esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Example  
### Example Description  
To illustrate the applicability of the proposed models in designing feeder bus network access for urban rail transit, this study has selected one rail station(M), six dispatch centers (D1-D6), and a total of fifteen demand points (C1-C15) in Nanjing City in China for a case study. Figure 3 is used to map the distribution of these vehicle visiting points, in which yellow circles represent the dispatch centers, red small balloons represent customer points, and the white square represents the rail station. Moreover, the number of passengers and their preferred time windows and expected ride time in rental points are shown in Table 2. Due to spatial constraints, we could not provide the distance matrix between rental points and depots in this manuscript. The key parameters used in the case study are given as follows:  
* Maximum capacity of feeder bus route: \(Q\) = 12 per;
* Maximum length of vehicle route: \(D_{\max}\) = 9 km;
* Minimum travel time of vehicle route: \(T_{\min}\) = 3 min;
* Operational cost: \(c_{1}\) = 6.5 yuan/km;
* The loss of passenger satisfaction reduction: \(c_{2}\) = 1 yuan/person;
* The parameters of the hybrid algorithm: \(N=100\), \(MaxIter=200\), \(\alpha=0.9\), \(\gamma=0.9\), \(l_{\max}\) = 5, and \(\theta_{\max}\) = 45\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \((I_{i},e_{i})\) & \(St_{i}\) & \(Lt_{i}\) & \(q_{i}\) \\ \hline C1 & [8:10–8:20], [8:00–8:33] & 5 & 15 & 3 \\ C2 & [8:00–8:10], [8:20–8:30] & 10 & 20 & 1 \\ C3 & [8:10–8:20], [10:05–10:15] & 5 & 15 & 1 \\ C4 & 8:15–8:25 & 5 & 10 & 2 \\ C5 & 8:15–8:25 & 5 & 20 & 2 \\ C6 & 8:20–8:30 & 10 & 20 & 3 \\ C7 & [8:05–8:15], [8:20–8:30] & 10 & 20 & 4 \\ C8 & 8:08–8:18 & 10 & 20 & 1 \\ C9 & 8:10–8:20 & 5 & 15 & 2 \\ C10 & [8:05–8:15], [8:10–8:20], [8:35–8:45] & 5 & 15 & 2 \\ C11 & 8:20–8:30 & 5 & 15 & 4 \\ C12 & [8:10–8:20], [8:25–8:35] & 5 & 20 & 1 \\ C13 & 8:00–8:10 & 5 & 20 & 3 \\ C14 & 8:10–8:20 & 5 & 20 & 1 \\ C15 & 8:20–8:30 & 5 & 20 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic information of demand points.
## 2 Design
### Sensing, Control, Communication, and Localization
#### 2.4.3 Communication  
A swarm of modular robots can communicate with each other via ZigBee-based wireless sensor networks. Each robot module has a Zigbee board with antennas covered by a plastic shell. In a group of robot modules, the Zigbee device of the head module operates as the coordinator, whereas the other devices of the body modules operate as the end-devices. If there are several groups of swarms, only one head is selected as the coordinator and other head modules are selected as the routers as messengers for communications between other body modules operating as end-devices. The robot can send messages to the target robot with a short address on the same channel. When the robot swarms finish the assembly process, they can communicate using CAN bus. In addition, WiFi is available to all the modules.
# Effect of Heat Treatment on Some Titanium Alloys Used as Biomaterials
## 3 Results and Discussions
### Indentation Results  
The indentation test is a method to characterize the behavior of a material under a complex load. The applied load and displacement are measured during the test, while the residual footprint can be measured after the test has been completed. The results of this test are commonly used for material testing to determine the deformation resistance of a material.  
Three determinations were made for each alloy for a more precise determination. The results of the modulus of elasticity of the Ti-Mo-Zr-Ta alloys are highlighted in Table 5.  
The modulus of elasticity obtained by the indentation method for the experimental alloys from the Ti-Mo-Zr-Ta system is between 36.25-66.24 GPa. The lowest value is presented by the Ti20 Mo7 Zr15 Ta1 Si alloy (38.57 GPa), and the highest value is presented by the Ti15 Mo7 Zr15 Ta0.75 Si alloy (66.24 GPa). The low values of the elastic modulus of the investigated alloys are due to the presence of \(\beta\)-stabilizing elements, such as Mo, Nb, and Si. According to the results in Table 5, as the Si content increases with a high content of 20% Mo, the modulus of elasticity decreases by about 30 GPa.  
However, for medical applications aimed at replacing hard tissues, it is known that too much hardness leads to high wear, and too little modulus of elasticity prevents the uniform distribution of mechanical stresses, thus favoring bone resorption [2].  
Great efforts are being made to produce implantable medical devices newly generated from Ti alloys that present a modulus of elasticity closer to that of bone and do not release ions with cytotoxic potential [3].
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Microalgal Culture  
The microcalga C. _vulgaris_ CCAP 211/11B, sourced from the Culture Collection of Algae and Protozoa (CCAP, Oban, UK), was cultivated over 7 d in a 120 L channel photo-bioreactor featuring LED lights (photoperiod of 24:0) within the walls (baffles) and bottom spargers at 8.75 L min\({}^{-1}\) were used for CO\({}_{2}\) supply (atmospheric air with 0.04% CO\({}_{2}\)) and culture mixing. The growth medium was based on the Organization for Economic Co-operation and Development (OECD) culture medium [23]: 0.500 g\(\cdot\)L\({}^{-1}\) NaHCO\({}_{3}\), 0.25 g\(\cdot\)L\({}^{-1}\) NaN\({}_{3}\), 0.45 g\(\cdot\)L\({}^{-1}\) KH\({}_{2}\)PO\({}_{4}\), 0.018 g\(\cdot\)L\({}^{-1}\) CaCl\({}_{2}\cdot\)2H\({}_{2}\)O, 0.015 g\(\cdot\)L\({}^{-1}\) MgSO\({}_{4}\cdot\)7H\({}_{2}\)O, 0.012 g\(\cdot\)L\({}^{-1}\) MgCl\({}_{2}\cdot\)6H\({}_{2}\)O, 0.415 mg\(\cdot\)L\({}^{-1}\) MnCl\({}_{2}\cdot\)4H\({}_{2}\)O, 0.1 mg\(\cdot\)L\({}^{-1}\) Na\({}_{2}\)EDTA\(\cdot\)2H\({}_{2}\)O, 0.08 mg\(\cdot\)L\({}^{-1}\) FeCl\({}_{3}\cdot\)6H\({}_{2}\)O, 0.185 mg\(\cdot\)L\({}^{-1}\) H\({}_{3}\)BO\({}_{3}\), 7 ug\(\cdot\)L\({}^{-1}\) Na\({}_{2}\)MoO\({}_{4}\cdot\)2H\({}_{2}\)O, 3 ug\(\cdot\)L\({}^{-1}\) ZnCl\({}_{2}\), 1.5 ug\(\cdot\)L\({}^{-1}\) CoCl\({}_{2}\cdot\)6H\({}_{2}\)O, 0.01 ug\(\cdot\)L\({}^{-1}\) CuCl\({}_{2}\cdot\)2H\({}_{2}\)O. Biomass concentration, monitored via optical density at 680 nm (OD680), was correlated with biomass dry weight (DW) concentration using a calibration curve (Equation (1)). Cultures were harvested at concentrations of approximately 340 mg\({}_{\rm DW}\) L\({}^{-1}\).  
\[\text{OD}_{680}=3.784\times\text{Biomass concentration (g}_{\rm DW}\cdot\text{L}^{-1})-0.0016 \tag{1}\]  
(R\({}^{2}=0.9902\), Limit of detection = 0.02 g\({}_{\rm DW}\cdot\)L\({}^{-1}\), Limit of quantification = 0.07 g\({}_{\rm DW}\cdot\)L\({}^{-1}\))
## 2 Composition of Berry Pomaces  
### General Information  
Contents of pomaces depend on the berry species, but as they are rich in the seeds and skins of fruits, dietary fiber is a dominant component of pomaces. For instance, chokeberry pomace powder consists of ca. 3.61% of fat, 5.97% of protein, 28.8% of carbohydrates and 57.8% [6] to 59.5% [7] of fiber (mostly insoluble fraction). [PERSON] and [PERSON] [8] studied the composition of raspberry pomace and the composition of the studied by-product was described as follows: 11.1% of fat, 10.0% of protein and 59.5% dietary fiber. In a different study conducted by [PERSON] et al. [9], the concentrations of constituents in raspberry pomace were: 9.1%, 8.7% and 54.2% for fat, protein and dietary fiber, respectively. In the same study, the composition of strawberry and blackcurrant pomaces was determined. In strawberry pomace, concentrations of nutrients were at levels 3.4%, 9.2% and 33.9% and in blackcurrant pomace at levels 0.7%, 6.9% and 38.5% for fat, protein and dietary fiber, respectively. Based on the studies conducted by [PERSON] et al. [7], it can be stated that the physicochemical properties of currant pomace depend on the color group of the berry. Blackcurrant (_Ribes nigrum_) pomace powder was found to contain about 61.0% of seeds. The percentage contents of the nutritional components of blackcurrant pomace were as follows: 20.21% fat, 15.71% protein, 2.20% carbohydrates and 59.13% fiber, with a predominance of insoluble fiber. Redcurrant (_Ribes rubrum_) pomace consisted of 40.4% seeds and, based on the obtained results, it can be treated as a source of 14.23% fat, 11.76% protein, 12.65% carbohydrates and 58.1% fiber, mostly insoluble fiber. Gooseberry pomace powder in turn consisted of 34.2% seeds, 10.93% fat, 12.40% protein and 56.6% dietary fiber, over 87% of which was an insoluble fraction.
This makes it possible to create interferograms by comparing the phase information of two SAR images; that is, an interferogram represents the phase difference between two images, calculated for each pixel. This difference in wavelength phase denotes the amount of displacement that took place between the acquisition dates of the images [28; 33; 40]. Differential synthetic aperture radar interferometry (DInSAR) has been used for generating large-scale surface deformation maps on a dense grid and with a centimeter to millimeter accuracy [41; 42; 43]. However, the traditional DInSAR method has some limitations, such as orbital errors, atmospheric errors, temporal, and spatial correlation distortion. The persistent scatterer interferometric synthetic aperture radar (PSInSAR) method [28; 44; 45] covers the previous limitations of conventional InSAR by analyzing only pixels which retain some degree of correlation, which we define as persistent scatterers (PS) [46; 47; 48]. Through the use of PSInSAR, information about surface deformations in large regions was obtained with millimeter precision and used for regional and large-scale research [20; 48]; it has shown good accuracy. Therefore, the aims of this paper are (1) to identify the seismic activity in the central coast of the Bio Bio region, Chile;(2) to analyze the surface deformations in areas of Central Chile using Sentinel-1B radar images and the PSInSAR method; and (3) to evaluate the linear trend deformations during the year 2017 in areas of Central Chile. The outcomes of this work can provide important information on landslide initiation, which can be applied to urban planning and/or safety assessment.
# A Novel Reduction Circuit Based on Binary Tree Path Partition on FPGAs
## 2 Background
While the reduction circuit uses the existing adder or multiplier as the reduction operator and then builds the peripheral circuit. The reduction circuit focuses more on data control and management. * The accumulator is sensitive to the data type. When changing the data type, the accumulator may need to modify some circuits. For example, if the data type is changed from 16-bit fixed-point to 32-bit single-precision floating-point, the accumulator needs to add circuits for processing exponent and fraction according to the data type of the floating-point number. As for the reduction circuit, it only needs to replace a reduction operator that supports single-precision floating point numbers, and then change the data width of the control circuit and buffer to 32-bit.
## 2 Materials and Methods
### Data  
We studied HSI from HJ-1A and MSI from Sentinel-2 for tree species classification. From the China Center for Satellite Data Application of Resource, HSI was obtained, and MSI was obtained from the United States Geological Survey (USGS). They are presented in Figure 1. Both kinds of data were captured from August to September 2016.  
The spatial resolution of the Sentinel-2A satellite image is 10 m [32]. There is a high-resolution image instrument with 115 bands in the HJ-1A satellite, the spatial resolution of which is 100 m, which could not obtain high accuracy in the present tree species classification studies [33]. Therefore, fusing Sentinel-2A with a high spatial resolution image was necessary to improve tree species classification accuracy. To provide the gap in the relative low resolution of the HSI, the spatial resolution of the HJ-1A HSI image was up sampled to 10 m with the bilinear interpolation method from ENVI 5.1 software. HSI and MSI of the study areas had the same spatial resolution of 10 \(\times\) 10 m\({}^{2}\) by resampling HSI. Furthermore, the labels of the dominant tree species were obtained from the second-class data surveyed in 2018.  
Due to the large room of the forest, the area with the most tree species was selected for the experiment, and the size of pixels from HSI and MSI was \(500\times 500\times 115\) and \(500\times 500\times 13\), respectively, and the spatial resolution of a pixel is 10 m. Birch, larch, spruce, Mongolia, willow, and poplar are studied as dominant species. In the experiment, three datasets are set up, and the number of samples from them is presented in Figure 2 and Table 1.  
Figure 1: Map of the study area.
## 7 Low-Temperature NH\({}_{3}\)-SCR Reaction Mechanism  
### Eley-Rideal (E-R) Mechanism  
The Eley-Rideal (E-R) mechanism (Figure 17) in the NH\({}_{3}\)-SCR reaction mainly involves the interaction between adsorbed NH\({}_{3}\) and gaseous NOx components [21]. On Mnx-based catalysts, NH\({}_{3}\) can adsorb onto Lewis acid sites to form coordinated NH\({}_{3}\) (primary) or onto Brensted acid sites to form NH\({}_{4}\)\({}^{+}\) (secondary) at low temperatures. The adsorbed NH\({}_{3}\) is oxidized by high-valence Mn\({}^{4+}\) ions to generate reactive NH\({}_{2}\). Mn\({}^{4+}\) is then reduced to Mn\({}^{3+}\), and NH\({}_{2}\) reacts with NO (g) to yield the active intermediate NH\({}_{2}\)NO, which decomposes into N\({}_{2}\) and H\({}_{2}\)O. Concurrently, the low-valence Mn\({}^{3+}\) is oxidized by O\({}_{2}\) to Mn\({}^{4+}\), completing the entire redox cycle.  
Figure 17: NO removal mechanism of the MnOx-Cu-AC catalyst. (NH\({}_{2}\)\({}^{*}\) and H\({}^{*}\) represent active NH\({}_{2}\)\({}^{-}\) and H\({}^{+}\)) (Reproduced with permission from Ref. [117]. Copyright 2020, Multidisciplinary Digital Publishing Institute, Basel, Switzerland).  
\[\text{NH}_{36}\xrightarrow{\text{Lewis acid sites}}\text{NH}_{3 \text{ad}} \tag{1}\] \[\text{NH}_{3\text{ad}}+\equiv\text{Mn}^{4+}=\text{O}\rightarrow\text{ NH}_{2\text{ad}}+\equiv\text{Mn}^{3+}-\text{OH}\] (2) \[\text{NH}_{2\text{ad}}+\text{NO}_{\text{g}}\rightarrow\text{NH}_{2 }-\text{NO}_{\text{ad}}\] (3) \[\text{NH}_{2}-\text{NO}_{\text{ad}}\rightarrow\text{N}_{2\text{g} }+\text{H}_{2}\text{O}\] (4) \[\equiv\text{Mn}^{3+}-\text{OH}+\frac{1}{4}\text{O}_{2}\rightarrow \equiv\text{Mn}^{4+}=\text{O}+\frac{1}{2}\text{H}_{2}\text{O} \tag{5}\]
# Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines
###### Abstract  
Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.  
genetic algorithm; parallel computing; B-spline fitting; scattered data Article
## 4 Architecture Design
### Continuous ML Pipeline in an Emulation Environment
#### 4.1.2 Deployment Environment View  
Figure 5 presents a deployment environment viewpoint of the system. The view is described based on the big data reference architecture (RA) framework, which was published earlier [24]. Architectural elements in the RA are described with ellipsis (data stores), rectangles (functionalities), and arrows (data flows). The architectural elements were placed/divided into vertical functional areas, where data flows typically from the data sources (bottom) towards the interfacing/visualization applications (up). Five deployment environments were defined for computing in the framework: in-device, on-site, MEC/edge, private cloud, and public cloud. The deployment environments are described horizontally in association with the functional areas of the high-level view of the RA as a matrix [24].  
The architectural elements of our system were deployed to edge, on-site, and in-device computing environments. Edge environment refers to the nodes (i.e., edge node in Figure 4) extracting and loading data from sources. On-site computing refers to nodes (i.e., the router node and on-site nodes 1 and 2 in Figure 4) one hop closer towards the point of use (control center). Finally, in-device computing refers to mobile devices with a user interface for visualization purposes. In the following, functionality in the deployment environments is presented.  
Figure 5: Deployment environment view of the system. X-axis represents deployment environments. Y-axis represents functional areas/processing pipeline of a big data system.  
* 1: Access point data, vehicle speed, direction, and location were extracted (at the router), transferred, and loaded into a training data store (InfluxDB) from the emulator.
* 2: OWD was measured/extracted with OWAMP tools and the results were saved.
* 3: Video data were streamed and transferred towards the control center for visualization purposes at end-user application (VLC).
* 4: A ML engineer/data scientist specified jobs for model training (deep analytics), and the results were logged to MLFlow.
* 5: The trained model(s) were evaluated via MLFlow UI, transformed to open neural network exchange (ONNX) format [25], and distributed to the inference service (RedisAI), and loaded into memory.
* 5b: Alternatively, a ML engineer/data scientist may manage incremental ML (stream analysis) at the router. In such a case, a local inference service would be executed at the router.
* 6: Predictions provided by RedisAI were utilized for decision-making purposes regarding multi-point routing (i.e., video stream).
* 7: Video streams were visualized with a video player (VLC).
* 8: Emulation data were served for 3D visualization purposes of the moving vehicles in a mining environment.
## 4 Results and Discussion  
This section presents the results and discussion based on the methodology, implementation, and experiments shown in the previous section. Moreover, we have also presented the description of software specifications used while implementing the proposed solution. Execution time analysis of SpMV computation with multiple random block sizes along with average- and worst-case scenarios are presented in Section 4.2. Speedup achieved by using optimal block sizes and compared them with average- and worst-case scenarios. Detailed predictive analysis, performance gain, and limitations are presented in the subsequent sections.
# Second-Harmonic Generation in Mie-Resonant GaAs Nanowires
## 1 Introduction
Although light is confined in only two dimensions, defined by the scattering plane orthogonal to the nanowires' axis, we show that, if the nanowires are judiciously designed, conversion efficiencies for SH generation are similar to those achievable in nanoparticles with 3D light confinement. Finally, we discuss the role of absorption losses in SH generation, and demonstrate that: (i) Nanowires produce intense SH light even when the SH photon energy is tuned above the electronic bandgap of GaAs, where the material is opaque and it is commonly assumed that harmonic generation is not efficient, and (ii) absorption of SH light is not negligible even when the SH photon energy is tuned below the electronic bandgap of GaAs.
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 2 Motivation
### Separation of Vascular Components  
Clustering methods have been widely used in image segmentation. Employing a fuzzy c-means (FCM) technique can determine approximate ROIs in medical images. The algorithm is related to the minimization of an objective function [23]:  
\[J=\sum_{i=1}^{N_{i}}\sum_{c=1}^{N_{c}}u_{ci}{}^{m}\|l(i)-v_{c}\|^{2},\text{ subject to }\sum_{c=1}^{N_{c}}u_{ci}=1,\ 0\leq u_{ci}\leq 1, \tag{1}\]where \(i\) and \(c\) are the pixel index and the cluster index, respectively. \(N_{i}\) is the number of pixels in the image. \(N_{c}\) denotes the number of clusters. \(u_{ci}\) indicates the membership between the \(i\)th pixel and the \(c\)th cluster, and \(v_{c}\) is the centroid of the \(c\)th cluster. \(I\) represents the attribute (intensity, color, etc.) of an image, and in this paper, \(I(i)\) is set to the gray value of pixel \(i\). \(m\) is the weighting exponent that controls the fuzziness of the clustering and is set to 2 in most segmentation tasks.  
\(J\) is optimized to its local minimum by updating \(v_{c}\) and \(u_{ci}\) iteratively [23]:  
\[v_{c}=\frac{\sum_{i=1}^{N_{i}}{u_{ci}}^{m}I(i)}{\sum_{i=1}^{N_{i}}{u_{ci}}^{m}} \tag{2}\]  
\[u_{ci}=\frac{\|I(i)-v_{c}\|^{-2/(m-1)}}{\sum_{k=1}^{N_{c}}{\|I(i)-v_{k}\|^{-2/( m-1)}}} \tag{3}\]  
Many methods based on the FCM provide ways to obtain better segmentation results by absorbing the spatial information. The intensities of pixels in a window around a pixel \(i\) were used as the spatial constraint for \(i\)[24; 25]. The spatial information was incorporated into the fuzzy membership function \(u_{ci}\)[26]. A fuzzy factor was proposed to flexibly change the influence of pixels within the local window around pixel \(i\), according to their Euclidean distance from \(i\)[27]. The image segmentation effect was improved by incorporating the adaptive local spatial information into the objective function [28]. Through morphological gradient reconstruction, superpixels were obtained to provide adaptive and irregular local spatial neighborhoods for color image segmentation [29].  
Simple linear iterative clustering (SLIC) can efficiently generate superpixels [30]. In this paper, we integrate SLIC into the clustering process to provide adaptive spatial information and reduce the amount of processing elements in the fuzzy clustering procedure.
## 2 Materials and Methods
### Liquid Chromatography/Mass Spectrometry  
The method was validated on an Agilent HP 1100 series G1946A LC-MSD (model VL, Chemstation software rev B.03.02), equipped with a degasser, quaternary pump, autosampler, thermostated column oven, photodiode array ultraviolet (UV) detector (DAD), coupled to a single quadrupole (Agilent Technologies, Inc., Palo Alto, CA, USA). An injection volume of 50 \(\mu\)L was applied to the HPLC-MS. Phytoestrogens separation was performed using a Water XBridge C18 reversed-phase (3.0 mm id \(\times\) 150 mm, 3.5 \(\mu\)m) column with a C18 guard column (3.0 mm id \(\times\) 20 mm, 3.5 \(\mu\)m), at 52 \({}^{\circ}\)C. The mobile phase consisted of water (Solvent A) and methanol/acetonitrile (80:20, _v/v_, Solvent B), both with formic acid (0.025%, _v/v_), at a flow rate of 0.6 mL/min. The gradient elution was programmed as follows: 35-40% B at 5 min, 100% B at 16 min, returned to 35% B at 19 min and re-equilibrated for 3 min. DAD was set at 290, 340, 260, 216 and 280 nm. Phytoestrogens were analyzed using electron spray ionization (ESI) interface in negative ion mode, with capillary voltage at 3.5 kV, drying gas flow of 11 L/min and 350\({}^{\circ}\)C, and nebulizer gas pressure of 60 psi. Ion confirmation was done using the four mass-selective detector (MSD) signals set in single ion monitoring (SIM) quantification.