esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Testing Results  
As mentioned above, the testing campaign included measurements from 39 classrooms in three different ETSIAE buildings (buildings A, B, and E). Table 1 shows the average illuminance values measured on student desks and blackboards in each of these classrooms, along with the surface areas of the classrooms. A first look at these results reveals rather low values in all classrooms in building A and in nearly all in building B, much lower than the 500 lx level stablished by the standards [43]. In some building A classrooms, the average lighting is below 200 lx. As expected, the situation is much better in building E.  
In building A, most of the classrooms have very poor lighting, with only three rooms recording a measured illuminance, \(E_{m}\), higher than 200 lx and none recording higher than 300 lx. The average illumination value on all desks measured in building A was 169 lx, well below the recommended 500 lx. In building B, better results were obtained, with 30% of the classrooms achieving the illuminance values recommended by regulations. In 50% of the classrooms, illuminance values of 300 lx to 400 lx were obtained, with only one classroom recording a value below 300 lx. Finally, all classrooms in the newest building, building E, had acceptable levels of lighting on the study desks. Based on the above results, it is clear that the improvements to be proposed should focus on increasing the average illuminance levels in the classrooms in buildings A and B while keeping the UGR glare index below the maximum level (maximum value of UGR should be below 19, according to UNE 12464-1).
## 4 Conclusions  
Gasoline is a valued and valuable energy source in the current energy market globally, and this resource is also frequently used as a solvent to remove bituminous binder in asphalt laboratories. This study investigated the use of mineral powder to adsorb part of the asphalt binder. In order to investigate the adsorption performance, X-ray Diffraction (XRD), X-ray Fluorescence (XRF), Atomic Absorption Spectroscopy (AAS) and Fourier Transform Infrared (FTIR) were employed to analyze the compositions of adsorbed sediments. Eventually, a case study compared the investigated solutions for bituminous binder removal according to costs and environmental impacts. The following conclusions can be drawn:  
1. Mineral powder mixed with gasoline is an effective solution to remove bituminous binder commonly found in experimental devices in asphalt laboratories. The mechanism can be explained as a physical interaction between the mineral powder and asphalt binder.
2. Almost 4% of the binder can be adsorbed by the gasoline solution, thus proving the beneficial effect generated by the use of mineral powder.
3. The investigated case suggests that removing 1 kg asphalt binder with pure gasoline costs CNY 71.20, consumes 362.90 MJ energy and emits 25.80 kg equivalent CO2, and that adding a mineral powder can reduce these quantities by 80%, 97% and 93%, respectively.
4. Although the rough surface of the mineral powder determines the physical adsorption strength, the low adsorption rate limits the benefits in practical application. Future research should focus on the improvement of the adsorption rate.  
Data Curation, F.W., Q.J., Y.L. and C.Y.; Formal Analysis, H.L. and C.Y.; Investigation, F.W. and Q.J.; Methodology, F.W. and S.W.; Project Administration, S.W.; Supervision, S.W.; Writing--Review & Editing, Y.L. and H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 71961137010 and No. 51778515), the Ministry of Science and Technology National Key R&D Plan (No. 2017 YFE0111600), the State Key Laboratory of Silicate Materials for Architectures (Wuhan University of Technology, No. SYSJ[2019-20) and the Fundamental Research Funds for the Central Universities. Finally, this study was supported by the Program of China Scholarships Council (No. 201906950014 and No. 201906950028).  
The authors declare no conflict of interest.  
The data that support the findings of this study are available within the article.
###### Abstract  
Methods based on electromagnetic time reversal (EMTR) have recently demonstrated promising results. In particular, EMTR, as a correlation estimator metric, has offered a quantitative definition for fault location candidates. As the correlation estimator already obtains the system transfer functions by simulating fault occurrences, these functions are subsequently correlated with the fault-generated transfer function. Therefore, it is necessary to explore the analysis related to the pre-fault processing of this metric. Firstly, the impact of fault impedance mismatch between the fault occurrence and fault estimation stages was investigated, and a simple approach is presented after observing the fault locations' error. Secondly, it was noticed that the existing correlation estimator approach does not accurately identify fault types; therefore, a pseudo approach was developed to address this issue. Finally, the spatial step considered during the fault estimation stage was investigated. It is demonstrated that larger spatial steps result in inaccuracies in fault location. The smaller spatial steps improved the performance, but increased the memory burden on the data storage devices, especially when simple and pseudo approaches must be employed. The memory issue was resolved by presenting a hybrid approach that makes use of regression analysis. A single-phase and a three-phase lossy transmission line system were used to illustrate the proposed analyses.  
transmission lines; fault location; correlation estimator method; regression analysis Article On the Characteristics of EMTR as a Correlation-Estimator-Based Fault Location Method in Transmission Lines [PERSON] 1,1 State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China 1
## 4 Results and Discussion
It was calculated that the initial wavelength interval of sensing units 1\({}^{\#}\) to 6\({}^{\#}\) is 4.91-5.25 nm, basically consistent with the wavelength design range of the sensing unit. According to Table 2, the wavelength varies from 2,688 to 2,900 pm when the sensing unit is subjected to 30 MPa, which is approximately 67.2 to 72.5% of the designed wavelength range (4 nm), indicating that the design range of the sensor has sufficient redundancy. In general, the performance of the sensor developed in this paper reaches the level of accuracy index 2.0, which meets the test requirements for engineering applications. However, due to the limitations of processing conditions and cost, the precision and assembly level of the sensor parts still require improvement, and the accuracy level could also be further improved. In addition, the sensor test used in this paper is limited to the routine calibration and stability tests, and the calibration results cannot be directly applied to the practical engineering environment. In order to apply the sensor in field, more numerical and/or physical simulation studies are needed to master the test performance of the sensor under different working conditions.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 4 Research on Multi-Dimensional Information Fusion and Enhancement Processing Algorithm
### Fusion Rule Based on the Maximum Absolute Value of the Coefficient  
After wavelet transformation, the low-frequency subband coefficients still reflect the average energy of the image, while the high-frequency subband coefficients reflect the details such as edges and textures of the image in different directions [14; 15; 16; 17]. The weighted average of low-frequency coefficients and the maximum absolute value of high-frequency coefficients are used for fusion, and the high-frequency part containing significant details of the image is fused with emphasis, which helps to improve the visual effect of the fused image and keep the significant details in the source image. Let the source images A and B be decomposed by J-layer wavelet to obtain coefficients \(\left\{\mathrm{c}\mathrm{A},\mathrm{d}\mathrm{A}_{\mathrm{j}}^{\mathrm{e}} \right\}\left\{\mathrm{c}\mathrm{B},\mathrm{d}\mathrm{B}_{\mathrm{j}}^{ \mathrm{e}}\right\}\), and the coefficient corresponding to the fused image F is \(\left\{\mathrm{c}\mathrm{F},\mathrm{d}\mathrm{F}_{\mathrm{j}}^{\mathrm{e}}\right\}\). Thus, the low-frequency scale coefficient of the image X in the J-th layer is represented, and the high-frequency wavelet coefficient in the layer direction of the image X is represented.  
The weighted average fusion criterion of low-frequency subbands is:  
\[\mathrm{a}\mathrm{F}(\mathrm{m},\mathrm{n})=\frac{1}{2}[\mathrm{c}\mathrm{A} (\mathrm{m},\mathrm{n})+\mathrm{c}\mathrm{B}(\mathrm{m},\mathrm{n})] \tag{7}\]  
where \((\mathrm{m},\mathrm{n})\) denotes the position of the low-frequency subband coefficients.
# Thermal Management Systems for Civil Aircraft Engines: Review, Challenges and Exploring the Future
## 1 Introduction
More comprehensive studies based on detailed testing and real applications were published in this phase. The ideas of new advanced structures for the TMS, fuel temperature control in TMS and using different cooling fluids like water, Therminol, which is a synthetic heat transfer fluid and thermally neutral heat transfer fluid (TNHTF) are presented and discussed as well. Obviously, the above-mentioned phases are concentrated on different ranges of power and technology development that was the state of the art in the date of research. Table 1 shows the quantitative analysis of different phases that would be discussed in next chapters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Thrust (lbf)** & \
\begin{tabular}{c} **Turbine Entry** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & **Bypass Ratio** &
\begin{tabular}{c} **Turbine Blade** \\ **Cooling Efficiency** \\ \end{tabular} \\ \hline
**Phase I** & \(\leq\)40,000 & \(\leq\)1200 & \(\leq\)5 & \(\leq\)0.55 \\
**Phase II** & 40,000–55,000 & 1200–1400 & 5–7.6 & 0.55–0.65 \\
**Phase III** & \(\geq\)55,000 & \(\geq\)1400 & \(\geq\)8 & \(\geq\)0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative analysis of different phases [12; 13; 14; 15; 16; 17]. Moreover, taking the new advances in material sciences into account, it could be seen that the turbine entry temperature has had an average increase of about 8 \({}^{\circ}\)C rise per year over the last 20 years [17]. It is due to the new advanced materials used for the turbine manufacturing in new aircraft engines (e.g., SC cast alloys and ceramics). So, more increase in thermal loads is expected for the future engines. Consequently, designing an optimal thermal management system architecture is a key factor for the heat management in future civil engines. The most important publications and milestones during phases 1 to 3 are discussed in detail in next sections. Section 2, focuses on pioneering work and concepts dating back to 1958. Section 3 describes the integration phase in thermal management system architecture design that occurred between 1978 and 1998. Section 4 concentrates on detailed design phase in the last two decades addressing the state of the art technologies for thermal load managements in civil aero engines. Based on the results of these sections, different classes of development for the future thermal management systems design are classified and described in detail and the pros and cons of each class is discussed in Section 5. Based on this discussion, two potential solutions for dealing with future challenges of thermal management systems are proposed and illustrated. The conclusion remarks are also presented in Section 6.
## 6 Discussion  
The power flow or dispatching model of single equipment unit or coupling unit in EGCS has already been established, and the corresponding optimal operation strategy also has been formulated through some technical methods [5; 6; 7; 8; 9; 10; 11], but all of them did not effectively evaluate the uncertainty and dispatchability of the EGCS. Also, the uncertainty of the system due to the integration of DG or active load has been evaluated or optimized [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27], while they have not made qualitative or quantitative analysis on the dispatchability of EGCS including DG, and there is no unified index for describing the dispatchability of EGCS. This paper proposes the EGCS economic dispatching model considering the uncertainty of new energy prediction error and the expected penalty of insufficient dispatchability. The optimal operation strategy of economic dispatching which can improve the dispatchability margin is obtained.  
The larger the controllable range of power output is, the more dispatchability it can provide; More uncontrollable and unpredictable resources lead to higher consumption of dispatchability. According to the factors contained in EGCS, the evaluation indexes of EGCS dispatchability are proposed, in which the expected indexes of dispatchability is a penalty item in the objective function of the dispatching model, while the indexes of dispatchability margin can directly reflect the dispatchability of the system. A simple three-machine pure electric system is established to verify the rationality of the indexes proposed in this paper, and the analysis shows that the reliability of the system has a negative correlation with the system dispatchability margin, but a positive correlation with the system operation cost. In addition, it can be seen from the dispatching model that the dispatchability of the system can be effectively improved by improving the prediction ability of the output of the DG in the system.  
Future work could include considering the power gird and the natural gas network under the multi-time scale and trying to perfect the theme of dispatchability by considering the transient state and dynamic state of EGCS.
## 2 Materials and Methods
### The 5G-V2X Antenna Model and Positioning  
Two 5G-V2X antennas at the working frequency of 3.5 GHz were modeled according to the technical specifications indicated by the H2020 5G PPP 5G Communication Automotive Research and innovation (5 GCAR) Project [41; 42]. Briefly, the 5 GCAR Project aimed at identifying 5G solutions to meet the key performance requirements in the most representative and emerging use cases of V2X communication. The 5G-V2X antenna model and setup identified in the 5 GCAR Project are based on the 3 GPP Release 16 and Release 17 specifications on the use of 5G-V2X in vehicular connectivity, resulting in Uniform Linear Array antennas with maximum 8 Tx elements operating at 3.5 GHz, ITS 5.9 GHz, 28 GHz and 64 GHz (mmWave band) [11; 12].  
In this work, the antenna model was simulated using the Sim4 Life software platform (www.zmt.swiss, accessed on 18 July 2022), and its dimensions and characteristics are displayed in the left part of Figure 1. In detail, each 5G-V2X antenna is modeled as an array antenna of eight elements disposed in two rows of four elements each. Each antenna element consists of a simple patch antenna with three layers: the ground and patch layers, modelled as PEC materials, and the substrate element, modelled as a dielectric material (dielectric properties: \(\varepsilon_{r}=2.2\) and \(\sigma=0.0005\) S/m). In order to have the array antenna resonate at the 5G-V2X frequency of 3.5 GHz, the dimensions of the single element were set equal to \(42.8\times 42.8\times 4\) mm, resulting in a total dimension of the eight-element array antenna of \(171.3\times 85.7\times 4\) mm. Each array antenna is driven with an input power of 1 W, simultaneously exciting the eight elements with a harmonic signal with a phase shift of \(0^{\circ}\) between the eight elements, as illustrated in Figure 1. The resulting antenna maximum gain is about 14 dBi, with the main lobe of the array antenna in the boresight direction (i.e., perpendicular to the plane of the antenna).  
We mounted the two antenna models in two different positions on the car body, following the typical settings used in real implementations of 5G vehicular connection [43; 44]. As we can see from the right part of Figure 1, the first 5G-V2X array antenna is positioned on the windshield glass, along the midline of the car, while the second antenna is placed on the roof top, in a posterior position, near the rear window.
## 4 Results and Discussion
### Steady-State Braking Analysis  
The effect of the contact interaction between the tyre and simplified pavements with different aggregate grain sizes can be further analysed in terms of the horizontal braking force. In this study, the steady-state braking of a vehicle travelling at initial speeds of 30, 60, 90, and 120 km/h was simulated using the established 3D tyre-pavement contact finite element model. After simulation, the horizontal combined force of the contact force and friction force is output in the post-processing of the software, which is the horizontal braking force, as shown in Figure 16. The cumulative occlusal depth of each simplified pavement for vehicles traveling at different speeds was calculated using Equations (1) and (2), as shown in Figure 17. To verify that the cumulative occlusal depth accurately reflected the contact interaction between the tyre and pavement aggregate, an analysis of the correlation between the cumulative occlusal depth and horizontal braking force of the simplified pavement was conducted, with the results shown in Figure 18.  
Figure 16: Horizontal braking force of each simplified pavement at different vehicle initial speeds.  
Figure 15: Cumulative occlusal depth of simplified pavement according to aggregate grain size.  
As shown in Figures 16 and 17, the horizontal braking force and cumulative occlusal depth for each simplified pavement decreased as the vehicle speed increased, with both the horizontal braking force and cumulative occlusal depth for the simplified pavements ranked from largest to smallest as aggregate grain sizes 9, 7, and 13 mm at all four speeds. As shown in Table 3, the _MTD_ values of the simplified pavements with aggregate grain sizes of 7, 9, and 13 mm were 1.61, 2.07, and 2.86 mm, respectively. Thus, the effect of the contact interaction between the tyre and simplified pavement aggregates was ranked in order of superiority as aggregate grain sizes of 9, 7, and 13 mm, indicating that the aggregate grain size does not exhibit any correlation with tyre-pavement contact interaction in the later stages of pavement skid resistance. Though the increase in aggregate grain size resulted in a larger _MTD_ of the simplified pavement, this does not indicate a better contact interaction effect between the tyre and pavement.  
As shown in Figure 18, the squares of linear correlation coefficients between the horizontal braking force and pavement cumulative occlusal depth were 0.921, 0.941, 0.889, and 0.894 for vehicle initial speeds of 30, 60, 90, and 120 km/h, respectively, indicating that using the pavement cumulative occlusal depth to characterise the effect of contact interaction between the tyre and pavement aggregate is accurate and can be used to evaluate asphalt pavement skid resistance.  
Figure 17: Cumulative occlusal depth of each simplified pavement at different vehicle initial speeds.  
Figure 18: Correlation analysis between horizontal braking force and cumulative occlusal depth.
# Continuous Adaptation with Online Meta-Learning for Non-Stationary Target Regression Tasks
## 3 The CORAL Algorithm
### Non-Stationary Target Adaptation  
Standard Bayesian linear regression utilises all the training data equally to compute the predictive posterior, which is why it has superior sample efficiency. However, in the case of a non-stationary target, equal treatment of the data causes serious performance degradation. To adapt to a non-stationary target, it is required to compute the predictive posterior with the recent data and forget older data that can degrade the prediction. Here, we propose using a sliding window approach that computes the predictive posterior with a time windowed data \(\{(y_{t},x_{t})\}_{t=\tau-n+1}^{\tau}\).  
There are other approaches to incorporate forgetting older data. We pick the sliding window because it works well with the online meta-learning algorithm, as we discuss in Section 4. We can implement the sliding window as shown in Equation (7). Compared with the conventional recursive update equations (Equation (4)), it has an extra term in the first and second equations to remove the older data. In this paper, we assume the time window size \(n\) is given and fixed. Adaptive window size algorithms are an interesting research topic, and we discuss some of them in Section 4.  
\[\begin{split}\Lambda_{\tau}&=\Lambda_{\tau-1}+\phi (x_{\tau})\phi(x_{\tau})^{T}-\phi(x_{\tau-n})\phi(x_{\tau-n})^{T}\\ Q_{\tau}&=Q_{\tau-1}+\phi(x_{\tau})y_{\tau}^{T}- \phi(x_{\tau-n})y_{\tau-n}^{T}\\ \overline{K}_{\tau}&=\Lambda_{\tau}^{-1}Q_{\tau}\\ \Sigma_{\tau+1}&=(1+\phi^{T}(x_{\tau+1})\Lambda_{ \tau}^{-1}\phi(x_{\tau+1}))\Sigma_{\epsilon}.\end{split} \tag{7}\]  
Here, \(Q_{0}\) is initialised as \(\Lambda_{0}\overline{K}_{0}\), and \(\Lambda_{0}\) is the prior's precision matrix. Again, we can simplify the above computation by updating \(\Lambda_{\tau}^{-1}\) directly using the Woodbury matrix identity. Here, we need to apply it twice--once for adding a new sample and the second time for removing the older data. The \(\Lambda_{\tau}^{-1}\) update can be written as  
\[\begin{split}\Lambda_{\tau}^{\prime-1}&=\Lambda_{ \tau-1}^{-1}-\frac{(\Lambda_{\tau-1}^{-1}\phi(x_{\tau}))(\Lambda_{\tau-1}^{-1 }\phi(x_{\tau}))^{T}}{1+\phi(x_{\tau})^{T}\Lambda_{\tau-1}^{-1}\phi(\tau_{t})} \\ \Lambda_{\tau}^{-1}&=\Lambda_{\tau}^{\prime-1}-\frac{( \Lambda_{\tau}^{\prime-1}\phi(x_{\tau-n}))(\Lambda_{\tau-1}^{\prime-1}\phi(x _{\tau-n}))^{T}}{-1+\phi(x_{\tau-n})^{T}\Lambda_{\tau-n}^{\prime-1}\phi(x_{ \tau-n})},\end{split} \tag{8}\]  
where \(\Lambda_{\tau}^{\prime}\) is an intermediate precision matrix obtained after adding the new data component \(\phi(x_{\tau})\phi(x_{\tau})^{T}\) and before subtracting the old data component \(\phi(x_{\tau-n})\phi(x_{\tau-n})^{T}\). By replacing the first line of Equation (7) with Equation (8), we obtain a recursive method for the predictive posterior with the windowed data.