esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 5 Experiment Design and Results Analysis
### Experiments and Results
#### 5.3.3 ECG-PPG Fusion Signal Based Authentication
The results of the multiuser ECG-PPG Fusion experiments are presented in Table 9. Multiple LSTM training configurations are used as mentioned in Table 9. It is observed that when the user group member size is increased, the accuracy decreases. However, when the window sample size is increased from two heartbeats to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline KNN Fine (3 Usr) & 70.2\% & 13.8\% & 79.0\% & 74.5\% & 0.21 \\ \hline Ensemble Bagged Trees (3 Usr) & 52.4\% & 32.7\% & 93.0\% & 67.5\% & 0.07 \\ \hline Naive Bayes (3 Usr) & 56.5\% & 6.9\% & 63.9\% & 60.1\% & 0.36 \\ \hline SVM (3 Usr) & 48.4\% & 17.6\% & 87.5\% & 62.8\% & 0.13 \\ \hline GAM (3 Usr) & 77.4\% & 28.2\% & 90.1\% & 83.6\% & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: PPG Result Details—Multiuser Authentication. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline KNN Fine & 70\% & 76.7\% & 98.4\% & 82.1\% & 0.15 \\ \hline Ensemble Bagged Trees & 19.5\% & 61.5\% & 98.5\% & 32.6\% & 0.40 \\ \hline Naive Bayes & 97.6\% & 29.9\% & 96.2\% & 96.9\% & 0.03 \\ \hline SVM & 0.0\% & 0.0\% & 98.4\% & 0\% & 0.50 \\ \hline GAM & 97.6\% & 44.4\% & 98.0\% & 97.8\% & 0.02 \\ \hline CNN (2 HB 1 Usr) & 97.2\% & 92.1\% & 99.8\% & 98.5\% & 0.21 \\ \hline LSTM (2 HB 1 Usr) & 94.4\% & 97.1\% & 99.8\% & 97.1\% & 0.16 \\ \hline CNN (8 HB 1 Usr) & 85.7\% & 73.2\% & 99.2\% & 92.1\% & 0.85 \\ \hline LSTM (8 HB 1 Usr) & 97.1\% & 94.4\% & 99.8\% & 98.5\% & 0.16 \\ \hline CNN (16 HB 1 Usr) & 88.2\% & 40.5\% & 97.4\% & 92.7\% & 2.56 \\ \hline LSTM (16 HB 1 Usr) & 70.6\% & 60.0\% & 98.6\% & 82.5\% & 1.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ECG-PPG Fusion Result Details—Single User Authentication. 8 heartbeats, the accuracy and EER change rate are changed to a negligible amount. It indicates that the model trained with LSTM and a minimum of two HBs can provide an excellent authentication scheme.
# Novel Algorithm for Multi-Time Data Implantation in a Special Cyber-Manufacturing Architecture
## 6 Result Analysis and Discussions
### Payload Comparison  
The payload was determined by the number of bits that a scheme implants in a DNA or RNA sequence. In our experiment, the proposed scheme implants in about \(N_{r}\) nucleotides. Therefore, at each cycle, the scheme implants \(|N_{r}|\times 2\) bits and finally, payload  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Genome Bank** & **No. of Nucleotides** & **Error Rates in \%** \\ \hline MN908947.3 & 29,903 & 0 \\ \hline NC045512.2 & 29,903 & 0 \\ \hline MW738481 & 29,847 & 0 \\ \hline OU055787.1 & 29,817 & 0 \\ \hline AC153526 & 200,117 & 0 \\ \hline OurDNA.1 & 100,000 & 0 \\ \hline OurDNA.2 & 10 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Error rate of proposed method.  
Figure 6: Checking the reversibility of the proposed method. Here, we first save the cover and stego nucleotides in two different files. Then, we rebuild the cover DNA sequence from the stego DNA sequence using our proposed algorithm. The rebuilt DNA sequence is saved to another file. The DOS operating system has a special function named fc, which is used to compare two files. We used the fc command to compare the files containing cover and rebuilt cover DNA sequences. The fc command encountered no differences. That means that the two files contain similar information. Thus, proof of reversibility is obtained.  
tallies to \((|N_{r}|\times 2)\times c\) for \(c\) time of data embedment, as showed in Equation (8). Again, as in Equation (9), capacity is a measure of bits that could be implantable per nucleotide.  
\[Payload=(|N_{r}|\times 2)\times c \tag{8}\]  
\[Capacity=\frac{Payload}{Total\ Nucleotides}=\frac{(|N_{r}|\times 2)\times c}{|N_{r}|}=2c \tag{9}\]  
where \(|N_{r}|\) stands for the number of cover nucleotides and \(c\) indicated the number of embedment cycles.  
Table 13 presents the results of payload. Our method strongly dominates the other competing schemes. The same result is noted at Figure 7. Even single cycle execution \((c=1)\) also implants a double of the [30] and three times of [31; 32]. Every higher cycle increases that payload amount to twice that of the one immediately before.
# Human Factors Analysis of the Improved FRAM Method for Take-Off Quality Lateral Shift
## 1 Introduction  
The take-off stage of an aircraft is the most accident-prone stage next to the landing stage. When the aircraft is granted a release permit, for the pilot, the environmental factors that affect takeoff are reaching their ability range standards. Therefore, the technical level of the pilot is another important standard for measuring the take-off quality of aircraft. The safety of the aircraft in the take-off phase is crucial to the safe operation of the whole flight. As important factors affecting the take-off quality in the take-off phase, human factors should be given sufficient attention. According to statistics, 65% of flight accidents occur in the take-off or landing phase [1]. From the perspective of the flight crew, the control of multiple navigation elements, such as speed, heading, braking, and altitude, during take-off has more stringent requirements and has a resonance response to multiple external environmental factors. Among the human factors that affect the take-off quality in the take-off stage, the pilot's status and operation are difficult to study and analyze through quantitative methods, while the commonly used qualitative analysis methods are highly dependent on subjective evaluations by analysts and experts and lack objectivity.  
The FRAM model is a network framework established by the upstream and downstream relationships between system sub-modules [2]; the variation in one sub-module has an impact on the other sub-modules, which can lead to unsafe events that cause the functional resonance of the entire system. However, the traditional FRAM method only uses the flight data collected in the current single open-loop analysis, which is only effective for the analysis of this event, and the analysis results are not universal for similar events; the results of this analysis and experience cannot be fully applied to other similar scenarios. Therefore, an improved FRAM method is proposed in this paper, which sets up a corresponding safety barrier through the resonance relationship between the sub-modules of the system and replaces the output result with the improved FRAM cycle. The validity of the output results is verified by using historical data and the new iteration's results.  
This study focused on the analysis of the influence of the pilot's control ability on the take-off quality by constructing a take-off quality analysis model. The process involved building different scenarios in the simulation environment, collecting the aircraft take-off data, and looking for the mutated function module through the relationship between the data and the safety envelope and then through the resonance effect of the correlation between the mutated module and the function module to determine the problems affecting the take-off quality and to establish safety barriers from the aspects of the system and human factors to reduce the take-off safety risk.
## 2 Methods
### Vehicle-Selection Criteria and Test Cycle
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**ID *} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Fuel**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Displacement (cc)**} & \multirow{2}{*}{**Engine**} & \multirow{2}{*}{**Mileage (km)**} & \multirow{2}{*}{**After-Treatment**} & **Emission** \\  & & & & & & & & & **Device** & **Standard** \\ \hline
1 & G1 & Passenger & Gasoline & 2015 & 1999 & MPI \({}^{\text{b}}\) & 44,238 & TWC \({}^{\text{c}}\) & ULEV \\
2 & G2 & Passenger & Gasoline & 2015 & 2999 & GDI \({}^{\text{d}}\) & 69,057 & TWC & ULEV \\
3 & G3 & Passenger & Gasoline & 2016 & 1991 & GDI & 25,944 & TWC & ULEV \\
4 & G4 & Passenger & Gasoline & 2015 & 1591 & GDI & 24,570 & TWC & ULEV \\
5 & L1 & Passenger & LPG & 2014 & 2999 & LPI \({}^{\text{e}}\) & 87,606 & TWC & ULEV \\
6 & L2 & Passenger & LPG & 2015 & 1519 & LPI 142,492 & TWC & ULEV \\
7 & L3 & Passenger & LPG & 2015 & 2999 & LPI & 70,324 & TWC & ULEV \\
8 & D1 & Truck & Diesel & 2016 & 2497 & DI \({}^{\text{f}}\) & 11,239 & DOC \({}^{\text{g}}\), DPF \({}^{\text{h}}\) & EURO 5 \\
9 & D2 & Passenger & Diesel & 2016 & 2199 & DI & 15,606 & DOC, DPF, LNT \({}^{\text{i}}\) & EURO 6 \\
10 & D3 & Passenger & Diesel & 2016 & 1582 & DI & 37,018 & DOC, DPF, LNT \({}^{\text{i}}\) & EURO 6 \\
11 & D4 & SUV & Diesel & 2002 & 1991 & DI & 183,472 & DOC & EURO 3 \\ \hline \hline \end{tabular}  
* Vehicle IDs beginning with G, D, and L indicate gasoline, diesel, and LPG fueling, respectively; \({}^{\text{b}}\) MPI: Multi Port Injection; \({}^{\text{c}}\) TWC: Three-way Catalyst; \({}^{\text{d}}\) GDI: Gasoline Direct Injection; \({}^{\text{e}}\) LPI: Liquid Phase Injection; \({}^{\text{f}}\) DI: Direct Injection; \({}^{\text{g}}\) DOC: Diesel Oxidation Catalyst; \({}^{\text{h}}\) DPF: Diesel Particulate Filter; \({}^{\text{i}}\) LNT: Lean NOx Trap.  
\end{table} 
Table 2: Specifications of vehicles considered in this study. NIER-09 (average speed: 34.1 km h\({}^{-1}\)), and NIER-12 (average speed: 65.4 km h\({}^{-1}\)) to assess driving conditions within metropolitan cities (Figure 1 and Table 3). Emission measurements during selected chassis-dynamometer tests were performed at ambient temperatures in the range of 20-27 \({}^{\circ}\)C and relative humidity of the order of 40-45% to minimize the effect of variations in the surrounding environment on emissions. Prior to performing measurements, the condition of each test vehicle was evaluated using an On-board Diagnostics (OBD) scanner. Diesel vehicles were evaluated for PM loading within DPFs to assess the need for regeneration. If regeneration was deemed necessary, the same was artificially induced, and gaseous species and particulate matter analyzers were employed to confirm complete regeneration.
## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Technique used** & **Accomplishment** & **Deficiency** & **Year** \\ \hline
[19] & Cooperative Cooperative-fashion algorithm, the best forwarder node is select by considering the lowest distance form the desired movable sink node, used sink mobility to reduce the latency and maintain a balance network. & Balance energy consumption, enhance the network throughput, decrease latency. & Select the channel link blindly which reduce the PDR. & \multirow{2}{*}{2016} \\ \hline
[20] & Non-cooperative algorithm, the best forwarder node selects by considering the node ID, depth information, and and the number of neighbors, used metric function for delay minimization. & Balance energy consumption, excess minimum energy, decrease latency. & Decrease the network reliability and maximum information lost due to packet loss. & \multirow{2}{*}{2016} \\ \hline
[21] & Non-cooperative region region-based protocol, best forwarder node is selected through nodes ID, residual energy and SNR value, used RSSI for localization more track technique is utilized for path establishment. & Ensure maximum packet reached to the sink node, improve the PDR, and hold a reliable network. & Exhaust maximum energy in the network, nodes died quickly. & Exhaust maximum energy in the network, nodes died quickly. & \multirow{2}{*}{2017} \\ \hline
[22] & Non-cooperative rectangular region region-based scheme, used two mobile sinks, best forwarder node is chosen by considering the nodes ID and nodes coordinates. & Ensure maximum packet received at the sink node, improve the network PDR, and and balance energy consumption. & The scheme has no mechanism to check the accuracy of the received information. & \multirow{2}{*}{2017} \\ \hline
[23] & Cooperative Cooperative-based routing algorithm, relay and destination nodes are chosen by considers the values minimum depth information and residual energy, the fuzzy logic technique is applied to balance energy. & The energy cost is minimum, increase the network lifespan, improve the link quality. & Absence of packet analyzing appliance, decrease network accuracy. & \multirow{2}{*}{2017} \\ \hline
[24] & Present two schemes non-cooperative and non-cooperative protocols, the best forwarder nodes are selected through a function of maximum remaining energy, less number of fewer hops, and and lowest BER. & Reduce the exhaust energy, balance energy consumption, improve the reliability. & Provide high latency during packets transmission. & \multirow{2}{*}{2018} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the existing reliable protocols.
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 1 Introduction
To analytically evaluate the performance of our proposed location verification scheme, we derive the typical two performance metrics and the statistical performance is analytically established. * Extensive numerical results are provided to demonstrate that the proposed scheme achieves desired performance. Numerical results are used to further show how the system parameters can affect the statistical performance. The remainder of this paper is organized as follows. Section 2 introduces the concerned system model and problem formulation. Sections 3 and 4 present the proposed location verification scheme and performance analysis, respectively. Section 5 shows the numerical results and performance evaluation. Finally, Section 6 concludes this paper. Notation: \((\cdot)^{H}\), \((\cdot)^{T}\), \((\cdot)^{*}\), \((\cdot)^{-1}\), \((\cdot)^{\dagger}\) denote the conjugate-transpose, transpose, conjugate, inverse and pseudo-inverse of a matrix, respectively. The \((m,n)\)-th element of a matrix \(\mathbf{H}\) is \(\mathbf{H}_{m,n}\). \(\text{diag}(a_{1},...,a_{N})\) is a diagonal matrix with \(a_{1},...,a_{N}\) on the main diagonal. \(\text{vec}(\mathbf{H})\) is the vectorization of matrix \(\mathbf{H}\). \(\mathbf{tr}(\cdot)\) and \(\triangleq\) represent the matrix trace function and definitions, respectively. \(\mathbb{E}\{\cdot\}\) represents the expectation operator. \(||\cdot||_{2}\) is the Euclidean norm operator. A Gaussian random variable \(x\) with mean \(\mu\) and variance \(c\) can be represented by \(x\sim\mathcal{N}(\mu,c)\). If let \(\langle\mathbf{X}\rangle\) be the subspace spanned by the columns of matrix \(\mathbf{X}\), then \(\mathbf{P}_{\mathbf{X}}\) denotes the orthogonal projection matrix onto \(\langle\mathbf{X}\rangle\), that is, \(\mathbf{P}_{\mathbf{X}}=\mathbf{X}(\mathbf{X}^{H}\mathbf{X})^{-1}\mathbf{X}^{H}\).
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
### Context and Related Works
A complete cryptographic proof of post-quantum security is proposed as future work. Therefore, with regard to the quantum component of the current approach, we focus only on quantum-inspired cryptography. Recent advances in quantum-inspired cryptography have demonstrated increasing experimental feasibility. For example, [PERSON] et al. [37] provide a practical exploration of the B92 protocol using pulsed lasers, while [PERSON] et al. [38] analyse the convergence of fibre- and satellite-based QKD networks. These developments motivate hybrid models such as QPIF.
## 2 Mathematical Model  
Consider the unsteady magnetohydrodynamic (MHD) flow of an Oldroyd-B fluid near an infinitely long plate with heat transfer under Newtonian heating. Suppose that the external magnetic forces act along the normal direction of the movement of the fluid and that the fluid is also electrically conducted. The impact of the thermal radiation is parallel to the plate but is assumed to be insignificant--the opposite is true in the horizontal direction. Velocity is considered at any point that does not only depend upon the radial distance in the proposed problem but also on the horizontal direction, i.e., the x distance. Thus, the flow in this problem is a two-dimensional flow. First of all, it is presumed that a system with no movement is a system under rest conditions. After a short interval of time, the fluid starts to move due to mixed convection, and the fluid flows along the plate as illustrated in Figure 1.  
Figure 1: Geometry of the considered model. The configured flow equations for an Oldroyd-B fluid model are given by [56; 57]  
\[\
abla.V=0, \tag{1}\]  
\[\
abla.T+\rho g+J\times M=\rho\bigg{[}(V.\
abla)V+\frac{\partial V}{\partial t} \bigg{]}. \tag{2}\]  
where \(V\), \(J\), \(T\), \(M\), \(\rho\), \(\rho g\) and \(t\) are the parameters used in the above equations to denote the velocity field, electric density, Cauchy stress tensor, total magnetic field (having both induced and imposed forces), fluid density, body force and time, respectively. The relation between the Cauchy stress and extra stress tensor is represented by \(T\) and \(S\), respectively, for the Oldroyd-B fluid formulated as  
\[S = pI+T, \tag{3}\] \[\bigg{(}S+\lambda_{1}\frac{\partial S}{\partial t}\bigg{)} = \mu\bigg{(}1+\lambda_{2}\frac{\partial}{\partial t}\bigg{)}A_{1},\] (4) \[A_{1} = (\
abla V)^{T}+(\
abla V), \tag{5}\]  
where \(\lambda_{1}\), \(p\), \(I\), \(\mu\), \(\lambda_{2}\), \(- pI\frac{D}{\partial t}\) and \(A_{1}\) denote the relaxation time, pressure, identity tensor, dynamic viscosity, retardation time, tensor's indeterminate part, convective time derivative and Rivlin-Ericksen tensor, respectively. Furthermore, the Maxwell equations for the electric and magnetic fields are written in the following form:  
\[\Delta.M=0,\quad\Delta\times E=-\frac{\partial M}{\partial t},\quad\Delta \times M=\mu_{m}I,\quad J\times M=-\sigma M_{0}^{2}V. \tag{6}\]  
where \(J\) represents the electric field, \(M\) is denotes the magnetic field, \(\sigma\) represents the fluid's electrical conductivity and \(\mu_{m}\) denotes the magnetic permeability. Furthermore, \(M=M_{0}+M_{1}\), in which \(M_{0}\) and \(M_{1}\) denote the imposed and induced magnetic fields, but the induced magnetic field is not considered herein. In the present work, the velocity field is considered in the following form:  
\[V=V(y,t)=w(y,t)i\quad\text{and}\quad S=S(y,t). \tag{7}\]  
where \(i\) and \(w\) represent the unit vector in the x direction and the x component of velocity \(V\), respectively.
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### IoT and CDS Impact on Business Models
#### 4.4.6 Enhanced Customer Experiences  
IoT and Distributed Computing Systems enable personalized and responsive interactions, thereby enhancing customer experiences. IoT machines could collect detailed data, for instance on client preferences, which allows product tailoring [74]. Distributed systems support these personalized experiences by processing and analyzing the information collected by IoT machines. They enable on-time personalization, where services and recommendations are dynamically adjusted based on current data [75]. For instance, a store can check client itineraries through IoT sensors. At the same time, distributed systems can analyze this data to provide personalized offers and product recommendations [76]. This level of personalization enhances customer satisfaction and loyalty by making interactions more relevant and engaging.
# Crop Land Change Detection with MC&N-PSPNet
## 3 Results
### Ablation Experiment  
To verify the effectiveness of the backbone feature extraction network before and after the replacement, the improvement of the replaced MobileNetV2, and the addition of the NAM module on the efficiency and accuracy improvement of the model, ablation experiments are conducted in this paper, and the results are shown in Table 3.  
As can be seen from Table 3, after replacing the backbone feature extraction network ResNet50 with MobileNetV2, the overall accuracy is improved by 3.37%, the Kappa coefficient is increased by 0.0729, and the training time is accelerated by 0.589h; after improving the backbone network and adding the CBAM module, the overall accuracy is improved by 3.51% and the Kappa coefficient is increased by 0.0762; after adding the attention mechanism NAM, the extraction accuracy is improved by 3.62%, and the Kappa coefficient is improved by 0.0785; after improving the backbone feature extraction network and adding the NAM module after feature fusion, the overall extraction accuracy is improved by 3.79%, the Kappa coefficient is improved by 0.0816, and the training time is accelerated by 0.413 h.
## 4 The Proposed Method
### BiLSTM Network  
After [PERSON] et al. (2016) proposed deep belief network (DBN) in 2006, deep learning has entered a stage of rapid development. Currently, recurrent neural networks (RNN) are the most commonly used neural network model. An RNN is a neural network used to process sequence data. However, RNNs cannot solve the long-term dependency problem. Therefore, in order to enhance the performance of RNNs, researchers have proposed many different methods.  
Long short-term memory (LSTM) ([PERSON] et al., 2017) is the most widely used method to improve the RNN model. Compared with ordinary RNN, the most obvious advantage is that it can perform longer sequences. LSTM includes three gates (i.e., an input gate, a forget gate, and an output gate) and a memory unit to save the state of each neuron. A standard maintains a hidden state vector \(h\) and a cell state vector \(c\), which control states regarding updates and output at each step. Let \(w_{t}\) be the input element of the \(t\)-\(th\) step; then, the architecture of the LSTM cell can be defined as follows:  
\[f_{t}=\sigma\Big{(}W_{f}[w_{t},h_{t-1}]+b_{f}\Big{)} \tag{2}\]  
\[i_{t}=\sigma(W_{i}[w_{t},h_{t-1}]+b_{i}) \tag{3}\]  
\[\widehat{C}_{t}=\tanh(W_{C}[w_{t},h_{t-1}]+b_{C}) \tag{4}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widehat{C}_{t} \tag{5}\]  
\[o_{t}=\sigma(W_{o}[w_{t},h_{t-1}]+b_{o}) \tag{6}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{7}\]  
where \(f_{t}\), \(i_{t}\), \(o_{t}\) indicate the activation vectors of the forget gate, input gate and output gate, respectively. \(h_{t-1}\) represents the hidden state at time \(t-1\). \(W_{f}\), \(W_{i}\), \(W_{C}\), \(W_{o}\) are the recurrent weight matrices. The biases are depicted using the notations \(b_{f}\), \(b_{i}\), \(b_{c}\), \(b_{o}\).  
The standard LSTM can only capture the historical information of the sequence. Due to the complexity of the natural language sentence structure, future information may be taken into consideration when labeling the sequence. On this basis, [PERSON] et al. (2016) proposed bidirectional long short-term Memory, which uses two LSTMs to work in the forward and backward time directions, respectively. This facilitates learning of bidirectional long-term dependencies across two time steps. We can splice BiLSTM into \(h_{t}=\left[\overrightarrow{h}_{t}\oplus\overleftarrow{h}_{t}\right]\).