esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Apparatus  
Collection of Heart Rate Variability Parameters: In this study, heart rate variability (HRV) parameters were collected using an Inner Balance device equipped with an ear sensor. This research apparatus includes a built-in ear sensor capable of capturing inter beat interval (IBI) parameters. The ear sensor can connect via Bluetooth, allowing HRV data to be transmitted to other mobile devices through an application. Additionally, the device is easy to install and suitable for dynamic testing, aligning with the experimental design requirements of this study. Eye Movement Data Collection: The Tobii Pro Eye Tracker was employed to analyze the eye movement behaviors of participants. This lightweight, head-mounted eye-tracking system minimizes restrictions on the subjects, ensuring that it does not interfere with their normal activities. The camera is mounted externally on the frame, positioned at the front of the forehead, and captures the controllers' field of view while tracking head movements. Under effective sampling conditions, the gaze position error is less than 0.5\({}^{\circ}\). However, the collection of eye movement data may be affected for participants wearing corrective glasses. To address this issue, myopic controllers were provided with contact lenses to ensure accurate eye-tracking results. Performance Assessment for Controllers: In conjunction with the performance evaluation standards of the control unit, the author designed a Working Memory Assessment Scale for controllers. This scale primarily evaluates the controllers' working memory performance during remote tower operations, specifically focusing on the understanding phase of situational awareness formation. To ensure accuracy and real-time feedback, the scale was administered immediately after task completion.  
Likewise, the author developed a Communication and Coordination Assessment Scale based on interviews with frontline controllers and related personnel. This scale assesses the controllers' abilities to process information and make decisions during the decision-making phase of situational awareness formation. To guarantee the accuracy and timeliness of the evaluation, this scale was also administered after task completion.
## 7 Conclusions
### Limitations and Future Research Directions  
This study acknowledges certain limitations. The analysis focuses on nine specific practices and seven evaluation criteria, which, while comprehensive, may not encompass all possible approaches or perspectives. Additionally, the results are based on expert evaluations, and a larger and more diverse sample of respondents could enhance the robustness of the findings. The model's reliance on manual calculations may also pose challenges for its wider adoption by practitioners who lack experience with MCDM methodologies.  
Accordingly, future research should expand the scope of biodiversity-oriented practices and evaluation criteria to include other aspects of business operations. Incorporating a broader range of stakeholders and larger respondent pools could improve the reliability and generalizability of the results. Automating the proposed methodology through user-friendly software or applications could enhance its accessibility for business managers.  
Another promising direction for future research is the application of the hybrid MCDM model to address other problems from this and other areas. Additionally, developing new hybrid models that build on the proposed approach could further advance the field of MCDM and its applications in sustainability.  
Conceptualization, M.K. and D.P.; methodology, M.K.; software, M.K.; validation, M.K., S.T. and P.P.M.; formal analysis, M.K.; investigation, M.K., S.T., P.P.M. and D.P.; resources, D.P.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, M.K., P.P.M. and D.P.; visualization, M.K.; supervision, D.P.; project administration, D.P.; funding acquisition, D.P. All authors have read and agreed to the published version of the manuscript.  
Project funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4--Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union--Next Generation EU [Project code CN_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP F87G22000290001, Project title \"National Biodiversity Future Center--NBFC\"].  
Not applicable.  
All data used in the study are disclosed within the paper.  
The authors declare no conflicts of interest.
## 4 Current Status and Identified Needs
### Level 2 Bio-Geophysical Land Products
#### 4.3.4 Existing Land Ground-Based Networks
TERN data are used in the frame of GBOV [53] to expand geographical coverage in Australia continent for energy budget and soil moisture validation. Despite the availability of this large ensemble of ground-based networks, the full exploitation of this wealth of data is still hindered by the disparity in the site characteristics, the used measurement practices, the adopted quality control procedures. One of the reasons is that the majority of these networks, except AERONET, were not primarily designed for validation purposes, but rather for ecosystem monitoring at regional and continental scale. There is a clear need for enhancing the interoperability of this ensemble of networks and to standardise the used protocols and quality assessment approaches. The use of the MM concept, as prototyped in the frame of GAIA-CLIM [62], can contribute to the endeavour of harmonising quality information and metadata across these networks and for supporting the network owners' in enhancing their readiness for supporting satellite Cal/Val needs.
## 3 Models' Development and Results  
### Data Preparation  
For developing the ML models, it was required to normalize data, since the data were unbalanced. Thus, the input variables were transformed using min/max transformation techniques wherein the minimum value was zero and the maximum was one. Details of inputs' distribution before and after the transformation are displayed in Table 3.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 5 The Proposed Security Scheme
### Surveillance UAV Network Operation Phase
#### 5.2.2 Join/Leave Procedures  
* _Join Process:_ When a new drone \(D_{n}\) is sent from \(CS\) to join a specific group, the operator provides \(D_{n}\) by the session keys \(k_{x}\) and \(k_{y}\) of the current cluster heads \(D_{x}\) and \(D_{y}\). Then, \(D_{n}\) encrypts two hello messages using \(k_{x}\) and \(k_{y}\); notice that the hello messages include timestamps and random nonce to prevent the replay attacks: \[c_{hello-x} =\mathbb{E}_{k_{x}}(m_{hello-x}),\] \[c_{hello-y} =\mathbb{E}_{k_{y}}(m_{hello-y}).\] Then, the drone forwards them to \(D_{x}\) and \(D_{y}\): \[D_{n} \xRightarrow{c_{hello-x}}D_{x},\] \[D_{n} \xRightarrow{c_{hello-y}}D_{y}.\] The cluster heads reply to \(D_{n}\) by acknowledgment messages \(ACK\) encrypted by their session keys. Figure 3(a) shows the join procedures.
# Theoretical Investigation for Angle Measurement Based on Femtosecond Maker Fringe
## 3 Simulation Results and Discussion
### The Investigation of Intensity-Based Angle Measurement
It is clear that the angle dependence of the \(\Delta n\) of p-s polarization is much faster than that of the p-p polarization and s-s polarization for all the nonlinear materials, and it leads to rapid changes in phase difference \(\psi\) and thus the rapid oscillations of the intensity. Therefore, the p-s polarization combination is more appropriate for the angular measurement for the z-cut nonlinear crystals. In addition to the visibility and sensitivity, the absolute magnitude of SHW intensity is also significant. It is evident from Equation (4) that the larger the \(d_{\text{eff}}\), the greater the measured intensity. Therefore, the proper azimuthal position \(\varphi\) should be set in the measurement process. For the p-s polarization, it is easy to see from Table 2 that the \(d_{\text{eff}}\) reaches its maximum value when \(\varphi\) = 0 because of the cosine dependence. The \(d_{\text{eff}}\) also affects the symmetry of the angle dependence. As shown in Figure 6, all the angle dependence of p-p polarization is not symmetrical because the \(d_{\text{eff}}\) of p-p polarization in this paper consists of two terms, one of which is an odd function and the other is an even function. In addition, there is a particular case in Table 2 in which the \(d_{\text{eff}}\) of the s-s case of KDP does not have any angle dependence. This means that an s-polarization incident femtosecond laser cannot produce an s-polarization second harmonic wave in a z-cut KDP, and thus the angle dependence of this case is not shown in Figure 6b.
## 2 Materials and Methods
### Anthropomorphic Phantom  
An anthropomorphic thorax phantom (Figure 1) with inserts that mimic the myocardium, lungs, liver, and spinal cord was used (Torso Phantom(tm) with Cardiac Insert(tm). Data Spectrum Corporation, Hillsborough, NC, USA). The myocardial insert (Figure 1b) consists of a fillable chamber that mimics the _LV_ wall and the _LV_ inner chamber (_LVIC_). To simulate pathological myocardial perfusion conditions, an insert filled with water (\"cold insert\") was used. This simulates a _PD_ in predefined positions inside the myocardial wall. A _PD_ insert with specific dimensions was used (Figure 1c): coverage angle on a short-axis plane of 45\({}^{\circ}\), 2 cm over the long-axis plane (45\({}^{\circ}\)\(\times\) 2 cm). The myocardial insert, liver, and torso were filled with \({}^{99\text{m}}\)Tc solution of different activity concentrations, as detailed below  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Imaging Device** & **Symbia T2** & **Symbia Intevo/Intevo Excel** \\ \hline Collimator & LEHR & SmartZoom \\ \hline Matrix size & 64 \(\times\) 64 & 128 \(\times\) 128 \\ \hline Zoom & 1.45 & 1 \\ \hline Pixel size & 6.6 & 4.8 \\ \hline Detectors & Both detectors & Both detectors \\ \hline Number of projections & 64 & 34 \\ \hline Starting angle & 45 & 59 \\ \hline Degrees of rotation & 90 & 104 \\ \hline Detector configuration & 90 & 76 \\ \hline Detector orbit & Non-Circular & Cardio-Centric \\ \hline Acquisition mode & Step and shoot & Step and shoot \\ \hline Energy window & \
\begin{tabular}{c} Dual-energy window: \\ photopeak and lower scatter \\ \end{tabular} &
\begin{tabular}{c} Dual-energy window: \\ photopeak and lower scatter \\ \end{tabular} \\ \hline Reconstruction algorithm & Flash 3D IRR & IQ Flash 3D IRR \\ \hline Corrections & SC & SC-AC \\ No corrections & No corrections \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acquisition and reconstruction protocol details.  
(Table 2). The lung insert filled with Styrofoam(r) beads and water mimicked low-density lung tissue. The spinal cord insert was made of Teflon(r).  
The volumes of the different compartments were determined experimentally to be able to determine the required activity. These data are essential to obtain the required activity concentrations based on the distribution of the radiopharmaceutical in the body.
## 2 IoV Distributed Intelligence
### Intelligence at the Edge for IoV  
#### 2.4.1 Applications  
The fusion of IoT and Artificial Intelligence (AI), known as AIoT, is transforming IoV to improve road safety, efficiency, and mitigate traffic issues [42]. The IoV landscape comprises three main categories: Autonomous Driving (AD), Safe Driving Monitoring Systems, and Cooperative Vehicle Infrastructure Systems (CVIS).  
**Autonomous Driving:** To address the challenges of massive data generation (i.e., 4000 TB per day) and the need for real-time decision making, edge computing emerges as a viable solution [43]. AD signifies a shift toward intelligent vehicles capable of AI-driven decision making. Edge computing, exemplified by vehicles such as HydraOne [44] and HydraMini [45], addresses the challenges of massive data generation and allows real-time decision making in critical scenarios. Furthermore, edge-based systems such as EdgeDrive improve safety through real-time Advanced Driver Assistance Systems (ADAS) applications [46].  
**Safe Driving Monitoring Systems:** Driver monitoring systems, crucial for safe driving, combat issues such as drowsiness. In [47], a Raspberry Pi 3-based system was implemented that uses a DL algorithm for real-time alerts by analyzing facial features captured in both day- and night-drive scenarios.  
**Cooperative Vehicle Infrastructure Systems:** CVIS establish real-time road information networks by connecting vehicles, pedestrians, and infrastructure. Using distributed infrastructure, including vehicles, base stations (BSs), and roadside units (RSUs), edge computing reduces transmission delays for timely communication. In [48], the authors proposed a you only look once (YOLO) DL model for car accident detection (YOLO-CA) system that uses 5G networks detects accidents promptly, using the CAD-CVIS dataset for improved accuracy.  
In conclusion, the integration of edge intelligence into IoV brings notable advances in AD, safe driving monitoring, and CVIS, addressing data challenges and fostering cooperative systems for improved road safety and traffic management. The reviewed literature emphasizes the transformative impact of AI at the edge in the IoV landscape.
## 4 Determining Travel Time Indicators and Predictors  
Relevant travel time indicators and predictors were determined based on previous research and on-field experience in the Zagreb tram network. The range of values for each segment is shown in Figure 6 relative to minimum travel time (0% means minimum travel time, \(-100\%\) represents zero, 100% means double minimum travel time, 200% means triple minimum travel time, etc.). Travel time is represented in box plot style: a short horizontal line for the median, the interquartile range in green, values outside the interquartile range in red, mild outliers as circles, and extreme outliers as stars.  
Outliers are usually a consequence of yellow lane contravention, trans entering traffic from terminals, and irregular stoppings in cases of congestion. Most segments have travel time 50% greater than the minimum, with some short segments (10, 21) having little disturbances and therefore their travel time is close to the minimum, and on the other hand, there are segments (01, 03, 22, 27) with travel time more than double the minimum. The number of measurements was 120 at the lowest-frequency segments (09, 22) up to 520 at the highest-frequency segments (05, 26).  
Figure 6: Travel time values for each segment in box plot style with outliers [39]. Interquartile ranges are marked in green, with the median as a short black horizontal line in-between. Values outside the interquartile range are marked in red, with mild outliers as circles, and extreme outliers as stars.  
To test the normality of the distribution for travel time on each segment, the following conditions were used:  
* The Shapiro-Wilk test must show at least 1% significance;
* The Kolmogorov-Smirnov test must show at least 5% significance;
* In the skewness test, the ratio of the value to its standard error must not exceed 2.5.  
The results are shown in Table 2. Segments that pass all three tests have an asterisk. Therefore, normal distribution appears in one third of all segments, which is not surprising, since the observation period was two hours, and traffic conditions change with traffic volume increasing from 15:00 to 17:00, confirmed by the skewness value column having mostly positive values, i.e., the distribution is right-skewed. The kurtosis column has mostly positive values, showing more data concentration around the average than in default normal distribution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Segment**} & **Sample Count** & **Kurtosis– Value** & **Skewness– Value** & **Skewness– Ratio** & **Kolmogorov– Smirnov** & **Shapiro–Wilk** \\ \hline
01 \(|\) \(|\) \(30|\) \(|\) \(|\) \(\mathrm{svms}\) \(|\) \(\mathrm{stdm}\) & 160 & \(+1.3\) & \(+1.0\) & 4.6 & 00\% & 00\% \\
*02 \(|
## 4 Experimental Results
### Hypervolume Analysis  
Table 3 presents Hypervolume averages. From the experiments, we could conclude the following:  
* If we compare the results obtained _just by the five MOEAs_, we could see that: (i) GDE3 obtained the highest results on five problems (P01, P04, P14, P16, and P17); (ii) IBEA was the best one on four problems (P02, P05, P07, and P13); (iii) NSGAII has the best average on four problems (P06, P10, P15, and P18); (iv) SPEA2 has the best average on five problems (P03, P08, P09, P11, and P12); and (v) mIBEA did not get the best results in any experiment.
* If we consider _just the four hyper-heuristics_, we have: (i) MOABHH has higher averages on nine problems (P01, P04, P07, P08, P10, P12, and P15-P17); (ii) HHLA has its best results on three problems (P05, P14, and P18); (iii) HHRL performs better than others on five problems (P02, P03, P09, P11, and P13); (iv) HHCF is the best algorithm just in P06.
* Finally, if we compare the results obtained by _all nine algorithms_, GDE3 has higher Hypervolume values on four problems (P04, P14, P16, and P17), IBEA has better averages on three problems (P05, P07, and P13), NSGAII has the best result on three problems (P06, P10, and P18), SPEA2 on three problems (P08, P09, and P11), MOABHH has better averages on three problems (P01, P12, and P15), and HHRL in two of them (P02 and P03). Moreover, mIBEA (among MOEAs), HHLA, and HHCF (both among HHs) did not excel in any problem.  
Finally, it is interesting to compare the results obtained by HHs with individual MOEAs. Table 4 presents a summary of the statistical pairwise comparison between HHs and individual MOEAs considering Hypervolume averages. An x in a certain cell means that the corresponding HH could not achieve a result as good as the one obtained by a particular MOEA, considering the Hypervolume indicator. First of all, we can notice that all HHs were overcome in problems P17 and P18. Moreover, MOABHH could not achieve statistically tied results with the best algorithms in three other problems (P10, P11, and P14), HHLA was bet in six other problems (P04, P06, P08, P09, P10, and P12), HHRL could not achieve the best result in two other problems
## 4 Use Case Evaluation  
Evaluation of the S2 NeTM in a use case scenario is crucial to identify its practical feasibility and effectiveness in achieving its intended goals. The Green Route use case scenario provides an evaluation of an S2 NeTM that aims to provide a sustainable commuting solution by enabling users to find the greenest and most efficient route to their destination. The evaluation considers the effectiveness of S2 NeTM in providing context-aware and  
Figure 5: Representation of social relationships in the S2 NeTM ontology.  
personalized recommendations for routes that meet the user's sustainability criteria while ensuring an efficient travel time. This section will provide a detailed overview of the Green Route use case, and assess the efficacy of S2 NeTM in fulfilling its objectives.
## 3 Results
### Detection Capability Improvement of Multi-GNSS for a Single Polar-Orbit Satellite
Furthermore, the meteorology LEO satellite's orbit track and velocity were along the longitude direction, and the GNSS RO events occurred in the fore and back occultation antenna fields of view; therefore, the GNSS RO events distributed along the longitude as shown in Figure 3a,g. Figure 4 illustrates the occultation number trend beside the GCF trend of the MET/BDS-3 and MET/multi-GNSS RO systems. As shown in Figure 4, the occultation numbers were proportional to time, and their growth rates were about 540 and 4200 events per day, respectively, while the 5 day cumulative occultation numbers were about 2700 and 21,000, respectively. The GCF increased with time accumulation, and the 5 day cumulative occultation GCFs were about 52% and 99%, respectively. Therefore, the multi-GNSS integration could significantly increase the occultation number and GCF of a single polar-orbit LEO satellite. Specifically, the occultation number and the growth rate of the MET/multi-GNSS RO system were about 7.8 times as many as those of the MET/BDS-3 RO system.
# An ECCD--Electronic Charge Compensation Device--As a Quantum Dissipative System
## 1 Introduction  
An electronic charge compensation device ECCD is a passive device that carries electrical currents, in time, to the electrical Earth field. The device we examined is a patent of DINNTECO International (P1); it is a security protector --DDCE, dispositivo de compensacion de cargas electricas-- that prevents lightning's impacts to infrastructure (telecommunication towers, sportive buildings, civil and heritage constructions, factories, ports, and airports, among others). The protection also includes derivative exogenous electric current pulses--generated by distant impact lightning--and reduces the radiofrequency disturbances in the protected area. This radiofrequency perturbation can have its origins in cloud-cloud lightning, in the emission of near telecommunication towers, in own friction between clouds by the wind and other atmospheric electrical changes, or by occasional external incidents. Obviously, the protection covers people, animals, electronic instruments, and all belongings in a wide coverage area (radius of \(\cong\) 200 m). It is quite convenient to start with a presentation of the statistical results of lightning's impacts around an ECCD, from 16 years ago until now. That related to empirical analysis: Section 2. The ECCD quantum description came up as consequence of the numerical simulation analysis developed at the INTA (Instituto Nacional de Tecnica Aeroespacial) of Spain to explore the radiofrequency interactions with an ECCD; in particular, with an exhaustive study of two models of ECCD produced by DINNTECO: DDCE 100 and DDCE 50 (SM 2). Section 3 shows the most significant results focused only on DDCE 100 model (see Figure 1), because that is the extensively used one, but the results are also applicable to similar systems. Finally, Section 4 show and justifies the different quantum implications that possibly involve the basic support of an ECCD. Spatial Distribution of Lightning Impacts around the ECCD: A Statistical Analysis and an Empirical Rule on Protection Efficiency  
The protection efficiency of the ECCD is by now supported by the test plan that DINNTECO International has been carrying out systematically and continuously for years in installations with a high risk of lightning impact, high lightning density, and high \"ceraunic\" activity. At all the facilities where these real tests are carried out, the condition that all their metallic parts should be at the same electrical potential as the lower hemisphere of the DDCE is fulfilled. These tests visualize the lightning activity detected within 2 km around the protected installation. The tests are based on data provided by an external company of recognized prestige: METEORAGE [1] (SM 2), whose lightning detection system is considered the most accurate in the world by the WMO (World Meteorological Organization) (SM 2) for data pertaining to Western Europe, with an assured accuracy higher than 98%. Following Table 1 shows the statistical results from seven stations based on the data provided by METEORAGE.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 4 Fusion Mapping on Cloud Server
### Semantic Aggregation  
After performing semantic alignment, the semantic elements observed by multiple tracks achieve good consistency in the global coordinate system, as shown in Figure 4A. However, this is still a composition of many semantic elements. To obtain a lightweight vector semantic map, it is necessary to aggregate the fusion map after multiple semantic alignments. The process of semantic aggregation includes instantiation and vectorization.