esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Method for UAV Electromagnetic Interference Suppression
### Anti-Electromagnetic Interference Method for UAV Hardware Equipment
#### 4.2.2 Filter  
Filtering is a method of removing interference signals. Filtering refers to the technology to filter out the electromagnetic interference energy in the conduction coupling and maintain the working level in the line. For the energy coupled in from the antenna part, a band-pass filter can be used at the external interface such as the antenna to filter and match the received signal. For energy coupled in from external lines, power filters can be used. To improve the high-frequency interference signal, the commonly used electromagnetic interference filter is the ferrite magnetic ring filter [78]. According to the frequency of interference suppression, choose ferrite materials with different permeability. The higher the permeability of the ring magnet, the greater the impedance at low frequencies and the smaller the impedance at high frequencies. The ferrite bead can be regarded as a resistance whose resistance changes with frequency. According to the characteristics of electromagnetic wave frequency, nickel-zinc ferrite or manganese-zinc ferrite can be selected. The high-frequency characteristics of the former are better than the latter. The higher the magnetic permeability of ferrite, the greater the impedance at low frequencies and the smaller the impedance at high frequencies. Therefore, when suppressing high-frequency interference, nickel-zinc ferrite should be used. Otherwise, use manganese-zinc ferrite. It is also possible to put manganese-zinc and nickel-zinc ferrite on the same bundle of lines at the same time, so that the interference frequency band that can be suppressed is wider. The greater the difference between the inner and outer diameters of the magnetic ring and the greater the longitudinal height, the greater the impedance, but the inner diameter of the magnetic ring must be tightly packed to avoid magnetic leakage. The installation position of the magnetic ring should be as close as possible to the interference source or the interface [69].
# Connectivity Analysis in VANETs with Dynamic Ranges
## 1 Introduction
### Related Works
#### 1.2.2 Propagation Channels  
To understand the channel effects on VANETs communication works in [7; 8; 9] analyzed the performance of VANETs. An in-depth VANETs performance analysis under different channel models is discussed by combining Physical and MAC layers [7]. While authors in [8] derived closed-form expressions of packet loss, packet error, and outage probabilities under different fading channels. For vehicle mobility and signal propagation environment effects on the link duration, ref. [10] uses a modified Markov model to show transitions in the inter-vehicle distribution using a path loss transmission model. An alternative path loss and shadowing model is used to analyze at 5.9 GHz the auto-correlation of the large-scale fading process [8]. The overall connectivity of V2V with both vehicle travel directions is analyzed for spectrum-agile VANETs in fading channels in [9; 11]. Lastly, authors in [11] analyzed how the effect of fading improves connectivity in VANETs.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 2 Background
### IoV  
IoV is a comprehensive platform which integrates IoT technologies and intelligent transportation systems [1]. It extends VANET's structure and applications. This platform can provide several services such as intelligent traffic control, driverless vehicles, safe driving, safe navigation, crash response, intelligent vehicle management, convenience services (remote door unlock, stolen vehicle recovery) and infotainment.  
There are numerous participants in an IoV system such as vehicles, drivers and other passengers that act as users, sensors (like on traffic lights) and On-Board Units (OBUs) which are devices installed on vehicles to provide smart features, a Central Authority (CA) which is responsible for network access and maintenance, cloud servers for communications and storage and Roadside Units (RSUs) which are scattered across the road and provide communication between vehicles and infrastructures. These participants communicate with each other in many different ways (Vehicle-to-Vehicle (V2V), Vehicle-to-Road (V2R), Vehicle-to-Human (V2H), Vehicles-to-Infrastructure (V2I), Vehicle-to-Sensor (V2S)). Thus, a social network is created with intelligent objects, which receive traffic information by exchanging Safety Beacon Messages (SBM) [2; 3]. IoV is an heterogeneous network as it involves several dissimilar participants and provided services. It faces various challenges due to its dynamic nature and real-time data requirements. Figure 1 shows the visual representation of an IoV system.  
The typical architecture of IoV which consists of four layers is based on a centralised system as it relies on Trusted Central Authorities (CA) [4]:  
* Environment sensing and control layer: In this phase, information is gathered by sensors which are stored within the vehicles. Those sensors detect SBMs, environmental and other vehicular information and also radio frequency identification perceive data such as satellite position, road environment, etc.
* Network access and transport layer: This layer is responsible for node management, data analysis and processing and communication between vehicles and other units in vehicular system.  
* Coordinative computing layer: In this layer, management of the IoV system is accomplished. In addition, resource allocation and processing of data are also fulfilled in this layer.
* Application layer: The application layer is in charge of storing and analysing information and deciding about several risk situations. It represents several applications such as traffic safety, infotainment, etc. These provided services are divided into two categories: open and closed services. Open services consist of services such as real-time traffic service, while more specific services like control platform and traffic command are considered as close services [2,4].
# Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition
## 4 Experiments
### Ablation Experiment  
In this section, this ablation experiment will shed light on the importance and effectiveness of the two-channel instrument representation, a pre-training process, and the embedded ACEVAE, providing insights into the individual contributions of these components to the model's performance.  
The AEDCN on mel-spectrogram is named AEDCN-mel-spectrogram, and the AEDCN without ECVAE is named AEDCN-single. For comparison, we also use AEDCN on 153 dimensional time-frequency representation (which is used in Muti-task ConvNet) in experiments, this model is named AEDCN-153. AEDCN without the pre-training process is denoted as AEDCN_without_pre. The results are shown in Table 6.  
As Table 6 shows, the three parts of AEDCN are both effective, the introduced two-channel instrument representation performs better compared with the 153 dimensional time-frequency representation. Furthermore, we verify that the constructed adversarial loss is effective for the instrument recognition task in Table 7.  
In Table 7, the AEDCN-likelihood is an AEDCN which uses the likelihood function (KL divergence) for training the ACEVAE, and the AEDCN-inverseKL is an AEDCN which uses inverse KL divergence for training the ACEVAE. As the results show in Table 7, the combined adversarial loss function of KL divergence and inverse KL divergence is more effective than the traditional loss function for instrument recognition.  
As this dataset is imbalanced, next, we evaluate AEDCN on each instrument class in Table 8.  
According to the results presented in Table 8, the AEDCN demonstrates improvements when compared to ConvNet-based and Transformer-based instrument recognition methods across various instrument categories. Particularly, while the AEDCN shares a similar backbone neural network structure with the Muti-task ConvNet, the generated features by ACEVAE in the AEDCN play a significant role in enhancing the instrument recognition performance for the Cla and Flu classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1 Micro** & **F1 Macro** \\ \hline AEDCN-mel-spectrogram & 0.64 & 0.61 \\ AEDCN-single. & 0.66 & 0.58 \\ AEDCN-153 & 0.66 & 0.62 \\ AEDCN\_without\_pre & 0.67 & 0.62 \\ AEDCN & 0.68 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of AEDCN in the ablation experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **F1 Micro** & **F1 Macro** \\ \hline AEDCN-likelihood & 0.66 & 0.62 \\ AEDCN-inverseKL & 0.63 & 0.61 \\ AEDCN & 0.68 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of adversarial loss in AEDCN.
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
Moreover, this paper attempts to analyse the features and similarities of the proposed configuration in the literature [5; 7; 8; 9; 10; 11; 24; 25; 26], in which graphene was used to operate in the NIR and, precisely, at 1.55 \(\upmu\)m. In particular, this work aims to analyse the graphene conductivity describing the properties of graphene (on which software environments are based making possible the simulation of real implementations) [5; 8; 9; 13; 14; 17; 18; 19; 21; 25; 26] and the transition of graphene from the dielectric regime with losses to the quasi-metallic regime [5; 9; 14; 18; 21]. Four groups of graphene-based devices have been considered: waveguide integrated optical modulators [11; 12; 16], Mach-Zehnder modulators [8; 9; 18], planar photonic crystal nanocavity-based modulators [7; 10; 19; 20] and micro-ring resonators [24; 25; 26]. It should be noted that both numerical [8; 9; 10; 14; 15; 16; 17; 18; 19; 21; 26] and experimental [7; 8; 9; 10; 11; 12; 14; 19; 20; 24; 25] works were considered. \begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\lambda\) and \(\Delta\lambda\) & wavelength and its shift \\ \(\sigma\) (\(\sigma_{\text{G}}\)), \(\sigma^{\prime}\) and \(\sigma^{\prime\prime}\) & surface conductivity (graphene surface conductivity), intraband \(\sigma_{intra}\) and interband \(\sigma_{inter}\) conductivities \\ \(\varepsilon\) (\(\varepsilon_{\text{G}}\)), \(\varepsilon_{\text{G}\_{-}\text{r}}\) and \(\varepsilon_{\text{G}\_{-}\text{i}m}\) & complex permittivity (graphene complex permittivity), its real \\  & and imaginary parts \\ \(n\) (\(n_{\text{G}}\)) and \(\Delta n\) & refractive index (graphene refractive index) and its change \\ \(n_{s}\) & graphene surface carrier density \\ \(k\) (\(k_{G}\)) & extinction coefficient (graphene extinction coefficient) \\ \(e\) & electron charge \\ \(\omega\) & radian frequency \\ \(h=h/2\pi\) & \(h=h/2\pi\), reduced Planck constant \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The list of symbols and notations. This paper is divided into two main sections. Section 2 details the graphene conductivity and the complex permittivity along with the related variables (chemical potential, thickness of GE, relaxation rates/scattering time, temperature, numerical methods, experimental data and manufacturing process) and the GE configurations, while Section 3 focuses on the device configurations and the possibility to tune the operating and/or the resonant wavelength. The last section sheds light on the possibility to extend and improve the tunability of graphene-based devices operating in the NIR range.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
###### Abstract  
This paper provides a study of the different alternatives that are being considered in the 5G-ROUTES project to establish seamless 5G connectivity in a maritime environment both from an architectural point of view and also from the definition of field trials to evaluate the performance and dependability of the proposed solution. As expected, the main challenge in providing 5G connectivity on the sea is to provide coverage over large areas of open water. Thus, as a starting point, this paper presents a measurement campaign that was conducted to assess the current coverage in the Baltic Sea, which concluded that the current terrestrial networks cannot guarantee sufficient coverage. Next, the solution architecture and trials proposed by 5G-ROUTES are described, which are based on the integration of satellite and leading-edge multi-hop connectivity in 5G networks. Utilizing satellite backhaul can potentially overcome the connectivity challenge from the terrestrial domain to the maritime domain, while multi-hop connectivity ensures that coverage is extended among the different ships that are navigating the sea. Furthermore, this paper describes how the project will evaluate, in field trials tailored to this maritime environment, common connectivity key performance indicators (KPIs) such as latency, throughput, availability and reliability. This paper concludes by providing a vision for applying the obtained results and insights to maritime transportation and other remote areas where the deployment of a suitable 5G infrastructure may be challenging or costly. The findings will be used to guide the design of future 5G networks for marine applications and to identify the most effective methods for providing secure and dependable communication in a maritime setting.  
5G; seamless connectivity; maritime connectivity; multi-hop; private networks; hybrid terrestrial-satellite; non-terrestrial networks +
Footnote †: journal: Sensors  
0023  
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## 2 Materials and Methods
### Methodology of the Health Impact Assessment  
Health Impact Assessment methodology allows calculating the share of health effects that can be attributed to air pollution. To determine the level of exposure to air pollution, a population-weighted average concentration of annual PM\({}_{10}\) and PM\({}_{2.5}\) was calculated from the modeling results for each scenario including a baseline. For this purpose, the population was taken from the 2011 national census with a 1 km \(\times\) 1 km grid ([[https://geo.stat.gov.pl/aktualnosci/-/asset_publisher/jNfjluicyRp/content/id/45261--accessed](https://geo.stat.gov.pl/aktualnosci/-/asset_publisher/jNfjluicyRp/content/id/45261--accessed)]([https://geo.stat.gov.pl/aktualnosci/-/asset_publisher/jNfjluicyRp/content/id/45261--accessed](https://geo.stat.gov.pl/aktualnosci/-/asset_publisher/jNfjluicyRp/content/id/45261--accessed)) on 3 March 2020). To calculate the share of health effects attributed to air pollution, the concentration-response function based on Relative Risk (RR) was used. WHO HRAPIE was used as the source since it provides meta-analysis results and is used in Europe by the European Environment Agency [7]. The RR used for an increase of 10 \(\mu\)g/m\({}^{3}\) of annual average concentration is as follows:  
* Premature death from natural causes--1.062; pollutant metric PM\({}_{2.5}\);
* Bronchitis among children--1.080; pollutant metric PM\({}_{10}\);
* Chronic bronchitis among adults--1.117; pollutant metric PM\({}_{10}\); and
* Work absence--1.046; pollutant metric PM\({}_{2.5}\). Relative Risk, concentration of air pollution and health data were used as stated below \[N_{HE\_pol}=N_{HE}*\left(1-exp^{-ln\frac{\mu\beta}{\beta}*conc}\right)\] (1)  
where \(N_{HE\_pol}\) is the number of health effects caused by air pollution; \(NHE\) is the number of health effects from all causes; \(RR\) is an indicator that is determined from the relative risk function for a given health effect; \(b\) is the concentration of air pollution for which \(RR\) was derived (for this calculation, 10 \(\mu\)g/m\({}^{3}\) was used); and \(conc\) is the value of the concentration of atmospheric air pollutants to which a given municipal population is exposed.  
This formula was used for each health impact individually for municipalities and particular scenarios. The difference in the number of adverse health effects between the emission reduction and baseline scenarios was defined as the potential health benefit for each municipality, and their sum as a gain for the region.
## 3 LiDAR-Inertial odometry
### IMU Preintegration  
During the optimization process of the factor graph, the robot's state is inevitably adjusted continuously. Each adjustment requires a re-integration of the pose from the current moment and the previous moment, which can be very time-consuming. To avoid reintegration and make the IMU integration independent of the previous state, preintegration strategy [26; 27; 28] is adopted.  
Firstly, modeling the angular velocity and acceleration observed by IMU under B  
\[\hat{\omega}_{t}=\omega_{t}+b_{t}^{w}+n_{t}^{w} \tag{8}\]  
\[\hat{a}_{t}=R_{t}^{BW}(a_{t}-g)+b_{t}^{q}+n_{t}^{q} \tag{9}\]  
where \(\hat{\omega}_{t}\) and \(\hat{a}_{t}\) are the original measurements at moment \(t\), and \(R_{t}^{BW}\) denotes the rotation matrix from W to B. \(b_{t}\), \(n_{t}\) are the bias and white noise, respectively. \(g\) is the constant gravity vector under W. Assuming that \(\hat{\omega}_{t}\) and \(\hat{a}_{t}\) in B remain unchanged, the motion of the robot is inferred from the measurement values of the IMU. The velocity v, pose p, and rotation q at moment \(t+\Delta t\) are \[\mathrm{v}_{t+\Delta t}=\mathrm{v}_{t}+g\Delta t+R_{t}(\hat{a}_{t}-b_{t}^{a}-n_{t}^ {a})\Delta t \tag{10}\]  
\[p_{t+\Delta t}=p_{t}+\mathrm{v}_{t}\Delta t+\frac{1}{2}g\Delta t^{2}+\frac{1}{2 }R_{t}(\hat{a}_{t}-b_{t}^{a}-n_{t}^{a})\Delta t^{2} \tag{11}\]  
\[R_{t+\Delta t}=R_{t}\exp((\hat{\omega}_{t}-b_{t}^{\omega}-n_{t}^{\omega}) \Delta t) \tag{12}\]  
where \(R_{t}=R_{t}^{WB}=R_{t}^{BW^{\mathrm{T}}}\), here it is assumed that the angular velocity and acceleration of the B are constant during the integration process. The relative motion of the robot between moments \(i\) and \(j\) is calculated using the IMU preintegration, and the preintegrated measurements \(\Delta\mathrm{v}_{ij}\), \(\Delta\mathrm{p}_{ij}\), and \(\Delta\mathrm{R}_{ij}\) between these two moments are:  
\[\Delta\mathrm{v}_{ij}=\mathrm{R}_{i}^{\mathrm{T}}(\mathrm{v}_{j}-\mathrm{v} _{i}-g\Delta t_{ij}) \tag{13}\]  
\[\Delta\mathrm{p}_{ij}=\mathrm{R}_{i}^{\mathrm{T}}(\mathrm{p}_{j}-\mathrm{p} _{i}-\mathrm{v}_{i}\Delta\mathrm{t}_{ij}-\frac{1}{2}g\Delta t_{ij}^{2}) \tag{14}\]  
\[\Delta\mathrm{R}_{ij}=\mathrm{R}_{i}^{\mathrm{T}}\mathrm{R}_{j} \tag{15}\]  
By using IMU preintegration, the poses of the robot can be jointly optimized using IMU bias and LiDAR odometry in the factor graph.
## 3 Results
### Perception and Concerns About Wildlife in the Community
#### 3.3.3 Do You Feel That There Are Fewer Wild Animals than Before?  
Students perceived fewer animals than before (84%), and the percentage increased as they progressed through school: 69% in upper primary, 83% in secondary school, and 93% in high school. There was a gender difference in upper primary, with 57% of boys compared to 43% of girls (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Question**} & \multicolumn{4}{c}{**Years of Schooling**} \\ \cline{2-5}  & **Overall** & **4 to 6** & **7 to 9** & **10 to 12** \\  & **\(n\) = 280** & **\(n\) (\%)** & **\(n\) (\%)** & **\(n\) (\%)** \\ \hline Do you think wild animals represent your community? & & & & \\  Yes & 238 (85\%) & 57 (74\%) & 71 (91\%) & 110 (88\%) \\  Female yes & 119 (50\%) & 25 (44\%) & 38 (54\%) & 56 (51\%) \\  Male yes & 119 (50\%) & 32 (56\%) & 33 (46\%) & 54 (49\%) \\  No & 42 (15\%) & 20 (26\%) & 7 (9\%) & 15 (12\%) \\  Which animal do you think is the most important? & & & & \\  Jaguar & 104 (37\%) & 26 (34\%) & 19 (24\%) & 58 (46\%) \\  Deer & 92 (33\%) & 26 (34\%) & 33 (42\%) & 35 (28\%) \\  Species of the Yucatan Peninsula & 42 (15\%) & 14 (18\%) & 11 (14\%) & 16 (13\%) \\  Species not of the Yucatan Peninsula & 42 (15\%) & 11 (14\%) & 15 (20\%) & 16 (13\%) \\  Do you feel that there are fewer wild animals than before? & & & \\  Yes & 235 (84\%) & 53 (69\%) & 64 (82\%) & 118 (94\%) \\  Female yes & 116 (50\%) & 23 (43\%) & 32 (50\%) & 61 (52\%) \\  Male yes & 119 (50\%) & 30 (57\%) & 32 (50\%) & 57 (48\%) \\  No & 45 (16\%) & 24 (31\%) & 14 (18\%) & 7 (6\%) \\  Do you think wild animals in your community are endangered? & & & & \\  Yes & 151 (54\%) & 50 (65\%) & 34 (44\%) & 67 (54\%) \\  Female yes & 66 (50\%) & 20 (40\%) & 14 (41\%) & 32 (48\%) \\  Male yes & 85 (50\%) & 30 (60\%) & 20 (59\%) & 35 (52\%) \\  No & 129 (46\%) & 27 (35\%) & 44 (56\%) & 58 (46\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Upper primary, secondary and high school students’ perceptions of the importance and decline of wild animals in their communities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Education Level**} & **Deer** & **Jaguar** & **Local Species** & **Non-Local Species** \\ \hline Upper primary school (4 to 6 years of schooling) & 34\% & 34\% & 18\% & 14\% \\ Secondary school (7 to 9 years of schooling) & 42\% & 24\% & 14\% & 20\% \\ High School (10 to 12 years of schooling) & 28\% & 46\% & 13\% & 13\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Species considered most important in the forest by children and adolescents in seven communities of José Maria Morelos, Quintana Roo, Mexico. Local species (excluding deer and jaguar).
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 3 Preliminaries and Methods
### Definitions
#### 3.1.2 Business Process Case and Prefix  
A business process case is a sequence of events occurring in time order, which can be denoted as \(\sigma=\langle e_{1},e_{2}\,,\ \ldots,\ e_{n}\rangle\), where \(e_{i}\) (\(1\leq i\leq n\)) denotes the \(i\)-th executed event in the instance, and \(n\) denotes the number of events contained in the instance, satisfying \(e_{i}.t<e_{j}.t\) for \(1\leq i<j\leq n\). A complete business process instance contains a start event \(e^{start}\) and an end event \(e^{end}\), i.e., \(e_{1}=e^{start}\), \(e_{n}=e^{end}\).  
A business process prefix is a case that has not yet ended and is usually a subsequence of a full business process instance. Given a business process case \(\sigma=\langle e_{1},e_{2},\ldots,e_{n}\rangle\), its prefix can be given as \(prefix(\sigma)=\langle e_{1},e_{2},\ldots,e_{n^{\prime}}\rangle\), where \(1\leq n^{\prime}\leq n\).