esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Wavelength VCSELs: Status and Prospects
## 3 Current Confinement and Tunnel Junctions
In addition to BTJs, current confinement can be achieved by ion implantation [34; 70; 71; 72; 73], leading to a planar device geometry. A disadvantage is the lack of inherent optical confinement [74]. To overcome the complicated implementation of high-level p-doping of InGaAs above 1 \(\times\) 10\({}^{19}\) cm\({}^{-3}\) GaAs\({}_{0.51}\)Sb:C/Ga\({}_{0.47}\)InAs:Si BTJs were suggested to realize 1300 nm VC-SELs [75] grown by MOCVD. Alternatives to BTJs were considered. In contrast to the traditional AlGaAs layer's high Al-composition in GaAs-based VCSELs, the use of InP-lattice-matched Al\({}_{\mathrm{x}}\)In\({}_{1-\mathrm{x}}\)As layers in InP-based VCSELs is complicated due to low oxidation speeds [3]. Selective oxidation of In\({}_{0.78}\)Al\({}_{0.22}\)As\({}_{0.47}\)P\({}_{0.53}\) layers for the fabrication of 1550 nm oxide-confined VCSELs was shown in [76; 77]. Results were also presented for developing current apertures by undercutting (selective chemical etching) the quantum wells (QWs) [21; 22; 78; 79; 80]. VCSELs with BTJ apertures, however, show better performance as compared to undercut QWs, implanted apertures, or selectively oxidized apertures.
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 4 Regulation of Key Genes Involved in Aroma Formation  
### Structural Genes  
During the whole development process of plants, the synthesis of volatile compounds differs in time and space, and these differences are often the result of changes in enzyme levels and activities [40]. This complexity is also reflected in the large number of genes encoding secondary metabolic enzymes, which account for approximately 15-25% of the 20,000-60,000 genes in the plant genome [41]. Through correlation and gene expression analysis, [PERSON] et al. [33] identified candidate genes related to aroma synthesis in peach fruit and reported that the _PPN018G03_ gene may affect the biosynthesis of phenylacetaldehyde through the transaminase pathway, thus affecting peach aroma. The _PPN023E05_ and _PPN052H12_ genes may regulate aroma synthesis by regulating the degradation pathway of fatty acids, and the fatty acid desaturase encoded by the _PPN052H12_ gene catalyzes the generation of unsaturated fatty acids to avoid entering the LOX/HPL pathway to produce undesirable aromas. Using RNA-seq, qRT-PCR, and other technologies, [PERSON] et al. [42] reported that ppa002510 and ppa002282 may be two important genes related to gluconolactone biosynthesis in the peach _ACX_ gene family. With the development and maturation of passion fruit, the expression level of PELOX4, LOX enzyme activity, and total ester content all increase [39]. Based on the metabolomics and transcriptomic analysis of strawberry fruits, 27 differentially expressed genes (DEGs) were found to be closely related to ester and lactone synthesis. These genes belong to the _AAT_, _LOX_, _ADH_, and cytochromatin P450 subfamilies, and may influence the production of aroma precursors [43]. In the biosynthesis of C-decalactone, AATs with esterification ability and ACX enzyme activity play a key role. Among AAT enzymes, PpAAT1 exhibits stronger esterification ability compared to the other AAT enzymes [35]. Changes in the expression of MdArAT, MdACPD, MdADH3, MdAAT2, and MdLOX may be the reason for the differences in the VOCs among different apple varieties [44].
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Optimal Values for Small Number of Nodes  
**Problem 3**.: _Find exact values of \(\Psi(r)\) for small \(r\) corresponding to the number of nodes in the tiny networks we consider._  
In the following, we shall determine the exact values of \(\Psi(r)\) for \(r\leq 15\). Note that it very quickly becomes the case that \(\Psi(r)\) can no longer be determined by exhaustive search due to the cardinality of the power set of \(\mathcal{R}=\{1,\ldots,r\}\). As we shall see, the results on \(\Psi(r)\) are somewhat surprising.
## 1 Introduction
In order to meet these conditions, this paper proposes a method for the online SOC estimation based on the Thevenin equivalent circuit model and the adaptive Kalman filter method. This paper aims to make the following contributions:  
(1) By comparing and analyzing ECMs, the most suitable ECM and parameter identification algorithms are decided;  
(2) A low-cost and accurate SOC estimator based on the adaptive Kalman filter (AKF) for the proposed model is developed, and its accuracy and robustness are verified by experiments under constant current working conditions and BBDST working conditions; and  
(3) The proposed method has strong robustness to model uncertainty and strong mutation to state tracking ability. The rest of this article is organized as follows: Section 2 describes a practical method for plotting the OCV vs. SOC curve for a battery, identifying the corresponding parameters of the Thevenin equivalent circuit. Section 3 describes the calculation method of the adaptive Kalman filter in detail. The experimental process is described in Section 4 and the experimental results are analyzed. Finally, Section 5 states the conclusions.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Assessment of Water Availability and Demand
#### 2.5.3 WEAP Model Calibration and Validation  
Before applying the WEAP model to the Lam Phaniang River Basin, calibration is a crucial and necessary step to adjust the parameters under the catchment node that affect streamflow generation within the study area, i.e., maximum infiltration rate and effective rainfall, until the simulated monthly discharge matches the historical observed data (2000-2008) collected at the E.68A gauging station (situated at Ban Khong Po, Mueang District, Nong Bua Lamphu Province as shown in Figure 1). To ensure that the calibrated parameters could properly produce outputs close to the observed data, model validation was then executed against a different period of data used for calibration; 2009-2017. It should be noted that the 2000-2017 period was considered due to limited data availability. Regarding the evaluation of WEAP model efficiency, two widely-used statistical indicators were employed; the Coefficient of Determination (R\({}^{2}\)), ranging from 0 meaning poor model performance to 1 showing good performance, and Nash-Sutcliffe Efficiency (NSE) ranging from negative infinity to 1 showing better agreement between the observed and simulated flows. In this study, the performance of the WEAP model was considered to be acceptable/satisfactory when the R\({}^{2}\) and NSE values were greater than 0.5, as suggested by [25; 26].
## 2 Materials and Methods
### Data Analysis Methods  
#### 2.3.1 Rate of Change Analysis  
Linear regression is a statistical method used to establish a linear relationship between a dependent variable and one or more independent variables for prediction and analysis. This study employs linear regression to explore the spatiotemporal trends in grassland productivity in Xinjiang and its relationship with meteorological factors (VPD, PRE, TEM, and SSR) from 2000 to 2020. By analyzing the data over this period, we aimed to reveal the spatiotemporal dynamics of these factors in Xinjiang [24].  
The linear regression trend for a single meteorological factor is calculated using the following equation:  
\[slope=\frac{n\times\sum_{i=1}^{n}i\times x_{i}-(\sum_{i=1}^{n}x_{i})(\sum_{i= 1}^{n}i)}{n\times\sum_{i=1}^{n}i^{2}-(\sum_{i=1}^{n}i)^{2}} \tag{1}\]  
In the equation, \"_Slope_\" represents the trend of the variable; \" \(n\)\" denotes the number of years (2000-2020, 21 years); \(x_{i}\) refers to the value of the meteorological factor for the i-th year; and \(i\) takes values from 1 to 21, corresponding to the years of study in the study area. Model parameter estimation and significance assessment were performed using R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). By estimating the model parameters, we quantified the trend in changes in the meteorological factors and assessed their significance.  
To more clearly identify temporal trends, we performed an F-test on different pixel values in R 4.4.1. The formula is as follows:  
\[U=\sum_{i=1}^{n}(\hat{y}-\overline{y})^{2} \tag{2}\]  
\[Q=\sum_{i=1}^{n}(y_{i}-\overline{y})^{2} \tag{3}\]  
\[F=U\times\frac{n-2}{Q} \tag{4}\]  
In the equations, \(U\) represents the sum of squared errors; \(Q\) denotes the sum of squares due to regression; \(n\) equals 21; \(\hat{y}\) is the predicted value for the i-th year; \(\overline{y}\) is the mean value of the dependent variable (GPP) over the 21 years; and \(y_{i}\) is the actual value of GPP for the \(i\)-th year.  
To more clearly illustrate the variation trends in each meteorological factor, we computed \(p\)-values using the F-test with different pixel values, thereby enabling a further classification of significance levels (as defined in Table 2) [25]:
# Optimal Energy Management within a Microgrid: A Comparative Study
## 5 Results
### Ciesol Building  
In this paper, several optimization methods are compared at a time to optimize the economic energy dispatch within a microgrid. Besides this, with the aim to evaluate the performance of each optimization method with different scenarios, real climatological data of solar radiation, wind speed, photovoltaic system data, and laboratory load saved during regular operation of the CIESOL building ([[http://www.ciesol.es](http://www.ciesol.es)]([http://www.ciesol.es](http://www.ciesol.es))) were considered. CIESOL is a solar energy research center placed inside the Campus of the University of Almeria in the south-east of Spain; see Figure 6.  
More specifically, this building is divided into two different floors with a total surface approximately equal to 1072 m\({}^{2}\). In addition, it has been designed to be a Nearly-Zero Energy Building (NZEB), and, thus, it has several bioclimatic criteria such as the use of photovoltaic panels to produce electricity and a Heating, Ventilation, and Air Conditioning system based on solar cooling which is composed of a solar collector field, a hot water storage system, a boiler, and an absorption machine with its refrigeration tower.  
Furthermore, the CIESOL building works as a research center which deals with the study of the implemented bioclimatic strategies, the analysis of their influence over energy efficiency, and greenhouse-effect gasses reduction, and, also, the development of optimization techniques to increase the ratio of renewable energies use against conventional ones. For this reason, it uses a wide network of sensors and actuators whose measured data is stored in a database by means of measurement and acquisition software. More specifically, the building has a meteorological station composed of different types of sensors (e.g., temperature, relative humidity, direct and diffuse irradiance, CO\({}_{2}\) concentration, etc.) and the building rooms measure air temperature, plane radiant temperature, globe temperature, relative humidity sensors, and power meters in order to measure the energy consumption in each room. In order to use a load profile to compare the different optimization methods, one of the representative rooms of the CIESOL building has been selected as a reference, specifically, Laboratory 6. This room was chosen among the characteristic ones mainly due to its occupancy profile. In more detail, this room works as an office and it is placed in the upper floor of the CIESOL building. In addition, it is characterized by having north orientation, a volume of 76.8 m\({}^{3}\), and a window with a total surface of 2.15 \(\times\) 2.09 m\({}^{2}\).  
Figure 6: The CIESOL building.
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Cellular Network Architecture  
The rapid growth in the demand for PCS has led the industry to intense research and development efforts towards a new generation of cellular systems. Nowadays, people can't live without cellular systems. The MS can perform the communication via the cellular system when the specific cell has a free channel. In this part, we will introduce two major systems in present cellular systems. The architectures of GSM and UMTS are described in the following subsections.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 4 Methodology Based on Cooperative Game Model for Platoon
### The Average Lexicographic Value  
The average lexicographic (\(AL\)) value utilizes the vector which is consisted of the lexicographically optimal points [32]. For a normal game, the \(AL\) (\(v\)) demonstrates the average value of the biggest vector in all possible lexicography. It is normally expressed in Equation (18).  
\[AL(v)=\frac{1}{n!}\sum\limits_{o\in\pi(N)}L^{o}(v) \tag{18}\]  
where \(n!\) represents the possibility of one sequence \(o\), and the \(L^{o}\) denotes its corresponding lexicographically optimal points.  
However, the sequence of a vehicle platoon is decided by physical environment which means that it is a balanced simplex game shown in Equation (19).  
\[\sum\limits_{S\in 2^{N}\setminus\{\varnothing\}}\lambda(S)v(S)\leq v(N) \tag{19}\]  
**Definition 5**.: _The game is a balanced simplex game, if its core \(C\)(\(v\)) is equal to non-empty imputation set i(\(v\))._  
**Theorem 1**.: \(AL(v)=CIS(v)\) _if \(v\) is a balanced simplex game. The center of invert set (CIS) can be obtained from the Equation (20)._  
\[CIS(v)=\frac{1}{n}\sum\limits_{k=1}^{n}f^{k}(v) \tag{20}\]  
where \(f^{k}(v)=(v(1),v(2),\ldots,v(k)+v(N)\ -\ \sum\limits_{i=1}^{n}v(i),\ldots,v(n))\).  
\(AL\) value satisfy the individual rationality, efficiency, core selectivity and symmetry. Note that the lexicighy maximum of the core \(C\) (\(v\)) is an extreme point of the core for this ordering. Similarly, \(AL\) value also meet the virtual participants.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 4 Advances in the Adsorbent Type Used in the Extraction Phase of the SPME Fiber
### Titanium Oxide Nanoparticles as Sorbents for SPME
#### 4.1.3 Carbon Nanoparticles in SPME
examined the identification of ultra-trace amounts of polycyclic aromatic hydrocarbons (PAHs) in complex coffee and tea samples using hollow-fiber solid-phase microextraction (HF-SPME) in conjunction with high-performance liquid chromatography and UV detection (HPLC-UV). They clarified that for such matrices, HF-SPMEis a better option than direct-immersion SPME (DI-SPME), since DI-SPME offers little sample cleanup in intricate, \"dirty\" samples. According to [PERSON] et al., the high porosity of the hollow fiber in HF-SPME enhances analyte selectivity and cleanup by preferentially adsorbing analytes from complicated mixtures. As the fiber sorbent, they created a new multi-walled carbon nanotube/zirconium oxide (MWCNT/ZrO\({}_{2}\)) nanocomposite, which they attribute to hydrophobic interactions and \(\pi\)-\(\pi\) stacking for its high PAH adsorption. The technique produced low detection limits (0.033-0.16 \(\mu\)g L\({}^{-1}\)) and a wide linear range (0.1-200 \(\mu\)g L\({}^{-1}\)) with high precision (RSD < 6.6%). The authors concluded that the MWCNT/ZrO\({}_{2}\) HF-SPME approach is very effective for PAH analysis in complex matrices after reporting relative recoveries of 92.0-106.0% for PAHs spiked in water, coffee, and tea samples [117]. This section covered a variety of nanostructured materials, such as carbon-based nanoparticles, metal oxide nanoparticles, and metal nanoparticles, that can be employed as fiber materials in SPME. Because of their high surface area, structural adaptability, and superior adsorption capabilities, carbon-based nanomaterials--in particular, carbon nanotubes--have demonstrated superior performance among these. These materials are very promising for upcoming SPME applications since they offer improved extraction sensitivity and efficiency.
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 4 Experimental Validation  
### Experimental Platform Description  
Figure 21 presents the experimental platform for validating cutting trajectory planning. The platform adopts the cutting trajectory planning method proposed in this paper, controls the roadheader to follow the planned path, and verifies the effectiveness of the method by comparing the differences between the actual and planned paths. The platform consists of a body vision positioning system [24], a cutting head positioning system, a control unit, and a target section (\(2\) m \(\times\) 1.9 m). The body vision positioning system includes explosion-proof industrial cameras, laser pointers, and an inertial navigation system. The cutting head positioning system includes displacement sensors for lifting, rotating, and telescoping cylinders, and calculates the attitude angle of the cutting arm using a mathematical model of the cutting arm joint mechanism.
## 2 Results
### Optimization of Solid-Phase Microextraction (SPME) Parameters
#### 2.1.3 Effect of Extraction Time and Temperature
The results obtained by [PERSON] et al. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{5}{c}{**Temperature (\({}^{\circ}\)C)**} \\ \cline{2-6}  & **20** & **35** & **50** & **65** & **75** \\ \hline Ethanethiol & 100.0 & 32.2 & 14.4 & 0.0 & 0.0 \\ Dimethyl-sulfide & 100.0 & 42.0 & 29.9 & 0.0 & 0.0 \\
1-Propanethiol & 100.0 & 41.3 & 37.7 & 5.8 & 2.6 \\ Thiophene & 100.0 & 83.7 & 59.5 & 11.6 & 6.6 \\ Diethyl-sulfide & 100.0 & 75.3 & 47.9 & 10.4 & 6.9 \\
1-Butanethiol & 100.0 & 79.3 & 47.7 & 14.6 & 6.3 \\ Dimethyl-disulfide & 100.0 & 88.6 & 57.4 & 18.5 & 5.7 \\ Ethyl-thioacetate & 100.0 & 89.9 & 69.5 & 31.1 & 16.1 \\
1-Pentanethiol & 100.0 & 88.2 & 58.6 & 18.8 & 8.7 \\ Dipropyl-sulfide & 100.0 & 99.0 & 77.5 & 30.8 & 14.6 \\ Diethyl-disulfide & 97.2 & 100.0 & 80.8 & 36.9 & 18.3 \\ Thiopheneh & 80.3 & 100.0 & 85.3 & 48.9 & 34.3 \\
2-Methyltetrahydrodrothiophene-3-one & 63.2 & 100.0 & 89.7 & 58.9 & 31.7 \\
3-Thiophenearboxaldehyde & 30.7 & 64.1 & 100.0 & 69.6 & 54.7 \\
2-Thiophenearboxaldehyde & 28.2 & 64.3 & 100.0 & 78.9 & 59.9 \\ Ethyl-3-(methylthio)propionate & 20.2 & 57.5 & 100.0 & 54.0 & 42.5 \\ Dibutyl-sulfide & 87.9 & 97.0 & 100.0 & 66.3 & 41.6 \\ Dipropyl-disulfide & 86.3 & 97.2 & 100.0 & 69.8 & 44.1 \\ Benzothiazole & 20.6 & 37.5 & 69.6 & 100.0 & 86.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effect of extraction temperature on the response of individual tested compounds. Data expressed as percent of maximum value obtained for each compound. clearly indicate that those compounds are present in fermented beverages and are not only the artifacts formed during analytical procedures.
## 3 Obstacle Avoidance Scenario
### Obstacle Detection Technologies
#### 3.3.2 Radar Mapping  
Radar (radio detection and ranging) imaging uses the same ranging principle as sonar. Both use time-of-flight or echo ranging to calculate the distance. Radar uses different radio wave signals, which are also a part of the radio spectrum [206]. Radar imaging uses massive mathematical calculations, but has the powerful capacity of being very long-ranging through space, and therefore has extensive use in aerospace technologies [207; 208]. This is why many works have been published on radar in rocket science and military technology [209; 210; 211; 212; 213]. However, our concern is obstacle detection for smaller vehicles with lower computational systems. A simple, short-range obstacle localization system was proposed by [PERSON] [214], where multiple 13-24 GHz radars were set up around a vehicle with a central digital processor system (DSP) system. Comprehensive sensing (CS) technology, addressed by [PERSON] and [PERSON] [215], is appropriate for monostatic bistatic and multistate scenarios. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [174] used four microwave doppler radars on a fixed-wing UAV to illuminate four forward field quadrants to determine an obstacle's position. A rotating radar was used for localization and mapping the surroundings by [PERSON] et al. [216]. [PERSON] et al. [217] proposed a 60 GHz imaging algorithm that can detect the nearby object and its location, orientation, curvature, and surface boundaries. [PERSON] et al. [218] experimented with 77-81 GHz radar for obstacle detection. [PERSON] et al. [219] used range-angle mapping with a single radar pulse to create a map, while [PERSON] et al. [220] used radar integrated with a MIMO array to determine an object's location. The frequency-modulated continuous wave, or FMCW radar sensor, is another popular radar sensor for obstacle imaging [221; 222; 223; 224].  
The rapid growth of autonomous car manufacturing increases miniature radar usage [225; 226]. Autonomous UAV usage is growing, and several studies also used radar for obstacle detection [47; 227; 228; 229]. The benefit of a radar detection system is that it can work under various hazardous conditions, such as rain, dust, sunlight, etc. [218].
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Temporal Normalization
#### 3.2.3 FAPAR Model Integrating FAPAR-P and 6S Models
The precise correction can be achieved by fitting each dashed line in Figure 5 by Equation (5), based on which the relationship between FAPAR measured at different time (SZAs) can be derived specifically. In this way a look up table of \(k_{1}\), \(k_{2}\) and \(k_{3}\) with varying LAI, SZA and VIS values could be established, which is precise but is complex. The RMSE of fitting FAPAR with LAI and SZA by \(k_{1}\), \(k_{2}\) and \(k_{3}\) were ranging 0.0002-0.0017, which were very low and were thus negligible. The changes of \(k_{1}\), \(k_{2}\) and \(k_{3}\) with LAI values followed the specific pattern, such as an exponential form for \(k_{1}\). If the relationship of LAI and coefficients \(k\) could be modeled further, the correction can be performed easily by deriving \(k_{1}\) and \(k_{2}\) from the simultaneously measured LAI values. The fitted models of \(k_{1}\) and \(k_{2}\) with LAI followed the pattern as \(k_{1}=a_{1}\cdot\exp(-a_{2}\cdot\mathrm{LAI})+a_{3}\) and \(k_{2}=a_{1}\cdot\exp(a_{2}\cdot\mathrm{LAI})+a_{3}\cdot\exp(a_{4}\cdot\mathrm{LAI})\). The fitting of \(k_{1}\) and \(k_{2}\) to LAI values were also very accurate, with RMSE ranging from 0.0002 to 0.0100.
## 5 The ATLAS MAC Protocol
### Medium Access Design  
The overall medium access operations of ATLAS follow a hybrid-approach based on the type of communication (_i.e._, Ch-to-G or S-to-Ch) and load-status information (_i.e._, low, moderate, high and overload). In Ch-to-G communication, the gateway node plays the coordinating role in selecting the mode of medium access according to the available traffic load-status information. More specifically, using the \"**wh**\" (whenever which is required) approach, the gateway exploits the CAP, CFP, and IP modes or intervals of a superframe. Conversely, in S-to-Ch communication, the medium access pattern is asynchronous and cluster-head controlled. In this case, the communication depends upon the mode of medium access operation between Ch-to-G.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Load Category** & **Load Differentiation Conditions** & *** LN Bits** \\ \hline Low-load & \((L_{i}\leq 0.74)\) & \(||\quad(q_{i}\leq q_{i}^{l})\) & 00 \\ Moderate-load & \((0.74<L_{i}\leq 0.83)\) & \& \((q_{i}^{l}<q_{i}<q_{i}^{u})\) & 01 \\ High-load & \((0.83<L_{i}\leq 0.92)\) & \& \((q_{i}^{l}<q_{i}<q_{i}^{u})\) & 10 \\ Over-load & \((L_{i}>0.92)\) & \(||\quad(q_{i}\geq q_{i}^{u})\) & 11 \\ \hline \end{tabular}  
* Load-status Notification.  
\end{table}
Table 1: Load-status settings.