esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Data Analysis
#### 2.4.2 The SOR Theory Framework  
After completing the Grounded Theory coding process, this study employed the SOR model for inductive data analysis and framework development. The SOR theory provides a behavioral psychology perspective, categorizing the interaction between the driver and voice dialogue into three dimensions [30]:  
Stimulus (S): In this study, voice dialogue is considered an external stimulus, encompassing the voice characteristics of the voice assistant, the type of dialogue content, and the interaction methods (e.g., humorous Q&A, casual conversation, task-oriented dialogue). These dialogue features, as external stimuli, directly influence the driver's emotional and cognitive states.  
Organism (O): This represents the internal state of the driver, including fatigue levels, attention levels, emotional states, and individual psychological needs. In this study, the organism level involves the driver's physiological fatigue, psychological fatigue, and personalized dialogue requirements. Different driver groups (e.g., long-distance truck drivers, taxi drivers, private car owners) have varying levels of fatigue and dialogue needs, which influence their responses to voice interactions.  
Response (R): The response refers to the behavior or psychological reaction exhibited by the driver after receiving the voice dialogue stimulus, primarily reflected in changes in driver alertness (e.g., attention restoration, increased fatigue). Based on the driver's responses, this study categorizes the dialogue arousal mechanism into effective and in effective responses, further analyzing the impact of different types of dialogue on driver alertness and safety.
## 3 Experimental Results and Analysis
### Accuracy of Image Registration  
The accuracy of image registration can be measured by RMSE of registration points, which is defined by  
\[RMSE(f)=\sqrt{\frac{1}{k}{\sum_{i=1}^{k}{\left\|f(x_{i})-x^{\prime}_{i}\right\| ^{2}}}} \tag{19}\]  
where \(x_{i}\) denotes the coordinates of grid points \(G_{A}\) in image \(I_{A}\), and \(x^{\prime}_{i}\) denotes the coordinates corresponding to \(x_{i}\) in image \(I_{B^{\prime}}f\) represents different image registration models, and the proposed algorithm and APAP algorithm use the local homography matrix, while the other algorithms use the global homography matrix as their image registration model; \(f(x_{i})\) denotes the coordinates transformed from \(x_{i}\) by using the image registration model \(f\), which is the estimation of \(x^{\prime}_{i}\); \(k\) is the total number of matching points in the pair of images, and it is set to 25 in the experiments.  
Table 1 shows the average RMSE of registration points achieved by several different image registration algorithms when implemented on the test set generated by the proposed method. To better present the performance of learning-based image registration algorithms, Table 1 gives in detail  
the registration accuracy of several deep learning-based image registration algorithms using VGG, Googlenet and Xception neural networks, respectively.  
From Table 1, it can be seen that the accuracy of the pixel-based ECC image registration algorithm is the lowest, and that of the feature-based SIFT image registration algorithm is higher. The APAP algorithm takes into account the locality of image registration, so it achieves the best result among the pixel-based and feature-based algorithms. The performance of the learning-based image registration algorithms is related to the used CNN models, and more advanced CNN models have higher image registration accuracy. The samples used by the [PERSON]'s algorithm and [PERSON]'s algorithm are relatively simple, so there is little difference in the accuracy of image registration under different neural networks. These two algorithms do not fully consider the locality of image registration, resulting in low accuracy of image registration. Compared with other algorithms, the proposed algorithm achieves the highest image registration accuracy by using the Xception network model. In addition, from Table 1, it is seen that the effect of the proposed algorithm under Xception network is better than that under Googlenet and VGG networks. This is because the samples and labels used in the proposed algorithm are more complex, and there are obvious differences under different neural networks. When combined with more advanced CNN models, the proposed algorithm can achieve higher accuracy of image registration.
## 5 Discussion
In further studies, it is advisable to expand the analysis by sensitivity testing and assessing the stability of the initial estimates under different configurations of membership functions. Therefore, verification of the intelligent analytical platform depends on the sample of flight instructors. However, the reliability of the obtained results was confirmed by the correct application of modern mathematical tools. The obtained results are fully consistent with the set scientific objectives and have significant practical value consisting of (1) the development of an intellectual and analytical platform for assessing the psychophysiological load on flight instructors; (2) integration of forecasting models, expert assessments, and fuzzy logic mechanisms to ensure a comprehensive analysis of the load; and (3) verification of the platform based on real data. The specified goals were achieved; the corresponding information models were implemented, data were collected and processed in real flight conditions, and an applied interpretation of the results was carried out. This confirms the validity of the proposed approaches and the practical value of the research results. Regarding the assessment of the effectiveness of the models, it is worth noting that this study did not involve a direct comparison of the accuracy between the models, since the goal was to create a holistic analytical platform with the integration of different sources of knowledge (dynamic data, expert assessment, predictive models). However, effectiveness was confirmed by the consistency of the obtained results with expert conclusions and the stability of the results when analyzing different flights. Regarding the first question (RQ1), the proposed intelligent and analytical platform provides effective integration of predicted psychophysiological load estimates with expert data on the mental health of instructors. The platform demonstrated an average prediction accuracy of 87.2% by the coefficient of determination for linear regression models. The reliability of the assessment was confirmed by the consistency of the results between different flight sessions. The instructors who participated in the study provided positive feedback on the usefulness of the load visualizations and the ease of interpretation of risk levels. Thus, the platform provides a basis for timely diagnostics and decision-making in relation to load optimization. Regarding the second question (RQ2), the developed platform enables automation of the process of objective assessment of psychophysiological load on instructors by combining multi-source information (noise indicators, cognitive load, expert assessments) with fuzzy multidimensional membership functions. Load prediction ensured compliance with real values within an error range of \(\pm\)7%, which demonstrates sufficient accuracy for practical application. As a result, the proposed system can be integrated into the training process to monitor critical states, optimize the work schedule, and increase the safety of flight training.
## 1 Introduction
### Paper Organization  
The rest of this article is arranged as illustrated in Figure 1. Section 2 introduces an evolution of wireless networks, a detailed progression from 1G to a future 6G network. Subsequently, an overview of 6G technology in terms of requirements, specifications, network architecture, usage scenarios, and currently ongoing research activities regarding 6G technology is provided in Section 3. Section 4 presents key enabling technologies and the role of ML techniques in addressing future 6G network concerns. Section 5 covers the ML capabilities in enabling different enabling technologies of 6G networks. The potential technical challenges, future research directions, and concluding remarks towards accomplishing the goals of 6G technology are then discussed in Section 6.
## 2 Current Technology
### User Satisfaction and System Acceptance  
The culminating phase of integrating the system into a given infrastructure is the end-user's overall evaluation of the technology. User satisfaction, technological self-efficacy, perceived enjoyment, and technological anxiety are all influenced by various factors, which can differ depending on the specific technological system in question. User satisfaction, which is closely related to a user's attitude towards the system, can be affected by factors such as ease of use, design, and overall functionality. Technological self-efficacy, or a user's belief in their ability to use and master new technologies, may be influenced by their prior experience with technology and level of comfort. Perceived enjoyment, or the level of pleasure a user derives from using the technology, can be influenced by factors such as the level of support provided and the overall usability of the system. Finally, technological anxiety, which refers to feelings of fear or apprehension towards new technologies, may be influenced by various factors such as the user's level of confidence and familiarity with the technology. By understanding these factors and how they vary depending on the specific system being used, designers and researchers can create more effective and user-friendly technologies that better meet the needs and expectations of users. Different examples can be found in the e-commerce field [48], where various attributes can be detected through sentiment analysis, attribute analysis, speech analysis, etc. In the nutritional/sports field, examples can be inferred from the medical field [49], where results obtained in the implementation of telemonitoring systems are reported, considering the percentage of system users, periods of activity on the platforms, and the type of communication with the contact center.
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 4 Machine Learning Models
### eXtreme Gradient Boosting (XGBoost)  
XGBoost utilizes gradient descent optimization and any differentiable loss function for development. It is a scalable model as it enhances the computational limits of the machines. In this model, ensembles are built from the D.T.s, which are combined, one at a time, with adjustments made to reduce the prediction errors resulting from the prior models. XGBoost is reported to have high execution speed and better performance compared with R.F. models. However, it employs its own technique of constructing trees where two metrics are used to specify the best node splits: similarity score and Gain. The similarity score (S.C.) is expressed mathematically by Equation (4) [75].  
\[Similarity\ Score\ (SC)=\frac{\left(\sum_{j=1}^{n}{Residual_{i}}\right)^{2}}{ \sum_{i=1}^{n}{\left({P_{pre_{i}}*\left({1-P_{pre_{i}}}\right)}\right)+\lambda}} \tag{4}\]  
where \(Residual\) represents the difference between the actual value and the predicted value. \(P_{pre}\) represents the probability of an event calculated at a previous step. \(\lambda\) is a regularization parameter. After finding the similarity core for each leaf, the Gain is evaluated using Equation (5).  
\[Gain=Left\ leaf_{SC}+Right\ leaf_{SC}-Root_{SC} \tag{5}\]  
Based on the gain value, the node split with the highest value is then selected for the updated D.T. Additionally, XGBoost can be further refined by setting up the value of a hyperparameter called \(Gamma\), which can be set manually or tuned using the grid search method. This hyperparameter can be used to prune the nodes with minimal \(Gain\) value, provided that they have \(Gain-Gamma<0\)[76].
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 4 Discussion
### The Strength Difference between the Onsoy and Tiller-Flotten Clays  
The absolute strength difference between the Onsoy and Tiller-Flotten clays was noticeable. The strength values for the Tiller-Flotten clay were \(\sim\)3-6 times higher than those for the Onsoy clay and \(\sim\)3.7 times higher on average. For CEM as a single binder, the strength was \(\sim\)2.9 times higher. It was noted that the deviations were similar between the two clays, which might reflect the fact that the specimen quality was similar for both clays. The strength difference is, therefore, believed not to be caused by, for example, differences in specimen quality. Further, based on previous research on similar types of clays, these differences seem too large to be explained by differences in, for example, the organic content alone, which was 3-4% for the Onsey clay and <1% for the Tiller-Flotten clay [13; 83]. Previous research has also shown that a lower liquidity index could imply a higher strength [98], but the opposite effect was observed herein as the liquidity indices were approximately 0.9-1.0 and 1.5-2.0 for the Onsey and Tiller-Flotten clays, respectively.  
Moreover, the difference cannot be explained by the differences in the water content relative to the binder quantity. This is typically analysed by using the water-binder ratio, i.e., the weight ratio between the water in the soil to the total \(\alpha\) added during stabilisation [6; 99; 100]. With a \(w\) of \(\sim\)42% and \(\sim\)73% for the Tiller-Flotten and Onsey clays, respectively, this corresponds to a content of water of approximately 530 l/m\({}^{3}\) and 660 l/m\({}^{3}\), respectively, which results in water-binder ratios of \(\sim\)9 and \(\sim\)11, respectively. Previous research on water-binder ratios vs. strength testing on Norwegian soils [6; 82] indicates that the strength difference should only be approximately 20%, i.e., this does not explain the large strength differences between the two clays studied herein.  
The strength differences between the two clays were also difficult to assess based on the compositional analyses. In addition, since previous studies have shown large strength differences between stabilised clays from different geographical regions without a satisfactory explanation [6; 82; 83; 101], there is a need for further studies on this topic.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 2 Background
### Tamper-Evident Logging Using Merkle Trees
#### 2.2.3 History Tree  
History Tree is a data structure used in tamper-evident history logging [12]. It is a versioned hash tree wherein each change to the tree is identified as a distinct version. Events are sequentially added as leftmost leaf elements, updating the tree into a new version each time. Like the Merkle tree, the interior elements of a History Tree are labeled with the hash of the concatenated labels of their child elements. This process continues recursively to the root. Consequently, the root hash of the History Tree becomes a commitment that fixes the integrity of the entire content of the tree. This commitment captures a snapshot of the History Tree representing all events up to the given version number.  
More formally, when a new event \(X_{i}\) is introduced, the History Tree is updated by appending \(X_{i}\), leading to the version-\(i\) tree, and a corresponding commitment \(C_{i}\) is computed. This commitment, \(C_{i}\) fixes the log containing events from \(X_{0}\) to \(X_{i}\). Each commitment thus represents a unique snapshot that captures the cumulative state of the log up to version \(i\).  
Frequent auditing by verifying the proofs is essential to maintain the integrity of logs. History Tree uses two types of proofs to support auditing: incremental and membership.  
Incremental proofs validate the continuity and integrity between different versions of the tree (i.e., different commitments), confirming that two commitments share the same historical log data. In contrast, membership proofs are used to verify the presence of a specific event in a particular version of the History Tree. In combining these two proofs, the History Tree ensures the historical consistency of the log.
## 2 Materials and Methods
### Testing
#### 2.2.3 Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD)  
The particle shape and surface texture were determined through Scanning Electron Microscopy (SEM). The electron beam was scanned directly on the material's surface, and this produced various signals, which were then analysed [24]. In this research, the backscattered electron imaging (BSE) was used to determine the shape of the particle at spot magnification levels 500\(\times\) and 10,000\(\times\). The acidic/basic character of fillers was examined through mineralogical analysis. XRD helped to quantify different types of minerals, which dictates the properties of fillers. Filler particles were composed of various minerals; each of these minerals has a different crystalline structure and chemical composition. The composition of minerals has its effect due to complex processes that occur at the surface of mineral aggregate and its affinity both for bitumen and water.
## 3 Materials and Methods
### Total Phenolic and Profile of Phenolic Compounds  
- Total phenolic content analysis  
The tomato extract (5 \(\mu\)L) was mixed on the plate well with 95 \(\mu\)L of distilled water, 10 \(\mu\)L of Folin-Ciocalteu reagent and 20 \(\mu\)L of 20% Na\({}_{2}\)CO\({}_{3}\). After 30 min of incubation, the absorbance was measured at 700 nm, and the obtained results were expressed as gallic acid equivalent per 100 g of fruit tissue.  
Tomato fruits collected at harvest maturity from the tested experimental variants were analyzed to determine the effect of the tested factors on the phenolic compound profile. The phenolic compound profile in tomato fruits was determined using the UPLC-PDA-MS/MS method.  
- Profile of phenolic compounds  
The determination of polyphenolic compounds was carried out using the ultra-performance liquid chromatography (UPLC) Waters ACQUITY system (Waters, Milford, MA, USA). The UPLC system was equipped with a binary pump manager, column manager, sample manager, photodiode array (PDA) detector and tandem quadrupole mass spectrometer (TQD) with an electrospray ionization (ESI) source. The separation of polyphenols was performed using a 1.7 \(\mu\)m, 100 mm \(\times\) 2.1 mm UPLC BEH RP C\({}_{18}\) column (Waters, Milford, MA, USA). For the investigation, the mobile phase consisted of 0.1% formic acid in water, \(\upsilon/v\) (solvent A) and 0.1% formic acid in 40% acetonitrile, \(\upsilon/v\) (solvent were used). The flow rate was kept constant at 0.35 mL/min for a total run time of 8 min. The system was run with the following gradient program: from 0 min 5% B, from 0 to 8 min linear to 100% B and from 8 to 9.5 min for washing and back to initial conditions. The injection volume of the samples was 5 \(\mu\)L, and the column was supported at 50 \({}^{\circ}\)C. The following TQD parameters were used: cone voltage of 30 V, capillary voltage of 3500 V, source and desolvation temperature 120 \({}^{\circ}\)C and 350 \({}^{\circ}\)C, respectively, and desolvation gas flow rate of 800 L/h. The characterization of the individual polyphenolic compounds was performed on the basis of the retention time, mass-to-charge ratio, fragment ions and the comparison of data obtained with commercial standards and literature findings. Obtained data were processed in the Waters MassLynx v.4.1 software (Waters, Milford, MA, USA) [43]. See Supplementary Materials S1.  
The following chemicals were used to determine the polyphenol profile of tomato fruit:  
- Folin-Ciocalteu reagent, (Pol-Aura, Zabrze, Poland)  
- sodium carbonate, purity 99,5% (Sigma-Aldrich, Saint Louis, MO, USA)  
- gallic acid, purity 99,85% (Sigma-Aldrich, Saint Louis, MO, USA)  
- 0.1% formic acid, pure about 85% (Sigma-Aldrich, Saint Louis, MO, USA)  
- 40% acetonitrile, purity \(\geq\) 99.5% (Pol-Aura, Zabrze, Poland)
## 4 Discussion
It is possible to estimate an offset value for M2 when trying to improve the position accuracy if there are some control markers with a known position visible during a trial; the mean error of these markers can be estimated and removed from the main data. However, since more reference markers in the pool are necessary for this approach, the usage of M1 is preferable, if possible. M2 shows the same behavior in the swimming protocol, despite some larger errors in the positions of the markers, since the results of leg kick amplitude were quite similar between the methods. The mean error in terms of velocity should be 0.8% and 1.5% for M1 and M2, respectively, considering the cycle duration and mean error of the hip coordinate compared with those of the static camera. The use of a static camera is the most common method in 2D kinematic analysis, and this is why M1 and M2 were compared to it. However, it is important to remember that the static camera is not a gold standard. In fact, based on the results of the two validation protocols, the results of M1 are more likely to be closer to the real value. In this way, a larger difference between cameras does not necessarily mean a larger error. Considering swimming velocity, M2 presents a slightly higher mean velocity with larger variability. Observing Figure 3, M2 evaluated a higher peak velocity and a lower minimal velocity, but the shape of the curves were quite similar: a single peak in the first arm stroke and a double peak in the second one, where all the peaks and minimal data were recorded approximately at the same moment for every method. The Bland-Altman plots reveal an acceptable number of outliers (about 5%) for all the comparisons, and the distributions of the differences appear uniform across the estimated values for most of the cases, except for the comparison between M2 and the static camera for the rod and swimming protocols. The behavior in these situations seems to be contradictory, while in the swimming protocol, M2 improves the results (overestimated the higher values and underestimated the lower values), the opposite occurs in the rod protocol. The translation of the moving camera in the direction perpendicular to the image plane could generate this behavior, but this was probably the most constrained degree of freedom of the camera caused by the trolley rail.