esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 2 Materials and Methods
### Fibre-Optic Coupling  
Ultimately, wavefront perturbations will cause a loss in optical power when coupling a beam into SMF. For an incoming beam, the coupling efficiency, \(\eta_{c}\), is the time-averaged fraction of incident power that is coupled into optical fibre. It is calculated from the overlap integral of the beam's field with the fibre's propagation mode. For a perturbed beam being focused onto an optical fibre that has fully filled some aperture diameter, \(D_{RX}\), the coupling efficiency is given by [20]  
\[\eta_{c}=8a^{2}\int_{0}^{1}\int_{0}^{1}\exp\left[-\left(a^{2}+1.1\left(\frac{D _{RX}}{r_{0}}\right)^{2}\right)(x_{1}^{2}+x_{2}^{2})\right]J_{1}\left(2.2\left( \frac{D_{RX}}{r_{0}}\right)^{2}x_{1}x_{2}\right)x_{1}x_{2}dx_{1}dx_{2}\, \tag{5}\]  
where \(J_{1}\) is the first order Bessel function of the first kind, and \(x_{1}\) and \(x_{2}\) are normalised distances from the centre of the aperture stop. The parameter \(a\) is determined by the geometry of the optics  
\[a=\frac{D_{RX}}{2}\frac{\pi W_{m}}{\lambda\,f_{\mathrm{eff}}}\, \tag{6}\]  
where \(W_{m}\) is the mode field radius of the fibre, \(D_{RX}\) is the diameter of the aperture stop, and \(f_{\mathrm{eff}}\) is the effective focal length of the optical system [20].  
In the absence of turbulence (where \(D_{RX}/r_{0}\to 0\)), \(\eta_{c}\) is maximised to 0.81 when \(a=1.12\). In the presence of turbulence, \(\eta_{c}\) will decrease with increasing \(D_{RX}/r_{0}\), shown as the blue curve in Figure 1. For any value of \(r_{0}\), setting \(a=1.12\) will yield a near maximum value of \(\eta_{c}\), showcasing how useful \(a\) is as a design parameter. The Fried parameter can be substituted with \(r_{0,3}\) in Equation (5) to find the TT-compensated \(\eta_{c}\), plotted in orange in Figure 1. The fractional improvement in \(\eta_{c}\) from TT compensation increases with increasing turbulence strength, up to a value of 1.7.
## 3 Study Area and Data
### Database Preparation and Analysis
(4) Human activity factor: Human engineering activities have always been a significant trigger for landslide formation, and the distance from road reflects the degree of influence of human engineering activities on landslide disasters [33]. Different buffer zones were set for roads in the study area, including 0-200 m, 200-400 m, 400-600 m, 600-800 m, 800-1000 m, and >1000 m. In the 0-200 m buffer zone from roads, the frequency ratio value is greater than 1 and reaches its maximum. However, in other distance ranges, the frequency ratio values are all less than 1, indicating that in these areas that are relatively far from roads, the impact of human activities on landslides gradually diminishes.
## 1 Introduction
Fiber treatment with NaOH increased density, and strengthened fiber's mechanical properties, with changes in O-H and C-H groups at 3330 cm\({}^{-1}\) and 2918 cm\({}^{-1}\), reduced lignin, more excellent thermal stability, and a rougher surface after treatment [27]. Corn husk fiber treated with NaOH showed better mechanical properties than fiberglass [28]. Fiber with NaOH treatment changed the mechanical properties for the better, and the absorption increased due to changes in the roughness and lumen structure [29]. Coir fiber's undergoing NaOH treatment changes the surface of the fiber, which is much cleaner and rougher, improves the tensile and flexural properties of the composite, increases the interfacial adhesion of the fiber and matrix, and effectively increases the compressive strength, flexural strength, and toughness [30]. The alkaline effect is evident from the changes seen on the surface of the Abaca fibers [31]. Pandanus tetorius fiber with alkaline treatment can strengthen the fiber, hence the pandanus tetorius fiber-reinforced composite is an environmentally friendly fiber alternative [32]. The H\({}_{2}\)O\({}_{2}\)/CH\({}_{3}\)COOH and HNO\({}_{3}\) treatment removed lignin, pectin, waxes and also increased cellulose crystallinity in the fibers, especially for HNO\({}_{3}\) treatment [33]. Alkaline treatment of pineapple leaf fiber increases the crystalline properties of the fiber [34]. Chemical treatment harms the environment, so environmentally friendly treatment materials are needed, such as the use of liquid smoke [35], where liquid smoke is a pyrolysis process for lignocellulosic materials and an environmentally friendly antibacterial [36, 37]. The use of liquid smoke has the potential as a natural fiber treatment material that protects fibers from bacteria and preserves and improves the micro-mechanical properties of natural fibers. Many processing materials are not environmentally friendly, in which case environmentally friendly processing materials such as liquid smoke are needed [12]. Liquid smoke contains phenolic compounds, carbonyl compounds, and acids that change the fiber'schemical, physical and mechanical properties. Therefore, it is necessary to research natural fibers, especially BSFs, to determine the effect of liquid smoke immersion treatment on physical (morphological), chemical (crystal index and functional groups), and mechanical properties (single fiber tensile strength); BSFs emerge as a composite reinforcement, lightweight and environmentally friendly.
## 4 Experimental Analysis and Results
### Experimental Setup  
The system requirements for the development environment are Windows 10 and an Intel(R) Core (TM) i5-1005G1 CPU running at 1.20 GHz to 1.19 GHz with 8 GB of RAM. The research models are developed using Python (version 3.11.1): [[https://www.python.org](https://www.python.org)]([https://www.python.org](https://www.python.org)) (accessed on 1 June 2023), a programming language, in a Jupyter: [[https://jupyter.org/](https://jupyter.org/)]([https://jupyter.org/](https://jupyter.org/)) (accessed on 9 July 2023) notebook. Data preparation is carried out using the Python package known as Natural Language Toolkit NLTK: [[https://www.nltk.org](https://www.nltk.org)]([https://www.nltk.org](https://www.nltk.org)) (accessed on 15 July 2023). The text has been vectorized by Keras: [[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/)) (accessed on 15 July 2023). Data processing and analysis are done using Pandas 1.16.0: [[https://pandas.pydata.org](https://pandas.pydata.org)]([https://pandas.pydata.org](https://pandas.pydata.org)) (accessed on 15 July 2023) and NumPy 1.24.1: [[https://numpy.org](https://numpy.org)]([https://numpy.org](https://numpy.org)) (accessed on 15 July 2023), respectively. The dataset of the study is collected from [30] and divided into training and test sets. An exploratory data analysis of training and test sets is established for understanding the relationships of the classes. Because the dataset used for evaluating the proposed approach is imbalanced, the F1-score metric is obtained on the test set. The F1-score is the most significant measure for unbiased evaluation of the classifier model trained on class-imbalanced datasets. In all experiments, the results of the F1-score are given as a major single evaluation metric of the approach. The precision and recall are already used for computing the F1-score. Therefore, it is sufficient to give the F1-score result as a single evaluator for comparing the models.
# Freeform Wide Field-of-View Spaceborne Imaging Telescope: From Design to Demonstrator
## 3 Telescope Proof-of-Concept Demonstrator
### Laboratory Demonstrator Setup  
The manufactured telescope is combined with an illumination source, aperture, and detector to validate its imaging performance (Figure 8). The incoming light is reflected on the primary mirror (M1), directing the light to the aperture stop and the secondary mirror (M2), which reflects the light to the fold mirror (M3) that guides the light to the detector, which is located beneath the telescope. For this, a three-step approach was followed during the laboratory demonstrator measurements: (1) alignment of the full setup, including source, detector, and aperture, using a collimated visible red laser source, (2) performance evaluation and spot-size measurement when replacing the red laser source with a near-infrared (NIR) laser emitting light within the envisioned wavelength range (1100-1700 nm), and (3) stray light evaluation using the red laser source with the aid of backward ray tracing. During each of the three steps, a collimated input beam is used, followed by the aperture, both mounted on translation and rotation stages, enabling the scanning of the performance along the FOV (120\({}^{\circ}\)). Preference is given to fiber-pigtailed sources to ease the mounting. Each of the three steps is explained in detail in the following subsections.
## 4 Challenges in Rainfall Estimation by CMLs
### Data Acquisition  
In general, CML data derives from the mobile network operator's Network Management System (NMS), which typically records maximum and minimum TSLs and RSLs at a 15 min time resolution. Although NMS can work at higher frequencies, for the purpose of checking the quality of communications, a time step of 15 min is satisfactory for mobile network operators. However, for rainfall monitoring, such a coarse sampling frequency may miss important information given the high temporal variability of rainfall. In addition, the sampling strategy of recording only extreme values poses difficulties in deriving the average rain rate over a 15 min period [157]. One solution is to calculate the maximum and minimum rain rates using maximum and minimum attenuation, respectively, to obtain an unbiased estimate of the mean rain rate according to the local rain rate probability function [10]. Another common approach treats a simple weighted average of the maximum and minimum rain rates as the average rain rate [49]. The literature [162] provides an introduction and comparison of three methods for estimating parameters from extreme values. Moreover, deep learning methods may also be an alternative [163; 164]. [PERSON] et al. [70] compared three sampling strategies for CML data and found that the performance of rainfall estimation was, in descending order, continuous measurements, 15 min averages and 15 min instantaneous values. [PERSON] et al. [165] further compared the sampling strategy of 15 min averages with 15 min maximum/minimum, demonstrating the slight advantage of the latter. In the Netherlands, rainfall estimation experiments using CMLs demonstrated that 15 min maximum/minimum sampling is superior to instantaneous sampling at the same time step because the former contains information over the entire time interval [166]. In addition, NMS records RSL values with a resolution of typically 0.1, 0.3 or even 1 dB. As shown in Figure 5, the coarse quantization resolution limits the precision of the CMLs in estimating the rain rate [64; 167]. Although this phenomenon can be mitigated with an increasing frequency and path length, it introduces other problems such as high energy consumption for high-frequency signals and rainfall variability over long paths. Moreover, coarse quantization also limits the analysis of the pattern of signal variation.
## 5 Evaluation
### Evaluation Scenarios  
Several testing scenarios are implemented to analyse the proposed RL-based solution algorithm. They demonstrate the importance of QoS parameters and their impact on video streaming quality perceived by customers. The goal is to evaluate the well-known routing algorithms such as Shortest Path First (SPF) with the proposed approach under varying traffic load with different network topologies scenarios. SPF is a primary mechanism to originate routing paths, which is extensively utilised in multiple protocols like Open Shortest Path First (OSPF). OSPF-based approach is compared with our solution to evaluate customers desired satisfaction across real-time DASH video streaming using both approaches. In each test, the reference video without degradation is distorted and recorded to generate the processed video with degradation. At this stage, by having both videos, an objective experiment of the perceived video quality is conducted. As an outcome of this experiment, VMAF and SSIM values are obtained.
## 3 Results and Discussion
### Evaluation of Antioxidant Activities
#### 3.4.2 Radical Scavenging Activity (DPPH)  
Free radical scavenging activity of cauliflower, red cabbage, Chinese cabbage, white cabbage and broccoli extracts increased in a concentration dependent manner and the concentration of extracts showed 50% scavenging (IC\({}_{50}\)) as shown in Table 1. Radical scavenging activity (IC\({}_{50}\)) of cauliflower, broccoli, red cabbage, white cabbage and Chinese cabbage extracts was ranged from 2.3 to 4.6 \(\upmu\)g/mL. The minimum IC\({}_{50}\) value was obtained by red cabbage extract (2.3 \(\upmu\)g/mL) and showed an excellent radical scavenging activity while the maximum IC\({}_{50}\) value was obtained by Chinese cabbage extract (4.6 \(\upmu\)g/mL) and showed lower radical scavenging capacity. The synthetic antioxidant, BHT, showed a better radical scavenging activity with the lowest IC\({}_{50}\) value (1.5 \(\upmu\)g/mL). Significant variations (\(p<0.05\)) in radical scavenging activity were observed among selected species of _Brassica_ genus. Red cabbage showed higher TPC and TFC and showed better radical scavenging activity [25]. Previous results showed that white cabbage, cauliflower, broccoli, Chinese cabbage and red cabbage extracts have IC\({}_{50}\) values of 1.64, 2.71, 3.85, 1.32 and 70.52 \(\upmu\)g/mL, respectively [24; 25].
## 3 Results and Discussion
### Quantitative Evaluations
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Soil Type** & **Accuracy** & **Precision** & **Recall** & **F\({}_{1}\)-Score** \\ \hline CLSTM & Primary & \(96.56\pm 9.35\times 10^{-3}\) & \(93.40\pm 3.50\times 10^{-1}\) & \(71.94\pm 1.31\) & \(80.45\pm 3.10\times 10^{-1}\) \\  & Frozen & \(97.79\pm 7.12\times 10^{-3}\) & \(99.10\pm 9.05\times 10^{-3}\) & \(61.92\pm 2.74\) & \(74.89\pm 1.61\) \\  & Thawed & \(97.71\pm 8.16\times 10^{-3}\) & \(99.88\pm 2.80\times 10^{-4}\) & \(63.79\pm 1.87\) & \(77.04\pm 9.82\times 10^{-1}\) \\ \hline U-Net & Primary & \(96.57\pm 1.19\times 10^{-2}\) & \(94.66\pm 4.33\times 10^{-1}\) & \(70.88\pm 3.19\times 10^{-1}\) & \(80.20\pm 1.48\) \\  & Frozen & \(97.28\pm 1.85\times 10^{-2}\) & \(74.79\pm 2.86\times 10^{-1}\) & \(99.55\pm 6.44\times 10^{-1}\) & \(85.16\pm 7.31\times 10^{-3}\) \\  & Thawed & \(98.47\pm 4.59\times 10^{-3}\) & \(99.94\pm 2.31\times 10^{-1}\) & \(77.73\pm 4.89\times 10^{-4}\) & \(87.25\pm 5.80\times 10^{-1}\) \\ \hline U-Net-LSTM & Primary & \(96.57\pm 1.57\times 10^{-2}\) & \(96.95\pm 1.37\times 10^{-1}\) & \(68.86\pm 1.69\) & \(79.67\pm 6.32\times 10^{-1}\) \\  & Frozen & \(98.43\pm 1.08\times 10^{-2}\) & \(98.71\pm 1.68\times 10^{-2}\) & \(73.03\pm 3.88\) & \(82.38\pm 1.70\) \\  & Thawed & \(98.50\pm 7.46\times 10^{-3}\) & \(99.65\pm 4.95\times 10^{-3}\) & \(76.49\pm 1.92\) & \(85.84\pm 7.24\times 10^{-1}\) \\ \hline BDULSTM & Primary & \(96.49\pm 1.01\times 10^{-2}\) & \(96.40\pm 1.45\times 10^{-2}\) & \(67.96\pm 7.64\times 10^{-1}\) & \(79.38\pm 2.92\times 10^{-1}\) \\  & Frozen & \(98.80\pm 9.14\times 10^{-4}\) & \(92.08\pm 3.08\times 10^{-1}\) & \(92.76\pm 4.05\times 10^{-1}\) & \(92.07\pm 4.25\times 10^{-2}\) \\  & Thawed & \(98.95\pm 3.03\times 10^{-3}\) & \(90.83\pm 2.05\times 10^{-1}\) & \(92.85\pm 6.09\times 10^{-1}\) & \(91.59\pm 1.81\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparisons of segmentation performance (%).
## 4 Discussions  
### Performance and Space Requirements  
In this paper, we propose a system that features extraction and image comparison modules that are fully analytical without trainable parameters. Such a system has little hardware requirements and does not require a GPU. Feature vectors are compact and require, on average, 3 KB of storage space per image. Due to simplicity of the operations (7)-(12), the analysis takes milliseconds even on an average-grade machine and supports multi-threading on a multi-core CPU. Specifically, feature extraction takes about 30 ms per image and the similarity value calculation for a query-test image pair takes 7 ms on an Intel i9-9900K 3.60 GHz CPU. The most time-consuming operation is feature extraction, as it requires one \"walk\" over the input matrix (query image). However, it is only performed once for every image as feature vectors are stored for future comparison. The accuracy results shown in Section 3, are comparable with certain cutting-edge DL models, while being less hardware-demanding. As such, this fact indicates the viability of the proposed approach. Moreover, we also discuss in Section 4.4, the much higher generalization potential when the results in Tables 4 and 5 are analyzed together.  
One might doubt the above claim as our approach still requires a parser. In this paper, we use an SCHP with an ResNet101 backbone (43 million parameters) [36; 37], which is larger than the backbones of most models, as is detailed in Tables 4 and 5. However, using such a large parser is not a necessary requirement. A more compact network could be used instead with little loss of accuracy as discussed in [64]. It was shown that rank-1 and mAP decrease by up to 2.5% and 3.7%, when the backbone is changed to MobileNetV2 or OSNet, respectively. At the same time, this leads from ten- to twenty-fold reduction in the number of model's parameters. Therefore, this method can be implemented with a compact parser for high-speed low-demand computations, which is especially promising for the purposes of edge computing applications. Still, this paper presents the results that can be achieved with an \"out-of-the-box\" parser [37].
## 2 Materials and Methods
### Forward Model
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Unit** \\ \hline \(x_{0}\) & \(x\) coordinate & meter \\ \(y_{0}\) & \(y\) coordinate & meter \\ \(z_{0}\) & \(z\) coordinate & meter \\ \(m\) & Magnetic moment & A\(\cdot\)m\({}^{2}\) \\ \(I_{0}\) & Magnetic inclination & degree \\ \(A_{0}\) & Azimuth & degree \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings of the single magnetic dipole approximation model. A simulated magnetic dipole source with parameters of \(x_{0}=2.5\) m, \(y_{0}=2.5\) m, \(z_{0}=5\) m, and a magnetic moment of 10 A\(\cdot\)m\({}^{2}\) was established to illustrate the effectiveness of the forward modeling based on the two-dimensional orthogonal basis decomposition method. The Earth's magnetic inclination is 60\({}^{\circ}\), and the declination is \(-10^{\circ}\). The dipole's inclination is 20\({}^{\circ}\), and the azimuth is 30\({}^{\circ}\). The spatial domain for forward modeling is a 5 \(\times\) 5 m area with a grid size of 0.5 m. The forward modeling results are illustrated in Figure 2a. A noise of \(-5\) dB was introduced into the forward modeling, and the outcomes after adding noise are displayed in Figure 2b. The formula for the SNR is as follows:  
\[\text{SNR}=10\text{log}_{10}\Bigg{(}\frac{\sum(F)^{2}}{\sum(\hat{F}-F)^{2}} \Bigg{)} \tag{12}\]  
where \(F\) is the original signal without noise; \(\hat{F}\) is the noisy signal. After the introduction of \(-5\) dB noise, discerning any dipole source targets in the figure becomes nearly impossible, as the noise completely submerges the magnetic dipole source anomalies. Figure 3 indicates that panels (a) to (e) represent the coefficients \(\alpha_{1-5}\), and panel (f) displays the sum of the squares of these five coefficients \(E\). Figure 3f indicates that the signal location is successfully recovered after processing with the two-dimensional orthogonal basis, with the noise being almost entirely suppressed. The research and proof presented in [39] demonstrate that the energy peak and distribution of the sum of squares \(E\) are only related to the horizontal position, burial depth, magnetic moment magnitude, and direction of the magnetic dipole source, forming the basis for subsequent parameter estimation. Figure 2: Simulation of a magnetic dipole source. (**a**) Magnetic dipole source without noise. (**b**) Magnetic dipole source with \(-5\) dB noise.
## 4 Experiment Results
### Test of Spectrometers
Spectral resolutions of two slits show high consistency, as shown in Table 6. The test result of the smile is 1.0% of a pixel, and the keystone is 2.2% of a pixel. The scatter diagram of the 1100 nm spectral line in Figure 21b also shows a slope of about 1.7 pixels, indicating the detector-to-slit rotation during the test process; the slope cannot be directly accounted for as the spectrometer's smile. Figure 20: Test of stray light. (**a**) Schematic diagram of the test device, (**b**) the image of the spectrometer when a ND filter is put in front of the slit, (**c**) spatial stray light tested by black spot method, (**d**) spectral stray light tested using the notch wave method. Test device for the B5 spectrometer is shown in Figure 22a. A high-temperature (800 K) blackbody, three bandpass filters, and a condenser lens are used to illuminate the slit at room temperature, and an uncooled LWIR detector is used. Filters used for testing are 8224 \(\pm\) 100 nm, 8834 \(\pm\) 105 nm, and 9350 \(\pm\) 110 nm, respectively. Figure 22b shows the spectral lines after background rejection. According to the centroid coordinates of spectral lines, the dispersion power of the B5 spectrometer is calculated to be 2052.1 nm/mm, and the spectral sampling distance is 197.0 nm with 3\(\times\) pixels binning. The response functions of three spectral lines are shown in Figure 22c, and the raw data contain the filter bandwidth. In order to remove the influence of the filter bandwidth, the transmission function of the filter needs to be deconvoluted. The SRFs after removing the filter bandwidth are shown in Figure 22d, and the spectral resolution of B5 spectrometer is given in Table 7. The spectral resolution is slightly larger than the design. The test results also show that the maximum smile is 2.80% of a pixel, and the keystone is 3.6% of a pixel. \begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Wavelength/nm**} & \multicolumn{2}{c}{**FWHM of SRF at Different Slits/nm**} \\ \cline{2-3}  & **Slit 1** & **Slit 2** \\ \hline
1100 & 12.1 & 12.1 \\
1400 & 12.0 & 12.0 \\
1700 & 11.9 & 11.9 \\
2000 & 12.0 & 11.9 \\
2300 & 12.2 & 12.1 \\ \hline \end{tabular}
\end{table} 
Table 6: Spectral resolution of B3 spectrometer with 2\(\times\) pixels binning. Figure 21: (**a**) Test device for B3 spectrometer, (**b**) spectral lines and SRFs of B3 spectrometer.