esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Traditional Controllers: PID and MPC Closed-Loop Control Strategies  
In the time domain, the general form of the PID control law in a closed-loop control strategy is given by the following input-output equation:  
\[u_{PID}(t)=k_{P}\varepsilon(t)+k_{I}\int\varepsilon(t)dt+k_{D}\frac{d \varepsilon(t)}{dt} \tag{8}\]  
where \(\varepsilon(t)\) represents the error between the actual measurement of system output \(y(t)\) and its desired value \(ysp(t)\), also called the setpoint, reference, or tracking value, as follows:  
\[\varepsilon(t)=y(t)-ysp(t) \tag{9}\]  
where \(u_{PID}(t)\) refers to the controller output.  
\(k_{P},k_{I}=\frac{1}{T_{I}},\;k_{D}=T_{D}\) represent the PID controller parameters, \(k_{P}\) is the proportional gain, \(k_{I}\) refers to the integral gain, \(k_{D}\) denotes the derivative gain, and \(T_{I}\) and \(T_{D}\) are the time constants for the integral and derivative blocks, respectively. For a particular parameter setting, different controller architectures (e.g., P, I, PI, PD, and PID) may be obtained.  
Figure 4: Open-loop linear simulation results for the MIMO ARMAX CCS state-space model.  
In the complex domain, the transfer function of an ideal PID controller is given bellow:  
\[H(s)=\frac{U_{PID}(s)}{\varepsilon(s)}=k_{P}+\frac{k_{I}}{s}+k_{D}s \tag{10}\]  
Furthermore, adding a compensator formula to Equation (10) using a low pass filter (LPF) of coefficient N can serve as a practical PID controller via the following transfer function:  
\[H(s)=\frac{U_{PID}(s)}{\varepsilon(s)}=k_{P}+\frac{k_{I}}{s}+\frac{k_{D}N}{1+ \frac{N}{s}} \tag{11}\]
# Data-Driven Analyses of Low Salinity Waterflooding in Carbonates
###### Abstract  
Low salinity water (LSW) injection is a promising Enhanced Oil Recovery (EOR) technique that has the potential to improve oil recovery and has been studied by many researchers. LSW flooding in carbonates has been widely evaluated by coreflooding tests in prior studies. A closer look at the literature on LSW in carbonates indicates a number of gaps and shortcomings. It is difficult to understand the exact relationship between different controlling parameters and the LSW effect in carbonates. The active mechanisms involved in oil recovery improvement are still uncertain and more analyses are required. To predict LSW performance and study the mechanisms of oil displacement, data collected from available experimental studies on LSW injection in carbonates were analyzed using data analysis approaches. We used linear regression to study the linear relationships between single parameters and the incremental recovery factor (RF). Correlations between rock, oil, and brine properties and tertiary RF were weak and negligible. Subsequently, we analyzed the effect of oil/brine parameters on LSW performance using multivariable linear regression. Relatively strong linear correlations were found for a combination of oil/brine parameters and RF. We also studied the nonlinear relationships between parameters by applying machine learning (ML) nonlinear models, such as artificial neural network (ANN), support vector machine (SVM), and decision tree (DT). These models showed better data fitting results compared to linear regression. Among the applied ML models, DT provided the best correlation for oil/brine parameters, as ANN and SVM overfitted the testing data. Finally, different mechanisms involved in the LSW effect were analyzed based on the changes in the effluent PDIs concentration, interfacial tension, pH, zeta potential, and pressure drop.  
low salinity waterflooding; carbonates; data-driven analysis; machine learning; SVM; ANN; DT +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Signals Involved in the System
#### 3.1.1 The Speech Signal  
The speech signal has most of its energy concentrated at frequencies lower than 8 kHz, as it results from [21] where a study about the bandwidth of the speech signal was conducted, for multiple languages, based on the long-term average spectra. High quality speech applications consider the bandwidth of speech to be 7 kHz according to the ITU-T recommendation shown in [22]. The insertion of the watermark in the recordings is made by making the speech and the watermark to coexist in the room. Because the watermark should be easily extracted from the recording at a later moment, it should not overlap the bandwidth of the speech signal. In this way, the SNR (Signal to Noise Ratio) is improved, and the extraction of the watermark is facilitated (i.e., in this situation, when computing the SNR, it was considered that the watermark plays the role of the signal and the speech signal is the noise). The situation was similar in the classical ENF criterion situation where the power grid signal that was picked-up had very low frequencies, below the inferior limit of the speech bandwidth, thus the bandwidth overlap was naturally avoided.
# Independent System Calibration of Sentinel-1B
## 6 In-Flight Antenna Model Verification
### Verification in Azimuth  
The azimuth pattern verification is performed using transponders (receive unit) by measuring the one-way transmit patterns during a satellite overpass (based on the same measurement procedure already described in Section 5.1 for detecting azimuth notch patterns). According to the in-flight verification approach [19], three StripMap swaths have been selected for this azimuth pattern verification to cover both near- (SM1, SM2) and far-range (S5) azimuth patterns. For each recorded data take, the pattern measured by the transponder is shifted and normalized such that the main beam maximum coincides with the one of the corresponding reference pattern derived by the antenna model. Then, for each of the three transponders that acquired the respective signal during a certain overpass, the difference between modeled and measured transmit pattern is calculated. In case more than one transponder recorded the satellite's transmit pattern, the final result is an average of all respective differences.  
The results are shown in Figure 18 (detailed view of one exemplary S2 data take). The figure depicts all measurements successfully acquired during this satellite pass. The top graph shows the measured patterns (in blue, yellow and red) as well as the calculated reference patterns (solid black line). At the bottom of this figure, the difference between the reference pattern and the measured patterns is shown in the same respective colors. Meanwhile, the averaged difference is shown in black.  
The results for all measured StripMap beams are summarized in Table 5. For cases in which all transponders were covered, the deviation between measured modeled transmit patterns is less than 0.035 dB (1\(\sigma\)) within the main beam.  
Figure 18: Azimuth pattern measurement for SM2 on 29 June 2016, shifted to coincide at the maximum of the main beam. The top plot shows the sidelobes as a function of azimuth angles. The bottom plot shows a zoom of the measurement/model difference over the main lobe area and the deviation between the averaged measured patterns; the calculated reference is shown in black.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 4 Challenges of Smart Grid Communication
### Use of encryption  
The security of the power grid is depends on authentication, authorization. Encryption of communication flowing between devices in the grid and data centers is crucial to reduce attackers ability to gain access to data or achieve system control. Depending on communication technology, different solutions are preferred, such as Advanced Encryption Standard (AES) and Triple Data Encryption (TDES) [112]. Encryption ensures identification and authorization.  
Authentication is the process of verifying the identity of a user, application, or device. Authorization in the process of verifying whether the user, application or device has permission or the rights to access the system, or perform an operation. Authentication, authorization, and access control is necessary in SGs due to the vast amounts of connected devices. Different users with different roles and level have access to control systems, sensors communication networks in the Smart Grid. Entities in the SG must be bidirectionally authenticated. Common types of authentication schemes in Smart Grids are device-to-device, device-to-network, and user-to-network [138].
## 3 Results and Discussion
### Geographical Distribution of DDREs
Yet, surface and atmospheric DREs as large as \(-215\) and 139 W/m\({}^{2}\)respectively, similar to ours, have been estimated in [53] using a similar version of the FORTH RTM, used in this study, at Crete island in the eastern Mediterranean. At the TOA, both positive (planetary warming, reddish colors) and negative (planetary cooling, bluish colors) DDREs are observed (left column of Figure 4). Specifically, negative values (as large as \(-40\) W/m\({}^{2}\)) are noted over the Mediterranean Sea and most of the European and North African land areas, while positive values (up to \(70\) W/m\({}^{2}\)) occur over areas mainly of N. Africa, which are covered by high dust loadings (see left column of Figure 2). The sign change of the dust DRE at the TOA is attributed to the presence of absorbing dust aerosols overlying high-surface-albedo desert areas [51; 55; 65; 78]. In Figure S11, the geographical distributions of the percent relative perturbation of the regional SW radiation budget, namely of the absorbed solar radiation at the TOA, in the atmosphere and at the surface, by dust aerosols during the DAEC are presented (Figure S11a-c). Significant modifications of the SW fluxes are observed, reaching 35% (decrease) and 45% (increase) at the MB's surface and in the atmosphere, respectively, especially over areas overloaded with the heaviest dust loadings (DODs). The radiative perturbations at the surface (cooling) and in the atmosphere (warming) are counterbalanced, leading to smaller perturbations (up to about 5%, either cooling or warming) at the TOA. The geographical features of the relative percent DDREs (Figure S11), including the gradual reduction in the tilt of the axis along which the dust export takes place over the MB, and its slow eastward shift, are identical to those of the corresponding absolute DDREs (Figure 4). The existence of maximum values, in both distributions, over areas with the largest dust loadings proves again that the dust optical properties, and in particular the optical depth, are the major drivers for DDREs. Figure 3: Geographical distribution of the absorbed solar radiation (in \(N\)/m\({}^{2}\)) at the top of the atmosphere (left column, (l)), in the atmosphere (middle column, (ii)), and at the Earth’s surface (right column, (iii)) for the first (first row, (a)), second (second row, (b)), and third (third row, (c)) day (at 1200 UTC) of the dust episode that took place during 16–18 June 2016 over the Mediterranean Basin. Figure 4: Geographical distribution of the dust direct radiative effect (in W/m\({}^{2}\)) on the absorbed solar radiation: at the top of the atmosphere (left column, (ii)), in the atmosphere (middle column, (iii)), and at the Earth’s surface (right column, (iii)) for the first (first row, (a)), second (second row, (b)), and third (third row, (c)) day (at 12:00 UTC) of the dust episode that took place during 16–18 June 2016 over the Mediterranean Basin.
## 3 Gear Ratio Optimization Problem
### Feasible Regions of Gear Ratio  
The gear ratio should satisfy the basic dynamic performance requirements of the vehicle and mechanical constraints of key components; the parameters of the vehicle and components involved in this paper are listed in Table 2. The basic dynamic performance of an EV generally refers to the maximum vehicle speed and maximum ascendable grade of the vehicle.  
According to the longitudinal vehicle dynamics [24], the range of the first gear ratio is determined by the maximum ascendable grade and the tire adhesion limit:  
\[\frac{rmg\left(f\cos\alpha_{max}+\sin\alpha_{max}\right)}{T_{M,max}\eta_{tot} }\leq i_{0}i_{g1}\leq\frac{rF_{z}\varphi}{T_{M,max}\eta_{tot}}, \tag{2}\]  
where \(g\) is the gravity acceleration, \(F_{z}\) is the support force for the ground to the driving wheel and is related to the axle load distribution of the vehicle, all other parameters are defined in Table 2.  
Similarly, the range of the second gear ratio is determined by the vehicle top speed and maximum motor speed, namely,  
\[\frac{rmgf+r\frac{C_{0}Av_{max}^{2}}{2L15}}{T_{M,max}\eta_{tot}}\leq i_{0}i_{g2 }\leq\frac{0.377 rn_{M,max}}{v_{max}}. \tag{3}\]  
In addition, given the space limitations of the two-speed transmission and the assembly relationship and adjacency condition of the planetary gear sets, the characteristic parameter of the planetary set should be limited to:  
\[\frac{4}{3}\leq k_{1},k_{2}\leq 4. \tag{4}\]  
In conjunction with the gear ratio expressions in Table 1, combining Equation (4) with Equations (2) and (3), then the ultimate feasible region of the gear ratios are as follows:  
\[\begin{split} 9.13\leq i_{0}i_{g1}\leq 12.29\\ 5.81\leq i_{0}i_{g2}\leq 7.19.\end{split} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Quantity (Unit)** \\ \hline \(m\) & Vehicle mass & 1865 kg \\ \(r\) & Wheel radius & 0.35 m \\ \(f\) & Coefficient of rolling resistance & 0.011 \\ \(C_{D}\) & Coefficient of aerodynamic drag & 0.24 \\ \(A\) & Frontal area & 2.34 m\({}^{2}\) \\ \(\eta_{tot}\) & Overall power-train efficiency & 0.96 \\ \(i_{0}\) & Final drive ratio & 3.91 \\ \(v_{max}\) & Maximum vehicle speed & 220 km/h \\ \(\alpha_{max}\) & Maximum ascendable grade & 35\% \\ \(\varphi\) & Adhesion coefficient & 0.75 \\ \(n_{M,max}\) & Maximum motor speed & 12,000 rpm \\ \(T_{M,max}\) & Maximum motor torque & 290 Nm \\ \(Q_{C}\) & Battery rated capacity (cell) & 50 Ah \\ \(N_{batt}\) & Number of the cell & 384 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vehicle and component parameters.
# Ultra-High-Cycle Fatigue Life Prediction of Metallic Materials Based on Machine Learning
## 1 Introduction
In the present study, an ultra-high-cycle fatigue life database containing ten types of metals was constructed, and subsequently fatigue life prediction models were built based on the gradient boosting (GB) and random forest (RF) algorithms. The mean square error and the neural network decision coefficient were used to evaluate the performance of the two models, and their advantages and application scenarios were also discussed. Finally, the generalization abilities of the models were evaluated by predicting the ultra-high-cycle fatigue life of GCr15-bearing steel. The models constructed in this work required simple input parameters and manifested excellent fatigue life prediction ability.
# Adaptive Kinetic Scrolling: Kinetic Scrolling for Large Datasets on Mobile Devices
## 4 Experiment
### User Study Application  
Each test participant was required to use the scrolling method on a mobile device to find a specific name on a list. Figure 4 shows a screen capture of the experimental application. This application was implemented to provide a list of names when scrolling. Each name on the list was generated randomly to prevent a learning effect, and the target name that the users were requested to search for was displayed at the top of the screen (shown as A in Figure 4). The items in the list ((B) in Figure 4) were organized in alphabetical order, and the users were requested to click on the target name to complete the task. This GUI was designed with reference to the contact application of the mobile phone and rendered in a straightforward manner for the users' instinctive use. Moreover, as it is not a familiar dataset that users generally use, we placed the target data explicitly at the top (A) (see Figure 4) so as to enable remembering it during the experiment. For this experiment, we conducted a pilot study to determine the triggering threshold (clutch time). The pilot study is described in Section 4.5.  
Figure 4: Screen capture of experimental application. In the experiment, the items on the list were written in Korean; however, in this figure, they have been replaced by sample data written in English for better understanding. (A) is a target data that the users were requested to search. (B) is a scroll list filled with names which alphabetical ordered.  
Before conducting the test, the users completed two sessions using a standard scroll as a training task. We conducted training tasks without recording the results to permit the users to understand the process of the specified tasks. During the experiment, the test application used a script to record the task completion time, the number of scrolling actions required to complete the task, and the number of overshoots. And at the end of each task, the test moderator checks the collected data.  
In this experiment, we applied the following values to the constants of Equation (1) presented in previous section.  
\[v=v_{0}\times(\log_{10}(\frac{RD}{20}+10)) \tag{2}\]  
In this configuration, when the remaining data (\(RD\)) is 1000 items, the speed is amplified by approximately 1.8, and when the target distance is 500 items, the speed is amplified by approximately 1.5.
###### Abstract  
The directionality of optical signals provides an opportunity for efficient space reuse of optical links in visible light communication (VLC). Space reuse in VLC can enable multiple-access communication from multiple light emitting transmitters. Traditional VLC system design using photo-receptors requires at least one receiving photodetector element for each light emitter, thus constraining VLC to always require a light-emitter to light-receptor element pair. In this paper, we propose, design and evaluate a novel architecture for VLC that can enable multiple-access reception using a photoreceptor receiver that uses only a single photodiode. The novel design includes a liquid-crystal-display (LCD) based shutter system that can be automated to control and enable selective reception of light beams from multiple transmitters. We evaluate the feasibility of multiple access on a single photodiode from two light emitting diode (LED) transmitters and the performance of the communication link using bit-error-rate (BER) and packet-error-rate (PER) metrics. Our experiment and trace based evaluation through proof-of-concept implementation reveals the feasibility of multiple LED reception on a single photodiode. We further evaluate the system in controlled mobile settings to verify the adaptability of the receiver when the LED transmitter changes position.  
visible light communication; multiple access communication; high data rate; spatial multiplexing; mobile VLC +
Footnote †: journal: Electronics
## 3 Results and Discussion
### Modeling Analysis Based on Full Wavelength  
To determine the optimal combination of preprocessing methods and models, raw spectral data and spectral data preprocessed by SG1, MSC, and SNV were entered into PLS-DA, LSSVM, RF, and ELM discriminant models for comparison. Figure 5 displays the accuracy of the prediction set of the above full wavelengths preprocessing methods combined with PLS-DA, LSSVM, RF, and ELM discriminant models, in which RAW represents the original spectral data. The analysis yields that the SNV-processed spectral data modeling accuracy is the highest and the prediction set has higher accuracy than that of the other two preprocessing methods and the original spectral data. This result arises from the differences within the samples and other factors. The hyperspectral instrument used in this experiment may lead to light scattering and further generate noise. SNV uses the average value and standard deviation as the correction parameters, which eliminates the differences within the samples and light scattering to a large extent [28].
## 4 Architecture and Implementation
### IoT Infrastructure
Moreover, CoAP has been designed to support HTTP bridging, so external devices (outside the network) not implementing the CoAP protocol may access node resources directly if an HTTP/CoAP bridge is included in the network.
## 2 The Cryogenic Refrigerator
### Test Apparatus  
As shown in Figure 2, the schematic view of the test rig is presented. The system used air as the working fluid and run in an open cycle. The normal temperature part is divided as the air supply section and the brake section. The screw compressor can provide 260 Nm\({}^{3}\) h\({}^{-1}\) compressed air at 1.00 MPa. The cooling and filtering devices (a filter, a freezing dryer and a molecular sieve) are applied to cool down the high temperature gas and remove the water and oil in the system. The cold box has a vacuum degree of 0.01 Pa, consisting of a cryogenic plate-fin heat exchanger, a cold load, and pipes.  
Figure 1: Flow process and \(T\)-\(s\) diagram of a cryogenic reverse Brayton refrigerator: (**a**) Flow process; (**b**) \(T\)-\(s\) diagram of energy consumption; (**c**) \(T\)-\(s\) diagram of energy recovery.  
The expander was the same as the one shown in Reference [15]. It has a diameter of 24.00 mm, and was supported by foil air bearings. The expander inlet operates in the range of 0.300-0.600 MPa, and the outlet is slightly higher than environmental pressure. The design isentropic expander efficiency is 0.578. The heat exchanger is a plate-fin regenerator and the design effectiveness is 0.930. The low temperature part and the air supply section are similar to those in References [4; 15]. While, the brake section is improved and quite different from the previous one in References [4; 15]. In the present study, the brake section is an independent and complete circuit. As shown in Figure 2, a centrifugal slower is applied to make the pressure in circulation can meet the demand of tests. When the required brake pressure is high, the valve is switched and the centrifugal blower runs to raise the pressure. When the brake pressure is low, the centrifugal blower stops working. The brake gas is not supplied by the air supply section any more [4]. It is a benefit for the stability of the system and accurate calculation of energy efficiency. The blower has a diameter of 27.00 mm. Based on the NI data acquisition board and the LabVIEW program, a real-time data acquisition, calculation, and display system was developed for the effectively dynamic control of the refrigerator.  
In the experiment, the temperatures were measured by platinum resistance thermometers and the rotating speed was measured by an eddy current sensor. The measure range and accuracy of tested parameters were listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Pressure/MPa (abs.)** & **Temperature/K** & **Rotating Speed/rpm** & **Flow Rate/kg s\({}^{-1}\)** \\ \hline Range & 0–1.000 & 55.0–320.0 & 0–300,000 & 0.003–0.060 \\ Accuracy & \(\pm\)0.25\% & \(\pm\)0.1 & \(\pm\)1 & \(\pm\)1.5\% \\ \hline \end{tabular}
\end{table}
Table 1: Measure parameters.  
Figure 2: Schematic view of the turbo-based air refrigerator.