esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background and Related Works
### Related Works
Following this approach, other works suggest collaborative distributed systems to determine the legitimacy of an alert or even to select the signatures to be used [36]. In a way, this can be viewed as a type of voting scheme. In a previous work [37], we combined ModSecurity [38] and Snort [39] to sanitize HTTP traces by combining the output of both detectors (\(\cup\) operation). Sanitization, however, is meant to maximize \(\mathit{TP}\) but not to minimize \(\mathit{FP}\). Regarding the process of adapting the ruleset to an operational target environment, we find few works aimed at providing guidelines (i.e., decision about which rules to apply which not) [40]. A natural way to reduce FP is to count on the feedback provided by the system operator when false alarms are identified [41; 42] but this can be a costly manual process. The authors of [36] proposed a metric to evaluate the quality of Snort's signatures by integrating the output from different data sources including blacklists, vulnerability scanners, etc. They analyzed 200 incidents during a period of 4 weeks in an operational network, which hinders the generalization of their results as it only suggests good practices for SIDS configuration according to their experiment. In [43], the authors suggest a self tuning SIDS that maintains a small distributed signature database for frequent attacks and dynamically updates it based on the change in the network environment, which is continuously monitored. Finally, in [44], the authors suggest adding a patch management tool to vulnerability scanners to improve and automate the tuning process according to [44].
# Fuzzy Adaptive Asymptotic Control for a Class of Large-Scale High-Order Unknown Nonlinear Systems
## 4 Simulations and Discussion
### Nonlinear System  
Consider a nonlinear system [33], which contains two subsystems  
\[\dot{x}_{1,1} = \theta_{1,1}x_{1,1}^{3}\sin^{2}(x_{1,1})+x_{1,2}\] \[\dot{x}_{1,2} = \theta_{1,2}x_{1,1}^{3}\sin^{2}(x_{1,1}x_{1,2}x_{2,1}x_{2,2}) \tanh(x_{1,1}x_{2,1})+u_{1}\] \[y_{1} = x_{1,1}\] \[\dot{x}_{2,1} = \theta_{2,1}x_{2,1}^{3}\sin^{2}(x_{2,1})+x_{2,2}\] \[\dot{x}_{2,2} = \theta_{2,2}x_{2,1}^{3}\sin^{2}(x_{1,1}x_{1,2}x_{2,1}x_{2,2})+u_{2}\] \[y_{2} = x_{2,1} \tag{50}\]where \(\theta_{1,1}=2,\theta_{1,2}=1,\theta_{2,1}=2,\theta_{2,2}=1\). \(f_{1,1}(\xi_{1,1})\) are nonlinear terms of the system only used to update the system states; they are not used in the designed controller.  
The initial conditions are chosen as: \(x_{1,1}(0)=0,x_{2,1}(0)=0,x_{1,2}(0)=1,x_{2,2}(0)=1\). The referenced trajectory \(y_{k,d}=\sin(t)\). FLS inputs are chosen as: \([z_{k,1},\ldots,z_{k,r_{k},r_{k},1},\ldots,x_{k,r_{k}}]\) for the \(k\)th subsystem at the \(r_{k}\)th step; \(\mu_{p}=[-1,-0.5,0,0.5,1]\) and \(\chi_{p}=0.5\). The control parameters are chosen as: \(k_{1,1}=10,k_{1,2}=10\), \(k_{2,1}=25,k_{2,2}=15\), \(\gamma_{1,1}=15,\gamma_{1,2}=15\), \(\gamma_{2,1}=10\), \(\gamma_{2,2}=10\), \(\tau_{1,1}=0.1\), \(\tau_{2,1}=0.1\), \(\sigma(t)=\
u e^{-\
u t}\) with \(\
u=0.001\), \(\beta_{1,1}=5,\beta_{2,1}=5\).  
Figure 1a,b show the output tracking performance of each subsystem using the MDSC; systems \(y_{1}\) and \(y_{2}\) can track the referenced trajectory asymptotically. Figure 1c,d show the smooth and bounded control input of each subsystem. Figure 1e shows the curves of the system parameters; it is clear that they are bounded. Figure 1f shows the system state, which is guaranteed to be bounded.
## 4 Test Results and Discussions
### Dynamic Compression Tests
#### 4.2.3 Dynamic Increase Factor and Energy Absorption
Compared with the BFRP and CFRP-confined high-strength concrete, the AFRP-confined high-strength concrete has a better energy absorption capacity. With an increase in the FRP layer number, the energy absorbed by the specimens generally increases, indicating that the FRP confinement can significantly improve the energy absorption capacity of concrete.
## 6 Summary and Conclusions  
Based on the technical work performed and the research carried out, the following conclusions can be drawn:  
1. The methodology of the solutions proposed in this article, leading to the creation of a copy of a museum exhibit with a description in Braille integrated onto its surface, turned out to be effective.
2. The selection of digitisation, modelling and 3D printing technology, along with the software used for scanning, modelling, post-processing and preparation of the layered model for replication turned out to be appropriate and made it possible to effectively solve all emerging problems.
3. Incorporating descriptions of objects onto their surfaces using the Braille alphabet will contribute to multifunctional recognition--integration of knowing the shape of the exhibit through recognition by touch and the content written on its surface.
4. The procedure of preparing descriptions in Braille on separate plates makes it possible to pre-print them and verify both their correctness and legibility by a blind person. This solution will save time and costs when reprinting the entire 3D model if the descriptions are found to be defective.
5. The use of 3D printing technology in the fused filament fabrication version allowed for the production of light 3D models to be recognised by touch, which the respondents could freely manipulate. The results of the pilot studies carried out on a group of people with simulated visual impairment and on a person who is blind from birth indicate that these models are useful for sharing cultural heritage objects.
6. Printed museum models in the form of 3D replicas of these objects will allow them to be used in many ways. They can be exhibited in museums, printed at home, used for the training of conservators as well as archives.
# A Novel 1-D CCANet for ECG Classification
## 5 Conclusions  
In this paper, we propose a novel heartbeat classification method based mainly on a new approach to the study of the correlation between the two ECG leads, to extract complex features. Our method also employ elementary hand-crafted time domain features, frequency domain features with a one-dimensional approach to spectrograms, and autoregressive coefficients. Our method is one-dimensional, allowing to explore a more complex neural architecture while maintaining a reasonable computational complexity, and providing better results. Our final model has an optimal structure and performs the classification of 15 and 7 heartbeat types for the MIT-BIH and INCART databases, respectively. Finally, our method outperforms [20] with a slightly better overall accuracy and mean ppv on the MIT-BIH database and a notably higher overall accuracy (+1.69%), mean sensitivity (+2.89%), and mean ppv (+1.78%) on the INCART database.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Authors** & **Year** & **Method** & **Type** & **\#Samples** & **Ov. ACC** \\ \hline [PERSON] et al. [13] & 2018 & Evol.Neural (1 lead) & 15 & 1000 & 91.00\% \\ [PERSON] et al. [39] & 2018 & PCANet (1 lead) & 15 & 3350 & 94.59\% \\ [PERSON] et al. [20] & 2019 & RandNet (1 lead) & 15 & 3350 & 94.39\% \\ [PERSON] et al. [20] & 2019 & DL-CCANet (2 lead) & 15 & 3350 & 95.25\% \\ Proposed method & 2021 & 1-D CCANet-SVD (2 lead) & 15 & 3350 & 95.52\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for various methods on the MIT-BIH database.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Authors** & **Year** & **Method** & **Type** & **\#Samples** & **Ov. ACC** \\ \hline [PERSON] et al. [39] & 2018 & PCANet (1 lead) & 7 & 1720 & 93.72\% \\ [PERSON] et al. [20] & 2019 & RandNet (1 lead) & 7 & 1720 & 92.91\% \\ [PERSON] et al. [20] & 2019 & DL-CCANet (2 lead) & 7 & 1720 & 94.01\% \\ [PERSON] et al. [20] & 2019 & TL-CCANet (3 lead) & 7 & 1720 & 95.52\% \\ Proposed method & 2021 & 1-D CCANet-SVD (2 lead) & 7 & 1720 & 95.70\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results for various methods on the INCART database.  
**Author Contributions:** All the authors contributed equally to the conceptualization; all the authors contributed equally to the methodology; all the authors contributed equally to the software; all the authors contributed equally to the validation; all the authors contributed equally to the draft preparation; all the authors contributed equally to the writing, review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
A review of metamaterial concepts focusing on future aeronautical applications has been presented. The aeronautical community is concentrating its research efforts on the reduction of community noise produced by aircraft, and the significant potential shown in acoustics by metamaterials can undoubtedly give a contribution in this sense. Despite the huge body of literature on acoustic metamaterials, this potential still remain undisclosed for applications that involve a moving fluid. Starting from these considerations, and bearing in mind the restrictions on weight and size in the specific application field, concepts of acoustic metamaterials have been selected as the most promising for aircraft community noise reduction. The achievable results promised by metamaterials include, e.g., an enhanced and/or selective frequency noise absorption, even at low-frequencies, with subwavelength thickness, that can be exploited for a new generation of acoustic liners. Anomalous reflection from the nacelle's internal walls can create virtual scarfing effects from the intake of engines may also be adopted for cabin noise mitigation. Metamaterials will, ultimately, be able to unlock new degrees of freedom for designers, allowing them to shape the acoustic signature of aircrafts to make them less annoying for the community. To achieve effective implementation of the analyzed metadevices, deeper investigations on the effect of the presence of a background flow must be conducted, but also, the effects of metamaterial designs on the flow have to be studied. In fact, another detriment for effective applicability of a metamaterial concept in aeronautics will be, alongside excessive weight or size, a negative impact on aerodynamic efficiency; this must be carefully avoided. Since metamaterials derive their properties primarily from their structures, complex designs are often adopted and complex visco-thermal losses and acousto-elastic interactions are involved in the generation of the related metabehaviors. Numerically reproducing such phenomena can be hard, resulting in very expensive simulations if standard direct methods, such as FEM, are applied. It is clear that new, fast, and reliable numerical tools are needed to tackle the design and optimization processes of metamaterial devices that are necessary to produce the highest TRL of this new breakthrough technology.  
[PERSON], [PERSON] and [PERSON] performed the literature survey and collected the relevant references for the review. The original draft was prepared by [PERSON] and [PERSON], who also followed the review & editing process together with [PERSON] and [PERSON] who also supervised the work as principal investigators of the Project AERIALIST.  
This work was supported by the European Commission through the Project AERIALIST (AdvanceEd aicRaft-nolse-Alleviation devIceS using meTamaterials), Grant Agreement No. 723367.
## 1 Introduction
Despite extensive research on WPT applications, the equivalent circuit analysis required to understand the electromagnetic characteristics of couplers remains insufficient. From a safety perspective, WPT systems can experience unnecessary power losses due to structural properties or interference from external objects. To address this, technologies using additional sensors for external object detection have been studied [37; 38; 39]. However, these techniques are primarily applied to IPT systems. For CPT systems, a method for detecting external objects or receiver position changes by evaluating the symmetry of the capacitance matrix has been proposed [40]. Ultimately, an equivalent circuit analysis is essential for examining coupling variations caused by changes in the electromagnetic characteristics of couplers. In addition, studies have demonstrated stable power transfer in experiments involving biological entities and uniform performance in multi-transmitter and receiver systems [41; 42]. This study provides a theoretical design of a practical equivalent circuit model for a basic DCPT coupler, accounting for loss resistance. The results offer validated findings that can support the design of couplers for various applications. The contributions of this study can be summarized as follows:  
1. This study presents high-accuracy validation results through a theoretical design based on the practical equivalent circuit (PEC) model of a four-plate parallel DCPT coupler, compared with electromagnetic simulation results. 2. It provides an analysis of the correlation between S21 and mutual capacitance in the four-plate parallel DCPT coupler. This study aimed to theoretically design a four-plate parallel DCPT coupler using an equivalent circuit approach. For this purpose, the basic equivalent circuit of a CPT coupler was transformed into a practical equivalent circuit that accounted for coupler losses. Each parameter of the equivalent circuit was defined based on the calculation of self-capacitance and mutual capacitance from a model designed using the 3D full-wave simulation software. The transmission matrix was used to calculate the power transfer efficiency of the equivalent circuit, including the impedance-matching network. The theoretical calculations were validated through equivalent circuit calculations and compared with 3D electromagnetic simulation results. Furthermore, the correlation between the theoretically calculated mutual capacitance and power transfer efficiency was analyzed to provide a detailed understanding of the four-plate parallel DCPT structure.
# Spatial Connections between Microplastics and Heavy Metal Pollution within Floodplain Soils
## 2 Materials and Methods
### Laboratory Analysis
#### 2.2.1 Metal Analysis  
An analysis of metal concentration was performed for (1) soil samples and (2) macroplastic and coarse microplastic particles obtained from microplastic analysis (see Section 2.2.2). For soil samples (1), analysis of the pseudo-total concentrations of the metals (Al and Fe), heavy metals (V, Cr, Co, Ni, Cu, Sn, Cd and Pb), and the metalloid As was performed after the digestion of 1 g prepared subsample with 20 mL aqua regia (12.1 M HCl and 14.4 M HNO\({}_{3}\); ratio 1:3; DIN ISO 11466:2006-12). Metal content was quantified with an inductively coupled plasma-mass spectrometry (ICP-MS; XSERIES 2; Thermo Fisher Scientific, Bremen, Germany). The ICP-MSsystem was calibrated with a certified multi-element standard solution (ROTI(r)STAR; Carl Roth GmbH, Karlsruhe, Germany). Each digest of a soil sample was measured three times and averaged. The resulting mean metal concentrations were converted into units (mg kg\({}^{-1}\)). Relative standard deviation (RSD) was quantified for all single measurements after threefold measurements to account for data reproduction and the effects of heterogeneous matrixes [55]. Data with an RSD \(\geq\) 20% were excluded from further evaluation [56]. Detection limits were calculated by multiplying the mean standard deviation of 10 repeated blank measurements by a factor of 3, but no measurement results were below the respective detection limit.  
To analyze the metal(oid) concentrations of macro- and coarse microplastic particles, 32 particles (from 9 soil samples) were analyzed (Table S1). All plastic samples had a weight of >10 mg. The plastic particles were crushed using stainless steel tongs, except for one sample, which had to be ground using a CryoMill (CryoMill, Retsch, Haan, Germany) due to an extraordinary hardness. Subsequently, the comminuted particles were digested with 6 mL purified HNO\({}_{3}\) (65%) at 260 \({}^{\circ}\)C and 120 bar for 20 min within acid-cleaned Teflon tubes (Turbowave; MLS-MWS, Leutkirch, Germany). The acid digestion protocol dissolves nearly all adsorbed and additive metals in the dissolved phase [57].  
Concentrations of the metals Al and Fe, the heavy metals V, Cr, Co, Ni, Cu, Sn, Cd, and Pb, as well as the metalloids As, Sb, and Se within digests were quantified with ICP-MS (Triple-Quadrupol ICP-MS 8800; Agilent, Santa Clara, CA, USA). Since no reference samples for plastics were available, certified water samples (SPS-W1, SPS-W2: Spectrapure Standards AS, Oslo/Norway; TMDA_64.3: CCRMP Canadian Certified Reference Materials Project, Ottawa (Ontario)/Canada) were used. The deviation of the recovery was consistently <10%. Metal concentrations were reported in ppm.
## 5 Experiments and Analysis of Results
### Risk-Aware Decision System Results
Figure 11 illustrates the flow of risk scoring across the three scenarios, highlighting the pre-defense and post-defense risk evaluation stages. Figure 12 compares the average risk reduction results for the three security configurations. To ensure robustness and statistical reliability, we conducted 30 independent trials  
Figure 11: Risk evaluation flow for T2G intrusion detection in three security scenarios. The red diamonds represent pre-defense risk evaluation, while the blue diamonds represent post-defense risk evaluation. using 10-fold stratified sampling, reporting the mean risk reductions along with standard deviations and confidence intervals. In Figure 12, the rule-based defense scenario exhibits a higher pre-defense risk score (mean = 27.85) due to its basic rule-based risk scoring, which may not effectively differentiate between attack types in T2G communication systems. After applying rule-based responses, the average risk is reduced by 39.79% (SD = 0.03). In contrast, both the Bayesian risk scoring and Full Defense scenarios achieve lower pre-defense risk scores (mean = 18.09) by utilizing Bayesian risk scoring, which provides a more accurate evaluation of risk in the context of T2G systems. This method accounts for attack severity, detection confidence, and environmental context, leading to more precise threat identification and better decision-making. The Full Defense scenario, which incorporates both Bayesian risk scoring and reinforcement learning-based adaptive defense, achieves a significant 63.56% risk reduction (SD = 1.60). This demonstrates the value of dynamic defense actions selected based on real-time risk scoring, which is crucial for the timely and effective mitigation of threats in railway communication systems. Additionally, statistical tests confirm that the Full Defense scenario outperforms both the rule-based defense and Bayesian risk scoring configurations, with \(p\)-values \(<1\times 10^{-20}\) in \(t\)-tests, indicating strong statistical significance.
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 2 Materials and Methods  
### Microalgae Strain and Biomass Production  
The microalgae strain _Microchloropsis salina_ (SAG 40.85), formerly referred to as _Nanochloropsis salina_, was acquired from the Culture Collection of Algae at the University of Gottingen (Gottingen, Germany). A modified artificial seawater (ASW) medium was used for all cultivations, including seed culture production [13]. ASW medium contained, per one liter of water, 27 g NaCl; 6.6 g MgSO4 \(\cdot\) 7 H\({}_{2}\)O; 1.5 g CaCl\({}_{2}\) \(\cdot\) 2 H\({}_{2}\)O; 0.3 g urea; 0.07 g KH\({}_{2}\)PO\({}_{4}\); 0.021 g Na\({}_{2}\)EDTA \(\cdot\) 2 H\({}_{2}\)O; 0.014 g FeCl\({}_{3}\) \(\cdot\) 6 H\({}_{2}\)O; and 1 mL of a trace element solution with ZnCl\({}_{2}\) (0.04 g L\({}^{-1}\)), H\({}_{3}\)BO\({}_{3}\) (0.6 g L\({}^{-1}\)), CuCl\({}_{2}\) \(\cdot\) 2 H\({}_{2}\)O (0.04 g L\({}^{-1}\)), MnCl\({}_{2}\) (0.4 g L\({}^{-1}\)), and (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\) \(\cdot\) 4 H\({}_{2}\)O (0.37 g L\({}^{-1}\)).  
To produce the microalgal biomass used in this study, _M. salina_ was cultivated in open thin-layer cascade photobioreactors using ASW medium under nutrient-replete conditions and physical simulation of outdoor climate conditions of a summer day in June 2012 in Almeria, Spain. The biomass was then deuterated using a dynamic settler with spiral plate technology (Evodos 50A, Evodos B.V., Raamsdonksveer, The Netherlands), allowing the harvest of a microalgae paste with \(\sim\)30% cell dry weight. The biomass was stored at \(-\)20 \({}^{\circ}\)C until further use. The exact cultivation and harvesting conditions were described previously in detail [7].
## 4 Case Study
### Determining the Rule Base  
To determine the level of risk, the expert knowledge of the FFH in a construction scenario in the form of rules is considered. A group of safety managers stated many levels of risk based on their experience. They stated five levels of risk (very low, low, medium, high, and very high risk). According to this levels defined by the experts, five fuzzy sets in the domain [0, 10] are stated and illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Low Risk** & **Medium Risk** & **High Risk** \\ \hline Detection of the Harness & attached & - & unattached \\ \hline Distance to the Virtual fence & \(\geq\)150 cm & 150–50 cm & \(\leq\)50 cm \\ \hline Altitude & 0–1 m & 1–2 m & \(\leq\)2 m \\ \hline Wind velocity & \(\geq\)0–15 km/h & 16–30 km/h & \(\leq\)30 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variables and level of FFH risk.  
Moreover, the panel of experts suggested different rules to pattern these risk situations. Specifically, after the methodology, to define the FLS mentioned before, and illustrated in Figure 3, they proposed 11 rules. For a better understanding, some of the cited rules are enumerated below.  
1. _IF Virtual fence distance IS Far THEN Risk IS Low_
2. _IF Virtual fence distance IS Medium AND Harness detection IS attached THEN Risk IS Low_...
4. _IF Virtual fence distance IS Medium AND Harness detection IS unattached AND Altitude IS Little THEN Risk IS Low_...
7. _IF Virtual fence distance IS Near AND Wind velocity IS High THEN Risk IS Medium_...
11. _IF Wind velocity IS very High AND Altitude IS High THEN Risk IS Very High_
## 3 The Proposed Method
### Feature Fusion
#### 3.2.1 Early Fusion  
In early fusion, embeddings of texts and images are input to the same CNN model simultaneously for feature extraction. Early fusion is also known as feature-level fusion, which can be expressed as follows: \(x_{m}=f\) (\(x_{1},\dots\ x_{n}\)), where an aggregated representation \(x_{m}\) of features is computed by function \(f\) that integrates individual features \(x_{1}\),..., \(x_{n}\). Early fusion combines different input sources into a single feature vector, which is used as inputs to the classification framework. The advantage of early fusion is that it learns all the features in one phase. This makes the training pipeline easier, but a lot of important information might be lost. The architecture is shown in Figure 3.  
Figure 3: The architecture for early fusion of text and image features.  
As shown in Figure 3, first, since the sizes of text and image embeddings are different, we use a fully connected (FC) neural network to fuse these vectors. There are two different ways of concatenation: text vector concatenated with images or image vector concatenated with texts. Next, we extract the output of different layers in the VGG16 model including the feature map and the final classification result or category as the combined representation for the texts and images.