esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Transient Temperature Field Model of Wear Band on the Flank of End Mills
### Verification of Temperature Field Model
#### 3.1.2 Comparison of Simulation and Experimental Results for Milling Temperature  
As shown in Figure 16, the simulation value of wear band in the temperature field model at the highest temperature is compared with the reported experimental value from [PERSON] et al. [34].  
By comparing the simulated value to the experimental value, it was found that the simulated value of the temperature field model was consistent with the experimental value with regards to the magnitude and trend of change. These results further verified the accuracy of the temperature field model. However, the relative error between them ranged from 9.28% to 15.22%. Such errors could be attributed to the following reasons:  
1. The established transient temperature field model of wear band on the flank of end mills assumed that all the physical works in the PSZ, RCZ and FWZ are converted into heat. Indeed, the actual situation may be that part of the physical works in the three deformation zones are at least partially stored in the form of potential, which exerts no effect on milling temperature.
2. The proposed temperature field model considers the wear band on the flank of end mills. However, the flank of end mills obtained from [PERSON] et al. [34] does not occur to wear phenomenon, which results in the lower temperature of end mills measured in the experiment compared to its actual temperature.
3. The reported method for measuring milling temperature is based on the artificial thermocouple approach of [PERSON] et al. [34]. It can only measure the temperature of the wear band on the flank of end mills at certain distances, but is not able to measure the temperature directly.
4. Thermocouple junctions have a specific volume and mass, which lags behind the temperature response of the milling process, owing to their interrupted cutting characteristics.
# A Novel Method for Ocean Wind Speed Detection Based on Energy Distribution of Beidou Reflections
## 3 Coastal Experiment Description
### Data Processing  
The data processing mainly included three parts: raw data preprocessing, averaged-waveform computation and observable computation.  
* Raw data preprocessing.  
The Direct intermediate frequency (IF) signals were tracked to calculate the precise code delay and the Doppler frequency. The reflected IF signals were cross-correlated against local generate code replicas with different delays which were estimated by using the direct signal code phase as shown in Figure 4.  
The output 1 ms complex waveform of the reflected signal is  
\[y_{r}(t,\tau)=I_{r}(t,\tau)+j\cdot Q_{r}(t,\tau) \tag{9}\]  
where \(I_{r}(t,\tau)\) and \(Q_{r}(t,\tau)\) are the in-phase component and quadrature component of the complex waveform, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Radio frequency (RF) Frequency & 1561.098 & MHz \\ Intermediate frequency (IF) Frequency & 3.996 & MHz \\ Bandwidth & 4 & MHz \\ Sample Rate & 16.369 & MHz \\ Quantization & 2 & bit \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of Global Navigation Satellite System (GNSS) raw signal collector.  
Figure 4: Simplified block diagram of data preprocessing.  
Figure 3: Scenario and experiment setup in the Yangjiang site.  
* Averaged-waveform computation  
In the experimental scenario, the collected reflected signals were contaminated by different kinds of factors, such as thermal noise, speckle noise and direct signal crosstalk. It is necessary to post-process the waveform from the Beidou-Reflected software receiver. As discussed in [33], the coherent and incoherent averaging were employed to increase the SNR of the waveform. Here, the coherent integration time was set to 50 ms. Then, utilizing the difference coherence properties between direct and reflected signals, the cross-talk of direct signals was removed by  
\[Y_{r}(\tau)\,=\,\frac{1}{N}\!\sum_{k=1}^{N}\!\left|y_{r,50}(t_{k},\tau)\,-\, \frac{1}{N}\!\sum_{k=1}^{N}y_{r,50}(t_{k},\tau)\right|^{2} \tag{10}\]  
where \(Y_{r}(\tau)\) is the averaged power waveform. \(y_{r,50}(t_{k},\tau)\) is the 50 ms coherent integrated complex waveform, \(N\) is the number of incoherent averages (\(N\) = 5000). Figure 5 shows the power waveforms at 12:00 on August 13, 2013 in the Yangjiang experiment. Figure 5a plots 250,000 consecutive 1 ms power waveforms. Figure 5b plots the averaged power waveform.  
* Observable computation  
The new proposed observables EMR and EDR were extracted from the normalized power waveform during the Yangjiang coastal experiment, respectively.
# Temperature Estimation during Pulsed Laser Sintering of Silver Nanoparticles
###### Abstract  
During the thermal sintering of metal nanoparticles (NPs) the process temperature plays the most important role in the outcome of the sintering results and the selection of a suitable substrate. Here, temperature calculation during pulsed laser sintering of silver (Ag) nanoparticles (NPs) isjcap-printed on glass substrates is presented. During the pulsed laser sintering of Ag NPs, a minimum line resistivity less than twice the bulk silver resistivity was obtained within around 52 s under a laser power of 276 mW. The temperature field during the pulsed laser sintering of Ag NPs was estimated via a three-dimensional numerical model in which a temperature-dependent thermal conductivity of Ag NPs was adopted. An algorithm for treating the thermal conductivity of the heated Ag NPs in a region by a previous laser shot was devised for the improvement of the temperature field estimation. A maximum temperature of approximately 204 \({}^{\circ}\)C over the Ag NPs line was obtained with a 276 mW laser power and a scanning speed of 135 \(\upmu\)m/s.  
silver nanoparticles; temperature estimation; thermal conductivity; pulsed laser sintering; inkjet-printing +
Footnote †: journal: Applied Sciences
## 2 Wireless Multimedia Sensor Networks
### Large Volume of Multimedia Data
#### 2.3.1 Reducing Data Redundancy
One solution is to use distributed source coding (DSC), which allows each camera sensor to independently encode their own piece of source information while leaving the complex joint decoding work to the sink [67; 68]. Compared to traditional downlink multimedia source coding technics, such as the JPEG 2000 and MPEG.x, the main advantages of DSC is the ability to shift much computation burdens from the encoder side to the decoder [34]. For WMSNs, this can significantly reduce energy consumption and also lower hardware costs [27; 30]. * In-network Data Storage and Query Processing: Traditionally, WSNs would transmit all sensing results to the sink for further processing and future inquiry. However, it is shown by [PERSON] et al. [69] and [PERSON] et al. [70] that with recent technology advances, it is now practical to equip camera sensors with more powerful processors and significantly larger flash memories for local processing and data storage. On one hand, with data being processed and stored on site, only the end result of data analysis needs to be sent to the sink. On another, in case of historical data inquiries, the query request (e.g., the number of traffic incidents happened at an intersection during the past year) can be pushed into the network and only the query results needs to be transmitted. In both scenarios, data transmission can be reduced and energy cost is also lowered. Nevertheless, local data storage schemes do impose a couple of challenges. First, when local storage space starts to fill up, proper data aging processes are required to clear space for new data without sacrificing fidelity on critical historical data [69; 71]. Second, sensor nodes in this scenario essentially form a distributed probabilistic database, thus respective database management techniques, have to be considered for efficient data querying [72; 73]. Figure 2: Local Processing in WMSNs.
## 2 Literature Review
### Position Estimation Based on the QR Code Recognition  
The QR code is one of the typical markers used for the inventory management. QR codes are used to provide information about the product through scanning by attaching to  
Figure 2: Previous work in drone-based inventory management (**left**). Proposed framework (**right**).  
the product, or to navigate unmanned vehicles and unmanned aircraft. Studies have been conducted to recognize QR codes in noisy environments, such as a warehouse. [PERSON] and [PERSON] [21] improved QR code detection performance by learning to detect the finder patterns (FIPs, a squared pattern present at three corners of the QR code) of the QR code by utilizing a single-shot detector architecture, a deep-learning-based object detection model. [PERSON] et al. [22] proposed a method to improve the performance of the QR code positioning by combining AdaBoost-SVM with Binarized Normed Gradients to solve the real-time positioning problem [23], which yields fast calculations, but provides low recovery rates. [PERSON] et al. [24] proposed MU R-CNN, which is a QR code detection model applicable to both low-definition and high-definition images, by combining Mask R-CNN [25], an object detection model, and U-Net [26], a segmentation model. [PERSON] et al. [27] proposed a method of correcting the QR code using the radius of cylinder to recognize the QR code whose curvature distortion occurred from being attached to the cylinder. [PERSON] et al. [28] corrected the distortion of the QR code by applying affine transformation to the contour of the QR code detected by the edge detection algorithm as the minimum bounding box surrounding the QR code.  
Studies have been conducted to estimate the position of drones and cameras using QR code recognition models. [PERSON] et al. [29] implemented autonomous navigation based on self-localization to calculate 2D coordinates and rotation angles using QR codes and a monocular camera. [PERSON] and [PERSON] [30] proposed an indoor positioning method based on a monocular camera and depth sensor using QR codes as a landmark. For position estimation of unmanned aerial vehicles, [PERSON] et al. [31] proposed the monocular camera-based position estimation method using the SolvePnP algorithm and Kalman filter. [PERSON] and [PERSON] [32] implemented a self-localization system that performs self-position and self-orientation estimation of the mobile robot in 2D space using the sensor and QR code. [PERSON] et al. [33] proposed a 3D position estimation method based on the monocular camera using two QR codes from an image. [PERSON] et al. [20] estimated 3D coordinates of remaining markers without 3D coordinates using 3D coordinates and images taken from multiple perspectives of initial markers. Furthermore, [PERSON] et al. [34] proposed position estimation of the camera using FIPs and alignment patterns of a single QR code. [PERSON] et al. [35] estimated the relative position of a drone with a fixed-sized QR code attached to the floor.
## 3 Results and Discussion
#### 3.1.4 Validation of the Reliability of the Flow Performance Evaluation Model  
To verify the reliability of the model, the broken gangue is sieved, the logarithms of both ends of Equation (2) are taken, and the fractal dimension is calculated to be 2.492. The fractal dimension is substituted into the flow characteristics evaluation model for calculation. The calculation process is shown in Figure 9, and the comprehensive score is 0.8548, indicating that the flow characteristics are excellent. At the same time, the flow characteristics of the original graded gangue are verified by traditional experiments. The experimental results show that the mini-slump is 143 mm, the expansion is 316 mm, the bleeding rate is 4.31%, the yield stress is 20.41 Pa, and the consistency coefficient is 0.00088. Combined with the practice of slurry filling, the slurry has excellent fluidity, which proves the reliability of the model evaluation results. At the same time, the model is simple to calculate and has high practical value.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flow Characteristic Index** & **Slump** & **Expansion Degree** & **Bleeding Rate** & **Yield Stress** & **Consistency Coefficient** \\ \hline Weight & 0.1536 & 0.1986 & 0.2201 & 0.2856 & 0.1421 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Index weight of flow characteristics.  
Figure 8: Evaluation model of flow performance.  
Evaluation and Analysis of the Settlement Performance of Gangue Filling Slurry Based on Fractal Grading
## 3 Results
### Expanded Ex Vivo Tregs Demonstrate the Nature Phenotype at Day Seven of Cultivation  
To investigate the expression profile of functional molecules, proliferating cells were labeled with relevant antibodies and analyzed by flow cytometry. There was a significant increase in cells expressing the Treg markers CD4\({}^{+}\)CD25\({}^{+}\)FoxP3\({}^{+}\)CD127\({}^{\text{low}}\) (Figure 2A-F). Over the period of cultivation, the proportion of CD4\({}^{+}\)CD25\({}^{+}\)FoxP3\({}^{+}\)CD127\({}^{\text{low}}\) Tregs gradually increased, while the proportion of conventional CD4\({}^{+}\)CD25\({}^{-}\) T cells decreased. After 7 days of cultivation, 88.7-92.8% of cells had the common phenotype CD4\({}^{+}\)CD25\({}^{+}\)FoxP3\({}^{+}\)CD127\({}^{\text{low}}\), which denotes homogeneity of expanded cells. Further cultivation did not increase the number of cells with a Treg phenotype. The proportion of cells expressing the transcription factor IKZF2 HELIOS, which is necessary for Treg maturation [39], also increased over the entire time of cell cultivation (Figure 2G,H). The expression of Helios remained stable during the first five days of cultivation and increased approximately twofold at day seven; 95.5-96.3% of mature eTregs expressed Helios. The proportion of cells expressing Treg functional molecules that participate in suppressive activity, cytotoxic molecules CD152 (cytotoxic T-lymphocyte associated protein 4, CTLA4) [40], CD39 (ectonucleoside triphosphate diphosphohydrolase 1, ENTPD1) [41] and CD223 (lymphocyte activating 3, LAG3) [42] increased in mature cells and reached maximal ratios at day 7 (Figure 2I,N), which also denotes the homogeneity of expanded cells. While the expression of CD152 increased on the fifth day and did not change during further cultivation, the expression of CD39 and CD223 increased gradually during seven days of Treg cultivation.  
Figure 2: The expression of Treg markers reached a maximum ratio at day seven of ex vivo expansion. Initial CD4\({}^{+}\) cells were cultured for 9 days to develop eTregs. Representative flow cytometry diagrams for cells labeled with the following antibodies: (**A**) CD25, (**C**) FoxP3, (**E**) CD127, (**G**) Helios, (**I**) CD152, (**K**) CD39, (**M**) CD223. (**B,D,F,H,J,L,N**) Mean expression of markers within the time of expansion. \(N=5\). \({}^{*}p\leq 0.05\) vs. initial cells at the 0-day time-point, measured by Bonferroni modification of Student’s \(t\)-test.
## 4 Conclusions  
In this paper, a nighttime vehicle detection system for detecting both oncoming vehicles and preceding vehicles based on headlights and taillights is proposed. Initially, a bright blob segmentation is performed to extract all the possible regions that may be vehicle lights. Then, a machine-learning-based method is proposed to classify headlights and taillights, and to remove false detections in the captured image. Next, each vehicle light is tracked in subsequent frames by using a light tracking process. As one vehicle is indicated by one or two light pairs, a modified light pairing process using spatiotemporal information is applied to the sets of detected headlights and taillights to determine the positions of the potential vehicles. Finally, the vehicle tracking process is applied to refine the vehicle detection rate under various complex situations on the road. The experimental results demonstrate that the proposed method significantly improves vehicle detection rate by identifying the vehicles in some situations that previous studies cannot solve.  
It is interesting to apply the proposed algorithm to the daytime condition in the future. We also investigate various weather scenarios, such as rainy or cloudy conditions. Furthermore, motorcycle detection and tracking will be examined for integration into the current system.  
[PERSON] proposed the idea, performed the analysis and wrote the manuscript. M.Y. provided the guidance for data analysis and paper writing. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology under Grant NRF-2018R1A2B6004371.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### The Anholt Offshore Wind Farm  
The wind farm site to be studied is the Anholt offshore wind farm in Denmark operated by Orsted, the world's largest offshore wind farm developer. The Anholt Offshore Wind Farm is located between Djursland and Anholt island in the Kattegat Sea of Denmark, and has a capacity of 400 megawatts. The Anholt offshore wind farm was the largest offshore wind farm in Denmark until 2019, before the completion of the Horns Rev Phase 3 offshore wind farm. It has a large-scale project area that is equivalent to four times the area of Anholt island (long axis length of 20 km, average width 5 km, total area 88 km\({}^{2}\)), and 111 Siemens SWT 3.6-120 model wind turbines are installed [42; 43].  
The substructure type is in the form of a monopile, and the water depth of the project sea area is approximately (15-19) m, which is not deep, and is about 15 km distance from the shoreline. The wind farm was completed in the summer of 2013, and is large enough to support the annual electricity consumption of 400,000 Danish households (4% of the total electricity consumption of Denmark). Figure 1 shows the site location and photography of the Anholt offshore wind farm, while Table 1 provides technical information of the wind farm and SCADA data. It was determined that a very large amount of data were required to analyze the power generation of all wind turbines, and the main wind shear effect could not be properly reflected, due to the overlapping wake effect. As will be introduced later, only five wind turbines (A1, A2, A3, B1, and C1) enlarged in orange color were selected and analyzed, considering that the main wind direction observed from LiDAR was southwest. This is to, as much as possible, use only the wind turbine located on the upwind side, to minimize the wake effect applied to the rotor equivalent wind speed analysis.  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Content** \\ \hline Wind turbines & Siemens Gamesa Renewable Energy, SWT 3.6-120 \\ Number of wind turbines & 111 \\ Wind turbine capacity [MW] & 3.6 \\ Total capacity [MW] & 400 \\ Hub height [m] & 81.6 \\ Gridshore wind farm & Rotor diameter [m] & 120 \\ Length of monopile [m] & 37–55 \\ Water depth [m] & 15–19 \\ Distance to shore [km] & 15 (Based on the nearest wind turbine) \\ Offshore wind farm area [km\({}^{2}\)] & 88 \\ Commissioned & Summer 2013 \\ \hline SCADA & Data & WTG \({}^{1}\) coordinates, SCADA data with min/max/mean/stddev \({}^{2}\) (Wind speed, \\ Year position, Blade pitch position, RPM, Active power, Ambient temperature) \\ \hline \end{tabular} \({}^{1}\) Wind turbine generator, \({}^{2}\) Standard deviation.  
\end{table}
Table 1: Key information of the Anholt offshore wind farm, wind turbines, and SCADA data information in Denmark.  
Figure 1: Left: Location of the Anholt offshore wind farm. Right: Arrangement of 111 offshore wind turbines. The offshore LiDAR is marked as dark-blue square to the west of Anholt wind farm. Only the five identified wind turbines (A1–A3, B1, and C1) marked in orange color in the enlarged figure are used for this analysis.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 5 Limitations  
In the gradually mature and practical path of VLC, there are still some issues that need to be addressed by in-depth study in future researches:  
(1) Thermal management of high power LEDs. The junction temperature of the LED may be changed with the variation of self-heating, drive current, ambient temperature and so on. Thus, in the VLC system, especially the ones run at high drive currents or where hundreds of LEDs are integrated, the high junction temperature may affect the dominant wavelength [89], spectral width [90], the internal efficiency, and finally leads to the degradation of signal power and reduces the SNR [7]. In addition, when the LEDs are suffered from long-term excessive heat, both the stability and the lifetime of the LED are drastically reduced [91]. Therefore, how to effectively monitor and reduce the LED junction temperature is a key issue of effective VLC implementation. (2) Bi-Directional Transmission. Basically, VLC is a kind of broadcast transmission, thus, how to provide an uplink path to realize a bi-directional transmission is challenging. To achieve the uplink path, several approaches have been proposed. For example, [PERSON] et al. [92] proposed a corner cube modulator for the uplink VLC path. The modulator is basically a retro-reflecting modulator,
## 2 Dynamics Modelling and Problem Formulation
### Aircraft Dynamics and Actuator Modelling
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Unit** & **Description** \\ \hline \(h\) & 0 & m & Altitude \\ \(V_{0}\) & 67.4 & m/s & Initial speed \\ \(\rho\) & 1.225 & kg/m\({}^{3}\) & Air density \\ \(\alpha_{0}\) & 0.148 & rad & Trim angle of attack \\ \(\gamma_{0}\) & 0 & rad & Trim flight path angle \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The trim conditions. \[\frac{\delta_{t}}{\delta_{tc}}=\frac{0.25}{s+0.25} \tag{5}\]  
where \(\delta_{e}\) is the elevator deflection angle, \(\delta_{t}\) is the throttle position changing, and \(\delta_{ac}\) and \(\delta_{tc}\) are commands. The maximum deflections and rates of elevator and engine are limited in order to ensure the aircraft's safety and comfortability, which are listed in Table 3[25]. Combining the linearized aircraft model and the actuators model, the linearized state equation can be written, as follows:  
\[\dot{x}(t)=Ax(t)+Bu(t) \tag{6}\]  
where \(x(t)=\left[\delta_{e}(t),\delta_{t}(t),V(t),\gamma(t),q(t),\theta(t),h(t) \right]^{T}\), \(u(t)=\left[\delta_{cc}(t),\delta_{tc}(t)\right]^{T}\), and  
\[A=\left[ \begin{array}{cccccc}-10&0&0&0&0&0\\ 0&-0.25&0&0&0&0\\ 0&0.98&-0.04&-10.56&0&-21.64&0\\ 0&0&0&-0.49&0.03&0.49&0\\ 0&0.01&0&0.42&-0.38&-0.42&0\\ 0&0&0&0&1&0&0\\ 0&0&0&221&0&0&0\end{array} \right],B=\left[ \begin{array}{cccc}10&0\\ 0&0.25\\ 0&0\\ 0&0\\ 0&0\\ 0&0\\ 0&0\\ 0&0\end{array} \right]\]