esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Design of Prediction Model
### Conceptual Scheme of Prediction System  
Figure 12 illustrates a conceptual scheme of the prediction system which implements the designed prediction model and ensures the communication with other systems (see Section 7). The prediction system uses ensemble prediction model consisting of three submodels.  
According to the _Tmcld_ and the precise determination of hour for which the prediction is to be issued (and possibly also according to the expected weather at the given time), the system will issue a prediction of the expected speed.  
In order to extend the possibilities of processing the results, other supplementary information is provided at the output: The TMC supplementary information (value of _FreeFlowSpeed_ per given segment and the length of such a segment in meters) and the Prediction supplementary information (code of the used submodel, the minimum and maximum speed recorded for the given segment at the given hour, the number of individual measurements in the history, and the number of hours for which there is at least one measurement available).  
Based on the analysis performed before, it was determined that it was not necessary to create a model for the period from 10 p.m. to 5.00 a.m. On the one hand, only a limited number of measurements was available. For another, the achieved speeds in this period were mostly affected by other factors than traffic density. An example is the identified slowdown on nights on motorways. Despite being evident from the data, it is insignificant for the purposes of navigation and when planning the route (roads are empty at night).  
Figure 12: Conceptual scheme of prediction system.  
A backup prediction is available for these hours, which was implied from the theoretical value attainable in the given segment (_FreeFlowSpeed_)--see Section 5.5.
## 4 Control and Monitoring of MRI-Assisted Interventions  
Intraoperative image-guided surgery that meets safety demands related to scanners and surgical procedures, in general, uses MRI and ultrasound scanners [16; 17; 19]. MRI scanners are increasingly being used in surgery, mainly because of their greater ability to distinguish tumors from healthy tissue in procedures involving tumor removal [52; 53; 54; 55]. As abovementioned, the MRI scanner is EM-sensitive. In addition to its possible disruption by external EMFs, the introduction of external substances could disrupt its operation; thus, robotic components introduced into the scaffold, near tissue parts, must be MRI-compatible, i.e., without magnetic or conductive constituents. However, robotic engagements typically require high-performance actuation actions, while a small number of actuator types behave in an MRI-compatible manner. One potential class of actuation mechanisms that are generally compatible with MRI exploits piezoelectric materials, which come in different types. Further detailed information about these actuators, their configurations, ingredients, fabrication, research, and uses are presented elsewhere, for example, in the studies of [56; 57; 58; 59; 60; 61; 62; 63; 64; 65], which correspond to different applications unconnected to the topic of this work but illustrate parameters such as positioning accuracy that are related to the topic. These tools are devoid of magnetic substances and made of dielectric piezoelectric materials but are equipped with thin conductive electrodes necessary for their excitation. In fact, piezoelectric materials are presumed to be compatible with MRI, while the compatibility of conductive electrodes depends on their structural configuration and therefore needs to be verified. This issue will be addressed in Section 5.2.
# CO\({}_{2}\) Adsorption Reactions of Synthetic Calcium Aluminum Ferrite (CAF)
## 3 Result and Discussion
### Characteristics of Carbo Reaction of SCAF
#### 3.2.2 Analysis of Crystalline Phase for Carbo Reaction of SCAF  
Figures 5 and 6 show the crystalline phases of SCAF formed with different carbonation reaction times and methods. In the case of carbonation with the dry method, the XRD peak pattens of the crystalline compounds produced by the carbo reaction show no significant differences with respect to the reaction time, irrespective of the sintering temperature.  
On the other hand, in the case of carbonation of SCAF with the wet method, in addition to the formation of new products, there are differences in terms of peak intensity compared to the products of carbonation with the dry method, and regardless of the sintering temperature of SCAF, after 3 h of carbonation reaction time, formation of a large amount of hydrates can be seen due to the reaction of CAF compound with CO\({}_{2}\). The products of the carbonation reaction include CAC(Ca\({}_{4}\)Al\({}_{2}\)O\({}_{6}\)CO\({}_{3}\cdot\)11H\({}_{2}\)O) and CACH(Ca\({}_{4}\)Al\({}_{2}\)O\({}_{6}\)CO\({}_{3}\cdot\)11H\({}_{2}\)O), which are calcium carbo aluminate compounds, and CAFC(Ca\({}_{4}\)Al\({}_{2}\)Fe\({}_{2}\)O\({}_{12}\)CO\({}_{3}\)(OH)\({}_{2}\cdot\)22H\({}_{2}\)O), which is a calcium carbo alumino-ferrite compound. In particular, a strong peak intensity is observed at 3 h of the carbonation reaction, and at 6 h, the peak of the calcite gradually increases, and it can also be observed that the peaks of CAC and CFC crystalline compounds, which are calcium carbo compounds, increase with the reaction time [11, 12].  
Figure 4: Calcite generation based on reaction time.  
Figure 5: XRD patterns for carbonation with dry method. (**a**) 1000 \({}^{\circ}\)C; (**b**) 1100 \({}^{\circ}\)C.  
Therefore, it can be seen that SCAF reacts with CO\({}_{2}\) to produce calcite and calcium carbo aluminate and/or calcium carbo ferrite compounds, and the carbo reaction becomes more active over reaction time.
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 3 Intelligent Power Management
### Data Acquisition  
The data acquisition steps and the DPMS strategy are the following:  
* Each sampling cycle has \(N\) = 512 measurements of Temp, SpO2, and HR. The measurement period takes 20.48 s at a sampling frequency of 25 Hz.
* The supercapacitor's voltage level V\({}_{scap}\) is also acquired every time the MCU wakes up (\(T_{Sleep}\)).  
Figure 4: Schematic diagram of the proposed PPG-based prototype. (**a**) main board top view, (**b**) sensors’ board top view and (**c**) sensors’ board bottom view.  
Figure 5: Flowchart diagram of the DPM strategy.  
* The moving average algorithm (MA) is implemented in the nRF52840 MCU. That is: \(\delta_{i}=\frac{1}{N}(x_{i,1}+x_{i-1,1}+...+x_{i-N,1};x_{i,2}+x_{i-1,2}+...+x_{i -N,2};x_{i,3}+x_{i-1,3}+...+x_{i-N,3})\). The \(N\) value is a power of 2, which means that the division is computed by a simple shift operation.
* ML-decision tree processing
* Data transmission \(\delta_{i}\) with the BLE protocol
* Sleep period of \(T_{sleep}=15\) min (default value) for IoWT sensing update.  
Recent studies have aimed to determine the minimal sampling frequency for PPG signals. One option is optimizing the signal sampling rate by decimating with a factor of 2, 5, 10, 20, 50, 100, 200, 500, then cubic spline and parabola interpolating back to 1 ms resolution [32; 33]. However, a 5 Hz sampling frequency is sufficient for the PPG signal's average monitoring. The interpolation method is used to improve the resolution from lower sampling frequencies [32]. In this work, the frequency of 25 Hz is selected for the PPG-based IoWT system. Finally, [PERSON] et al. [34] suggest taking three measurements per hour to assess disease conditions using photoplethysmography, with each measurement taking 20 min. In this study, a period ranging from 5 to 15 min was considered to achieve a balance between accuracy and energy savings.
## 4 Discussion
### 3D Mapping and Visualization Accuracy  
The 3D mapping system provided valuable insight into the drone's flight path and surrounding environment. By logging x, y, and z coordinates every 0.1 s, the system generated a spatial representation of obstacles as rectangular prisms positioned 0.35 m from the detection point. While the maps closely matched the lab's layouts, minor positioning inaccuracies were sometimes observed, likely due to improper coding adjustments. Such errors, though small, can compound over time and undermine the system's reliability, particularly in more complex environments where precise localization in essential. Prior research on SLAM (Simultaneous Localization and Mapping) systems suggests that incorporating adaptive filtering techniques, such as Kalman filtering, could improve mappingprecision [36]. Also, incorporating higher-resolution spatial data processing could enhance the system's ability to visualize complex environments [37].  
The addition of an AI-Deck for live video streaming introduced a new capability: beyond-line-of-sight control. This feature allows users to pilot the drone remotely using live footage, which has promising applications in search and rescue, reconnaissance, and surveillance. However, the AI-Deck camera was limited by its low resolution and fixed focus, resulting in poor image quality. Prior research has explored alternative small-scale camera modules that improve image quality while maintaining low latency [38]. Exploring better hardware options or implementing image-processing techniques such as super-resolution reconstruction could enhance the user experience [39].  
The integrated system functioned effectively as a cohesive unit, enabling intuitive control while maintaining a safety layer through obstacle avoidance. However, improvements are needed to enhance usability, responsiveness, and overall performance. The system's responsiveness was limited by occasional processing delays, likely caused by the computational overhead of gesture recognition and obstacle detection running simultaneously. Optimizing the software pipeline by utilizing multi-threading or GPU acceleration could improve processing efficiency [40]. Furthermore, reducing latency in the gesture recognition system could improve user experience, as research has shown that delays exceeding 100 milliseconds negatively impact interactive control systems [41].
## 4 Results and Discussion
### ORC Performances  
#### 4.2.1 Pure Fluid and Mixture Comparison  
During the experimental tests conducted in the mixture on the ORC and compared with the pure fluid tests, for the range of parameter variation as defined in Section 3.1, an increase in the net thermal efficiency of the overall system is observed as shown in Figure 6a. This increase in performance is in the order of 10% for an equivalent production of electricity. To complete the all-tests comparison, the second law efficiency was also studied to evaluate more specifically the conversion of the useful part of the heat source. In Figure 6b, the same increasing trend with the mixture can be identified, which confirms the interest brought to light with the thermal efficiency analysis.  
In the case of a comparison with all of the isoparameters, i.e., identical temperatures and flows of hot and cold sources for both the pure fluid and the mixture, the net thermal efficiency is identical and the power produced in the case of the mixture is reduced by 10% (see Figure 7).  
Figure 5: The expander electrical efficiency map for the Organic Rankine Cycle (ORC) up to 10 kW.  
Figure 6: The ORC performance according to electrical production in the pure fluid and the mixture. (**a**) Thermal efficiency; (**b**) Second law efficiency.  
The two previous analyses of the results obtained in the experimental tests seem to be contradictory as to the interest of using a mixture as a working fluid within an ORC. However, when using an Organic Rankine Cycle, the aim is to transform thermal energy, called degraded energy, from the heat source, into electrical energy, called noble energy. This type of technology, therefore, seeks to make the best use of the available thermal energy source, and, therefore, to obtain the highest possible thermal efficiency to produce the greatest amount of electricity. The results presented in Figure 6a,b confirm the interest in using fluid mixtures to increase the performances of an ORC installation. The results presented in Figure 7 highlight some of the limitations that can be achieved when using mixtures. Indeed, according to the isoparameters, the mixture is more efficient than the pure fluid for a given range of parameters and amount of electricity production. In this defined range, the zeotropic mixture will, therefore, allow for a better use of the heat source.
## 4 Simulation Results
### Comparison of Results Considering Different Standard Deviations of PV Power Generations  
The standard deviation of PV power generation is set to 3% of the mean value in Section 4.1. That is, the range of PV power generation can be estimated approximately to be (the mean value) \(\times\) (1 \(\pm\) 3 \(\times\) 3%), which covers 99.73% of possible PV generation cases. This section investigates impacts of other standard deviation (8%) on the results. Table 11 shows the comparisons of results by studying the same case in Section 4.1 with different standard deviations of PV power generations. The CPU times and iterations indicate that the proposed GA can still converge properly without premature convergence. The expected cost ($23,028.07) obtained by considering the standard deviation of 8% of the mean value is smaller than that ($23,737.68) obtained in Section 4.1. However, the difference is very small. Actually, the expected power generations of all micro-turbine generators obtained by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Population Size** & **Expected Cost (\$)** & **CPU Time (s)** & **No. of Iterations** \\ \hline
50 & 31345.96 & 25.72 & 15 \\
75 & 31300.69 & 54.32 & 23 \\
100 & 31321.59 & 56.07 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance of traditional GA considering different population sizes.  
Figure 12: **(a)** Probability; and **(b)** cumulative probability with respect to the percentage of difference between the best and worst costs.  
considering different standard deviations of PV power generations are almost the same because two location parameters of PV power generations in Equation (14) are utilized. These location parameters are centered at the same mean value for a given PV array although different standard deviations are considered and different location parameters are gained in Equation (14).
# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## 4 Discussion
### Suggestions for the Future  
The discovery that low-frequency ultrasound effects hemoglobin in RBCs (in vitro) to improve gas exchange has spurred us to start in vivo studies as soon as possible, and they are currently being prepared using the ultrasound transducer developed by the authors of this article [31] (patent pending). The clinical implications and therapeutic potential of ultrasound treatment in relation to these observed hematological changes need to be investigated. The mean, median, standard deviation, and interquartile range of blood parameters showed that ultrasound had an effect on granulocyte (GRAN) values as well as most other parameters. This parameter can be distinguished from the others because an increase in the granulocyte count indicates a bacterial infection [32]. However, statistically significant changes (decreases) in these values can be considered useful and may require further investigation, particularly regarding bacteria-related infectious blood tests. This finding suggests that ultrasound may have applications in the treatment of some bacterial diseases. Although ultrasound induces platelet aggregation, in vivo ultrasound may reduce the risk of thrombosis [33].  
MCH and MCHC measure hemoglobin, which carries oxygen to the body's cells from the lungs, improving the absorption of iron [34], folate (B9), and cobalamin (B12) to stop bleeding or blood loss. Under the influence of ultrasound, an increase in PLT reduces bleeding or blood loss and destroys the _S. aureus_ biofilm covering the surface of the prostheses, which increases antibiotic susceptibility [35]. Ultrasound is suitable for the treatment of endothelial dysfunction because it facilitates drug exposure to liver and kidney cells in order to identify clinically relevant biomarkers related to the function of these cells. Focused low-frequency ultrasound is a rapidly developing, non-invasive therapeutic technology that has the potential to improve the quality of life and reduce the cost of care for patients with heart valve calcification [36]. However, ultrasound can cause some biophysical effects, including thermal and non-thermal effects on cells, but these effects are significantly fewer with low-frequency ultrasound than with high-frequency ultrasound. Sonoporation, the most widely studied non-thermal biological effect of ultrasound, is considered to be a basis for new therapeutic applications. Ultrasound can increase the permeability of cell membranes due to the sonoporation effect, allowing molecules such as drugs, proteins, and DNA that cannot normally penetrate cell membranes to enter cells.
## 3 Proposed Method
### Adaptive Bin Estimator
#### 3.3.4 Bias-Prediction MLPs (\(B_{n}\))  
For low-resolution depth inputs, we only have a rough understanding of the depth distribution in the scene. Our general idea is to constrain the predicted depth range, making the bin prediction closer to the GT depth range. Since the minimum and maximum depths in a low-resolution depth map may not be identical to the true minimum and maximum depths in ground truth, a bias term is needed for compensating the predictive offset. The bias MLP consists of two inputs: the low-resolution depth map \(d_{pxp}\) and the embedding feature \(e_{n}\). The outputs are the estimated minimum depth and maximum depth of the scene. The embedding feature is passed through a bias-prediction MLP to estimate the minimum and maximum depths. The estimated biases are added to the minimum value and maximum value of \(\mathbf{d}_{p\times p}\) to obtain \(d_{\min}^{n}\) and \(d_{\max}^{n}\) given by  
\[d_{\min}^{n}=\min(\mathbf{d}_{p\times p})+bias_{\min}^{n}\text{and }d_{\max}^{n}=\max(\mathbf{d}_{p\times p})+bias_{\max}^{n}, \tag{7}\]  
where \(bias_{\min}^{n}\) and \(bias_{\max}^{n}\) are, respectively, the predicted minimum and maximum biases generated by the bias prediction of \(B_{n}\).  
The predicted minimum depth and maximum depth are then fed into the bin-initialization MLP and bin-splitter MLP with the more accurate dynamic depth range of the current scene. Table 4 details the architecture of the \(n\)th bias MLPs.
# Reconstructing Polarization Multiplexing Terahertz Holographic Images with Transmissive Metasurface
###### Abstract  
There is a growing trend towards the development of high resolution and multiplexing metasurface holograms. In this paper, we propose the reconstruction of polarization multiplexing terahertz (THz) holographic images based on transmissive metasurface. The metasurface composed of all-dielectric meta-atoms is designed as a multi-foci metalens and the focal points of the metalens are utilized as the pixels of a reconstructed image. We analyze the effects of focal length and phase pixel number of the metalens on focal point to achieve high-resolution holographic images. In addition, by switching the polarization of incident lights, holographic images with different patterns are reconstructed on its focal plane. Such high-resolution and polarization multiplexing metasurface holograms is promising for applications in THz communications, information engineering, and encryption.  
terahertz; transmissive all-dielectric metasurface; hologram; polarization multiplexing Article
## 4 Discussion
### Subcritical Crack Growth Parameter  
The subcritical crack growth parameter was between 30 and 37, depending on the loading conditions. The mean value of the subcritical crack growth parameter \(n\) by the monotonic load was 34, and the mean value of subcritical crack growth parameter \(m\) by cyclic load was 35, thereby showing similar values. Thus, although some deviations were observed, the subcritical crack growth parameter values were generally similar regardless of the loading conditions. This is because the subcritical crack growth was caused by environmental factors rather than by mechanical loading. Several potential mechanisms for generating subcritical cracks have been proposed. These mechanisms include stress corrosion, dissolution, diffusion, ion change, and microplasticity [7; 33]. The chemical effect on pore water in rock influences these mechanisms. Stress corrosion is thought to be the main mechanism for subcritical crack growth in rocks [7]. The reactions between strained bonds and the pore water result in a weak state, which can be broken at lower stress rates compared to string bonds [7]. During the interaction of crack tips with these environmental agents, the duration of their exposure to environmental factors is more important than loading. Therefore, regardless of the loading conditions, the values of the subcritical crack growth parameter are similar.  
Results similar to this study were found in other studies. [PERSON] [34] and [PERSON] et al. [35] showed that the subcritical crack growth parameters under shear and tensile loads are similar for Coconino sandstone. [PERSON] et al. [36] showed that the subcritical crack growth parameters were similar for the same rock type regardless of the test methods in compressive and tensile strength tests.
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 4 Case Study, Results, and Discussions
### Company Feedback on Results  
At the end of the computational step, with the clustering results, and after verifying the best performance of the Hierarchical, K-Medoids, and K-Means algorithms, the authors return to the automotive company to evaluate the findings. A meeting was held with the product engineering, manufacturing engineering, controllership, and purchasing teams to analyze the results critically. It was detected that three driver seats were in a cluster while the other was in a single group. When observing the original dataset, we found that the information in one of the dimensions of the single-seat was wrong, which led to a distortion in the results.  
Other similar errors have been observed, mainly in small components whose basic geometric dimensions X, Y, and Z sometimes correspond to the packaging itself and not the actual dimensions of the part. In other cases, it was observed as a set of pieces and not as a single sample. A similar situation has occurred with the weight of the components. Once the data were corrected, the new groups formed were coherent.  
Finally, we highlight that this investigation is a case study that tabulates results and displays a rank on the performance between 9 clustering algorithms considering six indexes primarily found in the literature. Although we are not introducing new or improved clustering techniques, clustering methods to identify price anomalies in an automaker is a relevant contribution to the field.