esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Soil water can either evaporate directly into the atmosphere or be absorbed by plants, and soil moisture levels also influence soil microbial respiration [33]. Additionally, carbon assimilated by plants contributes to the formation of soil organic carbon [34], while the soil organic carbon content, in turn, affects the soil water transport process [35]. In essence, the exchange of water and carbon fluxes in terrestrial ecosystems is an intricately linked and complex process. Therefore, incorporating this interaction into water and carbon flux estimation models has the potential to significantly enhance accuracy. In general, the transpiration and photosynthetic capacity of vegetation are determined by various factors, including vegetation growth, radiative energy, and soil conditions. However, it remains unclear which type of factor has the greatest impact on vegetation transpiration and photosynthesis, and which indicators are most critical when constructing models. Remote sensing technology offers a spatially and temporally continuous and efficient means of gathering information. Nevertheless, handling the vast amounts of remote sensingdata in terms of acquisition, storage, and computation demands high-performance computing resources. Google Earth Engine (GEE), a cloud-based platform, stores petabytes of earth observation data and serves as a cost-effective solution for online data processing [36]. GEE has found applications in diverse fields such as global forest mapping [37], surface water mapping and analysis [38; 39], cropland mapping [40], and crop yield prediction [41; 42]. Consequently, GEE holds the potential to serve as an efficient platform for the co-estimation of water and carbon fluxes. In this study, we utilized multiple variables, including vegetation growth, surface moisture, radiative energy, and other pertinent variables, as inputs for various regression algorithms. Specifically, we employed Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN) to estimate water (ET) and carbon fluxes (NEE). The estimated results were then compared with observations from six eddy covariance flux towers. The objectives of this study were threefold: (1) to assess the impacts of different input variables on the accuracy of evapotranspiration (ET) and net ecosystem exchange (NEE) estimations, (2) to compare the accuracy of the three regression methods, encompassing three machine learning algorithms and Multiple Linear Regression, and (3) to evaluate the performance of ET and NEE estimation models across various regions.
## 4 Numerical Results  
In this section, the presented antenna structure and design optimization algorithm are numerically validated. The design problem involves bi-objective optimization of the radiator for size reduction and minimization of reflection within the frequency band of interest. The obtained EM simulation results were verified based on evaluations performed using a frequency domain solver. Furthermore, the presented routine has been compared against a conventional multi-objective optimization algorithm in terms of computational cost and quality of the obtained Pareto designs. Finally, the smallest obtained antenna solution has been benchmarked against other structures from the literature. All computations have been performed using an Intel Xeon E5-2650 processor with 32 GB of RAM.
# Blood Pressure Estimation Algorithm Based on Photoplethysmography Pulse Analyses
## 2 Materials and Methods
A device capable of measuring PPG consists of a sensor module and an amplification, conversion, and signal transmission unit. The sensor, which is a measuring unit, consists of a red-light-wave measuring circuit for reflection-type light, using infrared light-emitting and light-receiving diodes. More than 20 PPG waveforms were sampled through two channels and measured in a constant area through filtering. The sampling reduced the time for repeated measurements by measuring in a narrow band around 64 Hz to reduce noise, and PTT set the peak value through differential filtering. In addition, to eliminate distortion due to noise around the measurement area, areas of 0.5 us or less and 10 us or more were filtered, and variable amplification signal bands (0.5 Hz to 5 Hz) were designated and processed. This study was approved by the institutional review board (IRB) of Nambu University, Gwangju city, South Korea (NBU-IRB-1041478-2017-HR-017) and conducted in accordance with the ethical standards of the declaration of Helsinki. All subjects were informed of the purpose of the study and study design, and all participants provided written informed consent. To verify the BP estimation algorithm, SBP and DBP were obtained through the automatic sphygmomanometer of the cuff method and PPG measurement device developed in this study. The experimental data were based on 8 men and 13 women (average age: 21.10 years, standard deviation: 1.64 years), who did not have circulatory diseases, which were obtained using the automatic BP cuff measurement method and PPG. After resting for 10 minutes before the test, the average values for SBP and DBP were obtained from both devices by measuring twice over a period of 10 days in the same posture, as shown in Figure 2. The automatic blood pressure monitor (ACCUNI, BP210, Seoul, South Korea) was used as a gold standard, as it is recognized as an absolute value of blood pressure measurement by medical institutions. It is a device that satisfies the standards of the American National Standards Institute (ANSI)/Association for the Advancement of Medical Instrumentation (AAMI) and was used for measurements in the same environment as PPG [25]. Figure 2: Equipment used for blood pressure measurements. (**a**) PPG measurement method, (**b**) Automatic blood pressure measurement method via air pressurization.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 4 Experiments and Results
### Identical Translation Experiment  
As shown in Figure 6a, the stereo-vision system in this experiment consisted of two MER-1070-14U3x cameras with image resolutions of 3840 \(\times\) 2748 pixels and lens focal lengths of 8 mm. The left and right cameras are all mounted directly onto an electronic control platform, which consists of a two-dimensional translation stage and a two-axis rotation stage. The accuracy of the translation motion is 0.03 mm, and the rotation accuracy is 0.05\({}^{\circ}\). A flat checkerboard placed at the opposite side of the stereo-vision system is the measured object. The size of each square is 25 mm \(\times\) 25 mm. Figure 6b shows the high-precision motion controller to achieve the independent translational and rotational movement by the electrical step motors. Figure 6c shows the definition of the coordinate system of the rotational stage. The \(Z\)-axis of rotational stage is parallel to the optical axes  
Figure 5: Relative rotation between two cameras in a stereo-vision system.  
(i.e., \(C_{l}Z_{cl}\) axis and \(C_{r}Z_{cr}\) in Figure 1) of the two cameras, and the X and Y-axes are parallel to the camera's CCD imaging plane.  
Before performing translational movement of two cameras, we employ [PERSON]'s method [39] to calibrate the initial camera parameters (\(A_{l},A_{r},R_{lr}\) and \(T_{lr}\)) and the lens distortion parameters (\(\kappa\)1, \(\kappa\)2, \(\kappa\)3, p1, p2). Thereafter, a pair of reference images of the checkerboard is acquired by the left and right cameras simultaneously, as shown in Figure 7a. Subsequently, both the cameras are translated together with the movement of the electronic control platform along the X and Z-axes, respectively. The two cameras are moved by 44 mm with an increment of 4 mm, and 11 image pairs are taken at 11 different positions by the stereo-vision system. Figure 7a also shows the stereo image pair captured in the state of identical translations of 44 mm along the X-axis. The five distortion parameters are used to calculate an ideal distortion-free image. After removing the distortions, we adopt the corner detection algorithm to extract the image coordinates of the corner points in each pair of undistorted images. The world coordinates of the corner points in all the pairs of images can be calculated through the triangulation algorithm. The difference between the 3D world coordinate \(\textbf  
of identical translation on the distance measurement can be neglected, which is consistent with the theoretical analysis in Section 3.1.
## 2 Materials and Methods
### Annotation of Volatile Compounds  
Solid-phase microextraction (SPME; Supelco, Bellefonte, PA, USA) combined with 7000D chromatography-mass spectrometry (GC-MS; Agilent, Santa Clara, CA, USA) was used to analyze the changes of volatile flavor compounds in the yellow rice wine fermentation process. A sample of 6 mL/6 g and 2 g of NaCl were added to a 20 mL extraction flask, which was then placed on a magnetically heated stirrer, and 65-\(\upmu\)m of PDMS/DVB fiber (Supelco, Bellefonte, PA, USA) was inserted into the flask and extracted at 60 \({}^{\circ}\)C and 600 r/min for 45 min. Next, the SPME installation was inserted into the GC injection port at 250 \({}^{\circ}\)C in a non-shunt mode for 5 min to allow complete desorption. The GC conditions were as follows: HP-5 MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 mm); carrier gas: He; flow rate: 1.0 mL/min; column oven temperature: the temperature was initially 40 \({}^{\circ}\)C and held for 5 min, then raised to 120 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held for 5 min, and finally increased to 240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and held for 5 min. The MS conditions were as follows: voltage 70 eV, ion source temperature 230 \({}^{\circ}\)C, mass scanning range \(m/z\) 30-450, and electron ionization (EI) mode.  
The mass spectra of the compounds were compared with the NIST20.L mass spectra database, and those with a matching degree of \(>\)80% were extracted and analyzed. The relative content of the annotated compounds was expressed by dividing the peak area of individual substances by the total peak area [24].  
The ROAV has been proposed to explain the contribution of a volatile substance to the overall flavor of a sample. Substances with ROAV \(>\) 1 are considered to make an important contribution to the overall flavor of the sample; those with 0.1 \(\leq\) ROAV \(<\) 1 are considered to have an effect on the flavor of the sample. The ROAV calculation method was based on the ratio of a volatile component's odor activity value (OAV) to the greatest odor activity value (OAVmax) among the volatile compounds in yellow wine [25].  
The thresholds were obtained by finding the threshold of perception of each volatile compounds in grain alcohol in the book [26]. (See Appendix A for section).
## Results and Discussion
### Purification of APs from MPTH
#### 3.2.3 Gel Filtration Chromatography of MUA-4  
As shown in Figure 3A, MUA-4 was split into two components of MUA-4-A and MUA-4-B using a Sephadex G-25 column. Figure 3B indicated that DPPH- and HO- scavenging activities of MUA-4-B were 86.39% \(\pm\) 4.32% and 80.56% \(\pm\) 3.55% at the concentration of 5.0 mg protein/mL, which were significantly higher than those of MUA-4 (DPPH- 76.64% \(\pm\) 3.43%; HO- 69.39% \(\pm\) 3.58%) and MUA-4-A (DPPH- 56.38% \(\pm\) 3.62%; HO- 55.26% \(\pm\) 4.39%) (\(p\) < 0.05). Gel filtration chromatography is a frequently-used prepared technique according to the MW of the separated substances and is applied to separate peptides from protein hydrolysates and their fractions, such as Spanish mackerel [30], miuoy croaker [10; 20], ark shell [19], and blue-spotted stingray [22]. Therefore, fraction MUA-4-B was selected for the following isolation process.  
Figure 2: Elution profile of MPTH-I in DEAE-52 cellulose anion-exchange chromatography (**A**) and radical scavenging activity of MPTH-I and its fractions (MUA-1 to MUA-6) at the concentration of 5.0 mg protein/mL (**B**). The data are presented as the mean \(\pm\) SD (\(n=3\)). \({}^{a-e}\) or \({}^{A-F}\) Values with the same superscripts indicate no significant difference (\(p>0.05\)).
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping
### Literature Review According to Analytic Methods and Approaches
#### 3.2.7 Vertical Displacement
\begin{table}
\begin{tabular}{c c} \hline \hline
**Prediction Model** & **Prediction Accuracy (\%)** \\ \hline SSI * with all six factors & 91.09 \\ \hline SSI with five factors (excluding 3D mine drift) & 88.80 \\ \hline SSI with five factors (excluding mined panel) & 90.32 \\ \hline SSI with five factors (excluding land use) & 90.53 \\ \hline SSI with five factors (excluding proximity to railroads) & 89.45 \\ \hline SSI with five factors (excluding proximity to roads) & 91.03 \\ \hline SSI with five factors (excluding the slope gradient) & 91.11 (Best model) \\ \hline \hline \end{tabular}  
* SSIs: subsidence susceptibility index.  
\end{table} 
Table 3: Sensitivity analysis: prediction accuracy of eight generated subsidence prediction maps representing combinations of the considered factors (revised from [PERSON] et al. [24]). displacement and surface influential range according to the size and shape of the underground mining area. [PERSON] et al. [45] proposed a GIS-based integrated approach for ground deformation within coal mining basins. This approach integrates GIS data analysis, 2D finite element numerical modeling analysis, a comparison between predicted data, and highly accurate measurement data (i.e., GPS or InSAR). From the calibration process, the proposed approach provides more accurate results than those obtained from other classical subsidence prediction methods.
## 3 Proposed Method
### Feature Fusion Module and Multi-Scale Skip Connection Based on Residual Network
#### 3.3.1 Feature Fusion Module
In the local context branch, it successively goes through \(1\times 1\) convolution, instance normalization, the ReLU activation function, \(1\times 1\) convolution, and instance normalization. In the global context branch, it successively goes through a global average pooling, then \(1\times 1\) convolution, instance normalization, the ReLU activation function, \(1\times 1\) convolution, and instance normalization. It is worth noting that \(1\times 1\) convolution preserves the size of the input features, which can preserve and highlight the fine details in the lower-level features. The tensor obtained from the local context branch and the global context branch is summed and then passed through a Sigmoid function to obtain a tensor with values in the range [0, 1], and this tensor is combined with the input tensor passed through a (1-Sigmoid) function to obtain the final output. The formulae are shown below  
\[O_{1}=IN(Conv(ReLU(IN(Conv(I))))), \tag{3}\]  
\[O_{2}=IN(Conv(ReLU(IN(Conv(GAP(I)))))), \tag{4}\]  
\[O=Sigmoid(O_{1}+O_{2})+(1-Sigmoid(O_{1}+O_{2}))\cdot I, \tag{5}\]  
where \(I\) is the input feature, \(O_{1}\) and \(O_{2}\) are the output features of the local context branch and global context branch, respectively, \(O\) denotes the output of the whole channel attention module, Conv denotes \(1\times 1\) convolution, IN denotes instance normalization, ReLU and Sigmoid denote activation functions, GAP denotes Global Average Pooling, and \((\cdot)\) denotes the product by element. Based on the channel attention module described above, the feature fusion module is shown in Figure 4. The two input features are first summed and then passed through the channel attention module to obtain the features after importance selection by the channel attention module, which are then multiplied by the two input features after the Sigmoid function and (1-Sigmoid) function, making it possible to adaptively adjust the proportion of the two input features in the fused features. To deepen the depth of feature fusion, the output features obtained are then repeated in the above process to fuse features of different semantics and different scales more deeply. Figure 4: The proposed feature fusion module. Figure 3: Channel attention module.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 3 Fracture Mechanism of MGs  
The fracture process in MGs discussed above is very different from that in conventional polycrystalline alloys. With work-hardening effect and the deformation mechanism of dislocations, the deformation of many polycrystalline alloys will experience an elastic, yielding, work-hardening, and fracture process. The mode I fracture process in these materials can be described by the classic linear elastic fracture mechanics (LEFM), which depicts the plastic zone by the contour line of yielding stress [52]. The material inside the plastic zone could deform plastically, and the material outside the zone will be still elastic. The plastic zone moves accordingly with the extension of crack-tip. The stress intensity for crack extension (fracture toughness) and energy absorption during the crack extension (fracture resistance) can be considered as material dependent constants. Compared to such a fracture process in polycrystalline alloys, the characteristics of fracture behaviors in MGs are summarized as follows:
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 2 Antenna Basics
### Beam Steering  
In order to scan the beam from one direction to the other, we have to change the relative phase \(\beta\) between the elements [7]. To find the \(\beta\) needed to get a maximum in a particular direction \(\theta_{0}\), the term \(\psi\) is set equal to zero, as given by Equation (14):  
\[\psi=kd\cos\theta_{0}+\beta=0 \tag{14}\]  
\[\beta=-kd\cos\theta_{0}=-\frac{2\pi}{\lambda}d\cos\theta_{0} \tag{15}\]  
By solving the above equation for a given frequency and angle, the phase difference necessary for a given scanning angle (Figure 3) can be derived. In the next section, we apply these equations to design an array to meet the requirements of Table 1.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Regression Analysis Using ELM
Once \(\mathbf{W}\), \(\mathbf{b}\) and \(\mathbf{B}\) are defined, the SLFN can then be used to predict the target variables \(\mathbf{\hat{y}}\) given a new vector of features \(\mathbf{x}\) as follows:  
\[\mathbf{\hat{y}}=sig\Big{(}\mathbf{x}^{T}\mathbf{W}+\mathbf{b}^{T}\Big{)} \mathbf{B}. \tag{5}\]  
This equation corresponds to the estimation of \(C\) targets for a single WT. If we want to estimate a single target (\(C=1\)), the equation can be simplified. In that case, the matrix \(\mathbf{B}\) is a vector, \(\beta\), and we obtain:  
\[\hat{y}=sig\Big{(}\mathbf{x}^{T}\mathbf{W}+\mathbf{b}^{T}\Big{)}\beta. \tag{6}\]  
In the general case in which we estimate the set of subsystems of the M turbines of an entire wind plant, the equation will be as follows:  
\[\mathbf{\hat{Y}}=sig\Big{(}\mathbf{x}^{T}\mathbf{W}+\mathbf{ub}^{T}\Big{)} \mathbf{B}. \tag{7}\]  
where now \(\mathbf{u}\) is a unit vector of size \(M\times 1\) and the matrix \(\mathbf{\hat{Y}}\) is of size \(M\times C\). In Figure 1 we can see the simple structure of a SLFN network for the particular case of a WT that uses the variables \(\mathbf{x}\) to predict two target variables at \(\mathbf{\hat{y}}\). Note that the normalisation process of the input variables is represented in the figure. Figure 1: Detail of the structure of the SLFN in terms of the parameters that define it (\(\mathbf{W}\), \(\mathbf{b}\) and \(\mathbf{B}\)), the vector of input features (\(\mathbf{x}\)), the z-score normalisation process and the output vector (\(\mathbf{\hat{y}}\)). Selection of the Network Size  
Once the input variables and targets are chosen, sizing the SLFN to fit the models only depends on the choice of the number of hidden nodes \(H\). The optimal selection of \(H\) will depend on the number of input signals and the number of targets to be estimated. By properly normalising the signals as explained in Section 2.5.1, there are no further issues related to calculating the Moore-Penrose pseudoinverse matrix.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 4 System Model
### Computation Model  
Firstly, the computation model will be illustrated. We assume that the system contains \(N\) terrestrial user terminals, \(M\) iSats as nodes of space edge computing, and a ground station as a terrestrial cloud computing platform. For terrestrial user terminals, each terminal can execute an application and generate a series of homogeneous service requests. So, the task \(i\) of the user terminal can present as:  
\[Task_{i}=\{c_{i},d_{i},P_{i}\} \tag{1}\]  
where \(c_{i}\) represents the number of computing resources required to accomplish the task; for example, \(c_{i}\) can be quantified by the number of CPU cycles. The variable \(d_{i}\) denotes the size of the computation input file describing some information of the task, such as the program codes or the corresponding data, and \(P_{i}\) represents the probability of the task being offloaded to the cloud. We assume that each satellite has the same computing power \(CP_{i}\) (for example CPU cycles/s). Therefore, the time required for the satellite to process the task \(i\) is \(T_{SC}\):  
\[T_{SC}=\frac{c_{i}}{CP_{i}} \tag{2}\]  
It is assumed that the resources of the terrestrial cloud computing platform are large enough, so the computation time for the task is negligible.  
For the energy consumption of satellites, it is proportional to the square of the frequency of the CPU [27; 28]. Thus, we have:  
\[E_{i,computation}=\kappa CP_{i}^{2}c_{i} \tag{3}\]  
where \(\kappa\) is the effective switched capacitance, which depends on the chip architecture [29].
# Bounce: A High Performance Satellite-Based Blockchain System
## 3 Bounce Protocol
### The Case for Satellites  
Now that we understand the protocol and its timing, let us take a step back to revisit the possible benefits of satellites over protected terrestrial data centers.  
* Satellites support broadcast. A satellite can send to many places on the Earth at once. At altitude 550 km, and elevation angle 40 degrees, a single Low Earth Orbit (LEO) satellite can cover an area of 1,000,000 km\({}^{2}\), bigger than the country of France [48]. This makes satellite communications more jam-resistant than a data center. Jam resistance can be further enhanced by having each satellite send information to other satellites.
* As anyone who has kept up with the hacking literature knows, proximity leads to compromise. Side channel power attacks for example are known to compromise keys, when the attacker is within a few meters of the device. The remoteness of satellites makes such attacks impossible.
* Finally, gaining physical possession of a 10 cm cube in space would be both challenging (even for state actors) and extremely obvious. Even if that were possible, tamper-resistance (i.e., self-destruction in case of tampering) is easy to build in.