esa-sceva/satcom-chunk-collection huggingface dataset:
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
## 4 Calibration Results  
To verify the effectiveness of the calibration method proposed in this paper, we carried out a 3D topographic mapping experiment by airborne STIL.  
The aircraft flew at an altitude of about 2000 m and a speed of about 70 m/s; the terrain was relatively flat, with an elevation of about 100 m, and the surveying area was a small town in northern China. After the same data processing operation as below, filtering noise, calibrating the installation angle of the inertial measurement unit and optical system, and correcting the inherent error in distance measurement, we compared the data of the 3D point cloud before and after the time and angle calibration proposed in this paper. The results are shown in Figures 12 and 13.  
Differently from the push broom scanning used in traditional STIL, we used a pendulum broom scanning method [21]. Due to the fact that the streak tube detection system could be seen as a linear array detector, the point cloud was composed of individual bands. From Figure 12, we can find that the overlapping effect of the two bands was better at the profile lines after calibration. Before calibration, even if the measured ground was flat, its streak signal was irregularly curved, and the curvature of streak targets at different distances was also different. After calibration, the streak signal on the flat ground became smooth, and the splicing parts of the 3D point cloud overlapped better.  
To evaluate the measurement errors before and after calibration, we measured the geographic coordinates of the 30 points listed in Figure 13 with a Leica Viva GS16 GNSS receiver. We took the measurement results of Leica Viva GS16 as the reference values and compared the point cloud data with them. We found that the range error (here, the  
Figure 11: The centroid points after thinning under different delays and angles.  
Figure 10: The centroid of the streak of the laser point.  
root mean square error) of the point cloud after calibration was reduced from 0.41 m to 0.27 m.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 6 Effects of Fiber Cutting Angle on Milling Force  
### Definition of Fiber-Cutting Angle  
The fiber-cutting angle of CFRP down milling can be defined according to the orthogonal cutting model of unidirectional CFRP material. In the orthogonal plane, the angle where the feed direction  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variance Source** & **Degree of Freedom** & **Sum of Squares (SS)** & **Mean Square (MS)** & **F** & **Significance** \\ \hline Regression & 3 & 0.536 & 0.179 & 18.182 & \\ Residual & 12 & 0.118 & 0.010 & — & 0.000 \\ Sum & 15 & 0.654 & — & — & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Regression variance of \(F_{x}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Coefficient \(t\)** & **b1** & **b2** & **b3** & **Significance of Regression** \\ \cline{2-5}  & \(|\mathbf{v}|\) & \(|\mathbf{a}_{e}|\) & \(|\mathbf{f}_{z}|\) & **Coefficient** \\ \hline \(t\) & 2.551 & 5.101 & 4.692 & \(b_{2}>b_{3}>b_{1}>2.201\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Significance test of regression coefficient.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Experiment No. 1**} & \multicolumn{3}{c}{**Experiment No. 2**} & \multicolumn{3}{c}{**Experiment No. 3**} \\ \hline \(\mathbf{v}\) (m/min) & & 75 & & 125 & & 175 \\ \(a_{e}\) (mm) & & 0.75 & & 1.25 & & 1.75 \\ \(f_{z}\) (mm/z) & & 0.015 & & 0.025 & & 0.035 \\ \hline Milling force (N) & \(F_{x}\) & \(F_{y}\) & \(F_{z}\) & \(F_{x}\) & \(F_{y}\) & \(F_{z}\) & \(F_{x}\) & \(F_{y}\) & \(F_{z}\) \\ Experimental (N) & 119.87 & 59.45 & 22.26 & 185.12 & 84.93 & 39.61 & 231.73 & 110.26 & 59.36 \\ Predicted (N) & 129.32 & 61.0 & 24.74 & 193.9 & 90.12 & 43.62 & 253.2 & 116.54 & 65.82 \\ Error (\%) & 7.88 & 2.61 & 11.12 & 4.74 & 6.11 & 10.12 & 9.26 & 5.69 & 10.88 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Test of regression model for milling force.  
rotates counterclockwise to the fiber axis is defined as the milling direction angle \(\beta\), and the angle between the cutting direction of the tool and the fiber axis is defined as the fiber-cutting angle \(\theta\). \(\beta\) and \(\theta\) are used to characterize the tool path during cutting. When the milling direction angle is constant, the instantaneous fiber-cutting angle changes with tool rotation. However, the machining surface is mainly determined by the cut-out state of the tool. When the tool mills CFRP material in different milling directions during down milling, the corresponding fiber-cutting angles are different, as shown in Figure 9.
# SNR-Based GNSS-R for Coastal Sea-Level Altimetry
## 7 Conclusions  
The Signal-to-Noise Ratio (SNR) observations from a GNSS station installed at the Haiti Coast Guard Base in Port-au-Prince is used to retrieve sea levels in the Caribbean Sea. GNSS-R sea levels are corrected for tropospheric delay and the dynamic state of the sea surface in the post-inversion processing. The geodetic GNSS-R sea levels are highly correlated with those of the collocated OTT radar level sensor installed vertically below the GNSS antenna, with a correlation coefficient exceeding 0.96.  
Due to the temporal resolution of the geodetic GNSS-R, the performance of geodetic GNSS-R capturing tidal variations is good above 6-h periods. A mm-level agreement for the eight major tidal constituents from the 15-min geodetic GNSS-R retrievals employing an 8-h moving window and OTT radar records is found.  
The GNSS coordinate time series at the POPR tide gauge station are estimated using GAMIT/GLOBK software. The GNSS-R-derived sea levels are corrected for the Antenna Phase Center (APC) relative to the Antenna Reference Point (ARP) in order to retrieve sea levels in the International Terrestrial Reference Frame 2014. Therefore, sub-daily sea levels at the Port-au-Prince tide gauge station are reported in ITRF 2014.  
In the future, we plan to include other navigation signals from the European Galileo and the Chinese BeiDou to improve the temporal resolution of geodetic GNSS-R sea levels. Furthermore, we will investigate the bias in the GNSS-R sea levels by conducting research to calibrate the radar level sensor.  
Conceptualization, S.T.; methodology, S.T.; software, S.T.; validation, S.T. and O.F.; formal analysis, S.T. and R.S.; investigation, S.T.; resources, S.T., R.S., K.G., G.M. and O.F.; data curation, S.T. and R.S.; writing--original draft preparation, S.T. and R.S.; writing--review and editing, S.T.; visualization, S.T.; project administration, O.F. All authors have read and agreed to the published version of the manuscript.  
This research was carried out at the Geophysics Laboratory of the University of Luxembourg and received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: Absolute sub-hourly sea level at the Haiti Coast Guard Base in Port-au-Prince.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data from the tide gauge in the Haiti Coast Guard Base Port-au-Prince is publicly available from the IOC. GNSS raw data are available from University of Luxembourg.  
**Acknowledgments**: We would like to thank [PERSON] and [PERSON] from the University of Luxembourg, [PERSON] from CNIGS, and [PERSON] from the State University of Haiti for technical support on the project. We thank [PERSON] for the valuable discussions.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 4 Discussion  
TADs have transformed several aspects of n orthodontic practice. Maximillary molar distalization is not an exception, and we think that TADs can provide more predictable and reliable maxillary molar distalization compared with traditional non-TAD devices. TADs do not require patient compliance, and their use also minimizes the adverse effects of maxillary molar distalization. Studies were conducted to validate the effectiveness of TAD-assisted maxillary molar distalizers, all of which demonstrated their efficacy not only in maxillary molar distalization, but also in effecting pure molar distalization without significant anchorage loss (mainly mesial tipping of the maxillary premolars and proclination of the incisors). Numerous maxillary molar distalization appliance designs utilizing TADs (mainly the miniscrew type) have been introduced, which either include TADs as adjunctive measures in combination with conventional appliances or TADs as the principal distalization tool. The two most reported types in the literature that are used in conjunction with fully fixed buccal appliances entail placement of the TAD in the palate and connected to a lingual arch appliance, or inserting the TAD on the buccal side of the maxillary dentition and directly connecting the TAD with the maxillary dentition using coil springs or elastomeric materials [10; 14; 16; 17]. En masse, one-step total distalization of the maxillary dentition is usually performed with bucally placed TADs [16]. All patients included in this study were treated with en masse retraction with TADs as part of the treatment protocol. Maximillary molar distalization was indirectly achieved in group 1 by en masse maxillary dentition distalization, whereas the maxillary first molars were subjected to direct distalization forces in groups 2 and 3. Group 1 underwent one-stage maxillary dentition distalization, while groups 2 and 3 underwent two-stage distalization (the molars were distalized first, followed by the premolars and mesial teeth). All treatment groups showed effective maxillary molar distalization, as revealed by statistical analysis (PtV to Mx, 7 mm). None of the three groups showed changes in the Frankfort-mandibular plane angle (FMA); therefore, there were no vertical skeletal changes in nongrowing patients with maxillary molar distalization. Studies analyzing the effects of conventional maxillary molar distalizers, such as pendulum and distal jets, have reported an increase in the FMA due to the extrusion of the maxillary molars after treatment and a decrease during follow-up [12; 19; 29; 30]. The reason for the lack of changes in the FMA in this study may be attributed to all participants being in the nongrowth stage, and fixed buccal appliances were simultaneously used during molar distalization. Similarly, several studies have found no vertical changes even though intrusion of the maxillary molars occurred after distalization with TADs [10; 31; 32; 33; 34].
# Impact Load Sparse Recognition Method Based on Mc Penalty Function
## 2 Impact Load Sparse Identification Model
### The Governing Equation for Identification of Multiple Impact Loads  
When subjected to multiple impact loads, its structural equation is as follows:  
\[M\ddot{Y}+\mathbf{C}\dot{Y}+\mathbf{K}\mathbf{Y}=F(t) \tag{11}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{K}\) are mass matrix, damping matrix, and stiffness matrix, respectively; \(\ddot{\mathbf{Y}}\), \(\dot{\mathbf{Y}}\), \(\mathbf{Y}\) are acceleration vector, velocity vector, and displacement direction maximum, respectively; and \(\mathbf{F}(t)\) is the unknown dynamic load vector to be identified. Equation (11  
\[\{\mathbf{F}(t)\}=\left\{\begin{array}{c}\mathbf{\theta}\\ \frac{\mathbf{F}(t)}{\mathbf{M}}\end{array}\right\} \tag{15}\]  
\[\mathbf{B}=-\frac{1}{\mathbf{M}}\mathbf{K}\mathbf{J}=-\frac{1}{\mathbf{M}}\mathbf{C} \tag{16}\]  
where \(\{\mathbf{Z}(t)\}\), \(\{\mathbf{F}(t)\}\) are \(2n\times 1\) dimensional column vectors, \(\mathbf{R}\) is \(2n\times 2n\) square dimension matrix. Similar to the derivation of single impact identification, Equation (12) can be ultimately simplified as:  
\[\mathbf{H}\mathbf{F}=\mathbf{Y} \tag{17}\]  
for the purpose of unifying the expression, the above page can be written as a compact matrices vector expression:  
\[\mathbf{H}\mathbf{f}=\mathbf{y} \tag{18}\]  
In general, the unknown load vector can be regarded as a multi-channel vector composed of \(N\) load vectors. Vibration response can be regarded as a multi-channel vector composed of multiple response vectors. In practice, when the system response is impacted by multiple loads, the calculation amount of exponential matrix will be huge, and the transfer matrix \(\mathbf{H}\) will become more sensitive to the noise in response. Since noise is unavoidable in the vibration monitoring process, the final load identification model is as follows:  
\[\mathbf{y}=\mathbf{H}\mathbf{f}+\mathbf{\delta} \tag{19}\]  
where the vector \(\mathbf{\delta}\) represents the amount of noise loss in the system response. The transfer matrix is \(\mathbf{H}\) obtained by the modal test method, which can be adapted to various complex mechanical structures, is relatively mature and has strong operability.
## 4 Results
### Results for the 1D Problem: Sound Absorption and ILR  
Figure 5 summarizes the theoretical and experimental results obtained using the 1D array of Helmholtz resonators in the impedance tube. We first evaluate analytically and experimentally the sound absorption coefficient along the range of frequencies of interest, as shown in Figure 5a. The results obtained from the TMM results indicate an almost perfect absorption \(\alpha>0.995\) for the whole targeted frequency range, including 5 peaks with increased absorption (\(\alpha>0.999\) at peak). Experimental results agree well with theoretical ones, especially at the resonance frequencies. Small differences between the theory and the experiments could be attributed to different reasons, including insufficient accuracy in the manufacturing process, small deviations placing the Helmholtz resonators modifying the length of the cavities (spacing between resonators) or even excitation of plate modes of some of the panels forming the resonators. The corresponding ILR values emphasise the aforementioned differences between theory and experiments. However, experimental ILR results are above 15 dB at all frequencies of interest, reaching experimentally peaks up to 32 dB.  
The reflection coefficient represented in the complex frequency plane is shown in Figure 5c. For a critically coupled system, where the coupling between the resonators and the propagating medium (energy leakage), and the intrinsic losses of the resonator are balanced, the zeros should be located exactly on the real frequency axis. However, for a set of \(N\) resonators, this is the case if the system is optimized to obtain perfect absorption at \(N\) discrete frequencies. In this work, the goal is to get the highest global absorption for a certain frequency range, which results in the zeros of \(|R|\) located very close, but not exactly at the real frequency axis, i.e., not all the resonances are critically coupled.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter (mm)** & **HR1** & **HR2** & **HR3** & **HR4** & **HR5** \\ \hline \(l_{n}\) & 5.20 & 32.39 & 38.61 & 24.78 & 10.46 \\ \(w_{n}\) & 4.63 & 1.00 & 1.00 & 1.00 & 1.67 \\ \(l_{c}\) & 49.77 & 34.92 & 42.06 & 47.13 & 75.52 \\ \(w_{c}\) & 46.34 & 10.00 & 10.00 & 10.00 & 16.74 \\ \(w_{s}\) & 36.03 & 23.69 & 10.34 & 19.09 & 5.02 \\ \(L_{s}\) & 66.35 & 30.00 & 30.00 & 30.00 & 36.74 \\ \(L_{w}\) & 25.00 & 25.00 & 25.00 & 23.00 & 20.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimized geometrical parameters obtained for each Helmholtz resonator composing the system to maximize the absorption coefficient in \(f=[400,560]\) Hz.  
Figure 5: Impedance tube results: 1D system. Theoretical and experimental **(a)** absorption coefficient and **(b)** Insertion Loss in Reflection (ILR), along the optimized range of frequencies. **(c)** Reflection coefficient along the complex frequency plane for the optimized design.
# Versatile Video Coding-Post Processing Feature Fusion:
## 4 Experimental Evaluations and Discussion
### Quantitative Analysis
In terms of coding efficiency measured by BD-Rate, the proposed method outperformed [45] with a value of \(-\)5.81% and \(-\)6.98% for RA and LD scenario, while [45] reported a BD-Rate value of \(-\)2.81% and \(-\)3.81% for the Y channel. Notably, the strength of the proposed method was evident in the results for class D, which represents the most challenging class due to its low resolution (416 \(\times\) 240). In this case, our proposed model surpassed [45], achieving a Y channel bit reduction of 7.69% and 8.56% compared to the reported rate of 4.76% and 4.72% for RA and LD configuration, respectively. Furthermore, it is worth mentioning that our proposed method comprehensively addressed all three channels (i.e., Y, U, and V) using a single network, while the network presented in [45] could only handle the Y component. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Sequence**} & \multicolumn{4}{c}{**BD-Rate (\%)**} \\ \cline{3-5}  & & **Y** & **U** & **V** \\ \hline \multirow{4}{*}{B} & MarketPlace & \(-\)3.10\% & \(-\)31.06\% & \(-\)25.53\% \\  & RitualDance & \(-\)4.54\% & \(-\)21.87\% & \(-\)30.52\% \\  & Cactus & \(-\)5.56\% & \(-\)24.13\% & \(-\)23.42\% \\  & BasketballDrive & \(-\)5.28\% & \(-\)25.01\% & \(-\)26.59\% \\  & BQTerrace & \(-\)7.77\% & \(-\)28.08\% & \(-\)23.87\% \\ \hline \multicolumn{5}{c}{Average} & \(-\)5.25\% & \(-\)26.03\% & \(-\)23.95\% \\ \hline \multirow{4}{*}{C} & BasketballDrill & \(-\)6.71\% & \(-\)25.45\% & \(-\)31.29\% \\  & BQMall & \(-\)9.17\% & \(-\)26.18\% & \(-\)29.91\% \\  & PartyScene & \(-\)6.94\% & \(-\)28.71\% & \(-\)25.51\% \\  & RaceHorses & \(-\)5.67\% & \(-\)28.84\% & \(-\)34.67\% \\ \hline \multicolumn{5}{c}{Average} & \(-\)7.12\% & \(-\)27.29\% & \(-\)30.35\% \\ \hline \multirow{4}{*}{D} & BasketballPass & \(-\)6.01\% & \(-\)29.03\% & \(-\)28.02\% \\  & BQSquare & \(-\)14.85\% & \(-\)27.14\% & \(-\)40.74\% \\  & BlowingBubbles & \(-\)5.43\% & \(-\)31.59\% & \(-\)25.97\% \\  & RaceHorses & \(-\)7.96\% & \(-\)32.28\% & \(-\)34.24\% \\ \hline \multicolumn{5}{c}{Average} & \(-\)8.56\% & \(-\)30.01\% & \(-\)32.24\% \\ \hline \multirow{4}{*}{E} & FourPeople & \(-\)8.50\% & \(-\)15.09\% & \(-\)19.36\% \\  & Johnny & \(-\)8.32\% & \(-\)9.08\% & \(-\)22.02\% \\  & KristenAndSara & \(-\)5.84\% & \(-\)11.27\% & \(-\)16.39\% \\ \hline \multicolumn{5}{c}{Average} & \(-\)7.56\% & \(-\)11.81\% & \(-\)19.26\% \\ \hline \multicolumn{5}{c}{Overall} & \(-\)6.98\% & \(-\)24.68\% & \(-\)26.74\% \\ \hline \end{tabular}
\end{table} 
Table 2: The compression performance of proposed method for LD configuration.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Filter Module
#### 4.3.2 Buffers  
The filter unit of the dual-edge deblocking filter architecture uses eight buffers, as shown in Figure 7, each of which can store a 4 \(\times\) 4 block of pixels (128 bits). These buffers are initialized with all zeroes. Before the control unit begins the filtering process, the block of pixels 1-4 indicated in Figure 4b from the external memory are stored into the corresponding buffers Q1 BUF, Q2 BUF, Q3 BUF, and Q4 BUF. The filtering operation is carried out along the vertical edges V1 and V2; at the same time, the block of pixels 5-8 is sent to the buffers P1 BUF, P2 BUF, P3 BUF, and P4 BUF, respectively. Figure 8 indicates the relative buffer for both the luma and chroma blocks, along with the outline of each 4 \(\times\) 4 pixel block. The filtered data are saved to the internal RAM after the edges V1 and V2 have been filtered. Similar techniques are used to filter V3 and V4 vertical edges. For horizontal filtering, the pixel data kept in the internal memory were subsequently transferred to these storage units. The same process is utilized for horizontal filtering as for vertical filtering.  
Figure 6: Filter unit of dual-edge deblocking filter architecture.  
Figure 7: Buffers to store the pixel block.
## 4 Aggregated Channel Attenuation Model  
In this paper, the channel model accounts for the cumulative impact of atmospheric turbulence \(h_{a}\), path loss \(h_{l}\) and pointing error \(h_{p}\). Therefore, the joint channel attenuation state can be expressed as \(h=h_{a}h_{l}h_{p}\).
## 5 Conclusions  
Our work demonstrated that BC patients have a distinct urinary volatile signature compared with cancer-free individuals, comprising higher levels of several alkanes and aromatic compounds, and lower levels of aldehydes, ketones and monoterpenes. This signature was able to detect BC with 70% sensitivity, 89% specificity, 80% accuracy and an AUC of 0.851. Regarding the detection of earlier BC stages, a remarkably high performance was found for stage T1 with 94% sensitivity, 80% specificity, 84% accuracy and AUC of 0.910, whereas stage Ta/Tis was less effectively detected with 65% sensitivity, 84% specificity, 78% accuracy and an AUC of 0.761. Surprisingly, the later stages of BC were not detected with higher sensitivity (60%), unveiling only better specificity (91%) and accuracy (86%). This may be explained by the distinct volatile profiles found between NMIBC stages (Ta/Tis vs. T1) and NMIBC vs. MIBC (stages \(\geq\)T2), with the latter characterized by higher levels of several alkanes and aromatic compounds, and lower levels of one aldehyde. These results emphasize that urinary volatile biomarkers are promising candidates for improving BC detection and staging. Indeed, the set of seventeen discriminant volatiles unveiled better overall sensitivity and specificity than some FDA-approved urinary biomarkers (NMP-22 and BTA TRACK). Future studies should entail a larger cohort of samples, ideally matched for other confounding factors (e.g., age, smoking habits, lifestyle) and comprising other populations in which BC incidence is high (e.g., Western Europe, North America). Moreover, it will be important to consider other concomitant conditions that may influence the urinary volatilome, such as hematuria and urinary tract infection. **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/metabo11040199/s1](https://www.mdpi.com/article/10.3390/metabo11040199/s1)]([https://www.mdpi.com/article/10.3390/metabo11040199/s1](https://www.mdpi.com/article/10.3390/metabo11040199/s1)), Figure S1: PCA scores scatter plots obtained for urinary volatile profile (full set of VOCs and VCCs concatenated matrix) of all samples (BC \(n=53\) and cancer-free controls \(n=56\), green circles) and QCs (\(n=24\), blue circles), Figure S2: Statistical validation of the PLS-DA model obtained for the urinary volatile profile of BC patients (\(n=53\)) and cancer-free controls (\(n=56\)), after variable selection (109 observations \(\times\) 4184 variables), by permutation testing (200 permutations; 2 components), Figure S3: (a) PLS-DA scores scatter plot and (b) ROC curve obtained for urinary volatile profile (VOCs and VCCs concatenated matrix), after variable selection (43 observations \(\times\) 495 variables), of stage T1 (red squares, \(n=17\)) compared with stage Ta/Tis (blue circles, \(n=26\)).