esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 1 Introduction
In 2019, [PERSON], [PERSON], [PERSON], and [PERSON] explored the effects of external shading systems on visual display terminal (VDT) users' cognitive performance, alertness, and visual comfort in naturally lit working environments. Their research found that the use of shading systems improved the performance on tasks requiring sustained attention, although it did not significantly affect other cognitive tasks [13]. Artificial lighting and daylight factors are two important elements in office lighting design. For artificial lighting, factors such as illuminance, color temperature, and illuminance uniformity are most easily perceived and consciously distinguished by occupants, providing direct guidance for designing and evaluating office lighting systems. Regarding natural lighting, the daylight factor under a cloudy sky model is beneficial for achieving a uniform illuminance and reducing natural glare [14]. On the other hand, the adoption of a cloudy sky model has minimal impact on the glare index for different building orientations and times of day, thus having little effect on office workers' comfort [15]. However, in locations like Dalian, where sunny days far outnumber cloudy days, conducting research based on a sunny day model is more relevant. In office building lighting design, natural light is generally preferred over artificial light sources, as it not only provides adequate lighting but also positively influences staff efficiency [16]. However, relying solely on natural light often fails to meet indoor illumination requirements. Therefore, this paper proposes a combined control strategy for integrating natural and artificial lighting. The goal is to improve the indoor lighting environment, while achieving optimal energy savings and visual comfort in office spaces.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 5 Experimental Results and Discussion
### Theoretical Coverage Study  
In this section, we present the theoretical coverage study from an accurate radio planning tool [20], which uses accurate terrain elevation data and the ITM Longley-Rice propagation model. We present a coverage graph showing the estimated RSSI interval for the propagation model that was tested. The configuration is set with the exact features corresponding to our experimental field test. All losses and gains of the system are considered to obtain an accurate study. The RSSI holds the received signal strength in dBm. Figure 17 shows the obtained coverage map for the 433 MHz and 868 MHz frequency bands. The positions in the figures are the exact location described in Table 4 and shown in Figure 7. The signal strength from the coverage heat map is described with colours. The red area indicates very good signal strength, where the blue areas represent a level of received power that is below the receiver's floor sensitivity. From the figure, the 433 MHz heat map shows large coverage with low RSSI values compared to the 868 MHz heat map.
## 3 Asset Localization
### Summary of Asset Localization  
Asset localization in IIoT systems can be implemented using a variety of technologies, including UWB, 5G, WiFi, BLE, etc.; however, UWB is the only technology that specifically concentrates on localization. Machine learning methods are generally used for the following two purposes in case of each technology:  
1. To learn the mapping between measurements and location
2. To improve the accuracy of the location deduced by closed-form, geometrical problems  
The first is mainly used in case of fingerprinting--here, machine learning models try to learn the mapping between the measurements (mostly RSSI) and the location. The baseline solution is often the kNN learner; however, a couple of other regression methods are used, sometimes using classification on grids. The second one combines geometric models with machine learning models, where the following two basic methods can be distinguished: predicting LOS or NLOS propagation and predicting the localization error. The methods usually use additional information, e.g., the channel impulse response. The references for different applications presented in the section are shown in Table 2.  
For further details, an in-depth study on indoor localization using machine learning techniques can be found in [103].
# A Low-Band Multi-Gain LNA Design for Diversity Receive Module with 1.2 dB NF
## 4 Experimental Results
Finally, this structure shows a better noise figure performance than other presented works except [8]. Nevertheless, we should consider the fact that our structure covers multi-bands with a 5 PST switch and the performance of this switch has also been integrated with our proposed LNA. Figure 8: Simulated and measured noise figure for different bands. Figure 7: Simulated and measured IIP3 for different bands. Figure 8.0 Phase discontinuity in an example frequency of 892 MHz. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **This Work** & **[7]** & **[8]** & **[9]** & **[10]** & **[11]** & **[12]** \\ \hline Freq. (GHz) & 0.61–0.96 & 0.9 & 0.868/0.9 & 0.45/0.9 & 0.05–1 & 4–11.5 & 0.3–3.5 \\ NF (dB) & 1.2\(\sim\)1.8 & 2.2 & 0.92/0.98 & \(<\)4.5/3.7 & 2.3\(\sim\)3.3 & \(>\)2.75 & \(>\)2.9 \\ S\({}_{21}\) (dB) & \(-\)12\(\sim\)18 & 11.3 & 14.2/13.8 & \(>\)50/30 & 24\(\sim\)30 & 21 & 14.6 \\ S\({}_{11}\) (dB) & \(<\)\(-\)8 & \(-\)20.6 & \(-\)18\(-\)16 & \(<\)\(-\)10\(-\)6 & \(<\)\(-\)10 & \(<\)\(-\)10 & \(<\)\(-\)10 \\ S\({}_{22}\) (dB) & \(<\)\(-\)15 & \(-\)23.9 & \(>\)\(-\)15 & \(<\)\(-\)15\(-\)10 & NA & NA & NA \\ IIf3 (dBm) & \(-\)6 @ @ G6 & - & \(-\)12 & \(-\)64 & \(-\)4.1 & 6.5 & 1.2 \\ Power (mW) & 10.8 & 12.78 & 5.23 & 0.96 & 19.8 & 5.15 & 14.8 \\ ESD & HBM, CDM & NA & HBM & Yes & NA & NA & NA \\ Multi-Gain & Yes & No & No & Yes & No & No & No \\ Tech. (nm) & 65 (RSB) & 180 & 130 & 180 & 65 & 65 & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance summary and comparison. Figure 9: Measured S-parameter results: (**a**) S\({}_{11}\), (**b**) S\({}_{22}\), and (**c**) S\({}_{12}\)/S\({}_{21}\) for 859–960 MHz frequency ranges from B26, B8, and LB_AUX1 ports (Figure 1), (**d**) S\({}_{11}\), (**e**) S\({}_{22}\), and (**f**) S\({}_{12}\)/S\({}_{21}\) for 728–821 MHz and 617–652 MHz frequency ranges from LB_AUX2 and LB_AUX3 ports (Figure 1).
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 4 Solar Array Fault Classification Model
### Model Validation  
In a real solar field, current-voltage curves were obtained from a string of 11 solar modules connected through an inverter, with the modules having an open-circuit voltage of 40.38 V and a short-circuit current of 10.85 A. The real solar field data comprised 23 entries, including 6 with minor crack faults, 14 with minor hot spot faults, 2 with small-area shading faults, and 1 with a large-area shading fault, all collected under an irradiance of 1000 W/m\({}^{2}\); the three types of faults are shown in Figure 26.  
After preprocessing, the real field data were input into the trained fault classification model. The results are displayed in Figure 27. The model correctly identified all shading and hot spot faults. However, it correctly identified only one of the crack faults, misclassifying the remaining five as hot spot faults, resulting in a model prediction accuracy of 78%. The  
Figure 26: Actual Field Measurement Data.  
Figure 24: Accuracy and Loss Curves.  
Figure 25: Confusion Matrix for the Model Test Set.  
misclassification can be attributed to the subtle differences between minor hot spot and crack faults in the current-voltage curves in real-world conditions, which are not distinctly different, leading to errors in model predictions.
## 5 Experiments
### Data Preparation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Buoy ID** & **\# Observations** & \begin{tabular}{c} **\# Training** \\ **Subsequences** \\ \end{tabular} & \
\begin{tabular}{c} **\# Validation** \\ **Subsequences** \\ \end{tabular} &
\begin{tabular}{c} **\# Testing** \\ **Subsequences** \\ \end{tabular} \\ \hline
42,001 & 5822 & 4325 & 731 & 730 \\ \hline
42,002 & 5807 & 4295 & 732 & 725 \\ \hline
42,003 & 1421 & 755 & 0 & 642 \\ \hline
42,036 & 5452 & 4304 & 372 & 729 \\ \hline
42,038 & 5811 & 4316 & 732 & 715 \\ \hline
42,039 & 2178 & 682 & 730 & 723 \\ \hline
42,040 & 5348 & 4297 & 274 & 723 \\ \hline BURL1 & 5760 & 4327 & 732 & 665 \\ \hline BYGL1 & 3953 & 2195 & 697 & 574 \\ \hline CDRF1 & 5625 & 4105 & 732 & 730 \\ \hline FWYF1 & 5231 & 4325 & 565 & 305 \\ \hline GDIL1 & 5782 & 4321 & 732 & 693 \\ \hline KYWF1 & 880 & 399 & 0 & 444 \\ \hline LONF1 & 5825 & 4326 & 732 & 731 \\ \hline MLRF1 & 5827 & 4328 & 732 & 731 \\ \hline NPSF1 & 2638 & 2044 & 100 & 397 \\ \hline SANF1 & 5818 & 4323 & 731 & 728 \\ \hline SGOF1 & 5573 & 4268 & 539 & 730 \\ \hline SMKF1 & 3622 & 2124 & 732 & 730 \\ \hline SPGF1 & 4172 & 2674 & 732 & 730 \\ \hline VCAF1 & 1437 & 108 & 706 & 573 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Total number of observations belonging to each buoy and number of subsequences created for training, test, and validation sets. extracted subsequences can be given as \(\lceil\)(L-T+1)/s\(\rceil\). This gives the maximum number of subsequences we can possibly extract. As shown in Figure 6, each buoy has a different number of missing data which results in data sequences with different length. Due to the high number of missing readings, we have tolerated missing data points on some time stamps by keeping the time difference limit between the end points of a subsequence at \(2\times T\). After having applied Algorithm 1 with window size set to 12 and step size to 1, we have generated training, validation and test sets which have different number of subsequences for each buoy as shown in Table 1.
## 3 Results and Discussion
### [PERSON]'s Color Properties of Yanggaeng  
Figure 1 illustrates the visual appearance of the Yanggaeng samples treated with CF and different alternative sweeteners, and Table 3 presents [PERSON]'s color measurements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proximate Compositions (\%)** & **Sug** & **Alu** & **Ste** & **Xyl** \\ \hline Moisture & 47.63 \(\pm\) 0.32 b  & 56.24 \(\pm\) 0.06 a  & 55.69 \(\pm\) 0.71 a  & 55.29 \(\pm\) 0.12 a  \\ Crude ash & 0.39 \(\pm\) 0.08 ns  & 0.43 \(\pm\) 0.08 ns  & 0.41 \(\pm\) 0.14 ns  & 0.38 \(\pm\) 0.03 ns  \\ Crude fat & 0.13 \(\pm\) 0.01 ns  & 0.15 \(\pm\) 0.01 ns  & 0.25 \(\pm\) 0.08 ns  & 0.27 \(\pm\) 0.10 ns  \\ Crude protein & 0.06 \(\pm\) 0.00 ns  & 0.07 \(\pm\) 0.01 ns  & 0.07 \(\pm\) 0.01 ns  & 0.06 \(\pm\) 0.00 ns  \\ Carbohydrate  & 51.80 \(\pm\) 0.31 a  & 43.11 \(\pm\) 0.03 b  & 43.55 \(\pm\) 0.82 b  & 43.99 \(\pm\) 0.16 b  \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proximate compositions of Yanggaeng made with _Corni fructus_ powder and alternative sweeteners.  
The lightness (\(L^{\star}\)) values were 24.20, 28.27, 23.70, and 19.40 for Sug, Alu, Ste, and Xyl groups, respectively. The \(L^{\star}\) values were the highest in the Alu group and the lowest in the Xyl group (\(p<0.05\)). Xyl had a stronger darkening effect on Yanggaeng than other alternative sweeteners. As a reducing sugar, xylose likely participates more actively in the browning reactions [33], significantly affecting the color of Yanggaeng. Interestingly, the Xyl group also exhibited higher \(a^{\star}\) and \(b^{\star}\) values than the Sug, Alu, and Ste groups. These results suggest that xylose can provide a richer and deeper hue for Yanggaeng. Similarly, [PERSON] et al. demonstrated that macarons with added xylose showed a decrease in \(L^{\star}\), resulting in a darker brown color and increased \(a^{\star}\) and \(b^{\star}\) values [34; 35; 36]. In terms of \(a^{\star}\) values, there were no significant differences in \(a^{\star}\) values among the Sug, Alu, and Ste groups. However, \(b^{\star}\) values were higher for the Yanggaeng samples with alternative sweeteners than for sugar-supplemented Yanggaeng.
## 3 Data
As land surface data, the soil was used as a factor related to groundwater potential. Soil texture invariably controls the penetration of surface water into an aquifer system. The soil texture of the study area was generated by a 1:25,000 scale soil map published by National Institute of Agricultural Science and Technology. 38 different categories of soil were extracted from the soil map: forest, grassland, gravel, gravelly sandy loam, fine gravelly sandy loam, sandy loam, loamy fine sand, fine sandy, loam, gravelly loam, silt loam, gravelly silt loam, clay silty loam, and silty clay loam. The spatial database was constructed with a resolution of 30 m \(\times\) 30 m on the basis of Landsat TM. The 19 hydrogeological factors were converted to ArcGIS grid format, and the GRID set comprised 767 rows by 1078 columns. In the study area, the total number of cells was known 502,456 and SPC in 124 cells (88 cells (including the SPC data of \(<\) 4.55 m\({}^{3}\)/day/m) for training and 36 cells for validation). Also, in the Pohang city, the total number of cells was known 990,495 and SPC in 83 cells (61 cells (including The SPC data of \(<\) 6.25 m\({}^{3}\)/day/m) for training and 22 cells for validation). Groundwater productivity data (Table 1) such as T, SPC, yield, well depth, well diameter, and water table were collected from the national groundwater survey in the Boryeong [9] and Pohang cities [17], the national groundwater monitoring network construction report [18], and the rural groundwater survey report [19]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**Factors**} \\ \cline{2-5}  & **Boryeong City** & **Pohang City** & **Data Type** & **Scale** \\ \hline \multirow{6}{*}{Topography \({}^{\text{a}}\)} & \multicolumn{3}{c}{Ground Elevation} \\  & Ground Elevation within 300 m & \multirow{6}{*}{Ground Slope} & Ground Elevation within 300 m & \multirow{6}{*}{Grid} \\  & Ground Elevation within 300 m & & Ground Slope & \\ \cline{1-1}  & Ground Slope within 300 m & & Ground Elevation within watershed Area & \\ \cline{1-1}  & Stream Power Index & & Ground Curvature & \\ \cline{1-1}  & Watershed Area & & Topographic Wetness Index & \\ \cline{1-1}  & & Cumulative watershed area & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hydrogeological factors related to groundwater productivity in the study.
Design and Experiment of a Large-Aperture Hollow Traveling Wave Ultrasonic Motor with Low Speed and High Torque  
[PERSON]  
1 State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (C.-S.Z.) College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Xi'an 712100, China; [EMAIL_ADDRESS] 1  
motors or single-stator with two-sided piezoelectric ceramics motors, to increase the input and output power and the output torque of TRUMs [6; 7; 8]. Nevertheless, those methods saddle TRUMs with disadvantages, such as large volume, complicated structure, and high power consumption, making them unsuitable for aerospace applications.  
Previous studies of low-speed-high-torque TRUMs have yielded the following results: [PERSON] et al. developed a \(\varnothing\)60 mm dual traveling wave rotary ultrasonic motor with a maximum speed and stall torque of 60 rpm and 0.85 N\(\cdot\)m, respectively [9]. [PERSON] et al. developed a \(\varnothing\)80 mm two-sided piezoelectric rotary ultrasonic motor with a maximum speed and stall torque of 40 rpm and 1.7 N\(\cdot\)m, respectively [10]. [PERSON] et al. developed a \(\varnothing\)70 mm double-piezoelectric-ceramic motor with a maximum speed and stall torque of 200 rpm and 2.1 N\(\cdot\)m, respectively [11]. [PERSON] et al. developed a \(\varnothing\)60 mm double-piezoelectric-actuator motor with a maximum stall torque reaching 1.2 N\(\cdot\)m [12]. [PERSON] et al. developed a \(\varnothing\)90 mm hollow-ring motor with a maximum speed and stall torque of 106 rpm and 1.8 N\(\cdot\)m, respectively [13]. [PERSON] et al. developed a \(\varnothing\)100 mm large-torque pressure-equalizing motor with a maximum speed and stall torque of 58 rpm and 2.2 N\(\cdot\)m, respectively [14]. Finally, [PERSON] et al. developed a \(\varnothing\)100 mm novel motor using the usual piezoelectric elastic body as the rotor, and the maximum speed and stall torque were 20 rpm and 4 N\(\cdot\)m, respectively [15].  
In the present paper, we propose the 70H: a hollow TRUM with an outer diameter of \(\varnothing\)70 mm and an aperture ratio of 53% for low-speed-high-torque applications. Combined with the advantage of the motion mechanism of a standing wave longitudinal-torsional hybrid motor [16], the stator was improved by ANSYS simulation and then fabricated. Two prototype motors were assembled, referred to as 70 HA and 70 HB, respectively. Tests of the mechanical characteristics showed that 70 HB had lower speed and higher torque.
# Optimizing Foreign Language Learning in Virtual Reality:
## 5 Case Study
### Comparative Analysis of VR Language Learning Platforms  
#### 5.3.1 Overview of Additional VR Language Learning Applications  
As the landscape of language learning continues to be reshaped by technological advances, several VR platforms have emerged, challenging traditional pedagogical approaches. Mondly VR, for instance, takes the vanguard by offering an immersive experience where learners are thrust into a variety of conversational scenarios. Users can converse with virtual characters in a simulated environment, which dynamically adjusts to their responses, promoting an interactive learning experience. The platform covers an extensive array of languages, allowing learners to practice common phrases and improve their pronunciation through speech recognition technology, a feature that sets it apart from many traditional and VR-based language programs.  
InmverseMe stands out with its approach to situating learners in virtual recreations of real-world settings, from ordering coffee in a Parisian cafe to asking for directions in Tokyo. Its strength lies in the contextual learning it provides, facilitating language acquisition through scenarios that mimic everyday life. The platform's use of photorealistic environments aims to bridge the gap between classroom learning and practical language use, thereby offering a spectrum of cultural contexts that enrich the learning process.
## 4 Simulation Results and Analysis  
In this section, we present numerical results to evaluate the performance of the proposed energy-efficient optimal power allocation schemes in integrated wireless sensor and cognitive satellite terrestrial networks. In the simulations, we consider the simulation parameters as shown in Table 1 unless otherwise stated [5; 15; 37]. Besides, the Average Shadowing (AS) scenario is assumed for satellite link [34]. Furthermore, all the simulation results are obtained through Monte Carlo simulations for Shadowed-Rician fading channel and Nakagami-\(m\) fading channel, which employ \(5\times 10^{3}\) realizations.
## 3 Experiment and Discussion
### Effect of AFM Cantilever Tip  
The AFM cantilever was one of the crucial factors that affected the evaluation accuracy of the diamond tool cutting edge radius. Therefore, another AFM cantilever (MPP-11100-10, Bruker, Billerica, MA, USA) was installed onto the AFM to scan the actual and the reverse diamond tool cutting edges for investigating the effect of the AFM cantilever tip on the measurement accuracy of the diamond tool cutting edge radius. The Olympus AFM cantilever and the Bruker AFM cantilever were referred to as Cantilever 1 and Cantilever 2, respectively. Differing from Cantilever 1, there was a long base at the top of Cantilever 2. The tip radius of Cantilever 2 was 12 nm, which was larger than that of Cantilever 1.  
Then, Cantilever 2 was used to scan the actual and the reverse cutting edges, which had been scanned by Cantilever 1. We confirmed that the AFM topographies of the cutting edges scanned by Cantilever 2 were similar to those measured by Cantilever 1, although the AFM images are not shown here for the sake of clarity. Figure 12 shows the measured diamond tool cutting edge radii using Cantilever 2. The average characterized cutting edge radius of the diamond tool was 29.67 nm, with a standard deviation of 0.16 nm. Characterized cutting edge radius using Cantilever 2 was the same as that using Cantilever 1. The measurement uncertainty using Cantilever 2 was evaluated. The results are also plotted in Figure 12. The standard deviation of the evaluated uncertainty values was calculated to be 0.028 nm. From the above results, it can be seen that the measurement accuracy of the diamond tool cutting edge radius based on the proposed edge reversal method was not affected by the AFM cantilever tip radius. This is consistent with the fact that the effect of the AFM cantilever tip radius can be removed in the proposed method.  
Figure 11: The measurement uncertainties of cutting edge radius of a diamond tool under various indentation depths.  
Figure 12: Experimental results using Cantilever 2.
## 3 Adaptation of COTS for space applications
In the case of these devices the reliability is conditioned to the magnetic cleanness of the pre and post test calibrations. Although the sensor head technology is demonstrated to be robust, the associated front-end can be susceptible to damage by the factors mentioned above. To produce robust FPGAs and ASICs is one of the big challenges for space applications manufacturers.
## 3 Link the Marginal PDFs of Wind Speed and Load by D-Vine Copulas
### D-Vine Copulas  
#### 3.3.1 Graphical Structure of Decomposition  
As shown in (9), \(x_{o}\) is picked up from \(\mathbf{x_{v}}\) arbitrarily. When we decompose (9) recursively, picking up \(x_{o}\) in different orders generates various conditional copulas, which determines the graphical structure of decomposition for (8).  
Generally, there are two graphical structures frequently used--C-vine and D-vine. C-vine is more suitable when there is a dominant RV, while D-vine performs better when the RVs have arbitrary correlation structures.  
There is no dominant RV in the correlation modeling of the wind speed and load, so we adopt D-vine copulas to capture the dependence features. We then decompose Equation (8) as:  
\[f(x_{1},\ldots,x_{n})=\prod_{i=1}^{n}f_{i}(x_{i})\prod_{j=1}^{n-1}\prod_{k=1 }^{n-j}c_{k,k+j|k+1,\ldots,k+j-1}=D(x_{1},\ldots,x_{n})\prod_{i=1}^{n}f_{i}(x _{i}), \tag{12}\]  
where \(D(x_{1},\,\ldots,\,x_{n})\) stands for the D-vine copula function.  
Graphically, the D-vine structure adopted in Equation (12) can be illustrated by Figure 1, which is a 4-dimension case.