esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Sensitivity Analysis with Respect to Neighborhood Size  
It is found that neighborhood size \(k\) of \(k\)NN graph can influence the results of GSSC. To investigate the sensitivity of SSCMG under different \(k\) values, we conduct experiments on ORL and Pose27. In the experiments, we fix \(Ls\) as 3 on ORL and as 5 on Pose27, we then vary \(k\) from 1 to 40. We also include the results of other comparing methods that utilize a \(k\)NN graph and report the results in Figure 2.  
From Figure 2, we can observe that SSCMG achieves better performance and it is less sensitive to \(k\) than other comparing methods when \(k\leq 10\). However, as \(k\) increasing, its accuracy downgrades gradually. This is because a large value of \(k\) means choosing more neighbors of a sample, so the constructed mixture graph is becoming denser and more noisy edges are included. On the other hand, the accuracy of GFHF and LGFHF first rise sharply as \(k\) increasing and then reduce gradually. The accuracy of LNP, RMGT and CGSSL increase as \(k\) rising and become relatively stable, and these methods sometimes get comparable (or better) results with SSCMG on ORL. The reason is that these three methods need a large number of neighbors to optimize a \(k\)NN graph for semi-supervised classification. CGSSL utilizes a compact \(l_{2}\) graph by using the direct neighborhood samples of a sample and the neighborhood samples of its reciprocal neighbors. As \(k\) increasing, more neighborhood samples are included and thus the similarity between samples is more distorted by noisy neighbors. Given that, CGSSL loses to LNP, which is also based on an \(l_{2}\) graph, but LNP only utilizes the direct neighborhood  
Figure 2: Sensitivity under different values of \(k\). (**a**) Accuracy _vs._\(k\) (ORL); (**b**) Accuracy _vs._\(k\) (Pose27).  
samples of a sample. As \(k\) increasing, the accuracy of GFHF and LGFHF on ORL and Pose27 downgrades much more than that of SSCMG, this observation suggests that mixture graph is less suffered from the choice of \(k\) than a single \(k\)NN graph. Figure 2 also shows that it is difficult to set a suitable \(k\) for GSSC methods. In contrast, there is a clear pattern and it is easy to select a suitable \(k\) for SSCMG. Although SSCMG, LGFHF and GFHF share the same classifier, the performance of SSCMG is always more stable than LGFHF and GFHF. This observation also justifies the advantage of mixture graph for GSSC.
## 3 Experimental Results and Discussion
### Comparative Analysis of the Optimization Performance of the Parallel EGO Algorithm
#### 3.1.1 Optimization Solution Accuracy
Although the strategy with a point-filling number of 10 still performs poorly on most test functions compared to other filling strategies, the overall accuracy of its convergence solutions has improved, with a reduction in the differences. Additionally, except for F1 and F3, the variation in the accuracy of the convergence solutions does not exhibit a linear decrease as the point-filling number increases. It is worth noting that, based on the statistical decision metric \(p\)-value, the difference in the accuracy of the converged solutions has significantly reduced, compared to the case based on OK model, except for F1 and F3. This indicates that the stability of the optimization performance of the parallel EGO algorithm based on GEK model has been better ensured.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.3 Markov Models  
Markov Models are stochastic sequence models based on probability distribution. The simplest Markov model is a Markov chain. In a Markov chain, a distribution of a variable that changes its value randomly over time depends only on the distribution of the previous state. A Hidden Markov Model is similar to a Markov chain, but it uses hidden states for fine tuning. Each state is represented by a probabilistic function of a state. Every HMM can be characterized by the number of the hidden states, the number of the system outputs and the state transition probability distribution [43]. In [22], the authors face motion intention inference, by enabling a cooperative vehicle perception. With the vehicles cooperative sensing, the perception range is increased, offering better perception quality and sufficient response time for autonomous triggered actions. This is achieved using a simplified version of a Coupled Hidden Markov Model (CHMM). The model is trained by using velocity records of various driving profiles of a vehicle sequence. A coupled HMM is a form of HMM, better suited for scenarios where multiple processes are taking place.  
To implement a parking space detection system, the authors in [14] use a combination of an SVM and a Markov Random Field (MRF) algorithm. The MRF is an undirected graph describing a set of random variables, which have the Markov property. The authors of this research first obtain input data from cameras fixed at parking lots. The images are pre-processed and the most useful features are extracted. Then the SVM is used to classify parking slots to free or occupied and finally MRF corrects possible conflicts in the SVM classification, improving it by 7.95%.  
Markov Decision Process (MDP) is another implementation of the Markov Model. In [36] a group routing suggestion algorithm is proposed based on MDP. Instead of finding the best route for a single vehicle, group routing will be suggested based on vehicle similarities and V2V communication to minimize traffic congestion. The MDP is a model suited for optimization and decision making problems. It is similar to the Markov chains but it introduces two new components. MDP has a set of actions that can lead to a certain state. The selection of the most appropriate actions is encouraged by MDP rewards.  
In the study above, the MDP uses a set of actions to make several decisions, the actions selection policy is performed using a reinforcement learning technique, named Q-Learning. Q-Learning is a model-free reinforcement learning method that offers the capability to estimate the reward or the penalty of an action in a Markovian domain. The algorithm learns by executing all the possible actions repeatedly and estimating the resulting state. In the end, the algorithm has learnt the optimum set of actions [44].
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Inertial Measurement Unit
#### 3.3.1 Accelerometers  
Accelerometers measure acceleration and can be employed to detect changes in orientation and movement. Placed strategically on a chair or worn on the body, accelerometers can identify different sitting positions. [PERSON] et al. [99] employed eight accelerometers--six on the seat and two on the backrest--to determine variations in inclination angles across the X, Y, and Z axes. These measurements formed the feature vector for training an RF model to classify nineteen different postures. Based on data from 20 subjects, the mean accuracy of the model was estimated at 80.1% only. This might be due to the large number of postures assessed. Moreover, it is only feasible to employ these sensors if the chair is prone to tilting or deforming when moved. [PERSON] et al. [100] employed three triaxial accelerometers to detect posture using unsupervised algorithms. These accelerometers were positioned on the user's back for gathering data. The data received from each sensor were changed into feature vectors and subsequently used to train different algorithms such as OCSVM, Isolation Forest, and K-means. Among all, the K-means achieved the highest accuracy of 99.3%. Ref. [95] used a commercial accelerometer sensor such as MPU-6050 mounted at various body positions to collect accelerometric and gyroscopic data from different body positions and recognize four different postures. Three machine learning classification algorithms were assessed, resulting in an overall accuracy of around 98.7% for real-time posture classification. Among these algorithms, the random forest model performed better in terms of precision, recall, accuracy, and F1-score.
## 2 Methods
### Test Stimuli
The 0 ms condition is equivalent to a vertical coincident microphone configuration [26], whereas the 1 ms condition represents the spacing between the main and height microphones being 0.34 m. This would be a practical spacing for a 3D microphone array, since, beyond this spacing, there would be no perceived increase in vertical image spread and the tonal quality would decrease [27]. In addition, the author's previous study on the VLT while using the same sound sources also tested the ICTD of 10 ms, but there was no significant difference found between 1 ms and 10 ms in the results. The test stimuli were created for each sound source, as follows. For the height channel signal, each source was first broken down into octave bands while using a linear-phase 8 th-order Butterworth filter (\(-\)48 dB/oct), which was created using the 'forward-backward' filtering method [28] in MATLAB (MathWorks, Natick, Massachusetts, USA). Each octave band then underwent the level reduction according to Table 1. The main channel signal was unaltered. Time delays of 0 and 1 ms were applied to the height channel with respect to the main. During the test, the main channel was routed to L and R, whilst the right was routed HL and HR. For blanket reduction, the amplitude of the height channel was reduced as a whole. From this, a total of 80 stimuli were created (five sources x two ICTDs x eight presentation methods). The playback levels of the main layers of all stimuli were calibrated at 70 dB LAeq at the listening position. The increase in amplitude when the stimuli were presented as vertically arranged quadraphonic phantom images was dependent on the VLT that was applied to the height layer, as shown in Table 2. It was decided not to match the overall sound pressure level (SPL) of the stimuli as a slight increase in SPL is an inherent result of VIC. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Centre Frequency (Hz)**} & \multicolumn{3}{c}{**Height Layer Attenuation (dB)**} \\ \cline{2-3}  & **Guitar, Speech, Oboe and Quartet** & **Conga** \\ \hline
63 & \(-\)6 & \(-\)6 \\
125 & \(-\)7 & \(-\)6 \\
250 & \(-\)7 & \(-\)5 \\
500 & \(-\)8.5 & \(-\)6 \\
1 k & \(-\)11 & \(-\)6 \\
2 k & \(-\)10 & \(-\)6 \\
4 k (0 ms) & \(-\)9.5 & \(-\)11 \\
4 k (1 ms) & \(-\)9.5 & \(-\)6 \\
8 k (0 ms) & \(-\)13.5 & \(-\)6 \\
8 k (1 ms) & \(-\)10 & \(-\)6 \\
16 k & \(-\)8 & \(-\)6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Vertical localisation threshold (VLT) values applied to different octave bands of the stimuli, based on [PERSON] and Lee [8].
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 5 Conclusions  
The paper designs a novel routing scheme based on node attributes for OppNets in oceans. The scheme first constructs the delivery competency to predict the existence of further relaying. Then a forwarding willingness mechanism of device capacity and node behaviors is introduced to qualify the probability that the nodes can participate in the delivery process. Finally, the utility metric is proposed to make decisions on message delivery. The results show that the proposed scheme improves network performance in terms of delivery ratio, average latency, and overhead ratio as compared to other schemes. In future work, the routing scheme will further consider the impact of packets dropped from the cache on system performance and develop the exploration of further relay nodes toward the destination as soon as possible to reduce waste of resources and network overhead.  
**Author Contributions:** Supervision, S.J.; Writing--original draft, L.G.; Writing--review & editing, L.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by The Innovation Program of Shanghai Municipal Education Commission of China (No. 2021-01-07-00-10-E00121).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The program code used in the research can be obtained from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Detachment Energy Evaluation in Nano-Particle Cleaning Using Lateral Force Microscopy
## 2 Measurement Method of Nano-Particle Detachment Energy Using Lateral Force Microscopy  
We used a method that can measure the shear action force to detach the nano-particles adhered to the substrate surface [21; 22]. The position and the size (height) (\(H_{\text{topgraphy}}(V_{T})\)) of the nano-particles were determined by Atomic Force Microscopy(AFM) before and after detachment. In order to measure the force required to detach the nano-particle from the surface, we detached the nano-particle from the surface and moved it on the surface by applying a force through contact with the tip of the cantilever using LFM (Figure 2). The elastic torsion of the cantilever was detected as a voltage signal (\(V_{L}\)) by the position-sensitive detector (PSD). This signal indicates the variation in the lateral force \(F_{\text{Lateral}}\) acting on the particle during the probe's movement in the \(x\)-direction and is converted to lateral force  
Figure 1: Nano-particle detachment involves breaking the bonding such as in post-CMP cleaning.  
through calibration [22; 23] (the calibration method is in the Supplementary Information). When the torsion is released, it can be regarded as the detachment of the particles.  
The energy required to detach (detachment energy) can be calculated by the variation of lateral force and the displacement \(\Delta d_{x}\) of the cantilever tip while pushing the nano-particle until it detaches. Then, the surface interrelationship between the particle and the film was quantitatively investigated.  
Experimental Results of Detachment Energy for Various-Sized Silica Particles from an Oxide Film Surface
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 3 Verification and Analysis Based on Numerical Simulation
### Temperature Distribution Reconstruction with Data Missing  
The original dataset \(T_{900\times 4\times 40\times 40}\) of the bimodal temperature distribution is processed with data missing, and the missing position is random. The temperature missing dataset is obtained and denoted as \(\mathbf{G}_{900\times 4\times 40\times 40}\). The missing position data in \(\mathbf{G}\) is replaced by 0, and the remaining data are unchanged.  
The temperature missing dataset \(\mathbf{G}\) is complemented by the reconstruction optimization algorithm, and the core tensor of the complemented dataset is extracted and reconstructed with 13 temperature measuring points. Due to the random data missing, five groups of test datasets of bimodal temperature distributions are reconstructed for many times, and the average reconstruction error is calculated. The reconstruction errors of different missing ratios are shown in Figure 6.  
As shown in Figure 6, the results show that the temperature distribution reconstruction algorithm based on Tucker decomposition and missing datasets can also be applied to the reconstruction after completing different proportions of missing datasets, and good reconstruction results are obtained. The reconstruction error of temperature distribution in the five groups of test conditions is less than 3.6 %. Observing the curve trend in the figure, it can be seen that as the data loss rate decreases, the overall reconstruction error also tends to decrease. The blue line is the temperature profile reconstruction error on the basis of the complete dataset. It is observed that the trend of the temperature distribution reconstruction error curve based on the missing dataset is basically consistent with it. Therefore, the temperature reconstruction optimization algorithm of the Tucker  
Figure 6: Reconstruction errors of different missing ratios.  
decomposition using the prior dataset with temperature missing can accurately complete the incomplete datasets and then achieve the temperature field reconstruction.
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 3 Applications
### Research Potential for Applications
#### 3.3.1. Data Monetization  
Data monetization refers to turning collected data into a monetary value [99]. In this context, data takes a new role where businesses can productize these data to promote new services, hence increasing their revenue streams [100; 101]. The monetization of the collected data from millions of CVs on the road promises to enable more innovative and compelling CV services [102; 103]. Many automakers are seeking to leverage CV data for variousinnovative services. GM, for example, introduced a new service called \"Marketplace,\" where drivers can use the infotainment dashboard to place orders (e.g., coffee and gas reservations) or to book restaurant seats in advance, while on the move [104]. Fuel depot is another service provided by GM that assists drivers with low fuel tanks to find the nearest gas station and, based on this information, provides offers and discounts that might be available at nearby gas stations [105].  
Automotive industries are racing to offer sophisticated and advanced in-vehicle services to improve the customer experience and to leverage data monetization opportunities. The Connected Car Customer Experience (C3X) framework proposed by McKinsey & Company [15] shows how CV customers' in-vehicle experience can be monetized and serves as a framework to estimate the data-driven value-generation opportunities aligned with increased user connectivity. The C3X framework defines five levels of customer in-vehicle experience in CVs extending from a basic to a high level of vehicle connectivity. These levels are scaled up to reach the ultimate cognitive artificial intelligence advanced level where vehicles can predict and achieve unexpected driving situations. More detailed information on the C3X framework can be found in [15,16].  
The high revenues expected to be generated from monetizing CV data are hampered by a number of challenges, one of which is promoting the added value associated with new connected services that require complex installation procedures and that might be faced with connectivity issues. The lack of a standard data monetization framework throughout the vehicle life cycle and struggling to model agile-based systems and non-scalable ecosystems are also other challenges [15]. Exploration of how to monetize data efficiently without privacy invasion is important for both OEMs and users. Moreover, it is important to consider cases where poor data quality exists and provide innovative solutions to improve data quality and integrity.
## 3 Results and Discussion
### Thermal Stability under Nanoindentation
Figure 12: Temperature dependence of hardness and compressive stresses for Ta-Zr-Si-B-C (**a**) and Ta-Zr-Si-B-N (**b**) coatings. increase in temperature to 750 \({}^{\circ}\)C, which is associated with the precipitation of hard Ta\({}_{\text{x}}\)Si\({}_{\text{y}}\) binary or (Ta,Si)\({}_{\text{x}}\)N\({}_{\text{y}}\) ternary compounds. The different effect of the crystallization process on the hardness of Ta-Zr-Si-B-N and Ta-Zr-Si-B-C coatings can be associated with their structure and the crystallite size of the main phases [54; 55; 56]. The nitrogen-containing coating, which is characterized by a maximum hardness, has a smaller crystallite size (\(\sim\)10 nm) compared to the carbon-containing coating (\(\sim\)20 nm), which is associated with the difference in crystallization temperatures and exposure times. Note that simpler Ta-Si-N coatings have thermal stability up to 900 \({}^{\circ}\)C and a hardness of 15 GPa in the temperature range of 20-900 \({}^{\circ}\)C [57]. The complex doping of these coatings with Ta-Si-N Zr and B makes it possible to improve thermal stability up to 1000 \({}^{\circ}\)C and hardness up to 27 GPa.
## 3 Design Patterns of Vectorized Programming for FIR Image Filtering
### Color Loop Unrolling  
In color loop unrolling, color channels in a pixel are vectorized to compute each color channel in parallel. Figures 4(b) and 4(a) depict the code and vectorization approach to color loop unrolling. In this pattern, a pixel that includes all color channels requires a length of SIMD register size. Typically, the color represents three color channels, namely, R, B, and G. As it is known today, the SIMD register has 4 elements in SSE, 8 elements in AVX/AVX2, and 16 elements in AVX512 for the case of single-precision floating point numbers. Therefore, the size of a pixel, including all color channels, remains smaller than the size of SIMD register, and we require zero padding. Using zero padding, aligned data loading is always possible because every loading data address is aligned. However, this pattern decreases valid vectorization efficiency by the amount of zero padding. Kernel weight is scalar; thus, this pattern has no constraint in vector operations for weight handling. Since vectorization is performed for color channels in all pixels, the pattern has no constraints in image size and kernel shape.  
Figure 4: Code of vectorization patterns. (**a**) Brute-force implementation; (**b**) Color loop unrolling; (**c**) Kernel loop unrolling; (**d**) Pixel loop unrolling. The size of the SIMD register is 4. Usually, the data structure \(I[y][x][c]\) represents RGB interleaving, where \(x\) and \(y\) are the horizontal and vertical positions, respectively, and \(c\) is the color channel. Splitting and merging the data by each channel are defined as follows: \(I[y][x][c]\Leftrightarrow I[c][y][x]\). For these data structures, the data in the final operator \([\cdot]\) can be sequential access.
## 5 Limitations and Future Works
Although an ensemble DCNN outperformed an individual DCNN, the ensemble classifier had a higher computational cost and a longer training time than a single primary classifier. Therefore, a trade-off between prediction accuracy and computational complexity should be taken into account. In addition, the proposed ensemble algorithm is a heterogeneous method, where diverse DCNNs on the same training dataset are combined via probability averaging. When a larger dataset is available in the future, we plan to build a homogeneous ensemble classifier that combines the same-type classification model on different settings of training data and learning parameters using bagging or boosting strategies. The combination of the prediction decision with the decision locations in the heat maps is more useful than the prediction alone. Unfortunately, mild pathologies are missed, especially when an image presents multiple TB lesions. Some images generated 'false' localizations because they did not focus on the right location for TB lesions. Some incorrect areas surrounded the lung area. This error is indeed a limitation of DCNN because it is difficult to explain why it activates at the wrong site. Since DCNN learned the features that were most predictive, it might use the features or regions that are insignificant to or ignored by humans. Further research, along with a radiologist, is required to investigate why the models used the wrong areas. The MC dataset is rather small, and it may simply be that more input cases are needed.
# Impact of Peri-Implant Inflammation on Metabolic Syndrome Factors: A Systematic Review
## 3 Results
### Peri-Implantitis and Hyperglycemia  
The study by [PERSON] et al. (2018) revealed that in patients both with and without type-2 diabetes, the presence of peri-implantitis was associated with significantly higher HbA1c levels (\(p\) < 0.001). In the non-diabetic group, HbA1c levels in patients without peri-implantitis were 4.3 \(\pm\) 0.3%, while those in patients with peri-implantitis were 5.2 \(\pm\) 0.1%. Meanwhile, in the type-2 diabetic group, HbA1c levels were 4.7 \(\pm\) 0.1% in patients without peri-implantitis. This value increased significantly to 9.3 \(\pm\) 1.5% in those diagnosed with peri-implantitis (\(p\) < 0.001).