esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Contour Error Evaluation  
To evaluate toolpath quality, a method of contour error evaluation is necessary. In the context of surface quality measurement, deviations from the ideal surface are typically evaluated using dedicated metrology equipment. However, these standard methods of surface quality evaluation are not applicable when evaluating deviations from the ideal surface based on position setpoint data obtained from the CNC interpolator. First, a physical workpiece cannot be machined based on the interpolator setpoint data only (i.e., without introducing the effects of machine dynamics and tool-workpiece force interaction). Therefore, the metrology equipment cannot be used. Second, the position datapoints are sampled with constant time frequency, potentially leading to a highly nonuniform distribution of these datapoints with respect to the toolpath length. Thus, the datapoints themselves cannot be used to evaluate surface quality according to standard ISO norms (as defined in ISO 4287 [33]).  
To avoid these issues, the following contour error evaluation method was formulated and applied in the presented research. The geometric profile obtained as the cross-section of the required surface with the YZ work plane was first uniformly sampled with respect to its arclength; then, the outer normal direction vectors at the sampling points were evaluated. Given a specific toolpath processed by the interpolator, the locations of the tool center point were interpolated by a piecewise linear curve (in case of the Fan toolpath) or surface (in the case of the Valley toolpath). The contour error was then calculated at the sampling points as the (signed) distance to the interpolated curve/surface in the direction of the respective outer normal vectors. For a schematic of the contour error computation, see Figure 4. The Fan and Valley toolpaths were defined as trajectories of a center point of a spherical tool with a radius of 4 mm. Note that the contour error is represented as a function of arclength.  
Figure 3: The Valley toolpath consisting of four identical segments in the YZ working plane with adjacent segments connected by a constant offset of 0.4 mm in the X-axis. This toolpath is an example of the zig-zag milling strategy. The angle sizes at Z = 285 mm (the bottom of the valley) are 159.5\({}^{\circ}\) and 135\({}^{\circ}\). The direction of the toolpath is indicated by red arrows.  
The presented definition of contour error evaluation was chosen for multiple reasons. First, by computing the contour error relative to the required surface, the effects of interpolator-introduced inaccuracies on the resulting surface geometry can be evaluated more directly compared to measuring deviations from the NC code toolpath. Second, by evaluating deviations at arclength-uniform sampling of the required surface-plane intersection, consistent estimates of the arclength-to-error function can be obtained across test results corresponding to different parameter value combinations.
## 2 Methods
### Assessment Approaches of Image Reconstruction Quality  
In this study, qualities of diffraction imaging results and simulated imaging results were assessed, and the criteria included relative diffraction efficiency (RDE), root mean square error (RMSE), and signal to noise ratio (SNR) [16; 17; 18]. Before the aforementioned assessments, signal reconstruction and area reconstruction needed to be defined, as in Figure 7. Signal reconstruction was the signal area of the reconstructed target image, while area reconstruction was the entire area for reconstruction. The reconstruction image area was assumed as X \(\times\) Y, including signal area and noise of the reconstructed target image, and the intensity of reconstruction signal and noise was assumed to be \(I_{s}\) and \(I_{N}\), respectively.  
Relative diffraction efficiency was used to determine whether or not the background noise affected image quality, where the intensity sum \(\sum I_{s}\) of each pixel in the signal reconstruction area was the numerator, and the intensity sum \(\sum(I_{s}+I_{N})\) of each pixel in the reconstruction area was the denominator. If the result was somewhere near 100%, there was nearly no background noise and the reconstruction image quality was good, with the equation as demonstrated in Equation (13) [16]:  
\[I_{RDE}=\frac{\sum I_{s}}{\sum(I_{s}+I_{N})}\times 100\% \tag{13}\]  
Root mean square error (RMSE) is an objective assessment, often used for defining reconstruction image qualities. It was used to calculate the mean difference between each pixel in diffraction reconstruction images and original images, mainly to judge the similarity of the signal distribution between diffraction reconstruction images and original images. If the RMSE value was closer to 0, it revealed the higher similarity of diffraction reconstruction images and original images and lower distortion, as in Equation (14) [17]:  
\[RMSE=\sqrt{\sum\frac{I_{N}^{2}}{XY}} \tag{14}\]  
Signal to noise ratio is the ratio of diffraction reconstruction signal intensity to noise intensity, which was used to compare the intensity level between the target signal and the background signal. Decibel (dB) normally serves as the unit; if the SNR was higher than  
Figure 7: Schematic diagram of area reconstruction, signal reconstruction, and noise.  
0 dB, it represented more reconstruction signals than noises, which could be an indicator of the diffraction reconstruction image being clearly reconstructed, as in Equation (15) [18]:  
\[SNR(dB)=10\times log_{10}\frac{I_{s}}{I_{N}} \tag{15}\]
# Near-Optimal Weather Routing by Using Improved A* Algorithm
## 1 Introduction
[PERSON] et al. [9] conducted routing to ensure the stability of ships based on the ant colony algorithm and proposed an optimal route to satisfy various conditions. [PERSON] [10] and [PERSON] [11] showed that route optimization using the genetic algorithm yielded highly accurate routing through various simulations. However, because these optimizations require a large amount of calculation and are difficult to use during actual navigation, routing algorithms suitable for grids or graphs are commonly used at sea to compensate for these shortcomings. The Bellman-Ford algorithm [12; 13; 14] and Dijkstra algorithm [15] are representative graph-based routing algorithms. In particular, the Dijkstra algorithm has been used in many studies because of its simplicity and flexibility. For example, [PERSON] et al. [16] proposed a routing methodology based on traffic density estimations using the Dijkstra algorithm. [PERSON] et al. [17] enabled consideration of various objective functions by applying the Dijkstra algorithm to a 3D graph in which the time domain was added. Despite these efforts for improvement, the Dijkstra algorithm has the disadvantage of requiring more search space than necessary in the graph. To solve this problem, an A* algorithm with a heuristic function applied to the Dijkstra algorithm was proposed, and time and space complexity were significantly improved when compared with the case of the Dijkstra algorithm. [18]. A number of improved algorithms based on the A* algorithm have been proposed. In particular, the D* algorithm enables the A* one to consider dynamic obstacles by reusing back-pointer information [19], and the Theta* algorithm aims to smooth the solution path of the A* algorithm by using line-of-sight inspection [20]. Although many routing algorithms have been proposed and many studies have improved the algorithm into a form suitable for weather routing, development in terms of the grid map used for weather routing has been insufficient. Thus, in this work, an adaptive grid system is proposed that can facilitate the efficient navigation of the rectangular map commonly used for weather routing. A learning-based regression model for ETA and a 16-way search method were applied to verify the more accurate and realistic alternative route-finding algorithm through simulation. The remainder of this study is as follows. The canonical A* algorithm, including the Bellman-Ford and Dijkstra algorithms, is briefly described in Section 2. In Section 3, properties of the improved A* algorithm, such as the adaptive grid and low-cost heuristic, are explained. In Section 4, a case study using AIS and marine weather data is discussed. Finally, in Section 5, the conclusions of this study and the direction of future research are presented.
# Analytical Model of Gear Thermal Stiffness Based on the Potential Energy Method
## 3 Gear Thermal Stiffness Based on Potential Energy Method
### Effect of Temperature on Contact Stiffness
#### 3.2.2 Contact Thermal Stiffness without Considering Thermal Stress  
Without considering the influence of thermal stress, the expressions of stress \(\sigma_{x}\) and \(\sigma_{y}\) on the \(x\)-axis and \(y\)-axis are given in Reference [30], as shown in Equations (12) and (13).  
\[\sigma_{x}=-\frac{p_{0}}{s}\bigg{[}\Big{(}s^{2}+2y^{2}\Big{)}\left(s^{2}+y^{2} \right)^{-1/2}-2y\bigg{]} \tag{12}\]  
\[\sigma_{y}=-p_{0}s\Big{(}s^{2}+y^{2}\Big{)}^{-1/2} \tag{13}\]  
where \(p_{0}=\max(p(x))=p(0)=2F_{\rm n}/(\pi s)\). Then, the thermal contact deformations \(\delta_{\rm T1}\) and \(\delta_{\rm T2}\) of the pinion and gear can be obtained by substituting Equations (12) and (13) into Equation (11) and integrating it along the \(y\)-axis in the interval (0, \(\rho_{ki}\)), as shown in Equation (14).  
\[\delta_{\rm T1}=\frac{2F_{\rm n}\big{(}1-v^{2}\big{)}}{\pi E}\ln\left(\frac{2 \rho_{ki}}{s}\right)+(1+v)\alpha_{\rm T}\int_{0}^{\rho_{ki}}\Delta T(y)dy \tag{14}\]  
According to Equation (14), the thermal contact deformation of the pinion and gear can be obtained by knowing the distribution function \(\Delta T(y)\) of the temperature rise along the \(y\)-axis. Thereafter, the total thermal contact deformation \(\delta_{\rm T}\) and the thermal contact stiffness \(K^{\rm Th}\) of the gear tooth pair considering the effect of temperature are determined by Equations (15) and (16).  
\[\delta_{\rm T}=\sum_{i=1}^{2}\delta_{\rm T1} \tag{15}\]  
\[K^{\rm Th}=\frac{F_{\rm n}}{\delta_{\rm T}} \tag{16}\]
## 4 Results and Discussion
Figure 8: Fields of: (**a**) estimated amplitude and (**b**), calculated thermal resistance based on the calibration curve in Figure 5a. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\epsilon_{Rth}\) & \(\lambda_{air}\) & \(Rth_{theo}\) & \(Rth_{est}\) & Error *** \\  & (**\(\upmu\)m**) & (**W.m\({}^{-1}\).K\({}^{-1}\))** & (**m\({}^{2}\).K.W\({}^{-1}\))** & (**m\({}^{2}\).K.W\({}^{-1}\))** & (**\%**) \\ \hline \hline \({}^{*}\) ROI 1 & [200–250] ** & 0.025 & [0.008–0.01] & 0.0087 & 3.4 \\ \hline \({}^{*}\) ROI 2 & [300–350] ** & 0.025 & [0.012–0.014] & 0.0126 & 3.1 \\ \hline \({}^{*}\) ROI 3 & [500–550] ** & 0.025 & [0.02–0.022] & 0.0221 & 5.2 \\ \hline \({}^{*}\) ROI 4 & [800–850] ** & 0.025 & [0.032–0.034] & 0.0328 & 0.85 \\ \hline \end{tabular} \({}^{*}\), The ROI order can be found in the schema in Figure 2 or the results in Figure 8; **, Systematic maximum machining uncertainty of +50 \(\upmu\)m given by the supplier; see Section 2; ***, Error \(100\times\left|1-\frac{Rth_{tot}}{Rth_{theo}}\right|\) calculated with the average value \(Rth_{theo}\) of the \(Rth_{theo}\) gap.  
\end{table} 
Table 1: Comparison between the theoretical and estimated values. Figure 7: Loglog dimensionless temperature response of the front face to a pulse of 1 ms duration and adiabatic boundary conditions as a function of the thermal resistance for a sample composed of one layer of polycarbonate, one layer of thermal resistance and one aluminum layer.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 5 DGNSS Positioning
### DBDS Positioning  
Coordinate accuracy processed with the three methods was compared based on the same BDS data used in the evaluation of DGPS positioning accuracy. Figure 8 presents a comparison of the three cases. The symbols used in Figure 8 are the same as those used in Figure 7.  
In the DBDS positioning result, using the real PRC had the most accurate results. RMSE accuracy in the DBDS system with real PRC was 1.14 m, but accuracy with predicted PRC and without PRC were 1.40 m and 2.01 m, respectively. Although RMSE of DBDS positioning calculated with the predicted values was 0.26 m larger than from using observed values, it improved by 0.61 m, compared to standalone positioning without PRC. As shown in Figure 8, most results using observed and predicted PRC showed errors within 3.0 m, whereas results without PRC had errors of up to 5.0 m. Positioning accuracy using the predicted values is lower than with observed values, but the degradation is not significant at all.  
For the vertical direction, RMSE was 11.46 m when PRC was not used, but 3.05 m and 2.80 m when using predicted and observed values, respectively. The positioning result using predicted values was 0.25 m larger than from using observed values, but improved by 8.41 m from the standalone case without PRC. From the experiment, the effectiveness of PRC modeling was also verified through  
Figure 8: DBDS positioning: (**a**) horizontal and (**b**) vertical error without PRC (\(\Box\)), with a predicted PRC (\(\Delta\)), and with the real PRC (\(\bullet\)).  
DBDS results. In particular, accuracy of the vertical component improved by about 70% compared to positioning results without PRC.
###### Abstract  
Red mud, a Fe-rich waste generated from the aluminum industry, was recovered as an adsorbent for wastewater treatment. The separation process of red mud from water after adsorption, including centrifugation and filtration, was complicated. This study demonstrated an alternative option to recycle red mud for preparing magnetic adsorbent via a facile hydrothermal route using ascorbic acid as reductant. Red mud is weakly magnetized and consists of andradite, muscovite, hematite, and cancrinite. After hydrothermal treatment, andradite in red mud was reductively dissolved by ascorbic acid, and transformed into magnetite and morimotoite. With increasing hydrothermal temperature, the dissolution of andradite accelerated, and the crystallite size of magnetite increased. When the hydrothermal temperature reached 200 \({}^{\circ}\)C, the prepared adsorbent P-200 showed a desirable saturation magnetization of 4.1 Am2/kg, and could be easily magnetically separated from water after adsorption. The maximum adsorption capacity of P-200 for Zn2+ was 89.6 mg/g, which is eight-fold higher than that of the raw red mud. The adsorption of Zn2+ by P-200 fitted the Langmuir model, where cation exchange was the main adsorption mechanism. The average distribution coefficient of Zn2+ at low ppm level was 16.81 L/g for P-200, higher than those of the red mud (0.3 L/g) and the prepared P-120 (1.48 L/g) and P-270 (5.48 L/g), demonstrating that P-200 had the best adsorption capacity for Zn2+ and can be served as a practical adsorbent for real-world applications. To our knowledge, this is the first study to report the conversion of red mud into a magnetic adsorbent under mild conditions.  
red mud; hydrothermal; magnetic adsorbent; heavy metal ions; wastewater treatment 2019 9 1519; doi:10.3390/app9081519
# A Review of Validation Methods for the Intracranial Response of FEHM to Blunt Impacts
###### Abstract  
The following is a review of the processes currently employed when validating the intracranial response of Finite Element Head Models (FEHM) against blunt impacts. The authors aim to collate existing validation tools, their applications and findings on their effectiveness to aid researchers in the validation of future FEHM and potential efforts in improving procedures. In this vain, publications providing experimental data on the intracranial pressure, relative brain displacement and brain strain responses to impacts in human subjects are surveyed and key data are summarised. This includes cases that have previously been used in FEHM validation and alternatives with similar potential uses. The processes employed to replicate impact conditions and the resulting head motion are reviewed, as are the analytical techniques used to judge the validity of the models. Finally, publications exploring the validation process and factors affecting it are critically discussed. Reviewing FEHM validation in this way highlights the lack of a single best practice, or an obvious solution to create one using the tools currently available. There is clear scope to improve the validation process of FEHM, and the data available to achieve this. By collecting information from existing publications, it is hoped this review can help guide such developments and provide a point of reference for researchers looking to validate or investigate FEHM in the future, enabling them to make informed choices about the simulation of impacts, how they are generated numerically and the factors considered during output assessment, whilst being aware of potential limitations in the process.  
Finite Element Head Models; FEHM; validation; mild Traumatic Brain Injury +
Footnote †: journal: Applied Sciences
# Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
## 5 Results and Analysis
### Positioning Accuracy of SPP and Characteristics of ISB Estimates
Figure 10: Epoch-wise RMS statistics of positioning errors in the 3D direction for IRNSS-only, GPS-only and GPS/IRNSS SPP based on the datasets collected at 14 MGEX stations on 7–13 July 2019.  
adopted SPP mathematical model is rigorous, and all types of biases and errors have been properly handled. Figure 13 shows the time series of epoch-wise ISB estimates between IRNSS and GPS at 14 MGEX stations on 7-13 July 2019, namely day of year (DOY) 188 to 194 of 2019. The ISB estimates are derived from the GPS/IRNSS combined SPP solutions. Based on Equations (3)-(6), the ISB parameter can be solved even when only one IRNSS satellite and at least four GPS satellites (note that there are generally about nine GPS satellites at each epoch in the datasets) are available. However, according to the least-squares adjustment, the code observation residuals of this IRNSS satellite will equal to zero, which results in unreliable ISB estimates [23]. Therefore, only the epoch-wise ISB estimates with at least two IRNSS satellites are used for analysis here. As a result of the unstable tracking for IRNSS satellites by some stations, such as the URUM and VACS stations, the ISB results are absent during partial time spans at these stations. Some common mode components of ISB throughout the 14 stations can be observed, which is caused by the time-offset between IRNSS and GPS systems. In addition, the epoch-wise ISB series have obvious periodic characteristics, and the period is consistent with the orbit repetition period of IRNSS and GPS satellites, namely one day. This can be well-explained by satellite distribution, namely the ISB-specific DOPs with periodic characteristics (see Figure 14). In addition, the single-epoch ISB estimates vary within 30 ns for the seven-day datasets, indicating that the ISB cannot be ignored when performing the data processing of GPS/IRNSS combined SPP. Figure 12: Distribution of code observation residuals derived from GPS/IRNSS combined SPP for GPS and IRNSS satellites. Figure 14: Time series of epoch-wise DOP corresponding to ISB estimates derived from GPS/IRNSS, integrated SPP at stations DARW, MAW1, and MIZU on 7–13 July (DOY 188 to 194), 2019. Figure 13: Time series of epoch-wise ISB estimates derived from GPS/IRNSS integrated SPP at 14 MGEX stations on 7–13 July (DOY 188 to 194), 2019.
## 2 Micro-Doppler Signals
### Radar Sensor Board and Specifications  
We designed the Doppler radar sensor board. Figure 3 shows the block diagram of the low-cost & short-range 24 GHz Doppler Radar and Figure 4 shows the radar sensor board implemented in this research. Radar sensor board has two main part of RF-Antenna circuits and processing circuits. RF-Antenna circuits include the micro-strip patch antenna, CW signal generator, Tx amplifier (Drive amp.), low noise amplifier and homodyne I/Q mixer (direct conversion) and two-stage cascaded baseband amplifier. The processing circuits have the micro-controller having AD converter and interface for the external data transmission and power circuits. Table 1 shows the specifications of 24 GHz Doppler radar.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Specifications** & **Ref.** \\ \hline Operating frequency & 24.125 GHz & \\ RF output & 16 dBm & TX port \\ Antenna Gain & 6 dBi & with feed loss \\ Detection range & 20 m (typ.) & Human walking (adult) \\ Detection angle & \(-40^{\circ}\sim+40^{\circ}\) & Azimuth \\ Micro-controller & Freescale Kinetis k60 & Clock: 100 MHz \\ Processing time & 1 s & \\ Sampling rate & 1024 samples/s & \\ Size & 60 mm \(\times\) 60 mm \(\times\) 15 mm & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specifications of 24 GHz Doppler radar.  
Figure 2: The binary classifier and multiclass classifier. (**a**) Binary classifier; (**b**) Multiclass classifier.
# A Semantic and Knowledge-Based Approach for Handover Management
## 4 Evaluation
### Prototype and Test Environment  
We implemented the SIM-Know prototype for WLAN, including \(KBP_{M}\), \(KBP_{N}\), and \(KBP_{S}\), by using the Python programming language version 2.7. We also deployed the prototype in a Mininet-WiFi emulator [39] (see Figure 6) running on an Ubuntu 16.04 Virtual Machine (VM) with a Core i7-3630 processor and 8 GB RAM. Mininet-WiFi adds virtual BSs and APs to classical Mininet [40] to enable the emulation of wireless network environments. The SIM-Know prototype, as well as all test scripts, are available in the project repository [41].  
Figure 6: Test Environment.  
Figure 6 shows the WLAN test scenario in which we evaluated and compared SIM-Know, SSF, and AHP-TOPSIS. The scenario, deployed in Mininet-WiFi, included seven APs, an end-user device associated with _User_1, and another end-user device linked to _User_2. In particular, we used three APs with a large coverage range (i.e., up to \(75m\) for \(AP_{1}\), \(AP_{4}\), and \(AP_{5}\) with \(802.11n\)) and four with a short coverage range (i.e., up to \(35m\) for \(AP_{2}\), \(AP_{3}\), \(AP_{6}\), and \(AP_{7}\) with \(802.11 ac\)). We also analyzed the performance when the _User_ 1 moved from Point A to Point B by following a straight line without directional change at a constant speed. We used three speeds for testing: 1.42 m/s corresponding to _SlowMobility_, 3.74 m/s to _ModerateMobility_, and 13.41 m/s to _HighMobility_. The end-user device associated with _User_ 1 transmitted traffic (Voice over IP (VoIP) or Transmission Control Protocol (TCP)) to the end-user device linked to _User_ 2, which was immobile. We repeated the experiments thirty-three times to obtain results with a 95% confidence level. Table 2 summarizes the setup of the experiments.  
It is worth mentioning that the described scenario was constrained to a small number of end-user devices because our main objective was to show the feasibility of SIM-Know. We will perform more extensive evaluations on large emulated environments in our subsequent papers.