esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Structure of the Bell Pepper Samples
This stage involves the breakdown of various compounds, including proteins, sugars (such as glucose and sucrose), and polysaccharides (including hemicellulose, cellulose, and lignin). In the third stage (480-600 \({}^{\circ}\)C), weight losses of 5.6% and 1.7% were observed for non-fermented and fermented samples, respectively, which can be attributed to the decomposition of residual compounds [31]. The effects of pre-treatment were noted, although they appeared to be inconsistent. The application of ultrasound facilitated the partial breakdown of polysaccharides, resulting in greater weight reduction and accelerated pyrolysis [56]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Step 1**} & \multicolumn{3}{c}{**Step 2**} & \multicolumn{3}{c}{**Step 3**} & \multicolumn{3}{c}{**Decomposition Temperature (\({}^{\circ}\)C)**} \\ \cline{2-10}
**Sample** & **Temp. ** & **Mass** & **Temp. ** & **Mass** & **Temp. ** & **Mass** & **Temp. ** & **Mass** & **Sum (\%)** & & & \\  & **Range** & **Change** & **Range** & **Change** & **Range** & **Change** & **Change** & **Change** & **2** & & \\  & (C) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & & & \\ \hline Fresh & 30-100 & 2.58 & 100-480 & 64.0 & 480-600 & 2.23 & 68.9 & 186.3 &  
The DTG curve (Figure S1) for the unfermetned pepper samples displayed two distinct peaks, occurring at approximately 190 \({}^{\circ}\)C and 312 \({}^{\circ}\)C for each sample. According to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [31], the degradation temperature ranges for glucose and sucrose are between 150-200 \({}^{\circ}\)C and 170-212 \({}^{\circ}\)C, respectively. In contrast, hemicellulose and cellulose have degradation temperatures ranging from 220-315 \({}^{\circ}\)C to 250-450 \({}^{\circ}\)C, respectively. These observations suggest that the identified peaks may correspond to the degradation of these specific compounds. As shown in Figure S1, which presents the DTG curve for the fermented pepper samples, at least five elongated peaks were detected at temperatures of approximately 150 \({}^{\circ}\)C, 190 \({}^{\circ}\)C, 260 \({}^{\circ}\)C, 290 \({}^{\circ}\)C, and 390 \({}^{\circ}\)C, respectively. The first two peaks may indicate the degradation of monosaccharides. Notably, these peaks are less pronounced than those observed in the non-fermented pepper samples (Figure S1), suggesting a lower concentration of monosaccharides in the fermented samples. Additionally, the peak around 190 \({}^{\circ}\)C may represent the point at which lactic acid degradation occurs [57]. The subsequent two peaks could be linked to the degradation of hemicellulose and cellulose. Furthermore, the peak at 290 \({}^{\circ}\)C might signify strong gas adsorption of the degradation products on the surface of the newly formed phase [53]. The peak at 390 \({}^{\circ}\)C, along with the smaller peaks observed at higher temperatures, could also provide essential insights.
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 2 Interpretability in Machine Learning
### Black-, White-, Grey-Box Models vs. Interpretability and Explainability  
ML models depending on their accuracy and explainability level can be categorized in three main domains: White-Box, Black-Box, and Grey-Box ML models.  
A _White-Box_ is a model whose inner logic, workings and programming steps are transparent and therefore it's decision making process is interpretable. Simple decision trees are the most common example of White-Box models while other examples are linear regression models, Bayesian Networks and Fuzzy Cognitive Maps [12]. Generally, linear and monotonic models are the easiest models to explain. White-Box models are more suitable for applications which require transparency in their predictions such as medicine and finance [5; 12].  
In contrast to the White-Box, _Black-Box_ is often a more accurate ML model whose inner workings are not known and are hard to interpret meaning that a software tester, for example, could only know the expected inputs and the corresponding outputs of this model. Deep or shallow neural networks are the most common examples of ML Black-Box models. Other examples are support vector machines and also ensemble methods such as boosting and random forests [5]. In general, non-linear and non-monotonic models are the hardest functions to explain. Thus, without being able to fully understand their inner workings it is almost impossible to analyze and interpret their predictions.  
A Grey-Box is a combination of Black-Box and White-Box models [13]. The main objective of a Grey-Box model is the development of an ensemble of black and White-Box models, in order to combine and acquire the benefits of both, building a more efficient global composite model. In general, any ensemble of ML learning algorithms containing both black and White-Box models, like neural networks and linear regression, can be considered as a Grey-Box. In a recent research, [PERSON] et al. [12] combined White- and Black-Box ML models following a self-labeled methodology in order to build an accurate and transparent and therefore interpretable prediction model.
## 2 Emerging Applications of 6G
### Virtual Presence (Telepresence)  
Virtual presence or telepresence is another renowned emerging technology utilized in many industries. Telepresence is similar to video conferencing, yet is more advanced considering the quality of the audio and video offered. This technology is not just used for conducting meetings and conferences remotely, but also for applications such as robotics. Researchers combine robotics together with telepresence to manipulate the reactions of robots deployed with human-oriented environments. With the aid of telepresence, the user responses are further analysed, and robots respond much accurately [22]. The same concept is applied in the healthcare industry, where robots are used to perform surgeries [23]. Virtual presence in conjunction with virtual reality is also used in military training, online gaming, and medical simulation operations [24]. In order to facilitate the abovementioned requirements, communication channels need to transmit higher amounts of information within a limited timeframe. Hence, high-speed connections and wider bandwidth channelsare essential needs. Furthermore, concepts such as performing surgeries with telepresence demand low latency, low jitter, and noise-free communication.
###### Abstract  
The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features.  
Keywords:air pollution; air quality prediction; machine learning; smart cities +
Footnote †: journal: Applied Sciences
# Efficient Sleep-Wake Cycle Staging via Phase-Amplitude Coupling Pattern Classification
## 2 Material and Methods
### Experimental Design
#### 2.4.1 Phase 1: Search for the Best MLP Architecture  
The four different protocols used for the construction of the input data (Figure 1A left) were as follows:  
* Protocol 1 or frequency points: The input layer comprised six neurons, each corresponding to _MI_ values of specific frequency pairs that are of general neurobiological relevance, sleep function included (Table 1). These pairs of frequency values were defined on the basis of past literature, including a previous study of the group.
* Protocol 2 or frequency ranges: Analogous to protocol 1, but instead of using single _MI_ values for each frequency pair, narrow bands centered around the values of Table 1 were used. For this protocol, all the _MI_ values inside each band were averaged, thus also resulting in 6 data points.
* Protocol 3 or full comodulogram: The whole comodulogram was incorporated as the data input, encompassing not only the previously mentioned frequency pairs but the entire spectrum of calculated cross-frequency couplings.
* Protocol 4 or half comodulogram: The same as protocol 3 but, instead of the whole comodulogram, only its first (left) half (0-10 Hz in the horizontal axis) was used.  
The set of _MI_ values (PAC patterns) representing each 30 s signal window was concatenated into a single column so that the input consisted of a matrix in which each line was the _MI_ values and each column represented a PAC pattern of a 30 s time window (Figure 1A right). This resulted in 6 \(\times\) 480 matrices for protocols 1 and 2, 800 \(\times\) 480 matrices for protocol 3, and 400 \(\times\) 480 matrices for protocol 4. The PAC patterns of the entire recordings (all mice and all days) were used. In this phase, the data were always standardized (z-scored) and, subsequently, they were randomly divided into training (70%) and testing (30%) sets.  
For each one of the four protocols, each possible network architecture, i.e., with the number of neurons in the hidden layer varying from 3 (number of output layer neurons) to the maximum number considered (equals the number of neurons in the input layer, 6,400, or 800) was tested in multiple proofs (10 simulations). In protocols 3 and 4, to improve the computation time, we used different step sizes for different ranges of the number of neurons in the hidden layer (see Figure 2). In this first experimental phase, the performance of each MLP architecture and input protocol was assessed using the area under the ROC curve (AUC) calculated for each output (Figure 1C) of all possible architectures. Here, the reported results refer only to the testing part of the dataset. The MLP with the highest average (across the distinct sleep stages) AUC value among all the proofs and stages was chosen as the best network for the second phase of the experiments.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## Appendix B Sensitivity to Elementwise Surface Deviation  
Real building materials may not be precisely shaped or perfectly smooth, raising the question of how sensitive the reflector configurations are to surface irregularities. In this appendix we consider the effect of local random deviations for the ideal \(2.4\times 2.4\) m reflector.  
Random elementwise delays were introduced to the 2.4 \(\times\) 2.4 m reflector, for the 1.2 m patch size at a distance of 2 m. The delay is indicated by the additional depth of an idealized well, \(d_{\text{rand}}\) per element \(i\) (Equation (A6)). The additional elementwise depths were determined from a uniform random distribution, spanning the range 0 to \(\delta\) m. Hence, the mean additional depth is 0.5\(\delta\), and the standard deviation 0.29\(\delta\), with a 'white' distribution of deviation over the two-dimensional spatial-frequency spectrum (offset by 0.5\(\delta\)).  
\[\Delta\tau_{\text{rand},i}=\frac{2d_{\text{rand},i}}{c},\ \ 0\leq d_{\text{rand},i}\leq\delta\] (A6)  
Results (Figure A3) indicate that uniformly distributed random delays equivalent to surface roughness with a range of \(\delta\leq\) 10 mm have little effect on the speech-A-weighted retroreflected energy level, but a larger range of random values degrades the retroreflected energy level. The degradation is greatest for focusing space-reversal patches.
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 3 Verification System Establishment
### Implementation of Verification System  
The verification system proposed in this paper can realize SIL testing and HIL testing of the AEB control algorithm. This section will describe the implementation process of SIL testing and HIL testing, respectively.  
The SIL testing method of the verification system is shown in Figure 6. TruckMaker provides a simulation environment for virtual vehicles, as well as vehicle parameters and environmental parameters for AEB braking decisions. The AEB control algorithm is loaded into the MATLAB/Simulink of the host. TCP/IP protocol is used for information transmission between the host and the IPG simulation platform.  
Figure 5: Schematic diagram of the radar sensor.  
During the SIL testing, the AEB control model of the host makes a decision according to the vehicle parameters and environmental parameters in the simulation process. When TruckMaker receives the decision information, the virtual vehicle presents the corresponding AEB braking status.  
The HIL testing method of the verification system is shown in Figure 7. The braking system in TruckMaker is replaced by the pneumatic braking bench. The AEB control algorithm is loaded on Simulink, and real-time code could be downloaded to the dSPACE controller through the RTI interface. The Xpack4 real-time system of the IPG simulation platform provides boards and interfaces for connecting external hardware, and data is transmitted with the dSPACE controller through the CAN bus. The communication between the pneumatic braking bench and the dSPACE controller is carried out through analog signals and digital signals.  
During the HIL testing, the dSPACE controller receives vehicle parameters and environmental parameters through the CAN interface, and receives braking pressure and pedal opening signals through the AIO and DIO interfaces. The current status of the VUT is calculated through the AEB control algorithm. Then the dSPACE controller sends the corresponding proportional relay valve control signal according to the braking decision, receives the braking pressure and pedal opening signals feedback, and transmits the braking pressure signal to the IPG simulation platform. In TruckMaker, braking pressure is converted into braking torque and applied to each wheel. The virtual vehicle presents the corresponding AEB braking status.  
The verification system including the IPG simulation platform, dSPACE controller, and pneumatic braking bench is shown in Figure 8.  
Figure 6: SIL testing method.  
Figure 7: HIL testing method.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 2 Communication Protocols GOOSE and R-GOOSE and Disadvantages of the IEC61850 Standard  
Within the electrical substations under the standard, the field level is distinguished, where the voltage and current transformers, disconnectors, switches, and merging units that digitize the voltage and current values of the transformers are located. The bay level, where the IEDs are located, applies control and protection over the field-level equipment and sends reports to the next higher level, the station level. There are three distinct types of messaging or information exchange, including GOOSE, SV, and MMS. The other two, R-GOOSE and R-SV, are used when this messaging must be sent to other networks [10; 11]. The protocol stack of IEC61850, shown in Figure 1, contains the different types of messages mapped to different layers of the OSI model.
## 4 Method for UAV Electromagnetic Interference Suppression
### Anti-Electromagnetic Interference Method of UAV Data Link
#### 4.1.3 Anti-Jamming Technology Based on Cognitive Radio  
The research on anti-jamming technology based on cognitive radio focuses on solving the problems of the shortage of spectrum resources and the increasing difficulty of user spectrum management, and considers applying the cognitive radio technology of spectrum sensing and system reconfiguration to drones [57; 58; 59; 60; 61].  
Aiming at the degree of interference of UAV data link in a complex environment composed of geographic environment, meteorological environment, electromagnetic environment, etc., the literature [62] proposes a prediction and evaluation method combining support vector machines and power criteria. The predicted results of this method can be used to reconstruct the UAV data link. Literature [63] proposes a UAV data link communication system based on cognitive radio technology. The author designed a cognitive engine that takes the signal-to-noise ratio, channel estimation result, spectrum sensing result, Doppler frequency shift, etc. as input. Through the designed cognitive engine, it outputs the reconstructed communication parameters of the UAV system and the Enter to determine the type of situation encountered by the system. Literature [64] based on the analysis of the current UAV data link problems, the concept of the UAV smart data link based on a cognitive radio is proposed, and the working process of the UAV smart data link is described. Index system and function classification of human-machine intelligent data link. On the basis of determining the function of the UAV data link, the architecture of the UAV intelligent data link is proposed. Literature [65] proposes a multi-parameter planning method for UAV data link based on a state machine, which provides a feasible and effective idea for UAV data link reconstruction.
## 3 Formula  
The analysis of the current flow path revealed that even when the length of the interconnect increases, the regions that do not come into contact with the via exhibit negligible current flow. This phenomenon occurs because, during the transition of current from the interconnect to the via and back to the interconnect, the segment of the interconnect outside the via contact area is effectively unused. Therefore, minimizing such unnecessary interconnect sections allows for a more compact layout design, contributing to reduced fabrication costs and enhanced electrical performance.  
In this study, FEM simulations were employed to analyze the current density distribution in a metal line-via-metal line structure, and based on these results, an analytical equation for resistance was derived. Traditional electrical characteristic analyses typically estimate resistance by considering only the geometric structure of interconnects and vias. However, this study aims to present a more realistic resistance model by incorporating the effects of interconnect-to-via contact area on the current flow. The FEM simulation demonstrated that the current density tended to concentrate in specific regions, including approximately 20% of the interconnect height and the diagonal edges of the via. By evaluating the total resistance for specific via dimensions (10 \(\times\) 10 \(\upmu\)m) and (20 \(\times\) 5 \(\upmu\)m), this study established a generalized resistance equation that accounts for structural parameters. This equation was validated by applying it to various structures and comparing the computed resistance values with simulation results. Since resistance varies depending on the current flow path, the resistance equation was formulated to reflect the structural characteristics of interconnects and vias.  
Figure 5: Current density distribution for via structures with misalignment conditions: (**a**) over-align, (**b**) 50% misalign, and (**c**) 90% misalign.  
Figure 7 shows the results of recalculating and comparing the simulation results by applying the modeling equation to each via dimension. The error rate between the computed and simulated resistance values remained below 3% across all data points, indicating a high degree of accuracy. Notably, for via lengths of 10 \(\upmu\)m and 12 \(\upmu\)m, the error was less than 1%. For practical fabrication scenarios, where a via length of 10 \(\upmu\)m is commonly used, the error remained under 1%, demonstrating the applicability of the proposed formula to real-world structures.  
The resistance equation derived in this study enables the calculation of resistance while considering the concentration of current density. This advancement can be applied to the design of metal interconnects and vias in semiconductor devices. Traditional design approaches often assume simple equivalent resistance models for the current flow. However, by leveraging FEM simulations to develop a resistance formula that reflects the actual current density distribution, this study enables more precise resistance predictions, offering significant implications for semiconductor interconnect design.
## 3 Results and Discussion
### Analysis of VOCs Obtained by GC-IMS and GC-MS
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline
**Count** & **Compound** & **CAS \#** & **Formula** & **MW \({}^{\text{a}}\)** & **RI \({}^{\text{b}}\)** & **R \({}^{\text{c}}\) [Sec]** & **D \({}^{\text{d}}\)** & **Comment** \\ \hline
1 & Methanethiol & C74931 & CH4S & 48.1 & 504.1 & 96.119 & 1.03955 & \\
2 & Ethyl Acetate & C141786 & C4H8O2 & 88.1 & 597.9 & 137.091 & 1.34375 & \\
3 & 3-Methylbutanal & C590863 & C5H10O & 86.1 & 645.1 & 157.712 & 1.41113 & \\
4 & 2-Methylbutanal & C96173 & C5H10O & 86.1 & 656.3 & 162.596 & 1.40419 & \\
5 & Pentanal & C110623 & C5H10O & 86.1 & 692.3 & 179.691 & 1.1862 & monomer \\
6 & Pentanal & C110623 & C5H10O & 86.1 & 693.7 & 180.776 & 1.42797 & dimer \\
7 & Ethyl Hexanoate & C123660 & C8H16O2 & 144.2 & 1002.1 & 568.154 & 1.33495 & monomer \\
8 & Ethyl Hexanoate & C123660 & C8H16O2 & 144.2 & 1002.5 & 568.932 & 1.80397 & dimer \\
9 & Heptanal & C111717 & C7H14O & 114.2 & 898.4 & 382.978 & 1.32916 & monomer \\
10 & Heptanal & C111717 & C7H14O & 114.2 & 896.2 & 379.088 & 1.69106 & dimer \\
11 & 2-Heptenal (E) & C18829555 & C7H12O & 112.2 & 951.9 & 477.9 & 1.25967 & monomer \\
12 & 2-Heptenal (E) & C18829555 & C7H12O & 112.2 & 952.7 & 479.456 & 1.67369 & dimer \\
13 & Acetone & C67641 & C3H60 & 58.1 & 530.9 & 107.842 & 1.12549 & \\
14 & 1-Propanol & C71238 & C3H8O & 60.1 & 569.6 & 124.745 & 1.25108 & \\
15 & 2,3-Butanedione & C431038 & C4H6O2 & 86.1 & 586.2 & 132.008 & 1.17563 & \\
16 & Dimethyl Disulfide & C624920 & C2H6S2 & 94.2 & 716.9 & 199.324 & 0.98008 & \\  
thyl Propanoate & C105373 & C5H10O2 & 102.1 & 701.9 & 187.341 & 1.45434 & \\
39 & Butanal & C123728 & C4H8O & 72.1 & 592 & 134.505 & 1.29064 & \\
40 & Dimethyl Trisulfide & C3658808 & C2H6S3 & 126.3 & 954.7 & 482.904 & 1.31729 & \\
41 & 3-Methyllithio) Propanol & C505102 & C4H10 OS & 106.2 & 984.9 & 536.605 & 1.464 & \\
42 & Octanal & C124130 & C8H16O & 128.2 & 1005.1 & 574.034 & 1.40534 & \\
43 & n-Nonanal & C124196 & C9H18O & 142.2 & 1106.2 & 770.91 & 1.47695 & \\
44 & Ethyl Pentanoate & C539822 & C7H14O2 & 130.2 & 896.9 & 380.317 & 1.27255 & \\
45 \({}^{\text{a}}\) & 2-Methyl-3-Heptanone & C13019200 & C8H16O & 128.2 & 930.7 & 440.309 & 1.27858 & monomer \\
46 \({}^{\text{b}}\) & 2-Methyl-3-Heptanone & C13019200 & C8H16O & 128.2 & 930.7 & 440.309 & 1.69262 & dimer \\ \hline \end{tabular} \({}^{\text{a}}\)  
\({}^{\text{b}}\) represents the molecular mass; \({}^{\text{b}}\) represents the retention index calculated using n-ketones C4–C9 as external standard on MCT-5 column; \({}^{\text{c}}\)  
\({}^{\text{c}}\) represents the retention time in the capillary GC column; \({}^{\text{d}}\)  
\({}^{\text{d}}\) represents the drift time in the drift tube; \({}^{\text{e}}\)  
\({}^{\text{e}}\) represents the Chemical Abstracts Service.  
\end{table} 
Table 1: The information on detected VOCs of Zhenba bacon by GC-IMS.