esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results
### Classification  
For online classification of an incoming vehicle to predict whether the vehicle is a Non-RLR or one of the four RLR types, we use MC-DCNN as described in Section 4.2. For training of the MC-DCNN model, we built a training dataset from traffic data consisting of time-series of vehicle speed, acceleration, DTI, and headways with cluster type determined through HCA so that each vehicle in the training dataset is labeled whether it is a Non-RLR, Type A RLR, Type B RLR, Type C RLR, and Type D RLR. Therefore, as shown in Figure 1, the trained MC-DCNN model gives a prediction to which class out of the above five classes an incoming vehicle is classified.  
To evaluate the classification performance MC-DCNN, we compare the classification accuracy of MC-DCNN with that of SVM using the validation dataset. Tables 2 and 3 are classification accuracy results using SVM and MC-DCNN, respectively. In the results, the classification accuracy is 100% if the classifier classifies all five classes, Non-RLR, Type A, B, C, D RLR correctly. The \"window size\" means the time-series length of input data, and the \"prediction time after yellow onset\" means the time when a classifier performs classification after yellow onset. In the case of SVM, if the window size is 0 s (i.e., there is only one data point in the input time-series), the accuracy is lower than about 60% regardless of prediction time. Table 2 also shows that the longer the input time-series length, the better the classification performance. The highest accuracy appears when the window size is 3 s and the prediction time is 2.5 or 3 s after yellow onset.  
Compared to the result from SVM, the classification accuracy of the MC-DCNN model is substantially better than that of SVM especially when the windows size is small. For instance, even the classification accuracies of MC-DCNN with 0 s window size in all prediction time cases are comparable to those of SVM with a 2 s window size. Also, the highest accuracy achieved by MC-DCNN with 1 s window size is 99.9% at 3 s prediction time while SVM with the same window size and prediction  
Figure 7: Clustering of RLR traffic dataset through Hierarchical Clustering Analysis (HCA).  
time can achieve only up to 87.5%. It is interesting to see that this 99.9% accuracy with 1 s window size is even better than the highest classification accuracy of SVM achieved with the longest window size. As a result of this comparison, it is shown that the MC-DCNN classification model proposed in this work can classify the class of an incoming vehicle more accurately than SVM even with shorter duration of vehicle motion measurement and also at a slightly earlier time after yellow onset. Furthermore, it is expected that the proposed MC-DCNN model can be applied to improve the performance of the system for imposing fines for vehicles violating traffic signals based on the accurate classification performance.
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 3 Results
### FTIR Spectroscopy Data of Hydrocarbon Samples Obtained Using the Cryogenic System

Table 4: Comparison of kerosene spectra results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{\mathcal{N}}\) & **Authors** & **Temperature, Type** & **Pressure, Type** & \begin{tabular}{c} **CH\({}_{3}\) Bending,** \\ **cm\({}^{-1}\)** \\ \end{tabular} & \
\begin{tabular}{c} **C-H Stretching,** \\ **cm\({}^{-1}\)** \\ \end{tabular} &
\begin{tabular}{c} **O-H Stretching,** \\ **cm\({}^{-1}\)** \\ \end{tabular} \\ \hline
1. & \
\begin{tabular}{c} Hudson, Gerakines, \\ and Yarnall [20] \\ \end{tabular} & Low (10 K) & Vacuum &
\begin{tabular}{c} Peak at 1459 \\ (occurs with O-H \\ bending as well) \\ \end{tabular} & Peak at 2952 & Peak at 3259 \\
2. & \
\begin{tabular}{c} [PERSON] et al. [21] \\ \end{tabular} & Low (30–135 K) & Vacuum & 1445–1472 & 2828–2985 &
\begin{tabular}{c} Centered near \\ 3250 \\ \end{tabular} \\
3. & \begin{tabular}{c} [PERSON] et al. [22] \\ \end{tabular} & Low (20–60 K) & Vacuum & \
\begin{tabular}{c} Peak at 1460 \\ (occurs with O-H \\ bending as well) \\ \end{tabular} & Peak at 2833 &
\begin{tabular}{c} Centered near \\ 3250 \\ \end{tabular} \\
4. &
\begin{tabular}{c} Our results \\ \end{tabular} & Low (80–100 K) & Atmospheric & 1450–1550 & 2840–2860 & 3200–3350 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of methanol spectra results. Figure 7: Characteristic peaks of kerosene at low temperatures.
# Effects of Various Extraction Factors on Protein Yield of Haliotis discus hannai (Abalone)
## 3 Results and Discussion
### Surfactant Effects and Synergistic Effects  
Increased SDS concentration gradually improved the extraction rate of abalone protein. The maximum yield was reached with 16 mM SDS (Figure 3a). In contrast, Triton X-100 did not alter the extraction rate of abalone protein (Figure 3b). In the presence of 0.6 M NaCl, the SDS concentration also showed a similar trend, but the addition of 32 mM SDS showed the highest protein extraction rate (Figure 4). Surfactants are categorized into ionic and nonionic. There are three types of ionic surfactants, namely, anionic, cationic, and amphoteric [24]. SDS is an anionic surfactant and is negatively charged. An anionic surfactant consists of a negatively charged hydrophilic head group and a hydrophobic tail group, while nonionic surfactants consist of a neutral hydrophilic head group and a hydrophobic tail. SDS is the most frequently used anionic surfactant, while Triton X-100 is a typical nonionic surfactant [25]. The role of surfactants in protein extraction is highly associated with their critical micelle concentration (CMC) [25,26]. In an aqueous solution, surfactants at or over their CMCs preferably form micelle structures. Indeed, over the CMC, the level of monomer surfactants did not increase, but symmetrical spherical micelles dynamically increased. It has been reported that the CMC of SDS was 8.08 mM in water and 1.99 mM in PB [27]. Thus, our results indicate that an increase in the micelle structure of SDS improved the efficiency of abalone protein extraction. On the other hand, Triton X-100 has a lower CMC, so it easily breaks the cell membrane. Although nonionic surfactants are suitable for breaking lipid-lipid and lipid-protein interactions, they are less effective in breaking protein-protein interactions, so the protein yield may not be altered by the addition of Triton-X 100 [28].  
Figure 2: Protein yield with the addition of different concentrations of NaCl in phosphate buffer with EDTA. Bars with different letters are significantly different (\(p<0.05\)).
## 2 Background  
Security of CC is a major concern for both CSP and users. Several attacks, such as denial of service (DoS), DDoS, and EDoS, threaten the security of CC. This section introduces CC, DoS, and DDoS attacks.
## 5 Feasibility Study Through System Simulations
### Border Conditions in the Area Intended for Positioning in the Scenario  
The tiling of the scenario is based on the discretization of the positioning area into tiles of a fixed size equivalent to that of the 2D sensor array. The use of fractions of a tile is not considered in our scope; therefore, the tiling cannot be carried out with fractions of a tile but only with entire tiles.  
For the tiling, we adopt a pragmatic approach in which we assume that the borders of the area of 13 m by 5 m intended for positioning in the scenario (Section 5.1) cannot be crossed by the 2D sensor array. This is equivalent to saying that in a real scenario, the assumed area intended for positioning has certain physical barriers (e.g., walls), which cannot be physically crossed by the 2D sensor array. In this context and with the constraint of using entire tiles, the tiles cannot cross the borders of the area intended for positioning.  
For cases in which an exact number of tiles fits into the entire area intended for positioning, the whole scenario is completely covered by tiles, as exemplified in Figure 12. To be specific, in these cases fractions of tiles are not required to cover the entire area intended for positioning, or, in other words, each one of the dimensions of the area intended for positioning, 13,000 mm and 5000 mm, is a multiple of the side length of the tile. For cases in which an exact number of tiles does not fit into the entire area intended for positioning, some regions of the area intended for positioning will be uncovered, at the top or right borders or at both. That is, starting the tiling from one border, we may be not able to reach the opposite border with an entire tile. See as an example the difference between the area intended for positioning and the positioning area (inner rectangle) in the figure of the second example presented in Section 6.8.3.  
In our simulations, we set in the function \(FTileCenters()\) (Pseudocode 1) as the initial coordinates the bottom-left corner of the area intended for positioning. Then, in the tiling, the area intended for positioning is covered from left to right and from bottom to top. In the case that the tiles do not fit exactly next to the top and/or right borders, the region(s) next to the corresponding border(s) remain uncovered.  
In the testing phase, moving tiles are used. Moving tiles are constrained to using the positioning area delimited by the fixed tiles. This is achieved by using the minimum and maximum fixed tile center positions returned by the function \(FTileCenters()\) and provided as input to the function \(FMovingTiles()\) (Pseudocode 3).
## 1 Introduction
Routing path selection is the crucial factor to underpin the overall Quality of Service (QoS) network performance, with wrong routing decisions, the QoS can be worse than the orthodox cellular network [20]. Orthodox cellular networking does not need to consider the device mobility, mobile users' equipment, and dynamic network topology. Due to this, in D2D technology, energy constraints, network stability, link failure, and traffic congestion factors need to be addressed when deciding an optimal routing [21]. These factors are briefly discussed in the following section. In D2D communications, every device has a limited battery capacity. Hence, once the device battery is drained, it will shorten the establish route lifetime among the devices, which extensively impacts the overall network performances. Since the device keeps moving in the network, the network topology is dynamically changing, causing intermittent interconnectivity between the devices. Furthermore, due to the number of devices continually fluctuating unpredictably (coming in and exiting the network), it leads to the deterioration of network QoS performances [22; 23; 24; 25]. Moving on to the traffic congestion factor, when the network devices transmit their data packets to a single device, there is a chance that data packet traffic congestion occurs at that particular device. Therefore, data packets need to wait until the queue is empty on the device that induces delay during data transmission between the source and destination devices. This results in the escalation of the network capacity, which attenuates the overall network coverage. Turning now to the last factor, links failure. In normal circumstances, the mobile devices in the network are changing their position frequently, and it causes the chance of link failure for an intermediate device in the established route. Hence, the source device again needs to reselect an alternate route based on the updated network topology information, which affects the packet drop in the established route.
## 4 Proposed System Design
### Oam/Ocdma-Fso Receiver  
Figure 4 illustrates the block diagram of the receiver of the OAM/OCDMA-FSO system. The MDM demultiplexer is used at the receiver first to separate the two OAM beams; then, the received signal undergoes the decoding process that has the SPD detection to detect the desired channel.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**City** & **Alexandria** & **Srinagar** \\ \hline Visibility (km) & 9.8 & 2.9 \\ Attenuation (dB/km) & 0.1 & 2.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Visibilities and attenuations of Alexandria and Srinagar cities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Post Level** & **LD** & **MD** & **HD** \\ \hline Visibility (km) & 1–10 & 0.2–1 & \textless{}0.2 \\ \hline Attenuation (dB/km) & 25.11 & 107.66 & 297.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dust storm level with visibility and attenuation [9; 24].  
In SPD detection, the received signal first passes through the decoder then the subtractive decoder (S-dec). The decoder has the same spectral profile as that of the encoder.  
As an example, let us consider that channel #1 is the required channel, so the decoder will have these wavelengths in nm (1550, 1550.8, and 1552.4), while for s-dec, it contains wavelength 1550.8 nm, which is the interference with channel #2. Then the s-dec is subtracted from the decoder.  
The resulting signal now carries the information of the desired channel #1 and then enters the PIN photodiode detector (PD) to make the electric/optical conversion. Finally, the output received signal is transferred to a low-pass Bessel filter.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 5 Simulation Results
### Average Throughput Analysis  
Average throughput is one of the most well-known measures used in the Internet of Things to assess how well communication methods function. It is defined as the quantity of sent packets or frames that are successfully received by the target device. Figure 5 illustrates a comparison of the performance of the suggested and current ways, in terms of average throughput, and it shows that the former mechanism was the best option for IoT networks, since the highest average throughput was possible at different network densities. Additionally, several networking typologies and a non-balanced clustering method, which is typical in IoT networks, were used to gather these data. Furthermore, we found that, unlike existing schemes, which have a direct link with these metrics, the performance of the proposed scheme was unaffected by changes in the number of member devices in a given cluster. This is similar to how previous systems function; the suggested approach's performance--in this case, average throughput--was unaffected by the proportion of active devices in a given cluster. Last but not least, the suggested solution supported member device movement, which did not impair the highlighted IoT network's overall performance.
## 5 Conclusions  
One of the main conclusions of this study is that the flexibility and modularity of an interface should not be replaced by standardized models unless they are based on robust results that converge toward a concrete and concise list of priorities and module selection. The main conclusions regarding information prioritization for each mission mode are summarized in Table 14, but in order to define standardized configurations according to types of missions, roles, and scenarios, it is necessary to have a group of professionals who, in addition to being representative, are familiar with the situational awareness modules investigated in the present European Rescue project. To optimize efforts, constant interaction with the different FR profiles is recommended so that both the interface design and the surveys to adapt its configuration are aligned with the diverse realities faced by FRs in the different rescue scenarios. This will save time and resources in generating prototypes that fit the needs of the end-users. Despite the discrepancy in converging toward standardized interfaces, it is worth noting that the experience of collecting feedback from the different FRs has been very productive, since the professionals themselves contributed many ideas for improvement. Moreover, the existence of this type of tool is beyond their imagination. Therefore, this type of project helps to generate a bridge between technology developers and rescue professionals. In addition, the rescue in the dark scenario is a realistic simulation to represent low visibility circumstances, but consideration should be given to adding some factor that generates a human stress response to simulate a realistic cognitive load so that the interface can be designed more appropriately. This could be achieved by developing additional technologies, such as virtual reality crisis simulations, to test the interface design in more immersive rescue scenarios. It is also important to mention the concern shown by some respondents about the implicit disadvantages of using these types of technologies. They indicated that the use of these tools could replace human references (collected through touch, smell and other senses) and that, in the event of blows or circumstances in which the technology becomes useless, the FR could find itself in more vulnerable situations than if it had used these references. In conclusion, despite the difficulties encountered in reaching a consensus on a standardized interface, through collaborative work with FRs, it is possible to design interfaces for smart helmets that help different professionals to perform rescue operations with more information than is available to them today.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 1 Introduction
### Objective  
The purpose of this paper is to minimize the effects of jamming attacks by adding a mitigation mechanism to well-known routing protocols, which we called MPH-M, AODV-M and DSR-M.  
We describe the organization of the rest of the paper. Section 2 introduces the model implemented in this work for the jamming attack. In Section 3, we present related work. Section 4 proposes a scheme for the mitigation of jamming attacks. Section 5 discuss the anomaly model and the behavior of the variables selected for the jamming detection. Section 6 presents the results. Finally, conclusions are given in Section 7.
## 3 Results and Discussion
### Profiling of Volatile Compounds in the Three Types of Daqu  
A total of 87 volatile compounds were detected from BQ, YQ, and WQ. There were 67, 61, and 54 volatile substances detected in BQ, YQ, and WQ, respectively (Table S1). The difference in metabolic composition between the three types of _Daqu_ was analyzed by PLS-DA. According to Figure 2a, nine samples were divided into three groups according to the types of _Daqu_, indicating that there were significant differences in the composition of volatile substances. Heterocyclic compounds (pyrazines [16; 17] and furans [18]) were the main Maillard reaction products in Sauce-flavor _Daqu_. Three furans and eleven pyrazines were detected from BQ, YQ, and WQ. Pyrazines are important flavor substances in Chinese baijiu, which are baked and nutty. Among the three kinds of _Daqu_, pyrazines accounted for the highest proportion of volatile components (Figure 2b), all of which were more than 30%. The proportion of pyrazines in BQ was 34% on average, which was higher than that in WQ and YQ. Tetramethylpyrazine [19], trimethylpyrazine [20], 2,6-Dimethylpyrazine [21], etc., (Figure 2c) are active flavor substances in Chinese baijiu, which were high-concentration pyrazines in _Daqu_, especially in BQ. By calculating the VIP values of each variable in the PLS-DA model, it can be seen that tetramethylpyrazine was a marked volatile substance of BQ (VIP \(>\) 1), and the content of which in BQ is significantly higher than that in YQ and WQ. The total concentration of furans in BQ was 23.18 mg/g, which was significantly higher than that in YQ and WQ, and only 2-Pentyfuran was detected in WQ. 3-Phenylfuran and 2-Furfuraldehyde are known Maillard reaction products. Among them, 2-Furfuraldehyde is almond-scented and roasted, which is a common substance in _Daqu_ and baijiu. The VIP values of both furans were greater than 1, and their concentrations in BQ were higher than those in other _Daqu_, which were marker substances of BQ. This result is consistent with the previous research [5]. The enrichment of Tetramethylpyrazine, 3-Phenylfuran, and 2-Furaldehyde in BQ is an important factor that the baking aroma in BQ is better than that in other types of _Daqu_. In addition to heterocyclic compounds, amines, the important precursor of the Maillard reaction, and aldehydes, the intermediate product of the Maillard reaction, were also detected from three kinds of _Daqu_ (Table S1). The content of amines in WQ was highest, while that in BQ was the lowest. Among the three kinds of _Daqu_, the  
Figure 1: Contents of melanoidin (**a**), reducing sugar (**b**) and free amino acids (**c**) in three types of _Daqu_.  
concentration of aldehydes in WQ was the lowest, and that in BQ was the highest. The results all showed that the degree of Maillard reaction of BQ was the highest and that of WQ was the lowest.
# Progress in THz Rectifier Technology: Research and Perspectives
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IR & Infrared \\ THz & Terahertz \\ DC & direct current \\ EH & energy harvesting \\ Si & Silicon \\ S\&Q & Shockley-Queisser \\ EM & electromagnetic \\ Rectifying antenna & Rectenna \\ SPPs & Surface Plasmon Polaritons \\ E & electric field \\ AC & alternating current \\ LPF & Low pass filter \\ \(\eta\) & Overall efficiency \\ \(\eta_{a}\) & Antenna collection efficiency \\ \(\eta_{s}\) & Losses in the material \\ \(\eta_{c}\) & Coupling or matching efficiency \\ \(R_{A}\) & Antenna resistance \\ \end{tabular}  
\begin{tabular}{l l} \(R_{D}\) & Diode resistance \\ \(C_{D}\) & Diode capacitance \\ \(\omega\) & Operating frequency \\ \(\eta_{q}\) & Quantum efficiency \\ MIM & Metal-Insulator-Metal \\ MI\({}^{\text{n}}\)M & Metal- multi-insulator-Metal \\ GD & geometric diode \\ \(f_{c}\) & Cut-off frequency \\ EBL & electron beam lithography \\ MBE & molecular beam epitaxy \\ SEM & scanning electron microscopy \\ MOCVD & metalorganic chemical vapor deposition \\ PR & Photoresist \\ ICP & inductively coupled plasma \\ TMAH & tetramethylammonium hydroxide \\ PECVD & plasma-enhanced chemical vapor deposition \\ FOMs & Figure of Merits \\ \(\Psi\) & Work function \\ \(\chi\) & Electron affinity \\ \(\varphi\) & Barrier height \\ \(\Delta\varphi\) & Barrier width \\ \(E_{F}\) & Fermi levels \\ DT & direct tunneling \\ FNT & Fowler-Nordheim Tunneling \\ I-V & current to voltage \\ TOV & Turn-on voltage \\ ZBR & zero-bias resistivity \\ Sym & Symmetry \\ _Asym_ & Asymmetry \\ _NL_ & Nonlinearity \\ \(S\) & Responsivity \\ RC & Resistance-Capacitance \\ BG & Band gap \\ H-BN & hexagonal Boron Nitride \\ MIG & Metal-insulator-graphene \\ Gr-h-BN-Gr & graphene/hexagonal boron nitride (h-BN)/graphene heterostructure \\ ALD & atomic layer deposition \\ GBG-TD & graphene/h-BN/graphene tunneling diode \\ CVD & chemical vapor deposition \\ GrIM & Graphene-Oxide-Metal Diode \\ AAO & Anodic Aluminum Oxide \\ AP-CVD & atmospheric pressure chemical vapor deposition \\ PEALD & Plasma-enhanced atomic layer deposition \\ PMMA & polymethyl methacrylate \\ MIBK: IPA & methyl-isobutyl ketone: isopropanol \\ PVD & Physical Vapor Deposition \\ MIM & metal-insulator-insulator-metal \\ M-I & Metal-interface \\ I-I & Interface-Interface \\ I-M & Interface-metal \\ QW & Quantum Well \\ MI\({}^{2}\)M & Metal-insulator-insulator-metal \\ MI\({}^{4}\)M & metal-insulator-insulator-insulator-metal \\ \end{tabular}