esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Result and Discussion  
### Scattered Signals Processing and ToF Extraction  
The signals scattered from the target were processed by wavelet threshold denoising to decrease the influence of white Gaussian noise. In order to extract ToF from the scattered signals, a Hilbert transform was applied in order to extract its envelope and the ToF information. The processed scattered signals recorded at receiving position 1 and its Hilbert transform are shown in Figure 8.
## 4 Discussion of Limitations
### Crash Severity Validation  
A major challenge to the validation of the predicted crash severity is the mining of the ground truth crash severity data. In-depth accident data studies reconstruct traffic accidents, which are an approximation of reality. Additionally, the GIDAS database contains reconstructed scalar values of delta-v, not crash pulses as time series. In order to obtain the crash pulses measured In-Crash, the event data recorder (EDR) signals must be collected.  
Figure 10: Pre-Crash system triggering confusion matrix.  
This has been performed only on a small number of GIDAS cases [33]. Another possibility in estimating the crash pulses for identified representative collision configurations are FEM simulations of car to car collisions; however, these are very cost-intensive.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 3 Approach
### Fusion Network  
The fusion network R2 FN, tailored for the dual-modal image fusion task, is designed based on the Res2 Net architecture. In the fusion network, the parameters of R2 FN vary across different layers. The structure of the R2 FN network is illustrated in Figure 7.  
Figure 5: Framework of proposed method.  
Figure 6: The structure of the enhanced Bneck.  
In the figure, \(\Phi^{\rm m}_{\rm irr}\) and \(\Phi^{\rm m}_{\rm vi}\) represent the infrared and visible depth features extracted by the encoder network, respectively. Initially, the outputs of Conv1 and Conv2 are concatenated and fed into a \(1\times 1\) convolution for channel transformation. The feature maps are subsequently partitioned into s subsets, each characterized by an identical spatial size and \(1/\)s of the total channel count, with s serving as the scale control parameter. The first subset, \(X_{1}\), remains unchanged and is directly propagated to \(Y_{1}\), while the remaining subsets undergo \(3\times 3\) convolution operations before being added to the next feature subset. Subsequently, the acquired feature maps are fed into the SE module to modify the weights of each channel, thereby improving the model's accuracy. Here, ReLU and h-sigmoid are sequentially utilized as activation functions. Finally, the results from the \(1\times 1\) convolution are added to the fusion convolutional layer Conv3 to obtain the final outcome C. In our experiments, we selected s = 4 as the scale control parameter.
## 3 Experiment and Results
### Performance Metrics and Benchmark Model  
Five fitting error indices and two main benchmark models were introduced to ensure the predictability of the proposed model. These indicators were MAE, RMSE, MAPE, STDAPE, DA, U1, U2, and \(R^{2}\) (the coefficient of determination), as well as seven ANN models. MAPE was a significant focus.
# Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
## 2 Materials and Methods
### Healthcare Data Quality Conceptual Model Development
#### 2.1.5 Consistency  
Consistency assesses the relationship between the inside and outside of the system by evaluating whether the data are consistent according to the system structure. For example, if a patient visits a hospital as an outpatient and receives a medication prescription, the value of their assigned patient number should exist in the visit information and medication information. This rule examines whether different data are linked to the foreground key in the database.  
The importance of validation rules for seven clinical experts, data managers, and data quality experts with more than three years of experience in the above five dimensions were evaluated. The weights developed using these are reflected in the evaluation index (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dimension** & **Sub-Dimension** & **Definition** & **Importance** & **Weight** \\ \hline Completeness & & This rule verifies that there are no missing required columns. & 9.6 & 0.23 \\ \hline Validity & Range & This rule checks whether a data value is within a given range. & 7.5 & 0.18 \\  & Format & This rule checks whether a data value conforms to the data type. & & \\ \hline Accuracy & Calculation & This rule verifies whether the values of different columns are correct. & 7.7 & 0.18 \\  & Timeline & This rule verifies the precedence of time. & & \\  & Business Rule & This rule verifies the hospital business rules. & & \\ \hline Uniqueness & & This rule verifies the value corresponding to the primary key. & 9 & 0.22 \\ \hline Consistency & Standard & If an international standard code is used, this rule verifies the standard code. & & \\ Relationship & If there is a referential relationship between tables, this rule verifies the referential integrity. & 7.9 & 0.19 \\ \hline \end{tabular}
\end{table}
Table 1: DQ4 HEALTH dimensions of specialist group review.
# Multi-View Visual Relationship Detection with Estimated Depth Map
## 4 Experimental Procedure
### Visual Relationship Detection  
#### 4.1.1 Dataset  
The VRD dataset [1] is widely used in performance evaluation for visual relationship detection framework. This dataset includes 4000 training images and 1000 test images, where the locations of the bounding boxes, categories of objects and the categories of predicates are annotated. The VRD dataset [1] contains 100 kinds of objects, 70 kinds of predicates, and 6672 different kinds of visual relationship triplets annotations. Furthermore, there are 1877 specific visual relationship triplets which never occur in the training set to evaluate the zero-shot learning performance and generalization ability of the visual relationship detection model.
## 4 Residual Error Optimization Strategy Based on Root-Mean-Square and Peak-to-Valley Maps
### Path Spacing Optimization Model Based on Surface Form Error
It is important to point out that several simulation experiments have shown that when the fluctuation range of the parent surface form error is large, expectations for the degree of residual error optimization should be reduced in one iteration, and the minimum path spacing should be selected to be larger, thereby increasing the critical value of bulk material removal depth determined by point P4 and reducing the variation range of the polishing path pitch. This is more conducive to reducing the impact of the distribution of the parent surface form error on the child residual error distribution, while avoiding distortion of the planned variable pitch path and affecting the final polishing effect. Equation (7) gives the empirical formula for the choice of minimum and maximum path spacing under the same parameters of the optimization strategy proposed in this paper. \[\left\{ \begin{array}{ll}p_{\max}=3R_{tif}\Big{/}5\\ p_{\min}=2p_{\max}\times[\max\left(Z_{0}\left(x,y\right)\right)-\min\left(Z_{0 }\left(x,y\right)\right)]\big{/}(3\max\left(Z_{0}\left(x,y\right)\right))\end{array} \right. \tag{7}\]  
where \(R_{tif}\) represents the radius of the TIF; \(\max(Z_{0}(x,y))\) and \(\min(Z_{0}(x,y))\) represent the largest and the smallest form error, respectively.
# Investigation of the Two-Way Injection Slurry-Supply Method for the Cu CMP Process
###### Abstract  
The effect of the two-way injection method during a copper chemical mechanical planarization (CMP) process was investigated. The two-way slurry-injection method has the advantage of not only preventing the degradation of the slurry, but also shortening the process time because the mixing process of the slurry and H\({}_{2}\)O\({}_{2}\) is not required. Compared to the conventional method, the two-way injection method has an equivalent level of less than 9% in material removal rate. In particular, when injecting near the center of the pad, the influence according to the positions and flow rates of the two nozzles is also insignificant, so it deserves to be studied as a slurry-supply method.  
chemical mechanical planarization; slurry-supply method; hydrogen peroxide +
Footnote †: journal: Applied  
1 SKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Seoul 16419, Republic of Korea 1,4  
Footnote 1: CSKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Seoul 16419, Republic of Korea 1,4
and novel slurry-injection system) using an ultraviolet-enhanced fluorescence system, and argued that the multihole injection method is effective in reducing slurry consumption by generating a thicker bow wave. In other studies, a method of supplying one slurry using multiple nozzles was used. In addition, there are many studies focusing on reducing slurry consumption to prevent CMP performance degradation. However, in this study, CMP performance was confirmed when the slurry was supplied through an on-plate mixing method in which the slurry excluding H2O2 and H2O2 was separately supplied. Through this method, the decomposition of H2O2 in the slurry can be minimized to prevent a degradation in CMP performance. The advantage of a two-way injection is that, since H2O2 and slurry are separately injected, degradation caused by mixing before the CMP process can be prevented, and the process of mixing the slurry and H2O2 is unnecessary, which is advantageous in terms of processing time. The CMP performance can be secured through this method. In terms of Cu MRR, the two-way slurry-injection method shows almost the same performance as the general (one-nozzle) method. Therefore, the two-way injection slurry-supply method can be considered an alternative to the general injection method that can prevent slurry degradation.
## 2 Materials and Methods
### Setup Data Collection  
First of all, for each patient and treatment fraction, basic clinical and treatment information were collected. Secondly, CVs were retrieved from the log files of the treatment rooms by means of an in-house script developed with MATLAB (The MathWorks Inc., Natick, MA, USA). Each CV is composed of 6 components, half translational, namely, \(x\), \(y\) and \(z\), and half rotational, namely, pitch (\(P\)), roll (\(R\)), and yaw (\(Y\)). With respect to the patient, \(x\), \(y\) and \(z\) represent the latero-lateral, cranio-caudal and antero-posterior directions, respectively. \(P\), \(R\) and \(Y\) represent rotations around the \(x\), \(y\), and \(z\) axes, respectively. As a preliminary analysis, the average value and standard deviation of each coordinate for each patient, throughout the treatment fractions, were computed. Additionally, the mean value of the modules (across patients) of the average corrections (across fractions for each patient), as well as the standard deviation (SD) for each coordinate, was computed. These basic statistical indexes make it possible to estimate a priori the impact of a pre-correction strategy based on averaging procedures to dampen systematic errors (i.e., the higher the average values of corrections, the higher the likelihood that an algorithm that cuts off systematic errors succeeds). For those patients who underwent two or more setup verifications throughout treatment, only the first one was considered and analyzed.
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 5 Conclusions  
A computationally efficient sector-matching PHD filter is proposed in this paper. With the radar sampling time diversity considered, the proposed filter is well adapted to the MTT problem during radar dynamic scanning. The proposed sector-matching structure quantifies the relationship between beam dwell time, false alarm rate and detection probability, and divides the scanning area into small sectors to obtain actual multi-target measurement times. The proposed filter rederives the prediction and updating steps based on the actual sampling time, and a state correction step is added. The performance is evaluated by simulations using the Gaussian linear model, and the results demonstrate that the proposed filter provides enhanced filtering performance in multi-target tracking. There are still some challenges with the proposed filter, including radars operating in TAS mode and target migration through resolution cells. We will address these issues in future research. In addition, as the performance of the TPHD [33] and TPMB [23] filters is much better than the basic PHD filter, future work will investigate the combination of \"sector matching\" structures with trajectory filters.  
Conceptualization, J.Y.; formal analysis, J.Y. and J.T.; investigation, D.J. and Y.G.; resources, D.J. and M.L.; data curation, J.Y. and Y.H.; writing--original draft preparation, J.Y.; writing--review and editing, J.Y., D.J., Y.G., X.L. and M.L. 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 under grant no. 61971179 and the Fundamental Research Funds for the Central Universities under grant no. B200202165.  
Not applicable.  
The authors declare no conflict of interest.
# Thermal Performance of Cryogenic Micro-Pin Fin Coolers with Two-Phase Liquid Nitrogen Flows
## Nomenclature  
A base area [m\({}^{2}\)]  
C constant in Equation (7)  
d\({}_{e}\) equivalent diameter of a fin [m]  
F enhancement factor  
f friction factor  
G mass flux [kg/m\({}^{2}\)s]  
H\({}_{fin}\) fin height [m]  
h heat transfer coefficient [W/m\({}^{2}\)-k]  
hbase heat transfer coefficient [W/m\({}^{2}\)-k]  
k thermal conductivity [W/m-k]  
N number of fin rows in longitudinal direction  
Nu Nusselt number  
Pr Prandtl number  
\(\Delta\)P pressure drop [Pa]  
\(\Delta\)P\({}_{f}\) pressure drop due to friction [Pa]  
\(\Delta\)P\({}_{acl}\) pressure drop due to acceleration [Pa]  
q heat transfer rate [W]  
Re Reynolds number  
S suppression factor  
\(S_{t}\) transverse fin spacing [m]  
\(S_{L}\) longitudinal fin spacing [m]  
Tinf fluid temperature [\({}^{\circ}\)C]  
T\({}_{\text{s}}\) surface temperature [\({}^{\circ}\)C]  
X Martinelli parameter  
x quality  
Greek symbols  
\(\alpha\) void fraction  
\(\rho\) density [kg/m\({}^{3}\)]  
\(\mu\) dynamic viscosity [Pa-s]  
\(\
u\) specific volume [m\({}^{3}\)/kg]  
\(\phi_{l}\) two-phase frictional multiplier  
\(\zeta\) adjustment parameter  
* [PERSON] et al. (2002) [PERSON]; [PERSON]; [PERSON] Performance of pin fin cast aluminum, coldwalls, part 2: Colburn j-factor correlations. _J. Thermophys. Heat Transf._**2002**, _16_, 397-403. [CrossRef]
* [PERSON] (1986) [PERSON] _Cryogenic Engineering_, 1 st ed.; Academic Press: Niwot, CO, USA, 1986.
* [PERSON] (2013) [PERSON] _CRC Chemistry/Physics Handbook_, 93 rd ed.; CRC Press: New York, NY, USA, 2013.
* [PERSON] (2000) 3M Fluorinert. Product Information. 2000.
* [PERSON] and [PERSON] (1990) [PERSON]; [PERSON] Parametric Investigation into the effects of pressure, subcooling, surface augmentation and choice of coolant on pool boiling in the design of cooling systems for high-power-density electronic chips. _ASME J. Electron. Packag._**1990**, _112_, 375-382. [CrossRef]
## 3 Results  
Of the twenty tackle cases recorded in the staged laboratory trials, nineteen could be reconstructed in the multibody modelling simulations (in one case, too many body markers fell off a participant during a tackle to generate valid kinematic measures). This resulted in eight mid/lower trunk tackles and 11 upper trunk tackles being executed. Table 1 reports the change in ball carrier head linear and angular velocity results for each tackle trial. For upper trunk tackles, the median percentage error (absolute value with quartiles) for the MADYMO predictions were 10% (6% to 45%) and 23% (16% to 39%) for change in head linear and angular velocity, respectively. For mid/lower trunk tackles, the median percentage error (absolute value with quartiles) for the MADYMO predictions were 46% (33% to 63%) and 60% (53% to 123%) for change in head linear and angular velocity, respectively. Table 1 also shows very high percentage errors, up to 336% and 366% for the change in head linear and angular velocity, respectively. The MADYMO simulations tend to respectively overpredict and underpredict the change in ball carrier head angular and linear velocity, for mid/lower trunk tackles (Figure 3). For upper trunk tackles (Figure 3), the results showed no clear trend with respect to underprediction or overprediction of the model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ball Carrier**} & \multicolumn{4}{c}{**Change in Resultant Head Linear Velocity (m/s)**} & \multicolumn{4}{c}{**Change in Resultant Head Angular Velocity (rad/s)**} \\ \hline \multirow{2}{*}{**Tackle ID**} & \multicolumn{2}{c}{**Closing**} & \multirow{2}{*}{**Vicon**} & \multirow{2}{*}{**MADYMO**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**\% Error (\%**} & \multirow{2}{*}{**Vicon**} & \multirow{2}{*}{**MADYMO**} & \multirow{2}{*}{**MADYMO**} & \multirow{2}{*}{**\% Error (\%**} \\  & **Speed** & & & & & & & & \\  & **(m/s)** & & & & & & & & \\  & & & & & & & & & \\ \hline Mid/Lower 2 & 4.04 & 1.03 & 0.59 & \(-\)0.44 & \(-\)43 & 2.52 & 3.92 & 1.40 & 56 \\ Mid/Lower 3 & 3.06 & 0.88 & 0.35 & \(-\)0.53 & \(-\)60 & 3.00 & 4.92 & 1.92 & 64 \\ Mid/Lower 4 & 3.70 & 1.06 & 0.70 & \(-\)0.35 & \(-\)33 & 1.73 & 4.09 & 2.36 & 136 \\ Mid/Lower 5 & 2.89 & 0.90 & 0.47 & \(-\)0.43 & \(-\)48 & 3.59 & 4.79 & 1.20 & 33 \\ Mid/Lower 6 & 4.20 & 1.45 & 0.50 & \(-\)0.95 & \(-\)65 & 3.42 & 5.28 & 1.86 & 54 \\ Mid/Lower 7 & 3.78 & 0.84 & 0.60 & \(-\)0.24 & \(-\)28 & 1.81 & 3.78 & 1.97 & 109 \\ Mid/Lower 8 & 3.85 & 0.92
## 2 ELM-AE and Shrinkage-Thresholding Algorithms
### The Shrinkage-Thresholding Class of Optimization Algorithms
#### 2.2.2 Greedy FISTA  
In general terms, G-FISTA [44] is a variant of FISTA that is characterized by a higher speed. Given the conditions imposed by G-FISTA, its architecture can restart the FISTA algorithm with the fixed impulse factor \(\gamma_{k}=1\). This algorithm updates the solution to the optimization problem in (4), setting \(\rho\in[\frac{1}{L_{f}},\frac{2}{L_{f}}]\), \(\xi<1\) and \(S>1\) until the maximum number of iterations is reached. The updating scheme is as follows:  
1. Update: \(\mathbf{\tau}_{k}=\text{prox}_{\rho\phi}(\mathbf{z}_{k}-\rho\
abla f(\mathbf{z}_{k}))\).
2. Update: \(\mathbf{z}_{k+1}=\mathbf{\tau}_{k}+(\mathbf{\tau}_{k}-\mathbf{\tau}_{k-1})\).
3. Restart: if \((\mathbf{z}_{k}-\mathbf{\tau}_{k})^{T}(\mathbf{\tau}_{k}-\mathbf{\tau}_{k-1})\geq 0\), then \(\mathbf{z}_{k+1}=\mathbf{\tau}_{k}\).
4. Safeguard: if \(\|\mathbf{\tau}_{k}-\mathbf{\tau}_{k-1}\|\geq S\|\mathbf{\tau}_{1}-\mathbf{\tau}_{0}\|\), then \(\rho=\max\{\xi\rho,\frac{1}{L_{f}}\}\).  
The G-FISTA algorithm improves the convergence rate and the reconstruction capability of FISTA, which uses a self-adaptive restart and adjustment scheme to obtain an additional acceleration and alleviate the oscillation problem in the reconstruction process. Based on the results discussed in [44; 49], G-FISTA has efficient performance when \(\rho\in[\frac{1}{L_{f}},\frac{13}{L_{f}}]\). For this reason, all experiments performed in this work were configured with \(\rho=1.3/L_{f}\), \(\xi=0.96\), and \(S=1.1\).
## 6 Simulation Results and Discussion
### Scenario (1)
#### 6.1.2 IEEE 69-Bus Test System  
The efficacy of the I-GWO algorithm was further tested on a medium-scale radial distribution system, specifically, the IEEE 69-bus system [26]. The capacity base for this system was set at 1 MVA, while its voltage base was 12.66 kV. The data for the system were obtained from [27] and can be found in Appendix B. This test system carried a total load of 3.80 MW and 2.69 MVAR. The total losses amounted to 224.99 kW, which translated to an annual cost of 118,254.41 USD/year. Notably, the minimum voltage was observed to be 0.90919 p.u. at bus 65, and the maximum voltage was 1.0 p.u. at the source bus.  
Table 4 indicates that in the case with two DSTATCOMs installed, the TACS amounts to USD 26,683.72 per year, which is superior to the other cases. The minimum voltage increased to 0.95144 at bus 65, as observed in Figure 11. Additionally, power losses were reduced to 153.73 kW, representing a 31.67% reduction compared to the base case.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Outputs**} & \multirow{2}{*}{**Base Case**} & \multicolumn{3}{c}{**Number of DSTATCOMs**} \\ \cline{3-5}  & & **Single** & **Two** & **Three** \\ \hline \multirow{3}{*}{Optimal size (kVAr) and location} & \multirow{3}{*}{1850.00 (30)} & 699.58 (14) & 953.38 (7) \\  & & & 1386.89 (30) & 477.22 (14) \\ \cline{1-1} \multirow{3}{*}{\% Reduction in Ploss} & \multirow{3}{*}{202.68} & 153.27 & 144.23 & 141.31 \\  & & 24.37 & 28.84 & 30.28 \\ \cline{1-1} \cline{2-5} \cline{4
## 2 Materials and Methods
### Preparation of Graphene Oxide Hydrogels  
#### 2.2.1 Preparation of Graphene Oxide  
Graphene oxide was prepared using a modified Hummer's method by dispersing 5 \(g\) of graphite powder in a 1000 mL conical flask containing 27 mL of a mixture of sulphuric acid (95-97%) and 3 mL of phosphoric acid and stirring for 30 min at 1000-2000 rpm-min\({}^{-1}\) in an ice bath (0-10 \({}^{\circ}\)C). After stirring, 15 \(\mathrm{\SIUnitSymbolOhm}\) of potassium permanganate was added to the mixture. The mixture was then raised to 50 \({}^{\circ}\)C and stirred for 12 h. The oxidized mixture was cooled to room temperature, 30% hydrogen peroxide solution was added, and the golden yellow mixture was left to stand without color change. The graphene oxide powder was obtained by washing with 1 mol-L\({}^{-1}\) hydrochloric acid solution and deionized water several times to obtain a dark brown precipitate, which was dried to a constant weight at 50 \({}^{\circ}\)C and ground and sieved through 100 mesh to obtain graphene oxide powder [24]. The graphene oxide powder was obtained by grinding and passing through 100 mesh sieves.