esa-sceva/satcom-chunk-collection huggingface dataset:
# DBCAN: DFormer-Based Cross-Attention Network for RGB Depth Semantic Segmentation
## 4 Results
### RGB-D Semantic Segmentation Model for Comparison
#### 4.4.1 NYUDepthv2 Semantic Segmentation

Table 4: Results on SUN-RGBD val dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **Params** & **Input Size** & **Flops** & **Acc** & **mAcc** & **mIoU** \\ \hline ACNet [7] & ResNet-50 & 116.6 M & 480 \(\times\) 640 & 126.7 G & - & - & 48.3 \\ SCNet [16] & ResNet-101 & 58.3 M & 480 \(\times\) 640 & 108.5 G & 76.8 & 63.1 & 51.0 \\ SA-Gate [28] & ResNet-101 & 110.9 M & 480 \(\times\) 640 & 193.7 G & 77.9 & - & 52.4 \\ CEN [29] & ResNet-101 & 118.2 M & 480 \(\times\) 640 & 618.7 G & 77.2 & 63.7 & 51.7 \\ CEN [29] & ResNet-152 & 133.9 M & 480 \(\times\) 640 & 664.4 G & 77.7 & 65.0 & 52.5 \\ ShapeConv [30] & ResNet-101 & 86.8 M & 480 \(\times\) 640 & 124.6 G & 76.4 & 63.5 & 51.3 \\ ESANet [31] & ResNet-34 & 31.2 M & 480 \(\times\) 640 & 34.9 G & - & - & 50.3 \\ FFNet [32] & ResNet-34 & 85.5 M & 480 \(\times\) 640 & 115.6 G & 77.6 & 66.5 & 53.6 \\ PGDENet [33] & ResNet-34 & 100.7 M & 480 \(\times\) 640 & 178.8 G & 78.1 & 66.7 & 53.7 \\ EMSANet [36] & ResNet-34 & 46.9 M & 480 \(\times\) 640 & 45.4 G & - & - & 51.0 \\ CMX [34] & MiT-B2 & 66.6 M & 480 \(\times\) 640 & 67.6 G & - & - & 54.4 \\ CMX [34] & MiT-B4 & 139.9 M & 480 \(\times\) 640 & 134.3 G & - & - & 56.3 \\ CMX [34] & MiT-B5 & 181.1 M & 480 \(\times\) 640 & 167.8 G & - & - & 56.9 \\ TopFormer [10] & DFormer-T & 6.0 M & 480 \(\times\) 640 & 11.8 G & 77.1 & 65.8 & 51.8 \\ TopFormer [10] & DFormer-S & 18.7 M & 480 \(\times\) 640 & 25.6 G & 78.1 & 66.4 & 53.5 \\ TopFormer [10] & DFormer-B & 29.5 M & 480 \(\times\) 640 & 41.9 G & 79.4 & 69.4 & 55.5 \\ TopFormer [10] & DFormer-L & 39.0 M & 480 \(\times\) 640 & 65.7 G & 79.9 & 70.2 & 56.9 \\ \hline DBCAN & DFormer-T & 22.7 M & 480 \(\times\) 640 & 87.3 G & 77.3 & 67.3 & 52.2 \\ DBCAN & DFormer-S & 38.1 M & 480 \(\times\) 640 & 113.7 G & 78.6 & 68.4 & 54.5 \\ DBCAN & DFormer-B & 48.8 M & 480 \(\times\) 640 & 129.9 G & 79.5 & 69.9 & 55.8 \\ DBCAN & DFormer-L & 59.2 M & 480 \(\times\) 640 & 158.1 G & 80.3 & 70.5 & 57.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result on NYUDepthv2 val dataset. Among the hybrid-based baselines, TopFormer's [10] results for Acc (79.9), mAcc (70.2), and mIoU (56.9) are lower than the results achieved by DBCAN. The multiple attention mechanisms in DBCAN enable the model to focus on more effective features during the decoding stage. As a result, DBCAN achieves the best performance across multiple evaluation metrics.
## 5 Limitations  
One major limitation concerning the current research is that the interface, at the time of publishing this paper, was an evaluation prototype, as was indicated in the title and clarified throughout the paper. As a result, it will be used for the dissemination and evaluation of the framework, so several things are expected to change, be added, or be modified in future versions. Moreover, as explained in Section 3, the implementation depends on several third-party components, such as pyTube for YouTube content downloading, and the YouTube API itself. Since such components can be modified without warning, the application will have to be maintained to follow the latest functionality, updates and syntax of every component that is used. From the prospective of UX design, an error page has been integrated into the application to handle such exceptions, and to provide contact information for troubleshooting and bug reporting. Since the applied procedures are computationally heavy and require significant resources to guarantee a fast response time, for the evaluation version there is a restriction on the allowed file size and YouTube video length. A restriction in the allowed file types has been also set. These restrictions are expected to be lifted when the application is in production.  
**Author Contributions:** Conceptualization, N.V., A.K., R.K. and C.D.; methodology, N.V., L.V. and R.K.; software, N.V., L.V. and R.K.; validation, A.K., R.K. and C.D.; formal analysis, N.V. and R.K.; investigation, N.V., R.K., A.K. and C.D.; resources, N.V., R.K. and A.K.; writing--original draft preparation, N.V., A.K., L.V., R.K. and C.D.; visualization, N.V. and L.V.; supervision, R.K. and C.D.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on the Flight Performance of Biomimetic Moth Based on Flapping Function Control
## 3 Calculation and Simulation
### Simulation of the Impact of Key Parameters on the Performance of Flapping-Wing Aircraft
#### 3.2.1 Impact of Quick Return Feature of Flapping Motion on Lift  
In Section 3.1, it is assumed that the upward and downward strokes occur at the same frequency. However, in fact, as can be seen from the lift calculation formula, the greater the difference in flapping speed between the upward and downward strokes, the greater the average lift generated. The quick return feature of the flapping motion will increase the difference in flapping speed. To qualitatively explore the impact of the quick return feature on aerodynamic performance, three groups of flapping functions are designed, simulated, and calculated. The control parameters of the right-wing flapping function are shown in Table 3, and the left-wing flapping function is opposite to the right wing.  
The simulation time is set to 1 s, including four flapping cycles. The simulation data is imported into the software Origin, and the lift curves of the three flapping motions over time are drawn, as shown in Figure 5. It can be seen that the quick return feature of flapping motion has a significant impact on aerodynamic performance. The average lift within a period is calculated (0.25 s-0.5 s). The average lift generated by the flapping motion without a quick return feature within a period is about 0.670 N. The average lift by the flapping motion with a quick upward stroke is about 0.505 N; the average lift by the flapping motion with a quick downward stroke is about 0.782 N. Therefore, it can be concluded that the quick downward feature can significantly improve the aerodynamic performance of the flapping motion.
## 4 Model Parameters and Simulation Scheme
### Determination of Basic Parameters  
#### 4.1.1 Spectral Characteristics Determination  
Through the interpretation and analysis of the 14.28 km\({}^{2}\) three-dimensional seismic data from the Yexian Salt Mine area, it has been found that the spectral characteristics of the processed seismic data are essentially in agreement with those of the pure wave seismic data. The dominant frequency of the target layer is 45 Hz, with a frequency band ranging from 10 to 80 Hz and a bandwidth of 70 Hz, as shown in Figures 6 and 7. These findings provide fundamental parameters for the subsequent forward simulation.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Safety and Security Concerns  
Safety and security issues have been significant concerns for a wide range of information regarding the TI and its applications. Data integrity, reliability, and accountability must be catered for if we rely on the TI to facilitate daily applications and avoid any harm if it does not work appropriately. Depending upon the application and various types of information, various dimensions of security are required. If we consider the latest security advancement available today, haptic communication can take place over the network. However, there is a significant increase in RTT when using the current implementation of security. However, the requirement of security not only exclusively constrains access to information but also there is an issue of restricting the RTT. It entirely relies upon the application whether priority is given to the security or RTT. For instance, in a military organisation, information requires high privacy. On the contrary, some VR applications require minimal latency and lower requirements for the security of data. Subsequently, it can be concluded that there is always a trade-off for security and latency requirements depending upon the type of application.  
Furthermore, to provide network coverage to catastrophically affected areas following events such as earthquakes, stunamis, etc., potentially emerging technologies such as 5G, blockchain and drones can be adopted in a TI environment. Therefore, drones can be mounted with small cellular base stations to provide cellular services in affected areas. The main challenge is to have data security in such flying cellular base stations. To tackle the concerns about data security, the blockchain-based framework [83] was used to provide a security layer in the communication between (1) drone to drone and (2) user to the drone.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 5 Discussion
### Limitations  
The following are limitations of this work. Where improvements were found when comparing with MOC from our previous work, CM-BBPSO did not demonstrate significantly large improvements. This could possibly be due to the conservative cluster number of two used in this study. So far, we applied the cluster-based approach only on MOEA/D BBPSO clustering. From Table 5, despite having issues on the CMC dataset, M-Fuzzy was found to produce good results. Further investigation of applications of the cluster-based technique on it can be considered. Though the latest cluster-based MOEA techniques were investigated in non-convex clusters in the decision space, we did not cover in this work which is of interest to our future investigation. Furthermore, we would like to investigate adding cluster representatives in our reproduction pool to guide the evolution of new candidates, which was conducted in [13], but we did not apply it in this manner. By including cluster representatives, this can increase diversity while applying local search in the decision space.
# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
## 4 Performance Evaluation
### Average Number of Hops  
The average number of hops is measured as the average number of hop counts in establishing an optimal, safe, and stable routing path from source to destination. Figure 7 shows the comparison of the average number of hops as a function of vehicular density. Depending on different cases and different visibility scenarios, the number of hops increases with increasing density for each source-destination pair. For case (_iii_), since the source stores and carries the packet until it comes within a range of any other node, the number of hops is less. For the other two cases, the increase in the number of hops occurs because the data packets travel through more vehicles or LCs to reach their destination. Moreover, when vehicular density increases, the hops in a sparse network become fixed. Because LCs are directly connected using LTE, the same number of hops is used for the remaining number of vehicles. The number of hops required to deliver packets in case (_ii_) is high because in this case, the vehicles change lanes, so it takes time for the whole platoon to become part of the second lane. Additionally, they ensure connectivity, which increases hops. Regarding this lane change scenario, it involves both LCs and HV forwarding PAMs to the trailing platoon to reduce its speed. Thus, in comparison to case (_i_), two links for the same destination are formed to avoid accidents in low visibility scenarios. However, case (_i_) has fewer hops in sparse conditions as LCs are used to convey messages to leading platoons. However, it becomes stable for the rest of the vehicle density due to increased connectivity. Figure 7 also shows that the number of hops for the reference schemes increases with the density of vehicles. This is due to the lack of LCs-based V2I communication in low-visibility scenarios.  
Figure 6: Performance comparison in terms of end-to-end delay as a function of vehicular density.
## 3 Methodology
### Balanced-Relative Density Peaks Clustering (BRDPC)
#### 3.3.1 Similarity Matching Algorithm  
The source domain sample cluster set \(Sc=\{Sc_{1},Sc_{2},\dots,Sc_{k^{\prime}}\}\) and the target domain sample cluster set \(Tc=\{Tc_{1},Tc_{2},\dots,Tc_{k^{\prime}}\}\) are obtained by the BRDPC algorithm. In this section, the maximum mean discrepancy (MMD) method is applied to find the most matching cluster in the source domain for each cluster in the target domain [45]. Such as \(Tc_{k^{\prime}}=\left\{{tc_{k^{\prime},1},tc_{k^{\prime},2},\dots,tc_{k^{ \prime},|Tc_{k^{\prime}|}}}\right\}\) is a cluster in \(Tc\). The most matching cluster \(Sc_{c^{\prime}}=\left\{{sc_{c^{\prime},1},sc_{c^{\prime},2},\dots,sc_{c^{ \prime},|Sc_{c^{\prime}|}}}\right\}(c^{\prime}=1,2,\dots,k^{\prime})\) with the smallest MMD value in \(Sc\) is found by Equation (19). Calculated the average MMD of all sample clusters in the target domain. Finally, the inverse of the MMD average value is taken as the overall similarity (\(Sid\)) between the source domain and the target domain samples. The higher the \(Sid\) value, the greater the similarity between the source domain and the target domain. The MMD is shown as follows:  
\[MMD=\underset{c^{\prime}=1,2,\cdots k^{\prime}}{min}\left({\left\|{\frac{1}{|Sc _{c^{\prime}}|}\sum_{j}^{|Sc_{c^{\prime}}|}\phi\left({sc_{c^{\prime},j}} \right)-\frac{1}{|Tc_{k^{\prime}}|}\sum_{j}^{|Tc_{k^{\prime}}|}\phi\left({tc _{k^{\prime},j}}\right)}\right\|}^{2}\right), \tag{19}\]
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 2 Background
### Rpl
Because the number of messages used to select the parent node is small, the number of messages required to select the new parent is small. Experiments based on various scenarios revealed that even if the speed and direction of the moving nodes are arbitrary, they perform better than existing algorithms. A mobility-aware routing protocol based on RPL was presented in EMA-RPL [13]. An EMA-RPL protocol exists as an RPL protocol for moving nodes. The algorithm always defines the mobile node as a leaf in the RPL tree to minimize data loss due to node movement. This also paper defines an associated node (AN) with the mobile node. Data messages generated by the mobile node are sent and received over the AN node, which serves as movement prediction, movement detection, and notification of the mobile node. The mobile node is supported in three ways: mobility detection, response and prediction, and notification. It has the advantage of reducing the energy and resource use of mobile devices through prediction based on the RSSI values of the mobile node and AN node and preventing sending multiple control messages. A summary of routing protocols for mobile node support in Table 1.
# An Overview of Nanofiltration and Nanoadsorption Technologies to Emerging Pollutants Treatment
## 3 Nanotechnological Approaches to the Treatment of Emerging Pollutants
### Nanoadsorbents
#### 3.2.2 Metal-Based Nanoadsorbents
[86] reported that under acidic conditions, the adsorption of amoxicillin onto silver-functionalized magnetic nanoparticles occurred by the interaction between the \(\pi\) bonds of the amoxicillin rings and the positive ions of the adsorbent. Hydrogen bonding between H of amoxicillin and O of adsorbent also contributed to adsorption. Therefore, the adsorption mechanism of emerging contaminants may vary according to different factors such as process parameters, characteristics of the adsorbent, and the contaminant. Metal-based nanomaterials have been applied as adsorbents to remove pharmaceutical and personal care products, pesticides, antibiotics, dyes, and heavy metals [58; 68; 69; 84; 85; 86]. Table 2 shows some examples of these applications.
## 3 Numerical Analysis
### Comparison of Different Methods
Furthermore, it is worth pointing out that the propagation constant in the \(y\) direction \(R_{y}\) is revealed to be a pure imaginary number in our calculation, which indicates that the surface waves propagate along the \(y\) direction in addition to the \(x\) direction, so that the propagating direction deviates from the \(x\) direction with some certain angle. The values of \(\mid\!\beta_{x}/R_{y}\!\mid\) as a function of \(t/\lambda_{0}\) with different slab widths are plotted in Figure 4b. The results indicate that \(\mid\!\beta_{x}/R_{y}\!\mid\) is very sensitive to the slab width. When the slab width increases, then the value of \(\mid\!\beta_{x}/R_{y}\!\mid\) increases significantly, meaning that the propagation direction is closer to the \(x\) direction.
# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 4 Problem Description and Optimal Solution
### Task Offloading Analysis Based on Starkelberg Game
#### 4.2.1 Second Stage Starkelberg Game  
We first analyze the utility of the requesting vehicle, and we have the first and second derivatives of (10):  
\[\frac{\partial D_{R_{i}}}{\partial q_{iv}}=\omega\left[\frac{2\mu\alpha Q_{i} }{C_{je}}-2q_{iv}\Bigg{(}\frac{\mu\alpha}{C_{v}}+\frac{\mu\alpha}{C_{je}} \Bigg{)}\right]-p_{iv}+p_{ie}-\gamma\bigg{(}\frac{\rho}{r_{v}}-\frac{\rho}{r _{e}}\bigg{)}, \tag{18}\]  
\[\frac{\partial^{2}D_{R_{i}}}{\partial q_{iv}}=-2\omega\Bigg{(}\frac{\mu\alpha }{C_{v}}+\frac{\mu\alpha}{c_{je}}\Bigg{)}<0. \tag{19}\]  
As is shown in (19), the second derivative of the utility function of the requesting vehicle is constantly less than zero, thus the utility function of the requesting vehicle is strictly concave, and the function has a maximum value. Therefore, it can be proved that the requesting vehicle can provide the best task offloading strategy regardless of the price strategies given by the edge server and the ECP.  
Let the first derivative of the utility function of the requesting vehicles be equal to zero, so we set (18) to be zero, then we can find the optimal offloading strategy of the requesting vehicle. Therefore, we can obtain:  
\[q_{iv}^{*}=\frac{Q_{i}c_{v}}{c_{je}+c_{v}}-\frac{c_{v}c_{je}[r_{v}r_{e}(p_{iv} -p_{ie})+\rho(r_{v}-r_{e})]}{2\omega\mu\alpha(c_{je}+c_{v})}. \tag{20}\]If the optimal amount of tasks offloaded to the edge server is less than the total amount of tasks, the rest amount of the tasks should be offloaded to the ECP, so that we have:  
\[\left\{\begin{array}{c}q_{iv}^{*}=\dfrac{Q_{i}c_{v}}{c_{je}+c_{v}}-\dfrac{c_{v}c _{je}[r_{v}r_{e}(p_{iv}-p_{ie})+\rho(r_{v}-r_{e})]}{2\omega\mu\alpha(c_{je}+c_{ v})}\\ q_{ie}^{*}=Q_{i}-q_{iv}^{*}\\ Q_{i}>q_{iv}^{*}\end{array}\right.. \tag{21}\]  
Otherwise, the optimal offloading strategy for the requesting vehicle is expressed as:  
\[\left\{\begin{array}{c}q_{iv}^{*}=Q_{i}\\ q_{ie}^{*}=0\\ Q_{i}\leq q_{iv}^{*}\end{array}\right.. \tag{22}\]
## 5 Experimental Results
### Evaluation on an Embedded System
#### 5.5.3 Cascade Approach on an Embedded System
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**FeatureSet**} & \multicolumn{2}{c}{**k-NN**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**Decision Tree**} & \multicolumn{2}{c}{**SVM**} & \multicolumn{2}{c}{**Naive Bayes**} \\ \cline{2-11}  & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** & **Accuracy [\%]** & **Timing [ms]** \\ \hline Airplane & & & & & & & & & & \\ Helicopter & & & & & & & & & & \\ Jekekhammer & & & & & & & & & & \\ Drilling & & & & & & & & & & \\ Cats & & & & & & & & & & \\ Dogs & Set 3 & **52.26**(4.53) & 144.09 (59.09) & **54.91**(4.42) & 41.85 (18.15) & 27.55 (4.62) & 5.41 (2.09) & 35.85 (5.43) & 7.69 (0.14) & 49.53 (4.34) & 27.33 (10.53) \\ Humans & & & & & & & & & \\ Children & & & & & & & & & \\ Cars & & & & & & & & & \\ Motorcycles & & & & & & & & & \\ Car hom & & & & & & & & & \\ Stern & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 17: Achievable accuracy and demanded execution time of the optimized classifiers performance using the non-cascade approach running on the Raspberry Pi. The Standard Deviation (SD) is in brackets.
## 1 Introduction  
Gas adsorption (physisorption) in porous solids can be used in industrial processes such as oxygen (and argon) separation from air [1; 2; 3; 4], sweetening of natural gas, upgrading of (landfill) biogas [5; 6; 7; 8] and purification of hydrogen obtained from synthesis gas (syngas) or from steam reformation of hydrocarbons [9; 10; 11], to name only a few examples. Other (prospective) applications include post-combustion carbon dioxide capture from the fine gas of coal fired power stations [12; 13; 14; 15], indoor air purification, e.g., in submarines and manned spacecraft [16; 17; 18; 19], and the use of porous solids for (alternative) hydrogen or methane fuel storage and delivery in the transportation sector [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32], including the advent of hydrogen fuelled drones and (possibly) airplanes [33; 34; 35; 36]. For gas separation and purification the gas adsorbent units are frequently operated in a transient mode, which involves alternative gas adsorption-desorption cycles referred to as temperature swing (TSA) or pressure swing (PSA) adsorption, depending on the strategy used to regenerate the porous adsorbent [10; 13]. Whichever the case, improvement of the gas adsorbent should be aimed at increasing differential gas adsorption capacity, while keeping the gas-solid interaction energy small enough to curb (as much as possible) the cost of adsorbent regeneration. To that end, precise knowledge about the structure and stability of the corresponding gas adsorption complex is of the utmost importance; and that is also the case when seeking the optimum porous adsorbent for gas transport and delivery. For such a purpose, classical infrared (IR) spectroscopy can give valuable structural information derived from analysis of the wavenumber shifts undergone by meaningful vibrational modes of the adsorbed molecule, and from the (relative) intensity of the corresponding IR absorption bands, but determination of the gas-solid interaction energy is out of reach unless a complementary technique (such as adsorption calorimetry) is also used. Nevertheless, the recently implemented variable-temperature IR (VTIR) spectroscopic method [37; 38; 39] facilitates direct access to both, structural characterization of the adsorption complex and (simultaneous) measurement of the standard enthalpy change (\(\Delta H^{0}\)) involved in the gas adsorption process, as will be shown below by reviewing several enlightening case studies.