esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ex Vivo Experiment Results Using Fuzzy Logic Control  
By the same method as described in the last section, the maximum temperatures obtained by other voltage outputs were also defined. These numbers are specified in the graph in Figure 8a with the minimum and maximum values at 55.9 \(\pm\) 0.3 \({}^{\circ}\)C and 64.9 \(\pm\) 0.2 \({}^{\circ}\)C corresponding to the output voltages of \(\pm\)6 V and \(\pm\)21 V. There are several different temperature peaks observed at 57.6 \(\pm\) 0.3 \({}^{\circ}\)C, 59.9 \(\pm\) 0.3 \({}^{\circ}\)C, 61.1 \(\pm\) 0.2 \({}^{\circ}\)C, 63.0 \(\pm\) 0.4 \({}^{\circ}\)C generated by \(\pm\)9 V, \(\pm\)12 V, \(\pm\)15 V, \(\pm\)18 V, respectively. Depending upon these peaks and the treatment target temperature, these output voltages acted as the outputs for the fuzzy logic control. Figure 8b shows the temperature observation graph in the entire 300 s of HIFU exposure in the ex vivo experiment using the fuzzy control system. The fuzzy controller helped the system to maintain a constant temperature in the target sample. Thus, the heated region is accurately located at the tumor model and avoid damaging the surrounding normal tissues during the HIFU treatment. The desired temperature was set to 60 \({}^{\circ}\)C and the system would actuate the HIFU transducer at the highest output voltage \(\pm\)21 V to attain this value quickly. The tissue at the focal region under acoustic pressure was heated up to the target temperature within 40 s. Afterward, the target temperature remained at 60 \({}^{\circ}\)C with tolerable deviations of 4 \({}^{\circ}\)C during the exposure.  
The thermal coagulation cross-sectional images of chicken breast tissues at various exposure times are shown in Figure 9b. The opalescent regions representing the treated area were marked by the half-dash-dot line and visibly had ellipsoidal shapes due to the HIFU transducer focal beam. The smallest denaturation area was 13.5 \(\pm\) 1.7 mm\({}^{2}\) after 60 s of treatment then linearly increased to 31.7 \(\pm\) 5.1 mm\({}^{2}\), 46.5 \(\pm\) 8.8 mm\({}^{2}\), 70.9 \(\pm\) 8.0 mm\({}^{2}\) for 120 s, 180 s, and 240 s exposure time, respectively. The largest damaged region was 89.4 \(\pm\) 9.1 mm\({}^{2}\), produced after activating the HIFU system for 300 s, and it was almost seven times the size of the smallest one, as described in Figure 9a. The fuzzy-assisted HIFU system shaped a rising trend of the coagulation area as a linear fitting. Thus, the extension of the thermal denaturation of the tissue could be anticipated.  
Figure 7: The observed temperature of chicken breast tissues during HIFU treatment at \(\pm\)21 V output voltage after 50 s (**a**) By thermal camera (**b**) By thermocouple.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Responses of transpiration to Elevated \(\mathrm{CO_{2}}\)  
Responses of transpiration to \(\mathrm{CO_{2}}\) concentration were measured at a constant \(\mathrm{PPF_{0}}\) using the same vegetative wheat canopy over a span of 8 days. During this time, chamber \(\mathrm{CO_{2}}\) was increased in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & **Coupling** & \(\mathrm{T_{Aero}}-\mathrm{T_{air}}\) & \(\mathrm{g_{A}}\) & \(\Omega\) & **Relative Magnitude** & **Transpiration Control** \\ \hline Conifer & coupled & small & low & \(\sim 0.1\) & \(\mathrm{g_{A}}>>\mathrm{G_{S}}\) & \(\mathrm{Radiation}\approx\mathrm{\Delta R_{net}}\) \\ Crop & decoupled & large & high & \(\sim 0.8\) & \(\mathrm{g_{A}}<<\mathrm{G_{S}}\) & \(\mathrm{Stomatal}\approx\mathrm{\Delta G_{S}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical land surface properties that influence the control of transpiration rate from conifers or crops.  
a stepwise fashion from 400, to 700, to 950, and to 1200 umol mol\({}^{-1}\). Canopy gas exchange fluxes and energy balance components were held at each CO\({}_{2}\) concentration for 48 h to allow the incremental buildup of sugar pools in tissues throughout the canopy. These data were used to measure canopy aerodynamic conductance and to determine the response of canopy transpiration to increased CO\({}_{2}\) concentration. Daily average values of E\({}_{\text{can}}\), P\({}_{\text{net}}\), LE, H, G\({}_{\text{S}}\), \(\Omega\), and WUE were calculated because G\({}_{\text{S}}\) and E\({}_{\text{can}}\) did not remain constant throughout the day due to diurnal changes in stomatal conductance.
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
## 5 Conclusions  
A comprehensive correction method is proposed for mitigating hypersonic aero-optical effects by adding MVGs and using blowing and suction flow control. Summarizing this work, the significant conclusions are drawn and listed as follows:  
(1) This approach not only reduces the temperature of the window but also achieves a more uniform temperature distribution compared to the single jet cooling method, thereby minimizing aerodynamic thermal radiation effects.  
(2) The temperature at the upstream position of the optical window is slightly higher with MVGs than without MVGs. The comparison of temperature distribution in the optical window at 5 Ma for jet cooling reveals that the inclusion of MVGs results in a more uniform temperature distribution across the entire window. This leads to a reduction in the temperature difference between the highest and lowest points, with only a 4.5 K variation observed. In contrast, when MVGs are not present, temperatures gradually increase along the flow direction, resulting in a significant temperature difference of 42.8 K.  
(3) The inclusion of MVGs effectively mitigates large-scale structures generated by airflow passing through the rear step. As the Mach number increases, the wavefront distortion at 5 Ma can be reduced by 21.7%. Thus, increasing the Mach number enhances the contribution of MVGs in minimizing wavefront distortion.  
(4) By implementing suction control at the lower step of the optical window, the refractive index fluctuations near the window are significantly reduced, leading to a more uniform surface temperature distribution. The application of this flow control technique results in a notable 14.7% reduction in wavefront distortion at 3 Ma and an approximate maximum value reduction of 20% at 5 Ma.  
It can be concluded that combining MVGs with spray and suction controls ensures consistent side window temperatures, while maintaining flow field uniformity near the window during cooling processes, thus achieving the effective correction of hypersonic aero-optical effects.  
[PERSON] completed the numerical simulation, processed the data, and wrote the article. [PERSON] and [PERSON] conducted the numerical simulation, processed the data, and revised the article. [PERSON] helped complete the numerical simulation and revised the article. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Aeronautical Science Foundation of China, Grant/Award Number: F2021110; the National Natural Science Foundation of China, Grant/Award Numbers: 11672133, 12002161; the National Science Foundation of Shaanxi Province, Grant/Award Number: 2021 JQ-078 and Open Foundations of EDL Laboratory, Grant/Award Number: EDL19092111.  
Not applicable.  
The data underlying this article will be shared on reasonable request to the corresponding author.
## 3 Results
### The Software Architecture  
The software back-end around LUMA was designed with the Model-View-Controller (MVC) design pattern in mind. The FHIR terminology server Ontoserver acts as one part of the model containing all data pertaining to LOINC as well as the standardized FHIR resources that describe how each LOINC _Property_ relates to UCUM. Ontoserver was developed with the intent of further increasing the uptake of clinical terminologies such as LOINC and the Systematized Nomenclature of Medicine (SNOMED) by supporting them out of the box and providing users with a powerful search mechanism for dealing with the complex and vast amounts of content in an effective and responsive manner. The stored terminologies are kept up to date via Ontoserver's syndication mechanism, which enables each instance of the tool to act as a client or a server for fetching or distributing data from other Ontoserver instances [27]. Possible UCUM units to map to are sourced from a ValueSet that may be specified beforehand. We ended up using the FHIR \"UCUM-Common\" ValueSet ([[https://build.fhir.org/valuset-ucum-units.html](https://build.fhir.org/valuset-ucum-units.html)]([https://build.fhir.org/valuset-ucum-units.html](https://build.fhir.org/valuset-ucum-units.html)) (accessed on 29 April 2022)) as it contained about 500 entries more than an aggregation provided by LOINC with the exception of roughly 45 units. The other part of the model consists of a RESTful UCUM service that deals with all requests regarding UCUM units, namely the canonization, conversion and display name generation for units. Ontoserver and the UCUM service never interact with each other. Instead there is a Controller, also implemented as a RESTful service, that encapsulates requests involving the model. Users do not interact with the back-end directly. Instead, they use the graphical user interface LUMA that communicates with the Controller. It should be noted that LUMA is only one imaginable way of making use of the back-end. The Controller is perfectly capable of being used for machine-to-machine communication, having been designed first. The LUMA interface was implemented later as one possible use case. A high-level abstraction of the component interactions is shown in Figure 5.  
Figure 5: This figure shows the interactions of each component. The Controller manages the flow of information and triggers the terminology server Ontoserver and the UCUM service in order to generate responses related to mapping tasks. Machines interact with the Controller via its REST interface, while humans do so via the graphical user interface LUMA. The communication happens via HTTP calls and the JSON file format is used for data interchange.
## 3 Results and Discussion
### Extraction of OGNs with Different Lengths and Modifications
\begin{table}
\begin{tabular}{c c} \hline
**Adsorbent** & **2.0 mg of MNP-Ac** \\ \hline \multirow{4}{*}{Conditioning} & 100 \(\mu\)L MeOH, mixing, \\  & magnetic separation, supernatant remove \\  & 100 \(\mu\)L 10 mM NH\({}_{4}\)OAc pH = 4.5, mixing, \\  & magnetic separation, supernatant remove \\ \hline \multirow{2}{*}{Sample load} & 50 \(\mu\)L sample + 50 \(\mu\)L 10 mM NH\({}_{4}\)OAc pH = 4.5, \\  & 15 min of mixing, magnetic separation, supernatant remove \\ \hline \multirow{2}{*}{Washing} & 100 \(\mu\)L 10 mM NH\({}_{4}\)OAc pH = 4.5, mixing, \\  & magnetic separation, supernatant remove \\ \hline \multirow{2}{*}{Elution} & 50 \(\mu\)L 10 mM NH\({}_{4}\)OAc pH = 9.5/MeOH 50/50 \(\
icefrac{{\
u}}{{\
u}}\), \\  & 30 min of mixing, magnetic separation, supernatant remove, \\ \multicolumn{2}{c}{centrifugation (14,000 rpm, 10 min), analysis} \\ \hline \end{tabular}
\end{table} 
Table 4: The final procedure used for the MDSPE of modified and unmodified OGNs. Abbreviations: MeOH—methanol; NH\({}_{4}\)OAc—ammonium acetate. Figure 6: The recoveries obtained for the OGNs with different modification types and lengths in MDSPE with the use of MNP-Ac (extraction conditions in Table 4). the highest values were obtained for unmodified OGN, they decreased with the decrease of hydrophobicity, and they are the lowest for the most hydrophobic MOE-20 (Figure 6). Similar tendencies can be seen for 11-mer OGNs. The lowest recovery was obtained for the LNA-11, the most hydrophobic in this group of analytes (Figure 6). It is worth highlighting that chemical modification of OGN can introduce additional interaction to the mechanism of retention of the analytes with MNP coating (e.g., C-H--\(\pi\) interactions). Nevertheless, the procedure, which was optimized for unmodified OGN, works in the case of OGNs with chemical modifications. Based on the obtained results, some conclusions about the influence of OGN chain length on the recovery can be made. Comparing unmodified DNA and phosphorothioate OGN with 20 and 11 nucleotide length, the higher values were obtained for the shorter ones (Figure 6). They have a lower charge, and their interaction with charged MNP coating is weaker. Thus, their desorption can be easier.
## 2 Literature Review
### 5G Network Slicing  
The concept of slicing a network end to end, forming multiple logical virtual network slices that share a physical infrastructure, has been a key enabler for 5G networks. The authors in [4] worked on a comprehensive survey of network slicing in 5G, discussing 5G-network-enabling technologies, their motivation, and the challenges involved. The study identified software-defined networking (SDN), network function virtualisation (NFV), cloud computing, and virtualisation as 5G-network-enabling technologies. [PERSON] et al. [3] highlighted the importance of efficiently orchestrating 5G network slices by studying resource allocation strategies and mobility management for 5G network slices.  
Software-defined networking (SDN) and network function virtualisation (NFV) were leveraged to propose a framework [5] in creating and managing 5G network slices efficiently. The need for automation to enable optimal network slice orchestration was emphasised. In [6], the authors studied the concept of softwarisation in 5G and 5G network slicing; they equally delved into and surveyed various solutions, technologies, and principles.
## 8 Challenges and Future Directions
### Future Directions
#### 8.2.1 Explainable AI  
Explainable AI techniques can be used to make AI models more transparent and trustworthy. They provide valuable insights into the decision-making process of these models. This will help users understand the reasoning behind the decisions of these AI systems and promote the adoption of HAR technologies. It would also address certain ethical concerns such as the possible of bias and discrimination. It would also allow researchers to identify and rectify points of errors in HAR systems. Some of the techniques for explainable AI include rule extraction, visualisation, attention mechanisms and feature importance. Applying explainable AI techniques for HAR systems would help in its adoption especially in healthcare.
## 4 Discussion
### Sensitivity Analysis of the Parameters on the Predicted Results
For example, under confining pressures ranging from 10 MPa to 60 MPa, an increase of 2.0 \(n\) results in \(\sigma_{1}\) variation ranges of 0.31\(\sim\)6.07% for granite, 5.13\(\sim\)25.01% for shale, and 0.75\(\sim\)8.47% for carbonate rock. In contrast, a 2.0 increase in \(B\) leads to \(\sigma_{1}\) variation ranges of approximately \(-\)8.12 to \(-\)3.74% for granite, \(-\)29.80 to \(-\)13.54% for shale, and \(-\)28.54 to \(-\)11.18% for carbonate rock.
## 4 Results
### Back-Calculated Results of Elastic Modulus
#### 4.2.2 Low-Strength Geopolymer  
The back-calculated elastic modulus results for Section 3 after grouting treatment are shown in Figure 8a. The average elastic modulus of asphalt in the pavement treated with low-strength geopolymer (without waterproofing agents) was 517.10 MPa, comparable to the left-side pavement and slightly higher than the right-side pavement, with a standard deviation of 15.44 MPa. The data indicate concentrated elastic modulus values for this material, with the lower standard deviation suggesting more consistent performance and reduced variability.  
The back-calculated asphalt elastic modulus results for Section 4 after grouting treatment are shown in Figure 8b. The pavement treated with low-strength geopolymer (with waterproofing agents) exhibited an average elastic modulus of 505.37 MPa, slightly lower than that of the left-side pavement, with a standard deviation of 17.16 MPa. These results indicate relatively uniform material performance (lower variability) but reduced strength, suggesting poor grouting effectiveness for this formulation.  
Compared to the back-calculated asphalt elastic modulus results of high-strength geopolymer-treated pavements (Sections 1 and 2), the low-strength geopolymer treatment yielded a significantly lower average elastic modulus, indicating lower material strength. This performance suggests its suitability for secondary roads or light-load structures.  
Figure 8: Elastic modulus of asphalt with low-strength geopolymer grouting. (**a**) Section 3; (**b**) Section 4.
## 2 Materials and Methods
### Model Formulation
#### 2.4.3 Kcp Parameter Function
The NDVI profile provides the timing and relative magnitude of water use. On the other hand, if the exercise is to simulate landscape water use by the natural vegetation, the maximum and minimum Kc (Kc\({}_{\text{max}}\), Kc\({}_{\text{min}}\)) can be adjusted as an area-weighted value to reflect this based on information on percent ground cover from such data sources as MODIS VCF. A justification of the 0.33 coefficient in Equation 7 is derived from the observation that 0.3 NDVIio value for crop/grassland areas also corresponds to approximately one-third plus the minimum NDVI of the range between the maximum NDVI (0.7) and minimum NDVI (0.1) generally found in crop and grassland systems (0.2 + 0.1). Thus, with a similar logic, the NDVIio for vegetation sparse regions is defined using Equation 7. These thresholds have been shown to produce reasonable results for the United States. However, sensor-specific adjustments may be necessary when using NDVI data sets from sources other than the AVHRR climatology. The main impact of the lower or higher NDVIio value is in the starting of vegetation growth simulation to the earlier or later part of the season, respectively. For example, a change from NDVIio of 0.25 to 0.3 may move the start of season by two weeks later. This kind of error is tolerable when the main objective is to monitor the performance of crops or vegetation at large spatial (\(>\) 5 km) and temporal (\(>\) monthly) time scales. This error is even more tolerable when the seasonal monitoring is compared with other years as an anomaly product. The Kcp function only uses these two parameters (NDVI\({}_{\text{max}}\) and NDVIio) to convert smoothed historical NDVI data sets from NDVI to Kcp according to Equation 4 or 5. For example, a generic cereal crop has a published Kc of 0.3 (Kc\({}_{\text{min}}\)) at the early stage and 1.15 (Kcmax) for the mature stage. Both NDVI\({}_{\text{max}}\) and NDVI\({}_{\text{min}}\) (NDVIo) are calculated from NDVIi data sets where \"i\" represents a \"historical\" NDVI time step. NDVIi data sets are available at weekly or longer time steps.
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 2 Modeling of Railway Vehicles and Wheel Flat
### Signals of Wheel Flat  
The movement of the wheel that has a wheel flat is represented in Figure 9. The wheel flat signal is generated by the impact force between the wheel and the rail according to Equation (5)-(8) reprinted from ref. [19].  
\[\omega\sqrt{\frac{2\times h}{g}}=2\times arcsin\frac{L}{2\times R}-\arccos\left(1 -\frac{h}{R}\right) \tag{5}\]  
where \(h\) is the dropping distance of the wheelset, and \(\omega\) is the rotation speed of the wheel. At this time, as shown in Figure 9, not only the dropping distance of the wheelset but also the vertical speed generated by the rotation of the wheel should be considered, and the vertical speed of the wheel is defined as follows;  
\[v_{tor.}=\omega\times R\times\ \sin\theta \tag{6}\]  
where, \(\theta\) is the angle shown in Figure 9, derived from the dropping distance \(h\), and is defined as follows;  
\[\theta=arccos\left(\frac{h}{R}\right) \tag{7}\]  
Finally, the impact force generated between the flat wheel and the rail is defined as follows:  
\[F_{im}=Q_{w}\left(1+\frac{2h+\frac{v_{tor}^{2}}{g}}{Q_{w}}\times\frac{K_{p}K_{ b}}{K_{p}+K_{b}}\right) \tag{8}\]  
Figure 8: Dz-Wheel raised in the SIMPACK post result.  
Figure 9: Principle of wheel and rail collision by wheel flat [19].  
Here, \(Q_{w}\) is the wheel load in the static state of the railway vehicle, and \(K_{p}\) and \(K_{b}\) mean the rigidity of the rail pad and the railroad bed, respectively.  
The acceleration of the x, y, and z axes generated by the impact force between the flat wheel and the rail is shown in Figure 10. This acceleration signal was obtained from the axle box point by simulation and proven by rig test [20]. In this paper, it is used as shown in Figure 11 adapted from ref. [21].
## 4 Results
### User Experience  
After using the AR and MR applications, the patients completed the short-UEQ questionnaire, and their responses were analyzed using the UEQ Data Analysis Tool ([[https://www.ueq-online.org](https://www.ueq-online.org)]([https://www.ueq-online.org](https://www.ueq-online.org)) (accessed on 18 February 2025)). Three graphs were generated (Figure 10) using one of the downloadable Excel templates from UEQ to assess the quality of our MR and AR applications in comparison with the products included in the UEQ benchmark dataset [22]. The graphs indicate that the Mobile AR application and the two MR applications with Quest 2 and Quest 3 significantly outperform the UEQ benchmark. According to the interpretation guidelines in the UEQ tool and reference [22], the three applications rank in the top 10% across all three variables. Figure 8: Box plot for the scores obtained by the patients before and after learning with the different learning methods. Abbreviations: Pre, initial score; Post, score after learning with the different methods; Mb, mobile AR; Q2, MR Quest 2; Q3, MR Quest 3, and Trad, traditional method. Figure 9: Box plot showing the learning gains with the different learning methods. Mann-Whitney \(U\) tests were used to check whether the learning application affected the three UEQ variables. The results of the analyses and the pairwise comparisons are presented in Table 5. From these analyses, it can be observed that there are significant differences in favor of the MR application with Quest 3 and the AR application when compared to the MR application with Quest 2. Furthermore, considering the data included in Table 5, the results favor the experience with Quest 3. Mann-Whitney \(U\) tests were used to check whether the learning application affected the usability and satisfaction questions listed in Table 1. After applying the test to the three applications compared two by two, it can be observed that in most of the tests, no significant differences were found. Significant differences were only obtained for three of these comparisons, which are shown in Table 6. From these analyses, it can be observed that there are significant differences in favor of the AR application when compared with the MR application with Quest 2 for US#2 and SA#4, and the MR application with Quest 3 for US#2. With regard to question US#2, \"I have gotten used to the application quickly\", the patients got used to the AR application more quickly given that a tablet was used and users are very familiar with devices of this type. With regard to question SA#4, \"I think I have learned with this application\", the patients believed they had learned more with the AR application than with the MR application with Quest 2, and, indeed, the learning results indicate that this was the case, although no statistically significant differences were found for such learning (Table 2). Figure 10: Comparison of the patients’ responses after using the AR and MR applications with the benchmark data from the UEQ Data Analysis Tool. (**a**) Mobile AR application; (**b**) MR application with Quest 2; (**c**) MR application with Quest 3.