esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The Smith predictor control scheme \(SP\) is effective at eliminating the effect of time delay in a closed-loop system [27]. However, its performance degrades when it is required to reject persistent disturbances. Thus, the main contribution of this paper is a modified Smith predictor controller specifically tailored to our \(TRMS\), which is effectively represented by a high-order dynamic model with time delay. The proposed controller is designed to accurately track the pitch and yaw angles in the \(TRMS\), while compensating for delay and rapidly rejecting disturbances. Few studies have proposed \(MIMO\) extensions of the \(SP\). Among them, most have focused on addressing robustness issues related to varying process parameters (e.g., [28]), while only a few have aimed to improve disturbance rejection. In most cases, the \(MIMO\)\(SP\) has been combined with a decoupler (e.g., [29; 30]), allowing the \(MIMO\) control problem to be decomposed into several \(SISO\)\(SP\) control problems. Most modifications to the \(SP\) aimed at enhancing disturbance rejection have been developed for \(SISO\) systems. Consistent with the approach mentioned above, extensions of the \(SP\) to \(MIMO\) systems typically rely on combining these \(SISO\)-based modifications with process decouplers. As a result, the disturbance rejection capabilities of the modified \(SISO\)\(SP\) are inherited by the corresponding \(MIMO\) control system. In previous work [31; 32; 33], we developed a modified \(SP\) specifically designed to mitigate the effects of step disturbances in \(SISO\) systems. In this paper, we propose extending this control strategy to the \(MIMO\) case to address the control requirements of our \(TRMS\). The most relevant disturbance rejection methods for \(SISO\) processes with time delay--including the filtered SP modification, internal model control (\(IMC\)), and feedforward strategies--were compared in [31] with our proposed \(SP\) structure in the context of a second-order plant with time delay. Our \(SP\) modification was further compared with several other \(SP\) variants (including a sliding mode controller) in [32] for \(SISO\) integrating processes with time delay. Additionally, it was benchmarked against the standard \(SP\) in the case of a first-order process with time delay in [33] In all these comparisons, our modified \(SP\) demonstrated superior disturbance rejection performance. Therefore, we anticipate that the \(MIMO\) extension of our modified \(SP\) will also outperform \(MIMO\) schemes based on other \(SISO\) disturbance rejection strategies combined with decouplers. The remainder of the paper is organized as follows. Section 2 is dedicated to the dynamic model of the \(TRMS\). Our proposed control scheme is presented in Section 3. Section 4 provides simulated results of a \(TRMS\), while Section 5 presents the experimental results. Finally, Section 6 presents the conclusions.
# Hydrogen vs. Batteries: Comparative Safety Assessments for a High-Speed Passenger Ferry
## 3 Proposed Designs  
### Case Study  
The case ship selected for the comparative assessment between the battery and the hydrogen version was the Stavanger demonstrator, for which the main particulars and ferry details are presented in Table 2.  
The battery-powered catamaran ferry, which is expected to be operational in 2022-2023, will operate in a multi-stop route in the Stavanger area in Norway up to 20 h per day with each round trip including up to 12 stops upon passenger's request. This ferry is part of the TrAM project, which is funded by the European Union [30]. Two more studies were carried out in London and Belgium for the same type of vessel.  
In this study, for the design of hydrogen systems and their arrangement onboard, the IGF code is mainly used [31], along with the guidelines provided by the recently published handbook for hydrogen-fuelled vessels by DNV [32]. Even though IGF rules are mostly applicable for LNG, they can also be used as a basis for LH\({}_{2}\) systems as well, considering their similar properties [20]. However, for novel projects, an equivalent level of safety with a referenced design should be demonstrated for the vessel through an alternative design approach [33; 34].  
For a maritime application, the design of a battery-powered system has different criteria, mainly focusing on the performance and safety levels. Regarding the performance of the battery-powered system, the biggest challenges are energy density, power density, charging duration, life span, cost, and sustainability [35]. DNV has also published technical guidelines to support the design of such vessels and the evaluation of safety and risk levels [36].
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 8 Generation of Synthetic Traffic with Generative Adversarial Networks
Finally, a cyber range can import datasets from third parties containing attacks and normal traffic that are subject to privacy or anonymity restrictions. As the network data used in the exercises by the blue and red teams are the synthetic ones generated by our GAN solution, no breach in privacy appears during the realization of such exercises. Moreover, exporting attacks and normal data (e.g., to other platforms in a federated cyber range) can be performed without incurring any privacy violation as the exported data to be shared with a third entity are exclusively the synthetic network traffic generated by our GAN solution. In Figure 8, we detail the way in which the synthetic traffic generated by our GAN solution can be integrated into SPIDER exercises and, in particular, in the ML processing model. Currently, we have developed and trained GANs for defensive exercises that can replicate with high fidelity the flow statistics of network attacks and normal connections. As can be seen in the figure, the GAN generator directly feeds the ML engine (solid red line). It is worth noting that from a functional perspective, the trainee (blue team) will not observe any difference during the exercise between receiving synthetic or real flow statistics since the ML engine will receive the input data with the same format either way. Nevertheless, future versions of our GAN solution (dotted red line) will be able to generate realistic sequences of network packets (attacks or normal traffic) that will be fed to the ML engine (contained in the STA module) in the same way as we feed the ML engine using a pcap file.
## 5 Cooling Calculations and Cooling System Design
### Liquid Cooling Module  
After taking the aspect lengths of the IGBT modules to be used and setting the optimum distance to be left between them, the dimensions of the water cooler module were calculated. The drawing in Figure 11 shows the module dimensions in millimeters. The  
Figure 10: Sample IGBT (0 mm) thermal analysis.  
Figure 9: Sample IGBT (20 mm) thermal distribution.  
module has a structure consisting of copper tubes embedded in an aluminum substrate through which water or coolant passes. In the design, drawings were made depending on the substrate thickness value, copper tube wall thickness, and the distance to be buried from the surface to the inside. The liquid cooler module is designed to remove the heat generated by the switching and conduction losses caused by the IGBT modules in the power stage of the power electronics and control circuit and to operate the power electronics circuit at the optimum temperature. The switching and conduction losses are calculated using the information obtained from the data sheets of the IGBTs used and the current, voltage, switching frequency, and physical dimensions of the system.  
An air-cooled system and Semikron's WP16 liquid cooling system were used in the first prototype studies. In later designs, these structures were revised and the liquid cooling system was designed as shown in Figures 12 and 13. This liquid cooling system was made with machining CNC processes.  
Figure 11: Liquid cooler module top and profile view.  
Figure 12: Liquid cooling module top view.  
As seen from the calculation in Equation (21), the module compensates for a loss of 2300 W and keeps the outlet water temperature at 20.5 \({}^{\circ}\)C when the inlet water temperature is 16 \({}^{\circ}\)C. It removes a temperature value of 4.5 \({}^{\circ}\)C from the system. In this case, the surface temperature of the cooler, that is, the surface temperature to which the IGBTs are connected, remains constant at 34.5 \({}^{\circ}\)C.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Reconfigurable Intelligent Surfaces
#### 3.2.6 State-of-the-Art RIS Designs  
We can assess the potential of RIS technologies with some example designs (see Table 5). Specifically, we present RFocus (ref. [66]), ScatterMIMO (ref. [67]), Active RIS (refs. [61; 62; 68; 69]), and dual band independent RIS (ref. [63]).  
RFocus is made of \(\lambda/4\times\lambda/10\)-unit cells to reflect or refract electromagnetic waves in order to improve signal propagation in poor link conditions. It is suitable for simple deployments and works quite well, achieving an increased signal strength of about 9.5 times. Because of the static structure, RFocus has restricted adaptability and demands precise placement and optimization (ref. [66]).  
ScatterMIMO uses rapid phase shifts of 0, \(\pi/2\), \(\pi\), and \(3\pi/2\). This perfectly enhances network performance by increasing the signal-to-noise ratio (SNR) by 4.5 dB and doubling the system's throughput. ScatterMIMO is usually used in crowded communication environments because of its ability to avoid noise. However, it is rather expensive, because of its complexity, and it consumes way more power than a typical Passive RIS system (ref. [67]).  
Active RIS, with its active components (low-noise amplifiers), addresses multiplicative fading effects and offers a 130% gain in sum-rate efficiency in contrast to the 22% of Passive RIS. This characteristic makes it ideal for high-demand applications and environments with a lot of obstacles and long-distance communications. Admittedly, disadvantages are the high power consumption and increased design complexity (refs. [61; 62; 68; 69]).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
Dual-Band Independent RIS combines sub-6 GHz and millimeter-wave (mmWave) technologies into a single aperture, enabling simultaneous and independent operation in both bands. It offers beam steering capabilities of \(-35^{\circ}\) to \(35^{\circ}\) for sub-6 GHz and \(-30^{\circ}\) to \(30^{\circ}\) for mmWaves. This design maximizes space utilization, but the complexity of combining separate control networks for two bands faces many challenges (ref. [63]).
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 1 Introduction
This paper presents a new approach to embedding information in radar waveforms by a hybrid FSK-FDM technique, which enjoys the benefits of both the modulations and multiplexing techniques. Note that the AM-based techniques are used when the communication receiver resides in the side lobe region of the radar. In contrast, the PM techniques perform better in communication receivers in the main lobe region. However, the proposed design facilitates the delivery of information bits to the communication receivers with an improved bit rate (BR) and bit error rate (BER) regardless of their locations in the radar's main lobe or side lobe regions. Initially, a lookup table containing the symbols mapped against the possible combination of two information bits is maintained. Since each symbol contains information about the orthogonal waveforms and frequencies, a composite signal is generated by adding multiple symbols using a linear adder. Eventually, the overall data rate significantly increased when adding the symbol through a linear adder in each PRI. Next, this composite signal is modulated at an intermediate frequency upon which the radar operates. Finally, the received signal at the communication receiver is passed through the bandpass filtering procedure to extract the individual symbol. Each combination of waveform and frequency is decoded using matched filtering. The main attributes of this paper are summarized as follows:  
* The proposed design facilitates the delivery of information bits to the communication receivers with an improved bit rate (BR) and bit error rate (BER) regardless of their locations in the radar's main lobe or side lobe regions. * The proposed method offers reduced inter-symbol interference as the decoding of each composite symbol at any communication receiver is independent of other neighbouring symbols. The rest of the paper is organized as follows. Section 2 explains the signal data model, while Section 3 presents the proposed information embedding approach at the transmitter side. Furthermore, performance analysis at the receiver is provided in Section 4, while Section 5 presents the simulation results, followed by the conclusion in Section 6.
###### Abstract  
Initialization methods for underwater wireless acoustic sensor networks (UWASNs) have been proposed as a subset of other network protocols under the simple assumption that all the nodes in the network can be initialized at once. However, it is generally time- and energy-intensive to initialize all nodes in a UWASN due to unstable underwater channel conditions. To improve network efficiency, we propose the Application-based Partial Initialization (API) protocol, which initializes only the same number of nodes as the number of activated nodes required to run a specific application. Reducing the number of active nodes is also particularly advantageous underwater since the replacement of batteries is costly. To the best of our knowledge, the API is the first approach that initializes nodes partially according to applications. Thus, we investigate the feasibility of the API for a UWASN by analyzing its performance via simulations. From the results, it is shown that the API provides similar data statistics compared with the conventional full initialization that initializes all nodes. Moreover, the API outperforms the full initialization in terms of the initialization time and message overhead performances.  
Keywords: initialization protocol; underwater wireless acoustic sensor network; partial initialization; application +
Footnote †: journal:
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
###### Abstract  
Low Earth orbit satellites collect and study information on changes in the ionosphere, which contributes to the identification of earthquake precursors. Swarm, the European Space Agency three-satellite mission, has been launched to monitor the Earth geomagnetic field, and has successfully shown that in some cases it is able to observe many several ionospheric perturbations that occurred as a result of large earthquake activity. This paper proposes the SafeNet deep learning framework for detecting pre-earthquake ionospheric perturbations. We trained the proposed model using 9017 recent (2014-2020) independent earthquakes of magnitude 4.8 or greater, as well as the corresponding 7-year plasma and magnetic field data from the Swarm A satellite, and excellent performance has been achieved. In addition, the influence of different model inputs and spatial window sizes, earthquake magnitudes, and daytime or nighttime was explored. The results showed that for electromagnetic pre-earthquake data collected within a circular region of the epicenter and with a Dobrovolsky-defined radius and input window size of 70 consecutive data points, nighttime data provided the highest performance in discriminating pre-earthquake perturbations, yielding an F1 score of 0.846 and a Matthews correlation coefficient of 0.717. Moreover, SafeNet performed well in identifying pre-seismic ionospheric anomalies with increasing earthquake magnitude and unbalanced datasets. Hypotheses on the physical causes of earthquake-induced ionospheric perturbations are also provided. Our results suggest that the performance of pre-earthquake ionospheric perturbation identification can be significantly improved by utilizing SafeNet, which is capable of detecting precursor effects within electromagnetic satellite data.  
earthquake; pre-earthquake anomalies; swarm satellites; ionospheric plasma; deep learning; physical mechanisms +
Footnote †: journal: 10 December 2021
## 2 Background and Motivation
### Scheduling Problem
Another important observation is the relationship between latency and resource allocation, as shown in Figure 2, which appears to be heavily influenced by cold start overhead, and latency decreases as resource readiness improves. When a sufficient number of pre-warmed containers is available (as in the PW case), cold starts are reduced or even eliminated, resulting in a significant drop in overall latency. However, this approach may lead to resource wastage and increased costs. Secondly, Resource Saturation: Overloading a single invoker causes increased queuing delays and sequential cold starts, both of which contribute to higher latency. From this simplified scenario, we can conclude that to minimize latency while maximizing utilization, schedulers must dynamically provision resources based on real-time demand. An efficient scheduling policy must simultaneously address the distinct requirements of global resource balancing (across invokers) and local container optimization (within a single invoker). Centralized multi-objective algorithms have been adapted to tackle these challenges [5]; however, this approach creates performance bottlenecks as the system scales because a single scheduler handles all objectives, such as load balancing, container reuse, and scaling [13]. Another challenge faced by such single-tier centralized schedulers is conflicting scheduling objectives. For example, minimizing cold starts may require over-provisioning containers, forcing the algorithm to prioritize one objective over another and resulting in suboptimal scheduling decisions. These challenges motivate us to implement a two-tier scheduling solution that separates concerns into infrastructure-level (global) and task-level (local) optimization. Each layer uses tailored strategies to address the multidimensional requirements of serverless environments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **RR** & **RS** & **PW** & **GP** \\ \hline Avg. CPU Utilization & 30\% & 25\% & 70\% & 15\% \\ Idle Containers & 0 & 0 & 20 & 0 \\ Cold Start Rate & 40\% & 45\% & 5\% & 65\% \\ Avg. Latency (ms) & 900 & 1200 & 250 & 1800 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Utilization Results for Different Scheduling Strategies. Finally, the scheduler must consistently maintain adequate resources to accommodate sudden spikes in invocations, especially since serverless platforms emphasize seamless scalability for handling fluctuating workloads.
###### Abstract  
The uncertainty of distributed energy (DG) and load in the electric-gas combined system (EGCS) requires EGCS to have higher dispatching capacity. A novel strategy is introduced in this paper to operate EGCS considering dispatchability evaluation indexes in order to improve the dispatchability of EGCS. Firstly, the paper describes the physical architecture of EGCS and its main devices. Based on the typical structure of EGCS, the main coupling modes between the two networks are analyzed and summarized, and a power flow model of deep coupling EGCS is established. Then, it proposes a unified quantitative modeling method of dispatchability, and qualitatively analyzes the dispatchability capability of different types of resources in the system through the definition, connotation, and multi-dimensional attributes of EGCS dispatchability. In order to characterize the strength of the overall dispatchability of EGCS, two evaluation indexes, upward/downward dispatchability margin, are proposed. The case study validates the applicability of the proposed dispatchability indexes through simulation. The uncertainties existing in various sources, namely networks and loads of EGCS, the output power of wind farms, and photovoltaic plants, are analyzed emphatically through actual data of a certain area. The EGCS economic dispatching model is established by considering the DG output prediction errors, introducing the expected penalty term of insufficient dispatchability into the objective function, and calculating the dispatchability margin through the simulation model to quantitatively analyze the dispatchability capability of the system.  
electric-gas combined system (EGCS); dispatchability; uncertainty; economic dispatching model +
Footnote †: journal:  
Article
## Appendix A Pertinence of the Representative Variable Detection Model
### Illustration of the Coherence  
We give Table 1 the coherence of four pairs of tested CORE-clusters on \(G_{1}\), \(G_{2}\) and their maximal spanning trees. Corresponding estimated representative variables are given in Table 2. The tested CORE-clusters are: (Test 1) The reference blocks of variables \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\), i.e., \(S^{1}_{71}=\{X^{0},\cdots,X^{6}\}\) and \(S^{2}_{71}=\{X^{8},\cdots,X^{14}\}\). (Test 2) Subsamples of \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\) of size three, i.e., \(S^{1}_{72}=\{X^{2},X^{3},X^{4}\}\) and \(S^{2}_{72}=\{X^{10},X^{11},X^{12}\}\). (Test 3) Samples \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\) in which a variable was replaced with the independent variable \(X^{7}\), i.e., \(S^{1}_{73}=\{X^{1},\cdots,X^{7}\}\) and \(S^{2}_{73}=\{X^{7},\cdots,X^{13}\}\). (Test 4) Samples \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\) in which the variables \(X^{4}\) and \(X^{10}\) were swapped, i.e., \(S^{1}_{74}=\{X^{0},\cdots,X^{3},X^{10},X^{5},X^{6}\}\) and \(S^{2}_{74}=\{X^{8},X^{9},X^{4},X^{11},\cdots,X^{14}\}\).  
Let us first interpret the results in Table A1. The coherences' first row on \(G_{1}\) in tests \(T1\) and \(T2\) first show that similar coherences were obtained with CORE-clusters of size 7 and 3, although the coherences are slightly higher for smaller CORE-clusters. These coherences are also clearly higher for those obtained in tests \(T3\) and \(T4\) where all variables are not contained in the same simulated block. Interestingly, the results obtained on graph \(G_{2}\) are similar to those obtained on \(G_{1}\). The only difference here is that the coherences of \(T4\) are slightly higher than in \(G_{1}\), but still relatively low. Note that the tested CORE-clusters may lead to disconnected subgraphs when tested on the maximum spanning trees. Equation (2) does not make sense in this case. When the simulated subgraphs were connected (12 cases out of 16), we obtained the same coherences on the maximum spanning trees and the whole graph. As discussed in Section 2.4, the coherence of a given CORE-cluster on a maximum spanning tree is indeed lower or equal to the coherence of the same CORE-cluster on the whole graph. We however computed the representative variables in all tested cases, as the centrality measure makes sense even on disconnected subgraphs (see Section 3.3). The results in Table A2 show that the estimated representative variables are always in the reference sets \(S^{1}_{Ref}\) and \(S^{2}_{Ref}\) in the tested configurations. They also correspond to the simulated representative variables, \(X^{3}\) and \(X^{11}\), in most cases, or are very close to these variables. Note that slightly inaccurate reference variables were detected in \(S^{1}_{Ref}\), and we can clearly see (Figure A1(top)) that the influence of its simulated representative variable is less obvious than in set \(S^{2}_{Ref}\).