esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Analysis
### Simulation and Analysis of GM-PHD and Pd-GM-PHD  
In this part, we use the same adaptive gating technical for the GM-PHD algorithm, then, we compare the GM-PHD algorithm with different constant detection probability and Pd-GM-PHD algorithm in a different clutter environment. Simulation results are shown in Figures 6-8, which show the estimation of target number. Also, the average time Optimal Subpattern Assignment distance (OSPA) [28] is used to evaluate the performance of the algorithm, which is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Target** & **Static** & **Initial State** & **Start Time (s)** & **End Time (s)** \\ \hline target 1 & (300 m, 10 m/s, 400 m, 8 m/s) & \(k=1\) & \(k=180\) \\ target 2 & (630 m, 2 m/s, 800 m, 10 m/s) & \(k=55\) & \(k=200\) \\ target 3 & (500 m, 8 m/s, 1700 m, \(-5\) m/s) & \(k=80\) & \(k=300\) \\ target 4 & (1200 m, 4 m/s, 200 m, 10 m/s) & \(k=80\) & \(k=300\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The state parameters of targets.  
Figure 5: Trajectory of four target in two dimensional scenario.  
From Figures 6-8, we can see that the GM-PHD filter algorithm with constant detection probability cannot estimate the target number accurately, while the estimation with Pd-GM-PHD can better approximated to the true target number. Also, after 80 s, the estimation of target number begins to deviate from the true value and there is a phenomenon that the higher detection probability, the greater the estimation of target number error. As the detection probability decreases with distance in practice,  
Figure 8: Comparison of target number estimation with different \(P_{d}\) in clutter intensity \(\kappa_{k}=20\).  
Figure 6: Comparison of target number estimation with different \(P_{d}\) in clutter intensity \(\kappa_{k}=5\).  
Figure 7: Comparison of target number estimation with different \(P_{d}\) in clutter intensity \(\kappa_{k}=10\).  
while the GM-PHD filter algorithm still believes that measurement is credible, eventually the estimation accuracy gets worse. As for the Pd-GM-PHD filter algorithm, the detection probability changes with distance, so its performance is better than the GM-PHD using the same adaptive gating technique with different constant detection probability.  
Table 2 shows the average OSPA distance between GM-PHD with constant detection probability and novel detection probability in different clutter environments. In the update process in the GM-PHD filter algorithm, the detection probability will affect the update results. The detection probability will decrease with distance, then unreliable measurements have the high weight in the update process. So the higher detection probability, the worse the tracking performance is and the bigger the average OPSA distance is. While this phenomenon will improve when using the novel detection probability, which not only can make the estimation of target number more reliable, but also can improve the performance of tracking accuracy. So we can get the similar results with Figures 6-8 that the Pd-GM-PHD has a good performance.
## 3 Materials Used for Restoration and Conservation of Masonry Elements
### Restoration/Conservation of Masorry Materials against Abiotic Factors
#### 3.3.2. Cleaning Agents  
Cleaning of stone artifacts is usually performed by mechanical (brushing, projection) [88] or chemical methods (using different solvents, such as methylene, acetone, acids, alkali or even commercial mixtures) [89; 90]. More recently, the use of lasers was proposed for a wide variety of stone types (marble, sandstone, etc.) [91]. Removal of biofilms is usually performed using the same techniques, followed by a biocidal treatment (presented in Section 3.1). Regardless of the chosen technique, great care should be paid to not provoke damages to the original material; the processes should be gradual, selective and economically feasible [92].  
Among the new materials used for cleaning purposes, the most encountered are the TiO\({}_{2}\) nanoparticles, which, due to their photocatalytic properties, can be used in self-cleaning protective layers. For example, [PERSON] et al. [93] proposed the use of this property of anatase-form nanoTiO\({}_{2}\) by incorporation in three types of commercial coatings (acrylic polymer in organic solvent--Paraloid B72, fluorinated polymer in an alcoholic solvent--Akeogard P, compared with a commercial aqueous suspension of TiO\({}_{2}\) and an acrylic polymer--Fosbuuild). The poorest results were obtained for the material using the nanoparticles/Paraloid composition (intense alteration of the surface, poor photo-degradation effect), while the best results were obtained for the commercial formulation (Fosbuild). The composition based on fluorinated polymers also led to good results, with significant photo-degradation and water repellency. A similar approach was adopted by [PERSON] et al. [94], combining the hydrophilic and photocatalytic photo-induced properties of the nanoparticles into a coating that decreased the water adsorption by 50%. Other examples regarding the use of nano-TiO\({}_{2}\) as self-cleaning materials are provided in Table 5.  
A particular and very important area of cleaning procedures is dedicated to the graffiti removing methods. This can be achieved by chemical or mechanical methods, laser cleaning [92] or biocelaning (using, for example, sulfate-reducing bacteria or enzymes) [95]. A preventive action (protective) is represented by the anti-graffiti coatings, based on organic or inorganic agents (either temporary--waxes, silicones, or permanent--polyurethanes, fluorocarbon, alkyl alkoxy silanes) [96].
## 6 Theoretical and Security Analysis
### Security Analysis  
#### 6.2.1 Rapid Content Publication Attack  
In the participation assessment algorithm, users can accumulate \(PV\) by publishing content. Thus, they might exploit this algorithm by publishing a large volume of false or low-quality content to garner extra \(PV\). To counter this type of attack, the NexoNet utilizes the hyperbolic tangent function's properties in the calculation of \(PV\) gained from content publication: \(GCP=\sum_{1}^{n_{1}}a_{gcp}\times\tanh_{1}\). If users publish a vast amount of content in a short period, the quantity of \(PV\) they can obtain becomes inversely related to the interval \(\Delta T\) between publications. The shorter the interval between user publications, the closer the gained \(PV\) will approach 0. This design prevents users from benefiting from such malicious behavior by making it impossible to gain any profits from rapidly publishing a large volume of content. Furthermore, publishing content on the system requires the payment of a transaction fee to cover the costs for system use and maintenance, imposing a significant cost on users who publish content extensively, thereby preventing rapid content publication attack.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Platform** & **NexoNet** & **Steemit** & **Mastodon** & **Minds** & **Diaspora** \\ \hline Decentralization & Fully Swarm & Partial & Federated & Partial & Fully \\ Data Storage & Distributed & Blockchain & Instanc-controlled & Blockchain & User-controlled \\ Incentive & Multiple & Token & None & Token & None \\ Mechanism & Incentives & Rewards & None & Rewards & None \\ Scalability & High & Limited & Instanc-dependent & Limited & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of consensus mechanism.
# Design and Assessment of an Austenitic Stainless Alloy for Laser Powder Bed Additive Manufacturing
## 3 Results
### Selection of a Specific Alloy
This composition is given in Table 1 and marked with red dots in Figure 2.  
Table 4 presents a comparison of the values of our defined objective functions for the selected material, calculated based on the effective powder composition specified in Table 1, and for 316L. Overall, the optimized alloy outperforms 316L, except for the thermal expansion coefficient, which is slightly higher than that of this reference alloy. The Scheil simulations of solidification and phase fractions with temperature for the selected alloy are depicted in Figure 3. The Scheil diagram in Figure 3a reveals that the alloy initially solidifies into \(\delta\)-ferrite (BCC_A2) for a solid fraction up to 20%, then austenite is formed (FCC_A1). By the end of the solidification, these two phases collectively make up 99% of the material. The predicted CTR is relatively low, with a value of 77 K, compared with the value of 92 K for the 316L alloy. Consequently, the risk of solidification cracking is minimal. According to the equilibrium calculation of Figure 3b, the alloy is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Hull min.** & **CTR** & **ST** & **\(\alpha_{\text{CTE}}\)** & **Vap. Flux** & **Vap. Flux** \\  & **Distance (a.u.)** & **(K)** & **[N.m\({}^{-1}\)]** & **SSH (a.u.)** & **[10\({}^{-6}\)/K]** & **Average** & **std.** \\ \hline Optimized alloy & 3.5 & 77 & 1.0 & 44 & 13 & 0.12 & 0.25 \\ \hline
316L & 1.3 & 92 & 1.3 & 31 & 11 & 0.13 & 0.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Calculated objective functions values for the optimized alloy and for 316L stainless steel. Figure 2: Violin plots of the optimized alloy population after 50 generations, with the median point (black diamond) and the selected composition (red dot). expected to maintain a single FCC structure within the temperature range of 950 to 1230 \({}^{\circ}\)C. Below 950 \({}^{\circ}\)C, an ordered BCC phase of the B2 type is predicted to form at equilibrium. Nevertheless, the Cr\({}_{\text{eq}}\) and Ni\({}_{\text{eq}}\) values, in accordance with the Hull diagram, suggest the alloy is anticipated to remain fully austenitic after solidification and cooling in a welding-type process. A negligible amount of chromium-rich M\({}_{23}\)C\({}_{6}\) carbides (<0.005%) is predicted below approximately 800 \({}^{\circ}\)C.
###### Abstract  
_Thrips hawaiiensis_ is a common thrips pest of various plant flowers with host preference. Plant volatiles provide important information for host-searching in insects. We examined the behavioral responses of _T. hawaiiensis_ adults to the floral volatiles of _Gardenia jasminoides_ [PERSON], _Gerbera jamesonii_ Bolus, _Paeonia lacifiom_ Pallas, and _Rosa chinensis_ Jacq. in a Y-tube olfactometer. _T. hawaiiensis_ adults showed significantly different preferences to these four-flower plants, with the ranking of _G. jasminoides_\(>\)_G. jamesonii_\(>\)_P. lacifiom_\(\geq\)_R. chinensis_. Further, 29 components were identified in the volatile profiles of _G. jasminoides_, and (Z)-3-hexenyl tglate (14.38 %), linalool (27.45 %), and (E3,E7)-4,8,12-trimethyltrideca-1,3,7,11-tetraene (24.67 %) were the most abundant. Six-arm olfactometer bioassays showed that _T. hawaiiensis_ had significant positive responses to (Z)-3-hexenyl tglate, linalool, and (E3,E7)-4,8,12-trimethyltrideca-1,3,7,11-tetraene tested at various concentrations, with the most attractive ones being \(10^{-3}\) uL/uL, \(10^{-2}\) uL/uL and \(100\) ug/uL for each compound, respectively. In pairing of these three compounds at their optimal concentrations, _T. hawaiiensis_ showed the preference ranking of (Z)-3-hexenyl tglate \(>\) linalool \(>\) (E3,E7)-4,8,12-trimethyltrideca-1,3,7,11-tetraene. Large numbers of _T. hawaiiensis_ have been observed on _G. jasminoides_ flowers in the field, which might be caused by the high attraction of this pest to _G. jasminoides_ floral volatiles shown in the present study. Our findings shed light on the olfactory cues routing host plant searching behavior in _T. hawaiiensis_, providing important information on how _T. hawaiiensis_ targets particular host plants. The high attractiveness of the main compounds (e.g., linalool, (E3,E7)-4,8,12-trimethyltrideca-1,3,7,11-tetraene, particular (Z)-3-hexenyl tglate) identified from volatiles of _G. jasminoides_ flowers may be exploited further to develop novel monitoring and control tools (e.g., lure and kill strategies) against this flower-inhibabiting thrips pest.  
**Keywords:** olfactory responses; flower host plant; VOC; Y-tube olfactometer; six-arm olfactometer
## 4 Feature Parameter Extraction
### Wavelet Packet Analysis  
The wavelet packet (WP), which can divide the frequency band into multiple levels, is used to process acoustic signals containing considerable crack information. Wavelet packet analysis, which uses Sym8 as the wavelet basis to carry out five-layer wavelet packet decomposition, is performed on transmitted waves and reflected waves, and kernel principal component analysis (KPCA) is used to reduce the wavelet coefficient dimension according to [15].  
Figure 10: 3D map of the angle–depth–reflection coefficient.  
In the time-frequency domain, WP coefficient peaks, WP energy peaks, and WP entropy are extracted and analyzed in this paper. Taking the transmitted wave as an example, the WP coefficient peaks, WP energy peaks, and WP entropy of transmitted waves at different crack depths and oblique angles are shown in Figure 12.  
Figure 11: 3D map of depth and angle versus deconvolution sum.  
Figure 12: _Cont._  
Figure 12 shows that when the depth increases, the WP coefficient peaks, WP energy peaks, and WP entropy shows different variation trends, which reflect different correlations with depth. Therefore, a selection standard is required to choose feature parameters that can fit the SVM model best and is proposed in the next section.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Routing Decision Process
#### 4.3.7 Acknowledgment  
When the sender UAV sends a packet to the custodian, the custodian sends back an acknowledgment (ACK) indicating that it successfully received the packet and took custody of it. Upon receiving the ACK, the sender discards the packet, considering that the custodian is solely responsible for forwarding the packet further. Following the best-effort approach, the destination does not send an ACK to the sender; instead, the destination sends an ACK only to the immediate custodian. By implementing this ACK mechanism, we ensure the minimum number of packet copies remains in the network at a time.
## 5 Implementation of the Proposed Control Algorithm
### Parameter Settings for SAKF  
For model (6), assume that its process noise covariance matrix \(\mathbf{R_{\mathrm{w}}}=\mathrm{diag}[\sigma_{\theta},\sigma_{\upsilon},\sigma_{d}]\), measurement noise covariance matrix \(\mathbf{R_{\mathrm{r}}}=\mathrm{diag}[\sigma_{\theta},\sigma_{\upsilon}]\). Therefore, in order to obtain \(\mathbf{R_{\mathrm{w}}}\) and \(\mathbf{R_{\mathrm{r}}}\), it is necessary to obtain \(\sigma_{\theta},\sigma_{\upsilon},\sigma_{d}\).  
Assuming that the resolution of the encoder is \(\delta\theta\) and the encoder's output value for the actual angle \(\theta\) is \(\overline{\theta}\), then the difference is \(\Delta\theta=\overline{\theta}-\theta\in(-\frac{1}{2}\delta\theta,\frac{1}{2} \delta\theta)\). Considering that the quantization error \(\Delta\theta\) is evenly distributed over the interval \((-\frac{1}{2}\delta\theta,\frac{1}{2}\delta\theta)\), so the angle variance is:  
\[\sigma_{\theta}=\mathrm{var}(\Delta\theta)=\int_{-\frac{\delta\theta}{2}}^{ \frac{\delta\theta}{2}}\frac{1}{\delta\theta}\Delta\theta^{2}\mathrm{d}( \Delta\theta)=\frac{1}{12}\delta\theta^{2}. \tag{17}\]  
Similarly, if the speed resolution is \(\delta\upsilon\), then the speed variance should be:  
\[\sigma_{\upsilon}=\frac{1}{12}\delta\upsilon^{2} \tag{18}\]  
As the disturbance is not easy to measure and model, the disturbance covariance \(\sigma_{d}\) depends on experimental tuning. If \(\sigma_{d}\) is set improperly large, the disturbance observation response will be faster and the suppression effect will be better. However, the observation result will be sensitive to the measurement noise, and the stability margin will decrease dramatically. Considering that most of the disturbances are originated by speed, it is reasonable to set the initial value of \(\sigma_{d}\) as:  
\[\sigma_{d}=B\cdot\sigma_{\upsilon} \tag{19}\]  
where \(B\) is the viscous friction coefficient, and then gradually increase \(\sigma_{d}\) until the servo performance and stability margin are both satisfactory.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method  
The sparse array optimization design method is the primary difficulty of sparse array optimization design. Currently, the optimization design methods of sparse planar arrays can be classified into four main categories: analysis methods, intelligent optimization methods, local optimization methods, and hybrid methods. This section focuses on these four types of methods; for easier understanding, Figure 11 provides a visual representation of the classification of sparse planar array optimization design methods.
## 4 Results and Discussion  
### The Effect of Pulse Density on Electrode Roughness  
The profile mapping of the 400 \(\times\) 400 \(\upmu\)m surface area reveals a consistent increase in roughness across all electrodes following the LSP treatment. Similar to the microstructural surface evaluation, a significant increase in roughness was observed among the treatments with 900 pulses/cm\({}^{2}\), 1600 pulses/cm\({}^{2}\), and 2500 pulses/cm\({}^{2}\), as shown in Figure 4b, 4c, and 4d, respectively. The roughness increase is further illustrated in Figure 5, where it becomes particularly evident after the laser treatment, with the maximum valley-peak distance reaching approximately \(\sim\)35.5 \(\upmu\)m for the electrode treated with 2500 pulses/cm\({}^{2}\). The increasing trend in Ra as a function of pulse density is detectable over relatively long profile distances, starting at 400 \(\upmu\)m. It is clear that the Ra of the Al6061 alloy increases linearly with higher pulse densities during the LSP process. This linear relationship aligns with the previously reported correlations between pulse density and surface roughness in the Al6061 To alloy [20].  
Figure 4: Profile mapping of the initial Al6061 alloy surface (**a**) and after LSP, (**b**) 900 pulses/cm\({}^{2}\), (**c**) 1600 pulses/cm\({}^{2}\), and (**d**) 2500 pulses/cm\({}^{2}\).  
Figure 5: Average roughness (R\({}_{\text{a}}\)) of the Al6061 To alloy electrodes after LSP treatment as a function of the pulse density.
## 2 Experimental Section
### Contact Angle and Surface Energy  
The surface hydrophobicity was quantified by measurement of the contact angle with a goniometer [21]. A drop of 5 \(\upmu\)L deionized water was used to probe the sample surface. The droplet images were recorded with a high-resolution camera. The contact angle was precisely measured in the picture. Five readings were taken from each sample and the mean was calculated. The measurement was done immediately after APP treatment. The surface energy of the plasma-modified samples was determined using the [PERSON]'s two-liquids method, as given in Equation (1) and (2) [22]. Two probing liquids, deionized water (72.8 mN/m) and glycerol (63.4 mN/m), were used for contact angle measurement according to the standard testing method ASTM D5946. Table 2 shows the discrete values of dispersion (\(\gamma_{1}\)d) and polar (\(\gamma_{1}\)P) parts of the surface tension (\(\gamma_{1}\)) used for calculation [22].  
\[\gamma l(1\,+\,\text{cos}\theta)\,=\,2\times[(\gamma\text{sp}\times\gamma l \text{p})1/2\,+\,(\gamma\text{sd}\times\gamma l\text{d})1/2] \tag{1}\]  
\[\gamma\text{s}\,=\,\gamma\text{sp}\,+\,\gamma\text{sd} \tag{2}\]  
where: \(\theta\) is the static contact angle between the rayon flock synthetic leather and the probing liquid; \(\gamma_{1}\) is the surface tension of the probing liquid; \(\gamma_{1}\)P and \(\gamma_{1}\)d are the polar and dispersive components, respectively, of the probing liquid; \(\gamma_{\text{s}}\) is the overall surface energy of the rayon flock synthetic leather; and \(\gamma_{\text{s}}\)P and \(\gamma_{\text{s}}\)d are the polar and dispersive components, respectively, of the rayon flock synthetic leather.
## 2 Materials and Methods
### Variation of Network Designs  
We designed a mask branch network with four types from MBN-V1 to MBN-V4, as shown in Figure 3. We explored the architecture of the mask branch network with several factors: the number of branches, starting positions of branches, and how to combine branch outputs in the case of multiple branches. Although the principle of operation is the same as that explained by MBN-V1 above, the suitable structure of the mask branch network can be diverse for each backbone network and task. We design single or multiple subnetworks parallel to the main network. MBN-V1 and MBN-V2 have a single branch, generating only one output map.  
MBN-V1 is attached to the 2 nd transition layer, extracting feature map \(\mathbf{F_{1}}\) and resulting in \(\mathbf{B_{1}}\) (Figure 3a). The mask loss \(L_{mask}\) is used to reduce the difference between \(\mathbf{B_{1}}\) and \(\mathbf{T_{1}}\). MBN-V2 is integrated into the backbone on the third transition layer, as shown in Figure 3b. In contrast to MBN-V1 and MBN-V2, the other versions of the mask branch network consist of two branches that were used for MBN-V1 and MBN-V2. In MBN-V3, the two branches are separated, resulting in \(\mathbf{B_{1}}\) and \(\mathbf{B_{2}}\), which have different shapes, as shown in Figure 3c. The branch outputs are matched to \(\mathbf{T_{1}}\) and \(\mathbf{T_{2}}\), calculating two corresponding mask losses. Finally, the losses are combined with the uncertainty loss weight method, and the branches are optimized by each loss. Conversely, MBN-V4 integrated the output maps to one single array by a summation operation (Figure 3d). In MBN-V4, after \(\mathbf{B_{1}}\) is downsampled to the same shape as \(\mathbf{B_{2}}\) through 3D average pooling, \(\mathbf{B_{1}}\) and \(\mathbf{B_{2}}\) are integrated by elementwise summation.  
We show the evaluation results of each case implemented in the experiments section.  
Figure 3: Architectures of the mask branch network we proposed with DenseNet. (**a,b**) MBN-V1, MBN-V2 with a single subnet, where each branch is attached to conv2 and conv3. (**c,d**) MBN-V3, MBN-V4 with multiple branch subnets containing branches of both MBN-V1 and MBN-V2. In MBN-V3 of (**c**), the losses are calculated with each _branch output_ separately. MBN-V4 combines the _branch outputs_ by summation and calculates loss in once, as shown as (**d**).