esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 3 Results
### Directional Dependency  
To find out if there is a directional dependency at this test site, the wind vector from the _USA_\(\vec{v}_{usa}\) at 98 m was projected onto the normal vectors \(\vec{n}_{lidar}\) of the laser beams of the SWE Scanner. The projection is done using Equation (1).  
The relative and absolute differences depending on the wind direction and the horizontal wind speed for each azimuthal position of the laser beam are shown in Figure 7. When looking at these plots, it should be noted that the sector disturbed by turbine wakes is from 341\({}^{\circ}\) to 171\({}^{\circ}\) (Figure 1). Figure 7 clearly shows increases in differences between the lidar and ultrasonic measurements in this sector. A closer inspection of the beam in the 288\({}^{\circ}\) direction shows that the positive differences (red dots) are mainly in the direction of the escarpment. There also appears to be an effect of the flow direction on the sign of the difference between the projected lidar wind speed and the value from the met mast in some sectors, whereby flow towards the lidar has a positive difference and flow away has a negative difference. This effect can be seen clearly in the beams at 288\({}^{\circ}\) and 216\({}^{\circ}\) but less so in the other directions. The particularly small differences do not appear to depend on direction or wind speed and also appear to have random distribution.  
When the wind is perpendicular to the laser direction (e.g., from 18\({}^{\circ}\) or 198\({}^{\circ}\) for the azimuthal beam from 288\({}^{\circ}\)), the lidar can only observe the existing vertical component \(w\) and the horizontal components should be zero if the alignment is perfect. This arises from Equation (3). From Figure 7 it can be seen that the points on the axes orthogonal to the beam do not have zero magnitude. However, because of different sampling rates, the chance that the lidar and met mast were not perfectly aligned (despite using best practice), and the possibility that the wind direction was not exactly the same at the lidar and mast, it is impossible to say that this difference is solely the vertical wind component \(w\).
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
## 3 Results  
### Experimental Setup  
For the sake of testing the ability to suppress random noise, we calculate the signal-to-noise-ratio (SNR) and local similarity. In general, the higher the SNR, the better the performance of the algorithm. The SNR formula is defined as follows:  
\[\text{SNR}=10\log_{10}\frac{\|\mathcal{X}\|_{F}^{2}}{\|\mathcal{X}-\hat{ \mathcal{X}}\|_{F}^{2}}, \tag{21}\]  
where \(\mathcal{X}\) are the original noise-free seismic data, and \(\hat{\mathcal{X}}\) are the denoised seismic data. Local similarity measures how locally similar two data samples are and is often used to indicate signal leakage. It is a cube with the same size as the samples and can be easily computed via Madagascar 4.0. In our experiment on synthetic data, we compute the local similarity between the original noise-free volume and the removed noise and normalize it to range of 0 to 1. Therefore, smaller values of this metric indicate better attenuating performance. In the experiments on F3-2020 and Penobscot, because there exist no ground truth data, we compute the local similarity between the denoised results and the corresponding removed noise.  
Regarding the training configuration of LRTNet, we set the number of layers to four. Our experiments indicate that using only two layers does not yield sufficient performance, while increasing the number to six leads to excessive computational cost without any significant performance gains. The remaining hyperparameters are chosen based on standard DL practices: we use the Adam optimizer to train the model for 100 epochs in total, start with a learning rate of 0.05, and reduce the learning rate by a factor of 0.8 every 20 epochs. For the experiments on F3-2020 and Penobscot, we use the LRTNet model tuned on the synthetic data without any additional fine-tuning, ensuring a fair comparison.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 4 Experimental Settings
### Our Code  
At this point in the paper, we want to explain the basic steps of the code used for the shape matching calculations. All steps of Algorithm 1 refer to the equations explained within this paper.  
```
Data: Shapes Result: hit rate between shapes
1 foreachShapedo
2 Compute \(W\) and \(D^{-1}\) for the LBO via (17) and (18).;
3 Compute eigendecomposition \((\lambda,\tilde{\mathbf{v}})\) of \(B\) (30) to get the eigenvectors of the LBO \(L\) with (31);
4 Keep the \(r\) lowest nonzero eigenvalues \(\lambda_{r}\) and the corresponding eigenvectors \(V_{r}\). Additionally, keep the maximal eigenvalue \(\lambda_{N}\) for the temporal domain calculation;
5 Compute temporal domain \(t^{*}\) with (32) and the time increment \(\tau=t^{*}/M\);
6 Compute \(H_{r}\) via (28);
7 foreach\(x\in\mathcal{P}\)do
8 Set initial condition \(\mathbf{u}(x_{i},0)\) with (8) or (9);
9 Transform the initial condition into the reduced system \(\mathbf{w}_{r}(x_{i},0)\) via (27);
10 Solve this with one of our three models (35), (36) or (41);
11 Transform the solution back into the full system via \(\mathbf{u}=V_{r}\mathbf{w}\);
12 Extract the damped feature descriptor \(f_{x_{i}}(t)\) from \(\mathbf{u}\) (3);
13
14 end for
15
16 end for
17 Compare feature descriptors of different shapes (4) to find the best matching ones;
18 Compute the hit rate using Equation (42);
```  
**Algorithm 1**Shape Matching MOR Method  
Overall, this code does not need much input. A basic version of the algorithm needs the shapes and two parameters, the number of the eigenvalues to keep for modal coordinate reduction \(r\), and the end of the time interval \(t_{M}\). Both parameters can be set to \(r=t_{M}=100\). That this is a suitable choice for the parameters has been verified in previous works [6; 7; 9].  
If we want to use the damped wave equation or the Gaussian initial conditions, we have to pass the damping parameter \(k\) and the width of the Gaussian function \(\sigma\) as well.
## 2 Theory
### Generation of Nondiffracting Beams with CGH
\begin{array}{ll}E_{1}(r_{1},z)&=Ak(n_{1}-1)\sqrt{\lambda z}\frac{h}{d}J_{0 }\left(\frac{k(n_{1}-1)ln_{1}}{d}\right)\\ &\times\exp\left[-\left(\frac{(n_{1}-1)\frac{2h}{d}}{\omega d}\right)^{2} \right]\exp\left[ik\left(z+\frac{r_{1}^{2}}{2z}\right)\right]\\ &\times\sum\limits_{m=0}^{N-1}\exp\left\{i\left[k\left(-\frac{(n_{1}-1)^{2}h^{ 2}z}{2d^{2}}+(n_{1}-1)mh\right)+\frac{\pi}{4}\right]\right\}\end{array}  \tag{19}\]  
Then, the intensity distribution at plane \(z\) behind the SLM can be obtained as:  
\[I_{(r_{1},z)}=2A^{2}\pi kz\left(\frac{h}{d}\right)^{2}(n_{1}-1)^{2}\exp\left[ -2\left(\frac{(n_{1}-1)2h}{\omega d}\right)^{2}\right]\!J_{0}{}^{2}\!\left[k( n_{1}-1)r_{1}\frac{h}{d}\right] \tag{20}\]  
Equations (2) and (20) represent the intensity distribution of an incident beam passing through the real axicon and the SLM-loaded holograms, respectively. Comparing Equations (2) and (20), if \(\theta=h\!d\), \(\omega_{0}=\omega\) and \(I_{0}=A^{2}\), the two equations are exactly the same. Therefore, we can use the hologram to simulate the positive axicon to generate zero-order Bessel beams (also called nondiffracting beams). The width \(\rho_{0}\) of the central lobe can be calculated by finding the first zero of the Bessel function and the maximum transmission distance \(z_{\rm max}\) of nondiffracting beams are given by the following equations [10]:  
\[\rho_{0}=\frac{2.4048\lambda}{2\pi\sin\beta}\approx\frac{2.4048}{k(n_{1}-1) \tan\theta_{\rm B}}=\frac{2.4048d}{k(n_{1}-1)h} \tag{21}\]  
\[z_{\rm max}=\frac{\omega}{\tan\beta}\approx\frac{\omega}{(n_{1}-1)\tan\theta_{ \rm B}}=\frac{\omega d}{(n_{1}-1)h} \tag{22}\]  
By comparing Equations (3) and (21) and Equations (4) and (22), it can be found that the center-From Figure 4b, we can get the inner radius \(r_{1}\) and outer radius \(R_{1}\) as:  
\[r_{1}=z_{\rm n}\tan\beta_{\rm n} \tag{23}\]  
\[R_{1}=(z_{\rm n}-h)\tan\beta_{\rm n}+L/2 \tag{24}\]  
where, \(\beta_{n}=arcsin[(n_{1}-1)\theta_{Bin}]\), and \(\theta_{Bin}\) is the conical angle. Accordingly, the width of the output beams is expressed as:  
\[R_{1}-r_{1}=L/2-h\tan\beta_{\rm n}\approx L/2-h(n_{1}-1)\tan\theta_{\rm Bn}= Nd-\frac{h^{2}(n_{1}-1)}{d} \tag{25}\]  
From Equation (25), it can be found that the width of annular hollow beams is only related to the cycles \(N\), the groove spacing \(d\), and the groove height \(h\).
## 3 DTN Structure and Implementation Issues
### DTN Structure in a Disaster  
In the event of a disaster, it is necessary to collect many types of information, such as information about the safety of disaster victims (safety confirmation information), information about the disaster area, information about the rescue operations, and information about first-aid tents if such tents have been installed. This paper focuses on the collection of safety confirmation information.  
We assume the following disaster situation. The base stations for mobile communication services are damaged. A telecommunication terminal (shelter terminal) is installed in each shelter in the disaster area. Victims want to use their mobile phones to send their safety confirmation information to a shelter terminal. A shelter terminal stores safety confirmation information, and sends messages that contain this information to a telecommunication terminal that is mounted on a truck that has come to the disaster area to bring in relief supplies. When the truck leaves the disaster area and comes to an area where a wide-area network is operating, its terminal accesses the Internet and sends the messages to the victims' loved ones who are outside the disaster area. Messages from the mobile terminals of victims are carried to a shelter terminal by what we call \"information carriers\" or just \"carriers\" in this paper. Specifically, they can be members of rescue teams, including those of the Self-Defense Forces. Carriers are assumed to have dedicated mobile terminals.  
As shown in Figure 1, the DTN assumed in this paper consists of terminals of victims (victim terminals), terminals of carriers (carrier terminals), and shelter terminals. These terminals communicate using wireless transmission. How information is sent from a shelter terminal to the victims' acquaintances outside the disaster area is outside the scope of this paper. The protocol stack used in the DTN is shown in Figure 2. The function of collecting safety confirmation information is implemented at the bundle layer.  
Figure 1: Delay/disruption Tolerant Networks (DTN) structure in a disaster area.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 3 Proposed MIA-LSTM Model
### Dataset  
The dataset used for the experimentations included air pollutants data (hourly) from three nationally controlled air quality monitoring sites in China. The air quality data and meteorological data were collected from twelve AQM sites by the Beijing Municipal Environmental Monitoring Center and China Meteorological Administration [60]. The meteorological data with the air quality data were matched with the closest weather station. Missing data were denoted by NA. The percentage of missing data is also given in the last row of Table 2. Another dataset for Ghaziabad city was obtained from the Central Pollution Control Board of India [61]. These data contain all the fields described in Table 2. These data contain hourly values of pollutants and meteorological parameters.  
Out of the above attributes available, wind direction was excluded for the prediction purpose from the Chinese dataset, and NOx values were excluded from the Indian dataset. The datasets from both countries contain missing values (the percentage of missing values is given in Table 2 for reference). For the application of various deep learning models, the data were initially split into three sets, i.e., training, validation, and testing, in the ratio of 60%, 20%, and 20%, respectively. The data were divided sequentially as it is time series data.
## 5 Experimental Results and Evaluation
### Comparison to Different Models  
The dataset considered in this research [38] trained by using various machine learning models (Support Vector Machine, SVM with Hog Features, Decision Tree Classifier, Naive Bias Classifier) and pre-trained CNN models (VGG16, VGG19, ResNet50). The performance of the proposed model has been compared against these eight models. Table 10 illustrates the comparison among these eight models including our proposed model by taking into account of the performance metrics such as Precision, Recall, F1-Score, and Accuracy. From the table, it can be observed that our proposed BRB-DL model outperforms eight models. The reason for this is that the values of Precision, Recall, F1-Score, and Accuracy of the BRB-DL model are higher than that of eight models.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Detection Effect of the Improved Model
#### 4.5.2 Effect of Resisting Background Interference  
As shown in the first column of Figure 10, the original model incorrectly detects the island as the target in the image; when there are background interferences such as snow, clouds and harbor ground in the image, it will interfere with the detection results, and the target cannot be detected. As shown in the second column of Figure 10, the improved model has no misdetection and detects the targets correctly, which indicates that our improved model can resist background interference. As shown in the third column of Figure 10, YOLOv7's ability to resist complex background interference has been improved compared  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AP\({}_{\text{Aircraft carrier (\%)}}\)** & **AP\({}_{\text{Submarine (\%)}}\)** & **AP\({}_{\text{Merchant (\%)}}\)** & **AP\({}_{\text{Warship (\%)}}\)** & **mAP (\%)** & **FPS (Frames Per Second)** \\ \hline YOLOv4 & 93.59 & 86.02 & 73.50 & 57.55 & 77.66 & 22.33 \\ YOLOv7 & 92.92 & 96.57 & 85.28 & 89.57 & 91.09 & 25.28 \\ Ours & 91.33 & 96.44 & 85.93 & 91.88 & 91.40 & 20.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the accuracy and speed of different modes.  
Figure 8: Curve of loss value change during training. (**a**) Loss value curve of 300 epoch training process. (**b**) Loss value curve of the post 285 epoch training process.  
with YOLOv4 (the first and second rows), but there are still missed detections (the third and fourth rows). According to the results, our method proves to be more effective at reducing background interference than all the other approaches compared.  
Figure 10: Comparison of the effect of resistance to background interference: the first column is the detection effect of the YOLOv4 model; the second column is the detection effect of our improved model; the third column is the detection effect of the YOLOv7 model.  
Figure 9: Small target detection effect comparison: the first column is the detection effect of the YOLOv4 model; the second column is the detection effect of our improved model; the third column is the detection effect of the YOLOv4 model.
# Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication
## 6 Conclusions  
In this paper, we proposed the 2-Level-Composite-Hash (2 LCH) to facilitate anonymous identification. Based on the 2 LCH, we proposed the anonymous device authentication schemes for the device-to-server (D2S) scenarios, for the device-to-server-to-device (D2S2D) scenarios and for the direct device-to-device (D2D) scenarios. We showed the application of our schemes on the MQTT context. The design of the 2 LCH effectively and efficiently facilitates anonymous device authentication using only simple hashing; it can effectively tackle both the desynchronization-based DoS attacks and the unreliable connection issues. The evaluations show that all our proposed schemes demand almost negligible computational costs, compared to the conventional pseudonym-bound PKC and pseudonym-bound CLPK schemes. Our schemes do not require the costly on-line certificate-renewing process. All these excellent merits make the proposed schemes very suitable for those resource-constrained IoT devices.  
There are several interesting challenges for the future work. How do we optimize the window size of the pseudonym vector under different scenarios? Another is how could we improve the storage efficiency of the level-2 pseudonym vectors of a device, especially when a device simultaneously builds several direct D2D sessions with several devices. The other is to extend the current works to the cases where the devices belong to different service domains.  
This project is partially supported by the Ministry of Science and Technology of Taiwan, under the grant No. MOST 108-2221-E-260-009-MY3.  
The authors declare no conflict of interest.
## 4 Discussion
### Vinyl Nitrile Foam vs. Expanded Polypropylene Foam  
The characteristics of the VN foams were compared to those of EPP foam, one of the most used and described foam for cushioning in helmet liners. In compression loading, the VN energy-absorption diagram and efficiency vs. stress curves at 100 s\({}^{-1}\) were compared to EPP results obtained by [PERSON] et al. [5] at 60 s\({}^{-1}\) initial strain rate. At equivalent density, EPP foam had a higher Young's modulus, higher plateau stress, and thus, absorbed more energy at maximum efficiency than the tested VN foam. For instance, at maximum efficiency, EPP106 (\(\rho^{*}\) = 94.6 kg/m\({}^{3}\), R = 0.10) absorbed more energy than VN-A with similar density (\(\rho^{*}\) = 97.5 kg/m\({}^{3}\), R = 0.09\(-\)0.11), but the same amount of energy as VN-B, which was denser (\(\rho^{*}\) = 125 kg/m\({}^{3}\), R = 0.09\(-\)0.11). However, VN-B had a higher efficiency than EPP106 (52% vs. 33%), and thus, transmitted less stress (1.05 vs. 1.57 MPa). Thus, to absorb the energy of 0.52 J/cm\({}^{3}\), VN would be a heavier, but more efficient choice than EPP. Similarly, the energy absorbed at maximum efficiency by VN-A (\(\rho^{*}\) = 97.5 kg/m\({}^{3}\), R = 0.09\(-\)0.11) was similar to that of EPP70 (\(\rho^{*}\) = 60.5 kg/m\({}^{3}\), R = 0.06) (0.28 vs. 0.34) with a better efficiency (41% vs. 36%). In combined compression and shear, the ratio between shear and compressive stress (\(\sigma_{s}\)/\(\sigma_{c}\)) in VN foams was compared to that in EPP foams measured by [PERSON] et al. [20]. They impacted a 43 kg/m\({}^{3}\) (R = 0.5) EPP foam, at an initial rate of 120 s\({}^{-1}\) with an experimental setup similar to the one used here (45\({}^{\circ}\) impactor). Based on their stress vs. strain curves, the ratio between shear and compressive stress (\(\sigma_{s}\)/\(\sigma_{c}\)) in EPP was about 0.8 near maximum efficiency, which is larger than the ratio measured in VN-A and VN-B (0.52 and 0.49). Hence, for the same oblique impact, EPP transmitted proportionally less force in compression, but more in shear than VN. In this comparison, the VN foams tested were denser than the EPP foam, and it was assumed that, as in VN, the stress ratio (\(\sigma_{s}\)/\(\sigma_{c}\)) in EPP would not change substantially with density. The difference in the ratio (\(\sigma_{s}\)/\(\sigma_{c}\)) between EPP and VN might partly explain why [PERSON] et al. [3] found that helmets with VN were better at reducing rotational accelerations than helmets with EPP, but worst at reducing linear acceleration. The differences observed between VN and EPP foam materials and the resulting differences in protection efficiency highlight the importance of testing foams in combined compression and shear, as combined loading is the most common loading for protective materials during an impact [9; 10; 11; 12].
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 4 Simulation Systems and Results
### Simulation Results  
In the simulations, the MSE performance of the estimated normalized frequency offset is simulated. The main simulation parameters are listed as follows:  
* The 5G NR SCS: \(\Delta f=15\) kHz;
* The 5G NR bandwidth: 10 MHz (i.e., the maximum number of available RBs is 52);
* The sampling rate: 15.36 MHz (i.e., the FFT size is \(N=1024\)[1]);
* The frame structure: shown in Figure 4;
* The simulation channel: AWGN + Doppler channel.
## 3 Results and Discussion
### Area Fraction  
Demonstrations of drop generation and surface renewal were then integrated into the simulation of DWC within a hybrid surface. Sample results are shown in Figure 7, where \(f\) tends to fluctuate within a certain range. Small fluctuations indicate drop growth, formation of new drops, and drop coalescing processes. Sharp declines signal surface renewal due to the sweeping of departing drops, which is evident in Figure 7 for \(R_{D}=0.125\) and Figure 7 for full DWC. The merging of small drops into film regions also contributes to fluctuation, whereas merging of larger drops is indicated by small declines.  
Figure 6: Simulation work for DWC within a hybrid surface: (**a**) \(R_{D}=0.5\) (**b**) \(R_{D}=1\).  
Figure 7 shows that \(f\) fluctuates between upper and lower limits. It was observed that the change in the upper limits was less sensitive, showing that condensation was at the same packing level. On the other hand, lower limit variation was more obvious and was dependent on the surface renewal mode. For small \(R_{D}\), in hybrid and full-DWC cases as shown in Figure 7, the lower limit of \(f\) was small due to sweeping. As \(R_{D}\) increased towards unity, the lower limits became higher and dependent only on merging.  
The average area fraction \((f)\) with respect to \(R_{D}\) is presented in Figure 8 for total temperature drops (\(\Delta T\)) of 2,4, and 8\({}^{\circ}\)C. For \(R_{D}=0\), the results are for a full-DWC surface with \(r_{max}\) set at 0.5 mm. The average \(f\) showed the lowest values when \(R_{D}\) equaled 0.125, where surface renewal was achieved by both sweeping and merging. As \(R_{D}\) approached 0.75, the average \(f\) increased as surface renewal became more dominated by merging. For \(R_{D}\) at 1, intensive merging occurred and, as a result, average \(f\) values dropped. Figure 8 shows that \(\Delta T\) has a minimal effect on the average \(f\), and it mainly affects the rate of growth process.  
From Figure 8, values of average area fraction \(f\) are bounded between 0.28 and 0.296, which are lower than the reported values in the literature for full DWC. The average \(f\) from simulation work [12; 22] is about 0.55, and surface coverage reported by [45] tends toward a constant value of 0.5. It is evident that surface renewal by sweeping causes alteration in surface coverage for a full-DWC surface, which ranges from 0.2 to 0.8 [19]. The deviation in surface coverage presented in the current work is not only due to the sweeping effect but also due to drops migrating sideways to the film regions.  
Figure 8: Average area fraction with respect to \(R_{D}\) variation for different total temperature drops.  
Figure 7: Area fraction fluctuation for simulation runs: (**a**) a comparison between the hybrid model at \(R_{D}=0.125\) and 1; (**b**) a comparison between the hybrid model at \(R_{D}=1\) and full DWC.