esa-sceva/satcom-chunk-collection huggingface dataset:
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 2 Site and Instruments
profile observations; the all-sector long-term \(z_{0}\) is 0.012 m. The villages of Boylingbjerg (\(\approx\)3 km east of the Hovsere meteorological mast) and Ramme (\(\approx\)5 km east of RUNE's experimental site) are the most prominent features that will impact the easterlies. The mixed forest area \(\approx\)10 km east of the site can also have an impact on the flow at high measuring heights during the campaign. The stability conditions that occurred during the campaign were estimated using the observations from a sonic anemometer at the Hovsere mast according to the Obukhov length \(L\) at 10-m height. The measurements from the sonic anemometers and stability conditions at Hovsere are extensively discussed in [15; 21]. Neutral (\(|L|\geq 1000\) m), stable (0 m \(<L<\) 1000 m) and unstable conditions (\(-1000\) m \(<L<\) 0 m) were occurring during \(\approx\)47%, 47% and 6% of the time, respectively. Unfortunately, the sonic anemometer mounted on the buoy was damaged after only two weeks of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Position** & **Name** & **Type** & **Usage** & \begin{tabular}{c} **Lasting** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Northing** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Height** \\ **ASL (m)** \\ \end{tabular} \\ \hline
1 & Koshava & WLS200S & Dual setup & 446,080.03 & 6,259,660.30 & 12.36 \\
2 & Vara & WLS200S & PPI & 445,915.64 & 6,261,837.49 & 26.38 \\
2 & Alizé & WLS70 & VAD & 445,915.64 & 6,261,837.49 & 26.38 \\
2 & WLS66 & WLS7v1 & VAD & 445,915.64 & 6,261,837.49 & 26.38 \\
3 & Steernn & WLS200S & Dual setup & 445,823.66 & 6,263,507.90 & 42.97 \\
4 & 3E & WLS7v1 & VAD & 446,379.30 & 6,263,251.46 & 43.18 \\
5 & Bura & WLS7v1 & VAD & 447,040.74 & 6,263,273.41 & 24.93 \\
6 & Lidar buoy & WLS7v2 & VAD & 438,441 & 6,262,178 & 0.00 \\
7 & Lidar buoy & WLS7v2 & VAD & 440,616 & 6,262,085 & 0.00 \\
8 & Hovsere mast & & Mast & 447,642 & 6,255,431 & 0.32 \\ \hline \end{tabular}
\end{table} 
Table 1: Positions, coordinates (UTM WGS84, Zone 32V), types and main scanning strategies (usage) of the lidars during the RUNE campaign (see details in the text), including the information of the Hovsere meteorological mast. The lidar buoy was used at two positions. The type is the commercial name given by the lidar manufacturer Leosphere (Orsay, France). Figure 3: Land-use description of the terrain around the RUNE site. The main positions described in Section 3 are also given. measurements and therefore the stability offshore could not be determined; it was likely predominantly unstable due to the influence of the warmer sea water. The impact of stability on the internal boundary layer is discussed in [21].
## 3 Results
That is, the lidar was designed to focus at a certain range to maximize the detection distance, which could lead to an artifact high backscatter coefficient layer. As mentioned before, the lidar was re-calibrated and rebooted in July 2019. Accordingly, the observations and classification might be influenced by the re-calibration. However, with a normalized input data set, the trained model can classify the measurements well. Figure 8 shows the results of 31 July. The physical properties of the aerosols are different in this case, compared to those on June 14 and 15. We can find a clear transition from the type II aerosols to the type III aerosols at around 15:00 UTC. The rain in the morning around 03:00 UTC was also classified but is underestimated since the rain data points do not reach the surface. In both examples (15 June and 31 July), we find that there are some missing data due to measurement gaps, which are labeled as others in the training data set but are classified as noise signals here. Figure 7: The lidar measurements on 15 June 2019, at Reykjavik (**a**) CNR, (**b**) backscatter coefficient, (**c**) depolarization ratio and the machine learning classification result (**d**) predicted classes. These trained models were applied to other data sets. Another dust event was observed from 9 to 10 July 2019. Figure 9 presents the results of the two models based on the measurements on 10 July. It can be seen that there is a mixture of three types of aerosols in the morning but dominated by type II (high \(\delta\) and lower \(\beta\)). The dust event lasted until the end of the day. Figure 8: The lidar measurements on 31 July 2019, at Reykjavik (**a**) CNR, (**b**) backscatter coefficient, (**c**) depolarization ratio and the machine learning classification result (**d**) predicted classes. Figure 9: The case with the data not used for model training: the lidar measurements on 10 July 2019, at Reykjavik (**a**) CNR, (**b**) backscatter coefficient, (**c**) depolarization ratio and the machine learning classification result (**d**) predicted classes. The same models were applied to the data collected from the lidar at Keflavik airport, which is identical to the lidar in Reykjavik, but the original calibration performed by the manufacturer is expected to be slightly different. Figure 10 shows an example from 31 July 2019. The CNR values of noise data points are slightly lower than from the lidar at Reykjavik, but the models still work well, except for an artifact layer of the \"other\" class around 600 m above the surface, which, as mentioned earlier, is a result of the focal effect. What should also be noted is that the input data were normalized over four days, but Figures 7-10 used the data normalized over those single days accordingly. In this case, the difference between instruments and calibration parameters can be ignored. More discussion regarding the normalization can be found in the next section.
## 2 Parallel Operation of the Power Converter  
### Parallel Operation System  
Figure 1 shows the equivalent circuit for analyzing the power-sharing characteristics of n parallel connected power converters [15].  
In Figure 1, \(V_{1}\) to \(V_{n}\) are the initial setting output voltage of number of n power converters, with each power converter having the same value. \(Z_{set}+\Delta Z_{1}\) to \(Z_{set}+\Delta Z_{n}\) is the line resistance between the output stage of the power converter consisting of n and the load resistance, and \(R_{L}\) represents the load resistance. The output current divided by the n power converter is \(I_{1}\) through \(I_{n}\). For the convenience of calculation, power current \(I_{1}\) and \(I_{n}\) are obtained under the assumption that there are only two power converters [16].  
\[I_{1}=\frac{(V_{1}-V_{2})R_{L}+V_{2}(Z_{set}+\Delta Z_{1})}{R_{x}} \tag{1}\]  
\[I_{2}=\frac{(V_{2}-V_{1})R_{L}+V_{1}(Z_{set}+\Delta Z_{2})}{R_{x}} \tag{2}\]  
\(R_{x}\) is as follows in Equation (3)  
\[R_{x}=Z_{set}((\Delta Z_{1}+\Delta Z_{2})R_{L}+(\Delta Z_{1}\times\Delta Z_{2})) \tag{3}\]  
If a parallel module is sharing the same current, it is \(I_{1}=I_{2}\), and from Equations (1) and (2) the following Equation (4) can be obtained.  
\[Z_{set}((V_{2}\times\Delta Z_{1})-(V_{1}\times\Delta Z_{2}))=2R_{L}\times(V_{ 2}-V_{1}) \tag{4}\]  
In assumption that the initial setting output voltage of each module is the same in Equation (4), it should be \(Z_{set}+\Delta Z_{1}=Z_{set}+\Delta Z_{2}\). However, \(Z_{set}+\Delta Z_{1}\) and \(Z_{set}+\Delta Z_{2}\) do not match, indicating that the initial setting output voltage \(V_{1}\), \(V_{2}\) must be controlled in order to share the same power current.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 4 Simulations and Results
### Simulation Scenario
To comprehensively evaluate the performance of the method, we localize the interference sources at different locations. The red squares in Figure 8 show the locations of spoofing interference sources, where the interference path is set to be a straight path. The starting and ending points are \([100\;500\;100]\) and \([100\;500\;1000]\), respectively. Equidistant sampling is conducted between them, and a total of 10 points are taken as interference source localization test points.
## 3 Data and Variables
### Data Sources  
The data of this study came from the IC card data of bus-metro transfer, at 88 metro stations along 12 metro lines in Shanghai. According to the bus-metro-transfer data of the metro IC card of Shanghai Shentong Metro Group Co., Ltd. (Shanghai, China), on 1 September 2016, there were 685,351 preferential records of bus-metro transfer, at all stations of the Shanghai metro network. By filtering the station information, missing data and abnormal data were deleted. Finally, we obtained 275,919 valid pieces of data, from 88 metro stations in the study area.  
Figure 1 depicts the transfer ridership of bus-metro at 88 metro stations. Among them, the metro station with the largest transfer ridership is People's Square, with 18,021 people, while Wuwei Road has the least transfer ridership, with only 366 people. There are 20 stations with more than 5000 people and 29 stations with less than 2000 people. The average daily transfer ridership is 3818 passengers per station.  
Many studies showed that the relationship between the public transport system and rail transit system determined the population coverage of existing public services [80; 81] and investment in transportation infrastructure [82; 83]. Bus-network density has a greater impact on transfer ridership than other attributes of bus-metro connection characteristics.  
Figure 2 depicts the bus-network density within the 1 km radius of 88 metro stations. As shown in Figure 2, the average bus-network density is 7.46 km per square kilometer. Jing'an Temple station has the largest bus-network density, which is 14.97 km per square kilometer, indicating that the metro station has the fastest information transmission in the whole transportation network. The bus-network density around Hongqiao Airport Terminal 1 is the smallest, only 1.45 km per square kilometer, so the bus-network density needs to be further improved. During peak hours, in addition to connecting buses, passengers can, also, choose other potential services, to ensure the efficiency of existing transfer transportation services [84; 85].
## 1 Introduction
Based on the Vernier effect for mode suppression, the effective free space range (FSR) becomes the least common multiple number of all FSR's. [PERSON] et al. [18] used a main ring cavity incorporated with a sub-ring cavity which included two fiber couplers and two optical circulators. The result shows that it could achieve an ultra\(-\)narrow linewidth of 459 Hz. Similar to [18], [PERSON] [19] replaced the sub\(-\)ring cavity of above structure with one fiber Fabry\(-\)Perot tunable filter and one fiber Fabry\(-\)Perot interferometer. The laser linewidth was less than 310 Hz. However, the method should take into account the FSR of filters, which means narrow linewidth of fiber laser could only be achieved by using an appropriate optical bandpass filter. Compared to [18] and [19], our proposed fiber laser has simpler structure and less cost. This paper presents the development of a narrow linewidth single-longitudinal mode (SLM) optical fiber laser based on the Rayleigh backscattering (RBS). RBS feedback is optimized by building a structure of linewidth measurement to maximize the quality of the light source and improve its performance. With a simple architecture, we achieve a low-cost narrow-line-width SLM fiber laser.
## 3 Results and Discussion  
### Color of Table Grape Skins  
Values of color coordinates of table grape skins, in both the Cartesian (L*a*b*) system and polar (L*C*h) system, are shown in Table 1.  
The effect of variety was statistically significant, and the darkest table grapes (the lowest L* values) were Autumn Royal and Black Magic, showing blue-black and violet-black skins. The lightest were Red Globe varieties, especially pink-colored ones. [PERSON] et al. [17] also reported that juice from Autumn Royal grapes presented lower L* values (18.4) than juice from Red Globe grapes (19.3). When analyzing chroma coordinates a* and b*, Autumn Royal, Black Magic, and Pallieri showed similar share of red color of the berries. The level of redness in Red Globe was the highest, and in Red Globe, pink-colored was the lowest among tested varieties. Significant differences were observed when taking into account the b* chroma coordinate. Negative b* values showed the high blueness of the samples, especially for Autumn Royal variety. Red Globe varieties with positive b* values showed yellowness of the skins. The higher redness (a*) and higher yellowness (b*) of Red Globe juice, in comparison to Autumn Royal juice, have been previously observed [17]. When analyzing the polar coordinates, such as hue angle (h), it could be observed that Autumn Royal, Black Magic, and Palieri varieties could be put to the fourth quadrant of the polar color space (270-360\({}^{\circ}\)), and the values around 360\({}^{\circ}\) indicated a rather dark red/violet color. Red Globe varieties could be placed in the first quadrant of the polar color space (0-90\({}^{\circ}\)), indicating that Red Globe was rather light red/orange-colored, whereas Red Globe pink was rather orange/yellow-colored variety. [PERSON] et al. [10] reported previously the hue angle for black grape was 215.5, whereas for red grape, it was 25.6. The value of the h coordinate, obtained in this study for Red Globe red-colored, is similar to the result shown by the authors [10]. The color of the table grape skins is the result of phenolic compound content, which was partially discussed by others [8; 9; 18; 19]. Generally, the darker color is associated with the higher total phenolic content [18; 19], but the content of individual phenolic compounds also plays a crucial role [8; 9; 19], which is discussed later in this manuscript (Section 3.3).
# Improved Branch-and-Bound Antenna Selection Algorithm for Massive MIMO
## 2 Representative Methods in Antenna Selection
### Exhaustive Search Algorithm  
The core principle of the exhaustive search algorithm [26] lies in its global exploration of all potential antenna combinations, subsequently selecting the optimal subset based on specific performance metrics, such as channel capacity, signal-to-noise ratio (SNR), or Frobenius norm. The standard search procedure follows these steps:  
1. Enumerate all possible antenna combinations: Calculate all feasible antenna subsets using the combinatorial formula defined as: \[\text{C}(N_{r},\ M_{r})\ =\begin{pmatrix}N_{r}\\ M_{r}\end{pmatrix}=\frac{N_{r}!}{M_{r}!(N_{r}-M_{r})!},\] (2)
2. Produce the sub-channel matrixes: Select the corresponding columns from the full channel matrix \(\mathbf{H}\) to form the submatrix \(\mathbf{H}_{s}\), \(\mathbf{H}_{s}\in\mathbb{C}^{M_{r}\times\ N_{t}}\).  
Calculate system performance indicators: For each submatrix, calculate the corresponding performance indicators.
* Select the optimal antenna combination: Find the antenna combination \(S_{opt}\) that makes the performance optimal.  
An exhaustive search algorithm can guarantee the identification of the optimal receive antenna combination, thereby achieving peak system performance. However, its critical limitation lies in the exponential growth of subset cardinality as \(N_{r}\) and \(M_{r}\) increase, which rapidly escalates the computational complexity. This inherent drawback renders it practically unfeasible for implementation in large-scale MIMO systems. Consequently, it necessitates the investigation of suboptimal algorithms characterized by reduced computational overheads while maintaining near-optimal performance, aiming to establish an optimal trade-off between algorithmic efficacy and computational tractability.
# Soil Is Still an Unknown Biological System
## 2 Properties of Soil as a Biological System
### Interactions between Microbes, between Microbes and Plants, and between Microbes and Fauna
Beneficial, pathogen, and neutral microorganisms inhabit the rhizosphere soil. Among the beneficial microorganisms, the plant growth-promoting bacteria can stimulate plant growth by providing nutrients and protecting plants from various abiotic and biotic stresses (among the latter, the biological control of pathogens) [30]. Some of microorganisms can positively infect plant roots, giving origin to symbiosis after a complex molecular cross-talk involving both the release of root exudates and microbial exudates. The most studied molecular cross-talks are those between rhizobia and legumes [31] and mycorrhizae and plants [32]. Generally, microbial diversity decreases when passing from the bulk to the rhizosphere soil as the result of rhizodeposition [30]. However, soil plays a fundamental role in affecting the microbial diversity of the rhizosphere. Indeed, denaturing gradient gel electrophoresis (DGGE) profiles showed that the microbial diversity of the rhizosphere soil of _Carex arenaria_, a non-mycorrhizal plant species so as to avoid confounding the effects of mycorrhizae, depended on the soil type [33]. The use of sequencing confirmed that soil could impose a larger selective pressure on plant-associated bacterial communities than plant species [34; 35]. However, the soil effect decreases by comparing microbial diversity of rhizosphere soil with that of the endosphere (the interior of roots colonized by endophytes) [36]; indeed, 40% of the variation in microbial diversity depended on the host species in the endosphere but only 17% in the rhizosphere soil when 30 angiosperms were grown in a garden soil [37].
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication  
This section explores the techniques used for wet chemical etching to modify the surface of BSi. Wet etching offers a cost-effective and versatile approach to achieving nanostructured surfaces. The most common wet-etch-based methods for BSi are discussed herein, including metal-assisted chemical etching (MACE), potassium hydroxide (KOH) etching, and electrochemical etching.
# Implementation of Climate Change Effects on Slope Stability Analysis
## 4 Study of the Effect of Climate Change on Slope Stability  
Remedial works must be carried out for landslides and remaining stable slope. It should be noted that such a measure must be carried out to ensure that the slope next to the landslide will be stable in the future in the face of climate change.  
Figure 10: Slope with critical failure line and safety factor after 3 days of rainfall, at decreased water net infiltration due to increased runoff.  
Figure 9: Slope with porewater pressure, critical failure line and safety factor after 3 days of rainfall.  
The analysis considers climate change impact on slope instability due to: increased precipitation, increased air temperature, and increased wind speed.  
It was found that the increased precipitation is most important for slope stability, while increased air temperature and increased wind speed are less important, as assessed by ELGIP [27], however, it should be emphasized that net water infiltration is the result of all three combined. Consequently, main climate change impacts on slope instability are degradation of material strength parameters, increased surface runoff, increased surface and ground water level and flow, and the change in porewater pressure.  
Projected climate change by 2050 and correspondingly increased precipitation were considered. For the landslide site, information from the Precipitation Changes report [29] was used to calculate changes in precipitation levels by 2050 due to increases in precipitation and temperature, using a selected ensemble of regional climate models. The most significant changes are expected in the winter months when precipitation in the lowlands is projected to fall as rain rather than snow. Thus, snow cover will be less frequent than it is today, so more problems associated with large amounts of rain (e.g., landslides) can be expected [29].  
The estimate of current extreme rainfall (return period of 100 years) and rainfall in 2050 for the selected landslide site using the climate change scenario RCP4.5 is shown in Table 3.
## 3 Methodology
### Robotic Grinding Process Parameters Optimization
#### 3.2.2 Robotic Parameters Optimization on BPNN  
To optimize the response variable \(y\), the GA is used to determine the optimal parameter combinations in the BPNN. However, due to its reliance on random search, it is susceptible to becoming trapped in local optima. To address this issue, SA is incorporated into the GA process (GA-SA), further optimizing individuals with higher fitness, therefore improving overall optimization performance.  
The procedure works as follows: first, the Genetic Algorithm runs for 50 generations, generating a set of solutions and selecting individuals based on their fitness. Individuals with higher fitness are chosen as the initial candidates for local search. Then, Simulated  
Figure 3: BPNN construction method and backpropagation parameter optimization process.  
Annealing is applied to these individuals to further optimize them and potentially find better local optima. In the SA process, the initial temperature is set high, allowing worse solutions to be accepted, and as the temperature decreases, the algorithm converges toward the optimal solution.  
To quantify the fitness of each individual, the objective is to minimize a loss function, such as the MSE:  
\[f=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{9}\]  
where \(f\) is the fitness value, \(y_{i}\) is the actual output, \(\hat{y}_{i}\) is the predicted output, and \(N\) is the number of data points.  
Additionally, a regularization term can be added to the objective function to prevent overfitting:  
\[C(w)=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2}+\alpha\|\mathbf{w}\|^{2} \tag{10}\]  
where the first term represents the MSE, the second term is the regularization term, \(\|\mathbf{w}\|^{2}\) is the L2 norm of the weight vector, and \(\alpha\) is the regularization coefficient.  
This hybrid optimization approach combines the global search capability of GA and the local optimization power of SA, enhancing both the precision and efficiency in solving complex optimization problems.
## 2 Materials and Methods
### Gas Sensor Fabrication and Sensing Measurement  
Here, commercial SnO\({}_{2}\) powders (sizes \(\leq\) 100 nm, Sigma Aldrich, St. Louis, MO, USA) were employed to realize H\({}_{2}\) gas sensors. First, a clean tubular alumina substrate with gold electrodes was fixed on a support (Figure 1a), and by the rotation of the substrate utilizing a small motor, the sensing material was applied to it with the help of a small brush (Figure 1b). The fabricated sensor was placed on a support (Figure 1c), and by applying an external voltage to the heater inside the tubular sensor, it was possible to increase the temperature to the desired values via the Joule heating effect. Synthetic dry air was mixed with the target gases utilizing mass flow controllers (MPCs) at the expected concentrations, and then it was introduced into the gas chamber. To prepare H\({}_{2}\) gas with different concentrations of 10, 50, 100, 500, and 1000 ppm, the amounts of dry air and H\({}_{2}\) gas were set to 495/5, 475/25, 450/50, 250/250, and 0/500, sccm, respectively, by MPCs. It should be noted that the total gas flow rate was fixed to 500 sccm in all experiments.  
Since in resistive gas sensors, variations of the resistance are an important parameter, the resistances of the gas sensors were continuously measured in air (R\({}_{\text{a}}\)) and in the presence of target gas (R\({}_{\text{g}}\)) using a Keithley (2400) source meter, and the obtained data were saved to a PC connected to source meter. The sensor response was calculated as R = R\({}_{\text{a}}\)/R\({}_{\text{g}}\). The response time was calculated as the time required for the resistance to achieve a 90% change in the presence of H\({}_{2}\) gas, when it was introduced into the gas chamber.