esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
### Future Work
  
PoQF  
Proof-of-Quality-Factor  
PoR  
Proof-of-Reputation  
PoRe  
Proof of Reputation  
PoRep  
Proof-of-Replicated-Storage  
PoRX  
Proof-of-Reputation-X  
PoS  
Proof-of-Stake  
PoST  
Proof-of-Scalable-Traceability  
PoSv  
Proof-of-Sovereignty  
PoTN  
Proof-of-Negotiation/Proof-of-Trust Negotiation  
PoUW  
Proof-of-useful-work  
PoW  
Proof-of-Work  
PoWaP  
Proof-of-Work-at-Proximity  
PoWork  
Proof-of-Work-or-Knowledge  
PoX  
Proof-of-Exercise  
PPE  
Private Proof-of-Effort  
PPoM  
Predictive Proof of Metrics  
PRNG  
Pseudorandom number generator  
PUF  
physical unclonable function  
QoS  
Quality of Service  
RBA  
Robust Byzantine Agreement  
RBFT  
Reputation-based Byzantine Fault Tolerance  
RBitcoin  
Regulated Bitcoin  
RDV  
Register, Deposit, Vote  
ReCon  
Reputation Consensus  
RHC  
Reverse Hash Chain  
RPBFT  
Practical Byzantine Fault Tolerance Based on Reputation Value  
RSP  
Rock-Scissors-Paper  
RUW  
Reciprocally Useful Work  
SAGA-PBFT  
Security-Aware Genetic Algorithm based Practical Byzantine fault Tolerance  
SBFT  
Scalable Byzantine Fault Tolerance  
SDMA-PBFT  
Scalable Dynamic Multi-Agent Byzantine Fault-Tolerance  
SeBFT  
Scalable Efficient Byzantine Fault Tolerance  
SENATE  
Sybil-Proof Wireless Network Coordinate Based Byzantine Consensus  
SharPer  
Sharding Permissioned Blockchains Over Network Clusters  
SHBFT  
Scalable Hierarchical Byzantine Fault Tolerance  
SMP  
Synergistic Multiple Proof  
SodsBC  
Stream of Distributed Secrets for Quantum-safe Blockchain  
TCNS  
Twice Verifications and Consensus of Blockchain  
TEE  
Trusted Execution Environment  
UAMNET  
unmanned aerial vehicle ad hoc network  
VRF  
Verifiable random function  
WBFT  
Weight of Authentication Byzantine Fault Tolerance  
WMCA  
Weighted Majority Consensus Algorithm
## 2 Materials and Methods
### Neural Networks Architectures for TEC Forecasting
#### 2.2.3 Temporal Convolutional Neural Networks  
Convolutional neural networks have become widespread in solving image processing problems and have also been successfully applied to time series modeling. It has been shown that convolutional neural networks are able to give results comparable to recurrent neural networks or even surpass them. At the same time, they have more flexibility in meta-parameters management and also have a higher performance in terms of computation time.  
The temporal convolutional network consists of a set of dilated causal convolution layers that are processing time steps of each input sequence.  
The common approach for 1 d temporal convolution implementation is using a stack of consecutive residual blocks including several dilated convolutional layers and an arbitrary selection of utility, normalization, and activation layers, supplemented by an optional convolution skip connection.  
Assuming that \(d\) is the dilation factor, \(x\) is the input sequence, \(s\) is the element of the input sequence, \(f\) is the applied filter, \(k\) is the filter size, and \(s-d\cdot i\) represents the dilation in the backward direction, the 1D dilated convolution can be represented as follows:  
\[F(s)\ =\ (x*_{d}f)(s)\ =\ \sum_{i\ =\ 0}^{k-1}f(i)\cdot x_{s-d\cdot i}. \tag{11}\]  
Depending on the dilation factor, this equation can be reduced either to the usual convolution when \(d=1\) or can be used to increase the receptive field of a neural network by introducing different time steps between filter passes.
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Quantitative Evaluation
#### 4.3.4 Natural Image Quality Evaluator (NIQE)  
In image quality evaluation, sometimes the evaluation result index of PSNR and SSIM is very high, but the viewing effect of human eyes could be better. Especially in recent years, following the introduction of GAN networks, in image super-resolution reconstruction tasks, the higher the PSNR and SSIM are, the lower the super-resolution reconstruction effect is. In addition, PSNR and SSIM require reference images to calculate, but in practical applications, the corresponding high-quality reference images cannot be obtained. Therefore, NIQE [31] was proposed as an indicator of image reconstruction quality. It extracts a series of features from high-quality natural images and then uses them to fit a multivariate Gaussian model. This Gaussian model is then used to measure the difference in multivariate distribution between the image and these features, and the image quality is estimated through this difference. The smaller the value of NIQE, the better the perceived quality of the image. The NIQE algorithm has better prediction stability, monotonicity, and consistency. It has better consistency with the subjective quality evaluation of the human eye, is closer to the human visual system, and can effectively evaluate image quality.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
###### Abstract  
The smart factory environment has been transformed into an Industrial Internet of Things (IIoT) environment, which is an interconnected and open approach. This has made smart manufacturing plants vulnerable to cyberattacks that can directly lead to physical damage. Most cyberattacks targeting smart factories are carried out using malware. Thus, a solution that efficiently detects malware by monitoring and analyzing network traffic for malware attacks in smart factory IIoT environments is critical. However, achieving accurate real-time malware detection in such environments is difficult. To solve this problem, this study proposes an edge computing-based malware detection system that efficiently detects various cyberattacks (malware) by distributing vast amounts of smart factory IIoT traffic information to edge servers for deep learning processing. The proposed malware detection system consists of three layers (edge device, edge, and cloud layers) and utilizes four meaningful functions (model training and testing, model deployment, model inference, and training data transmission) for edge-based deep learning. In experiments conducted on the Malimg dataset, the proposed malware detection system incorporating a convolutional neural network with image visualization technology achieved an overall classification accuracy of 98.93%, precision of 98.93%, recall of 98.93%, and F1-score of 98.92%.  
smart factory; IIoT; edge computing; malware detection; deep learning; cyberattack; cybersecurity
## 3 Results and Discussion
### Application in Intelligent Cockpits  
In recent years, automobiles have been gradually transforming into intelligent terminals, and the concept of \"intelligent cockpits\" has been proposed. The user's \"emotional experience\" has also been given increasing importance by car manufacturers. From the perspective of textiles, interior components undoubtedly serve as excellent carriers for human-machine interaction. With the innovative development in the automotive industry, interior lighting has been endowed with important functions for human-machine interaction. It can provide users with a pleasant driving experience through adjustments in color, brightness, and the application of rhythmic patterns. It can also enhance the user's sense of involvement and belonging through customization, emotional linkage, and intelligent interaction.  
Therefore, this developed pressure-sensing OP fabric holds great potential in the field of intelligent cockpits. Users can directly control the luminescent component or interact with other interior components by touching such OP fabric components, thus achieving more diverse interaction modes. Figure 13 simulates the effect of applying the luminescent OP fabric in automotive interior components.
## 4 Discussion
### Limitations/Trade-Offs with the Proposed Reconfiguration Method
#### 4.2.2 Trade-Offs  
While integrating adaptive panels increases the initial cost, the long-term increase in energy production can offset this expense. Additionally, the use of semiconductor switching elements in SMC requires no maintenance, enhancing system reliability.
# Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks +
## 4 Proposed Solution  
### Reduction in Beam Coverage  
Section 3.1 calculated the NB-IoT receiver range. Currently, this study's residual Doppler shift that needs to be compensated for is 2100 Hz [21]. This study proposes methods for solving the problem. The proposed method sets the beam diameter to be smaller than the beam diameter suggested by 3 GPP. Although this method is simple, the benefits of satellites are lost as the coverage size becomes smaller. Therefore, proper size is essential. This study expresses the relationship between beam coverage and residual Doppler shift as an equation. In addition, beam coverage that satisfies 950 Hz or lower is calculated through satellite orbit simulation.  
Equation (5) described in Section 2.3 is expressed in detail as follows:  
\[f_{residual} =f_{reference}-f_{edge}\] \[=f_{c}\times\frac{\upsilon}{c}\times|(\cos\theta_{1}-\cos\theta_ {2})|, \tag{10}\]  
where \(\theta_{1}\) is the angle between the satellite and the reference position. \(\theta_{2}\) is the angle between the satellite and the target UE. The maximum residual Doppler shift occurs when the angle formed with the reference position is 90 degrees. The equation for calculating the beam coverage is as follows:  
\[f_{max} \geq f_{c}\times\frac{\upsilon}{c}\times|(-\cos\theta_{2})|,\ \theta_{1}=90^{\circ}\] \[\geq f_{c}\times\frac{\upsilon}{c}\times\frac{R}{\sqrt{R^{2}+h_{0 }^{2}}}\] \[R \leq\sqrt{\frac{h_{0}^{2}}{\frac{\upsilon\times f_{c}}{c\times f_{ max}}^{2}-1}}, \tag{11}\]  
where \(R\) is the radius of the beam coverage, \(h_{0}\) is the satellite's altitude, and \(f_{max}\) is the maximum residual Doppler shift. Equation (11) is calculated assuming that the Earth and the satellite are in two parallel lines.  
This paper calculates the maximum coverage size the NB-IoT receiver can compensate for through simulation. Figure 9 shows the simulation results. Figure 9a shows the results of the residual Doppler shift in each target UE. During the simulation time, the residual Doppler shift is satisfied [0.95 \(-\)0.95], and the beam diameter is 30 km. Figure 1b shows the target position applied in Figure 9a.
# Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
## 2 Materials and Methods
### Feature Extraction with Fast Fourier Transform (FFT)  
The feature represents a differentiating property or an operative component identified in a section of a pattern, and a recognizable measurement. Feature extraction is a critical step in EEG signal processing. Consequently, minimizing the loss of valuable information attached to the signal is one of the goals of feature extraction. Additionally, feature extraction decreases the resources required to describe a vast set of data accurately. When carried out successfully, feature extraction can minimize the cost of information processing, reduce the complexity of data implementation, and mitigate the possible need to compress the information [23].  
The extraction of informative statistical features from the EEG signal is necessary to perform sleep stage classification efficiently. In general, the EEG signal is highly complex and non-linear, so it would be better to use a non-linear model [24]. In this study, the fast Fourier transform (FFT) is utilized to extract the features of EEG signal for sleep stage classification. Hence, the values of a given time-series data as a numeric sequence data are converted into a finite set of the frequency domain. Then, to deconstruct signals into segmented EEG signal sequences, we divided them into equal time intervals called epochs. The length of each epoch was set to every 30 s of EEG signal. Accordingly, the epochs were then processed using frequency analysis in which frequency spectra were generated using FFT. We used FFT to convert a signal from its original, time domain signal to a representation in the frequency domain signal [25]. Figure 3 represents in the form of time-domain signal and frequency-domain signal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\# of Classes** & **AWA** & **REM** & **S1** & **S2** & **S3** & **S4** \\ \hline
6 & 74,827 & 11,848 & 4,848 & 27,292 & 5075 & 3773 \\ \hline
5 & 74,827 & 11,848 & 4,848 & 27,292 & 8848 \\ \hline
4 & 74,827 & 11,848 & 32,140 & 8848 \\ \hline
3 & 74,827 & 11,848 & 40,988 & \\ \hline
2 & 74,827 & & & 52,836 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of samples in Sleep-EDF dataset (R & K criteria).  
Previous studies have shown that FFT is a promising tool for stationary signal processing, and enjoys a speed advantage over virtually all other available methods in real-time applications, and it is more appropriate for sine waveforms such as in EEG signals. However, the disadvantage is that it does not have excellent spectral estimation and cannot be employed for analysis of short EEG signals [23].
## 3 Results  
### Geometry Analysis  
The optical scanning result of the vase was digitized into a high-resolution mesh model with an average point spacing of 0.21 mm and 4,338,126 polygons. The dimensions of the model were 133 mm (width) \(\times\) 133 mm (length) \(\times\) 213 mm (height). The surface texture and detailed patterns of the vase were well expressed, and glaze cracking caused by the firing process was clearly visible (Figure 5a). The RGB texture was mapped after removing the diffuse reflection areas, and the real colors were well represented (Figure 5b). Despite showing a precise surface shape, the optical scanning model was unable to represent the geometry of areas where light could not reach. As observed in the 3D cross-sectional view (Figure 5c), the thickness and internal shape of the vase were not recorded.  
Hence, X-ray CT scanning was performed to record the internal shape. The X-ray CT model showed the internal thickness and shape information effectively, compensating for  
Figure 4: Aligning points on the spout for the registration accuracy.  
Figure 5: Optical scanning results of prunus vase replica: (**a**) 3D polygon mesh model, (**b**) RGB texture mapping model, and (**c**) cross-sectional view.  
the limitation of optical scanning. Based on thickness measurements through sampling analysis, the spout thickness was determined to be 4.6 mm and the upper, middle, and lower body parts were 3.9, 5.2, and 9.5 mm thick, respectively, with a tendency of thickening downward (Figure 6a,b).  
Next, acquired X-ray CT data were converted into a mesh model based on the surface determination (i.e., thresholds) between the material and air layers (Figure 6c). This data conversion process resulted in the inclusion of 569,791 polygons in the X-ray CT mesh model, with an average point spacing of 0.80 mm. In particular, this model had a closed structure with no unscanned areas throughout, providing sufficient surface area (147,987 mm\({}^{2}\)) and volume (395,123 mm\({}^{3}\)) information.  
As X-ray CT reconstructs a 3D model using tomographic images, it has the disadvantage of low precision and poor material texture representation of the external surface. As shown in the 3D voxel and polygon mesh models acquired from the X-ray CT data, the glaze cracking and inlaid patterns, which were well visualized in the optical scanning model, were not represented well. In addition, in the curvature mapping results, the optical scanning model exhibited clear changes in the curvature values depending on variations in the surface microtexture (Figure 7a). The X-ray CT model showed a considerably lower curvature range of \(\pm\)0.41 and a near-zero curvature for the body, which indicated that the surface features were not well represented (Figure 7b).  
Figure 6: X-ray CT scanning results of the prunus vase replica: (**a**) 3D voxel model, (**b**) measuring the thickness via sampling analysis, and (**c**) converting the acquired data to a mesh model.  
Figure 7: Curvature mapping results obtained using (**a**) optical scanning model and (**b**) X-ray CT model.
## 4 Energy Consumption Modeling
### System Model  
Finally, the relationship between path trajectories and the FPTLIR energy consumption is established after a series of modelling derivations. The transfer of relationships between the sub-models is shown in Figure 5.  
The power of a single rotor motor can be expressed by Equation (19).  
\[p(t)=U_{l}(t)I_{l}(t) \tag{19}\]  
where \(p(t)\) is the power of a single rotor motor. The total energy consumption of the FPTLIR during the flight time \(T\) can be determined by Equation (20).  
\[W(t)=\int_{0}^{T}P(t)dt \tag{20}\]  
where \(W(t)\) is the total energy consumed by the FPTLIR during the flight time \(T\), and \(P(t)=\sum\limits_{i=1}^{6}p_{i}(t)\) signifies the aggregate power generated by all six rotor motors of the FPTLIR.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 4 The Adaptive Polling Medium Access Control (APMAC) Protocol
### Data Transmission Phase  
Data transmission phase follows the association phase. In this phase, actual data transmission between VLAP and VLNs occurs. The VLAP starts receiving DTF from each VLN that issued ARF. Each VLN transmits DTF within its specific time interval as stated in the AGF. DTF contains the physical address of the VLAP and VLN, payload, and the remaining buffer size at the VLN. If the remaining buffer size at the VLN is zero, then the VLAP knows that there is no more data to be transmitted. Moreover, if the buffer size in the DTF is greater than zero, it means there is more data to be transferred. In this case, the VLAP calculates the next time slot for this payload based on the next time-slot available. VLAP replies to the VLN with the DAF in which it specifies the next allowed MTU that the VLN can transmit and the slot time in which it can perform next data transfer. VLAP updates its polling table by changing the new RTT and buffer size for each VLN.
## Materials and Methods
### Room Segmentation
#### 3.3.2 Room-Space Segmentation
The working details are shown in Algorithm 2. Figure 5: Results of room-space segmentation with different upper thresholds. (**a**) Results when the upper threshold is 5 in this dataset. (**b**) Results when the upper threshold is 25 in this dataset. (**c**) Results when the upper threshold is 50 in this dataset. (**d**) Results when the upper threshold is 100 in this dataset. Figure 6: Offset space for closing doorways. Figure 7: Offset space for discerning an open door from an opening in a wall. The thresholds are chosen by the room size in the data. The upper threshold is approximately the size of the largest room, while the lower threshold is approximately the size of the smallest room. Two thresholds are set to 20 and 70 in the current dataset. Input: _binary image_: generated binary image after projecting point clouds on one floor  
maxerode: iterations  
_lower threshold_: lower limit of area  
_higher threshold_: higher limit of area  
_cell size_: size of one cell  
Initialize: \(labels\leftarrow\varnothing\); // the zero set, which has the same size as the _binary image_  
\(Outmap\leftarrow\varnothing\); // resegmentation result  
count = 0;  
for \(i=1\) to maxerode  
_binary image_ = erode(_binary image_, strel ('disk',1));  
_region_ = edge detection on _binary image_;  
for each \(\alpha\in region\)  
_room area = cell size \({}^{\star}\) cell size \({}^{\star}\)_ area of \(\alpha\);_  
if _room area > lower threshold&&room area < higher threshold_  
_labels_ in \(\alpha\) = count;  
count++;  
end if  
end for // \(labels\) shown in Figure 4b  
for \(i=1\) to unique(_labels_)  
if \(labels_{i}\) is surrounded by \(labels_{j}\)  
\(labels_{i}=j\);  
end if  
end for // \(labels\) shown in Figure 4c  
\(Outmap\) = wavefront algorithm on _labels_; // \(Outmap\) shown in Figure 4d  
Return \(Outmap\)  
Figure 8: Room-space segmentation. (**a**) Binary image after cutting off connections between rooms via projecting the offset space onto the x-y plane. (**b**) Labeled regions. (**c**) Results after merging. (**d**) Final results after the wavefront algorithm.
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### Correlation Analysis of Research Variables  
The results of the correlation analysis are shown graphically, in the form of a correlogram in Figure 4. Each value of the correlation coefficient in the interval between \(-1\) and \(1\) is represented by a certain shade of blue for positive and red for negative coefficients. Based on Figure 4, it can be concluded that, according to the model for estimating the \(QoE_{i}\) value, the strongest correlation (by absolute value) with user ratings of the quality of experience has transitional variables used to define satisfaction indicators D (0.41) and dissatisfaction indicators C (\(-0.55\)). According to the correlogram, the highest correlation exists between variables X\({}_{6}\) and X\({}_{7}\) and is 0.63, and then between X\({}_{5}\) and X\({}_{9}\), with a coefficient of 0.43. Additionally, it can be noted that a correlation equal to zero exists between the independent variables X\({}_{1}\) and X\({}_{6}\) and between X\({}_{1}\) and the dissatisfaction indicator C.