esa-sceva/satcom-chunk-collection huggingface dataset:
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## 3 Entity Oriented General Description of Battlefield Situation Information
### Structure of the Concept-Logic Tree  
As Figure 5 shows, some relations between \(E\), \(C_{EC}\), \(A\), \(S\), and \(R\) exist. For example, the entity classes to which an entity belongs have relationships such as inclusion or intersection from the perspective of abstraction; the attributes of an entity are closely related to the entity classes that the entity belongs to; there are unary or multiple constraint relationships between attributes and states; a hierarchical  
Figure 4: Schematic diagram of information sharing based on a unified information space.  
structure and multi-semantic relations exist among entity relationships, and so on. Realizing a structured organization of concepts used in \(ES(t)\) with these relations explicitly described will be useful. It can be used to realize a hierarchically structured organization of information in information systems and to improve the efficiency of information management and sharing among systems. Thus, we designed a concept-logic tree model to organize these concepts. Besides, to organize concepts in a structured way, we analyzed the logical and semantic relations between concepts that describe entities and constructed the concept-logic tree of both attributes and entity relationships, respectively.
# Elucidating Super-Compressible States in Inner Structure of Vehicular Flow
## Appendix A  
### Class of Balanced Densities  
A function \(g(x):\mathbf{R}\to\mathbf{R}\) is called _a balanced density (balanced density function)_ if fulfilling the following axioms:  
1. Piece-wise continuity: \(g(x)\in PC(\mathbf{R})\), i.e., \(g(x)\) is piece-wise continuous on \(\mathbf{R}\);
2. Non-negativity of the range: \(\mathrm{Ran}(g)\subset\langle 0,+\infty\rangle\), i.e., \(g(x)\) is non-negative;
3. Non-negativity of the support: \(\mathrm{supp}(g)\subset\langle 0,+\infty\rangle\), i.e., \(g(x)=\Theta(x)g(x)\);
4. Lebesgue integrability: \(g(x)\in L(\mathbf{R})\), i.e., \(g(x)\) is Lebesgue integrable;
5. Balancing tail: \(\exists\omega>0\) so that \[\forall\alpha>\omega:\quad\lim_{x\to+\infty}g(x)\mathrm{e}^{\alpha x}=+\infty,\] (1) and \[\forall\alpha<\omega:\quad\lim_{x\to+\infty}g(x)\mathrm{e}^{\alpha x}=0.\] (2)  
The parameter \(\omega\) is called _a balancing index_. The class of balanced densities is denoted by \(\mathcal{B}\). The balanced density \(g(x)\) will be referred to as _the normalized (balanced) density_ if \(\int_{\mathbf{R}}g(x)\,\mathrm{d}x=1\). Therefore, every normalized density can be understood as the probability density function of a certain continuous random variable. Moreover, according _the balancing criterion_, density function \(g(x)\) is balanced (with the balancing index equal to \(\omega>0\)) if and only if  
\[\lim_{x\to+\infty}\frac{\ln g(x)}{x}=-\omega. \tag{3}\]  
Representatives of the set \(\mathcal{B}\) are, for example, exponential distribution, Erlang distribution, Gamma distribution, inverse Gaussian distribution, and generalized inverse Gaussian distribution. Contrarily, log-normal distribution or normal distribution does not belong to \(\mathcal{B}\).
# Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
## 5 Experimental Design and Analysis
### Experimental Evaluation  
#### 5.2.1 Algorithm Performance Comparison  
The feature extraction algorithm is crucial in our model. Therefore, to explore the feasibility of our feature extraction RAGRU algorithm, it is compared with the traditional feature extraction algorithms CNN, BiGRU, and BiLSTM. The experimental results are shown in Figure 16a. Figure 16b shows the comparison results of each algorithm's gesture recognition rates since the number of hidden nodes in our BAGRU algorithm is an important parameter. To explore the optimal number of nodes, we chose different numbers of neurons and conducted comparison experiments in a conference room environment, and the results are shown in Figure 16c.  
Figure 16: Algorithm performance comparison: (**a**) Comparison of feature extraction algorithms. (**b**) Impact of different feature extraction algorithms on gesture accuracy. (**c**) The effect of different numbers of hidden nodes on the accuracy of gestures.  
Figure 15: The effect of position on gesture accuracy.  
Figure 16a shows different methods' Receiver Operator Curves (ROC). The x-axis represents the False Positives Rate (FPR), and the y-axis represents the True Positives Rate (TPR). When the RAGRU algorithm's true positive reaches 0.8, the false positive is only 0.08, which is the best. When the TPR of the BiGRU algorithm is 0.8, the FPR is 0.16, which is slightly lower than that of RAGRU. When the TPR of the BiGRU algorithm is 0.8, the FPR is 0.18, performance somewhat lower than BiGRU. When the TPR of the CNN algorithm is 0.8, the FPR is 0.27, and the performance is the worst. Combined above, RAGRU has the best performance because it not only extracts features from space and time but also introduces an attention mechanism, so it is better than BiGRU and BiLSTM. BiGRU and BiLSTM are about equal; BiGRU is a variant of BiLSTM, but the parameters are reduced, the complexity is low, and the performance is not much different from LSTM. Both consider past and future data information, so the performance is better than CNN. Figure 16b shows the gesture recognition results of the four algorithms, from which we can see that our proposed method has the best performance, so the RAGRU algorithm is feasible. From the results in Figure 16c, when the number of hidden nodes is 50, the gesture recognition rate is shallow, but when the number of hidden nodes increases from 50 to 300, the gesture accuracy rate reaches more than 95%. When the value of the hidden node rises again, the gesture recognition rate changes slightly. Since the number of hidden nodes increases, the training time will be longer since more hidden nodes lead to longer training time. Therefore, our BAGRU chooses 300 hidden nodes for gesture recognition.
## 6 Final Considerations  
The implementation of a cyber-physical system with 3D visualization has improved the management of the Lerma Basin's water supply network, marking a significant advancement in the use of technology for resource management. By integrating SCADA systems, real-time data analytics, and 3D visualization, the system provides operators with unprecedented tools to monitor, control, and optimize the water supply network. This approach not only enhances operational efficiency but also addresses critical challenges such as water scarcity, infrastructure complexity, and the need for sustainable resource management.  
The success of this system also highlights the importance of integrating modern technologies into critical infrastructure. By leveraging IoT devices, hybrid communication networks, and cybersecurity measures, the SCADA system ensures reliable and secure operation, even in geographically dispersed and challenging environments. This technological integration not only improves the efficiency of the Lerma Basin's water supply network but also sets a precedent for other regions facing similar challenges.  
In conclusion, the implementation of this cyber-physical system with 3D visualization has not only improved the operational efficiency of the Lerma Basin's water supply network but also provided a scalable and sustainable model for water management. This approach demonstrates how technology can bridge the gap between historical infrastructure and modern demands, ensuring the reliable and efficient supply of water.  
As for future work, the development of usability tests has been considered to improve the interaction between the operators and the system interface. Also, it is necessary to consider the present limitations of the system in order to correct them or reduce their effect.  
Conceptualization, G.S.-C. and E.V.-R; methodology, G.S.-C., E.A.P.-F. and E.V.-A.; software, G.S.-C.; validation, E.V.-R. and E.A.P.-F; formal analysis, G.S.-C., E.A.P.-F. and E.V.-A.; investigation, E.A.P.-F. and E.V.-A.; writing, G.S.-C., E.A.P.-F. and E.V.-A.; supervision, G.S.-C. and E.V.-R.; project administration, G.S.-C. and E.V.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Virtual Wave Control. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Simulation Results and Analyses
### Results and Analyses
#### 4.2.4 Simulation Results and Analyses for 17:00 to 22:00  
Figure 16 is the results of the period 17:00-22:00 taken from Figure 7. During 17:00-22:00, sub-flowchart a operates and the \(k_{L\_CRIT}\) is set as 80%. At the beginning of this period, the PV generated power is greater than the load demand power, the \(soc_{BS}\) reaches the \(SOC_{BS\_MAX}\), so the power from the public grid is the only power to support \(\Delta p\). Therefore, a small amount of PV power is shed when the public grid power reaches its limit \(p_{G\_MAX}\). The other part of the period shows that the PV generated power is less than the load demand power, so the simulation results are the same as the results analyzed for the period 6:00-8:00.  
During the period 17:00-22:00, the DG is turned on also at 19:36, 20:18, 21:00, and 21:43 and shows the same results as those depicted above at 6:27, load shedding also happens at 19:34 and shows the same results as those depicted above at 10:42, and the SC is also recharged at 17:27, showing the same results as those depicted above at 9:33.
# Digital Twin Framework for Road Infrastructure Management
## 3 Digital Twins in the Road Infrastructure Industry  
### DT Concept in Road Infrastructure Management  
Numerous definitions of the term \"digital twin\" have been provided across both academia and industry. Generally, a DT is a virtual representation of a physically built asset (e.g., buildings, roads) that remains connected to its physical counterpart. According to a previous study [19], a DT typically consists of the following components, as illustrated in Figure 2.  
* Physical part: This refers to physical assets such as roads and associated infrastructure facilities (e.g., bridges, tunnels). It may also represent a process (a recurring decision-making activity requiring data collection, analysis, and intervention) or a system (a set of processes or assets that provide a service) that can be implemented through DTs.
* Data flow: This basic element of twinning involves data transmission from physical assets using sensors and tools (IoT devices) to enable contextual understanding and informed decision-making. The data quality, type, and frequency depend on the DT's intended purpose, with some applications requiring real-time data flow.
* Digital part: This component of a DT facilitates improved decision-making by analyzing, simulating, and visualizing data received from the physical twin. The type of visualization varies based on the use case and purpose of the DT, ranging from raw data visualization to immersive virtual experiences.
* Intervention: This represents the outcome of a DT, which can be delivered either automatically or manually depending on the intended function of the DT. The outcome may support preventive actions, fault detection, or responses to external events affecting the physical twin.  
Figure 1: Research methodology flow diagram.  
Figure 2: Concept of a DT in the road infrastructure domain.  
Several reports and papers have documented the potential benefits of applying DTs in the built environment and construction sector. For the construction industry, these benefits include reduced construction and operating costs, increased productivity and collaboration, improved safety, optimized asset performance and sustainability, and ongoing compliance with regulations [16]. Furthermore, DTs can support countries in fulfilling their commitments to the United Nations' Sustainable Development Goals (SDGs), particularly SDG 8: Decent Work and Economic Growth; SDG 9: Industry, Innovation, and Infrastructure; SDG 11: Sustainable Cities and Communities; and SDG 16: Peace, Justice, and Strong Institutions [17].
# Seismic Reflection Analysis of AETA Electromagnetic Signals
###### Abstract  
Acoustic and electromagnetics to artificial intelligence (AETA) is a system used to predict seismic events through monitoring of electromagnetic and geoacoustic signals. It is widely deployed in the Sichuan-Yunnan region (22\({}^{\circ}\) N-34\({}^{\circ}\) N, 98\({}^{\circ}\) E-107\({}^{\circ}\) E) of China. Generally, the electromagnetic signals of AETA stations near the epicenter have abnormal disturbances before an earthquake. When a significant decrease or increase in the signal is observed, it is difficult to quantify this change using only visual observation and confirm that it is related to an upcoming large earthquake. Considering that the AETA data comprise a typical time series, current work has analyzed the anomalism of AETA electromagnetic signals using the long short-term memory (LSTM) autoencoder method to prove that the electromagnetic anomaly of the AETA station can be regarded as an earthquake precursor. The results show that there are 2-4% anomalous points and some outliers exceeding 0.7 (after normalization) in the AETA stations within 200 km of the epicenter of the Jijuzaigou earthquake (M. 7.0) and the Yibin earthquake (M. 6.0) half a month before the earthquakes. Therefore, the AETA electromagnetic disturbance signal can be used as an earthquake precursor and for further earthquake prediction.  
AETA; anomaly detection; LSTM autoencoder; earthquake precursor; unsupervised learning
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 5 Conclusions and Future Work  
This paper reviews some of the current technologies used for the noncollaborative detection and tracking of UAVs and proposes a collection of simulation models, composed by integrating preexisting models of radar and acoustic sensing and by adapting them to  
Figure 8: Distance error of each of the plots as distance to each of sensors’ receivers increases.  
Figure 6: Plots generated by MIMO and microphone-based sensors corresponding to the actual incoming drone.  
Figure 7: Angle error of each of the plots as distance to each of sensors’ receivers increases.  
our application. These models allow for a lightweight simulation of the most important effects on detection and estimation performance of the C-UAS sensors and sensor networks. There are some limitations on the current models, such as:  
1. The radar models are not fully compatible with some newer Electronic Scan Antenna radars with adaptable track update rates.
2. The problem of target resolution has not been addressed, which could impose limitations to the simulation of nearby targets and drone swarms.
3. Multistatic sensors with several receivers have not been implemented.
4. Systematic errors (biases) related to sensor alignment, propagation, etc. have not been included in our models.  
The presented simulation results show the capability to derive realistic measures using those simulation models, following the expected behaviors regarding both detection and estimation accuracy performance. Finally, there are a collection of future lines for research related to this paper:  
1. Improvements of the models to alleviate the previous limitations.
2. Completion of the simulation with models adequate for RF UAS signal detection. The model is expected to be similar to that of the acoustic system, with specific modifications to define the noise power and the emitter signal power.
3. Completion of the simulation with models adequate for camera/vision systems.
4. Definition of simulation means to evaluate integrated deployments using different collaborating sensors and potentially managing the collection of sensors for specific tasks (long-range detection, short-term classification, clutter removal, etc.)
5. Integration with actual UTM systems and tracking systems.  
Conceptualization, J.A.B., I.C. and D.C.; Funding acquisition, J.A.B.; Methodology, J.A.B. and G.d.M.; Software, I.C., D.C. and L.B.; Supervision, J.A.B.; Validation, I.C. and G.d.M.; Writing--original draft, I.C.; Writing--review and editing, J.A.B., D.C., L.B. and G.d.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Science and Innovation under Grant PID2020-118249 RB-C21.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Moreover, due to possible cyber-attacks, the communication network should be isolated properly. One solution is to use the virtual private network (VPN) for encryption and isolating data. However, to overcome the routable communication requirements, the network enhancement through special technologies is necessary. The contribution of this paper is to address the above-mentioned weaknesses for fast, multicast, and reliable IEC 61850 R-GOOSE communications over WAN in modern power systems. Therefore, the state-of-the-art of this work could be summarized as follow:  
* Introducing a DDS-based communication framework for multicast R-GOOSE messages;
* Describing unique features of this framework in terms of scalability, reliability, and multicasting;
* Addressing the network traffic issues and minimize the end-to-end time delay;
* Verifying the proposed approach through an experimental setup by measuring the real-time end-to-end delay of R-GOOSE message communication over the network layer. In other words, we are going to address this challenge for IEC 61850 routable communications by introducing a data-centric communication framework for R-GOOSE messages using the data distribution service standard. The proposed solution is a fast and reliable framework that covers all the IEC 61850 routable communication requirements along with the feature of multicasting, which could enable us to develop advanced protection schemes for modern power systems. Furthermore, we develop an experimental setup to measure the real-time end-to-end delay of R-GOOSE message communication over the network layer to verify the effectiveness of the proposed framework. The rest of this paper is as follow: In Section 2, the message centric and data-centric communication approaches are described and compared. Section 3 is devoted to present the data distribution service as a data-centric communication standard. In Section 4, the proposed multicast routable GOOSE messages (MRGM) communication framework is presented in detail. The proposed test framework along with hardware setup and experimental results are reported in Section 5, and finally, the paper is concluded in Section 6.
## 2 Materials and Methods
### Data Source and Data Processing  
A critical issue in monitoring long-term changes in land use/cover is the appropriate selection of remote sensing data [34]. In this work, long-term remotely sensed images collected by Landsat instruments, including MSS, TM, ETM + and OLI, were obtained from Landsat Collection 2 Surface Reflectance Product through the Google Earth Engine (GEE) platform. All data were atmospherically rectified to eliminate errors from atmospheric scattering, absorption and reflection, which provided direct access to detect changes in the earth's surface among multiple images of the same area [35].  
In addition, datasets reflecting different bioclimatic and topographic features were collected as auxiliary data based on the GEE cloud platform to provide a broader range of land use classification features. The elevation, slope, and aspect data were obtained from the  
Figure 1: The location of the study area: (**A**) land over in 2019 (**B**), digital elevation model (DEM) (**C**), annual mean air temperature (TMP) from 1990–2019 (**D**), annual mean precipitation (PCP) from 1990–2019, and (**E**) the administrative boundaries of 12 districts and counties in the Dongting Lake Region.  
Shuttle Radar Topography Mission (SRTM) [36] data ([[https://lpdaac.usgs.gov/products/strmgl1v003/](https://lpdaac.usgs.gov/products/strmgl1v003/)]([https://lpdaac.usgs.gov/products/strmgl1v003/](https://lpdaac.usgs.gov/products/strmgl1v003/)), accessed on 15 April 2023). Annual mean temperature and precipitation were obtained from the WorldClim V1 Bioclim dataset (GEE). Imaging of water bodies was obtained from the JRC Global Surface Water Mapping Layers, v1.3 dataset (GEE).
###### Abstract  
We propose and demonstrate, using optical experiments, a new reflex reflector structure called SuperPin Plus. The structure is composed of special pin groups with dihedral-angle offsets in corner cubes. One of the specular features brought by this new design is that it can comply with both the US SAE (US Society of Automotive Engineers) standard and the EU ECE (Economic Commission for Europe) standard, so that manufacturing costs of reflex reflector for both European and American automobile markets can be reduced. By using genetic algorithms for optimization, the angles and the positions of the pins, which are the building elements of corner cube reflectors, serve as the parameters to tune up the performance of the SuperPin Plus curved reflex reflector. Compared with conventional ECE flat regular retro-reflectors, we found that not only can we achieve a 41% higher retro-reflection efficiency with the ECE SuperPin Plus flat reflex reflector, but that SuperPin Plus can also act as a reflex reflector within SAE standards. In addition, we demonstrate that the retro-reflection efficiency is 30.5% higher (SAE standard) and 42.7% higher (ECE standard), and that a 32% increase in working area can be achieved if double pin groups are used to construct the corner cubes instead of a single pin arrangement, in a curved SuperPin Plus reflex reflector.  
optics design; double pins with corner cubes; SuperPin Plus curved retro-reflector; EU ECE regulations (Economic Commission for Europe); US SAE regulation (US Society of Automotive Engineers)
# Lemongrass Essential Oil Components with Antimicrobial and Anticancer Activities
## 3 LEO Therapeutics
### Antimicrobial Potential
#### 3.1.1 Antibacterial Activity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Microbe**} & \multirow{2}{*}{\
\begin{tabular}{c} **Family/** \\ **Kingdom** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} _Cymbopogon citratus_ \\ **Essential Oil** \\ \end{tabular} } & \multicolumn{2}{c}{**Ctrl**} \\ \cline{3-6}  & & & **IC\({}_{50}\) [\(\mu\)L/L]** & **MIC [\(\mu\)L/L]** & **IC\({}_{50}\) [\(\mu\)L/L]** & **MIC [\(\mu\)L/L]** \\ \hline _Candidia famata_ & fungi & Saccharomyces & 177 \(\pm\) 19 & 3684 \(\pm\) 271 & 37 \(\pm\) 7 & 142 \(\pm\) 19 \\ _Cryptococcus albidus_ & fungi & Tremelaceae & 199 \(\pm\) 25 & 265 \(\pm\) 31 & 2\(\pm\) 0 & 20 \(\pm\) 6 \\ _Candidia albicans_ & fungi & Saccharomyces & 571 \(\pm\) 109 & 2734 \(\pm\) 250 & 83 \(\pm\) 8 & 110 \(\pm\) 15 \\ _Mycobacterium susemgmatis_ & bacteria & Mycobacteriaceae (Gram positive) & 860 \(\pm\) 89 & 3409 \(\pm\) 775 & 109 \(\pm\) 12 & 137 \(\pm\) 19 \\ _Protein vulgaris_ & bacteria & Enterobacteriaceaeiaceae (Gram negative) & 992 \(\pm\) 37 & 1453 \(\pm\) 40 & 97 \(\pm\) 12 & 163 \(\pm\) 34 \\ _Staphylococcus aureus_ & bacteria & Staphylococcus & 1841 \(\pm\)199 & 5830 \(\pm\) 198 & 77 \(\pm\) 2 & 92 \(\pm\) 2 \\ _Pseudomonas aeruginosa_ & bacteria & Pseudomonas & 2385 \(\pm\) 162 & 5308 \(\pm\) 339 & 41 \(\pm\) 2 & 93 \(\pm\) 8 \\ _Salmonella enterica_ & bacteria & Enterobacteriaceaeiaceae (Gram negative) & 2626 \(\pm\) 301 & 4693 \(\pm\) 634 & 66 \(\pm\) 8 & 97 \(\pm\) 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Antimicrobial activity of _Cymbopogon citratus_ (DC.) Stapf essential oil and citral. MIC, minimum inhibitory concentration; IC\({}_{50}\), half maximal inhibitory concentration. Adapted from [PERSON] et al. [48].