esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 3 Range Determination for a Specular Target
### Simplified Algorithm
#### 3.2.1 Doppler Velocity Estimation  
There are several options for the Doppler velocity estimation needed in coherent summing. The simplest, as previously discussed, is based on the fact that the Doppler is very close to zero in the optimum duration, and in fact exactly zero over a wide river. In this case, it is possible to sum the echoes, eliminating the \(e^{-i\omega k}\) term. A second option is by geometrical consideration of the satellite's location at each echo with respect to the water centerline. If neither of the above is applicable, the third option is estimating the Doppler from the same data. Here, we propose the usage of the Fitz [17] algorithm:  
\[\omega=\frac{\sum_{m=1}^{J}m\text{arg}\sum_{k}z_{n+k,r}z_{n+k+m,r}^{*}}{\sum_{m =1}^{J}m^{2}} \tag{7}\]  
with the same \(2K+1\) burst sum as before. \(J\) is the number of lags used in the estimate. For \(J=1\), Fitz is identical to the well-known frequency estimator based on the phase change between consecutive complex samples [18; 19]. The Fitz formula with more lags provides more accurate Doppler estimation. Figure 7 shows examples of altimeter passes over a small river in Peru (Rio Nahuapa), discussed later in Section 4. The picture shows the Doppler estimate with \(J=1\) compared with \(J=5\). The plot points represent the Doppler velocities obtained by a moving window comprising 25 echoes. The Doppler velocity varies gradually, with Doppler = 0 corresponding to the river centerline (as it is confirmed by the overlay of the data on Google Earth images). Further details on the Fitz method are in Appendix A. Five-lag Fitz (panel b in Figure 7) is significantly better than its 1-lag counterpart (panel a). The improvement is most noticeable further from the river centerline, where the echo power has dropped 15-20 dB from the central peak.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Vehicle Density  
Apart from the maximum number of vehicles, another important QoS parameter that can be affected by the decoding algorithm is the vehicle density in terms of vehicles/km [35]. Similar to the previous subsection, we first calculate the maximum possible vehicle density for each QoS scenario in urban and highway traffic environments according to the 5G V2X specifications [9], which are shown in Table 12. As expected, the maximum vehicle density is common for all QoS scenarios since it is a QoS parameter independent of the range of the scenario.  
In Tables 13 and 14, we show the maximum vehicle density in urban and highway traffic environments, respectively, in which the maximum end-to-end latency requirements of Table 2 are achieved for each QoS scenario and decoding algorithm. QoS scenarios Q2, Q4, Q5 and Q6 achieve the maximum possible number of vehicles (Table 12) that can communicate at the same time in an urban traffic environment, whereas in scenarios Q1 and Q4, this cannot be achieved. The superiority of polar coding in the latency parameter is again evident in scenarios Q1 and Q4. Additionally, scenario Q4 highlights the effect of the low transmission rate on the delay and the limitation of simultaneous transmissions that a receiver can accept within the maximum end-to-end latency requirements. However, BCJR-based algorithms can serve enough vehicles simultaneously and respond efficiently to medium-density urban traffic environments for the Q4 scenario and to high-density urban traffic environments for the other QoS scenarios. The behavior of vehicle density in a highway traffic environment is similar to that of an urban traffic environment, with the only difference that in QoS scenario Q6, the BCJR and logBCJR algorithms cannot reach the maximum possible limit since they have the greatest complexity and since the maximum end-to-end latency requirements are very short. However, these two algorithms achieve a very large upper bound in scenario Q6, which is acceptable in real-life traffic cases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**QoS Scenario** & **Maximum Latency** & **BCJR** & **logBCJR** & **maxlogBCJR** & **SOVA** & **LDPC** & **Polar** \\ \hline Q1 & 1000 & 178 & 191 & 297 & 535 & 313 & 531 \\ \hline Q2 & 1000 & 571 & 571 & 571 & 571 & 571 & 571 \\ \hline Q3 & 100 & 571 & 571 & 571 & 571 & 571 & 571 \\ \hline Q4 & 1000 & 32 & 34 & 54 & 97 & 57 & 96 \\ \hline Q5 & 1000 & 571 & 571 & 571 & 571 & 571 & 571 \\ \hline Q6 & 20 & 571 & 571 & 571 & 571 & 571 & 571 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Vehicle density that achieves maximum end-to-end latency requirements for each decoding algorithm and QoS scenario in an urban environment.
## 3 Methodology
### System Overview  
**Drivable Region Mapping**: For drivable region mapping, both semantic information and the 3D map are required. We employed deep learning-based semantic segmentation method and visual SLAM with a single RGB-D Camera such as Asus Xtion and Intel Realsense. When the camera captures \(I_{t}\), \(\widetilde{P}_{t}\) is generated via deep-learning-based semantic segmentation results \(\mathbf{u}_{i}\) and \(D(\mathbf{u}_{i})\). PIDNet [39] is used for semantic segmentation, and \(\widetilde{P}_{t}\) represents a collection of points in the camera coordinates. The results of visual SLAM include \(\mathcal{X}=\left\{\mathbf{x}_{t}\right\}_{t=1:t}\) and \(\mathcal{M}_{j}\). ORB-SLAM3 is used for visual SLAM. Subsequently, \(\widetilde{P}_{t}\) is transformed to \(\widetilde{P}_{t}^{W}\) according to \(\mathbf{x}_{t}\) and combined with \(\mathcal{M}_{j}\). This sequence is repeated to create \(\mathcal{M}_{j}\). When this transformation occurs, our method effectively addresses the mismatch between the 3D map and semantic information.  
**Map-merging**: In this step, two \(\mathcal{M}_{j}\) are merged. First, the overlapping region is specified, and the corresponding point clouds are extracted. Then, \(\mathcal{F}_{j}\) is extracted from the extracted point clouds using FPFH [45]. The KNN and KDTree [52] algorithms are subsequently used to find correspondences and perform 3D point cloud registration via TEASER++ [51]. By applying the transformation matrix from TEASER++, the two maps are merged to generate a global map. This process is repeated for all \(\mathcal{M}_{j}\), creating final global map \(\widetilde{\mathcal{M}}\).
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 9 Conclusions  
The quality assessment of any algorithm implemented in hardware is strongly required as the optimization techniques do not affect the quality of the reconstructed video data in any of the processing steps. Among the various quality assessment metrics, we use the metric _PSNR_ to assess the quality of the video owing to its simplicity. Raw video data of two different resolutions (QCIF and CIF) are taken and the quality of the filtered video is checked with the quality of the video obtained from the HEVC Test Model. It is noted that the quality of the parallel edge filter architecture does not affect the quality of the reconstructed video, and it shows slight improvements compared to the HEVC Test Model. In one second, ten to twelve separate images can be processed by the human visual system by perceiving each image discretely. One image is held in the visual cortex for around one of fifteen parts in a second. Therefore, as the frame rate is higher, perception of the moving picture will be smooth. Hence, the processing time decreases in the dual-parallel edge DBF, the frame rate will increase, and, thus, the perceptual quality is increased.  
Conceptualization, P.R.C. and S.S.; methodology, P.R.C. and S.S.; validation, P.R.C.; formal analysis, P.R.C.; investigation, P.R.C. and S.S.; resources, P.R.C.; data curation, P.R.C.; writing--original draft preparation, P.R.C.; writing--review and editing, P.R.C. and S.S.; supervision, S.S. All authors have read and agreed to the published version of the manuscript. The APC is funded by Vellore Institute of India, Vellore, Tamil Nadu, India.  
Not applicable.  
The reference software for HEVC called HM (HEVC Test Model) is from HM software repository [[https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware](https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware)]([https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware](https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSoftware)) (accessed on 1 October 2022), and the test video sequences used for quality assessment are from the Video Trace Library of Arizona State University [[http://trace.eas.asu.edu/yuv/index.html](http://trace.eas.asu.edu/yuv/index.html)]([http://trace.eas.asu.edu/yuv/index.html](http://trace.eas.asu.edu/yuv/index.html)) (accessed on 1 October 2022) and Video Information Processing Lab of National Chiao Tung University [[http://vip.cs.nctu.edu.tw/resource_seq.html](http://vip.cs.nctu.edu.tw/resource_seq.html)]([http://vip.cs.nctu.edu.tw/resource_seq.html](http://vip.cs.nctu.edu.tw/resource_seq.html)) (accessed on 1 October 2022). The authors would like to acknowledge Vellore Institute of Technology, Vellore, Tamil Nadu, India, for providing all the necessary facilities for the research. The authors declare no conflict of interest.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 6 Conclusions
#### Future Work  
eNIDs provide protection for the lower 64 bits of the address field. The upper 64 bits--the routing prefix under IPv6, or ILNP Locator (L64)--cannot be changed in the same way, as it encodes essential topological location information used for routing. Even when eNIDs are in use, an attacker could perform a coarse-grained correlation attack based on the prefix/L64 bits, or attack their semantics to compromise location privacy. ILNP provides a potential mechanism for weakening such attacks through the rewriting of mutable locator values at various points on the path across the network [54; 55]. We have work in progress to implement such a mechanism by adapting FreeBSD's IPFW firewall. Once ILNP firewall processing has been demonstrated in software, a high-performance hardware implementation is also of interest.  
Conceptualization, G.T.H. and S.N.B.; methodology, G.T.H. and S.N.B.; software, G.T.H.; validation, G.T.H. and S.N.B.; formal analysis, G.T.H.; investigation, G.T.H.; resources, S.N.B.; data curation, G.T.H. and S.N.B.; writing--original draft preparation, G.T.H. and S.N.B.; writing--review and editing, G.T.H. and S.N.B.; visualization, G.T.H.; supervision, S.N.B.; project administration, S.N.B.; funding acquisition, S.N.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The research data supporting this publication can be accessed as [56].
## 4 Stochastic Traffic Model
### Data Source and Instantiation
Furthermore, the observed data [38] has also been used to instantiate the observation traffic model, as shown in Figure 7. The data were measured on a typical highway bridge in China, in which vehicles were classified into six groups according to the axle number, axle spacing, and axle weight. The brief description of each group is listed in Table 3, along with the lane occupancy rate. Other details about the observed data could be found in the article [38]. Since the in-lane position was not provided, the distribution model previously discussed has been employed in the observation model. Meanwhile, since the data were measured in China, the footprint is kept the same as in the Chinese code JTG [20], i.e., \(300\times 200\) mm for the front axle and \(600\times 200\) mm for the rear axle. Similar to the AASHTO model, an extra impact factor of 1.15 should be applied when using the observation model in the analysis since the dynamic effect is not considered. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Description**} & \multicolumn{3}{c}{**Lane Occupancy (\%)**} \\ \cline{3-5}  & & **Fast** & **Middle** & **Slow** \\ \hline
1 & Two-axle cars & 41.23 & 25.74 & 9.70 \\
2 & Three-axle trucks (I) & 0.03 & 0.26 & 0.36 \\
3 & Three-axle trucks (II) & 0.10 & 0.87 & 0.60 \\
4 & Four-axle trucks & 0.04 & 0.67 & 0.92 \\
5 & Five-axle trucks & 0.06 & 1.17 & 1.31 \\
6 & Six-axle trucks & 0.57 & 7.94 & 8.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Vehicle groups and occupancy rate in the observation model. Figure 6: Probability distribution of the lateral position of vehicles. Figure 7: Vehicle model in the observation model.
# Multi-Defect Detection Network for High-Voltage Insulators Based on Adaptive Multi-Attention Fusion
## 4 Experiment Preparation
### Evaluation Indicators  
As the primary objective of this research was to enhance detection accuracy, various performance metrics were utilized to evaluate the algorithm. These metrics include the average precision (\(AP\)), recall rate (\(R\)), accuracy rate (precision, \(P\)), overall accuracy \(F1\) (\(F1\) score), frames per second (FPS), and mean average precision (mAP). \(P\) and \(R\) assess the algorithm's comprehensiveness and accuracy in detecting the target, while the \(F1\) score provides a comprehensive evaluation based on both precision and recall. \(AP\) measures the detection accuracy for each category. FPS evaluates the speed of object detection by representing the frame rate per second, indicating the number of pictures that can be processed within a second. mAP represents the overall detection accuracy across all the categories. These evaluation indicators are calculated using the following equations:  
\[R=\frac{TP}{TP+FN}\times 100\%, \tag{15}\]  
Figure 12: Labeling process of LabelImg tool.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Category** & **Label** & **Number of Targets** \\ \hline Insulators & insulator & 1825 \\ Breakage defect & posun & 1091 \\ Flashover defect & wushan & 2423 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample statistics of the insulator dataset.  
Figure 13: Insulator defect sample diagram. (**a**) Breakage insulators. (**b**) Flashover insulators.  
\[P= \frac{TP}{TP+FP}\times 100\%, \tag{16}\] \[F1=2 PR/(P+R),\] (17) \[AP=\int_{0}^{1}P(R)dR, \tag{18}\]  
The evaluation metrics used in this study include \(TP\) (number of correctly detected targets), \(FP\) (number of wrongly detected targets), \(FN\) (number of missed targets), \(N\) (total number of detected categories), and \(P(R)\) (a function with \(R\) as the independent variable and \(P\) as the dependent variable). The mAP is calculated as the mean value of the \(AP\) values for each category.
## 3 Interference Analysis and Dynamic Frequency Allocation  
### Interference Analysis Model  
We consider Signal to Interference plus Noise Ratio (SINR) to be the signal quality indicator and its formula is:  
\[\gamma=\frac{\frac{P_{\mathrm{D}}G_{\mathrm{TD}}G_{\mathrm{RD}}}{L_{\mathrm{D} }}}{\frac{R_{\mathrm{CI}}G_{\mathrm{RD}}}{L_{\mathrm{I}}}+kT_{\mathrm{n}}B}, \tag{1}\]  
where \(P\) represents the transmit power; \(G_{\mathrm{T}}\) represents the gain of transmit antenna; \(G_{\mathrm{R}}\) represents the gain of receive antenna; \(L\) represents the free space path loss; \(T_{\mathrm{n}}\) represents the equivalent noise temperature of receiver; \(B\) represents the transponder bandwidth, and \(k\) is Boltzmann constant. D in subscript denotes the variable related to the desired link whereas I for the interference link. The free space path loss is:  
\[L=\bigg{(}\frac{4\pi d}{c/f}\bigg{)}^{2}, \tag{2}\]  
where \(f\) and \(d\) represent the frequency and the distance between the transmitter and receiver, respectively, and c is the velocity of light. The expression of the antenna gain is [44]:  
\[G=G_{0}\bigg{(}\frac{2J_{1}(\mu)}{\mu}\bigg{)}^{2}, \tag{3}\]  
where \(\mu=\pi fD\sin\theta/c\); \(J_{1}\) is the first order Bessel function; \(\theta\) is the off-boresight angle of the transmitter or receiver; \(G_{0}\) is the maximum antenna gain corresponding to angle zero, and its expression is:  
\[G_{0}=\eta\bigg{(}\frac{\pi D}{c/f}\bigg{)}^{2}, \tag{4}\]  
where \(D\) represents the antenna diameter, and \(\eta\) represents the antenna efficiency.  
It can be inferred that the value of SINR mainly depends on the off-boresight angles and the distances. Due to the LEO motion, the angles and the distances that are determined by the geometrical configuration vary with time. Particularly, the off-boresight angles of the desired receiver are zero because the antennae of GEO and LEO users keep pointing their corresponding satellites.
###### Abstract  
Recently, 3D printing of large, structural polymer parts has received increasing interest, especially for the creation of recyclable structural parts and tooling. However, the complexity of large-scale 3D polymeric printing often dictates resource-intensive trial and error processes to achieve acceptable parts. Existing computational models used to assess the impact of fabrication conditions typically treat the 3D-printed part as a continuum, incorporate oversimplified boundary conditions and take hours to days to run, making design space exploration infeasible. The purpose of this study is to create a structural model that is computationally efficient compared with traditional continuum models yet retains sufficient accuracy to enable exploration of the design space and prediction of part residual stresses and deformations. To this end, a beam-based finite element methodology was created where beads are represented as beams, vertical springs represent inter-bead transverse force transfer and multi-point, linear constraints enforce strain compatibility between adjacent beads. To test this framework, the fabrication of a large Polyethylene terephthalate glycol (PETG) wall was simulated. The PETG was modeled as linearly elastic with an experimentally derived temperature-dependent coefficient of thermal expansion and elastic modulus using temperature history imported from an ABAQUS thermal model. The results of the simulation were compared to those from a continuum model with an identical material definition, showing reasonable agreement of stresses and displacements. Further, the beam-based model required an order of magnitude less run time. Subsequently, the beam-based model was extended to allow separation of the part from the printing bed and the inclusion of part self-weight during fabrication to assess the significance of these effects that pose challenges for existing continuum models.  
additive manufacturing; finite element; modeling; polymer Article
# Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11 ax Networks +
## 3 System Model
In the following sections, we describe the detailed procedure for each step of the proposed scheduling method.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 3 Results  
In this part, the design procedure of the monopole antenna and monopole antenna array with simulated and measured return loss curves and radiation pattern plots are presented.
## 4 Discussion
### Human Exposure to Other VOCs  
Exposure to toluene, ethylbenzene, and other VOCs is scarcely investigated in scientific literature. The WHO have also set values for toluene, ethylbenzene, xylenes, 1,3,5-trimethylbenzene and 1,2,4-trimethylbenzene [25]. On the other hand, no limits were found in the scientific literature for 1,3,5-trimethylbenzene and 1,2,4-trimethylbenzene. Therefore, although findings are relative to benzene for an exposure time of two hours, it is plausible to assume that the levels of the VOCs emitted by incense are well below the suggested threshold values.  
Toluene and _o-_, _m-_, and _p-_xylenes are classified as non-carcinogenic for humans, yet their adverse effects are not underrated [32]. It has been reported that the concentrations of ethylbenzene, _m-_, _p-_xylenes, _o-_xylene, and toluene increased with incense combustion as benzene did. However, benzene and toluene are emitted in higher concentrations, confirmed by scientific evidence [33]. People exposed to toluene, _m-_, _p-_xylenes, _o-_xylene, and ethylbenzene can have adverse effects in both the short- and long-term. Effects can range from mild to severe, such as eye, nose, throat or skin irritation, narcosis, neurotoxicity, aplastic anemia, cardiovascular conditions, respiratory problems, and kidney or liver damage [33].  
It is important to note that experimental testing has demonstrated that when an aeration condition is established in a space that guarantees 95% volatile organic compounds (VOCs) removal, the concentrations of VOCs released can be substantially decreased within approximately 2 h following the cessation of combustion [34]. Based on experimental evidence, the emission of VOCs from the burning of incense sticks has a reduced impact on health in a room with a ventilation condition [34]. As a result, considering the exposure value suggested by the WHO, eight incense sticks showed a VOC release above the suggested guidelines value after two hours.  
In any case, it should be considered that some practices of high use, frequency and duration, presence of vulnerable populations, together with indoor characteristics, and layout (volume of rooms/spaces, air exchange rate, etc.) can lead to chronic exposures that exceed the guideline or reference values (WHO), which does suggest the need to limit this type of exposure.
## 2 System Model and Connection Setup
### Channel Statistics Estimation  
During CS, channel statistics estimation may take a long time to achieve reliable information. Indeed, transmitting several symbols in a short time interval does not allow for observation of remarkable channel changes. On the other hand, using long guard intervals (in the order of the frame time \(T_{f}\)) between two consecutive pilots allows the receiving nodes to acquire more significant channel statistics. In principle, there are several ways to estimate the channel coefficients starting from the received signal in Equation (9).  
Based on the a priori knowledge of the nodes about the signal shape \(x(t)\), the channel autocorrelation can be retrieved from an observation time \(T_{e,\text{CS}}=Q_{e}T_{f}\), with \(Q_{e}\), being the number of frames spent for channel acquisition. In fact, similar to the interference statistics estimation in Equation (3), the channel autocorrelation can be evaluated as:  
\[c_{h}^{(CS)}[m]=\frac{1}{E_{s0}(2K_{e,\text{CS}}-1)}\sum_{p=1}^{K_{e,\text{CS}}} r_{e,\text{CS}}^{*}[p]r_{e,\text{CS}}[p+m] \tag{10}\]  
with \(K_{e,\text{CS}}=Q_{e}T_{f}/T_{c}\) being the number of samples used for estimation.  
Analogously to the interference autocorrelation estimation, during EC, channel autocorrelation, updates can be performed exploiting a shorter time interval \(T_{e,\text{EC}}=M_{e}T_{s}\) (\(M_{e}\), being the number of employed pilots/frame slots) and, according to:  
\[c_{h}^{(EC)}[m]=\frac{1}{E_{s0}(2K_{e,\text{CS}}-1)}\sum_{p=1}^{K_{e,\text{CS}} }r_{e}^{(\mu)*}[p]r_{e}^{(\mu)}[p+m] \tag{11}\]  
being \(r_{e}^{(\mu)}[p]\) defined in vector format as:  
\[\mathbf{r}_{e}^{(\mu)}=\left[\mathbf{r}_{e}^{(\mu-1)}(K_{e}:K_{e,\text{CS}}) \ \mathbf{r}_{e}\right], \tag{12}\]  
where \(K_{e}=M_{e}T_{s}/T_{c}\) is the number of most recent autocorrelation samples. Similarly to the analysis reported in Equation (5), the superscript \((\mu)\) in Equation (12) indicates the number of updating procedure, the position \(\mathbf{r}_{e}^{(0)}=\mathbf{r}_{e,\text{CS}}\) is assumed, and \(\mathbf{r}_{e}^{(\mu-1)}(K_{e}:K_{e,\text{CS}})\) refers to the elements of the vector \(\mathbf{r}_{e}^{(\mu)}\) ranging from the \(K_{e}\)-th till to the \(K_{e,\text{CS}}\)-th one. Finally, \(\mathbf{r}_{e}\) defines the new estimation used for updating the channel autocorrelation, that is the sampled version of Equation (9).