esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
Consequently, resilience must be regarded as a dynamic concept that requires continuous study during crises triggered by extreme events, such as earthquakes or terrorist attacks, and even their aftermath [11; 12]. This highlights the critical importance of leveraging IoT technologies for the real-time monitoring of CIs. In this context, resilience quantification emerges as a crucial approach to understanding, assessing, and enhancing the resilience of CIs. While existing methodologies often rely on metrics and indicators derived from historical data [13; 14; 15], a significant gap persists in real-time resilience monitoring. Addressing this gap is essential to effectively capture the dynamic behavior of CIs during crises. To bridge this gap, this study seeks to answer a pressing and emerging question: _Is it feasible to monitor the resilience of IoT-integrated CIs in real-time using sensor data for specific KPIs?_  
By exploring this question, the study underscores the urgency and relevance of real-time resilience monitoring in advancing the safety and functionality of CIs. Therefore, this article presents an empirical study based on real-world data, observations, and experimentation to derive conclusions. The research design involves developing and operating a cyber-physical testbed to implement various data-driven algorithms for real-time resilience monitoring. The testbed is a smart PV panel, with its resilience being evaluated under a FDIA. The primary contributions of this empirical study are outlined as follows:  
* The development and validation of a comprehensive method for real-time resilience monitoring of IoT-integrated CI. * The implementation and testing of different data-driven algorithms tailored to R-KPIs, informed by observed system behavior. * The construction and operation of a practical cyber-physical testbed for empirical resilience assessment using a Digital Twin-integrated smart PV system. The remainder of the article is structured as follows: **Section 2 Research Landscape**: An overview of the current state of resilience assessment of CI. In **Section 3**, we explain the theoretical foundation of the resilience KPIs that are used in this article. **Section 4 Statistical and AI methods**: A detailed description of the cyber-physical system under study and methodology for implementing and measuring the selected R-KPIs. **Section 5 Cyber-Physical System**: A synthesis of the testbed results, with a validation of the resilience monitoring method based on sensor data in the real time. **Section 6 Case Study**: A detailed description of test configuration, and discussion of results from conducted tests results. **Section 7 Limitations, Challenges, and Future Research**: Discussion of study limitations, encountered challenges, and potential future research avenues. **Section 8  
**Conclusions**: Summarizes key findings and their implications for the resilience monitoring of IoT-enabled CI.
## 5 Results  
### Insights into the Negativity of Tweets Related to COVID-19  
In this subsection, we aim to answer RQ1, i.e., what sentiment was present in COVID-19-related tweets posted during the first three pandemic waves and how did the amount of negative sentiment change over time? Initially, we trained the classification models for the three annotated categories: Negative, neutral, or positive and evaluated the performance of different models. After applying the best performing model (multilayer perceptron with Cro-CoV-cseBERT embeddings) to the Cro-CoV-Tweets dataset, the numbers of tweets per classes were as follows, neutral: 99,469; negative: 96,511 and positive: 10,216. As the percentage of positive tweets (5%) is low in comparison to the neutral (48.2%) and negative tweets (46.8%), we merged the neutral and positive tweets into one non-negative group enabling a better visualization on the relationship between the negative and non-negative tweets. This way, we obtained insights into the negativity of COVID-19-related tweets.  
In Figure 3, the sentiment is visualized through time by averaging sentiment over a 30-day sliding window. To calculate the average sentiment, the negative sentiment is treated as 1 and non-negative (neutral and positive) as 0. The higher the value of the sentiment, the more negative it is, hence, we reference it as negativity. In May 2020, the negativity was at its lowest, which coincides with the time when there were almost no new COVID-19 cases and strict lockdown measures were starting to relax. In May 2021, the negativity decreased again as the summer was getting closer and the number of COVID-19 cases was getting lower.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline Naive Bayes & 0.55 & 0.58 & 0.55 & 0.71 \\ Random Forest & 0.50 & 0.51 & 0.50 & 0.75 \\ Support Vector Machine & 0.50 & 0.51 & 0.50 & 0.74 \\ Multilayer Perceptron & 0.60 & 0.58 & 0.59 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline evaluations with fastText embeddings in terms of precision, recall, and F1-score calculated as macro averages (the complete results are in Appendix B.2).
# Properties and Recognition of Atom Graphs
## Atom Graph Subclasses
### Atom Graphs of \(\mathcal{G}_{nine}\)
: (of Characterization 14) \(1\Rightarrow 2\) follows from Lemma 5, \(2\Rightarrow 4\) follows from Lemma 8, \(4\Rightarrow 3\) follows from Lemma 6, and \(3\Rightarrow 1\) is evident. The \(5\)-sun shown in Figure 1 is a graph of \(\mathcal{G}_{\text{ninc}}\) and its atom graph is a block graph, with \(\{F\}\) as unique minimal separator. Let \(G\) be this atom graph and let \(G^{\prime}=\text{RetroCG}(G)\). The \(5\)-sun, \(G\) and \(G^{\prime}\) are shown in Figure 9. \(G^{\prime}\) is a block graph whose atom graph is isomorphic to \(G\). Note that \(G\) is also isomorphic to the atom graph of \(G^{\prime}-\{F\}\), as the presence in \(G^{\prime}\) of the node \(\{F\}\) is not necessary since \(N_{G^{\prime}}(\{F\})\) is already a maximal clique of \(G^{\prime}\). The graph shown in Figure 2 is not a graph of \(\mathcal{G}_{\text{ninc}}\). Its atom graph is not a block graph, and is isomorphic to the atom graph of no graph of \(\mathcal{G}_{\text{ninc}}\) by Characterization 14. It follows that block graphs, and in particular trees and complete graphs, are atom graphs. We have the following complexity results. Lemma 9: _Let \(G\) be a connected block graph. Then RetroCG\((G)\) can be computed on \(O(n^{2})\) time._  
Proof. : Let \(G^{\prime}=RetroCG(G)\). Its node set is computed in linear time since \(G\) is chordal. The edges of \(G^{\prime}\) that are incident to a node of size \(1\) are computed in linear time too by scanning the maximal cliques of \(G\), as the sum of their sizes is bounded by \(n+m\). The other edges are computed by making \(N_{G^{\prime}}(\{v\})\) into a clique for each vertex \(v\) of \(G\). Each edge is added only once by this process since two distinct non-disjoint maximal cliques of \(G\) have exactly one vertex in common since, by Lemma 6, their intersection is a minimal separator of \(G\) and \(G\) is a block graph. So this process takes \(O(n^{2})\) time, as \(G\) has at most \(n\) maximal cliques, and therefore \(G^{\prime}\) is computed in \(O(n^{2})\) time. Theorem 2.2: _Recognition of an atom graph of a graph of \(\mathcal{G}_{\mathit{ninc}}\) takes linear time, and if it is the case then a block graph whose atom graph is isomorphic to the input graph can be computed on \(O(n^{2})\) time._  
Proof. : The first result results from Characterization 14 and the fact that recognizing a block tree takes linear time. The second one results from Lemmas 6, 8 and 9. A block graph can be the atom graph of a graph not belonging to \(\mathcal{G}_{\mathit{ninc}}\). For instance, if \(G\) is the chordal graph whose maximal cliques are \(\{1,2,3\}\), \(\{1,2,4\}\), and \(\{1,5\}\), its minimal separators are \(\{1,2\}\), and \(\{1\}\), so \(G\) is not in \(\mathcal{G}_{\mathit{ninc}}\) but its atom graph is a block graph since it is a complete graph.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods
### Testing Environment  
For our testing, two distinct setups were employed for evaluating CAM and DENM handling. The testing was conducted using C-ITS SIM connected to two OBUs, which were responsible for transmitting and receiving messages during different phases of the test. Third-party units were involved both in logging the messages sent by the C-ITS SIM setup and in broadcasting their own messages, which were then captured and analyzed by the C-ITS SIM system.  
All communication between the units was captured in PCAP format, which was subsequently analyzed using the tool Karlos (see Section 2.2.1). The entire testing environment was designed for controlled laboratory conditions, focusing on validating the content of the messages.
# Sage-Husa Algorithm Based on Adaptive Double Forgetting Factors
## 3 Simulation and Analysis
The Sage-Husa algorithm with a fixed-value forgetting factor, whether one or two, improves upon the EKF by introducing an update mechanism for the noise statistics, addressing the issue of limited filtering accuracy due to unknown noise characteristics in the EKF. However, the effectiveness of the Sage-Husa algorithm relies on the choice of the forgetting factor. The fixed-value forgetting factor lacks adaptability. This limitation can result in cumulative errors during the noise statistics update process, leading to reduced filtering accuracy or even divergence. In contrast, the AEKF algorithm employs an exponential update mechanism for the forgetting factors, allowing them to dynamically adjust while ensuring they remain within a stable range of 0.95 to 1. Additionally, the relationship between the prior residuals and the theoretical residuals is compared, and based on specific criteria, it is determined whether to re-update the statistical characteristics of the measurement noise. This adaptive mechanism effectively mitigates cumulative errors, resulting in state estimates that are closer to the true values. Thus, the AEKF algorithm demonstrates both feasibility and superior performance, highlighting its practical effectiveness and reliability.
## 2 Materials and Methods
### Root Canal Contamination with E. faecalis  
The _E. faecalis_ strain ATCC 29,212 (American Type Culture Collection, Manassas, VA) was used to inoculate the root canals. _E. faecalis_ was grown on brain heart infusion agar (BHI; Difco Laboratories Inc., Detroit, MI, USA) mixed with 5% sheep blood at a temperature of 37 \({}^{\circ}\)C for a period of 48 h. Colonies were inoculated into sterile BHI broth to acquire a 1-McFarland bacterial suspension, which was diluted to obtain 1 \(\times\) 10\({}^{8}\) colony-forming units (CFU) per milliliter. The Eppendorf tubes with the sterilized samples were opened under a laminar flow hood, and each root canal was inoculated with the bacterial suspension using a sterile pipette. A #15 K-file (Dentsply Maillefer) was inserted into the canal within 1 mm of the working length, and the canal was filed circumferentially for a period of 10 s. The incubation of the specimens was performed at 37 \({}^{\circ}\)C for 21 days. Fresh BHI was added to the root canals every other day. Five teeth were randomly selected as a control group and were submerged in BHI broth for 1 day to ensure the effectiveness of sterilization.
# Thermal and Mechanical Assessment of PLA-SEBS and PLA-SEBS-CNT Biopolymer Blends for 3D Printing
###### Abstract  
Polybactic acid (PLA) is one of the most promising biopolymers often used as a raw material in 3D printing in many industrial areas. It has good mechanical properties, is characterized by high strength and stiffness, but unfortunately, it has some disadvantages; one is brittleness, and the other is slow crystallization. Amounts of 1-5% SEBS (styrene-ethylene-butylene-styrene) thermoplastic elastomer were blended into the PLA and the thermal and mechanical properties were investigated. DSC (Differential Scanning Calorimetry) measurements on the filaments have shown that SEBS increases the initial temperature of crystallization, thereby acting as a nucleating agent. The cooling rate of 3D printing on the other hand, is too fast for PLA, so printed specimens behave almost amorphously. The presence of SEBS increases the impact strength, neck formation appears during the tensile test, and in the bending test, the mixture either suffers partial fracture or only bends without fracture. Samples containing 1% SEBS were selected for further analysis, mixed with 0.06 and 0.1% carbon nanotubes (CNTs), and tested for thermal and mechanical properties. As a result of CNTs, another peak appeared on the DSC curve in addition to the original single-peak crystallization, and the specimens previously completely broken in the mechanical tests suffered partial fractures, and the partially fractured pieces almost completely regained their original shape at the end of the test.  
PLA; SEBS; 3D printing; biopolymer; CNT Article  
Themal and Mechanical Assessment of PLA-SEBS and PLA-SEBS-CNT  
1
# Streaming-Based Anomaly Detection in ITS Messages
###### Abstract  
Intelligent transportation systems (ITS) enhance safety, comfort, transport efficiency, and environmental conservation by allowing vehicles to communicate wirelessly with other vehicles and road infrastructure. Cooperative awareness messages (CAMs) contain information about vehicles status, which can reveal road anomalies. Knowing the location, time, and frequency of these anomalies is valuable to road users and road authorities, and timely detection is critical for emergency response teams, resulting in improved efficiency in rescue operations. An enhanced locally selective combination in parallel outlier ensembles (ELSCP) technique is proposed for data stream anomaly detection. A data-driven approach is considered with the objective of detecting anomalies on the fly from CAMs using unsupervised detection approaches. Based on the experiments carried out, we note that ELSCP outperforms other techniques, with 3.64 % and 9.83 % better performance than the second-best technique, LSCP, on AUC-ROC and AUCPR, respectively. Based on our findings, ELSCP can effectively detect anomalies in CAMs.  
anomaly detection; data streams; intelligent transportation systems; traffic incident detection Article  
Streaming-Based Anomaly Detection in ITS Messages
## 3 Research Method
### Data Collection
#### 3.1.2 UAV Data  
This study uses UAV to extract information on soil and water conservation measures in SDAs of HSRs. UAV has the characteristics of rapidity, efficiency, stability, and convenience, which can effectively make up for the shortcomings of low resolution and slow update of traditional RS imagery. Through on-site investigation of the SDAs in HSRs, the take-off and landing location of the UAV was determined, its flight route was formulated, and the UAV data was extracted.  
The DJI Phantom 4 RTK UAV (SZ DJI Technology Co., Ltd., Shenzhen, Guangdong, China) was utilized for an aerial survey of a SDA. The UAV has the following performance characteristics: a maximum take-off altitude of 6000 m, a maximum ascent speed of 6 m/s, a maximum descent speed of 3 m/s, and a maximum horizontal flight speed of 58 km/h. The hovering accuracy is \(\pm\)0.1 m vertically and \(\pm\)0.1 m horizontally. Equipped with a 20-megapixel imaging sensor, it offers a maximum photo resolution of 5472 \(\times\) 3648 and a 4K video resolution of 3840 \(\times\) 2160. The UAV meets the specifications of China's GB/T 7930-2008 standard for 1:500 topographic aerial photogrammetry [58]. The maximum operational area per flight is approximately 1 km\({}^{2}\).  
Using the automatic flight mode, the flight route range was scientifically designed, with appropriate route planning and measurement methods selected. The routes were reasonably segmented and merged to ensure that the final flight path was precisely adapted to the specific dimensions of the SDA, guaranteeing complete site coverage. Flight parameters were optimized to capture detailed imagery, including an elevation of 250 m for broad area coverage, a minimum side overlap of 70%, and a heading overlap of 80% for imagery consistency, complemented by a full 360\({}^{\circ}\) main course angle to capture all site aspects.  
Following aerial imagery acquisition, the data is processed using Pix4 Dmapper 4.4.12, a professional drone mapping software. The processing includes initial data preparation, aberration correction, aerial triangulation, and digital elevation modeling, followed by orthorectification, imagery mosaicking, cropping, and other key steps, culminating in the creation of an orthophoto map of the UAV survey area.
## 2 Main Content of the Special Issue
The atom-resolved magnetic moments at the interface and subinterface layers were calculated and compared with the values obtained from bulk materials. The spin polarizations were calculated to further illustrate the effective range of tunnel magnetoresistance (TMR) values. [PERSON] et al. [36] systematically investigated the effect of Fe doping in Ti\({}_{2}\)CoSi and observed the transition from gapless semiconductor to nonmagnetic semiconductor. [PERSON] et al. [37] used the spin-polarized density functional theory based on first-principles methods to investigate the electronic and magnetic properties of bulk and monolayer CrSi\({}_{2}\). Their calculations show that the bulk form of CrSi\({}_{2}\) is a nonmagnetic semiconductor with a band gap of 0.376 eV. Interestingly, there are claims that the monolayer of CrSi\({}_{2}\) is metallic and ferromagnetic in nature, which is attributed to the quantum size and surface effects of the monolayer. [PERSON] et al. [38] conducted a theoretical investigation with the help of the density functional theory and showed that the creation of small, isolated, and stabilized skyrmions with an extremely reduced size of a few nanometers in GdFe\({}_{2}\) films can be predicted by 4d and 5d TM (transition metal) capping. Magnetic skyrmions is an exciting area of research and has gained much attention from researchers all over the world. We hope that this work may add value to the scientific community and be helpful for reference in future work. Finally, we introduce two manuscripts in this SI related to computational materials. Although these two papers are not in the field of spintronics, they belong to the field of computational materials science. The interaction of hydrogen with metal surfaces is an interesting topic in the scientific and engineering world. In this SI, [PERSON] et al. [39] investigated the hydrogen adsorption and diffusion processes on a Mo-doped Nb (100) surface and found that the H atom is stabilized at the hollow sites. They also evaluated the energy barrier along the HS\(\rightarrow\)TIS pathway. Due to their unique physical properties and wide application, Bi-based oxides have received extensive attention in the fields of multiferroics, superconductivity, and photocatalysis. In this SI, [PERSON] et al. [40] investigated the electronic structure as well as the optical, mechanical, and lattice dynamic properties of tetragonal MgBi\({}_{2}\)O\({}_{6}\) using the first-principles method. This research was funded by the Program for Basic Research and Frontier Exploration of Chongqing City (Grant No. cstc2018 jcyjA0765), the National Natural Science Foundation of China (Grant No. 51801163), and the Doctoral Fund Project of Southwest University, China (Grant No. 117041). We would like to sincerely thank our assistant editor, [PERSON] ([EMAIL_ADDRESS]), for all the efforts she has made for this Special Issue in the past few months. The authors declare no conflict of interest.
## 1 Introduction  
Numerical analyses using the finite element method (FEM) present a wide range of possibilities for analysing structures composed of various materials, e.g., reinforced concrete or masonry [1; 2; 3]. Analyses often include load-carrying capacities of structures and their damage [4; 5; 6; 7], stresses and deformations in zones of concentrated stresses [8; 9; 10], adhesive strength and anchorage of reinforcement [11; 12; 13], behaviour of superficial strengthening [14; 15], effects of contraction deformations and creeping [16], effects of temperature on ageing mass concrete constructions [17], flows of heat and moisture flux [18; 19], and new structural and material solutions [20]. Despite the significant progress in knowledge and continuously increasing processing power of computers, it is still impossible to build a numerical model for the global analysis of all physical effects taking place in built, ageing, and used constructions. For that reason, some simplifications are introduced to the models by adjusting them according to the planned computational objectives, the available information on the modelled material, and the processing powers of the software applied in the calculations.  
The detailed numerical analysis of multi-material constructions is complex, because it requires the determination of many material parameters. Determination of the failure surface requires many tests under uniaxial or complex load states [21; 22; 23; 24; 25]. Apart from preparing the material model of failure, many parameters are required to describe effects observed at the interface of bonded materials, e.g., reinforcement and concrete and masonry units and mortar. Appropriate modelling requires specific contact elements, of which the parameters are taken from laboratory tests [8; 22; 26].  
The aim of the tests performed in this study was to determine the interaction of autoclaved aerated concrete (AAC) intells with brickwork above them, as well as the effect of confinement on the behaviour of walls under axial compression. The main purpose of numerical calculations was to diagnose the stress state in the masonry wall, comparing relationships between load and displacement, and those between crack morphology in the masonry and the lintel, using the masonry micro-model with advanced non-linear material models implemented into ATENA software.  
This paper describes results from numerical calculations for reinforced intells made of autoclaved aerated concrete, bonded with the masonry wall made of autoclaved aerated concrete, reinforced concrete tie beams and cores. The used material models of concrete, autoclaved aerated concrete, reinforcement and contact elements were based on material tests. Results from numerical calculations were compared with laboratory tests conducted on parts of walls containing the lintel and the tie beam, as well as full-scale walls.
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 7 Conclusions  
This paper proposes a novel ZigBee-to-WiFi cross-technology communication approach with the throughput 1000 times greater than the state-of-the-art CTC design. The proposed CTC design is transparent to the ongoing ZigBee communication. The existing CTC designs either are too slow or need to interrupt the ongoing ZigBee communication. This paper investigates how the ZigBee signal impacts the WiFi preamble detection and proposes a chip-level CTC design based on modifying some ZigBee chips to extend the sinusoidal waveform in the ZigBee signal. Moreover, the principle and analysis of this design was presented. Finally, the evaluation shows that the performance of this design can outperform all current CTC approaches significantly despite the fact that the CTC communication range should be shorter than 10 m.