esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
## 5 Control Strategies for the Mitigation of the Impacts Induced by Induction Motor Loads
### VSM Voltage Magnitude Control Strategy  
From the results obtained in Section 5.1, it was concluded that the prolonged reactive current injection provided by the CI-RES during the post-fault period contributes to increasing the network voltage profiles (as shown in Figure 19), which increases the IM loads' electromagnetic torque and contributes to a successful reacceleration. In this section, an alternative control solution which exploits the VSM post-fault reference voltage modulation is presented, which can be temporarily increased in order to boost the reacceleration torque developed by IM loads. Note that in this section, the CI-RES FRT strategy corresponds to the one presented in Section 2.2.  
As explained in Section 2.1.3, the VSM output voltage amplitude is defined as a function of the voltage reference (constant value) and of a reactive power/voltage modulation term. However, in order to temporarily increase the VSM reference voltage during the post-fault recovery period, a hysteresis control approach is considered, modulating the VSM reference voltage as a function of the VSM terminal voltage (at the grid side), as shown in Figure 27. Thus, during steady-state operation \(V_{ref}\) will be defined by the green curve, being fixed at \(V_{1}^{\prime}\). However, for voltage dips lower than \(V_{1}\), the VSM reference voltage will be defined by the blue curve, returning to the green curve when the VSM terminal voltage reaches \(V_{4}\). In order to smooth the transition from the blue curve to the green curve, a fall limitation ramp is imposed on the VSM reference voltage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IM Load (\%)** & **IM Load** & **CCT (ms)** \\ \hline
11\% & L5 & 107 \\
15.5\% & L3 & 117 \\
21\% & L4 + L5 & 107 \\
26.5\% & L3 + L5 & 104 \\
27\% & L1 & 110 \\
36.5\% & L3 + L4 + L5 & 94 \\
36.5\% & L2 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Network fault critical clearance time vs. motor load percentage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**IM Load (\%)** & **IM Load** & \(S_{\text{BESS}}\) **(MVA)** \\ \hline
11\% & L5 & 12 \\
15.5\% & L3 & 12 \\
21\% & L4 + L5 & 12 \\
26.5\% & L3+L5 & 12 \\
27\% & L1 & 12 \\
36.5\% & L3+L4+L5 & 17 \\
36.5\% & L2 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Grid-forming BESS power converter sizing vs. IM load percentage.  
Figure 27: Virtual synchronous machine reference voltage modulation.
## 1 Introduction
### Motivation
This scheme aims to find an optimal policy for the data encryption decision to maximize the long-term security level of the system. More specifically, the scheme uses a well-known symmetric encryption method called the Advanced Encryption Standard (AES) [21] for the same data block length with different key sizes (AES-128, AES-192, AES-256). The SU can encrypt data using an algorithm with longer key lengths to enhance security, and then transmits the encrypted data on an idle licensed channel. Furthermore, the SU determines the encryption key length based on the impact of spectrum sensing error, the energy causality constraint, and the effect of the current decision on future time slots. The problem is first formulated as the framework of a partially observable Markov decision process (POMDP), and is then solved by using value iteration-based dynamic programming to find the optimal policy. However, this solution is rarely directly useful in reality. It is akin to an exhaustive search, looking ahead at all possibilities, computing the probabilities of occurrence and their desirability in terms of expected rewards (i.e., security levels) [22]. The solution relies on the assumption that we know in advance the dynamics of the environment (i.e., an arrival of harvested energy), which is rarely true in practice. Consequently, this paper is going to investigate the problem from a different point of view in which the solution does not require prior information about the environment's dynamics.
The modified algorithm introduces three improvement strategies. Firstly, the Quadratic Interpolated Beetle-Antennae Search (QIBAS) [31] is embedded into the GTO that can get the diversity of the silverback's position. In addition, Teaching-Learning-Based Optimization (TLBO) [32] is hybridized with GTO to stabilize the performance between the silverback and other gorillas. Finally, the Quasi-Reflection-Based Learning (QRBL) [36] mechanism is used to enhance the quality of the optimal position. 2. To verify the effectiveness of the MGTO, 23 classical benchmark functions, 30 CEC2014 benchmark functions and 10 CEC2020 benchmark functions are adopted to conduct a simulation experiment. The performance of the MGTO is evaluated through a variety of comparisons with the basic GTO and eight state-of-the-art optimization algorithms. Furthermore, the MGTO is applied to solve the welded-beam-design problem, pressure-vessel-design problem, reducer problem, compression/tension-spring problem, three-bar-truss-design problem, crash-worthiness-design problem and string-design problem. The experimental results indicate that MGTO has a strong convergence ability and global search ability. The rest of this paper is organized as follows: Section 2 introduces the basic GTO. In Section 3, three strategies and the modified GTO named MGTO are proposed. The experimental results and the discussion of this work are presented in Section 4. In Section 5, the MGTO is tested to solve seven kinds of real-world engineering problems. Finally, the conclusion and future work are given in Section 6.
## 4 Simulation Setup
### Aerodynamic Disturbance Model  
In real operative scenarios, wind is a typical cause of undesired motion of the UAV and thus can be a source of error in the positioning of the end-effector. For this reason, a model of this disturbance is implemented in the simulator.  
Most aerodynamic disturbance models on UAVs that can be found in the literature are based on empirical formulas with coefficients that come either from data derived from expensive experiments on UAVs or from sophisticated CFD simulations (see, for example, [25; 26]). The purpose here is to have a simple model of disturbance to test the rejection capabilities of the proposed inverse kinematics control algorithms. We therefore model the aerodynamic disturbance as a random concentrated force acting on the UAV CoG due to wind. First, a wind speed profile is generated using the well-known Dryden model [27], and then a simple model is used to associate the wind speed profile with the force on the base.
# Perspectives on the Structural Health Monitoring of Bridges by Synthetic Aperture Radar
## 5 Conclusions  
This research aimed to estimate the m-m of bridges. Large infrastructures need continuous maintenance because of materials degradation due to atmospheric agents and their persistent use. This problem makes it imperative to carry out persistent monitoring of infrastructure health conditions in order to guarantee maximum safety at all times. The main issue of early warning infrastructure fault detection is that expensive in-situ distributed monitoring sensor networks have to be installed. This research proposed a new global infrastructure monitoring paradigm using m-m estimation of critical sites, performing spaceborne SAR data exploitation. We designed a complete procedure for damage early-warning detection, using modal proprieties analysis. m-m is processed to extracted  
Figure 35: Optical representation of “Morandi” bridge (longitudinal cracks).  
modal features such as natural frequencies and mode shapes generated by vibrations of large infrastructures. Several case studies of bridges are considered in this paper. In addition, the case of the \"Morandi\" Bridge (Polcevera Viaduct) in Genoa (Italy) is analyzed in depth where the proposed method showed abnormal vibration modes during the period before the bridge collapsed. Finally, estimating cracks, by evaluating any vibration maps anomalies, will permit an efficient structural health monitoring and predictive maintenance.  
Each author dealt with a specific study case and reviewed the other study cases. All authors reviewed the manuscript.  
No founding is used for this paper.  
The authors would to thank the Italian Space Agency (ASI) for providing the SAR data. This research used the software SARPROZ: [[https://www.sarproz.com/](https://www.sarproz.com/)]([https://www.sarproz.com/](https://www.sarproz.com/)), for performing the precise geocoding of the estimated vibration maps.  
The authors declare no conflict of interest.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.6 System Limitations and Responsible Application Guidelines  
While the uncertainty quantification system demonstrates exceptional performance within its development scope, responsible deployment requires acknowledging several important limitations that affect the interpretation and application of predictions.  
The system was developed using clayey silt from the northwestern lasi region, and its learned relationships reflect the specific physicochemical properties of this experimental soil. Application to soils with substantially different mineralogical compositions, plasticity characteristics, or organic content should be approached with appropriate caution. Engineers working with different soil types should consider using the system as a starting point for estimation while validating predictions through limited laboratory testing to ensure applicability to their specific conditions.  
The experimental program explored cement contents ranging from 2.5 to 10 percent and compaction velocities from 0.75 to 1.25 mm per minute. Predictions remain most reliable within these experimental ranges, and extrapolation substantially beyond these bounds may introduce additional uncertainty. The relationship between cement content and UCS appears to accelerate at higher dosages, suggesting that the model may underestimate strength for cement contents beyond ten percent if this non-linear pattern continues. Similarly, substantially different compaction velocities or procedures may introduce effects not captured in the current training data.  
All experimental data originated from controlled laboratory conditions with standardized mixing procedures, consistent environmental conditions, and uniform specimen preparation. Field implementation of soil-cement mixing inevitably introduces additional variability sources not captured in the current uncertainty model. These include mixing heterogeneity, moisture variations, and environmental factors such as temperature, humidity, and pH levels that may influence hydration processes and strength development. Additionally, operator-related variability including differences in mixing technique, compaction consistency, and curing practices can further affect performance of cement-treated soils.  
The temporal scope of the experimental program examined curing periods up to twenty-eight days, which captures the primary hydration phase but may not fully represent long-term strength development mechanisms such as continued pozzolanic reactions or carbonation effects that can influence strength over months or years. For projects requiring long-term performance assessment, engineers should consider these extended timeframes in their design approach.
## 5 Conclusions  
We have shown that DNNs can recognize distorted images at the output of a MMF from their corresponding intensity speckle patterns. In particular, the deep learning approach presents a remarkable robustness against wavelength drift of the light source within an extended bandwidth for which total decorrelation of the speckle pattern intensity is observed. Despite the randomization of the input-output mapping introduced by the various speckle drifting, image classification of high accuracy of about 70% is reported for almost 100 nm wavelength drift, while more than 80%classification accuracy is shown for a 50 nm bandwidth. The results presented in this article suggest that deep learning can overcome distortion in the signal after propagation through MMFs in the presence of severe wavelength drift. The potential of using a single trained DNN model for retrieving image information in multiple wavelengths is suggested by using the intensity-only images of the respective speckle patterns, which could allow multicolor illumination imaging without extensive system recalibration.  
Conceptualization, E.K., and D.P.; methodology, E.K.; software, E.K. and N.B.; validation, E.K., N.B. and D.P.; formal analysis, E.K.; investigation, E.K.; resources, E.K.; data curation, E.K.; writing--original draft preparation, E.K.; writing--review and editing, E.K., N.B., B.R., U.T., D.P. and C.M.; visualization, E.K.; supervision, E.K. and D.P.; project administration, E.K.; funding acquisition, D.P. and C.M. All authors have read and agreed to the published version of the manuscript.  
This project was partially conducted with the support of the Swiss program: \"CEPF SFA, CERAMIC X0: High-precision micro-manufacturing of ceramics\".  
The authors declare no conflict of interest.
# Robot Joint Vibration Suppression Method Based on Improved ADRC
## 3 ADRC Control of Robot Joint Module
### ESO Design
#### 3.2.3 Mathematical Stability Analysis of ADRC  
The theoretical basis of ADRC mainly comes from the work of Professor [PERSON], who first proposed the core concept of an ESO but did not provide strict mathematical proof. In the subsequent studies of scholars, the convergence of an ESO has been further proved. For instance, [PERSON] et al. studied the relationship between the stability of the ESO bandwidth and the system cut-off frequency, and for the first time established the convergence theory of an ESO based on frequency-domain analysis and the Lyapunov method [26; 27].  
The design of an ESO can usually be expressed as  
\[\left\{\begin{array}{l}e_{1}=z_{1}-x_{1}\\ \dot{z}_{1}=z_{2}-\beta_{1}e_{1}\\ \dot{z}_{2}=z_{3}-\beta_{2}e_{1n}+b_{0}u\\ \dot{z}_{3n}=-\beta_{3n}e_{1n}\end{array}\right. \tag{29}\]  
When the bandwidths of an ESO satisfy \(\omega_{0}>3\omega_{c}\), \(\omega_{c}\) is the cut-off frequency, the estimation error satisfies  
\[\lim_{t\rightarrow\infty}|z_{i}(t)-x_{i}(t)|<=O(\frac{1}{\omega_{0}^{i}}) \tag{30}\]  
where \(z_{i}\) is the estimation of the system state and disturbance, \(x_{i}\) is the input signal, \(e_{i}\) is the estimation error, and \(O(x)\) is the upper bound of the estimation error proportional to the negative power of \(\omega_{0}\). The disturbance suppression conditions in the stability analysis are  
\[|\overset{\sim}{d}(t)|=|z_{3}-d(t)|<=\varepsilon \tag{31}\]where \(\widetilde{d}(t)\) is the total perturbation estimation residual, \(d(t)\) is the perturbation value, \(\varepsilon\) is the perturbation suppression accuracy threshold, and \(\omega_{0}\) is positively correlated with the ESO bandwidth.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 5 Experiments
### Data Preparation  
In this study, we analyzed real-world data from a class-B network with over 1000 users and 30,000 network devices, e.g., personal computers, servers, and mobile devices [59; 61; 70]. For anonymity purposes, the six IDSs are referred to as appliances A-F. We used two datasets collected from the same network at different times, which we refer to as the NSOC2017 and NSOC2022 datasets. The NSOC2017 dataset contains alert logs generated by all six IDSs from 1 January to 31 October 2017. The NSOC2022 dataset comprises alert logs from two appliances (A and B) from 18 June 2021 to 30 November 2022. These two appliances are considered the main sources of information for incident handling due to their long operation time and high-quality alert information. Appliances C-F were  
Figure 4: Visualization of the event segmentation algorithm results using ATGs. (**a**) Example 1 of event segmentation; (**b**) Example 2 of event segmentation. In the ATGs, hosts are depicted as disks, adhering to the color scheme defined in Figure 3b. Alerts are illustrated by lines connecting the source and destination hosts in the panels, with colors indicating the grouping outcome—alerts belonging to the same group share a common color. Ellipse A highlights a cluster of alerts indicating attacks on port 22 with prolonged time intervals. Ellipse B exemplifies a cluster of alerts targeting the same destination port but originating from different source ports within a short timeframe. Ellipse C highlights a cluster of alerts targeting the same destination port. However, alerts within this cluster are associated with distinct attack types, resulting in their grouping into multiple events. Ellipse D showcases another example where alerts targeting the same port are distributed across multiple events due to inconsistencies in other attack characteristics.  
discontinuued for performance and commercial reasons. Using these datasets, we assessed the performance of the proposed SIEM scheme to improve incident handling.  
The SOC security team examined the alerts and escalated those that could potentially impact critical systems. They analyzed the communication content associated with each escalation, checked the accessed URLs against commercial blacklists, analyzed downloaded files using a sandbox, and then recorded the results in the system. To minimize the impact of overlooked critical alerts caused by alert fatigue and single alerts that were escalated accidentally, we performed a review using data grouping techniques, ATGs, and signature-matching using various information, e.g., the URLs and hash values of the downloaded files, to identify alerts that were closely related to escalations. The SOC investigation and subsequent secondary review process helped us accurately identify critical alerts and improve the quality of the dataset.
## 5 Baseline 10 MW OWT
### Parametric FEA  
A parametric finite element analysis model of the OWT support structure and the soil system was developed using ANSYS software package, a widely used multi-purpose finite element analysis software. The parametric FEA model enables modeling OWT support structures with stochastic variables, such as environmental loads and material properties. The environmental loads, i.e., wind, wave, and current loads, are applied to the structure in an uncoupled way, ignoring the fluid-structure interactions, as illustrated in Figure 2. The parametric FEA model is applied to the jacket support structure of the baseline 10-MW OWT, of which the main parameters are summarized in Table 3. The geometry, material, mesh, and boundary conditions used in the FEA model are presented in the upcoming subsections.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Rated power (MW) & 10 \\ Blade length (m) & 87.70 \\ Hub radius (m) & 3.05 \\ Rotor diameter (m) & 181 \\ Tip radius (m) & 90.74 \\ Rotor-nacelle assembly mass (kg) & 1,072,000 \\ Blade vertical clearance (m) & 2 \\ Precone (\({}^{\circ}\)) & 3 \\ Shaft tilt (\({}^{\circ}\)) & \(-\)5 \\ Overhang (m) & \(-\)9.10 \\ \hline \end{tabular}
\end{table}
Table 2: Main properties of the baseline 10-MW OWT model [5].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Deck height above MSL (m) & 16 \\ Water depth (m) & 50 \\ Leg OD (m) & 1.74 \\ Leg wall thickness (m) & 0.030 \\ Number of bays & 4 \\ Jacket batter & 8.47 \\ Height of TP (m) & 7 \\ Number of legs & 4 \\ Pile OD (m) & 1.59 \\ Pile wall thickness (m) & 0.037 \\ Mud brace OD (m) & 0.762 \\ Mud brace wall thickness (m) & 0.017 \\ x-brace outer diameter (m) & 0.61 \\ x-brace wall thickness (m) & 0.016 \\ RNA mass (kg) & 1,072,000 \\ Tower length (m) & 88.4 \\ Tower-top OD (m) & 3.85 \\ Tower-top thickness (m) & 0.03 \\ Tower-base OD (m) & 7 \\ Tower-base thickness (m) & 0.055 \\ Thrust associated with rated power UL case (kN) & 1700 \\ Maximum rotor thrust taking into account dynamic amplification UL case (kN) & 3400 \\ Thrust FL case (kN) & 203 \\ Torsional moment UL case (kN-m) & 7876 \\ Torsional moment FL case (kN-m) & 3483 \\ Tilting moment UL case (kN-m) & 38,567 \\ Tilting moment FL case (kN-m) & 3687 \\ \hline \end{tabular}
\end{table}
Table 3: Baseline 10-MW support structure parameters [5; 8; 80; 83]. MSL—mean sea level; OD—outer diameter; TP—transition piece; RNA—rotor–nacelle assembly; UL—ultimate limit; FL—fatigue limit.
## 3 Results
### Classification Analysis  
The classification analysis revealed that Automated neural networks displayed the best result, from the methods we applied (Table 3). The prediction accuracy was 75.86. K-Nearest Neighbours showed the worst result.  
Figure 4: Topographic maps of the event-related potentials during dynamic facial emotion recognition. For neutral faces they represent N170 at 170 ms, posterior P2 at 200 ms and EPN at 500 ms. In response to the gradual appearance of a facial emotional expression the topoplot displays another posterior P2 at 300 ms, EPN at 800 ms, and LPP at 1300 ms.  
Figure 5: ERP at posterior sites (P3, Pz, P4, O1, Oz, and O2) depending on the accuracy of facial emotion recognition. Participants with a lower level of accuracy displayed a larger P2 amplitude. Each curve represents the averaged data from 56 participants, 55–120 trials for each participant. The asterisk indicated the time windows of significant differences.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy, \%** \\ \hline K-Nearest Neighbours & 62.07 \\ Naive Bayesian Classificator & 68.97 \\
**Automated Neural Network** & **75.86** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The classification accuracy of the recognition speed on the basis of the posterior brain waves.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Internal vs. External  
Depending on the origin of the attacker, threats can be internal (launched by compromised nodes within the network) or external (launched from outside the network).  
The following sections explore some of the most prevalent attacks targeting MANETs [29].