esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Perceptual Behavior of Visual Elements  
#### 4.2.1 Visual Behaviors of Different Groups of Viewers  
The eye movement index of one image was randomly selected from the landscape architecture images viewed by each group on the observation decks, and six indicators of the residents (n = 15) and tourists (n = 15) were analyzed. Finally, the eye-tracking experiment data for the three landscape architecture images of A-01, B-02, and C-02 were selected as the data sources. Based on the experimental results for the different groups of participants on the same observation deck, SPSS 21.0 (IBM Corp.) was used to perform an independent sample _t_-test (Figure 5, Supplementary Materials Table S1), and the significance of the six visual behaviors in the two groups was compared.  
The data in Table S1 show that the F-value (i.e., the Levene F-statistic) has a _p_-value > 0.05 for six variables, and these six eye-tracking data all show the same variance. In the two-tailed test, the _p_-value is also >0.05; combined with the F-statistic, this suggests that the visual behavior of the two groups toward the same landscape architecture elements has no significant difference, and the visual behavior characteristics of the same view are maintained.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 4 Proposed DRL-Based Method
### Design of State, Action and Reward  
#### 4.3.1 State  
In the deep reinforcement learning framework, the state is defined as a complete observable description of the current environmental conditions. For this problem, the state design is primarily composed of the following parts:  
* Terrain: Based on a rasterized topographic map, a binary encoding scheme is applied to distinguish deployable areas from restricted zones. Roads, flatlands, and grasslands are classified as deployable regions and assigned 1, while buildings, water, and forests are marked as restricted zones with 0.
* Deployable Range: Determine the communication range between adjacent elements based on the network topology, with values in the range [0, 1].
* Optical Cable Access Point: When the deployment element is the optical terminal equipment, the area within a certain range around the optical cable access point is set to 1, while other areas are set to 0.
* Microwave Directionality: For microwave relay vehicle deployment scenarios, a directional weight matrix is constructed, where the value increases as it approaches the direction of the opposite microwave terminal. The range of values is [0, 1].
* Type: The requested element type as an integer corresponding to the index of current element.
## 3 Results
### Bio-Membrane Characterization Coupled Fuel Cell  
Once the bio-membrane was evaluated in a water electrolysis system, it was coupled to a fuel cell. Considering the results obtained from the electrolyzer analysis, thermal treatment was regarded as the most adequate alternative. The performance of this bio-membrane was compared to a commercial reference (Nafion membrane) in the fuel cell.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment** & **Potential (V)** & \(\sigma\)**mS cm\({}^{-2}\)** & **Half-life (h)** \\ \hline \hline
**Without treatment** & 9.3 & 1.39 \(\pm\) 0.01 & 0.05 \\  & H2O & 3.9 & 0.03 \(\pm\) 0.01 & 0.06 \\
**Immersion** & NaOH 0.1 M & 3.8 & 0.02 \(\pm\) 0.03 & 0.08 \\  & H2 SO4 0.1 M & 3.6 & 0.02 \(\pm\) 0.06 & 0.08 \\  & chloroform & 4.4 & 0.11 \(\pm\) 0.03 & 0.05 \\
**Clean-up** & ethyl ether & 3.5 & 4.26 \(\pm\) 0.02 & 0.16 \\  & hexane & 3 & 6.25 \(\pm\) 0.03 & 0.06 \\  & methanol & 3 & 0.07 \(\pm\) 0.01 & 0.25 \\
**Polymeric coating** & Nafion & 2.7 & 0.02 \(\pm\) 0.01 & 1.85 \\  & **Scraping** & 3.4 & 5.13 \(\pm\) 0.02 & 0.65 \\  & **Thermal** & 2 & 10.00 \(\pm\) 0.03 & 336 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Activation treatments of the bio-membrane.  
Electric parameters were recorded during the performance assessment of the bio-membrane in the hydrogen and methanol fuel cell. Resistance between plates was measured in order to determine the membrane ionic conductivity. For all experiments a contact area of 4.0 cm\({}^{2}\) between the plate and the membrane was set with a membrane thickness of 0.08 mm.  
All the experiments were run for a week, and the potential obtained did not change in this time for both the _Agave salmiana_ bio-membrane and the Nafion membrane. Table 2 shows the potential (V) obtained in the fuel cell using hydrogen and methanol as fuel. When using hydrogen, the potential value was greater than that obtained with the Nafion membrane. The bio-membrane was not completely hydrated, and its efficiency was affected in comparison to the Nafion membrane. Nonetheless, the obtained potential is deemed to be competitive. On the other hand, when using methanol, the potential was greater for the _Agave salmiana_ bio-membrane compared to the reference Nafion membrane; the bio-membrane was observed to be completely hydrated, which led to an increased ionic conductivity. The results obtained using the thermally-treatment bio-membrane in the methanol fuel cell are similar to those reported in methanol fuel cells with Alginate and Nafion membranes [11; 38]. Results demonstrate that the proposed bio-membrane is a competitive option to replace Nafion in hydrogen and methanol fuel cells.
# Optimal Algorithms for Sorting Permutations with Brooms
## 5 Analysis of Algorithm \(D^{*}\) on Millipede Tree  
The first known upper bound \(f(\Gamma)\), for sorting permutations using transposition trees, was computed by [PERSON] and [PERSON] [1] in 1989. The time complexity for calculating \(f(\Gamma)\) is \(\Omega(n!n^{2})\), since it performs pair wise distance computation of each of the possible \(n!\) permutations. Subsequently, various polynomial time approximation algorithms were designed [23; 24; 25; 29; 30]. [PERSON] introduced algorithm \(D^{{}^{\prime}}\) in [24] which finds the upper bound \(\delta^{\prime}\) in polynomial time, which is the tightest known upper bound when the upper bounds of all trees are summed together. Subsequently, algorithm \(D^{*}\) which identifies corresponding the upper bound \(\delta^{*}\) was designed in [29]. It was shown that \(\delta^{*}\) is tighterfor _balanced-starburst_ tree [29]. In [31], an analysis of both \(D^{{}^{\prime}}\) and \(D^{*}\) was performed and shows that \(\delta^{*}\) is tighter for full binary tree too. In this section we define a class of trees called millipede tree for which \(\delta^{*}\) is tighter, compared to \(\delta^{\prime}\).  
A _millipede tree_\(M_{k}\) is obtained by connecting the centre vertices \(c_{1},c_{2},\ldots c_{k}\) of \(k\) path graphs \(p_{1},p_{2},\ldots p_{k}\) arranged in the order, such that \(k\) is odd and the number of vertices in \(p_{i}\) is given by the following function:  
\[n(p_{i})=\begin{cases}2i+1&\text{if }1\leq i\leq\lceil k/2\rceil\\ 2(k-i)+3&\text{Otherwise}.\end{cases} \tag{5}\]  
The diameter of \(M_{k}\) is \((k+1)\) and the total number of vertices \(n=2*[\lceil k/2\rceil*(\lceil k/2\rceil+1)]-1\). \(v_{c}=c_{(k+1)/2}\) is the center of the tree. The center has an eccentricity of \((k+1)/2\).  
We call the subtree above the vertex \(v_{c}\) the _upper millipede tree_ and the subtree below \(v_{c}\) the _lower millipede tree_(refer figure 6).
## 3 Results and Discussion
### Atomic Force Microscope (AFM)  
Figure 12a-c show the AFM three-dimensional images of the matrix asphalt, SBS-modified asphalt (with 4 wt% SBS), and GF-WTB/SBS composite-modified asphalt (with 4 wt% SBS and 2 wt% GF-WTB), respectively. In Figure 12, the dark areas are pits and the bright areas are bumps. It can be seen from Figure 12a that the microscopic morphology of the matrix asphalt surface is relatively uniform. The surface is distributed with alternating dark and pale areas, which are close to bee-shaped structure. Research shows that these be-shaped structures are the clustering of the asphaltene micelles not completely dispersed in the dispersion medium [34]. It can be seen from Figure 12b that the surface of SBS-modified asphalt presents a network structure, which is formed by the SBS block copolymer crosslink and swelling by absorbing light components in asphalt. This network structure can effectively change the elasticity, plasticity, and ductility of asphalt, thereby improving the high- and low-temperature performance of asphalt.  
From Figure 12c, the convex GF-WTB particles (white areas) can be clearly seen in the SBS-modified asphalt network structure, and the two modifiers have good compatibility. During preparation, the GF-WTB/SBS composite-modified asphalt was stirred and developed at a high temperature. Swelling, adsorption of the asphalt light components, and reaction between some organic functional groups in asphalt and the vinyl functional group on grafted GF-WTB particles surface occurred. Then, the GF-WTB agents were evenly dispersed in the SBS-modified asphalt in the form of particles and chains, and the  
Figure 12: AFM of: (**a**) matrix asphalt; (**b**) SBS-modified asphalt; (**c**) GF-WTB/SBS composite-modified asphalt.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Series** & **Weight \%** & **Atomic \%** & **Error \%** \\ \hline C & K & 33.31 & 45.25 & 10.20 \\ N & K & 2.27 & 2.65 & 20.96 \\ O & K & 34.26 & 34.94 & 9.54 \\ Na & K & 0.66 & 0.47 & 17.17 \\ Mg & K & 0.71 & 0.48 & 12.25 \\ Al & K & 1.82 & 1.10 & 6.57 \\ Si & K & 23.20 & 13.48 & 3.05 \\ S & K & 0.86 & 0.44 & 14.94 \\ Cl & K & 0.11 & 0.05 & 23.64 \\ K & K & 0.28 & 0.12 & 13.49 \\ Ca & K & 2.52 & 1.03 & 7.85 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Element content of UP152-modified GF-WTB.  
GF-WTB/SBS composite-modified asphalt obtained better toughness and low-temperature crack resistance.
###### Abstract  
The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).  
SDWSN-IoT; multi-objective routing; reinforcement learning; energy-efficient routing +
Footnote †: journal: _Sensors_
## 2 The Structure of the UPQC System  
The traditional UPQC is a multifunctional power quality management device that integrates both voltage and current compensation functions [24]. The series side functions as the voltage compensation unit, primarily aimed at improving the quality of the grid voltage. The parallel side serves as the current compensation unit, with its core function being the dynamic compensation of harmonic currents introduced by nonlinear loads, thereby maintaining the sinusoidal and symmetrical nature of the grid current and improving the overall power quality. The overall structure of the UPQC is shown in Figure 1.
## 4 Network Layer: The Main Building Block of Fixed Networks  
### Five Generations of Access: From PSTN to 50G EPON  
Access network is the easiest and least costly part of upgrade compared to (for instance) core, in order to improve QoS and increase the availability to end users. Over the last fifty years, various ways of accessing the network have been proposed, as illustrated in Figure 4, each having its pros and cons. The first generation of fixed networks includes PSTN, ISDN and hybrid fiber-co-axial (HFC) standards to provide access to the end users, while the majority of DSL and passive optical network (PON) technologies were standardized during the second generation. More details regarding copper-based standards (e.g., PSTN, ISDN, HFC, Data Over Cable Service Interface Specification (DOCSIS) and DSL) can be found in Table 3, while a more elaborated picture of the PON family is shown in Table 4. In the rest of this subsection, we elaborate on the access technologies for each of the five fixed network generations.
## 3 Methodology
### Point Cloud Confidence Based on Rerendering  
For direct generation of a 3D point cloud instead of 2D images, the algorithm mentioned in Section 3.3 is typically not used. Our methodology facilitates the inclusion of multiple source views to generate a more precise point cloud, necessitating the introduction and aggregation of confidences.  
By leveraging the principle of the algorithm from Section 3.3 for introducing and aggregating confidence across multiple views, our work possesses two unique characteristics:  
* The precision of the point cloud augments with an increase in the number of input views.
* Different input views contribute varying confidences to the same point in the point cloud, and these contributions are interpretable.  
This implies that our point cloud can achieve high precision given sufficient input and computational resources.  
Given the exponential decrease in light intensity when a ray passes through a 3D object's surface, this intensity, represented as follows:  
\[T_{i}=\exp(-\sum_{j=1}^{i-1}\sigma_{j}\beta_{j}) \tag{10}\]  
should be lower for parts of the 3D point cloud reconstruction that are obscured from view and higher for visible portions.  
The voxel confidence for the point cloud point at the neural radiance point \(T_{i}\) is given by the following:  
\[T_{i}^{j}=T_{i}\frac{W_{i}^{j}}{\max(W_{i})} \tag{11}\]  
where \(W_{i}^{j}\) denotes the inverse distance weight.  
By performing 2D neural radiance rendering on the point cloud using the camera poses from the source views, voxel confidences for these viewpoints are derived, alongside the neural rendered images from the source views. The color confidence is calculated as follows:  
\[S_{i}=\max(1-\frac{||I_{i}-O_{i}||}{L},0) \tag{12}\]  
The aggregated point cloud confidence, combining voxel and color confidences, is given by the following:  
\[t_{i}^{j}=S_{i}T_{i}\frac{W_{i}^{j}}{\max(W_{i})} \tag{13}\]  
In the case of multi-view aggregation, the confidence for each point is aggregated using the maximum value.
# Experimental Study on Femtosecond Laser Processing Performance of Single-Crystal Silicon Carbide
## 3 Results and Discussions  
### Significance Analysis  
Table 2 shows variance analysis results of the groove depth D, groove width W, MRR, HAZ width and side wall inclination angle \(\theta\). The influence of fluence and numerical aperture on groove depth, groove width, HAZ width, MRR and side wall inclination angle is very significant. The scan speed has a very significant effect on the groove depth, groove  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Experiment 1**} \\ \hline
**Parameter** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** \\ \hline Fluence (J/cm\({}^{2}\)) & 1.77 & 1.89 & 2.01 & 2.13 & 2.26 \\ Scan speed (\(\upmu\)m/s) & 50 & 150 & 250 & 350 & 450 \\ Repetition rate (Hz) & 200 & 400 & 600 & 800 & 1000 \\ Multi-scanning & 1 & 2 & 3 & 4 & 5 \\ NA & 0.4 & 0.6 & - & - & - \\ \hline \hline \multicolumn{6}{c}{Experiment 2} \\ \hline Parameter & Level 1 & Level 2 & Level 3 & Level 4 & Level 5 \\ \hline Fluence (J/cm\({}^{2}\)) & 1.77 & 1.89 & 2.01 & 2.13 & 2.26 \\ Scan speed (\(\upmu\)m/s) & 5 & 10 & 50 & 100 & 300 \\ Polarization angle (\({}^{\circ}\)) & 0 & 30 & 60 & 90 & - \\ Multi-scanning & 1 & 5 & 10 & 30 & 60 \\ Feed step size Az (\(\upmu\)m) & 0 & 0.1 & 0.3 & 0.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Single factor experimental scheme.  
Figure 2: Laser beam movement mode.  
width, HAZ width and side wall inclination angle but has an insignificant effect on the MRR. Repetition rate and multipass scanning have a very significant effect on groove depth, HAZ width, MRR and side wall inclination angle, and a moderately significant effect on groove width. The effect of scan speed on MRR is insignificant, while the effect of repetition rate on groove width is moderately significant.  
The femtosecond laser processing of silicon carbide grooves is mainly used for micro-cavities, micro-mechanical systems, water droplet based micro-fluidic devices, micro-fluidic sensors and biosensors. Grooves with high aspect ratio, high MRR and small HAZ width help reduce friction in micro-fluidic devices and improve flow ability. A larger scan speed has insignificant impact on MRR, while a smaller scan speed has a very significant impact on MRR. A smaller number of multipass scanning has a moderately significant impact on groove width, while a larger number of multipass scanning has a very significant impact on groove width. In order to inject more laser energy into the workpiece, a smaller scanning speed and larger number of multipass scanning were used in Experiment 2. Table 3 shows the variance analysis results of groove depth, groove width, MRR, HAZ width and aspect ratio. The fluence, multipass scanning, scan speed and z-layer feed all have a significant impact on the machining characteristics. The polarization angle only has a significant effect on the HAZ width but has insignificant effect on the groove depth, groove width, aspect ratio and MRR.
## 5 Obstacles and Challenges in Evolving V2X Communication
### RAN Obstacles and Challenges
#### 5.1.2 Subcarriers or CSMA/CA--The Perks and Drawbacks
The fixed radio resource blocks can result in degraded utilization in the case of varying packet lengths, which is typical in standardized direct V2X communication (like CAM messages). As we can see, the modulation of these communication technologies is relatively similar. However, the channel access (the MAC) design has several implications for the performance of these communication technologies. Figure 6 from [135] highlights the differences relatively well. The DSRC performs slightly better in the case of low distances; however, the C-V2X outperforms the other technology in the case of higher distances.
## 6 Experimental Results
### Comparison Between Joint-Approach and LDA
#### 6.7.1 TF-IDF Weight
It is based on the concept of probability distribution; indeed, it constructs a probability distribution over pairs of high-dimensional objects in such a way that similar objects have a high probability of being picked, whilst dissimilar points have an extremely small probability of being picked. A key feature aspect of t-SNE is a tunable parameter, _perplexity_, which we have presented as a quality metric to evaluate the goodness of the probabilistic LDA modeling. This parameter says how to balance attention between local and global aspects of the data under analysis. The parameter is related to the concept of the number of close neighbors had by each point. The perplexity value has a complex effect on the resulting pictures; in fact, since the algebraic model is not suited to measuring perplexity in probabilistic terms, the good value to be set for its plot could be complex to infer. In Figure 15, we have reported the representations of the t-SNE visualization for the joint-approach (top) and for the probabilistic approach (bottom). The shape is quite similar; however, the plot using the LDA model converges better in the presented figures. It is probably bad news that, to see a global geometry shape, a fine-tunable perplexity parameter is necessary. Moreover, since real data are characterized by multiple clusters with different cardinalities (i.e., number of documents), it could happen that using only one single perplexity value is not enough to capture distances across all clusters. Indeed, the perplexity metric is a global parameter defined for the entire model. Thus, an interesting area for future research could be the fixing of this problem. Figure 14: D1 graph representation, TF-IDF weighting scheme, \(K=10\), probabilistic approach, considering the top-20 (**a**) and the top-40 (**b**) words.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 5 Two Modes of Signature Proofs  
### Mode 1: Computationally Limited Signer  
In scenarios where the signer has limited computational resources, such as a digital camera, VerITAS employs a lightweight signing process optimized for efficiency. The process involves the following steps:  
1. Hashing the Image: The image is first hashed using a lattice-based collision-resistant hash function, which produces a compact 4 KB digest. Lattice-based hashes are particularly suitable for zk-SNARKs due to their reliance on linear operations, which are more efficient to prove in zero-knowledge settings.
2. Further Compression: The 4 KB digest is then hashed again using a Poseidon hash function, reducing it to 32 bytes. In our work, we propose a methodology to adapt the Poseidon hash function for post-quantum security. Poseidon is a zk-SNARK-friendly hash function designed for efficient proof generation.
3. Signing the Hash: Finally, the 32-byte hash is signed using a standard signature scheme. In our work, we use CRYSTALS-Dilithium.  
Advantages:  
* The lattice-based hash function's linear operations make it highly efficient for zk-SNARK proofs.
* This mode is well-suited for large images (e.g., 30 MP) and resource-constrained devices.  
Trade-off:  
* The editing proof generation time is slower compared to Mode 2, as the prover must verify the signature within the zk-SNARK.
# Compact Full Ka-Band Waveguide Directional Coupler Based on Rectangular Aperture Array with Stairs
## 5 Conclusions  
In this paper, a compact full Ka-band waveguide directional coupler is presented, which has good coupling flatness and broad bandwidth. Over the full Ka band from 26.5 GHz to 40 GHz, the S\({}_{11}\) magnitude and S\({}_{41}\) magnitude were below \(-\)17.46 dB and \(-\)20.82 dB, respectively. The S\({}_{21}\) magnitude varied from \(-\)2.89 to \(-\)4.26 dB, while coupling strength (S\({}_{31}\)-magnitude) changed from \(-\)2.74 to \(-\)3.80 dB. A power-split unbalance within 0.76 dB was achieved. The length of the coupling region is only 15.82 mm. The overall sizes of the coupler are 55.03 \(\times\) 20.02 \(\times\) 25.02 mm\({}^{3}\). The novel coupler can be easily designed to other frequency ranges and different coupling coefficients by following the design process given above. The proposed coupler could be a good candidate for miniaturized microwave system applications such as radar systems, communication systems, and microwave imaging systems. Based on this article, the research of deformable directional coupler, terahertz directional coupler may be carried out in the future.