esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The findings presented in the paper for reference boards (with simulated defects) show that the noncontact measurement technique and Lamb waves can be effective tools for defect detection in flat boards and will serve for interpretation of the results obtained for other flat (composites, plasterboards) or corrugated boards (cellulose-cement, extruded glass or acrylic boards). The experiments performed in the present paper were conducted in laboratory conditions. However, following the proper calibration, the developed measuring method can be used to test board materials in industrial conditions.  
The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection and in the presented ultrasonic method, they are the first described in this way.  
This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future applications of Industry 4.0/5.0.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app142210638/s1](https://www.mdpi.com/article/10.3390/app142210638/s1)]([https://www.mdpi.com/article/10.3390/app142210638/s1](https://www.mdpi.com/article/10.3390/app142210638/s1)).  
Conceptualization, A.K., R.D. and M.P.; methodology, A.K., R.D., M.P., D.M. and I.R.; software, A.K., R.D., M.P., D.M. and I.R.; validation, A.K., R.D., M.P., D.M. and I.R.; formal analysis, A.K., R.D., M.P., D.M. and I.R.; investigation, A.K., R.D., M.P., D.M. and I.R.; resources, A.K., R.D., M.P., D.M. and I.R.; data curation, A.K., R.D., M.P., D.M. and I.R.; writing--original draft preparation, A.K., R.D., M.P., D.M. and I.R.; writing--review and editing, A.K., R.D., M.P., D.M. and I.R.; visualization, A.K., R.D. and M.P.; supervision, M.P. and I.R. project administration, M.P. and I.R. funding acquisition, M.P. and I.R. All authors have read and agreed to the published version of the manuscript.  
The work presented in this paper has been financed under a grant to maintain the research potential of Kazimierz Wielki University.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Thermal Characteristics Study of the Bump Foil Thrust Gas Bearing
## 3 Thermal Analysis
### Analysis of Influence Factors of Bearing Temperature
As the rotor speed increases, the convective heat exchange effect between the outer edge of the thrust plate and the environment becomes much stronger. Figure 14: Relationship between thrust plate temperature and speed. Figure 13: Relationship between bearing temperature and speed. Figure 15 shows the relationship between bearing temperature and the flow rate of cooling gas under the rotor speed of 100 k\(\mathrm{rpm}\) and static load of 100 N. The results show that when the flow rate of cooling gas gradually changes from 0 to 0.2 m\({}^{3}\)/min, the bearing temperature drops rapidly. If the gas flow rate is further increased, the cooling effect will be weakened gradually. Therefore, in engineering applications, the appropriate flow of cooling gas passed into the foil structure should be designed to decrease the bearing temperature with the maximum efficiency.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 3 Constellation Observation Optimal Scheduling  
### Overview of Task Scheduling  
Due to the large number of space objects [26], as shown in Figure 7, the space-borne optical telescope can only track one space object at a time. In order for the satellite constellation to monitor space debris during all weather events and at all times and achieve multi-satellite coordination, make full use of space-based observation equipment resources, reduce inefficient repeated observation, and improve the observation efficiency, it is necessary to develop an optimal scheduling algorithm for the observation tasks of space-based satellite constellations. This includes formulating efficient observation plans for each satellite and improve the monitoring efficiency.  
The goal of space-based observation equipment detection task scheduling is to allocate detection resources to significantly improve the detection and prediction capabilities for space objects to a certain extent. This allocation must consider many influencing factors, such as the observation capability of each sensor, the orbit accuracy of each observation object, and the detection frequency required to maintain a catalog of space objects. Space-based optical satellite observation task scheduling is a combined optimization problem under complex constraints. Its observation equipment and observation targets move at high speeds; it exhibits multi-time window constraints, multi-resource constraints, and  
Figure 6: The airspace coverage performance of each constellation.  
Figure 7: Growth trend in the number of space objects.  
high conflicts; and it has become one of the leading issues in the field of space observation. When the amount of observation equipment and targets increases, the complexity of the optimization problem will increase geometrically.
## 3 Chidroid Architecture  
The Chidroid architecture is presented in Figure 2 and describes how the application collects data from Android devices and distributes them to other endpoints. Once the application is installed and configured, it collects logs and system metrics.  
The Chidroid architecture (Figure 2) allows the efficient collection and secure distribution of logs and system metrics from Android devices. By using shell commands and the API, Chidroid can provide valuable information for monitoring and optimising the performance and security of Android devices. Chidroid generates, collects, and distributes the following logs: (i) logcat.log, (ii) packages.log, and (iii) privileges.log, along with (iv) metrics. Internally, Chidroid uses a scheduler to run user-defined scripts or shell commands to generate the logs or data. This scheduler follows the familiar CRON syntax, extended to add seconds. After collecting logs and system metrics, Chidroid encodes them in JSON and distributes them to the chosen target collector for analysis. This collector can be a cloud service or another web server that is able to receive and process JSON data.  
Chidroid can be remotely configured using the POST method, and the API can replace the configuration files, namely config.tml and rules.tml. When Chidroid starts up, it reads these configuration files and sets up the log input sources, internal settings, and output sinks from _config.tml_. From _config.tml_, Chidroid reads the processing and transforming rules, for example, regular expressions or event-breaking rules. It is noteworthy that the rules.tml file possesses the capability to integrate privacy-centric rules, which can be invoked during the course of log collection. Such rules serve to enable data transformation, which is geared towards privacy enhancement objectives. This is achieved through the application of anonymisation and other pertinent processes to the collected logs, adding privacy features. As for distribution, Chidroid can ship logs in the raw format or JSON encoded. If needed, traffic can be relayed on another Chidroid listener (peer-to-peer distribution).
## 3 Prototype and Performance Test
### Field Test of Proposed TOBV in CCPPs  
The prototype of the proposed TOBV was installed at the gas turbine in the CCPPs and was operated for three months to evaluate durability and suitability for the actual working environment. Leakage could not be measured at the rear end of the turbine, so sealing performance of the new  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Internal Pressure (MPa)** & **Duration Time (s)** & **Result** \\ \hline \multirow{2}{*}{Internal pressure test} & Regulation & 1.5 & 160 \\  & Actual & 1.7 & 182 \\ \hline \multirow{2}{*}{Leakage test} & Regulation & 1.1 & 125 \\  & Actual & 1.15 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of performance tests.  
Figure 20: Conditions and results of the performance tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Allowable Strength (MPa)** & **Maximum Equivalent Stress (MPa)** & **Yielding** \\ \hline Body & 184.25 & 158.30 & No \\ Shaft & 385.25 & 143.19 & No \\ Disc & 137.35 & 117.51 & No \\ SS & 137.35 & 56.50 & No \\ G & 4.69 & 0.24 & No \\ \hline \hline \end{tabular}
\end{table}
Table 9: Structural safety of the proposed TOBV.  
model was predicted by comparing its temperature with that of the conventional valve, as shown in Figure 21. Perfect sealing blocks the hot gas flow into the outlet pipe, which allows the temperature in the rear end to decrease. The measurement results show that the gas (350 \({}^{\circ}\)C), which entered into the inlet pipe during the disc opening, elevated sharply the temperature at the rear end from 27 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. After then, the temperature decreased at fully shut-off position, and it kept in an almost steady state. The time to be required for opening and closing the disc was 4.25 s, and its cycle was one day. Temperature of the new model (32 \({}^{\circ}\)C) decreased by 20%, compared with that of the conventional one (40 \({}^{\circ}\)C), which demonstrates that the proposed laminated seal improved the sealing ability of the TOBV.
## 3 Results
### Classification Method Comparison
#### 3.1.3 Airline Passenger Satisfaction  
Figure 5 presents the same classifications methods as Figures 2 and 4. This dataset, however, was far more predictive than the previous datasets, with accuracies over 95% for all methods except logistic regression. Additionally, all methods were consistent with a slighter higher precision than accuracy, and a slightly lower recall than accuracy. WoC-Bots performed similarly to XGBoost, AdaBoost, and Random Forest, better than logistic regression, and slightly worse than the deep neural network. With most methods already performing well, the meta-swarm did not improve over any of them, though, did reduce variability between accuracy, precision, and recall. Table 7 shows the confidence intervals for the meta-swarm for this dataset. Over 15% of samples fell in the \"Very High\"  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Interval** & \(n=3699\) & **\% of \(n\)** & **Accuracy (\%)** \\ \hline Very High Confidence & 51 & 1.4 & 96.1 \\ High Confidence & 958 & 25.9 & 91.6 \\ Medium Confidence & 1261 & 34.1 & 84.5 \\ Low Confidence & 1428 & 38.6 & 77.3 \\ \hline Very High + High + Medium & 2271 & 61.4 & 87.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confidence Interval Distribution and Accuracy—Meta-Swarm for Hollywood Success.  
Figure 4: Comparison of five classification methods with two versions of the swarm (first and last columns) for the Hollywood movies dataset.  
interval, over 70% in the \"High\" interval with the remaining 15% split almost evenly between \"Medium\" and \"Low\" confidence. The accuracy values for each interval were very similar, with the \"Very High Confidence\" interval being outperformed by the \"High\" and \"Medium\" intervals. We did not find the confidence intervals to be useful for this dataset; the airline data are highly predictive which made it difficult to stratify samples into confidence intervals.
## 1 Introduction
The proposed method recommends TV programs by merging different time contexts without additional external information with minimum computation costs, which is a great advantage for standalone TVs compared to the conventional TV recommender systems. We choose channels and genres of programs as features for the recommendation. In general, channels and genres are available information in standalone TVs. The channels chosen by users are known in TVs and the genres of chosen programs are usually available in the EPG. Also, they have strong correlations with user preferences on programs. For example, a user who frequently watches news tends to watch news channels or programs in the news genre. So, user preferences on TV programs can be effectively modeled based on channels and genres of watched programs. In order to effectively model complicated periodic patterns, the proposed method performs multi-time contextual profiling and constructs several time contextual preference matrices of channels and genres. The proposed method combines those time contextual preference matrices to infer user preferences on channels and genres. Since multiple preference models are constructed with different time contexts, there can be conflicts among them. To merge the preferences with the minimum number of conflicts, we develop a quadratic programming (QP) model. The optimization problem is formulated with a minimum number of constraints so that the optimization process is scalable and fast even in a standalone TV with low computing power. The channel preferences and the genre preferences are merged to calculate recommendation scores of live programs. The proposed method is validated with a real-world dataset. The remainder of this paper is organized as follows. Section 2 surveys the related works. Section 3 presents the proposed TV recommender system and explains each part of the system. Section 4 reports the experimental results and offers some discussions. The conclusions are presented in Section 5.
## 2 Related Work
### Inverse Rendering  
The formation of face images is mainly affected by intrinsic face properties and external factors. Intrinsic properties consist of shape (geometry) and albedo (skin properties), while external factors include pose, illumination, expression, camera setting, etc. Inverse rendering refers to reversely decomposing internal and external properties in facial images. Many inverse rendering methods have been proposed. CNN3 DMM [44] represents shape and albedo, respectively, as a linear combination of PCA bases and uses a CNN to regress the combination coefficients. SfSNet [45] mimics the process of imaging faces based on physical models and estimates the albedo, light coefficients, and normal of the input face image.  
As one of the intrinsic properties, albedo has natural advantage for face recognition owning to its robustness to variations in view angle and illumination. However, most inverse rendering algorithms pay more attention to recovering a more accurate and detailed 3D face shape, and treat the albedo as an ancillary result. As one of the few algorithms using albedo to assist face recognition, [PERSON] and [PERSON] [46] captured the personal specific shape and albedo properties by fitting a morphable model of 3D faces to 2D images. The obtained model coefficients that are supposed to be independent of external factors can be used for face recognition. However, due to the limited representation ability of the statistical model, the recovered albedo would lose its discrimination to some extent. To solve this problem, [PERSON] et al. [47] proposed to generate albedo images with frontal pose and neutral expression from face images of arbitrary view, expression, and illumination, and extract robust identity features from the obtained albedo images. They experimentally showed that albedo is beneficial to improving face recognition. However, they only realize the synthesis of normalized albedo images in two-dimensional image space, lacking the exploration on the principle of physical imaging, which leads to a poor performance on a cross-database.
# Recent Case Histories of Carbon-Neutral Activity Using Ground Improvement Technology in Japan
## 3 Recent Developments in and Application of Environmentally Friendly Ground Improvement Techniques  
### Soil Densification Techniques  
(1) Use of industry waste  
The sand compaction pile method (SCP) is one of the densification techniques, in which many compacted sand piles are constructed in the ground to improve stability, for reducing ground settlement, and for preventing liquefaction [3]. Sand has been used in this method, but since the 1990s, many types of granular materials, including construction by-products such as slag [7], granular coal fly ash [8] and granular stabilized soil [9], have been beneficially used in this method. By promoting the beneficial use of these materials, these methods can provide a reduction in natural materials that can produce CO\({}_{2}\) emission.  
(2) Discharge of biochar into the ground together with granular material  
Recently, a new type of SCP method has been developed to achieve carbon dioxide reduction, in which biomass is discharged into the ground together with sand [10,11]. The sand is expected to increase the strength of the compacted pile, and the biomass is expected to store CO\({}_{2}\) in the ground. Since the amount of carbon dioxide stored in the ground is overwhelmingly larger than the CO\({}_{2}\) emitted in ground improvement work, this new technology can be described as being a \"negative emission technology\". A field test revealed the high applicability of the biomass material, bamboo chips, in which the strength and shape were confirmed to be equivalent to those of conventional sand piles (Figures 4 and 5 [11]). In the SCP method, a total of 5 kg CO\({}_{2}\) per m\({}^{3}\) of improved ground is emitted in construction when natural sand is used. In this new SCP method, however, carbon dioxide can be stored in the ground and the amount of CO\({}_{2}\) emission can be decreased to \(-20\) kg CO\({}_{2}\) due to a larger amount of stored CO\({}_
# Evaluation of Urban Traffic Accidents Based on Pedestrian Landing Injury Risks
## 2 Methodology
### Parametric Research Proposal  
Figure 1 shows the extraction and loading process of boundary parameters for the pedestrian-to-ground impact simulations. First, six real-world pedestrian traffic accidents  
were completely reconstructed with reference to the accident reconstruction method proposed by [PERSON] et al. [16]. The detailed workflow of accident reconstruction is depicted in Figure 2. The key parameters related to accidents with vehicles, pedestrians, roads, and the environment are summarized in Table 1. The reconstructed pedestrian kinematics were compared with the video recordings, as shown in Figure A1 (Appendix A). The quantitative validation of reconstruction results and the linear and angular velocity components at the COG of the pedestrian's body at the moment of 2 milliseconds before the body-to-ground contact were extracted and summarized in Table 2. The average linear velocities along the \(x\), \(y\), and \(z\) axes were calculated to be 2.4 m/s, \(-1.0\) m/s and \(-5.6\) m/s respectively, and the average angular velocity around the \(y\)-axis was 4.7 rad/s\({}^{2}\). Second, the average linear velocity and angular velocity components were loaded into the COG of THUMS v4.0.2 for body-to-ground collisions. To explore the effect of PFO on body-to-ground contact injury, three PFOs were formed by rotating the initial orientation of a standing pedestrian around the \(z\)-axis at 90-degree intervals, coding the PFOx+, PFOx-, and PFOy+, respectively. Third, each PFO is rotated around the \(y\)-axis in 45\({}^{\circ}\) increments, resulting in eight different PRAs. Finally, a total of 24 pedestrian-to-ground impact configurations were defined.
## Appendix A Calculation of Jump Height from Flight Time  
It is noted that the assumption for this calculation is that the height of the COM\({}_{\text{wb}}\) is the same at takeoff and landing of the jump (\(H_{\text{v}}\) is equal to \(H_{\text{t}}\)). Once an object is projected into the air, the COM\({}_{\text{wb}}\) must follow a parabolic trajectory, and the trajectory cannot be altered in the air until landing because only the gravitational acceleration is applied to it. Therefore, the vertical velocity of the COM\({}_{\text{wb}}\) is calculated as  
\[V(t)=\ V_{0}-gt \tag{1}\]  
where \(V(t)\) represents the vertical velocity, \(V_{0}\) represents the initial velocity, and \(t\) represents the time of travel. As \(V(t)\) becomes zero at the highest point during flight phase the time from the takeoff to the highest point (\(t_{\text{top}}\)) is expressed as  
\[V_{\text{to}}-gt_{\text{up}}\ =\ 0 \tag{2a}\] \[V_{\text{to}}\ =\ gt_{\text{up}} \tag{2b}\]  
\(t_{\text{up}}\) should be half of the flight time, with the peak of the jump happening at exactly the midpoint of the flight time, expressed as follows:  
\[V_{\text{to}}\ =\ \frac{1}{2}gt_{\text{flight}} \tag{3}\]  
Substituting Equation (3) for Equation (2), we obtain Equation (3) as follows:  
\[H_{\text{t}}\ =\ H_{\text{v}}\ =\ \frac{1}{2g}\bigg{(}\frac{1}{2}gt_{\text{ flight}}\bigg{)}^{2} \tag{4}\]  
\[H_{\text{t}}\ =\ \frac{1}{8}gt_{\text{flight}}^{2} \tag{5}\]  
Appendix B Calculation of the Flight Time from the Vertical Displacement of the COM\({}_{\text{wb}}\)  
The vertical displacements of the COM\({}_{\text{wb}}\) travelling from takeoff to the highest point (vertical velocity becomes zero) and from the highest point (vertical velocity is zero) to landing are both expressed as  
\[h(t)\ =\ \frac{1}{2}gt^{2} \tag{6}\]  
where \(h(t)\) represents the vertical displacement. During the time from the takeoff to the highest point (\(t_{\text{up}}\)), \(h(t)\) is equal to \(H_{\text{v}}\), and Equation (6) gives  
\[H_{\text{v}}\ =\ \frac{1}{2}gt_{\text{up}}^{2} \tag{7}\]  
\[t_{\text{up}}\ =\ \sqrt{\frac{2H_{\text{v}}}{g}} \tag{8}\]  
On the other hand, during the time from the highest point to landing (\(t_{\text{down}}\)), \(h(t)\) is the sum of \(H_{\text{v}}\) and \(\Delta COM_{wb}\), and Equation (6) gives  
\[H_{\text{v}}+\Delta\text{COM}_{\text{wb}}\ =\ \frac{1}{2}gt_{\text{down}}^{2} \tag{9}\]\[t_{\text{down}}\ =\ \sqrt{\frac{2(H_{\text{v}}+\Delta\text{COM}_{\text{wb}})}{g}} \tag{10}\]  
The flight time (\(t_{\text{flight}}\)) is the sum of \(t_{\text{up}}\) and \(t_{\text{down}}\). Therefore, \(t_{\text{flight}}\) is expressed as  
\[t_{\text{flight}}\ =\ \sqrt{\frac{2H_{\text{v}}}{g}}\ +\ \sqrt{\frac{2(H_{\text{v}}+ \Delta\text{COM}_{\text{wb}})}{g}} \tag{11}\]