esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Novel Concrete Curing Method Using Induction Heating System
## 2 Optimization of Induction Heating System for Concrete Curing
### Finite Element Method (FEM) Analysis for Concrete Curing
This steel form was heated in accordance with  
Figure 3: Temperature distribution for different formwork materials: temperature contour of forms made with (**a**) aluminum, (**b**) stainless steel, and (**c**) steel; (**d**) temperature distribution comparison. the temperature regime, shown as a red line in Figure 4. It is noted that this temperature regime is similar to that of a typical steam curing method that is currently in use for the production of precast concrete products in Korea [17]. It is also noted that the sections of this curing regime where the temperature either increases or decreases were selected to prevent delayed ettrinoutput current is adjusted to control the temperature measured by the thermocouples attached inside the formwork. The magnetic flux created by the three-turn coil heats the surface of the formwork. It is important to obtain a uniform surface temperature to cure the concrete inside the formwork uniformly. The experiment was performed to assess the temperature distribution in a temperature equilibrium state after the formwork surface temperature had attained the maximum value. This is because temperature variation is important in the step where the maximum temperature is maintained (Step 3; see Figure 4). In the experiment, the temperature at each thermocouple was recorded after 30 min at the maximum temperature equilibrium. Figure 7a shows the temperature values obtained from each thermocouple attached to the inner surface of the formwork, over a period of 25 min. During the measurement  
Figure 5: Temperature distribution by a three-turn coil: (**a**) temperature distribution on the surface of formwork; (**b**) temperature distribution according to height. Figure 6: Experiment set-up: induction heating system and inside the metallic form with thermocouples. time, the temperature values measured by the thermocouples mostly remain constant. Thermocouples 1 and 7, which were located at the top and bottom ends of the formwork, recorded relatively low temperatures of 51-62 \({}^{\circ}\)C and 55-60 \({}^{\circ}\)C (dot lines in Figure 7a), respectively. The other thermocouples maintained 65-75 \({}^{\circ}\)C (solid lines in Figure 7a). Figure 7b shows the average temperatures measured by each thermocouple, obtained by averaging the data captured during the 25 min in Figure 7a, together with the temperature variation obtained in the FEM simulation (Figure 5b), for comparison. The temperature values measured by thermocouples 2-6 in Figure 7b range from 65 \({}^{\circ}\)C to 75 \({}^{\circ}\)C. Meanwhile, those measured by thermocouples 1 and 7, which are located at the top and bottom ends of the cylinder, are close to 55 \({}^{\circ}\)C and 57 \({}^{\circ}\)C, respectively. That is, by comparing the temperature values obtained from the tests and FEM simulations, it was verified that the test results of the induction heating of formwork were highly similar to the results of the FEM-based temperature analysis.
## 4 Discussion
It should be noted that, besides the copper depletion of the CIGS surface, an intermixing between In\({}_{\text{x}}\)S\({}_{\text{y}}\) buffer and CIGS absorber could also take place. A possible sulfur incorporation into the CIGS absorber, which we did not observe clearly in the ToF-SIMS profiles, would lead to a lowered valence band maximum and a different band alignment to In\({}_{\text{x}}\)S\({}_{\text{y}}\). Future research directions could address one or several of the following approaches: Sodium can be intentionally inserted in the In\({}_{\text{2}}\)S\({}_{\text{3}}\) target to increase the bandgap energy, similar to the sodium-containing In\({}_{\text{x}}\)S\({}_{\text{y}}\) buffer used by the company Avancis deposited by thermal evaporation [7]. In addition, post-annealing procedures or heat-light soaking could be an adequate approach to diffuse sodium into the In\({}_{\text{x}}\)S\({}_{\text{y}}\) buffer and to the CIGS/buffer interface, which was already successful applied to CIGS cells with CBD-Zn(O,S)/(Zn,Mg)O buffer system. Oxygen can be intentionally introduced via reactive sputtering with oxygen in the sputtering gas [2], via variation of the base pressure with water vapor as oxygen source [18], the use of mixed In\({}_{\text{2}}\)(O,S)\({}_{\text{3}}\) targets, or co-sputtering from pure In\({}_{\text{2}}\)S\({}_{\text{3}}\) and In\({}_{\text{2}}\)O\({}_{\text{3}}\) targets. Another approach could be the use of a very thin In\({}_{\text{x}}\)S\({}_{\text{y}}\) layer with a thickness of 20 nm or even less. In this case the subsequent high-resistive layer should be optimized, e.g., (Zn,Mg)O or ZnTiO\({}_{\text{x}}\)[38] could be candidates to replace i-ZnO. Furthermore, different window layers--such as InZnO\({}_{\text{x}}\) or InSnO\({}_{\text{x}}\) instead of ZnO:Al--can influence the device performance of CIGS solar cells in combination with sputtered In\({}_{\text{x}}\)(O,S)\({}_{\text{y}}\) buffers [2].
# Algorithms in Tomography and Related Inverse Problems--A Review
## 7 Optical Coherence Tomography
A novel aspect introduced in this paper is the concurrent placement of dual-axis MEMS at the pupil location. Creating a detailed block diagram for optical coherence tomography algorithms involves representing the key components and stages in the process. Keep in mind that the actual implementation can vary based on the specific OCT system and application. Here is a simplified block diagram for OCT algorithms:  
1. **Data Acquisition:** _Light Source:_ Generates coherent light. _Interferometer:_ Splits the light into sample and reference arms. _Sample Arm:_ Directs light onto the sample. _Reference Arm:_ Sends light to a reference mirror. _Interference Detection:_ Combines sample and reference beams; interference is detected. 2. **Signal Processing:** _Interference Signal Processing:_ Extracts the interference signal. _Fourier Transform:_ Converts the interference signal from time to frequency domain. _A-Scan Generation:_ Produces an A-scan (depth profile). 3. **Image Reconstruction:** _B-Scan Formation:_ Combines multiple A-scans to form a B-scan (cross-sectional image). _En-face Image Generation:_ Constructs en-face images at different depths. Image Enhancement and Analysis:  
_Speckle Reduction:_ Techniques to reduce speckle noise. _Contrast Enhancement:_ Improves visibility of structures. _Segmentation:_ Identifies boundaries and structures in the OCT images. _3D Rendering:_ Creates three-dimensional representations of the imaged volume. Image Display and Analysis:  
_Visualization:_ Displays OCT images in real-time. _Quantitative Analysis:_ Extracts numerical information from images. _Clinical Decision Support:_ Provides support for medical diagnoses. Advanced Algorithms:  
_Motion Correction:_ Compensates for motion artifacts. _Doppler OCT:_ Measures blood flow within tissues. _Polarization-Sensitive OCT:_ Provides additional tissue information based on polarization properties. _Machine Learning:_ Incorporates machine learning techniques for image analysis and pattern recognition. Data Storage and Management:  
_Database:_ Stores acquired OCT data. _Archiving:_ Manages storage of large datasets for future reference. Integration with Other Modalities:  
_Multimodal Imaging:_ Integrates OCT with other imaging modalities for comprehensive diagnostics. Clinical Applications:  
_Ophthalmology:_ Retinal imaging, anterior segment imaging. _Dermatology:_ Skin imaging. _Cardiology:_ Cardiovascular imaging. _Endoscopy:_ Imaging within body cavities. Feedback Loop:  
_System Calibration:_ Ensures accuracy and reliability. _User Feedback:_ Allows for adjustments based on user input. _System Optimization:_ Continuous improvement based on performance feedback.
## 3 Approach  
### Overall Architecture  
An overview of our method is depicted in Figure 1. Our method consists of a stack of L = 6 stages, with the temporal length halved except for the first stage, thereby generating a temporal feature pyramid structure. All stages have an identical structure, which is used to aggregate multi-range context features. Moreover, every stage includes three modules: multi-scale local convolution (MLC), window self-attention (WSA), and global attention (GA).
## 2 The Bus Synchronization Problem
### Mathematical Formulation
#### Parameters  
\(T=\) duration of the planning horizon, measured in minutes.  
\(Dem_{b}^{hg}=\) the estimated demand for transfers between lines \(h,g\in H\) at transfer zone \(b\in B\) during the whole planning horizon. Demand is uniform throughout that period.  
\(Co^{h}=\) cost per trip for bus line \(h\in H\).  
\(Wd_{b}^{hg}=\) time that takes for users to walk between bus stops of bus lines \(h\in H\) and \(g\in H\) in transfer zone \(b\in B\).  
\(Tol_{b}^{hg}=\) maximum threshold time that users are willing to wait for a transfer from bus line \(h\in H\) to bus line \(g\in H\) in transfer zone \(b\in B\).  
\(TT_{b}^{h}=\) travel time that requires bus line \(h\in H\) to travel from the departure station (beginning of the line) to transfer zone \(b\in B\).  
\(N_{h}=\) minimum number of trips of bus line \(h\in H\) to guarantee the quality of service for users who do not transfer.  
\(Hd_{h}^{min}=\) minimum bound for the headway of bus line \(h\in H\).  
\(Hd_{h}^{max}=\) maximum bound for the headway of bus line \(h\in H\).
## 2 Overview of Available Countermeasures to Support Low Conventional System Inertia
The use of frequency deviation in the two-loop model makes it possible to reproduce the behavior of the synchronous generator more accurately and thus ensure that the frequency of the electrical power system is restored to nominal values more quickly. In modern electrical power systems, a great deal of attention is paid to the development of electricity networks using HVDC transmission lines. The use of this type of network has several aims, e.g., to optimize the losses of active power in the transmission of large power flows over long distances, and to connect the power systems of different synchronous zones. The advantages of HVDC are that it provides fast and bidirectional power control, frequency support, and blackout recovery (with VSC technology only), and it suppresses power fluctuation. The SI algorithm method based on the frequency derivation principle of the electrical power system can also be implemented in HVDC transmission lines [17; 29]. To reduce the number of errors, a low-order filter is used in conjunction with a derivative block, and the selection and calculation of the parameters are quite complicated. To evaluate the frequency derivative and pass the command to the VSC system, it is necessary to ensure a high-quality transmission/communication connection [30]. The control concept of other HVDC systems that connect two power systems is explored below, one of which is the Virtual Synchronous Power (VPS) control method [31], which is adapted to control VSC. This control method is based on the traditional generator electric operating model, which is responsible for the control of the VSC converter. The principle of the SI model is based on the motion equation of a traditional generator and a common electromechanical control loop. The power-locked loop is modeled by applying power output measurements and DC input power signal to the Power Loop Controller (PLC) to maintain the frequency of the electrical power system by simulating the rotor speed of the traditional generator. The frequency integral allows for the rotor angular velocity to be modeled, which is used to calculate the HVDC output power. The authors of [32] presented a method used to model a virtual synchronous machine that is applied in microgrids for the provision of SI and ancillary services. The output power is calculated according to the principles of active power frequency variation, considering the motion Equation (1), which relates the active power to the angular velocity deviation. \[\dot{\dot{\varpi}}=\frac{1}{H}\Big{(}P_{ref}-P-D_{p}\big{(}\omega_{vsm}- \omega_{g}\big{)}\Big{)}. \tag{1}\]
###### Abstract  
Multiple-input multiple-output (MIMO) radar is playing an important role in military and civilian fields. However, radio frequency (RF) multi-links have a large data-processing capacity and the system is bulky and complex. Therefore, a single-link RF MIMO has been the subject of heated discussed in recent years. Single-link technology shares the same regularity with that of the multi-link MIMO system, which makes the MIMO system simpler in structure without affecting its performance. In this study, a 2 \(\times\) 2 array single RF link MIMO radar at X band was designed by combining MIMO radar with the concept of a single RF link in the communication field, and a simulation platform for studying its properties was established using SystemVue software. The whole system is controlled by the exclusive OR (XOR) operation results of two transmission signals, and one signal can control the antenna-switching signal to form a variety of orthogonal bases. The received two original signals are demodulated and the control signals enter the corresponding matched filters. The signal is processed by fast Fourier transformation (FFT) and the cell-averaging constant false alarm rate algorithm (CA-CFAR) is used to judge the signal. For multiple measurements, the Monte Carlo method was adopted to obtain its mean value as the final result. Compared with the traditional MIMO radar performance in acquiring the target position, speed, and detection probability, the results show that the computation time can be greatly reduced (due to decrease in the number of links in MIMO radar system) without any deterioration in the MIMO radar performance. The single RF link technology provides a broad prospect for the miniaturization of small radar applications.  
single-link; multiple-input multiple-output (MIMO); constant false alarm rate (CFAR); detection probability +
Footnote †: journal: _Journal of the Royal Society Interface_  
1
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Federated Multi-Layer Perceptron Algorithm  
Here, we propose a federated multi-layer perceptron algorithm (FMLP) based on the traditional multi-layer perceptron. FMLP can train a simple model for each client in a multi-party data aveolation environment through gradients' sharing. The multi-layer perceptron, also known as a deep feed-forward network, is a typical deep learning model. An example of its architecture is shown in Figure 3.  
The loss function that calculates the distance between the output and the ideal value can be defined as:  
\[c=loss(f^{*}(x),out) \tag{3}\]  
The function of the back-propagation is to calculate the gradients and propagate them from the loss function backwards to help the network adjust the parameters according to the gradient to reduce the error between the output value and the ideal one. The back-propagation process can be defined as:  
\[grad=bp(x,\theta,c) \tag{4}\]  
The model-update process is to adjust the network parameters based on the gradient obtained by backpropagation, which can be expressed as:  
\[\theta^{{}^{\prime}}=\theta-lr\cdot grad \tag{5}\]  
Through the federated network realized by MLP, we can get a federated MLP (FMLP). Then, a copy of the MLP model is stored in the local memory of each learning client. It contains an input layer with \(x\) units, \(n\) hidden layers with \(y\) units each, and an output layer with \(z\) units. The size of \(x\) depends on the feature dimensions of the input data. In addition, the size of \(z\) depends on the required output of the network that is closely dependent on target output of the real applications.  
The main function of the computing server is to fuse the gradient data, helping the model to accelerate the gradient descent while learning the data from each client. Before the model is updated, each learning client passes the gradients to the computing server for model training. In addition, then the computing server integrates all the gradient data from all clients, and returns the calculated new gradient to each client for model updates. Finally, when the loss of each client is less than \(\epsilon\), the model converges. In addition, then all the clients can get the same federated model. The specific steps of FMLP are shown in Algorithm 1.  
```
0: Dataset \(x\)
0: Model \(\theta_{final}\)
1: Initialize model parameters \(\theta\)
2:for\(i\) in \(iteration\)do
3: Forward propagation: \(out_{i}=fp(x_{i},\theta_{i})\);
4: Compute loss: \(c_{i}=loss(f^{*}(x_{i}),out_{i})\);
5:if\(c_{i}<\epsilon\)then
6: Break
7:else
8: Back propagation: \(grad_{i}=bp(x_{i},\theta_{i},c_{i})\);
9: Send gradients to computing server and get new gradients;
10: Update: \(\theta_{i+1}=\theta_{i}-lr*grad_{new}\);
11:endif
12:endfor
13:return Model with parameters \(\theta_{final}\)
```  
**Algorithm 1** Federated Multi-Layer Perceptron
## 5 Conclusions  
In this study, the draught reduction performance of the tool with upcutting continuous belt motion was investigated through comparative DEM simulations. The results showed that the belt motion can significantly reduce the draught resistance, and the reduction can be explained by the changes in soil disturbance characteristics, particle velocity distribution, and soil deformation and movement patterns. The main findings from the results were summarized as follows:  
1. The belt motion significantly reduces the draught force by 13.18-44.64%, but increases the downward vertical force within a reasonable range.
2. The belt motion increases the area of transverse disturbance, but reduces the area of longitudinal disturbance. The belt motion also changes the shapes of soil disturbances.
3. The belt motion causes more soil particles to move upward at a higher speed relative to the tool preventing the development of soil buildup.
4. The belt motion relieves soil compression and transports the soil upwards by changing the soil deformation and movement patterns.
5. The changes in soil disturbance, particle velocity distribution, and soil deformation and movement patterns contribute to the reduction in draught force.  
This study indicates that the belt motion has great potential in reducing the draught force of subsoiling. The study also provides detailed scientific guidance and a theoretical basis for the future design of low-draught subsoilers and other earth-moving machines based on the upcutting continuous belt.  
Conceptualization, P.G. and Y.M.; Data curation, P.G. and X.L.; Formal analysis, P.G. and J.L.; Funding acquisition, Y.M.; Investigation, P.G. and J.L.; Methodology, P.G. and J.L.; Project administration, Y.M.; Resources, Y.M.; Software, P.G. and X.L.; Supervision, Y.M.; Validation, J.L., H.Q. and X.L.; Visualization, P.G. and X.L.; Writing--original draft, P.G., J.L. and H.Q.; Writing--review and editing, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52275288); National Key Research and Development Program of China (grant number 2023 YFD2000903); the Jilin Province Science and Technology Development Plan Item (grant number 20210202021 NC).  
Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors are grateful to the editors' immediate processing, and the anonymous reviewers for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Lane-Changing Behavior  
In the lane-changing scenario, five vehicles are involved (Figure 11): the lane-changing vehicle (LCV), the leading vehicle in the current lane (LV), the following vehicle in the current lane (FV), the leading vehicle in the target lane (LVT), and the following vehicle in the target lane (FVT). The LCV attempts to merge from the current lane into the target lane, influenced by the dynamic interactions with both the LV and FV in the original lane, as well as the LVT and FVT in the target lane. The relative positions, speeds, and accelerations of these vehicles significantly impact the success and safety of the lane-changing maneuver.  
In mixed traffic flow, CAVs and HDVs are randomly distributed among the lane-changing vehicles and the target lane. To describe the impact of CAV degradation during lane-changing behavior, the following three typical lane-changing scenarios involving DCAVs have been selected for analysis, as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **ACC** \\ \hline Gains on positioning errors \(k_{a1}\) & 0.23 s\({}^{-2}\) \\ Gains on speed errors \(k_{a2}\) & 0.07 s\({}^{-1}\) \\ Safe time headway \(T_{a}\) & 1.1 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: The parameter values of the ACC model.  
Figure 11: Lane-changing scenario.
# A Method for Ferulic Acid Production from Rice Bran Oil Soapstock Using a Homogenous System
## 2 Materials and Methods
### Quantitative Analysis Method of \(\gamma\)-oryzanol, FA, and Ethyl Ferulate  
The HPLC system (GL-7480, GL Science Inc., Tokyo, Japan) equipped with a photo diode array detector (PDA) (GL-7452, GL Science Inc., Tokyo, Japan) was used. The analytical column was a reversed-phase Inertsil ODS-3 C\({}_{18}\) (4.6 mm x 250 mm, 5 \(\mu\)m). The mobile phases were an aqueous phosphoric acid solution (0.283 M) (A) and a mixed solution of ACN and MeOH (\(v/v\) 1:1) (B). The analytical conditions were as follows: a gradient from 30:70 (A:B) to 100:0 for 15 min, and held for 25 min, then a gradient to 70:30 for 5 min, and then, 30:70 for 5 min. The eluent flow rate was 0.8 mL/min. The UV absorbance was monitored at 325 nm. The calibration curves of _trans_-FA, EF, and \(\gamma\)-oryzanol showed the good linearity, and their coefficient of determination (R\({}^{2}\)) was more than 0.9990. The calibration curve of \(\gamma\)-oryzanol was estimated by the sum of the areas of four main components versus the corresponding concentrations (Figure 2). The obtained values were the averages of triplicate measurements.  
The _cis_-FA concentration was estimated based on the _trans_-FA calibration curve. The percentage of _trans_-isomer presented in the mixture of two isomers was calculated as follows:  
Figure 1: The reaction vessel placed on the transducer in a temperature controlled water bath (**a**) and the planar type of transducer and its controller (**b**).  
\[Pt=\frac{St}{St+Sc}\times 100\% \tag{1}\]  
where \(Pt\) is the percentage of _trans_-FA in the mixture, \(St\) and \(Sc\) are the concentrations of _trans_- and _cis_-FA, respectively.