esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Main Subject
### Sensor System for Active Driving Assistance
#### 2.1.2 Imu  
In this study, the IMU sensor was used to correct the direction data of the UWB sensor. In the case of direction estimation through the UWB sensor, data tremors caused by the radio wave reception strength and obstacles to the UWB sensor data occur, and the UWB sensor-based direction estimation can cause strong errors. Therefore, the direction estimation was corrected through angular velocity data for IMU sensors with a high short-range measurement accuracy. IMU sensors are usually mounted at the center of the robot drive shaft and used to measure the robot direction. They consist of acceleration, geomagnetic, and angular velocity sensors, and assuming that the robot moves in a plane, the angle of the robot is limited to the yaw axis, which can be calculated from angular velocity and geomagnetic sensors. However, in the case of geomagnetism, the noise caused by iron or structures that are heavily affected by the electromagnetic field is strong, and it is difficult to use when considering the electromagnetic field through the motor [33]. Therefore, the estimation in this study used only angular velocity data. However, in the case of the angular velocity data, it is difficult to know the initial direction and has a drift error in which errors accumulate over time. Therefore, in this study, the direction of improving the shortcomings through fusion with the UWB sensor was estimated.
## 2 Materials and Methods
### Data Analysis  
To prevent potential initial isometric force control adjustment and early termination effects, the first 5 s and final 1 s of force data were removed. Thus, the middle 14 s of force data was analyzed for each trial. Using a bidirectional fourth-order Butterworth filter (a cut-off frequency = 30 Hz), all force data were additionally filtered. Force control performances were estimated by (a) force accuracy: root-mean-square error (RMSE), (b) force variability: standard deviation (SD), and (c) force regularity: approximate entropy (ApEn). Force regularity is a temporal structure of force variability so that ApEn values denote regularity of future force signals in a time series in comparison to the prior force signals. The values of ApEn close to 2 represent less regularity of force data indicating more adaptive force productions, whereas the values of ApEn close to 0 mean more regularity of force data. Using the Pincus algorithm (Formula 1), we calculated the ApEn value for each trial [31; 32].  
\[\text{ApEn }\left(\overset{\rightarrow}{X},\ m,\ r\right)=\ln\left[\frac{C_{m} (r)}{C_{m+1}(r)}\right] \tag{1}\]  
where \(C_{m}\) (\(r\)) indicates number of repetitive patterns of length m of vector \(X\) within \(r\) criterion of similarity. \(C_{m}+1\) (\(r\)) indicates number of repetitive patterns of length \(m+1\). Consistent with previous studies [33; 34], we set \(m\) as 2 and \(r\) as 0.2 \(\times\) SD.  
Low-frequency oscillations in force signals were calculated by performing power spectrum analysis with a fast Fourier transform using a custom Matlab Program (Math WorksTM Inc., Natick, MA, USA). Consistent with prior studies [11; 12], we quantified absolute power (N\({}^{2}\)) for the four frequency bands: (a) 0-0.5 Hz, (b) 0.5-1.0 Hz, (c) 1.0-1.5 Hz, and (d) 1.5-2.0 Hz.
## 3 System Design
### Application Server
#### 3.2.1 Data Analysis Module  
As mentioned in the literature review, several analysis packages for statistical analysis in climate research have been developed. This system intends to integrate the commonly used analytical functions in the data analysis module.  
The workflow of executing analysis requests is shown in Figure 4. Users define their analysis requirements such as variable, spatiotemporal coverage, analysis type and representation form. The requests are then sent to the application server through HTTP. Input parameters in HTTP are interpreted and the corresponding analytical functions are invoked on the server side. When executing the analysis, selected input data (e.g., 1-year annual means) are retrieved from the original database and processed to satisfy the data input requirement of the analysis functions. The analysis process may generate some temporary data (e.g., 10-year annual mean) which are deleted after the completion of the analysis. The final output may be new NetCDF data or graphs of statistical plots. The NetCDF data which cannot be rendered by clients directly should be converted into images. The response is returned to users as an XML stream which includes the information about analysis output such as path, title and legend.  
Figure 4: Workflow of the application server.  
The entire workflow is enabled by HTTP requests, Java servlets and XML. NetCDF Operator (NCO) software [34] is used for data pre-processing (e.g., data permutation and metadata editing) and calculating descriptive statistical analysis (e.g., calculating the mean and standard deviation). The R language [35] is applied to perform advanced statistical analysis and drawing statistical plots (e.g., calculating correlations and generating scatter plots). NCO and R scripts are invoked by Java servlets.
# Integrating Pareto Optimization into Dynamic Programming
## 2 Key Operations in Pareto Optimization and Dynamic Programming
Beyond this, we will not bother the reader with the details of algebraic dynamic programming, nor with the syntax of GAP-L. The explanation can be given at the abstraction level used in the Introduction; we borrow the expositional example from [21]. Let \(f\), \(g\) and \(h\) be a binary, a unary and a nullary scoring function in an evaluation algebra \(A\). A nullary scoring function constitutes the base case of an empty or trivial subproblem. A unary scoring function extends a single subproblem in a particular way. A binary scoring function combines scores from two subproblems to the score of the composed subproblem. Aside from scoring functions, the evaluation algebra also holds a choice function \(\phi_{A}\), which satisfies [PERSON]'s principle with respect to the scoring functions in \(A\). The tree grammar rule:  
specifies a part of some problem decomposition (beyond this example, algebra functions can have arbitrary arity and productions of the tree grammar arbitrary height) and can be read intuitively as:  
A subproblem of type \(W\) can either be decomposed into subproblems of type \(X\) and \(Y\), with their scores combined by function \(f\), or its score can be computed by function \(g\) from the score of a subproblem of type \(Z\), or it constitutes a base case, where \(h\) supplies a default value. Note that the decomposition into \(X\) and \(Y\) in the first clause must happen in all possible ways, say splitting an input string (posing the subproblem of type \(W\)) at all possible positions. For a string of length \(s\), this gives a list of \(s+1\) results. Eventually, results from all cases must be combined and the objective function \(\phi\) applied. The overall computation can be defined by the introduction of three operators, \(\otimes,\oplus,\ \#\), respectively called \"extend\", \"combine\" and \"select\". \(\otimes\) performs splits and computes combined scores; \(\oplus\) collects alternatives of the decomposition; and \(\#\) applies the choice function \(\phi\) defined on lists. In this way, \(W\) is evaluated as:  
\[W=(\otimes(f,X,Y)\ \oplus\ \otimes(g,Z))\ \oplus\ h)\ \#\,\phi. \tag{2}\]In the standard, non-Pareto optimization, the implementation of the operators can be described as:  
\[l\,\#\,\phi = \phi(l) \tag{3}\] \[l_{1}\oplus l_{2} = l_{1}{+}{+}l_{2}\] (4) \[\otimes(f,X,Y) = [f(x,y)\mid x\in X,y\in Y]\] (5) \[\otimes(g,X) = [g(x)\mid x\in X], \tag{6}\]  
where \(l,l_{1},l_{2}\) denote lists of intermediate results and \(++\) denotes an appendedlist. Moving on from simple choice functions, e.g., maximization, to Pareto optimization, \(\phi\) will be replaced by some version of **pf**, and \(\otimes\) and \(\oplus\) will become more sophisticated. While \(\phi\) expects a totally ordered domain \(A\), **pf** expects a Cartesian product domain \(A\times B\) and optimizes with respect to the domination partial ordering defined by \(>_{A}\) and \(>_{B}\).
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 3 Modelling
### Mass and Species Conservation
#### 3.2.1 Generator  
(1) In a transient process, the solution mass storage in the generator depends on the entering weak solution, leaving strong solution and the desorbed vapor refrigerant, as illustrated in Figure 1. The mass conservation equation is:  
\[m_{w}-m_{s}-m_{v,des}=\frac{dM_{s,g}}{dt} \tag{1}\]  
where \(m_{w}\) is the mass flow rate of the weak solution entering the generator (point 9), kg/s; \(m_{s}\) is the mass flow rate of the strong solution leaving the generator (point 10), kg/s; \(m_{v,des}\) is the mass flow rate of the refrigerant desorbed from the solution, kg/s; and \(M_{s,g}\) is the solution storage mass in the generator, kg.  
(2) The species conservation for the generator is:  
\[m_{w}\cdot x_{w}-m_{s}\cdot x_{s}=\frac{d(M_{s,g}\cdot x_{s})}{dt} \tag{2}\]  
where \(x_{w}\) is the weak solution mass concentration (point 9); \(x_{s}\) is the strong solution mass concentration (point 10).  
The properties of the fluids stored in the containers are assumed to be same as those of the leaving fluids [16; 26].  
(3) For the vapor in the generator, the mass conservation equation is:  
\[m_{v,des}-m_{v,out,g}=\frac{dM_{v,g}}{dt} \tag{3}\]  
where \(m_{v,out,g}\) is the mass flow rate of the refrigerant leaving the generator (point 1), kg/s; and \(M_{v,g}\) is the refrigerant vapor storage in the generator, kg.  
(4) The volume of the solution storage plus that of the vapor storage is the volume of the whole generator:  
\[\frac{M_{s,g}}{\rho_{10}}+\frac{M_{v,g}}{\rho_{1}}=V_{g} \tag{4}\]  
where \(\rho_{10}\) and \(\rho_{1}\) are the densities of the solution and the vapor stored in the generator, kg/m\({}^{3}\); and \(V_{g}\) is  
where \(z_{g}\) is the solution height inside the generator, which is calculated by:  
\[z_{g}=\frac{M_{s,g}}{\rho_{10}\cdot A_{g}} \tag{7}\]  
where \(A_{g}\) is the bottom area of the generator, m\({}^{2}\).
# A Dual-Polarized and Broadband Multiple-Antenna System for 5G Cellular Communications
## 6 Comparison  
Table 2 provides a performance comparison among the newly introduced MIMO array and various other designs discussed in the existing literature. Various fundamental properties, such as the employed antennas, efficiency, Envelope Correlation Coefficient (ECC), among others, are discussed for each design. The results of the comparison reveal that the developed array exhibits improved functionalities and offers several advantageous characteristics, particularly in terms of the operational frequency band. The designed array offers an impressive frequency bandwidth of more than 2800 MHz, making it capable of covering a wide range of sub-6 GHz frequencies. This broad bandwidth is crucial for supporting various 5G applications that require high data rates and reliable connectivity. An additional advantage of the presented antenna array is its planar schematic and ease of integration on a single-layer substrate. Furthermore, the presented antenna provides a diversity function in terms of both pattern and polarization characteristics, offering full coverage while supporting various sides of the board. These significant advantages position the proposed antenna as a competitive and practical solution for enabling advanced 5G communication technologies.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Antenna Type**} & \multirow{2}{*}{**Bandwidth (GHz)**} & \multicolumn{2}{l}{**Efficiency**} & \multicolumn{2}{l}{**Size**} & \multicolumn{2}{l}{**Isolation**} & \multirow{2}{*}{**ECC**} & \multirow{2}{*}{**Diversity**} \\  & & & **(\%)** & & & **(mm2)** & & **(dB)** & \\ \hline
[10] & Monopole-slot & 2.55–2.65 (0.1) & 50–70 & \(136\times 68\) & 12 & \textless{}0.15 & Yes \\
[11] & L-shaped monopole & 3.4–3.6 (0.2) & 50–5 & \(136\times 68\) & 15 & \textless{}0.10 & No \\
[12] & Self-isolated monopole & 3.4–3.6 (0.2) & 60–70 & \(150\times 75\) & 19 & \textless{}0.02 & No \\
[13] & Balanced open-slot & 3.4–3.6 (0.2) & 60–75 & \(150\times 80\) & 17 & \textless{}0.05 & No \\
[14] & Inverted-F antenna & 3.4–3.6 (0.2) & - & \(110\times 60\) & 19 & - & Yes (Limited) \\
[15] & Decoupled open-slot & 3.4–3.6 (0.2) & 40–60 & \(150\times 75\) & 12 & \textless{}0.40 & No \\
[16] & Rectangular Slot & 3.4–3.6 (0.2) & 50–60 & \(136\times 68\) & 11 & \textless{} 0.05 & No \\
[17] & H-shaped monopole & 3.4–3.6 (0.2) & 60–70 & \(150\times 75\) & 11 & \textless{}0.15 & No \\
[18] & Gap-Coupled Loop & 3.4–3.6 (0.2) & 40–60 & \(150\times 75\) & 12 & \textless{}0.20 & No \\
[19] & S-shaped monopole & 3.45–3.55 (0.1) & 50–68 & \(130\times 50\) & 15 & \textless{}0.30 & No \\
[20] & Shorted loop & 3.4–3.6 (0.2) & 50–78 & \(150\times 77\) & 10 & \textless{}0.1 & No \\
[21] & L-shaped monopole & 3.4–3.6 (0.2) & 40–70 & \(145\times 75\) & 15 & \textless{}0.15 & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison table of the suggested broadband MIMO antenna.  
Figure 16: (a) Talk-mode and SAR analysis at (b) 3.5 GHz and (c) 5.5 GHz.
###### Abstract  
Land surface temperature (LST) is an important parameter in determining surface energy balance and a fundamental variable detected by the advanced geostationary radiation imager (AGRI), the main payload of FY-4A. FY-4A is the first of a new generation of Chinese geostationary satellites, and the detection product of the satellite has not been extensively validated. Therefore, it is important to conduct a comprehensive assessment of this product. In this study, the performance of the FY-4A LST product in the Hunan Province was authenticly tested with in situ measurements, triple collocation analyzed with reanalysis products, and impact analyzed with environmental factors. The results confirm that FY-4A captures LST well (R = 0.893, Rho = 0.915), but there is a general underestimation (Bias = \(-\)0.6295 \({}^{\circ}\)C) and relatively high random error (RMSE = 8.588 \({}^{\circ}\)C, ubRMSE = 5.842 \({}^{\circ}\)C). In terms of accuracy, FY-4A LST is more accurate for central-eastern, northern, and south-central Hunan Province and less accurate for western and southern mountainous areas and Dongting Lake. FY-4A LST is not as accurate as Himawari-8 LST; its accuracy also varies seasonally and between day and night. The accuracy of FY-4A LST decreases as elevation, in situ measured LST, surface heterogeneity, topographic relief, slope, or NDVI increase and as soil moisture decreases. FY-4A LST is also more accurate when the land cover is cultivated land or artificial surfaces or when the landform is a platform for other land covers and landforms. The conclusions drawn from the comprehensive analysis of the large quantity of data are generalizable and provide a quantitative baseline for assessing the detection capability of the FY-4A satellite, a reference for determining improvement in the retrieval algorithm, and a foundation for the development and application of future domestic satellite products.  
FY-4A/AGRI; land surface temperature; in situ measurement; triple collocation analysis; environmental factors; comprehensive evaluation +
Footnote †: journal: Atmosphere  
0000-0002-2313-1300]Qinzhe Han  
4
## 2 Methodology
### Frequency Domain Analysis Theory  
For large-scale marine structures, the velocity potential is generally solved based on the three-dimensional potential flow theory. The motion frequency domain response equation of the platform can be written as:  
\[\left[-\omega^{2}\left(M_{f}+A\right)+\left(-i\omega C+K\right)\right]u=F_{w} \tag{5}\]  
where \(M_{f}\) is the mass matrix of the floating body system, \(A\) is the Additional mass matrix, \(C\) is the additional damping matrix, \(K\) is an additional water static matrix. \(u\) is the motion response of floating body in complex form, \(F_{w}\) is wave excitation force, \(\omega\) is the incident wave frequency.
# VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android
## 1 Introduction  
Recently, technological advances in computing, electronics, and telecommunications and their convergence in our daily lives have led to the emergence of new needs, especially with the widespread use of intelligent devices. Among these devices, unmanned aerial vehicles (UAVs), or drones, have been used in various missions. They were used for the first time towards the end of the First World War by the U.S. Army [1]. The idea was to guide piloted planes remotely without the pilot in the cockpit. Since then, UAVs have become the subject of innovation in various fields and for several types of missions. They have long been a military tool; however, in recent years, their use has been extended to the civilian domain. They present a promising solution for the most dangerous, delicate, and unsuitable missions for human pilots, which saves lives as they allow for the replacement of piloted planes and helicopters.  
There are several designs for UAVs, among which the quadrotor offers real advantages such as:  
* Their reduced size and maneuverability allow them to fly in closed or open environments and close to obstacles, unlike conventional helicopters.
* The can perform vertical take-off and landing.
* This configuration is controlled by only varying the speed of the four engines.  
However, the quadrotor has a number of drawbacks that still prevent it from becoming a leading technology among UAVs. Many works have addressed the problems of modeling, designing, and controlling such complex systems. Despite its four motors and stationary equilibrium, the quadrotor remains an underpowered and dynamically unstable system. The objective is to equip these quadrotors with more sensors and intelligence with the development of control algorithms to ensure their stability, as well as a certain degree of autonomy.  
To the best of our knowledge, there is no prior work that has presented an intelligent and autonomous drone with Android as the operating system. The use of Android gives us a large array of applications for direct use by the drone depending on the context of the mission. Therefore, the main objective of this paper is to develop a quadrotor-type flying robot that ensures autonomous navigation and stability, as well as guarantees streaming in real time.  
The remainder of this paper is organized as follows. Section 2 discusses related works. Section 3 presents the architecture of VAGADRONE such as the hardware and software used and the features of our UAV. Section 4 describes the simulation and experimental results in various scenarios. Finally, Section 5 provides some conclusions.
## 4 Results
### Statistical Analysis  
Our dataset consists of dependent observations since there are multiple samples of the same participant in the same group and/or medication state and/or severity level. Thus,an analysis that assumes independent observations in each class or between the classes themselves, such as ANOVA, cannot be employed. To determine which gait features are affected by the existence of the disease, the medication state or the severity level of PD, a Mixed Linear Model (MLM) analysis with Bonferroni adjustment, and with the disease group (Adults, Elderly, and PD patients), medication state or MDS-UPDRS:Part III, as a fixed factor, and participant ID as a random factor was employed for each test. The level of statistical significance was set at \(p<0.05\). We present the results of MLM analysis regarding the statistical significance of features derived from slow, normal, and high-pace walking (elaborating the WST Slow, Normal, High tests and mTUG), to explore the effect of gait velocity in differentiating gait patterns.  
The mixed linear model analysis revealed significant correlations of features between ON/OFF medication states and between severity levels based on the MDS-UPDRS-Part III Total Score, as shown in Table 7, for each different type of test. The results indicate the importance of using different gait velocities since different features are identified as being significant for each type of test. This allows for not only examining the behavior of features in relation to different gait velocities but also comparing results with the literature by exploiting a range of velocities, as the normal pace of walking may differ significantly per participant.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Zirconium-Doped cadmium Oxide Films  
Zirconium-doped cadmium oxide thin films were deposited on stainless steel substrates by [PERSON] and co-workers [159]. The SEM analysis showed that grain sizes were reduced by increasing the dopant percentages during the experiment. The 2%-doped cadmium oxide films showed the best electrochemical features, with a band gap of about 2.07 eV and a cubic phase, and they could be used in optoelectronic devices.
## 2 Related Studies
### Identified Gaps in the Literature
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Ganepola \& Wirasingha \& Wirasingha \& Wirasingha \& This Review \\ \multicolumn{1}{c}{**(2021) [15]**} & **(2021) [15]** & **(2021) [16]** & & \\ \hline Search Strategies & EA, RL, Gradient-based & RL, Gradient-based & EA, RL, Gradient-based & EA, RL, Gradient-based \\ \hline Datasets & CIFAR-10, STL-10 & CIFAR-10, STL-10 & CIFAR-10, STL-10 & CIFAR-10, STL-10, CelebA, LSUN, MNIST \\ \hline Evaluation Metrics & IS, FID, GPU days, Search space & IS, FID & Search space, Computational Cost \\ \hline Limitations Addressed & Focus on early NAS-GANs & Limited evaluation criteria & Metric reliability, Reproducibility, Discriminator NAS gaps \\ \hline Novel Contributions & Initial NAS-GAN taxonomy & Multi-criteria analysis & Analysis of recent NAS-GAN works, dataset diversity \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative Analysis of NAS-GAN Reviews. This comparison underscores our review's unique focus on understudied challenges in NAS-GANs, such as reproducibility, while expanding the scope of datasets and evaluation criteria. Subsequent sections detail these contributions through quantitative and qualitative analyses.
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
## 4 Field Application  
### Application Details  
To verify that the permeability enhances the effect of water jet slotting, a field experiment was conducted in the J\({}_{15}\) coal seam in the Pingdingshan No. 12 Coal Mine in Henan province. The average thickness of the coal seam was 3.2 m, and the inclination angle was 6\({}^{\circ}\). The gas pressure of the coal seam was 1.7 MPa, and the gas content was 17.4 m\({}^{3}\)/t. Three types of boreholes were considered in this test: optimized slotted borehole, conventional slotted borehole, and conventional borehole. The application parameters for the conventional slotted borehole were 20 MPa and 5 r/min. For the optimized slotted borehole, the parameters were 26 MPa and 4 r/min. Optimization was carried out as follows: the water jet pressure increased by 1 MPa every 2 min starting from 20 MPa. When the water discharge from the borehole started to decrease, it was considered that the coal slag had begun blocking the borehole and preventing the water pressure boost. Thus, the water pressure was the optimized pump pressure. The rotational speed can be optimized in the same manner. These boreholes were located at the same drill site, with a drilling spacing of 10 m. The geological column and borehole layout parameters are shown in Figure 12.  
Figure 11: Vertical CT scan image of slots.  
Figure 12: Detailed description of test site and borehole layout parameters: (**a**) location of No. 12 Coal Mine; (**b**) geologic column; (**c**) plan sketch of boreholes; (**d**) schematic diagram of boreholes.