esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 MAP-DBSCAN Method
### DBSCAN Algorithm  
The merged clusters comprise seismicity concentrated in time. However, events with temporal proximity can be spatially sparse and are falsely assigned into the same cluster. To overcome this ambiguity, a density-based clustering algorithm, DBSCAN, is applied to separate events in space based on a distance metric on the earthquakes' epicentral distribution. Depending on the adopted distance metric, the algorithm can be used for grouping events with waveform similarities [4] as well as earthquakes with related rupture styles and orientations (focal mechanism clustering) [43]. Density-based algorithms search for areas where the event density exceeds a threshold, \(e\). The boundaries of these areas are set where the spatial density falls below that threshold. The DBSCAN algorithm in particular requires as input two parameters, the upper threshold, \(\epsilon\), and the minimum number of neighboring events, \(N_{pts}\). A cluster is defined if an earthquake \(i\) exists along with at least \(N_{pts}\) events within distance \(d\leq\epsilon\), including itself. Earthquake \(i\) is then considered a core point of the cluster and the algorithm moves to the investigation of the other events. If \(N_{pts}\) neighbors are identified, they are also considered core events; otherwise, they consist of the boundary points of the cluster and the algorithm stops. Events that have not been assigned to any cluster at the end of the procedure are included to the background seismicity and are merged with events that occurred during periods with estimated rate under the rate threshold, \(\lambda_{thr}\). In this way, the algorithm can remove events that are sparsely distributed in space. It has been efficiently applied for detecting similarities among earthquake locations, origin times and focal mechanisms [34; 44]. An advantage of the algorithm is that it does not require as input a predefined number of earthquake clusters, such as the k-means algorithm, where further optimization techniques for the determination of the clusters number are necessary [45].
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 2 Background
### Defence of Identity Privacy
#### 2.4.1 Network Address Translation (NAT)  
Network Address Translation (NAT) (and its derivatives) allows the mapping of one address range into another. A common deployment scenario is to use NAT to allow a private network to use a single public address, reducing IPv4 address exhaustion, as is common for home broadband services.  
With multiple devices using a single source address, the precision of correlation attacks is reduced; an attacker cannot trivially spot individuals--only the crowd. However, even with 1000 users behind the same NAT, fingerprinting attacks can identify individual users [20].  
A NAT is also problematic as a privacy defence, as it is beyond the user's control. A small home network NAT provides minimal protection, as the crowd will be small, and only contains devices associated with the user in some way. Hiding in larger crowds requires using larger NATs, such as a service provider's Carrier-Grade NAT (CGNAT). However, CGNATs are operated by the service provider, upstream, so a user's domain of trust must extend beyond the device in use. The user:  
* must trust the upstream provider regarding the CGNAT function and state;
* cannot opt to use or not use the CGNAT as a privacy feature;
* cannot determine how many others are online to infer how strong the privacy protection of the crowd is.  
NAT is not as widely used with IPv6, due to the absence of address exhaustion concerns, but might be used in enterprise networks for easing address management locally. Network Prefix Translation (NPT) [11] is a similar mechanism to NAT for facilitating private addressing for IPv6, but, as the name suggests, only adjusts the routing prefix and not the IID value, so it does not provide even the limited privacy benefits of NAT.  
Overall, address mapping systems like these do not improve identity privacy in a meaningful and verifiable way.
# A Meshless Radial Point Interpolation Method for Solving Fractional Navier-Stokes Equations
## 3 RPI Methodology for Fractional-Order Navier-Stokes Equations
Finally, errors are calculated to assess the accuracy, and the algorithm outputs the approximate solution. ```
1:Initialize Parameters: Define all necessary parameters, including constants, coefficients, and initial settings for the problem. 2:Setup Data Points and Centers: Specify the number and types of data points and centers. Load these into the computational framework. 3:Define Time Discretization: Set the time discretization step \(\Delta=\frac{1}{\text{num\_times}}\) and define the time interval \([0,1]\). 4:Set Initial Conditions: Establish the initial conditions for the problem. 5:Compute Exact Solution: Evaluate the exact solution at the data points for each time step. 6:Define Weight Function and Coefficients: Define the weight function \(w_{j}=(j+1)^{1-\alpha}-j^{1-\alpha}\) and compute \(b_{\alpha,\Delta}=\frac{\Delta^{-\alpha}}{\Gamma(2-\alpha)}\). 7:Initial Approximation Solution: 1. Compute shape functions \(\Phi(x)\) and their derivatives at data points and centers. 2. Formulate approximation matrix \(M=b_{\alpha,\Delta}A+B^{1}\). 3. Calculate force function \(F=b_{\alpha,\Delta}AU_{0}+F^{1}\). 4. Compute initial approximate solution \(u_{\text{approx}}=\Phi^{\top}(M^{-1}F)\). 8:Iterate for Subsequent Time Steps: Repeat for \(t=2,\dots,\text{num\_times}\): 1. Recompute shape functions \(\Phi(x)\) and their derivatives. 2. Update matrix \(M=b_{\alpha,\Delta t}A+B^{n}\). 3. Update force function \(F=b_{\alpha,\Delta t}A(U^{n-1}-\sum_{k=2}^{n}\omega_{\alpha,k}(U^{n-k+1}-U^{n- k}))+F^{n}\). 4. Compute approximate solution \(u_{\text{approx}}=\Phi^{\top}(M^{-1}F)\). 9:Compute Errors: Calculate the maximum error and RMS error on the evaluation grid: 1. Maximum error: \(maxerr=\|u_{\text{approx}}-u_{\text{exact}}\|_{\infty}\). 10:Output Results: Return the final approximate solution \(u_{\text{approx}}\). ```  
**Algorithm 1** The proposed RPIM for solving two-dimensional conservation-form FNSE problem (1)
# Retrospective Study of the Association between Peri-Implantitis and Keratinized Mucosa
###### Abstract  
**Background:** Peri-implantitis affects a high percentage of patients treated with dental implants. Among the risk factors that can cause this pathology, limited evidence exists regarding the absence of keratinized mucosa. The main **objective** of this study was to compare the amount of keratinized mucosa around healthy implants versus implants in patients with peri-implantitis, in order to analyze its influence on esthetics and the development of this pathology, and to study the possible influence of keratinized mucosa on various clinical parameters. **2. Methods:** A retrospective case-control study was carried out in which 203 implants were evaluated, 103 of which presented a healthy peri-implant state, versus 100 which had peri-implantitis. The following elements were recorded: keratinized mucosa, recession, plaque index, bleeding, suppuration, probing depth, and bone level. **3. Results:** Keratinized mucosa was present in 51% of the peri-implantitis group versus 97.1% of the healthy implants. In implants without keratinized mucosa, both recession and plaque index, bleeding on probing (96.2%), suppuration (57.7%), mean probing depth and bone level were higher. Implants without keratinized mucosa presented worse eshetics. **4. Conclusions:** Absence of Keratinized mucosa around dental implants seems to be associated with the appearance of peri-implantitis modifying certain clinical parameters: there is a greater probability of recession, bleeding, suppuration, deficient hygiene, greater probing depth and greater bone loss in implants without keratinized mucosa.  
Keywords:dental implants; peri-implantitis; keratinized mucosa; peri-implant tissues +
Footnote †: journal:
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
## 2 Methods
### Numerical Methods
For the airfoil with the chord-normal slot \(\theta_{jet}=80^{\circ}\), \(1125\times 158\) grids are used (see Figure 2). For the airfoil with the tangential slot \(\theta_{jet}=22^{\circ}\), \(1150\times 158\) grids are used. Slightly more grids on the OA209 airfoil with the tangential slot are used to resolve the interaction between the jet and the boundary layer downstream of the jet exit, compared to the chord-normal slot. For the chord-normal and the tangential slot, \(41\times 31\) and \(71\times 31\) grids are used, respectively. The asymmetric sides of the tangential slot require more grids inside the slot for capturing flow phenomena on the corner between the suction side and the slot end, compared to the chord-normal slot. The freestream is located at \(50c\) away from the airfoil. The boundary conditions at the freestream and at the actuator are listed in Table 1 along with the SA boundary conditions. The no-slip and adiabatic conditions are applied to the wall. Figure 2: Grids around the OA209 airfoil, near the leading edge, and near the jet slots. Every 4 th point is shown. The time step size is \(\Delta t=1\times 10^{-4}\) s \(\simeq t_{c}/30\) for the baseline airfoil without jet actuation at fixed angles of attack. The convective time scale \(t_{c}\) is defined as \(t_{c}=c/U_{\infty}\). This time step size is sufficiently small to obtain accurate unsteady RANS computations for the baseline OA209 airfoil. The time-averaged value of unsteady RANS is obtained over one flow-through time which is about \(100t_{c}\) after the initial one flow-through time. The corresponding Courant-Friedrichs-Lewy (CFL) number for \(\Delta t=1\times 10^{-4}\) s is as large as \(CFL=15\) in the current grids. The implicit time marching scheme of [PERSON] (1998) allows using such a high CFL number. The time step size for a pitching airfoil or jet actuation is adequately reduced because of additional flow phenomena in the simulation. The time step size for dynamic stall is \(\Delta t=2\times 10^{-5}\) s \(\simeq t_{c}/150\). For jet actuation in both static and pitching airfoil cases, time step is further reduced to \(\Delta t=1\times 10^{-5}\) s \(\simeq t_{c}/300\) in order to resolve the near sonic jet in the current simulation. The number of sub-iterations per time step is 200 in all simulations, which guarantee a sufficiently small residual of \(\mathcal{O}(10^{-11})\) for the conserved variables of Equations (3)-(5) with a residual drop of more than five orders of magnitude per time step. For the pitching airfoil, a cycle-to-cycle convergence is achieved after two initial pitch cycles in the current simulation. Time steps are listed in Table 2.
# An Improved Dictionary-Based Method for Gas Identification with Electronic Nose
## 1 Introduction
adopted the SRC algorithm to solve the breath sample classification problem [23]. The basic function of the SRC classification algorithm is to calculate the residual value between the reconstructed sample and the original sample, select the class label corresponding to the smallest residual value as the result, and complete the pattern recognition task [24; 25; 26]. In sparse decomposition models, the dictionary is an important part, and a good dictionary can have a large effect on the algorithm. At present, many dictionary learning algorithms have attracted academic attention and are highly rated by many scholars [27; 28; 29; 30]. However, when dealing with coefficients, the dictionary learning methods usually adopt the l0-norm or l1-norm method to solve this problem, which are time-consuming approaches to computation. In response to this problem, many researchers have proposed fast algorithms to accelerate the operation speed of dictionary-based learning algorithms (such as iterative shrinkage, proximal gradient, gradient projection threshold, etc.) [31; 32; 33]. This paper conducted a comprehensive study on the traditional dictionary learning algorithm. Although the recognition rate of the algorithm was high, only one atom could be updated in one iteration, which was not efficient. Therefore, it is interesting to study whether the dictionary learning algorithm can have a high recognition rate and a fast calculation speed. A new classification model was adopted to optimize electronic nose performance. Unlike the single-column atomic update of the traditional dictionary learning algorithm, the proposed algorithm utilized a multi-column atomic update. Based on this, aiming at the problem that the singular value decomposition of the k-means (K-SVD) algorithm has little discriminative power, a new model was proposed to make the K-SVD dictionary more discriminative. The rest of this article is structured as follows. Section 2 introduces the experimental device and the dataset. Section 3 resolves the improved algorithm, and Section 4 discusses the recognition results.
## 2 Materials and Methods
### Datasets  
#### 2.2.1 Map of Plantation Forests  
We applied the PF map from [PERSON] et al. (2023) [30] to investigate the variation in PF area in Jiangsu Province. The map was generated by integrating multi-source remote sensing data, including Landsat-5/7/8 time-series spectral imagery, Sentinel-1 radar imagery, a Digital Elevation Model (DEM), and Chinese Forest Canopy Height data. The temporal extension of the dataset was from 1990 to 2020. The spatial resolution was 1 km.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 4 CFD Analysis of Ramjet Nozzles
### Conical Nozzle  
For the conical nozzle, the best thrust was obtained from the longest horizontal divergent section length of 3.8 m, agreeing with the literature in Section 3.3.1. The pressure contour accompanied by the corresponding Mach contour in Figure 16 depicts the presence of a shock which results in an increase in pressure across the exhaust jet axis following the exit from the nozzle while the Mach decreases simultaneously. The pressure at the exit is 12 kPa above the ambient pressure. In an ideal situation, the flow would under-expand. However, the ambient pressure causes an adverse pressure gradient on to the boundary layer at the nozzle lip which causes it to detach at the exit, inducing a shock surface line with a smaller slope towards the jet axis. The shock strengthens as it propagates towards the jet axis resulting in the reflection of the shock without producing a Mach disk. At the exhaust plume boundary, the slip line borders the jet flow, caused by the mixing of the two velocity streams. This is where the shear layer highlighted in Figure 16 is obtained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Boundary** & **Altitude (m)** & **Total Pressure (bar)** & **Temperature (K)** & **Velocity (m/s)** \\ \hline
**Inlet** & N/A & 11.5 & 2000 & N/A \\
**Ambient** & 10000 & 0.262 & 216 & 417 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Boundary conditions for computational fluid dynamics (CFD) validation.  
Figure 15: CFD model validations: (**top**) literature baseline compared against (**bottom**) dual bell optimised nozzle results obtained in this study.  
Thrust is mostly radial, as discussed in Section 3.3.1, where after applying the divergence factor (\(\lambda\)), which was found to be 0.95, the resultant gross axial thrust is 1.47 \(\times\) 10\({}^{6}\) N. Referring back to Figure 6 in Section 3.3.1, for a divergence angle of the best conical nozzle, i.e., 25\({}^{\circ}\), there is a likelihood of separation for the nozzle designed. Divergence loss results in poor nozzle performance at lower altitudes resulting from flow separation. The divergence factor also accounts for the formation of separation shock in the given case. Figure 17 depicts the decrease in pressure along with an increase in Mach number along the jet axis, which validates the CFD contour maps in Figure 16. The drop in Mach at the 1.61 horizontal position is caused by the shock emanating from the sharp turn of the flow as it passes through the nozzle.  
Figure 16: **(Top)** Pressure contour and **(Bottom)** Mach contour thrust = 1.47 \(\times\) 10\({}^{6}\) N, Nozzle Pressure Ratio (\(NPR\)) \(\approx\) 100, H1 = 3.8, V1 = 1.8.  
Figure 17: Mach and pressure plots against nozzle position.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 5 Experiments and Results Analysis
### Ablation Study
#### 5.3.2 The Loss Function during the First Stage of Training  
The first stage of training focuses on the feature extraction capability of the encoder and the reconstruction ability of the decoder, independent of the fusion layer. To balance the magnitudes of different loss terms in the loss function, we introduce the balancing parameters \(\lambda\) and \(\mu\), where \(\lambda\) is used to balance the magnitude difference between \(L_{\text{sim}}\) and \(L_{\text{grad}}\), and \(\mu\) is used to balance the magnitude difference between \(L_{\text{ssim}}\) and \(L_{\text{mse}}\). We assessed the average values of objective metrics under different magnitude combinations to validate the optimal combination of balancing parameters. The experimental results are presented in Table 2. The optimal values are highlighted in bold font.  
From Table 2, it can be observed that when \(\mu\) is set to 100 and \(\lambda\) is set to 1, the image fusion network exhibits better performance.
Besides, perfectly vertical grating coupling [27; 28; 29; 30], which enables fiber packaging without angle polishing, can be very useful to decrease the fiber packaging cost [31; 32; 33]. In this paper, an 800 Gbps WDM silicon photonic transmitter with surface-normal optical interfaces is proposed and numerically demonstrated. This optical transmitter is comprised of eight silicon Mach-Zehnder optical modulators and an 8-channel interleaved angled MMI (AMMI) (de)multiplexer, all of which contain a high-efficiency bidirectional grating coupler functioning as both the perfectly vertical grating coupler and 3-dB power splitter. To improve the coupling performance, an apodized overlay grating structure with subwavelength gratings is proposed and carefully designed. Based on the simulation and modeling work of the grating coupler, the optical modulator and the interleaved AMMI, the system performance was analyzed with the circuit simulation model using Ansys Lumerical interconnect. The data transmission experiment was carried out and the bit error test results were analyzed as well.
## 3 End-to-End Modeling
### Lidar Signal Distortion  
#### 3.3.1 Surface Slope  
As highlighted by [PERSON] _et al_. [65] and [PERSON] _et al_. [66], surface slope has an effect on the lidar accuracy for large footprint lidar systems. This lidar signal distortion due to the surface slope may be no longer negligible when considering a footprint of 20 m in diameter. It affects both the canopy and the ground echoes and may modify their locations in a lidar profile and the SNR level. In this way, it impacts the link budget of the lidar system. Simulations were operated over a simulated homogenous forest  
Figure 10: Uncertainties 1 on _TTH_, _QMCH_ and _FOT_ for different GE SNR. 1 Mean values for all sampling sites.  
Figure 9: The probability density functions (PDF) of the good detection of the ground echo (GE) _vs._ SNR at the ground level (GE SNR). The optimal SNR of 6 is highlighted by the black vertical dotted line.  
containing identical trees (see the illustration of Figure 11a). On the one hand, different ground slopes (0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\) and 45\({}^{\circ}\)) were applied for a combined lidar signal in a 20 m footprint. Results (Figure 11b) show that the lidar profile is strongly affected by slopes larger than 30\({}^{\circ}\). On the other hand, different footprint sizes (5m, 20m and 40 m) were used for a ground slope of 30\({}^{\circ}\) (Figure 11c). As expected and shown in Figure 11, the lidar signal decreases and the distortion increases when the slope or the footprint size increases. This leads to a dispersion of the ground echo on a larger altitude range and to a loss of precision when retrieving the structural parameters. For instance, a slope of 30\({}^{\circ}\) and a footprint of 20 m in diameter lead to a relative uncertainty of 10%-30% on _TTH_, _QMCH_ and _FOT_. This means the surface slope effect has to be considered in the link budget of spaceborne lidar observations; which is equivalent to a decrease of \(\sim\)50% of the GE SNR and then leads to significant increase of the necessary lidar payload (e.g., emitted energy, telescope diameter).
## 4 SNR Estimation
### Proposed Frequency Domain CAZAC-FD SNR Estimation  
The CAZAC-FD SNR estimation algorithm is developed based on the second-order moment criteria where the SNR estimation is performed in the frequency domain after performing FFT processing on the STBC decoder output signal, \(Y_{dstk}\). The CAZAC-FD SNR estimator utilizes the modified CAZAC preambles, shown in Figure 4, having \(Q\) equal parts, each of \(N_{S}/Q\) length, where \(Q\)>2 and \(N_{S}\) is the FFT size. For the CAZAC-FD estimator algorithm, where the modified CAZAC preamble, \(X_{P}(m)\) for \(m=0,\ldots(N_{S}-1)\)  
Figure 7: At \(SNR=12\) dB, the autocorrelation plots of (**a**) the transmitted OFDM signal and (**b**) the received STBC-decoded signal.  
is loaded on even sub-carriers, and the odd sub-carriers are not used (null). Therefore, the transmitted CAZAC preamble signal on the \(k\)th sub-carrier can be expressed as:  
\[X(k)=X(2m+q)=\begin{cases}X_{p}(m),&q=0\\ 0,&q=1\end{cases} \tag{16}\]  
Thus, the received signal on the loaded sub-carrier can be expressed as [18]:  
\[Y_{dstbc}(k)=Y_{dstbc}(2m)=\sqrt{S}\,X_{p}(m)\,H_{p}(m)+\sqrt{W}\,\sigma(m) \tag{17}\]  
where \(S\) is the total transmit power and \(H_{p}(m)\) is the channel response on the loaded sub-carriers. \(W\) is the noise power on each sub-carrier and \(\sigma(m)\) is the corresponding sampled zero-mean AWGN with unit variance.  
The received signal on the nulled sub-carriers consists of the only noise signal and is given as:  
\[Y_{dstbc}(k)=Y_{dstbc}(2m+1)=\sqrt{W}\,\,\sigma(2m+1) \tag{18}\]  
The CAZAC-FD SNR estimation algorithm was developed based on the second-order moment of the OFDM demodulated signal to estimate the SNR at the receiver. Thus, the STBC-decoded signal is OFDM-demodulated and denoted as \(Y_{(d-ofdm)}\). Therefore, the second-order moment on \(Y_{(d-ofdm)}\) by using equations in [18]:  
\[P_{RS}=\frac{1}{N_{s}}\sum_{k=1}^{N_{s}-1}|Y_{(d-ofdm)}(2m)|^{2} \tag{19}\]  
Similarly, the received noise power from the nulled sub-carriers is given as:  
\[P_{RN}=\frac{1}{N_{s}}\sum_{m=1}^{N_{s}-1}|Y_{(d-ofdm)}(2m+1)|^{2} \tag{20}\]  
Thus, the SNR estimation can be determined using the following:  
\[SNR_{MIMO-est}=\frac{P_{RS}-P_{RN}}{P_{RN}} \tag{21}\]  
The NMSE performance of the CAZAC-FD SNR estimator can be quantified using Equation (15).