esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Purification of APs from MPTH  
#### 3.2.1 Fractionation of MPTH  
Using MWCO membranes of 3 and 5 kDa, MPTH was divided into three fractions including MPTH-I (MW \(<\) 3 kDa), MPTH-II (3 kDa \(<\) MW \(<\) 5 kDa), and MPTH-III (MW \(>\) 5 kDa). At the concentration of 5.0 mg protein/mL, DPPH- and HO- scavenging activities of MPTH-I was 65.52% \(\pm\) 3.86% and 58.78% \(\pm\) 3.05%, respectively, which were significantly higher than those of MPTH, MPTH-II, and MPTH-III (\(p\) \(<\) 0.05) (Figure 1). Previous literature indicated that the antioxidant activities of protein hydrolysates were negatively related to their average MW distribution [5,30,31]. In the report, peptides in MPTH-I fraction with small MW revealed strong radical scavenging activity, which agreed with those previous reports. Therefore, MPTH-I was selected for the subsequent chromatographic separation.
## 2 Theoretical Background
### Measurement Functions  
To characterize the frequency content of the vibration signal in both regimes, namely, transient and steady states, it is divided into two parts, one for each regimen. For their characterization, two indicators, i.e., energy (\(ENE\)) and total harmonic distortion (\(THD\)), have been selected. The \(ENE\) index is used for the transient state because it can serve as a representative measure of the time-varying amplitudes of each frequency component in their time-frequency representation. On the other hand, the \(THD\) index is used for the steady state because it can represent how much the harmonic content can change relative to the fundamental frequency component. It is important to note that, in both cases, it is expected that the frequency content changes in the presence of a SCT condition. The proposed indices are calculated as follows [33]:  
\[THD =\frac{E_{Harmonics}}{E_{f_{0}}} \tag{6}\] \[ENE =\sum_{f=0}^{f_{s}/2}[X|f|]^{2} \tag{7}\]  
where \(E_{Harmonics}\) is the energy related to the harmonic components, \(E_{f_{0}}\) is the energy related to the main frequency, \(X[f]\) is the signal in the frequency domain, and \(f\)s is the sampling frequency.
# Tracking Sensor Location by Video Analysis in Double-Shell Tank Inspections
## 2 Materials and Methods
### Tracking Sensor Location by Video Analysis
Tracking sensor location by offline video analysis means that the movement of the sensor is under external control. We acquire video while the sensor is moving and analyze the video after the sensor has stopped to predict how far it has moved. In the laboratory, we can measure how far the sensor moved to test the accuracy of predictions by offline video analysis. Tracking sensor location by real-time video analysis means that we both acquire and analyze video while the sensor is moving and use the results for motor control. An operator starts the movement of the sensor and expects the real-time video analysis to stop the movement after the sensor has gone a prescribed distance, such as the distance between data collection sites. In the laboratory, we can measure how far the sensor moved and compare it to the results of real-time video analysis, but we are more interested in  
Figure 6: Application of Python algorithms SIFT and FLAMM to match a target in frame 4100 with the same target in frame 4117. The red line connects the target as seen in the 2 frames. the comparison of the actual distance moved to the prescribed distance to discover the trustworthiness of real-time video analysis for moving the sensor a prescribed distance. At the velocity of sensor movement that is typical of our laboratory experiments, video acquired at 6-10 fps with a frame buffer size of 1 allowed us to grab the newest frame and analyze it for how far the sensor had moved since the last grabbed frame. Typically, 10-20 matches on both upper edges of the air slot channel are selected to estimate incremental movement in the range of 10-15 mm. When the accumulation of incremental movements approaches the chosen distance between sites of data acquisition, usually 6 inches, the computer sends a signal to stop the motors on the crawlers. A ground-truth measurement of actual distance traveled is taken before the user restarts crawler movement.
## 2 Materials and Methods  
### Discretized Network Model  
In this study, we used a gene-wise discretized network model as presented in the previous study [43]. The discretized network can be modeled as a directed graph \(G(V,A)\), where the vertex set \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) corresponds to \(N\) genes, and edges \(A\subseteq V\times V\) encode regulatory interactions between them. The state of a gene \(v\) at time \(t\), denoted as \(v(t)\), assumes one of \(l\) discrete values from the set \(\{0,1,2,\ldots,l-1\}\). When \(l=2\) for all genes in \(V\), the network is referred to as a Boolean network [44]. Suppose that \(v\in V\) denotes a target node whose dynamics are governed by the interaction with \(k\) regulatory genes \(u_{1},u_{2},\ldots,u_{k}\), where \(u_{i}\in V\). Let \(E\) and \(E_{i}\) denote the sets of possible discrete expression values for the gene \(v\) and the regulatory genes \(u_{i}\), respectively. The state of \(v\) at the next time step, \(v(t+1)\), is determined by a discrete function \(f:E_{1}\times E_{2}\times\cdots\times E_{k}\to E\), which maps the expression values of the \(k\) regulatory genes at time \(t\) to an updated state. Consequently, the update rule for \(v\) can be expressed as follows:  
\[v(t+1)=f(u_{1}(t),u_{2}(t),...,u_{k}(t)). \tag{1}\]  
In this study, we adopted an update time lag of one. The total number of possible functions \(f\) is given by \(l^{\Gamma_{1}l}\), where \(l=|E|\) and \(l_{i}=|E_{i}|\) denote the cardinalities of the sets \(E\) and \(E_{i}\), respectively.
## 3 Methodology
### A1: Feature Extraction in Video Stream  
**Step 1: Hardware installation**  
In Step 1, a general-purpose computer and a USB fixed-point camera are installed to acquire a video stream of a hospital outpatient examination room. The viewing angle of the USB fixed-point camera can be either flat-angle or wide-angle, but its location should be where it can capture the doctor's and patient's faces.  
**Step 2: Acquisition of the video stream**  
Step 2 is to display the video stream from the USB fixed-point camera on a Web browser using a Web browser on a general-purpose computer. Because it is necessary to permit the camera connected to the computer to be accessed from the Web browser, the getUserMedia method of the Web API's navigator.mediaDevices object is used for access.  
**Step 3: Introduce bone sensing**  
In Step 3, a bone sensing PoseNet model driven by TensorFlowjs is used to obtain the 2D coordinates of the bone keypoints of 17 human body parts in real-time using a Web browser (see Figure 3). The PoseNet model detects a single bone feature or multiple bone features from a person in a video image.  
Figure 3: Overview of image-based bone sensing using PoseNet models on edge.  
Figure 2: Technical architecture in the proposed method.
## 3 Results and Discussion
### The Fourier Transform Infrared (FTIR) Test
#### 3.2.4 Sulfur Compounds  
The SO\({}_{2}\) and SO groups of sulfur compounds for any organic material produce strong infrared bands in the range of 1400-1000 cm\({}^{-1}\)[30]. In most of the studies regarding bitumen ageing, the sulfoxide band at wave number 1027 cm\({}^{-1}\) was given special attention. It is well known that sulfur is an element that can be found in bitumen in the range of 0-6% [1]. Most of the studies confirmed that, during ageing, sulfoxides are formed due to an oxidation reaction within the molecules and lead to an increase in polarity [1; 4; 31; 32; 40]. After ageing, they are found in the resins or asphaltenes [31].  
Based on the results presented in Figure 7a, sulfoxides are originally found in all modified bitumen. The S=O stretching shows stretching bands at 1060-1015 cm\({}^{-1}\), but its absorbance peak may differ from the modified bitumen due to the addition of NC particles and ageing.  
As can be seen in Figure 7b, the recorded absorbance at wave number 1027 cm\({}^{-1}\) is similar for all modified binders before ageing. No changes are recorded due to adding NC particles. The absorbance values at wave number 1027 cm\({}^{-1}\) are 0.009, 0.008 and 0.009 for 0 NCMB, 2 NCMB and 4 NCMB, respectively. The same observation has been noted after applying STA, where there are no clear changes for the absorbance of the binders. LTA showed a different trend. An increase was recorded for the sulfoxide band for all the binders. This increase in the peak of the band is an indicator of an increase in the concentration of the sulfoxide. However, as in previous ageing stages, no large changes were recorded for the absorbance values between the binders. For all the binders tested, there was a small difference between unaged and STA with regard to the formation of the sulfoxide band, while LTA had a clear effect.
# A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning
## 5 Application of Mobile Robot Path Planning Based on FMMPA
### Path Planning Length Functions and Conditions  
In practical path planning application problems, setting every predator is a solution. The set of position updates of each predator represents a path of the robot, finding an optimal path from the many path sets that meet the constraints. Since the model built by the grid method will discretize the space, to facilitate the analysis and rationalize the path planning, this paper requires the relevant constraints as follows:  
1. Environmental boundary and obstacle constraints. The node \(hi\) of the planned path is not on the grid where the obstacle is located, and the connection between the adjacent nodes \(h_{i}\) and \(h_{i+1}\) cannot cross the obstacle grid [35]. The motion path must be within the grid space and cannot exceed the grid boundary.
2. The path is not circuitous. The movement route cannot be circuitous. If the grid coordinates of the current position \(h_{i}\) are \((x_{i},y_{i})\), the next position node should satisfy \(x_{i+1}>x_{i}\) or \(y_{i+1}>y_{i}\).
3. The target path is the shortest. Among the many grid locations that satisfy the above two constraints, row \(j\) (\(j\) = 1,2,... \(N_{y}\)) should take the abscissa minimum \(I\) (\(1<I<N_{x}\)) as the path node to achieve the minimum path minimum condition.  
The mathematical model of the path planning length according to the above modelling requirements can be expressed as:  
\[H(path)=\sum_{i=0}^{n-1}d(h_{i},h_{i+1}) \tag{23}\]  
Figure 6: Grid map construction process.  
where \(d\) (\(h_{i}\), \(h_{i+1}\)) is expressed as the distance between positions \(h_{i}\) and \(h_{i+1}\), so the length function of path planning in the global coordinate system can be expressed as:  
\[\left\{\begin{array}{l}H(path)=\sum\limits_{i=0}^{n-1}\sqrt{\left(x_{i+1}-x_{ i}\right)^{2}+d^{2}}\\ d=\left(y_{i+1}-y_{i}\right)\end{array}\right. \tag{24}\]
## 4 Conclusions  
1. The united approach for modelling the hot deformation behavior, fracture, and microstructure evolution was developed and approved for the AISI 316 Ti steel.
2. The constructed strain compensated constitutive model of hot deformation behavior for the AISI 316 Ti steel in the temperature range of 1173-1473 K and at the strain rate of 0.1, 1, and 10 s\({}^{-1}\) showed a high accuracy with an average absolute relative error of 4.38%.
3. The modified Rice and Tracy fracture model was realized to predict the investigated steel's fracture behavior. It was shown that the critical value of the fracture criteria is dependent on the Zener-Hollomon parameter by a power law.
4. The model of the AISI 316 Ti steel microstructure evolution during dynamic recrystallization was constructed. The comparison of the effective activation energies' values shows that the limiting factor of the dynamic recrystallization proceeding is the growth of the new grains in the deformed matrix.  
Conceptualization, A.Y.C.; methodology, A.Y.C.; investigation, S.V.M., O.I.M., A.A.K.; writing--original draft preparation, T.A.C., A.Y.C.; writing--review and editing, A.Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation (project N18-79-10153).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 5 System Model
### Forecast Horizon  
The EGAT system in Thailand forecasts conditions for 2 pm on the next day, along with 10 to 34 h in advance. Since the EGAT office is not open on weekends, EGAT must forecast up to Monday from Friday. Thailand usually uses data up to 106 h ahead for short-term forecasting, particularly during extended holidays. However, the scope of this study is restricted to forecasting for the following day utilizing data up to 2 pm. Simplifying the model, Figure 11 displays the forecast horizon and lag terms specific to Thailand's practice. To predict demand on a given day, forecasting begins 10 h in advance, at 2 pm. The variable load1d_cut2 pm now represents the historical demand data from HH = 0 to HH = 28 of d\(-\)1 (the preceding day) and HH = 29 to HH = 47 of d-2. Likewise, our model incorporates the lagged demand for the next two days, which is denoted by a parameter called load2d_cut2 pm.  
Adding dummy variables and their relationships has been recommended by earlier research to increase forecasting accuracy. [PERSON] et al. (2019) suggested the usage of four types of dummy variables--deterministic, temperature, load, and past errors--and their interactions. The term \"deterministic\" describes predictable variables, like the day of the week, the month, and the year (2019). The variables were grouped into two categories: deterministic and lagged, as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Variables** & **Description** \\ \hline \multirow{6}{*}{Deterministic} & WD & Weekday dummy (Mon, Tue, \(\dots\), Sat, Sun) \\  & MD & Month dummy (Jan, Feb, \(\dots\), Nov, Dec) \\  & Holiday & Binary 0 or 1 \\  & Day After Holiday & Binary 0 or 1 \\  & Day After long Holiday & Binary 0 or 1 \\  & Holiday Type & Type of holiday (Songkran, Newyear, \(\dots\), etc.) \\ \hline \multirow{6}{*}{Lagged} & Load1d\_cut2 pm & 1 day ahead until 2 pm and 2 days ahead after 2 pm load \\  & Load2d\_cut2 pm & 2 days ahead until 2 pm and 3 days ahead after 2 pm load \\  & Load3d\_cut2 pmR & 3 days ahead until 2 pm and 4 days ahead after 2 pm load \\  & Load4d\_cut2 pmR & 4 days ahead until 2 pm and 5 days ahead after 2 pm load \\ \hline \multirow{6}{*}{Temperature} & Temp & Forceasted temperature \\  & MaxTemp & Maximum forecasted temperature \\  & Square temperature & Square of the forecasted temperature \\  & MA2 pmTemp & Moving average of temperature at 2 pm \\ \hline \multirow{2}{*}{Interaction} & WD: Temp & Interaction of weekday dummy to temperature \\  & MD: Temp & Interaction of month dummy to temperature \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of selected input variables (2019).  
Figure 11: Demand forecast horizon: the practice of EGAT in Thailand (2019).
## 3 System Validation Methods
### Audio Fingerprint Matching
#### 3.2.1 Chromaprint Process  
Chromaprint converts the audio input to mono and downsampled to 11,025 Hz. The audio signal is converted to the frequency domain by performing a short-time Fourier Transform (STFT) with a frame size of 4096 samples (371 ms) and a 2/3 overlap (2731 samples). The resulting spectrum is converted to 12 bins representing the chroma of the signal. This information is called \"chroma features\". Each bin in the chromagram represents the energy that is present in a musical note. The 12 bins represent the 12 notes of the chromatic scale. In order to transform the bins in a more compact form to carry out the fingerprint matching, a 12-by-16 sliding window is moved over the chromagram one sample at a time. On each of them is applied a pre-defined set of 16 filters that capture intensity differences across musical notes and time. Each of the filters quantizes the energy value to a 2-bit number. The 2-bit value is encoded using Gray coding. The 2-bit hash values from each of the 16 filters are converted to a single 32-bit integer representing the subfingerprint of the 12-by-16 window. The window is advanced one sample to calculate the next subfingerprint. The full fingerprint is composed by the all subfinngerprints.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Total (\(n\) = 176)**} & \multicolumn{6}{c}{**4 \(\times\) 4 Pixels**} & \multicolumn{6}{c}{**2 \(\times\) 2 Pixels**} \\ \cline{2-10}  & **Max (\({}^{\circ}\)C)** & **RMSE** & **QRMSE** & **Mean (\({}^{\circ}\)C)** & **RMSE** & **QRMSE** & **Mean (\({}^{\circ}\)C)** & **RMSE** & **QRMSE** \\ \hline \multirow{3}{*}{Left} & LS & 35.77 \(\pm\) 0.85 & 0.9781 & 0.5379 & 35.59 \(\pm\) 0.85 & 0.9809 & 0.5417 & 35.66 \(\pm\) 0.83 & 0.9522 & 0.5165 \\  & MC & 35.77 \(\pm\) 0.83 & 0.9646 & 0.4948 & 35.59 \(\pm\) 0.87 & 1.0061 & 0.5398 & 35.66 \(\pm\) 0.77 & 0.8939 & 0.4584 \\  & LC & 35.77 \(\pm\) 0.86 & 1.0198 & 0.5369 & 35.59 \(\pm\) 0.86 & 1.0103 & 0.5370 & 35.66 \(\pm\) 0.85 & 0.9881 & 0.5292 \\ \hline \multirow{3}{*}{Right} & LS & 35.80 \(\pm\) 0.84 & 0.9664 & 0.5116 & 35.62 \(\pm\) 0.82 & 0.9543 & 0.5062 & 35.68 \(\pm\) 0.82 & 0.9574 & 0.5086 \\  & MC & 35.80 \(\pm\) 0.82 & 0.9151 & 0.5033 & 35.62 \(\pm\) 0.81 & 0.9823 & 0.4902 & 35.68 \(\pm\) 0.79 & 0.8664 & 0.4815 \\ \cline{1-1}  & LC & 35.80 \(\pm\) 0.93 & 1.0412 & 0.6014 & 35.62 \(\pm\) 0.89 & 1.0134 & 0.5717 & 35.68 \(\pm\) 0.92 & 1.0398 & 0.5985 \\ \hline \hline \end{tabular} ROI, region of interest; LS, lacimal sac; MC, medial canthus; LS, lateral canthus.  
\end{table} 
Table 4: Maximum, mean temperature, root mean square error (RMSE), and quantile RMSE (QRMSE) results of three ROIs in the horse’s eyes for each rectal temperature (RT, \({}^{\circ}\)C) after statistical bias correction. For animals, maintaining the homeostasis of body temperature can be important because it is an indicator of health. The most suitable for ET measurement is medial canthus, and use of the average temperature of the smallest ROIs (2 \(\times\) 2 pixels) is recommended for temperature analysis. Moreover, it is suggested that the measurement distance is maintained at 30-50 cm and 90\({}^{\circ}\) to the sagittal plane. This study offers a validated protocol for using IRT to measure stress and welfare. Regarding animal welfare, it is observed that it is sufficient to use ET to verify whether ET can be used as an indicator of health. Further studies are warranted to evaluate the utility of the measured ET in assessing stress levels and pain states in horses.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 2 Method of Gaze Guided System  
The intention of the system's architecture is to achieve an influential integration of eye tracking technology with practical GGC system. The overall concept of gaze guided robot control system can be demonstrated in Figure 1. In this structure, it is illustrated how different physical elements can be attached and communicate. The system architecture is divided into three parts. The first part is (user side) the user inferences, which are related to the eye tracking subsystem and the eye movement translation into robotic platform commands (1), the second part (host-IT2 FIS) is data processing and command execution (2), the third part (robot side) is a robot moving environment under the visibility of an overhead camera. This framework includes four parts: an eye tracking system which can track user's eyes, an overhead camera system that supplies the video feedback to the user, a wheeled mobile robot and the host computer system which is accountable for collecting the gaze data and commentate it into robot motion commands. The mobile robot used in this platform has an ARM9-based processor at 300 MHz Linux-based operating system. Detailed properties can be viewedin [50]. A well-developed programming interface based LabVIEW programming language allows us to transmit movement information, depending on the direction of the gaze, to the robotic platform via Bluetooth. The image acquisition camera [51] is able to provide live video with a resolution of \(480\times 640\) at 30 fps. The overhead camera height from the floor is approximation 2 m. The system software principally consists of two parts: the vision detection (eye tracking, robot tracking) and motion control algorithm. Sub-titles and details of the system are given below.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Discussion  
This section compares SL_GAS with the related schemes (SGMS, SEIP, and CGM6) to highlight the advantages of SL_GAS concerning security, signaling costs, computational overhead, and transmission delay.
## 3 Modeling the Relationship between Field AGB and Satellite Data
### Evaluation of ABG Models  
Based on measured AGB samples, the mean error (ME), mean absolute error (MAE), and RMSE as defined by [PERSON] and [PERSON] (1989), with \(r\) between the measured and estimated ABG, were used to evaluate the performances of different interpolation methods [55; 96].
# A Machine Learning Method for Modeling Wind Farm Fatigue Load
## 4 Case Study
### Parameters  
Wind farm simulation involves many parameters, including wind precursor, wind turbine and farm layout, and simulation control parameters.  
1. Wind precursor: A precursor is the wind data used in a simulation to generate ambient wind. There are nine wind precursors with different wind speeds and turbulence intensities generated by TurbSim [28]. The average wind speed (hub level), wind direction, and turbulence intensity of precursors are shown in Table 1. A wind precursor case is shown in Figure 5. The U, V, and W components of the wind speed represent the horizontal, cross, and vertical directions of the wind speed in space, respectively.
2. Turbine and layout parameters: The NREL 5 MW wind turbine is used, with a diameter of 126 m and a nacelle height of 90 m [29]. The wind farm contains two or three wind turbines, which are placed on a straight line, with a distance of five times the diameter of the wind turbine. Figure 6 shows the velocity of FAST.Farm simulation at 1900 s under precursor P7 and yaw offset (\(\theta_{1}=5\) and \(\theta_{2}=0\)).
3. Yaw angle control strategy: The control strategy mainly focuses on yaw-based control. Each simulation case runs at fixed yaw angles for 2000 s. The last 500 s of data are used to calculate DELs. The range of yaw angles command in two turbine cases is shown in Table 2. For example, in the control mode of Y1, the upstream wind turbine WT1 of each case will move to a fixed angle (\(\theta_{1}\)), and the downstream wind turbine will maintain an angle of 0 (\(\theta_{2}=0\)). Under Y2, the last turbine keeps zero yaw angles. Moreover, the load types include blade root edgewise moment (BREM) and blade root flapwise moment (BRFM), and their damage equivalent load values are DEL-BREM and DEL-BRFM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Precursor** & **Wind Speed (m/s)** & **TI (\%)** &
\begin{tabular}{c} **Wind Direction** \\ **(Degree)** \\ \end{tabular} \\ \hline P1 & 5 & 5 & 0 \\ P2 & 5 & 10 & 0 \\ P3 & 5 & 15 & 0 \\ P4 & 7 & 5 & 0 \\ P5 & 7 & 10 & 0 \\ P6 & 7 & 15 & 0 \\ P7 & 9 & 5 & 0 \\ P8 & 9 & 10 & 0 \\ P9 & 9 & 15 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of wind precursor.