esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
## 3 Numerical Simulation of the Conveying Process  
### Fluid Parameter Settings  
To enhance computational accuracy while minimizing computational overhead, the fluid domain mesh was optimized through a grid independence study involving the following three distinct meshing configurations: (1) a baseline coarse global mesh with 740,000 elements; (2) an adaptively refined mesh featuring localized encryption at critical regions (the orifice flow zone and chute junction) totaling 890,000 elements; and (3) a fine global mesh comprising 1,190,000 elements. Velocity contour analysis conducted at the mid-chute plane (Figure 3) demonstrated that while the maximum flow velocities showed minimal variation between configurations 2 and 3 (Figure 4), the computational resource requirements for configuration 3 were substantially higher. This trade-off analysis justified the selection of configuration 2 as the optimal meshing strategy (Figure 5), achieving  
intense gas-solid two-phase turbulence. The computational parameters were set as follows: time step size of 0.05 s and automatic data saving every 10 steps (0.5 s interval). The simulation utilized a Eulerian multiphase flow model, with the standard k-epsilon model for turbulence and the Gidaspow model for drag force calculation. The physical properties of standard air at normal temperature and pressure were selected as the working fluid, with detailed parameters presented in Table 1.
# Fragile Watermarking of 3D Models in a Transformed Domain
## 7 Discussion
### Security Considerations  
The security of the method was based on the secrecy of the KLT basis. Thus, this set of orthonormal vectors constituted the secret key. This basis may be derived from any sample of vectors belonging to a vector space of the right dimension: in our case, the EU was composed of 28 bytes; thus, the vector space should be of dimension 28.  
For example, considering a grayscale image and building vectors from pixel values taken in raster scan order allowed the computation of a covariance matrix from which to derive an orthonormal basis (as shown in Section 3): if the image were kept secret, so would be the KLT basis and consequently the embedding space of the watermark bit string. This prevented an attacker from creating forged vertices embedding a correct watermark given that she/he would not know the kernel basis of the transform.  
If the length of the watermark stored in each EU were \(m\) bits (i.e., the payload was \(m\) bpv), then the probability that a forged vertex would not be detected as such would be \(1/2^{m}\). Nonetheless, as previously said, if the modification were in the higher part of the vertex data, then all the polygons sharing that vertex would detect the modification: given that for each polygon, the probability to fail detection would be \(1/2^{m}\), if the forged vertex were shared amongst \(k\) polygons, then the probability that the tampering went undetected by all polygons would be \(1/2^{mk}\).
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 2 Materials and Methods
### Population Estimation at Fine-Scale
#### 2.3.2 Population Estimation Model  
Residential houses in buildings are the main focus of a population's spatial distribution. Thus, changes in the number of housing units reflect changes in the size of the population. The era of big data and improved dynamic data acquisition technology have increased the availability of building data, thereby meeting the requirements for population estimation at a fine scale. Our proposed method involves multi-source data processing to classify urban buildings, before selecting residential houses from the classification results.  
The traditional residential unit method is used to calculate the population by multiplying the number of residential units by the average number of people per household in a statistical area. In addition, the overall living area of buildings is an important feature used for estimating populations. The contribution of buildings with different floor numbers to population estimates will vary. Based on the relevant national regulations, we classify residential buildings with 0-3 stories as low-rise residential buildings, residential buildings with 4-6 stories as middle-rise residential buildings, and residential buildings with seven stories or more as high-rise residential buildings. We can obtain the following characteristics of subdistrict buildings based on their classifications: (1) total floor area of buildings, (2) total floor area of low-rise buildings, (3) total floor area of middle-rise buildings, (4) total living area of high-rise buildings, (5) number of low-rise buildings, (6) number of medium-rise buildings, and (7) number of high-rise buildings. According to the number of floors in residential buildings, the characteristic variables are counted such as the perimeter, area, and total floor number. The random forest method is then employed for variable selection and association analysis.  
The inputs for the random forest method comprise the predictor variable X and the response variable Y. X representing seven statistical features of buildings in a subdistrict, and Y is the actual population of the subdistrict. Random forest regression training is conducted after normalizing the input variables. The importance values are calculated for the seven building features, and the first K are selected in order to construct the multiple regression model.
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 2 Methodology  
Papers were found using major search engines and through publishers including Google Scholar, IEEE Explore, Research Gate, Elsevier, Springer, and the Omni database [9]. As this is primarily an operations research topic, operations research journals were investigated thoroughly. In addition, in order to fully capture the entire perspective, papers from the domains of computer science, engineering, and medicine. The following journals and conferences were reviewed: Operations Research for Health Care, Canadian Medical Association, INFORMS Journal on Applied Analytics, Telecommunications and Signal Processing, IIE Transactions on Healthcare Systems Engineering, Production and Operations Management, Canadian Journal of Surgery, International Journal of Production Economics, Expert Systems with Applications, International Conference on Computing & Communication Technologies, Institute for Operations Research and the Management Sciences, Health Care Management Science, Health Systems, Annals of Operations Research, IEEE Explore, PATAT (Practice and Theory of Automated Timetabling), Decision Support Systems, Omega (United Kingdom), Journal of Medical Sciences, Production and Operations Research, Expert Systems with Applications, Principles and Practice of Constraint Programming, Machine Learning for Health, and European Journal of Operations Research. These journals were chosen as they contained papers which met all of our criteria. The journals were found through citations and the above mentioned search engines. If a journal is not mentioned we did not find a paper which met the requirements.  
Papers were filtered based on location, date, and focus. In order to be accepted the paper must either have come from a Canadian university or worked directly with a Canadian hospital. Papers that were more recent were also weighted higher than those which were older. We made sure to have at least 8 in each category. About half of the papers are from the last 5 years with the oldest being from 1987. The selected papersneeded to present an implementation for an optimization model and presented the results. Additionally, supplementary algorithms such as discrete simulation models were accepted if they directly contributed to creating a schedule.  
Over 100 papers were reviewed to ensure that this selection is encompassing of the domain we wish to survey. This review involved an initial read to ensure that they were relevant to the problem. These 100 were filtered to approximately 75 papers that mentioned Canadian healthcare, of which 40 worked with Canadian hospitals. The 40 which worked with Canadian hospitals were then thoroughly read and surveyed below. We believe these represent the literature's current state of improving scheduling within Canadian healthcare centres. Papers beyond the 40 were discarded as they did not meet the standards.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 3 Results
### DGPS Test  
As a final test of the proposed methods, a DGPS test using a Double Difference DGPS setup as defined in [2] was performed. The choice of DGPS for the test of the proposed methods, instead of RTK, stems from the fact that, in the present paper, only pseudorange-based positioning was performed, which makes DGPS the most suitable method to benchmark the results of the present research. This test used the u-blox 6T receiver connected to an RF2 antenna from the previous tests as a base station, and another, identical receiver-antenna pair as a rover, with the antenna designated as RF6. This test took the mean position estimate of the base station since the start of the survey at 4 h intervals, using both EKF and UKF to estimate the base station position, and used that estimate to determine the rover receiver during a 4 h survey. The resulting mean error is presented in Table 4, with the rover position error during the survey presented in Figures 12 and 13.  
Figure 12: Rover position error with base station position obtained with EKF and averaged after a set number of hours.  
Figure 13: Rover position error with base station position obtained with UKF and averaged after a set number of hours.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 5 Simulation
### Performance of the Proposed Algorithm with Different Array Geometries  
We first evaluate the performance of the proposed algorithm with different array geometries. As claimed before, the \(7\times 7\) URA is used as the benchmark. Then, we deploy two larger URAs with the sizes of \(10\times 10\) and \(12\times 12\). \(K=2\) sources come from \((35^{\circ},25^{\circ})\) and \((68^{\circ},57^{\circ})\), respectively. The numbers of impaired sensors in these URAs are all set to 5. The signal-to-noise ratios (SNR) of sources are equally set to 10 dB, and the number of snapshots is fixed to \(T=100\). We run 10 trials, and present the estimation results in Figure 3. It is clear that all URAs with different geometries can accurately estimate the source DOAs under sensor failure. Meanwhile, with a larger array size, the estimation accuracy increases.
## 5 Implementation and User Scenario
### Technical Validation and Experimental Results
#### 5.4.6 Discussion
**Latency in Real-Time Adaptation:** Despite AI-MDD-UX's ability to dynamically track and respond to user interactions, latency issues may arise in high-traffic scenarios. The computational demands of KMeans++ clustering, RL-based policy learning, and UI rendering updates contribute to potential delays. Future improvements will integrate intelligent caching mechanisms and edge computing infrastructure to optimize processing time while reducing latency. This will be combined with an asynchronous RL architecture that separates policy updates from real-time interaction handling, enabling continuous model improvement without compromising system responsiveness. 4. **Interpretability and Explainability Issues**: The black-box nature of GANs and RL-based UI adaptation presents challenges in explainability, making it difficult for UX designers and business stakeholders to fully understand how UI changes are generated. Enhancing transparency through Explainable AI (XAI) techniques could improve trust and provide actionable insights into UI decision-making. Future development will integrate Explainable AI (XAI) tools, particularly SHAP-based visualization tools, to make UI adaptation decisions transparent. We will concurrently assess how these system explanations impact user trust, evaluating statements such as \"this interface adjusted based on your frequent interactions with a given feature\". Future enhancements to AI-MDD-UX would benefit from meta-learning techniques to improve UI adaptation for new users and adaptive reinforcement learning strategies to improve decision-making speed.
# Simultaneous Lightwave and Power Transfer for Internet of Things Devices
## 3 System Design and Experimental Methodology
### Energy Harvesting  
Figure 5 shows the charging of the system's supercapacitor using sunlight and various light sources placed 30 cm from the solar panel, including a 430-nm blue laser diode, 50-W LED, and a 100-W incandescent lamp. The measured illuminance from the sun during the experiment is 82,300 lux (obtained using an EXTECH 401025 Light Meter). The illuminance values of the LED and incandescent lamp are 11,300 and 2860 lux, respectively.  
The fastest charging is when the device is directly exposed to sunlight. Charging the device using a 430-nm laser takes the longest. Besides the low lux output of the laser compared to the other light sources, the collimation nature of laser beams leads to a small filled area of the solar panel when placed at a short distance of 30 cm. The weak responsivity of silicon at the blue wavelength region also decreases the charging efficiency.
## 2 Results and Discussion
### Assembly Assessment  
By comparing our results to _Gentiana_ sequences downloaded from the NCBI (Available online: [[http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov)]([http://www.ncbi.nlm.nih.gov](http://www.ncbi.nlm.nih.gov))), we demonstrated that the assembly succeeded in constructing a large amount of transcripts with desirable length. Of 43,611 _Gentiana_ sequences, 33,773 (77.4%) sequences were represented in our assembly (Megablast, _E_-value was 10\({}^{-9}\)), among which 23,908 (70.8%) sequences were matched with more than 80% identity and 80% coverage. RNA-Seq reads were mapped back to the assembly to calculate the proportion of reads assembled, indicating a statistic report comparable to other _de novo_ assemblies. The total alignment rate was 92.72% (Table 2), and 78.3% of the mapped paired-reads aligned concordantly, which showed good physical evidence of sequence contiguity. Transcript length (such as N50, average length) is another broadly used parameter to overview the quality of the transcriptome assembly. As shown in Figure 1, the unigenes ranged from 201 to 16,728 bp, with a mean length of 753 bp and an N50 length of 1384 bp, which is comparable to similar RNAseq reports. Thus, we have successfully constructed a desirable assembly from Illumina paired-end sequencing.  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Contigs** & **Unigenes** \\ \hline Total number & 189,576 & 76,717 \\ Total length (bp) & 228,624,912 & 57,734,637 \\ Mean length (bp) & 1206 & 753 \\ N50 (bp) & 1996 & 1384 \\ GC content (\%) & 39.7 & 39.5 \\ Number of length \(\geq\) 500 bp & 120,525 & 29,795 \\ Number of length \(\geq\) 1000 bp & 81,919 & 16,332 \\ Reads mapping rate (\%) & 92.72 & **-** \\ \hline \end{tabular}
\end{table}
Table 2: Summary of the transcriptome assembly of _G. rigescens_.  
Figure 1: Length distribution frequency of the unigenes in _G. rigescens_.
## 5 Case Study
### Maintenance Period of Secondary Critical Machines for the Automotive Part Production Line  
As mentioned in Section 5.2, the cumulative failure probability of machine should be lower than the \(F_{\max}\) and \(F_{\max}\) = 1 \(-\) 0.7 = 0.3. The value of \(F_{\max}\) was taken into Equation (16), and the preventive maintenance period \(T_{s}\) under reliability constraints was gained, which was equal to 13 days. According to Equation (18), the cumulative failure probability of each secondary critical machine \(F_{m}(T_{s})\) during \(T_{s}\) period were gained. The values of \(F_{m}(T_{s})\) for the secondary machine are shown in Table 5.  
As shown in Table 5, the cumulative failure probability \(F_{m}(T_{s})\) of each secondary critical machine is less than \(F_{\max}\) (\(F_{\max}\) = 0.3), while the preventive maintenance period \(T_{s}\) is 13 days. According to Equation (20), the operating rate \(Y_{m}\) is always equal to 1 with the values listed in Table 5. Hence, the preventive maintenance period of each secondary critical machine \(T_{PMm}\) is 13 days (\(T_{PMm}\) = \(T_{0}\cdot Y_{m}\)). Namely, six machines evaluated as the secondary critical machines (M01, M08, M12, M16, M18 and M19) were in a group for the preventive maintenance with the 13 days period.  
The fixed cost \(C_{0}\), the average cost of the parts replacement \(C_{m}\), and the average cost of the breakdown maintenance \(C_{fm}\) of each secondary critical machine in a preventive maintenance period were obtained from the maintenance data provide by the enterprise and shown in Table 6.  
When the preventive maintenance period \(T_{\mathrm{S}}\) was 13 days and the production period was one year, there were about 26 periods for the preventive maintenance. The total maintenance costs could be calculated by Equation (19) and \(TC(T_{s})\) = 353,306 RMB. Compare with the results in literature [12; 28], it could be found that, although the number of machines and the maintenance costs were different, the average maintenance cost of each machine in the present case study was lower. The preventive maintenance strategy for the secondary critical machine in this study should reduce the maintenance cost; meanwhile, the production line reliability was ensured.
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 2 Theoretical Conceptualization
### Shewhart \(T^{2}\) Control Chart
Although other metrics exist, such as the MSE and the Original-to-Reconstructed-Signal Ratio (ORSR), \(T^{2}\) proved to be more effective in identifying anomalies in the analyzed data. Equation (6) presents the mathematical formulation of \(T^{2}\):  
\[T^{2}=R(\mathbf{\tilde{h}}-\mathbf{\tilde{h}})^{T}\mathbf{S}^{-1}(\mathbf{ \tilde{h}}-\mathbf{\tilde{h}}), \tag{6}\]  
where \(\mathbf{\tilde{h}}\) represents the sample mean vector, while \(\mathbf{\tilde{h}}\) and \(\mathbf{S}\) are the reference mean vector and the reference covariance matrix, respectively. The UCL is defined as the value that exceeds 95% of the \(T^{2}\) values of the training data, establishing a threshold for anomaly detection [27; 37]. Thus, \(T^{2}\) values above the UCL indicate significant changes in the data, suggesting the presence of structural damage or other irregularities. This approach allows for continuous monitoring of the structure's condition and the implementation of corrective actions before catastrophic failures occur. It is important to emphasize that, by definition, 5% of the \(T^{2}\) values from the training data will exceed the UCL, which is not a cause for concern if they remain close to the threshold [27; 37].
## 3 Materials and Methods
### Stage 2: Project Contingency Prediction Models
\begin{table}
\begin{tabular}{c c} \hline
**Prediction Models** & **Hyperparameters** \\ \hline Linear regression & ElasticNet (‘alpha’ = 1, ‘11\_ratio’ = 1) \\ \hline SVM & SVR(‘C’ = 500, ‘degree’ = 2, ‘epsilon’ = 2, ‘gamma’ = ‘scale’, ‘kernel’ = ‘linear’) \\ \hline Decision tree & DecisionTreeRegressor (‘criterion’ = ‘poisson’, ‘max\_depth’ = None, ‘max\_features’ = None, ‘min\_samples\_split’ = 2) \\ \hline Random forest & RandomForestRegressor (n\_estimators = 100, criterion = ‘squared\_error’, \\  & max\_depth = None, min\_samples\_split = 2, min\_samples\_leaf = 1) \\ \hline Gradient boosting & GradientBoostingRegressor (‘learning\_rate’ = 0.1, ‘loss’ = ‘huber’, ‘n\_estimators’ = 100) \\ \hline XGBoosting & XGBoRegressor (objective = ‘reg:squarederror’, n\_estimators = 300, learning\_rate = 0.01, \\  & max\_depth = 3, subsample = 0.8, colsample\_bytree = 1, gamma = 0, min\_child\_weight = 20, \\  & reg\_alpha = 0, reg\_lambda = 0, random\_state = 31) \\ \hline ANN & MLPRegressor (hidden\_layer\_sizes = (256, 128), activation = ‘relu’, solver = ‘adam’, \\  & alpha = 0.0001, max\_iter = 600, random\_state = 0, learning\_rate = ‘constant’, \\  & learning\_rate\_init = 0.001, shuffle = True, verbose = True, validation\_fraction = 0.1, \\  & momentum = 0.9) \\ \hline \end{tabular}
\end{table} 
Table 3: Fine-tuned hyperparameters for the model algorithms. According to the evaluation of model performance metrics, the high-performing models were then selected for further analysis due to their superior predictive capabilities and reliability.
## 5 Conclusions
Therefore, Topology Optimization can be used as a first stage in the design of flexure elements, after that it can be followed by adjusting the material distribution by means of Cellular Material topology, therefore having a controlled structure-property relation and perhaps a simplification of the design. Finally, further trends in research in this area and recommendations to extend the study of tailoring stiffness of flexure elements via Cellular Materials and Topology Optimization are provided based on the available literature reviewed here. Due to their advantages in tailoring the mechanical properties, exploring the use of closed-cell structures, 3D arrays, auxetic, and zero-Poisson architectures in tailoring the stiffness of flexure elements could lead to benefits compliant systems. As flexures base their functioning on the elastic response of their elements, other material properties, i.e., Poisson's ratio, could complicate their use to build up other kinematic pairs, e.g., cylindrical. However, a correct selection of cellular pattern could be designed so that it restricts the Poisson's ratio to 0. This opens up a new range of possibilities in compliant systems applications. Combining both Cellular Materials and Topology Optimization approaches (TOAUC, MTOCM, and TOFCM) to expand the possibilities in tailoring stiffness of flexure elements. This combination leads to obtain the best out of each. An example of the use of these two could be by implementing Topology Optimization at the conceptual design stage for the proposal of a flexure element. Then, employ a Cellular Material to adjust the stiffness by modifying the parameters that define the structure. Analytical parameterization of the Cellular Material employed can result in an accurate and precise tracking of the stiffness. This allows a controlled tailoring of stiffness in compliant systems. Another aspect to keep in mind is that lattice honeycombs might have in-plane anisotropy. Therefore, when used in flexures, the proper selection of orientation becomes crucial. Failure in considering this could lead to deformations in non-desired directions, i.e., parasitic motions. Conceptualization, M.A.-S. and E.C.-U. ; methodology, M.A.-S., E.C.-U., and A.G.-E.; investigation, M.A.-S.; writing--original draft preparation, M.A.-S.; writing--review and editing, E.C.-U. and A.G.-E.; supervision, E.C.-U. and A.G.-E.; project administration, A.G.-E. All authors have read and agreed to the published version of the manuscript. Funding: The authors would like to acknowledge the financial support of Tecnologico de Monterrey, in the production of this work. The authors would like to acknowledge support from CONACyT for PhD studies of first author (scholarship number 863078) and nTopology for providing access to research and academic licenses. The authors declare no conflicts of interest. Abbreviations