esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper simulated the wind flow around a high-rise building using different k-\(\epsilon\) turbulence models with a polyhedral mesh system. The accuracy of the simulation results was evaluated against the AIJ wind tunnel experiment results. It was concluded that:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Recommended Models** & **Not-Recommended Models (Much Different from the Experiment)** \\ \hline Y = 0 & STLKE & RTLKE, RKE, AKNKE, EBKE \\ \hline z/b = 0.125 & STLKE, EBKE, RTLKE & RKE, AKNKE \\ \hline z/b = 1.25 & STLKE, EBKE, RKE & RTLKE, AKNKE \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison of turbulent kinetic energy in high wind-speed area.  
Under polyhedral mesh conditions, the modified k-e models applied with the wall function and the damping function improved the accuracy of the simulations but could still not perfectly match the wind tunnel experiment results. The correction of the models (e.g., optimizing the closure coefficients of the RANS turbulence model) needs to be further studied in order to significantly improve the accuracy of CFD simulations.
2. In the low-wind-speed area, the simulation results of the RTLKE model were the closest to the experimental results of the wind tunnels. In the high-wind-speed area, the simulation results of the STLKE model were the closest to the experimental results of the wind tunnels. So, these two models are recommended to be used for the simulation of wind around high-rise buildings under different circumstances.
3. In practice, it is recommended to use the STLKE model to explore high-wind-speed areas around high-rise buildings (e.g., the high-wind-speed areas around buildings during a typhoon, the maximum wind speed area around high-rise buildings, etc.). It is recommended to use the RTLKE model to explore low-wind-speed areas around high-rise buildings (e.g., the size of the static-wind area around high-rise buildings, the diffusion time of pollutants around buildings, etc.).  
The source files of numerical simulation can be downloaded at: [[https://pan.quark.cn/s/92](https://pan.quark.cn/s/92) ac5a356278]([https://pan.quark.cn/s/92](https://pan.quark.cn/s/92) ac5a356278).  
Conceptualization, H.Z., M.X. and B.C.; methodology, M.X.; software, H.Z.; validation, M.X.; formal analysis, B.C.; investigation, H.Z. and Y.Q., resources, H.Z.; data curation, M.X.; writing--original draft preparation, H.Z.; writing--review and editing, M.X. and B.C.; supervision, H.Z. and B.C.; project administration, H.Z.; funding acquisition, H.Z. and B.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY19E080001, and Research Development Fund of Xi'an Jiaotong-Liverpool University, grant numbers RDF-20-02-19 and RDF-15-01-19.  
Not applicable.  
Data Availability StatementData are contained within the article or supplementary materials.  
The authors would like to thank the editor and anonymous reviewers for their support to improve the quality of this manuscript.  
The authors declare no conflict of interest.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.2 Holographic Metasurface Requirements  
The demands for holographic metasurfaces within the 6G era range from energy-efficient and high-capacity to reconfigurable systems. One of the works (ref. [71]) applies the genetic algorithm to optimize phase-only holograms at 90 GHz, which shows that one can accurately sculpt the electromagnetic field for holography and imaging. It enhances the pattern distribution and allows for high-density, low-noise applications. A closely related but separate work (ref. [73]) proposed holographic MIMO (HMIMOS) surfaces and claimed a reduction in power consumption and enhancement in spectral efficiency. Another potential complement to ultra-massive MIMO systems is reconfigurable holographic surfaces (RHS), which enable holographic beamforming with almost continuous spatial aperture, relatively cheaper hardware, and better power efficiency compared to phased arrays (ref. [74]). Nonetheless, scalability, channel estimation, and resource allocation are still key factors to explore further (refs. [75; 76; 70]).
## 4 Use Cases
### Problem-Solving Strategy  
MultiScaleAnalyzer enabled the identification of different problem-solving strategies employed by students. Using the aggregated data view, two distinct approaches to solving mathematical problems were observed. The first approach was exemplified by S7, who demonstrated sustained fixations on areas with relevant information throughout the problem-solving process (Figure 5). In contrast, S16 also maintained focused attention on the problem, but individual fixation durations were significantly longer, with many exceeding one second (Figure 6). According to [PERSON] et al., fixations lasting over 500 ms can be classified as long fixations, indicating extended periods of cognitive processing [38; 40]. This highlights a significant difference between S7's frequent but short fixations and S16's fewer but prolonged fixations.  
Furthermore, through the aggregated data view 2--referred to as \"Elements Vis\" (elements' visualization), it was observed that S7 tended to read through the entire problem comprehensively (Figure 7a), whereas S16's long fixations were concentrated on specific keywords like \"a total of\", \"gave\", \"brother\", and \"left\" (Figure 7b). These findings align with [PERSON] et al.'s [42] identification of two distinct mathematical problem-solving strategies. The first is the model-based problem-solving strategy, which involves understanding the problem and constructing a mental model. The second strategy is the keyword strategy, which focuses on identifying and leveraging key terms within the problem statement. Correspondingly, it suggests that S7 employed a model-based problem-solving strategy, while S16 adopted a keyword strategy. These insights demonstrate the utility of MultiScaleAnalyzer in uncovering diverse cognitive approaches and strategies to solve mathematical problems.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 2 Numerical Computational Model for Distribution Grid Fault Location
### Basic Principle of Distribution Network Fault Location
#### 2.2.5 Numerical Computational Expression for the Objective Function  
When considering the minimum fault set constraint, the objective function shown in Equation (6) can be simplified and rewritten in matrix form, as shown in Equation (25):  
\[\text{min}f(\boldsymbol{X})=\boldsymbol{e}^{\boldsymbol{T}}[\boldsymbol{I} \oplus\boldsymbol{J}(\boldsymbol{X})] \tag{25}\]  
where \"\(\oplus\) \" means that the corresponding elements of two same-dimension vectors undergo logical \"XOR\" operations.  
The vectors \(\boldsymbol{I}\) and \(\boldsymbol{J}(\boldsymbol{X})\) are both 0-1 vectors. Because for any 0-1 vector \(\boldsymbol{a}\), \(\boldsymbol{b}\), we have \(\boldsymbol{e}^{\boldsymbol{T}}(a\oplus b)=(a-b)^{\boldsymbol{T}}(a-b)\), we can translate Equation (25) into a numerical expression as shown in Equation (26).  
\[\text{min}f(\boldsymbol{X})=[\boldsymbol{I}-\boldsymbol{J}(\boldsymbol{X})]^{ \boldsymbol{T}}[\boldsymbol{I}-\boldsymbol{J}(\boldsymbol{X})] \tag{26}\]  
In summary, we complete the equivalent transformation of the logical operations in the fault location model by using Equation (26) as the objective function, Equation (19) as the equality constraint, and Equation (20) as the inequality constraint to construct the numerical computational model for fault location, as shown in Equation (27).  
\[\left\{\begin{array}{l}\text{min}f(\boldsymbol{X})=\boldsymbol{e}^{ \boldsymbol{T}}[\boldsymbol{I}\oplus\boldsymbol{J}(\boldsymbol{X})]\\ \boldsymbol{J}(\boldsymbol{X})=\boldsymbol{e}-\prod\limits_{x_{k}\in \boldsymbol{X}}^{*}(\boldsymbol{e}-\boldsymbol{P}_{k})\\ \forall x_{i},x_{j}\in\boldsymbol{X},\boldsymbol{P}_{i}\
eq\boldsymbol{P}_{i}* \boldsymbol{P}_{j}\end{array}\right. \tag{27}\]  
It can be seen in Equation (27) that the numerical computation model for fault location does not contain logical operations, which lays a theoretical foundation for a highly efficient solution for the model obtained through strict mathematical derivation.
# Improved Solar Operation Control for a Solar Cooling System of an IT Center
## 2 Materials and Methods  
### Solar Assisted Cooling System  
In Figure 1, a simplified process and instrumentation diagram of the SAC system at the Federal Environment Agency (UBA) in Dessau, Germany with five heating circuits and four cooling circuits, each combined into one symbol, is depicted. The cold demand is dominated by the cooling circuit of the IT center with an average cooling load of approximately 20-25 kW around the clock. During summertime, the absorption chiller is powered either by district heat from a central CHP plant or by solar heat from a field of vacuum heat pipe collectors (216 m\({}^{2}\) absorber area, 3 \(\times\) 7.5 m\({}^{3}\) storage volume). The idea of the relatively large storage volume and collector field with respect to the cooling load is to accumulate solar heat during daytime in order to cover the cooling load by solar heat operation of the absorption chiller during nighttime as well. The design values of the absorption chiller are provided in Table 1.  
Figure 1: Simplified process and instrumentation diagram of the SAC system at the Federal Environmental Agency (UBA) in Dessau, Germany. Only the temperature probes and flow meters that are used in the result section are labelled.  
A compression-type chiller is used for the peak load and as redundancy for the IT center. During wintertime, solar heat is used for space heating and the reject heat device (RHD) is used directly for the cold supply. In this case, neither the compression-type chiller nor the absorption chiller is running. Nominal values of the reject heat device in dry-cooler mode are provided in Table 2. A more detailed description of the seasonal solar cooling concept can be found in [16].
## 6 Results and Discussion
### Array Metamaterial Results  
A unit cell itself cannot satisfactorily function independently. To obtain the expected electromagnetic properties from a metamaterial, an array of the unit cell was used. The \(1\times 2\) and \(2\times 2\) arrays of the proposed unit cell with the same substrate material are illustrated in the Figure 17a,b. To investigate the coupling effect among the proposed unit cell for different orientations of an array, a parametric study was performed whereby it generated an almost similar response as S\({}_{21}\) to the unit cell. Therefore, the efficiency of the array was also observed. Figure 18a,b present the statistical results of S\({}_{11}\) and S\({}_{21}\), respectively, by CST of arrays with the proposed unit cell. By assessing the simulated results of the arrays, a change in the resonance frequencies was observed for the new array metamaterial. A comparative view of three resonance frequencies of two arrays in addition to the unit cell is shown in Table 4. The third frequency (9.42 GHz) is shifted nearly 2%, while the other 2 frequencies (2.38 GHz and 4.55 GHz) shifted only by 1%.  
Figure 16: Refractive index (n) real and imaginary of unit cell.  
Figure 15: Permeability (\(\mu\)) of real and imaginary of unit cells.  
A, B, C, and D are the four positions of unit cell in the 2 \(\times\) 2 array. If the two elements of the diagonal BD rotate at an angle of 90\({}^{\circ}\) compared to another two diagonal elements of the Figure 17b, as shown in Figure 19a, interesting and magical changes are observed in the numerical result. Four resonance peaks were observed at 2.27 GHz, 4.46 GHz, 7.76 GHz and 9.21 GHz for the S\({}_{21}\) result at the new orientation, whereas, for Figure 17b, the simulated result shows three resonance peaks at 2.40 GHz, 4.60 GHz and 9.60 GHz. Figure 19b compares the two settings.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Simulation** & **Unit Cell** & **1\(\times\) 2 Array** & **2\(\times\) 2 Array** &
\begin{tabular}{c} **Max. Shift of** \\ **Frequency (\%)** \\ \end{tabular} \\ \hline First resonance (GHz) & 2.38 & 2.36 & 2.40 & 0.84 \\ Second resonance (GHz) & 4.55 & 4.54 & 4.60 & 1.09 \\ Third resonance (GHz) & 9.42 & 9.34 & 9.60 & 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variation in resonance frequencies for different arrays with the PMA unit cell.  
Figure 17: (a) 1 \(\times\) 2 array; (b) 2 \(\times\) 2 array.  
Figure 18: Comparison of S-parameter: (a) reflection coefficient (S\({}_{11}\)); (b) transmission coefficient (S\({}_{21}\)).
## 6 Error Analysis and Model Reliability  
ML models are imperfect and often contain systematic errors, even if the level of accuracy is satisfactory when evaluated based on given error metrics. Recall that the error plot in Figure 6 shows that the distribution of MSB error is not perfectly Gaussian. Since the trained model generates single predictions without giving information on confidence interval, it is possible that the model seems over-confident even when the prediction is likely to be inaccurate. Therefore, it is important to analyse and understand model error so that model accuracy can be consistently monitored and improved. In this section, we aim to apply several error analysis approaches to the MSB framework, so that individual predictions that are considered unreliable and may be subject to higher error than average can be discovered at the same time the predictions are made. Hereinafter, an 'unreliable prediction' refers to an individual prediction point with prediction error higher than overall metrics. As a benefit, when a weather forecast from MSB is potentially unreliable, customers will be notified and advised to rely on alternative sources of information for these points.  
This section presents the various methods incorporated into MSB to discover unreliable and potentially less accurate predictions before the observation data are made available, so that alternative information sources can be sought in advance as a supplement to MSB predictions. The reliability study is only applied to forecasts with lead day 0 because forecasts with longer lead days can be subject to change over time as new observations become available. Note that since our goal is to provide foresight to the predictions, we do not apply posterior analysis approaches that require the knowledge of ground truth in this study. Meanwhile, we aim at keeping the process fully automatic and time efficient, so the adopted approaches are selected with consideration of their simplicity together with their effectiveness. Meanwhile, it should be noted that studies in this section aim at identifying high-error predictions only, without separating the source of error into aleatoric and epistemic uncertainty.
## 3 Analysis of Shield Tunneling Parameters
### Cutter Speed  
The correlation between the cutter speed and the shield tunnel ring number is illustrated in Figure 12. Table 8 presents the statistical characteristics of the cutter speed across three distinct stratum conditions.  
Figure 11: The curve of the cutter torque changing with the ring number.  
Figure 12: The curve of the cutter speed changing with the ring number.  
Analysis of Figure 12 and Table 8 reveals that within the same stratum, the cutter speed undergoes minimal change. The cutter speed in formations I, II, and III gradually increases. In the relatively weak stratum I of the shield, the cutter speed of the shield tunneling cutter is deliberately maintained at a low level, with an average cutter speed of about 1.23 r/min\({}^{-1}\). This is done to minimize the disturbance caused by the cutter to the stratum. In stratum II and stratum III, where the soil strength is higher, the cutter torque is reduced by increasing the cutter speed, with the average cutter speed controlled at 1.34 to 1.36 r/min\({}^{-1}\).  
Contrary to the influence of the stratum on the cutter torque in shield tunneling through volcanic ash strata, the cutter speed is intentionally kept low. This is due to several reasons. (1) A lower cutter speed ensures enough torque is transmitted to the cutter, effectively overcoming stratum resistance. (2) Reducing the cutter speed is necessary to avoid an increase in tool wear. (3) Lowering the cutter speed of the cutter is beneficial in reducing the accumulation of particles on the cutter surface, thereby minimizing the tunneling resistance.
## 3 Results and Discussion
### Chemical Exergy (\(e^{h}\))  
One of the effective ways to calculate energy quality is by analyzing the exergy of the biomass. [PERSON] [42] defines exergy as the maximal theoretical useful work acquired if the system is in thermodynamic equilibrium with the surroundings via reversible process. In context of biomass, exergy plays a vital role in evaluating the energy potentiality that is stored in the form of chemical bonds of the compounds.  
From Figure 6 it can be seen that the overall increase in chemical exergy increases with increase in torrefaction residence time and temperature. At 200 \({}^{\circ}\)C, there is a gradual increase in the exergy but a significant increase is observed at 300 \({}^{\circ}\)C followed by 250 \({}^{\circ}\)C and the least value is projected by 350 \({}^{\circ}\)C. The graph obtained for chemical exergy can be correlated with O/C molar ratio and degree of torrefaction. The decrease in the O/C molar ratio is due to the increase in the relative carbon content which in turn increases the chemical exergy. This may be due to the fact that elemental oxygen present in the biomass operates as an oxidizing agent instead of combustible matter in contrast to the elemental carbon of the biomass, which undergoes oxidation during the chemical reaction releasing heat, which can be converted to work. In addition, the trend followed by degree of torrefaction and chemical exergy is comparable. For 200-300 \({}^{\circ}\)C, the degree of torrefaction and chemical exergy increases with an increase in torrefaction residence time. At 350 \({}^{\circ}\)C, the degree of torrefaction decreases with an increase in residence time; whereas, there is a negligible increase in the chemical exergy. This again can be associated with the increase in calorific value of the biomass due to the increase in the relative carbon content with the increase in torrefaction residence time. The highest value of exergy obtained in this study was 17.05 MJ/kg for 300 \({}^{\circ}\)C at 50 min residence time. The greater the value of exergy the greater is the useful work of the torrefied biomass, which in turn can enhance the thermochemical processes, such as pyrolysis, combustion, and gasification. For better-torrefied yield product on the basis of chemical exergy it can be concluded that 250-300 \({}^{\circ}\)C can be used as the optimal torrefaction temperature for sewage sludge.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Torrefaction** & **Temperature** & **Torrefaction** & **TI (Obtained in** **this Study**) \\
**Regimes** & **(\({}^{\circ}\)C)** & **Index (TI)** [40] & **TI (Obtained in** **this Study**) \\ \hline Light & 200 to 235 & 0.93 to 0.95 & 0.89–0.96 \\ Medium & 235 to 275 & 0.95 to 0.97 & 0.94–0.99 \\ Severe & 275 to 300 & 0.97 to 1.0 & 0.96–1.0 \\ - & 350 & - & 0.94–0.92 \\ \hline \end{tabular}
\end{table}
Table 2: Torrefaction Index in different Regimes.  
Figure 6: Chemical exergy of sewage sludge as a function of torrefaction residence time.
## 3 Background
### Feature Extraction
#### 3.5.2 Bidirectional Encoder Representations from Transformers (BERT)  
BERT [50] is a language model that is pre-trained and is extensively used for various natural language processing tasks. Due to its foundation on the Transformer architecture, which utilizes a multi-head attention mechanism, BERT is highly proficient in representing and encoding textual information. Initially, text is represented by a sequence of tokens and is initialized as one-hot vectors. Subsequently, processing the layers of the Transformer involves computing the attention of words with words adjacent to them to generate the word representation in the subsequent layers. In the BERT model, additional positional information is utilized. BERT is then trained by masking certain words (using MASK) during the input and predicting them in the output layer.  
Incorporating BERT as a preprocessing tool has enabled numerous NLP tasks to achieve state-of-the-art performance. Therefore, in our research, we also leverage BERT to represent textual information such as reviews and descriptions. Specifically, we employ two variations of BERT: BERTBASE (L = 12, H = 768, A = 12, total parameters = 110 M) and BERTLARGE (L = 24, H = 1024, A = 16, total parameters = 340 M). For generating word vectors in our study, we opt to use BERTBASE.
## 4 Numerical Results  
### Simulation Parameters  
The main simulation parameters of the IRS-aided VLC system are listed in Table 2. Assume that two parallel vehicles are driving along the centerline of the neighbor lanes and the width of each lane is 3.5 m. The height of the high-beam headlamp is 0.62 m, and the separation between two headlamps is 1.12 m. IRSs are installed on the street light pole, and the height of the center is consistent with the height of the headlamp.  
The coordinate values of the transmitter and receiver can be obtained as follows.  
\(x_{s}\ =\ \left(\frac{3.5}{2}-\frac{112}{2}\right)=1.19\), \(y_{s}\ =\ 10\), \(z_{s}\ =\ -\left(w_{m}\cdot\frac{n_{l}}{2}\right)\ =\ -\left(0.05*\frac{n_{l}}{2}\right)\).  
\(x_{d}\ =\ \left(3.5+\frac{3.5}{2}\right)=\ 5.25\), \(y_{d}\ =\ y_{s}\ =\ 10\), \(z_{d}\ =\ 0\).  
For the transmitter signals, set A = 2, \(\varepsilon=1\), and \(l_{DC}^{2}=45\) dBm. The noise power is \(\sigma^{2}=-98\) dBm. The maximum value of the electrical power of the system \(P_{max}=50\) dBm, and the hardware static power consumed in the transmitter and receiver \(P_{hsp}=30\) dBm. The size of each mirror in IRS is 0.01 \(\times\) 0.01 m\({}^{2}\), and the spacing between mirrors is zero. To ensure traffic safety and avoid collision with the IRS when driving, we set the IRS to have 60 mirrors in each row and 60 mirrors in each column, so the maximum number of mirrors is \(N_{max}=60\times 60=3600\). The power consumption of each mirror is \(P_{m}=20\) dBm. The mirror reflection efficiency is 0.8. The modulation mode is optical OOK modulation and the \(BER_{t}\) is \(10^{-6}\).
## 2 Materials and Methods
### Identification of Chemical Compounds  
The plant extracts were chromatographically analyzed using high-performance liquid chromatography (HPLC)/mass spectrometry (Kyoto, Japan), CBM-20A (CONTROL BUS MODULE), CTO-30A (COLUMN OVEN), LC-30 AD (LIQUID CHROMATOGRAPH), SIL-30 AC (AUTO SAMPLER), and LCMS-8030 (LIQUID CHROMATOGRAPH MASS SPECTROMETER) (Triple Quad Ms). Briefly, 25 mg of the dry crude extract was dissolved in 1 mL of methanol, and 10 \(\upmu\)L of the sample solution was injected. Separations on an ACE 5 C18 (250 mm \(\times\) 4.6 mm; 5 mm) column were accomplished with a flow rate of 0.8 mL/min at 30 \({}^{\circ}\)C. Mobile phases A:B:C (80:12:8) water with acetic acid (0.1%) (A), methanol with 0.1% acetic acid (B), and acetonitrile with 0.1% acetic acid (C), were used for the analysis (C). The elution program continued at 80:12:8 (A:B:C) for 8 min. The polarity was gradually increased by 25:60:15 at 40-45 min, then returned to the mobile phase (80:12:8) to recondition the column for 5 min. Extract samples and solvents for mobile phases were filtered using a 0.22 mm filtration system before processing HPLC injections (Millipore Corp., Billerica, MA, USA). Each sample was examined in triplicate. The chemical components of the plant samples were detected by comparing their retention indices (RI) and mass spectra (MS) with those in the NIST and WILEY mass spectral libraries.