esa-sceva/satcom-chunk-collection huggingface dataset:
# A Forecast Model of the Number of Containers for Containers by Voyage
## 4 Example Analysis
### Determining the Weight of Influencing Factors  
As indicated by the data in the table, the order of magnitude of the sequences was quite different, and the two sequences were standardized using Equation (1). The correlation between each influencing  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**No.** & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(X_{4}\) & \(X_{5}\) & \(X_{6}\) & \(X_{7}\) & \(X_{8}\) & \(X_{9}\) & \(X_{0}\) \\ \hline
1 & 2395 & 75 & 2461 & 10.3 & 11 & 20 & 21 & 5200 & 77 & 1100 \\
2 & 27,689 & 66 & 776 & 85 & 38 & 10 & 170 & 1700 & 85 & 279 \\
3 & 29,960 & 77 & 2521 & 102.3 & 10 & 15 & 230 & 4700 & 69 & 1739 \\
4 & 29,841 & 82 & 4123 & 162 & 49 & 13 & 201 & 3410 & 64 & 177 \\
5 & 13,562 & 63 & 2357 & 68.9 & 39 & 20 & 150 & 1200 & 73 & 110 \\
6 & 17,369 & 59 & 2037 & 60.3 & 22 & 26 & 120 & 800 & 62 & 205 \\
7 & 14,650 & 71 & 1521 & 59 & 13 & 29 & 147 & 2800 & 73 & 347 \\
8 & 30,550 & 58 & 798 & 47.7 & 26 & 15 & 128 & 3600 & 88 & 561 \\
9 & 25,103 & 54 & 567 & 110.3 & 13 & 10 & 235 & 2000 & 65 & 850 \\
10 & 14,650 & 65 & 668 & 85.6 & 25 & 12 & 164 & 2590 & 86 & 496 \\
11 & 14,023 & 49 & 732 & 77.7 & 16 & 30 & 139 & 2810 & 59 & 594 \\
12 & 19,776 & 67 & 651 & 56 & 30 & 21 & 98 & 1400 & 71 & 350 \\ \hline \end{tabular}
\end{table}
Table 1: Training samples.  
factor and the number of containers allocated to the container ship for one voyage was calculated using Equations (2), (3) and(4), and the calculation results are presented in Table 2.  
As shown in Table 2, the correlation degrees of \(X_{1}\), \(X_{4}\), and \(X_{7}\) were all approximately 0.17, indicating that the three influencing factors had the lowest effect on the number of allocated containers for one voyage and could be ignored. The correlation degree of \(X_{5}\) was 0.3998, signifying that this factor had little effect on the number of allocated containers for one voyage; the correlation degrees of \(X_{2}\), \(X_{6}\), \(X_{3}\), and \(X_{9}\) were higher than 0.6, indicating that these three factors had a significant effect on the number of allocated containers for one voyage. However, the correlation degree of \(X_{8}\) was 0.8345, signifying that this factor had the greatest effect on the number of allocated containers for one voyage. The weight of each influencing factor was calculated using Equation (5), and Table 3 shows the results.  
As shown in Table 3, the weight values of \(X_{1}\), \(X_{4}\), and \(X_{7}\) were relatively low (all lower than 0.091), and the weight values of the other influencing factors were higher than 0.14, with no significant difference. This is mainly because the other factors had greater effects on the number of allocated containers for one voyage, and their weight values were scattered.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 7 Discussions and Findings  
There has not yet been any trust-based system, in the existing literature, that has been examined for both static and mobile scenarios under attacks. Most of the existing trust-based systems for routing security are for a static environment, such as a smart home [25]. To the best of our knowledge, only one study, DCTM [27], has been tested for mobile sender nodes. However, the performance of trust-based secure routing protocol is not tested under a mobile sink node environment. The simulation performance for SMTrust under routing attacks is determined and analysed for various parameters, including node rank changes, power consumption, packet loss rate, and throughput. The results indicate that SMTrust outperforms MRHOF [28], SecTrust [25], DCTM [27], and MRTS [26] in terms of network performance under Rank and Blackhole attacks.  
For power consumption, SMTrust shows a negligible increase as compared to existing systems. This is due to the difference in trust index computations, attack detection, and trusted parent selection. As compared to MRTS, SMTrust shows negligibly lesser throughput under Blackhole attack and higher packet loss rate under Rank attack. This is because MRTS uses an IDS approach for attack detection and isolation, as well as a hardware security chip embedded in each node. This is justified by the fact that SMTrust employs simplified equations to compute trust metrics and trust index, thus avoiding integration of IDS or hardware separately.
# Study on Dynamic Mechanical Properties and Failure Pattern of Thin-Layered Schist
## 5 Discussion
### Influence of Schistosity Spacing  
The schistosity spacing has great influence on the strength of thin-layered schist. It is difficult to collect multiple groups of schist samples with different schistity spacing within the same or similar areas, which makes it impossible to carry out laboratory rock tests. The above numerical models have verified the feasibility of numerical methods in studying the dynamic characteristics of schist. Therefore, in order to study the influence of schistity spacing on dynamic mechanical properties, numerical models with schistity spacing of 22 mm, 17 mm, 12 mm, 9 mm and 7 mm are established. The failure pattern and dynamic compressive strength are, respectively, shown in Figures 12 and 13.  
Figure 11: Comparison of failure patterns at different schistity angles.  
In Figures 12 and 13, it is observed that the main failure pattern is significant shear failure, and the rock specimens are mainly destroyed along the schistity under impact load, which indicates that the change in schistity spacing has little effect on the overall failure pattern. However, with the decrease in schistity spacing, the damage degree of the rock specimen is higher and more radial cracks are generated along the failure plane of schistity, which results in a more obvious characteristics accompanied by local compression/shear composite failure. In addition, the dynamic compressive strength decreases gradually with the decrease in schistity spacing and the change trend is obtained by data fitting. The value drops by up to 20.3% from schistity spacing of 22 mm to 7 mm.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 5 Discussion  
### Parameters  
Since a series of parameters, such as continuity constraints and construction budgets, are involved in the proposed method, we discuss their sensitivities and the corresponding effects on the results. We set the adjustment factor \(\alpha=1.0\), the traffic flow constraint \(f=90\) vehicles/h, the continuity constraint \(k_{c}=6\), \(k_{cmon}=2\), and the construction budget \(B=20\) km to perform parameter analysis.  
To examine the continuity constraint parameter, we set the continuity constraint to \(k_{c}=0,3,6\), and \(10\) to plan the bus lanes and discuss the corresponding effect on the results. Figure 8 shows the Pareto front for different continuity constraint parameters. As the continuity constraint parameter increases, the punctuality rate function score shows an upward trend, and the utilization function score initially shows an upward trend. When the continuity constraint parameter \(k_{c}\) reaches a certain value (\(k=15\)), the scores are reduced. The utilization objective function involves the continuity of the bus trajectories. The existence of more continuous road segments increases the utilization score to a certain extent, but when the continuity constraint parameter reaches a certain level, the candidate road segments may be connected to continuous roads, which may lead to a lower score.  
To examine the construction budget parameter, we set the construction budget to \(B=10,20,\) and \(40\) km to plan the bus lanes and discuss the effect of the construction budget on the results. Figure 9 shows that as the construction budget increases, the Pareto front of the final result continues to advance in the direction of increasing the multiobjective function score, and the distribution of the solutions expands. Combined with the dotted  
Figure 7: Comparison of the model results with actual planning scheme in Nanshan District, Shenzhen City.  
line of the objective function extremum, the objective function scores show approximate linear growth with increasing construction budgets.  
Figure 8: Planning results achieved with differing values of continuity constraint parameters \(k\).  
Figure 9: Planning results achieved with differing values of construction budgets \(B\) (km).
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.4 System Commands  
The number of commands for the reactive BCI category varies from 2 [18; 23; 31; 35] to 36 [21; 41]. The most frequently used number of commands is four, and it appears in 10 studies, [14; 15; 19; 22; 26; 29; 30; 36; 39; 40]. The authors of [20; 24; 27; 44] designed a system with eight commands, while refs. [17; 42] chose three commands for their system. In the works of [16; 37], a six-command system was developed, whereas ref. [25] employed nine commands. Additionally, Refs. [32; 33] designed systems incorporating three and four commands, while ref. [41] developed a system with 9, 16, 25 and 36 commands.
## 2 Correlation Algorithm
### Autoencoder  
The autoencoder [17; 18; 19; 20] is an unsupervised learning neural network. It leverages the backpropagation algorithm along with optimization techniques like gradient descent to learn the mapping between input \(x\) and its corresponding reconstructed output \(x^{\prime}\).  
This model consists of two primary components: the encoder and the decoder. The encoder converts high-dimensional input data \(x\) into latent space variables \(h\), which are encoded representations. The decoder reconstructs the latent space variables \(h\) back into the original dimensions of the output \(x^{\prime}\), which is an approximation of the input. A well-performing autoencoder will yield a reconstructed output \(x^{\prime}\) that closely resembles the original input \(x\). The principle is illustrated in Figure 1.  
The objective of an autoencoder is to train a network to learn data features and reproduce the original input as faithfully as possible. Leveraging this characteristic, autoencoders can be applied to rating prediction within recommendation systems. Consider a scenario with a sparse rating matrix \(R\). Treating \(R\) as the input, it undergoes encoding to transform it into a latent space representation. The transition from the hidden layer to the output layer is executed by the decoder, which reconstructs the input from the latent space representation. Notably, autoencoders possess the property of having input and output dimensions equal, and they learn to reconstruct a user's ratings using the acquired latent space representation.  
This utilization of autoencoders proves beneficial in tackling the issue of sparse data in recommendation systems, as they can effectively learn meaningful latent representations from the input data and aid in accurate rating predictions.  
The basic form of an autoencoder is as follows; where \(f(x)\) represents the encoding function and \(g(x)\) represents the decoding function:  
\[x\overset{f(x)}{\rightarrow}\mathrm{h}\overset{g(x)}{\rightarrow}x^{\prime} \tag{4}\]  
During the training process, the constraint conditions are:  
\[x\approx x^{\prime} \tag{5}\]  
Figure 1: Autoencoder model.  
To ensure that the input and output of the autoencoder closely match, it is imperative to define a loss function:  
\[x^{\prime}=g(f(x)) \tag{6}\]
## 4 Numerical Simulation and Analysis of Result
### Results of Different Seismic Excitations
The results show that every spike was at the same frequency for all six cases without correlation with either excitation type or excitation frequency, which means the inner water is under free vibration, and the vibration frequency is the natural frequency of the inner water. The simulation results show that inner free surface waves are generated due to seismic excitation. Figure 16 shows a snapshot of an inner water wave on the right side of the arch rib. The figure shows that there are a series of waves on the water's surface, and a large vortex formed in the inner water. After careful calculation, the wave frequencies for every case are shown in Table 4. It is shown that the inner water wave frequencies for all cases are 1.1-1.15 Hz, which is very close to the first spike frequency (1.125 Hz). This result also implies that the first spike frequency is the natural frequency of the inner water. The natural frequency of the inner water depends on the depth of the water. Therefore, the simulation of different water depths was carried out and the frequency of pressure oscillation is investigated in the next section.
## 4 Measurement and Discussion
### Reflection Coefficient  
As shown in Figure 10a, in free space, the impedance bandwidth (S\({}_{11}\)\(<\)\(-\)10 dB) is 2.2-2.64 GHz in simulation and 2.18-2.61 GHz in measurement when the antenna is flat. The slight discrepancy between simulation and measurement may be caused by manual fabrication errors. For the conformal cases, a slight frequency shift is observed. Nevertheless, the antenna still shows a good performance. Further, the on-body results are shown in Figure 10b. The simulated impedance bandwidth is 2.21-2.64 GHz, and the measured bandwidth of on-arm and on-chest is 2.18-2.59 GHz. It is evident that, due to the benefit of ground shielding, the proximity of the body results in a limited influence when the antenna is worn on the arm and on the chest. Overall, we can conclude that the simulation and the measurement results match well. The antenna shows a robust impedance performance in free space and on-body scenarios.
## 4 ACE and Blockchain-Based IoT Knowledge Sharing Scheme
### Multi-Level ACE-Based IoT Knowledge Sharing Algorithm
\begin{split}(pp^{1}_{i},msk^{1}_{i})\leftarrow\text{ACE}^{1}. Setup(1^{\lambda});\\ \text{ }ek^{1}_{i}\leftarrow\text{ACE}^{1}.Gen(msk^{1}_{i},sen); \\ dk^{1}_{i}\leftarrow\text{ACE}^{1}.Gen(msk^{1}_{i},rec);\\ \text{ }rk^{1}_{i}\leftarrow\text{ACE}^{1}.Gen(msk^{1}_{i},sen). \end{split}  \tag{5}\]  
**KeyGen. ** Once the security level \(i\in[1,\ldots n]\), the identity \(\{sen,rec,san\}\), and \(msk\) are input, the algorithm outputs the three kinds of keys in the system:  
\[ \begin{split} ek_{i}&=\{ek^{1}_{j}\}_{j\in M},\text{ where }M(i)=\{j|P(i,j)=1\};\\ dk_{i}&=dk^{1}_{i};\\ rk_{i}&=\{rk^{1}_{i}\}_{j\in[1,n]}.\end{split}  \tag{6}\] \begin{table}
\begin{tabular}{c l l l l l l l} \hline \(i\) & **Encryption Key \(ek_{i}\)** & **Ciphertext \(c_{i}\)** & & **Sanitizer Key \(rk_{i}\)** & **Decryption Key \(dk_{i}\)** & **Decryption Result \(m^{\prime}_{i}\)** \\ \hline
1 & \(\{ek_{1}\}\) & \(\{Enc(ek_{1},m_{1}),\quad R_{12}\quad,\quad R_{13}\quad\}\) & \(\{rk_{1},rk_{2},rk_{3}\}\) & \(dk_{1}\) & \(\{m_{1},\bot,\bot\}\) \\
2 & \(\{ek_{1},ek_{2}\}\) & \(\{Enc(ek_{1},m_{2}),Enc(ek_{2},m_{2}),\quad R_{23}\}\) & \(\{rk_{1},rk_{2},rk_{3}\}\) & \(dk_{2}\) & \(\{\bot,m_{2},\bot\}\) \\
3 & \(\{ek_{1},ek_{2},ek_{3}\}\) & \(\{Enc(ek_{1},m_{3}),Enc(ek_{2},m_{3}),Enc(ek_{3},m_{3})\}\) & \(\{rk_{1},rk_{2},rk_{3}\}\) & \(dk_{3}\) & \(\{\bot,\bot,m_{3}\}\) \\ \hline \end{tabular}
\end{table} 
Table 2: The encryption and decryption process of the ACE3 with the BLP access control policy. **Enc. ** Once the message \(m\) and encryption key \(ek_{i}\) are input, the algorithm computes the ciphertext \(c=(c_{1},\ldots,c_{n})\) for \(t\in[1,\ldots n]\):  
\[c_{t}= \begin{cases}c_{t}^{1}\leftarrow\text{ACE}^{1}.Enc(ek_{t}^{1},m),&ek_{t}^{ 1}\in ek_{i};\\ c_{t}^{1}\leftarrow_{\text{g}}\text{C}_{t}^{1},&ek_{t}^{1}\
otin ek_{i}.\end{cases}  \tag{7}\]  
**San. ** Once the ciphertext \(c\) and a sanitizer key \(rk_{i}\) are input, the algorithm sanitizes each part of the ciphertexts and outputs the sanitized ciphertext \(c^{\prime}=(c_{1}^{\prime},\ldots,c_{n}^{\prime})\):  
\[c_{i}^{\prime}\leftarrow\text{ACE}^{1}.San(rk_{i}^{1},c_{i})_{i\in[1,n]}. \tag{8}\]  
**Dec.** Once a sanitized ciphertext \(c^{\prime}\) and a decryption key \(dk_{i}\) are input, the algorithm recovers a message:  
\[m^{\prime}\leftarrow\text{ACE}^{1}.Dec(dk_{t}^{1},c_{i}^{\prime}). \tag{9}\]  
In Figure 2, an example of key distribution and knowledge sharing in the ACE\({}^{2}\) system is depicted.
## 4 Simulated Results and Analysis
### Double Lane Change (DLC) Condition  
The DLC test is used here [32]. The testing paths are set in Carsim. The road surface adhesion coefficient is 0.4, initial vehicle speed is 80 km/h, and simulation time is 10 s (Figure 7). The simulation results are shown in Figure 8a-j.  
Figure 8: _Cont._  
Figure 7: The double lane change (DLC) test Lane layout.  
Figure 8: The simulation results of the front wheel sinusoidal steering test: (**a**) the comparison of estimated sideslip angle and real value; (**b**) the sideslip angle estimation error; (**c**) the vehicle yaw rates; (**d**) the vehicle sideslip angle; (**e**) the phase plane of the vehicle sideslip angle and the vehicle sideslip angle rate; (**f**) the vehicle lateral acceleration; (**g**) the vehicle lateral displacement; (**h**) the vehicle longitudinal velocity; (**i**) the estimated of value \(d\); (**j**) brake pressure of the braking wheels by AFRBF-SMC controller.  
The ADUKF can well estimate the vehicle sideslip angle with low errors and can meet the demand for vehicle stability control (Figure 8a,b). With the absence of controller intervention, the yaw rate and sideslip angle both severely fluctuate, and after about 4.9 s, the yaw rate quickly increases and diverges, but the sideslip angle rapidly rises and diverges after about 2.3 s (Figure 8c-d). With the intervention by AFRBF-SMC and by SMC, the yaw rate and sideslip angle of the vehicle both follow the ideal values. The AFRBF-SMC can better trace compared with SMC, and the yaw rate and sideslip angle are closer to the ideal values. With the absence of controllers, the phase trajectory of the sideslip angle gradually diverges from convergence, but with the presence of controllers, the sideslip angle fluctuates slightly and is under the stable converging state (Figure 8e). Moreover, the phase trajectory of the sideslip angle under the control by AFRBF-SMC is more convergent than that under the control by SMC. AFRBF-SMC, compared with SMC and the control-less status, makes the vehicle have better dynamics performance (Figure 8e-j). Figure 8i,j visually shows the changes of the uncertainty term and vehicle cylinder pressure under the control by AFRBF-SMC. When AFRBF-SMC is used to estimate the uncertainty term of the vehicle, the controlled vibration is less compared with SMC, and the additional yaw moment controlling rate is finer. As a result, the longitudinal vehicle speed under the control by AFRBF-SMC is smaller than that of SMC. As for lateral kinetics, AFRBF-SMC can ensure smaller lateral displacement and lower-fluctuation lateral acceleration. During DLC condition, the overall performance of AFRBF-SMC is better.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
### Physiological Responses for Assessment of Driver State  
Among all studies that measure physiological signals of drivers, driver drowsiness and fatigue are the most often researched topics. Chronologically, the first studies on detection of physiological signals for drowsiness detection were using video analysis with image processing techniques [2; 3]. Now, modern research often uses electroencephalography (EEG) [4; 5; 6; 7] or a combination of EEG with other physiological signals [8; 9]. Another common measure for detecting drowsiness besides EEG, is cardiovascular activity. Drowsiness has been assessed with different cardiovascular signals including photoplethysmography (PPG) [10; 11; 12], blood volume pulse (BVP) [11], and electrocardiogram (ECG) [8; 12; 13; 14]. Some studies report on using heart rate variability (HRV) [12; 9; 13], electrooculography (EOG) [8; 15; 16], and galvanic skin response (GSR) [9; 11].  
Another often studied topic regarding the driver's physiological signals is monitoring of individual driver's state such as situational awareness [17], emotional states [18; 19; 20], or driver physical fitness [21; 22; 23]. Similar methods have also been used for observing effects of use of different modalities when interacting with in-vehicle information systems [24] and to assess drivers' cognitive workload due to use of such systems [25; 26] or experienced stress due to demanding driving conditions [27]. By observing EEG signals, physiological data can also be used for prediction of drivers' intentions [28; 29].  
Difficult driving conditions, e.g., interaction with reckless drivers and bikers, impatient pedestrians crossing the road, and operating the vehicle, e.g., gear shifting, can significantly increase the load on drivers [30]. Different levels of driving demand reflect in drivers' mental and also physical load. What makes the situation even worse is that the driving demand is increasing every day [31]. The driver's mental workload is usually assessed by subjective reports, measures of task performance, or physiological measures [32]. As [PERSON] and [PERSON] suggest, physical and mental workload have clear impact on physiological signals [31], especially on heart rate, heart rate variability, galvanic skin response, and blood pressure [33].  
In summary, the list of common physiological measures for assessing drivers consists of:  
* Electroencephalography (EEG),
* Electrocardiography (ECG),
* Photoplethysmography (PPG),
* Heart rate (HR),
* Galvanic skin response (GSR),
* Electromyography (EMG) and
* Eye tracking (pupil diameter--PD).  
Combinations often include EEG and ECG or PPG and GSR, sometimes in combination with eye tracking.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 5 Energy and Delay Aware Data Aggregation in RPL
### Parent Rank Calculation  
Rank indicates the root of the DODAG, how far from the node of the participant. The node 'x' rank computes from the parent node(x) rank and its value, Rank_Increase_Value. The value of Rank Increase calculates the value of residual energy and the value of MinHop_RankIncrease. The MinHop_RankIncrease value is 256, which is the default value in the rank calculation [33; 34]. The calculation of the rank is shown in Equations (6) and (7).  
\[\text{Rank(x)}=\text{Rank(parentNode(x))}+\text{Rank\_Increase\_Value} \tag{6}\]  
\[\text{Rank\_Increase\_Value}=\text{RER}+\text{MinHop\_RankIncrease} \tag{7}\]  
The EDADA-RPL parent selection algorithm is given in Algorithm 1.  
```
Input: DIO, DAO, DAO-ACK, DIO_RER Output: Optimal Parent
1:For preferred_ParentNodeparentNode-listdo
2: compute RER \(\text{RER}(\text{Ni})=\frac{\text{E}_{\text{Initial}}-\text{E}_{\text{ Depleted}}}{\text{E}_{\text{Initial}}}\)
3:compute the Rank(N) \(\text{Rank(x)}=\text{Rank(parentNode(x))}+\text{Rank\_Increase\_Value}\)
4:Calculate the Rank_Increase_Value \(\text{Rank\_Increase\_value}=\text{RER}+\text{MinHop\_RankIncrease}\)
5:IfBest_ParentNode\(>=\) Preferred_ParentNodeThen Best_ParentNode=Preferred_ParentNode
6:End
7:Whilepreferred_ParentNode==Best_ParentNodedo SourceNode=Preferred_ParentNode
8:End
9:End
10:Return Optimal Parent
```  
**Algorithm 1**EDADA-RPL parent selection