esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 5 Array Antenna
### Planar Array Antenna  
Planar array antennas enhance the antenna directionality compared to LAAs. In Figure 8, a rectangular planar array consists of \(n=n_{x}\bullet n_{y}\) identical antenna units, where \(dx\) and \(dy\) are the intervals between antenna units along the x and y axes, respectively. The equation for the planar array antenna pattern is expressed in Ref. [34] as follows:  
\[F(\theta,\varphi)=F_{x}(\theta,\varphi)\bullet F_{y}(\theta,\varphi) \tag{19}\]  
Figure 7: (**a**) Uniform LAA geometry; (**b**) sparse LAA geometry.  
\[F(\theta,\varphi)=\sum_{n=1}^{N}I_{n}e^{|2\pi d_{x}\sin\theta\cos\varphi}e^{|2\pi d _{y}\sin\theta\sin\varphi} \tag{20}\]  
where \(N\) is the total number of antenna elements in the array; \(\theta\) is the elevation angle with respect to the \(z\)-axis; \(\varphi\) is the azimuth angle with respect to the \(x\)-axis; \(I\) is the excitation amplitude of the \(N\)th element; \(d_{x}\) and \(d_{y}\) are the intervals between antenna units along the \(x\)- and \(y\)-axes, respectively; \(d_{x}=d_{y}=0.5\lambda\); \(\lambda\) is the wavelength.
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 2 Design Method of Proposed Dual-Band Phase Shifter
### Parasitic Susceptance Compensation  
Each \(\lambda/4\) shunt open stub works a short-circuited condition at node A for the co-operating band. However, the \(\lambda/4\) shunt open stub acts as an additional susceptance at the operating band, which affects the PSR at the operating band. Therefore, the additional susceptance at the operating band should be compensated to achieve a wide PSR.
# The Effect of Blue Noise on the Optimization Ability of Hopfield Neural Network
## 3 Materials and Methods
### Improvement of Neural Network Model Based on Colored Noise  
Through the above experiments, this paper proves that colored noise can improve the optimization of the neural network. To verify that the colored noise neural network model can meet the needs of more scenarios, the original Hopfield neural network model is improved, and the excitation function of the original neural network model is transformed from Sigmoid function to a combination of continuous wavelet and Sigmoid excitation function. The chaos generation mechanism of the improved neural network is also introduced through the exponential decreasing of the self-feedback terms. Since the excitation function of the wavelet chaotic neural network does not monotonically increase but generally increases, the addition of wavelet function can not only cause the excitation function non-monotonically to increase but also to allow the excitation function to have wavelet function advantages. The improved model is applied to the traveling salesman problem in 30 cities through the segmented simulated annealing mechanism and the disturbance of blue noise. Comparing the data from previous experiments, it can be intuitively felt that the network model, adding the wavelet function and the segmented simulated annealing mechanism, has significantly improved the solving rate. Even in the face of more complex combinatorial optimization problems, colored noise perturbation can still improve the solution of chaotic neural networks.  
The improved Hopfield neural network model is as follows:  
\[U_{c}=Cn(U), \tag{18}\]  
\[V=S(U_{c},\varepsilon_{0})+coef\cdot M(U_{c},\varepsilon_{1}), \tag{19}\]  
\[V_{c}=Cn(V), \tag{20}\]  
\[Cn(\xi)=ABGN(\xi,SNR,m,n), \tag{21}\]  
\[S(U_{c},\varepsilon_{0})=\frac{1}{1+e^{-U_{c}/\varepsilon_{0}}}, \tag{22}\]  
\[M(U_{c},\varepsilon_{1})=\frac{2}{\sqrt{3\sqrt{\pi}}}(1-(\frac{U_{c}}{ \varepsilon_{1}})^{2})\exp(-\frac{\left(U_{c}/\varepsilon_{1}\right)^{2}}{2}), \tag{23}\]  
Equation (19) is the excitation function of wavelet chaotic neural network, which is composed of Sigmoid function and Mexican_Hat function. Where \(S\) represents Sigmoid function and \(M\) represents Mexican_Hat function; \(\varepsilon_{0}\) and \(\varepsilon_{1}\) are steepness parameters of Sigmoid function and Mexican_Hat wavelet function; \(coef\) is a coefficient (\(coef\geq 0\)); and other parameters have the same meaning as the parameters of Hopfield neural network model chaos with blue noise.
## 4 Space-Qualified RF Generator Techniques  
Several RF generator techniques can be used in order fulfil the requirements listed in Table 1. Five different techniques are described, each able to work in the harsh environment of space and using Commercial-Off-The-Shelf components (Table 3). The components used in these generator setups have to withstand tests such as radiation, vibration, shock, thermal cycling, and vacuum. Commercial electronics are not capable of surviving these tests. Hence, the selected components linked to the RF generator techniques are all available in a space-grade package or have a space-qualified version.  
The different solutions can be analog or digital, each with their own pros and cons. These five techniques are listed and tested in the following paragraphs. For each technique, the design is described, test results are discussed, and a conclusion is drawn in the frame of implementability for space applications.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 4 Methodology  
This paper aims to propose a tensor-based road traffic pattern extraction method for the purpose of spatiotemporal anomaly detection. The proposed methodology is presented in Figure 3, and encompasses the main steps: (i) Data preprocessing, (ii) grid-based map segmentation with STMs computation, and (iii) anomaly detection based on the CoM estimation.  
Figure 3: Proposed methodology for the anomaly detection.
# Biomimetic Liquid-Repellent Surfaces by Ultrafast Laser Processing
## 1 Introduction
### Laser-Irradiated Structures
#### 2.2.2 Columnar Microstructures  
Columnar microstructures--conical, quasi-ordered micrometer-sized structures--were reported to be generated on semiconductors as well as on metals. These structures are also referred to in the literature as pillars, bumpy, and spikes. Micro-sized columns can be specified by the average column height and the average distance between the neighboring columns, or by the density of the columns. The effects of different processing parameters on the morphology of columnar structures were studied in detail in the past (see the references summarized in Table 1). It is important to note that the fabrication of laser-irradiated columnar microstructure does not have a universal character and cannot be fabricated, for example, on copper [28]. Figure 6 shows ablation craters on steel and copper, generated by 100 laser pulses (30 fs) at 10 J/cm\({}^{2}\). Figure 5: Detailed pictures of LIPPS on shows that LSFL generated by ultrafast laser radiation are densely covered by nanostructures (reprinted from [22] by permission from Springer, copyright 2007). Figure 6: SEM images of ablation craters on steel (**left**) and copper (**right**), generated by 100 laser pulses (30 fs) at fluence of 10 J/cm\({}^{2}\). As can be seen in Figure 6, at the same experimental conditions for the laser ablation of copper are more efficient compared to for steel. In both cases a specific surface structure in the middle of the ablation crater can be observed, which is formed from melted and re-solidified material. Therefore, the height of the individual structures is in the range of 20 to 30 \(\upmu\)m for steel and in the range of 2 to 3 \(\upmu\)m for copper. This difference can be explained by the different physical characteristics of these materials and their melts. However, columns on copper, if required, can be produced only as laser-inscribed structures, as was reported for the fabrication of biomimetics liquid-repellent surfaces, for example, in [29]. As distinguished from smooth surfaces of columnar structures produced with long laser pulses or under stationary conditions (without sample translation with respect to the laser beam) with ultrafast laser radiation, columns produced by non-stationary ultrafast laser radiation are superimposed with LIPSS or nanoroughness [30]. The nanostructures develop on the microcolumn surfaces due to the irradiation at fluences near ablation threshold at the trailing edge of the moving laser beam. Due to their unique morphology, which resembles the naturally superhydrophobic surfaces of plants, columnar structures were intensively used for the fabrication of biomimetic surfaces and have great potential for future applications. Although the arbitrary change of column morphology cannot be realized, some fine tuning of their shape can be done by adjusting process parameters enabling better fit to potential application. Main tendencies of influence of process parameters on morphology of columnar microstructures are summarized in Table 1.
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 2 System Configuration  
### Overview of WDPS Development Methodology  
To verify the operation feasibility of heavy haul freight in Korea, we developed a WDPS system that applied the MIMO-OFDM. For that, the railway undertaken, the vehicle manufacturer, the braking and propulsion control device manufacturer, and the wireless device manufacturer were involved in the WDPS development and test. Figure 1 is a flowchart that briefly introduces the methodology we developed for WDPS.  
First, functional requirements, system requirements, and interface requirements of WDPS are derived based on the operational requirements of heavy haul freight (ex, target vehicle, length of train, operating speed, test section, etc.). The second step, the design and development for each technical field, is performed based on the requirements derived in the previous step. In the locomotive field, the S/W modification for the control logic of propulsion/braking and TCMS and the control circuit modification for the safety circuit configuration are performed. In the wireless field, a design to minimize interference factors through analysis of the propagation environment in the test section and a design for interface with the locomotive are performed. When development is completed for each technical field, a self-test is conducted, and if there is no problem, a system static test is performed with the heavy haul freight configuration by the operation requirements. At this step, in order to safely  
Figure 1: WDPS development methodology.  
operate heavy haul freight, the braking performance, the main functions of the locomotive, and the safety functions that must be operated in an emergency must be confirmed. When all the essential functions and performance are satisfied in the static state, the next step is to check the function and system requirements of WDPS and the normal operation of heavy haul freight under the operating requirements. If the requirements of each stage are not satisfied, return to the system design stage to check the problems, draw up improvements and reflect them in WDPS.  
The main operation requirements of heavy haul freight in this test were as follows.  
* Configuration of heavy haul freight: That consisted of a master locomotive, 50 freights and a slave locomotive. The master and slave locomotives are equipped with WDPS to distribute traction and braking control command data.
* Operation speed: An average speed of 100 km/h
* Test section: The approximately 21 km from Jilley station to Busansinhang station in Korea.
* Common requirement: The heavy haul freight with WDPS should perform the same main function and safety function of the freight train operation using the existing centralized control.
## 3 Data and Methods
### Organizing Team Roles and Coordination of Logistics  
For the organization and implementation of the Ecuadorian and Bolivian events, a training team comprising three specific roles was established for each. These roles can be summarized as follows:  
* Facilitators delivered talks on the essential aspects of Copernicus's services, as well as other advanced technical topics. This role also involved selecting the content, conducting exercises and evaluations, and collecting feedback from stakeholders.
* Event assistants facilitated the activities, established a communication channel with participants, and organized the logistics of the event. They also provided administrative support and assisted in the technical aspects of presentations and execution of the exercises.
* Stakeholder coordinators bridged the gaps among the facilitators, the workshop assistants, and the participants. They also secured special talks from high-ranking representatives, arranged the venues, and formally communicated with and invited the stakeholders.  
For the training workshops in Ecuador, the organizing team consisted of one facilitator, three event assistants, and three stakeholder coordinators. For the in-person days, the venue was a conference center at the Geographic Institute of Ecuador. During the training workshops, coffee breaks and lunch were provided, and a record of the sessions was maintained. The participants were asked to bring their own laptops, but a few desktop computers were also available upon request. After the second day and the switch to online mode, the participants convened via Microsoft Teams, with the sessions recorded to later edit and share the presentation materials. For communications, a WhatsApp channel was opened with the participants.  
In the case of the panel discussions in Bolivia, the organizing team comprised two facilitators, two event assistants, and two stakeholder coordinators. The headquarters of the Vice Presidency of the Plurianational State of Bolivia served as the venue, which had different meeting rooms as well as a conference room. Since the participants were different in each session, only coffee breaks were provided. The presentations were not recorded, except for the session on the overview of Copernicus, which was transmitted online. The participants were not asked to bring their own presentation materials; however, some of them provided these, which were useful in understanding their experiences and needs. No communication channels were opened with the participants, except through the stakeholder coordinators, due to the sensitive nature of some discussions.
## 4 Results
### Model Evaluation Metrics  
Four metrics were introduced to evaluate the model performance: root mean square error (_RMSE_), mean absolute percentage error (_MAPE_), mean absolute error (_MAE_), and coefficient of determination (\(R^{2}\)), as expressed in Equations (23)-(26). For _RMSE_, _MAPE_, and _MAE_, a smaller value indicates better prediction results. On the other hand, for the coefficient of determination \(R^{2}\), a higher value indicates better prediction results.  
\[RMSE =\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}}} \tag{23}\] \[MAPE =\frac{100\%}{n}{\sum_{i=1}^{n}}{\left|\frac{y_{i}-\hat{y_{i}}}{ \hat{y_{i}}}\right|}\] (24) \[R^{2} =1-\frac{\sum_{i=1}^{n}\left(y_{i}-\widetilde{y_{i}}\right)^{2}}{ \sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}} \tag{25}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factors** & **Rg** & **Rd** & **H** & **T** & **W** & **Wd** \\ \hline Correlation & 0.98 & 0.8 & \(-\)0.46 & 0.42 & 0.32 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lag Time** & **Correlation** & **Lag Time** & **Correlation** \\ \hline
1 & 0.988 & 11 & 0.633 \\
2 & 0.965 & 12 & 0.576 \\
3 & 0.959 & 13 & 0.499 \\
4 & 0.925 & 14 & 0.427 \\
5 & 0.908 & 15 & 0.358 \\
6 & 0.889 & 16 & 0.294 \\
7 & 0.836 & 17 & 0.231 \\
8 & 0.789 & 18 & 0.155 \\
9 & 0.742 & 19 & 0.081 \\
10 & 0.695 & 20 & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Autocorrelation analysis.  
\[MAE=\frac{100\%}{n}{\sum_{i=1}^{n}}{\left|{{y_{i}}-{\hat{y}_{i}}}\right|} \tag{26}\]  
In the above formulas, \(y_{i}\) and \(\hat{y_{i}}\) are the true and predicted values of PV power at time \(i\), respectively. \(\widetilde{y_{i}}\) represents the average of true PV power values, and \(n\) is the number of test samples.
###### Abstract  
Time-varying underwater acoustic (UWA) channels are the key challenge of underwater acoustic communication (UAC). Although UAC exhibits time-variance characteristics significantly in time domains, its delay-Doppler (DD) domain representation tends to be time-invariant. Orthogonal time-frequency space (OTFS) modulation has recently been proposed and has acquired widespread interest due to its excellent performance over time-varying channels. In the UWA OTFS system, the novel DD domain channel estimation algorithm that employs a multidirectional adaptive moving average scheme is proposed. Specifically, the proposed scheme is cascaded by a channel estimator and moving average filter. The channel estimator can be employed to estimate the time-invariant channel of the DD domain multidirectionally, improving proportionate normalized least mean squares (IPNLMS). Meanwhile, the moving average filter is used to reduce the output noise of the IPNLMS. The performance of the proposed method is verified by simulation experiments and real-world lake experiments. The results demonstrate that the proposed channel estimation method can outperform those of benchmark algorithms.  
orthogonal time frequency space (OTFS); underwater acoustic communications; adaptive channel estimation; improved proportionate normalized least mean squares (IPNLMS); moving average (MA) filter; denoising strategy; multidirectional filter
## 2 Results and Discussion
### Gas Chromatography-Mass Spectrometry Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Retention**} & \multirow{2}{*}{**Area (\%)**} & \multirow{2}{*}{**Tentative Compounds Identification**} \\  & **Time (min)** & & \\ \hline
1 & 7.869 & 16.11 & 2(3H)-Benzofuranone, hexahydro-3-methylene- \\ \hline
2 & 7.228 & 10.61 & 5-Methyl-2-ethylamine-2-thiazoline \\ \hline
3 & 9.709 & 3.62 & 2-Propenal, 3-(2-nitrophenyl)- \\ \hline
4 & 8.518 & 3.37 & Heptyl caprylate \\ \hline
5 & 12.372 & 3.23 & 2-(furan-2-yl)ethan-1-amine \\ \hline
6 & 1.572 & 2.97 & 2-Chloroethyl methyl sulfoxide \\ \hline
7 & 8.788 & 2.95 & Silane, [3-(2,3-epoxypropoxy)propyl]leethoxydim \\ \hline
8 & 11.201 & 2.66 & Benzoic acid, 4-hydroxy-3.5-dimethoxy-, methyl \\ \hline
9 & 4.603 & 2.54 & Thiophene, tetrahydro-2-methyl- \\ \hline
10 & 14.123 & 2.24 & 4-[3-(4-Fluorobenzyloxy)propyl]-1H-imidazole \\ \hline
11 & 3.21 & 2.07 & 1-Methylcyclopropanethanol \\ \hline
12 & 11.631 & 1.95 & 3,4,5-Thirhydroxybenzyl methyl ether \\ \hline
13 & 3.402 & 1.69 & Propanedio acid, oxo-, diethyl ester \\ \hline
14 & 7.484 & 1.62 & 4H-Pyran-4-one, 5-hydroxy-2-(hydroxymethyl) \\ \hline
15 & 2.098 & 1.57 & Propane, 1,2-bis(difluoroamino)-2-methyl- \\ \hline
16 & 5.627 & 1.41 & L-Valine, N-ethoxycarbonyl- \\ \hline
17 & 10.385 & 1.0 & 2,4-Dimethoxy-5-pyrimidine carboxaldehyde \\ \hline
18 & 12.053 & 0.99 & 3,5-Methano-2H-cyclopenta[b]furan-2-one, 6-b \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Gas chromatography–mass spectrometry analysis of tested manuka honey samples. Figure 1: GC–MS chromatogram of the tested manuka honey sample (X-axis: retention time in minutes; Y-axis: intensity).
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 2 Materials and Methods
### Data Preparation
An architecture that details the physical infrastructure of the environment was not implemented; instead, an existing IoT infrastructure was used. The raw data collected over six months represents a wealth of information about the functioning of the smart city and its critical infrastructure. This data underwent cleaning and preparation to ensure its quality and consistency. Additionally, feature engineering processes were performed to extract relevant patterns and improve data quality for anomaly detection.
# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### AI-Based Monitoring Systems for Elderly Care  
AI-based monitoring systems for elderly care have advanced significantly, integrating multimodal technologies like computer vision, voice interaction, ML, and predictive analyses to balance safety with autonomy. These systems prioritize real-time anomaly detection, adaptive responses, and user-centric design, with recent innovations focusing on hybrid approaches that combine sensing modalities and conversational AI for proactive care [47].  
Recent studies have focused on:  
* _Activity recognition_ that uses video feeds to detect anomalies or dangerous patterns. Systems analyze skeletal poses and movement patterns to detect falls or unsafe behaviors, achieving real-time processing with minimal latency [48];
* _Voice interaction_ that allows elderly users to interact naturally with assistive systems. However, setup complexity remains a barrier, but studies show that touchscreens and simplified instructions improve adoption among older adults [49]. These findings are relevant to the design of our system, as they show that ease of setup and interaction directly influence the acceptance of voice technologies among older adults;
* _ML for Predictive Analytics_, where models analyze gait patterns, historical health data, and environmental factors to predict fall risks. Logistic regression and neural networks generate personalized risk scores, enabling preemptive interventions like balance exercises [50].  
The combination of real-time monitoring with intelligent responses, especially using conversational AI, is considered the next step in proactive and user-centric elderly care. By enabling not only the detection of incidents but also dynamic interaction with the user, these systems bridge the gap between passive surveillance and responsive assistance.