complex structures with the BFO algorithm [64], skin detection based background removal for enhanced face recognition with adaptive BPSO [89], identifying malicious web domains with BPSO in [88], and web page classification with ACO [83].
# Data-Driven Robust Control Using Reinforcement Learning
## 2 Materials and Methods
### Robust Control Using Reinforcement Learning
#### 2.1.2 Policy Improvement by the Actor  
Linear feedback controllers have been widely used as a stabilization tool for nonlinear systems where dynamic behavior is considered approximately linear around the operating condition [16; 17; 18]. Hence, in this paper, we use linear functions of the states with gain \(K_{i}\) as the control policy at iteration \(i\):  
\[u(t)=\pi(x(t))=-K_{i}x(t), \tag{17}\]  
and the level of uncertainty is constant during the controlling process. The task of the actor is to robustly improve the current policy such that the value function is guaranteed to be reduced during the next policy implementation. If the following differential inequality is satisfied:  
\[\dot{V}_{i}(x(t))+\alpha V_{i}(x(t))\leq 0 \tag{18}\]with some positive constant \(\alpha\), then by using the comparison lemma (Lemma 3.4 in [19]), the derivative of function \(\hat{V}_{i}(x(t))\) can be bounded by  
\[\hat{V}_{i}(x(t))\leq V_{i}(x(t_{0}))e^{-\alpha(t-t0)}. \tag{19}\]  
Therefore, maximizing the rate \(\alpha\) will ensure a maximum exponential decrease in the value of \(\hat{V}_{i}(x(t))\).  
The following part shows the main results of the paper, which describe how the policy gain can be improved during the learning process. Derivations of the results are provided in the stability analysis (Section 2.1.3).  
**Definition 1**.: _Assume \(A\) is a square matrix with dimension \(n\times n\) and \(x\) is a vector with dimension \(n\times 1\). The maximize operation on matrix \(A\) and vector \(x\) is defined as follows:_  
\[\text{maximize}(A,x)=C, \tag{20}\]  
_where_  
\[C_{ij}=\left\{\begin{array}{ll}\max(A_{ij})\text{ if }x_{i}x_{j}\geq 0\\ \min(A_{ij})\text{ if }x_{i}x_{j}<0\end{array}\right.\text{ with }i,j=1\ldots n. \tag{21}\]  
Assuming that the sign of all state variables cannot be changed between each policy update interval, the improved policy \(K_{i+1}\) can be obtained by minimizing \(\alpha\) subject to  
\[\left[\begin{array}{cc}V&K_{i+1}^{T}B^{T}\\ BK_{i+1}&-\gamma I\end{array}\right]\leq 0 \tag{22}\]  
and  
\[\left[\begin{array}{cc}\zeta&K_{i+1}\\ K_{i+1}^{T}&-I\end{array}\right]\leq 0, \tag{23}\]  
where:  
\[V=M+\Delta P_{i,\text{max}}^{T}\Delta P_{i,\text{max}}\gamma-\hat{P}_{i}BK_{i +1}-K_{i+1}^{T}B^{T}\hat{P}_{i}+\alpha(\hat{P}_{i}+\frac{1}{2}\Delta P_{i, \text{max}}^{T}\Delta P_{i,\text{max}}+I) \tag{24}\]  
and  
\[M=-Q-K_{i}^{T}RK_{i}+\hat{P}_{i}BK_{i}+K_{i}^{T}B^{T}\hat{P}_{i}+H_{i} \tag{25}\]  
with \(\Delta P_{i,\text{max}}=\text{maximize}(\Delta P_{i},x)\) and \(H_{i}=\text{maximize}(\Delta P_{i}BK_{i}+K_{i}^{T}B^{T}\Delta P_{i},x)\), utilizing the maximize operation defined in Definition 1. Inequality (22) provides the stable condition and its derivation is provided in Section 2.1.3. Inequality (23) provides the upper bound for the updated gain \(K_{i+1}\) through the user-defined parameter \(\zeta\). The value of \(-\zeta\) limits the maximum \(L^{2}\) gain of \(K_{i+1}\) since inequality (23) is equivalent to \(K_{i+1}K_{i+1}^{T}\leq-\zeta\).
# Kinematic and Workspace Analysis of RRU-3 RSS: A Novel 2T2R Parallel Manipulator
## 5 Conclusions  
In this paper, we have presented the RRU-3 RSS manipulator, which is a new non-over-constrained fully PM with 2T2R motion. The kinematic problem of this manipulator has been studied and a methodology for obtaining its useful workspace has been described. This methodology, implemented in MATLAB, allows for the calculation of the workspace free of singularities, collisions and kinematic joint range limitation for a given set of geometric parameters. The four-dimensional WS of the mechanism has been illustrated using two types of bidimensional sections, namely, the translational and rotational WS. The analysis of the WS has been carried out for the 16 working modes of the RRU-3 RSS manipulator. By studying the results for all the working modes, it is possible to have a comprehensive kinematic characterization of the manipulator and to see the suitability of its use in different types of applications wherein 2T2R motion is required.  
A further extension of this work could be an optimization method in which, in addition to the kinematic factors, the dynamics will be considered. This optimization method will ease the dimensional synthesis process of the mechanism, which in turn will allow for the construction of a prototype.  
All authors contributed to the conceptualization and methodology; writing--original draft preparation, P.D.; writing--review and editing, E.M. and S.H.; software and visualization, P.D., E.M. and S.H.; supervision, J.C., M.D., F.J.C. and C.P.; project administration, F.J.C. and C.P.; funding acquisition, F.J.C. and C.P. All authors have read and agreed to the published version of the manuscript.  
This research received financial support for the project PID2019-105262 RB-I00 funded by MCIN/AEI/10.13039/501100011033, for the project PDC2022-133787-I00 funded by MCIN/AEI/10.1 3039/501100011033 and by the European Union Next Generation EU/PRTR and, for the project PID2023-150982 OB-I00 funded by MCIU/AEI/10.13039/501100011033/FEDER, UE. This research was also funded by the Basque government for the project 201822013 and PhD grant PRE_2023\(2\)0168.  
Not applicable.  
Figure 23: Influence of the initial position (black rectangle) on rotational WS subsets (\(\varphi\) and \(\theta\) range is \(\pm\)180\({}^{\circ}\)).  
Data Availability Statement: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.
## 3 Results and Discussion  
### Histopathology of Infections Caused by P. melonis 502 on the Root Tissues of Cucumber  
After 6 weeks of growing the plants on infected soil, the cortex cells had a distorted appearance (Figure 1a). _P. melonis_ 502 densely colonized the parenchymal and epidermal tissues of the root. Hyphae were of different thicknesses and were observed not only in the intercellular space but also inside the cells, forming intracellular mycelium (Figure 1b-d). Figure 1f shows the hyphae of the fungus inside the cell, as well as the necrotic cell walls of root cells, which indicates the synthesis of hydrolytic enzymes by the fungus, providing the degradation of cells (Figure 1f).  
The leading tissues (xylem and phloem) did not colonize (Figure 1a), which indicates the impossibility of plant vessel clogging and shows the fungus's biochemical effects on plants, which causes the process of pathogenesis, but in xylem there were tilloses (Figure 2a). Tiloses were also present in the roots of plants affected by the fungi _Phaemoniella chlamdospora_ and _Phaeoacremonium_ sp. [37]. Our results confirm the results of [PERSON] and [PERSON] [3], who also recorded the presence of tilloses in leading root tissues in the defeat of _A. cucurbitacearum_ (syn. _P. melonis_). The etiology of tilloses formation is quite ambiguous and debatable, but it is known that these structures reduce water supply to the plant under the conditions of high demand for moisture, which may be the reason for their depletion [3,38]. In addition, we found microsclerotia in the affected tissues of the roots of cucumber plants (Figure 2b), which completely fill the cell of the host plant. Microsclerotia are known to increase the survival of a micro-organism in soil under adverse environmental conditions [39].  
Figure 1: Longitudinal section of the main roots of cucumber plants affected by _P. melonis_ 502 (**a**). Cross section of the main cucumber roots infected with _P. melonis_ 502 (**b–f**). Colonization by the fungus _P. melonis_ 502 of the roots of cucumber plants, which leads to necrosis of the cell wall (**e**). Lysis of root cells of cucumber plants affected by _P. melonis_ 502 (**f**). 900\(\times\) magnification.  
We previously reported that _P. melonis_ 502 can synthesize the phytohormone ethylene (ET) in vitro and regulate plant growth [19]. Phytohormones can promote the penetration of the pathogen into the tissues of host plants, create a niche for the pathogen and/or trigger other disease processes [40]. ET activates and accelerates the aging process of plant tissues [41]. At the same time, that tissue shows increased activity to cellulase, which leads to the destruction of intercellular connections [42]. ET modulates plant susceptibility and fungal growth in plant tissue also [43].