esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.3.1 Biome-BGC Model  
The Biome-BGC model is a typical terrestrial ecosystem process model that comprehensively accounts for differences in ecological characteristics and parameters. It accurately describes the cycles of carbon, nitrogen, and water, as well as energy flow for the different vegetation types [16]. The Biome-BGC model was jointly developed and maintained by the National Center for Atmospheric Research (NCAR) and the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana. It originated from the Forest-BGC model initially [37]. Currently, the model is stable at version 4.2. It can simulate the state of ecosystem carbon and water fluxes on a daily scale from 1 m\({}^{2}\) to a global scale, with given geographical parameters, meteorological conditions. and specific vegetation eco-physiological parameters [18]. The Biome-BGC model has been widely used to simulate and analyze the fluxes of different ecosystems, such as forests, grasses, and crops [18].  
The Biome-BGC model's structure is mainly composed of physical processes and eco-physiological processes. The physical processes primarily include solar radiation transmission and evapotranspiration, while the eco-physiological processes contain photosynthesis, respiration, and flux calculations. These processes are interconnected and mutually influence each other. The Biome-BGC model runs in two steps: (1) The first step is to carry out a spin-up model, starting from low levels of carbon and nitrogen until the system reaches equilibrium, which provides the initial state of the simulation; (2) Then, a normal model is conducted to simulate the GPP flux at each grid during the study period.
# Effects of Bogies on the Wake Flow of a High-Speed Train
## 5 Results and Discussions
### Mechanism of Generation and Evolution of Trailing Vortices  
Based on above analyses, a mechanism of the generation and evolution of the trailing vortices in a high-speed train is obtained and illustrated as follows. Two sources of vortex generation are inherent in the trailing car of the simplified model and they are the result of the unique geometry of the train. The first source is the cowcatcher of the trailing car; the pressure on the cowcatcher is lower than that on the bottom of the trailing car and the air flows over the edge of the cowcatcher from the bottom of the trailing car due to a pressure gradient, resulting in the formation of a pair of internally rotating vortices near the edge of cowcatcher. This vortex generation mechanism is quite similar to that at the wing tips in airplanes. The other source is the nose of the trailing car due to its unique geometry; the flows on the upper and the lower surfaces of the nose slow down significantly, whereas the flow almost maintains its velocity on the sides of the nose. Two pairs of vortices are formed because of the flow velocity gradient. Without an interaction with the flow from the bogies, the strengths of the upper two pairs of vortices, which form due to the nose geometry, decrease rapidly in the wake, whereas the strength of the lower pair of vortices, which form as a result of the cowcatcher, is maintained for a longer distance in the wake.  
Unlike in the simplified model, bogies exist in the real model and have two main effects on the external flow of the train. First, the adjacent flow is enhanced by the bogies, especially by the last bogie; this greatly enhances the wake flow of the real train model. Second, strong perturbances are also introduced into the flow of the real train model by the bogies, resulting in greater unsteadiness in the wake flow.  
Figure 27: Reconstructed flow fields of the wake of the real model.  
Therefore, on the basis of the geometry of the simplified model, the existence of bogies in the real model, especially the last one, introduces strong perturbances into the wake flow, which results in the significant enhancement of the lower pair of vortices (in the wake flow field of the simplified model). Subsequently, these two vortices become larger and they affect a broader range, resulting in greater unsteadiness and periodicity of the wake flow of the real model. However, the effects of the bogies on the upper two pairs of vortices (in the wake flow field of the simplified model) are comparatively weaker. These four vortices would be inherently damped or merged with the strong lower vortices. Consequently, in the real model, the wake flow, which is enhanced by the perturbances of the bogies, is characterized by two large counter-rotating streamwise vortices, which are generated by the cowcatcher of the trailing car.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 3 Discussion  
In wearable health monitoring systems, energy efficient functioning of wearable devices is highly dependent on the selection of appropriate communication protocols. This is because wireless communication, unlike sensing and computation, consumes a significant amount of energy in the sensor nodes. Thus, a suitable selection of low-power communication technology can substantially increase the useful lifetime. This section highlights some of the important features of possible low-power communication technologies that must be taken into account when choosing a particular technology choice. There are a number of low-power wireless communication protocols that can accomplish this task. Out of these protocols, ZigBee and Bluetooth are most broadly used. The preference of Zigbee over Bluetooth/BLE or vice versa can be made based on the following factors. See Tables 2-5 for a detailed comparison between ZigBee and BLE.  
Figure 2: Intra-body communications combined with existing low-power protocols.
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 5 Role of Microbial Biopolymers in Wastewater Treatment Processes  
Wastewater describes any water that has been contaminated by industrial processes or human activities, including both residential and commercial sources and natural sources, such as stormwater runoff and infiltration or inflow into sewer systems [258]. Wastewater contains a range of contaminants, including inorganic pollutants (heavy metals and rare earth minerals), organic pollutants (dyes, food, detergents, pesticides, herbicides, and pharmaceuticals), and other pollutants (oil, radioactive waste, spill, grease, etc.), which have detrimental effects on the environment and human health [259]. Until now, step-by-step processes like preliminary, primary, and secondary treatment have been employed to ensure water meets the required quality standards for safe discharge or reuse [258]. Various approaches have been tested in the removal of contaminants, including aerobic and anaerobic biological methods and the use of physical and chemical treatments. Coagulation and flocculation comprise fundamental stages in the removal of large, coarse, solid materials, like suspended solids, heavy metal ions, and dye molecules. Oxidation methods tackle the removal of organic impurities, while adsorption mechanisms have proceeded to capture soluble particles. Membrane-based treatment technologies have also demonstrated the capacity to separate impurities from wastewater, enhancing overall water quality [260]. Recently, the use of microbial biopolymers has gained significant attention as a value-added and sustainable approach to wastewater treatment. By acting as filtration media, adsorbents, coagulants, and flocculants, microbial biopolymers can be integrated into various stages of the treatment process, potentially further enhancing the efficiency and effectiveness of wastewater treatment methods [171]. The upcoming subsections will delve into the diverse roles of microbial biopolymers in wastewater treatment.
## 4 Conclusions  
In summary, the high permeability and high saturation magnetostriction of the magnetic materials are two key factors for high ME coupling performance. After investigating three typical ME composites, Permendur/PZT, Metglas/PZT, and Tefenol-D/PZT, with the same dimensions and different saturation magnetostriction and magnetic permeability, the most excellent ME performance was observed in the Permendur/PZT laminates, which agrees well with the predicted results from the figure of merit. The low-frequency and resonance ME coefficients of the Permendur/PZT composite are \(\sim\)23.1 V/Oe.cm and \(\sim\)309 V/Oe.cm at the optimal dc bias magnetic field of \(\sim\)250 Oe, respectively. The Permendur/PZT composite has potential in practical magnetic sensitive device applications.
###### Abstract
This approach enables more effective utilization of the information in the data, thereby enhancing the model's predictive capabilities. In order to solve the above problem, this study proposed a similarity clustering deformation prediction model based on GNSS/accelerometer time-frequency analysis. The model's excellent predictive performance was verified through a shake table simulation experiment. The main contributions of the proposed method are summarized below. 1. The CEEMDAN algorithm is employed to decompose the original monitoring data, effectively reducing the nonlinearity and randomness of the deformation sequence and establishing the time-frequency characteristics of the deformation data. 2. A similarity clustering algorithm is proposed to cluster monitoring sub-sequences based on frequency domain features. The reconstruction and integration of monitoring sequences are accomplished. The novelty of this algorithm lies in its adoption of clustering analysis, a method that effectively captures key features of structural deformation processes, thereby significantly enhancing data processing efficiency. 3. A CEEMDAN-SC-LSTM deformation prediction model based on GNSS/accelerometer time-frequency analysis is proposed. Experimental results verify that the proposed model achieves a significant improvement in the prediction performance. The rest of the paper is organized as follows. Section 2 discusses the methods used and introduces the processes and details of the methods used in this study. Section 3 validates the effectiveness and superiority of the proposed prediction model through experiments. It also reconstructs the overall deformation characteristics based on the predicted values. Finally, Section 4 concludes the entire paper, providing analysis and conclusions.
# Cleaner Processes for Making Laundry Soap from Vegetable Oils and an Essential Oil
## 2 Materials and Methods
### Methods and Analyses
#### 2.2.2 Idone Number (IN)  
The IN is a measure of the amount of unsaturated fatty acids present in the triglycerides/oils/fats used in the soap formulation. The IN value influences the final properties of the soap, such as its cleaning power, texture, stability and durability.  
IN values give anticipatory indications on the final properties of the soap, as follows:  
1. _Cleansing capacity:_ when the IN value of the triglycerides is higher (as a measure of the unsaturation of the component fatty acids), the cleansing capacity of soap is higher; in this case, the soaps have a greater capacity to extract/displace and emulsify oils and dirt.
2. _Texture and hardness:_ Low IN values often occur with saturated fats and indicate the attainment of harder and more solid soaps at room temperature. High IN values are specific to unsaturated fats and indicate the creation of softer soaps.
3. _Stability and durability:_ high IN values indicate a risk of rancidity, which is caused by the oxidation of unsaturated fatty acids and can be accelerated by factors such as exposure to light, heat, and air; in this case, the soap has a shorter shelf life [65].  
The saponification recipe is designed using a mixture of olive oil (with high IN) and coconut oil (with low IN) precisely to create a soap that has both moisturizing properties and a long-lasting, bubbly lather in a moderate amount, as is necessary for a laundry soap.  
The IN is determined according to AOCS Method cd 1-25/1993 [63].  
In an Erlenmeyer flask, over a quantity of 0.3 g of oil, 20 mL of CCl\({}_{4}\) and 25 mL of iodine/KI solution are added, with the mixture being shaken vigorously. This is followed by 30 min of incubation, in the dark, at room temperature. Then, 200 mL of distilled water is added to stop the reaction and then titrated with 0.1 N sodium thiosulfate solution.  
The titration is completed when the yellow-violet color disappears. Then, 0.5 mL of a soluble starch solution, with a concentration of 1%, is added when the blue color appears. The titration is continued using the same 0.1 N sodium thiosulfate solution until the blue color disappears and the solution in the beaker becomes colorless [66].  
The volume of sodium thiosulfate used in the titration is read on the burette.  
The IN index is calculated with the mathematical Equation (2) [67]:  
\[\text{IN}=[(\text{V}_{\text{B}}-\text{V}_{\text{S}})\times\text{N}_{\text{ thiosulfate}}\times 12.69]/\text{W}\ [\text{mg iodine}/\text{g oil}] \tag{2}\]  
where \(\text{V}_{\text{B}}=\text{V}_{\text{blank}}\), for blank sample vs. = \(\text{V}_{\text{sample}}\) = volume of 0.5 N sodium thiosulfate used in the titration; 0.1 = normality of the sodium thiosulfate solution; 126.9 = molecular weight of sodium thiosulfate (g); W = mass of the oil sample = 0.3 g.  
The IN value can be converted to g iodine/100 g oil to express the number of grams of iodine absorbed by 100 g of oil.
## 3 Proposed 7/10 Modulation Code for Mitigating ITI  
### Encoding Scheme  
The modulation coding schemes, such as run-length limited code and maximum transition run code, make transmission that is suitable for a channel possible using constraints. Normally, modulation codes are used for preventing error patterns, timing recovery, and DC balance in data storage systems. To accommodate specific constraints, a lookup table, a finite state machine, and so on are utilized in the modulation encoder. In general, in the modulation coding schemes, the performance is excellent when the code rate is low.  
In this paper, a 7/10 modulation code that prevents serious ITI is proposed. The proposed modulation code using a lookup table and one-to-one mapping encodes the 7 bits of user data sequence \(a=[a_{0},a_{1},a_{2},\)\(a_{3},\)\(a_{4},\)\(a_{5},\)\(a_{6}]\) to the \(5\times 2\) (= 10 bits) array of coded data sequence \(c=[c_{0},\)\(c_{1},\)\(c_{2},\)\(c_{3},\)\(c_{4},\)\(c_{5},\)\(c_{6},\)\(c_{7},\)\(c_{8},\)\(c_{9}]\), as shown in Figure 3. To improve the performance of the proposed 7/10 modulation coding scheme, the codeword selection process was divided into Step 1 for removing error patterns causing the ITI effect, and Step 2 for providing enough Hamming distance among codewords to improve the correcting capability. The two islands on the upper track and the other two on the lower track affect one island on the main track in a staggered array BPMR. Therefore, a constraint for four neighboring islands is necessary to effectively reduce the ITI effect. However, heavy constraints on the islands cause the code rate to decrease. Thus, to increase the code rate, the proposed code restricts two pixels on the neighboring tracks not containing error patterns, which cause the ITI effect, such as \([1,\)\(0,\)\(1]^{\mathrm{T}}\) and \([0,\)\(1,\)\(0]^{\mathrm{T}}\).  
For Step 1, Figure 4 shows the available patterns for each column (\([c_{0},\)\(c_{1},\)\(c_{2},\)\(c_{3},\)\(c_{4}]^{\mathrm{T}}\) or \([c_{5},\)\(c_{6},\)\(c_{7},\)\(c_{8},\)\(c_{9}]^{\mathrm{T}}\)). The number of combinations in one column is 32 (=2\({}^{5}\)). Out of 32 patterns, only 16 patterns that do not have patterns of \([1,\)\(0,\)\(1]^{\mathrm{T}}\) and \([0,\)\(1,\)\(0]^{\mathrm{T}}\) were selected for making codewords. Thus, 256 (=16\(\times\) 16) codewords can be included by combining 16 codewords obtained in each column.  
In Step 2, codewords having a distance of at least 2, which is the Hamming distance among nonidentical codewords, were found. The distance among the codewords enables the original codeword from the received sequence in the decoding process to be recovered correctly. Table 1 shows a list of 128 codewords obtained through Steps 1 and 2. For instance, the user data sequence of \(a=[0,\)\(0,\)\(0,\)\(0,\)\(0,\)\(0,\)\(1]\) is encoded to \(c_{1}=[0,\)\(0,\)\(0,\)\(0,\)\(0,\)\(0,\)\(0,\)\(0,\)\(1,\)\(1]\) by one-to-one mapping.
## 3 Method Application
### The Rack Tool Case
#### 3.1.1 Analytical Modeling  
Figure 1 shows the couple of running centrodes formed by \(C_{1}\)--the workpiece centrode (circle of radius \(R_{r}\)) --and \(C_{2}\)--the centrode of the generating rack (straight line tangent to the circle of radius \(R_{r}\)).  
At the same time, the reference systems associated with the centrodes are defined, respectively:  
* \(\chi\)yz is the fixed system, with the \(z\)-axis as the axis of workpiece rotation;
* \(\chi\)YZ--mobile system, attached to \(C_{1}\) centrode;
* \(\xi\eta\zeta\)--mobile system, attached to \(C_{2}\) centrode.  
The variable parameters associated with the workpiece and rack tool motions are as follows:  
* \(\varphi\), meaning the rotation angle around the \(z\)-axis;
* \(\lambda\)--translation parameter, along the \(\eta\)-axis.  
By using these parameters, the running condition of the two centrodes is defined as:  
\[\lambda=R_{r}\cdot\varphi. \tag{4}\]  
Within the space \(\xi\eta\zeta\) associated with the rack tool, the shapes of the tool flanks are defined as follows (see Figure 1):  
\[\overline{AB}\left|\begin{array}{c}\tilde{\xi}=u\cdot cos\alpha\\ \eta=\lambda_{0}+u\cdot sin\alpha\end{array};\ \overline{BC}\left|\begin{array}{c} \tilde{\xi}=ucosa_{max}\\ \eta=\lambda_{0}+u_{1}\end{array};\ \overline{CD}\left|\begin{array}{c} \tilde{\xi}=u_{2}\cdot cos\alpha\\ \eta=\lambda_{0}+\frac{m+\pi}{2}-u_{2}\cdot sin\alpha\end{array}.\right.\right. \tag{5}\]  
The parameters \(u\), \(u_{1}\), and \(u_{2}\) give the position of the current point on tooth sides, the parameter \(\lambda_{0}\) represents the initial value of translation parameter \(\lambda\), and the parameter \(\alpha\) is the inclination angle of the rack tool flank (see also Figure 1).  
The expression of the relative motion between the mobile systems \(\xi\eta\zeta\) and XYZ is as follows:  
\[X=\omega_{3}(\varphi)\cdot[\xi+a]. \tag{6}\]  
where \(\omega_{3}\) means the matrix of coordinates transformation corresponding to rotation around the \(z\)-axis and \(a\)--the matrix of coordinates transformation corresponding to translation along the \(\eta\)-axis.  
Figure 1: Kinematics of teeth generation with a rack tool [26].
## 1 Introduction
### ECMWF Reanalysis  
ECMWF is the European Centre for Medium-Range Weather Forecasts, this organization is advancing numerical weather prediction through global collaboration. The ECMWF reanalysis dataset was chosen for different studies, because of its high temporal and spatial resolution [36]. ECMWF dataset covers a vast timespan starting from 1979 to the present time [37]. ECMWF Reanalysis datasets, including ERA5, ERA-interim, ERA-interim/land, CERA-SAT, CERA-20C, ERA-20 CM, ERA-20C. Furthermore, ECMWF data gathers information from the global observation system comprehending different kind of satellites, meteorological, buoys, weather stations and ships.  
These limitations are mentioned above, that do not include RS satellite technology and reanalysis datasets [38], such as ECMWF, MERRA, NARR and ERA. A reanalysis dataset is the only source resolved long-term information of spatial wind information at wind turbine height. It provides data and potential assessment of this sector strongly related to the quality form of the meteorological situation [39]. Due to high costs and limitations of the measuring device, including coastal stations, buoys, ships, masts, the use of this kind of dataset can give possibility to understand wind speed with good quality in wind farm site assessment. This step is a very important criterion in possible alternatives that can be provided by high-resolution reanalysis data. Furthermore, this kind of analysis can reduce the cost of on-site wind measurement [39]. [PERSON] et al. [40], described the limits of offshore buoys, pinpointing that wind source assessment would be a challenging task. One of the best alternatives for inexpensive data and for filling the data gaps by providing a huge volume of data for extended periods is satellite RS. The assessment of wind source from single scatterometer could lead to inconsistency where there is a requirement of multiple satellite scatterometer. Therefore, four scatterometers viz. OSCAT, ASCAT-A, ASCAT-B and QuikSCAT with long-term in situ wind datasets over the North Indian Ocean were considered. It has been observed that QuikSCAT and OSCAT wind data have lesser bias with the range of 0.15 m/s (2.4%) to 0.83 m/s (15.1%) before adjustments. Linear regression was used for adjustment and the synergetic approach of linear regression adjustments and the combination of scatterometer data have resulted in smaller differences.
## 3 Results and Discussion
### Comparative Analysis  
The proposed antenna, which is a compact and low-profile structure, exhibits high gain. A comparison of the proposed antenna characteristics with other antennas presented in the literature [32; 33; 34; 35] is given in Table 1. Since the operational bandwidth is from 23.25 GHz to 24.1 GHz, it can receive LHCP signals at 23.4 and 23.7 GHz through port 2 and radiate components in RHCP through port 1, indicating that the proposed structure can operate as a tag antenna in the aforementioned applications.
## 2 Materials and Methods
### Data  
In February 2013, the Landsat 8 satellite was launched, and it is equipped with Operational Land Image (OLI) capabilities. It has a 30 m spatial resolution and is scheduled to be revisited every 16 days. The aforementioned data consists of surface reflection data, which were obtained by physically normalizing the image values throughout time, irrespective of variations in meteorological conditions [55]. These images unequivocally comprise five VNIR bands, two SWIR bands, and one TIR band, all skillfully processed to deliver accurate orthorectified surface temperatures. They also include QA bands and intermediate bands that are utilized to calculate the ST products.  
All analyses in the study were performed on the GEE platform. The workflow of the methodology is shown in Figure 1. GEE is a feature-rich platform that makes geographic dataset analysis and visualization easier for scientists. It caters to diverse users, including academics, non-profit organizations, businesses, and government entities. Earth Engine is a platform that hosts satellite imagery and preserves it in a publicly accessible data archive. This collection contains historical earth photographs spanning over four decades. The images, which are taken daily, are subsequently accessible for the purpose of global-scale data mining. In addition, Earth Engine provides APIs and other tools that facilitate the examination of extensive databases. Images captured by Landsat 8 are associated with the identifiers \"LANDSAT/LC08/C02/T1_L2\" on the GEE cloud platform. The LaSRC algorithm creates data from Landsat 8 SR [56].  
It is noteworthy to mention that regarding cloud-related issues, a criterion employed for analyzing Landsat images involved the creation of a digital processing script to mask clouds and their shadows within a designated range for satellites and sensors. The script was modified in accordance with the guidelines provided by the basic algorithm.