esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contribution by Keesom Interaction  
In Section 3.2, regarding the irradiation intensity dependence of the absorbance change, we proposed a plausible model based on the concept that those molecules within the transition dipole-transition dipole interaction distance in the light irradiation volume interact with one another to create the absorption spectra characteristic of J- or H-aggregates. Although this is qualitatively reasonable, it needed to be validated quantitatively.  
Figure 6: The absorbance change spectrum includes shorter wavelength area than F monomer absorption peak excited by 548 W/cm\({}^{2}\) intensity laser (solid line) and sample absorption spectra (dotted line).  
We considered the Keesom interaction as a possible intermolecular interaction in the model. The Keesom interaction [59; 60; 61] is known as the electric dipole interaction between molecules in thermal motion, as described below:  
\[U(r)=-\frac{1}{3k_{B}T}\Bigg{(}\frac{\mu_{1}\mu_{2}}{4\pi\varepsilon_{0} \varepsilon_{r}}\Bigg{)}^{2}\frac{1}{r^{6}}, \tag{3}\]  
(\(r\): distance between dipoles, \(\mu_{i}\): electric dipole of molecule i, \(T\): absolute temperature, \(k_{B}\): Boltzmann constant, \(\varepsilon_{0}\): permittivity of vacuum, \(\varepsilon_{r}\): relative permittivity of solution)  
The concentrations of the TPPS molecules were 59 \(\upmu\)mol/L in the water solution and 52 \(\upmu\)mol/L in the ethanol solution. For these concentrations, the average center-to-center distance between neighboring TPPS molecules was 72 nm in the water and 76 nm in the ethanol. Taking the TPPS oscillator strength as 1.723 at 413 nm [62], the Keesom potential energy between molecules in the average distance was \(-1.2\times 10^{-19}\) eV in the water and \(-9\times 10^{-20}\) eV in the ethanol. This potential energy was far smaller than the light-gradient force potential. Therefore, the Keesom interaction could not quantitatively explain the light-induced aggregation.
## 4 Experimental Results and Discussion  
Various experiments were conducted in this study, including the observation of the changes in the performance by models fed with different amounts of data, the searching of suitable hyperparameters, the study of the performance of the model with our collected and labeled data, some visual comparisons between the different training data sets and modes used, and the study of the model performance under extremely dark conditions.  
Our real traffic nighttime data sets were collected in Taipei, Taiwan, under various road and weather conditions, including normally moving traffic, congested traffic, rainy weather, and foggy days. The illumination degree of the collected data can be classified into two categories: Urban city illumination and extremely dark conditions. For the case of urban city illumination, the data were collected at night under usual illumination, such as under street light, headlight, and taillight illumination. For the case of extremely dark conditions, the data were collected at night under extremely weak illumination. Only few lighting effects existed in these data; some of these effects could not even be distinguished by human eyes.  
We collected our data sets by recording street views from dashboard cameras within the first half of the year, spanning spring and summer. After the collection of videos, we sampled the view videos with an interval of 30 seconds. Next, we viewed the sampled images and deleted images that were similar or seriously unclear before labeling them. We collected 9003 samples of data, which we labeled according to our seven predefined classes: Bikes (bicycles), buses, cars, motorbikes, pedestrians, vans, and trucks. The definition of the labels is provided in Section 3.2.1.
# Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
## 4 Results
### The Temporal Trends of NTL in High-Intensity Intervals  
Hotspots of global PAs can be revealed from their spatial maps and temporal trends of light pollution in high-intensity intervals, while showing that the continuously polluted PAs are highly correlated with urbanization (Figure 7). Spatially, PAs with a high value of NTL in the high-intensity polluted intervals from 1992 to 2018 are mainly distributed in the USA, Europe, East Asia, Venezuela, and Brazil (Figure 7a). However, when regarding their temporal trends of NTL over the past decades, the USA and Japan have decreasing NTL (Figure 7b). Meanwhile, we found that the temporal trends of light pollution are increasing in the Middle East, Indian, and South Asia. In the USA, the temporal trends of NTL in polluted PAs translated from decreasing in the north to increasing in the south (Figure 7c). The main polluted category in Europe is continuously polluted (Figure 7d). The temporal trend of NTL is increasing, except northern Europe, where the population has been slightly decreasing over the past decades [59]. In Japan, the continuously polluted PAs are close to the urban, whereas the time trend decreases (Figure 7e). This phenomenon mainly contributes to well-planned ecological conservation policies; e.g., Japan is the first country to develop guidelines for light pollution [57]. The trend of light pollution in South America is increasing, especially in Venezuela (Figure 7f) and Brazil (Figure 7g). What these places have in common is that continuously polluted PAs are near urban areas larger than 100 km\({}^{2}\).  
Figure 7: Distribution of global polluted PAs with high value of NTL in the high-intensity interval (**a**) and their temporal trends of NTL over the past decades (**b**). Detailed cases in (**c**–**g**) in (**a**) can be found in enlarged views. Note: the mean value of NTL and temporal trend of annual NTL sum in the high-intensity interval were visualized by the kernel density approach to identify these five global hotspots.
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 3 Methods
### Multi-Band Graph Neural Network
#### 3.3.3 Graph Aggregation  
The attention mechanism in machine learning is an emulation of the human visual system, which aims to automatically extract the critical information for a given task. For the EEG classification task, the features of different frequency bands should be evaluated and flexibly integrated to provide the more distinguishable information. Thus, in this paper, a soft attention mechanism is used in the form of a multi-layer perceptron (MLP).  
Supposing that \(H_{f}\) is the output vector of the front-end graph network corresponding to the \(f\)-th sub-band EEG signal, the band-wise attention mechanism is defined as follows:  
\[\begin{split}\eta_{f}=\tanh&\Big{(}\mathbf{W_{e}} H_{f}+b_{e}\Big{)}\\ \alpha_{f}=\frac{\exp(\eta_{f})}{\sum_{f}\exp(\eta_{f})}\\ Output=\sum_{f}\alpha_{f}H_{f}\end{split} \tag{15}\]  
where \(\mathbf{W_{e}}\) and \(b_{e}\) are trainable parameters. The integrated vector exported from the band-wise attention layer is forwarded to the linear layer and then to the softmax layer.
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Metaheuristics  
[PERSON] introduced the term \"metaheuristics\" ([PERSON], 1986) by combining two words, meta (Greek prefix _meta_, \"beyond\" in the sense of \"higher-level\") and heuristic (Greek _heuriskein_ or _euriskein_, \"find\" or \"discover\" or \"search\"). Metaheuristics are habitually described as (1) problem-independent algorithms that can be (2) adapted to incorporate problem-specific knowledge and definitions [4; 27; 33]. Consequently, they can resolve several categories of optimization problems efficiently in a flexible way because they do not have \"strong\" links with any specific problem.  
A metaheuristic (MH) can be seen as a _higher-level heuristic_, composed of a master heuristic (_metaheuristic cycle_) that drives and coordinates a set of subordinated heuristics (SH)--in an iterative cycle--to explore the search space [2; 4; 6; 55; 56; 57]. This relationship between master heuristics and subordinated heuristics (SH) is illustrated in Figure 2. Each subordinate heuristic performs a different role in the metaheuristic's search strategy. Thereby, the MH cycle arranges the SH to work together--in a higher level of abstraction--to search for an optimal solution for a given problem in a coordinated way.  
Informally, it is possible to say that the metaheuristics perform \"blind exploration\" in a vast search space that they do not know. Consequently, they cannot \"see\" the whole landscape to identify \"hills\" and \"valleys\" (optimal solutions). For that reason, the strategies used by the heuristics (master and subordinated) to navigate in the search space are crucial to investigate and map promising spots of the search space. Thus, everything that a metaheuristic knows about the landscape is the \"visited places\" (solutions) and the quality of these solutions (fitness). Therefore, after spending considerable time exploring the search space, the algorithm will have some information about parts of the search space. As a result, the best solution found is the return of the algorithm.  
A metaheuristic will be successful in discovering optimal solutions on a given optimization problem if it can conduct a good balance between exploration and exploitation [2; 16; 34; 58; 59]. _Exploration_ and _exploitation_ are related concepts in some sense because both techniques are employed to navigate and examine the search space. However, there is a subtle difference between them. _Exploration_ can be seen as diversification because it aims to identify promising areas in the search space with high-quality solutions. In contrast, _exploitation_ can be understood as intensification because it intensifies the search in a good region (solutions with excellent quality) of the search space to find a better solution [2; 4; 34; 44; 58].
## 2 Background and Previous Research
### Customer Perception by Using Text Mining  
The use of text mining applied to customer perception has a great field of application because it helps measure the user's perception of a product, brand, or public figure, even though metrics or standard measurement scales have not been established in this field. A key study is presented in [17]. Here, an evaluation of the perception of Twitter users is proposed with the following metrics: user sentiment score, tweet sentiment score, positive-negative tweet ratio, and positive-negative user ratio. These are calculated from the classification of the tweets (positive and negative) of the followers of some public figures recognized worldwide through the LIWC tool (software for cognitive and emotional evaluation of texts) and the implementation of different dictionaries.
# A Deep Learning Method for the Prediction of Pollutant Emissions from Internal Combustion Engines
## 2 Materials and Methods
### Artificial Neural Network Setup  
#### 2.2.1 Description of the Initial Dataset  
This study evaluates the effectiveness of the back propagation neural architecture in predicting pollutant concentrations, in particular, NOx, CO, CO2, and HC emissions, in ppm (Figure 3) over 5 different dynamic cycles, at the exhaust port of a spark ignition engine. The initial dataset, for training and validating the architecture, comprises experimental data collected under a constant \(\lambda\) value (i.e., \(\lambda\) = 1.0), with varying throttle valve openings (TVOs) ranging from 5% to 100% and engine speeds ranging from 500 rpm to 2250 rpm (Figure 4).  
The dataset, detailed in Table 3, includes 100 operating cases, with each case featuring 100 consecutive combustion events. The machine learning model utilized the following four parameters as inputs for each combustion event:  
* Engine speed [rpm].
* Ignition timing [CAD aTDC].
* Throttle valve opening [%].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Description** & **Specifications** \\ \hline \multirow{4}{*}{Athena GET HPUH4} & \multirow{4}{*}{Engine control unit} & Control the injector energizing time and IT by sending a trigger signal to the igniter control unit \\  & & Output: AFR, \(\lambda\), and [O\({}_{2}\)] \\ \multirow{4}{*}{Horiba Mexa 720} & \multirow{4}{*}{Fast lambda probe} & Adjustable for various fuels through setting the O/C and H/C ratios \\  & & H/C ratios \\ \cline{1-1}  & & H/C ratios \\ \cline{1-1}  & & Output: HC, CO, CO\({}_{2}\), NOx, \\ \cline{1-1}  & & SO\({}_{2}\), O\({}_{2}\), and THC \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: The trend in the (**a**) NOx, (**b**) CO, (**c**) CO2, and (**d**) HC concentration at the exhaust pipe, for different engine speeds, with one particular operating condition taken as an example (i.e., TVO = 50%).  
Based on the observations previously outlined and presented in Table 3 and Figure 4, Figure 5 shows the division of the primary dataset into training and validation subsets, comprising [100 \(\times\) 4] input variables and [100 \(\times\) 4] output variables. The dataset is composed of 100 experimental cases, as detailed in Table 3, with each case defined by 8 variables (Figure 5a). The input parameters (i.e., engine speed, IT, TVO, and torque) are organized into a [4 \(\times\) 100] matrix, while the output parameters (i.e., NO\({}_{\text{x}}\), CO, CO\({}_{2}\), and HC) are represented by another [4 \(\times\) 100] matrix; see Figure 5b. The dataset was partitioned so that 90% of the data were utilized for training and the residual 10% were reserved
## 3 Results
### Frequency Sweep Test
On the other hand, at this loading, the composite with Ti particles showed frequency-dependent behaviour in a wide frequency range, and the G\({}^{\prime}\) curve started to flatten at around 1 rad/s. It can also be seen that the values of storage modulus for the composite with GB particles at 60 vol.% were higher compared to all other materials, while the composites with 316L and Al exhibited remarkably similar behaviour. This indicates the strongest particle network formation for GB particles and the weakest for Ti particles at this concentration. The influence of filler concentration on dynamic properties in the LVR is shown in Figure 7 for all four filler particles separately. As expected, the increasing filler concentration increased storage, G\({}^{\prime}\) and loss, G\({}^{\prime}\) modulus, respectively, for all the composites used (similar as in amplitude sweep tests). As discussed, the behaviour of Al and 316L materials were similar and can also be viewed in a side-by-side comparison in Figure 7a,b. In both cases, the increase of filler content from 30 to 45 vol.% resulted in an increase of G\({}^{\prime}\) for approximately one order of magnitude in the whole frequency range. Much higher increase can be observed for 60 vol.%, where G\({}^{\prime}\) drastically increased, for example at frequency 10 rad/s from 130 Pa to around 5000 Pa.  
On the contrary, a much smaller increase of the viscoelastic properties could be observed for Ti particles (Figure 7c), as the values of G\({}^{\prime}\) from 30 to 60 vol.% increased by \(\approx\)200 Pa in the low-frequency area. The behaviour of GB material was at 30 and 45 vol.% similar to the behaviour of Ti particles, however at 60 vol.% this composite showed the highest change of properties and the values of G\({}^{\prime}\) and G\({}^{\prime\prime}\) were even higher than those obtained for Al and 316L composites. There are several ways to characterise the transition from liquid to solid-like behaviour [37; 54; 55]. For example, the transition can be determined when the slope of G\({}^{\prime}\) curve changes to 0, when tan 8 is frequency independent or at the crossover of G\({}^{\prime}\) and G\({}^{\prime\prime}\) functions (G\({}^{\prime}\) = G\({}^{\prime\prime}\)). From Figure 7 it can be seen that the crossover point shifted to higher frequencies as the filler loading increased in all composite materials studied. The crossover point was separately analysed and is presented in Figure 8.  
The transition from solid to liquid-like behaviour for Al and 31  
Figure 7: The effect of filler concentration on storage, G\({}^{\prime}\) and loss G\({}^{\prime\prime}\) modulus as a function of angular frequency: (**a**) Al filler, (**b**) 316L filler, (**c**) Ti filler, and (**d**) GB filler.
# Generalization Investigation for Artificial Intelligence-Based Positioning in IIoT
## 3 System Model
### Modeling of Receiving Timing Error  
The result of receiving time delay involved in the reception of a signal before reporting measurements are obtained from the signal. It is the uncalibrated receiving time delay, or the remaining delay after the device's internal calibration/compensation of the receiving time delay, involved in the reception of the positioning signals. The calibration/compensation may also include the calibration/compensation of the relative time delay between different RF chains in the same device and may also possibly consider the offset of the receiving antenna phase center to the physical antenna center.  
The timing errors are modeled according to TR 38.857, which means that the device receiving timing error is modeled as one truncated Gaussian distribution with zero mean and standard deviation of T1 ns, with a truncated range as [\(-\)2T1,2T1]. As a clarification, the device receiving timing error is generated randomly for each device drop but is fixed for all BSs linked with this device, while the network synchronization errors are independent and identically distributed among 18 BSs and generated randomly for each device dropping.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 5 Key Cooperative and Cognitive Technologies  
### NOMA  
NOMA has gained significance recently as an efficient access technique with the capability of improving spectral efficiency. NOMA is an essential enabling technology to meet the heterogeneous demands for low latency, high reliability, massive connectivity, improved fairness, and high throughput. Considering its capabilities to achieve better spectral efficiency and massive connectivity [151; 152], the role of NOMA has been discussed in cooperative HSTNs.
# A Systematic Review of Radio Frequency Threats in IoMT
###### Abstract
Hospital settings are vulnerable to various attacks, such as simulation and clone attacks in LFID (low-frequency identification), privacy leakage attacks (PLA) on contactless cards, replay and brute force attacks on pressure systems, sniffing and jamming attacks on Wi-Fi, the transmission of fake uplink data, and clone the tracker attacks and LTE sniffing attacks on mobiles. Information security incidents caused by intruders and unethical hackers are becoming more common, as evident from various research efforts on the IoT security and wireless sensor networks security and challenges, including medical devices and personal body area networks. Information spilling, session hijacking, and phishing attacks are frequent in healthcare infrastructure. Moreover, attack types based on the layer are becoming common in the hospital sector due to the low level of the cyber security maturity model, penetration testing to identify the vulnerabilities, and lack of cyber security awareness among business users and stakeholders. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Attack** & **IoT Layer** & **Attack Area Description** \\ \hline Primary emulation & Perception & The primary transmitter/antenna emits information or spilling of information. \\ \hline Spectrum sensing & Perception & Fake identification and observation of spectrum sensing process. \\ \hline Control channel attack & Network & Untrusted system or process collecting confidential information of trusted system/process. \\ \hline Cross-layer attack & All layers & Parallel attack on all the layers of IoT. \\ \hline SDR device attack & All layers & SDR device antenna/battery and other core part disruption. \\ \hline Jamming attack & All layers & Decreasing signal to interference noise ratio by blocking the information transfer between transmitter and receiver in the communication channel. \\ \hline Replay attack & All layers & Interception of signal between transmitter and receiver to accomplish fake transmission. \\ \hline Sniffing attack & All layers & Closely monitoring the sensitive or unauthorized information between transmitter and receiver in the communication channel. \\ \hline Tampering attack & All layers & Closely monitoring the sensitive or unauthorized information between transmitter and receiver in the communication channel and modification of process/parameters to compromise the system. \\ \hline Denial attack & All layers & Closely monitoring the sensitive or unauthorized information between transmitter and receiver in the communication channel, modification of process/parameters, and disrupting the availability. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: RF attack by IoT layer and attack area description.
## 1 Introduction
The most recent mode probabilities are then used to construct a global estimate of the user's condition. The method implements a gentle switch between the various modes by modifying the probabilities of each mode, which are used as weightings in the aggregate global state estimate. This combined estimate's covariance matrix considers the similarities and differences between the mode-conditioned estimates. Figure 1: Example of outdoor multipath interference. Two models were constructed for this study's many models, each describing a different aspect of observation. The IMM algorithm weighs the inputs and outputs of a bank of parallel filters at each instant using model probabilities. The idea of entropy has recently found widespread use in information theoretic learning (ITL), where the minimum error entropy (MEE) and maximum correntropy criteria (MCC) [13; 14; 15; 16; 17; 18; 19; 20] are most often employed for robust filter design dealing with non-Gaussian system applications. By integrating the probability density function (pdf), the statistical measure correntropy calculates the similarity between two or more variables. To mitigate non-Gaussian noise, the accompanying MCC is used as a new cost function for robust filtering. The MCC was created using an ITL methodology and has been applied effectively in various filter design applications, mainly when non-Gaussian sounds are present. Furthermore, to address the estimation problems in non-Gaussian noise settings, the MCC was used to replace the MSE criteria in developing a robust EKF known as the MCEKF. Some other well-known robust filter designs are available based on the relative entropy (also called the Kullback-Leibler divergence) or minimax theory [21; 22]. The MCEKF frequently performs far better than the conventional EKF. It has been stated that MCC is better suited for unknown noise and undetermined parameters and has a close link with M-estimators. A new general answer for researching random systems with generalized noise is also shown. MCEKF uses the MCC rather than the MMSE as the optimization criterion for GNSS navigation processing to improve the performance of EKF applications where non-Gaussian disturbances are present. The ranging error space may be constrained using the measured distance and its variance, controlled to exclude the impacts of colored Gaussian noise. By integrating the MCEKF and IMM technologies, the proposed IMM-MCEKF offers higher non-Gaussian signal processing performance. Furthermore, the IMM-MCEKF was demonstrated to be more effective in processing non-Gaussian data by combining IMM and MCC-based EKF algorithms. As a result, the MCC is used in this work in place of the conventional MMSE criteria for GPS navigation. The rest of this paper is structured as follows. The interacting multiple-model based filter is introduced in Section 2. The maximum correntropy-extended Kalman filter is shown in Section 3. The proposed IMM-MCEKF's performance compared to the EKF, IMM-EKF, and MCEKF techniques is assessed using illustrative examples based on simulation experiments in Section 4. Finally, Section 5 provides conclusions.