esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Open Challenges and Future Research Directions
### Future Research on the Impact of Quantum Computing on IoE Security  
Quantum computing has the potential to break traditional cryptographic methods by using mathematical approaches such as Shor's Algorithm and Grover's Algorithm. Therefore, IoE-based security systems must be reinforced with post-quantum cryptographic solutions to withstand these threats [33; 49; 54; 66].  
Resistance of Current Cryptographic Systems to Quantum AttacksClassical cryptographic algorithms like RSA and ECC can be rapidly compromised in the face of quantum attacks. As a result, research is focusing on lattice-based cryptography, hash-based signatures, and code-based cryptography as quantum-resistant alternatives [52; 53; 68; 132].  
Quantum-Enhanced Security SolutionsQuantum technology is not only a threat but also an opportunity to enhance security. For example, QKD can provide secure key exchange in IoE networks, and Quantum Random Number Generators (QRNG) can generate stronger encryption systems [53; 54; 66; 70].  
Quantum Computing-Based Security Protocols (Hybrid Security Models)To minimize the impact of quantum computing on IoE systems, existing blockchain and AI-based solutions must be made quantum-resistant. Research should focus on Quantum-Resilient Blockchain Protocols and Quantum AI-supported IoE security mechanisms [52; 66; 68; 127].  
Open Research AreasDevelopment of quantum-resistant blockchain models [52; 58].  
Implementation of QKD for key management in IoE systems [53; 54; 66].  
Investigation of quantum computing-AI-supported security mechanisms for IoE applications [127; 130].  
The potential impacts of quantum computers pose a serious threat to classical encryption algorithms. Since IoE systems are expected to be long-lived infrastructures, they must be prepared for the post-quantum era. However, lattice-based cryptographic systems, while promising, still face challenges related to computational overhead and standardization.  
Research DirectionFuture studies should focus on integrating hybrid encryption systems, including post-quantum digital signatures and ZKP, into IoE architectures. Additionally, it is crucial to investigate how quantum AI techniques can be employed for advanced cyber threat analysis [161; 132; 162; 163; 164].
# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 2 Attention Mechanism
### Spatial Attention  
Apparently, for a video, it has not only global temporal features, but also local temporal features. These local temporal features are usually the representations of the fine-grained features of actions in a video, such as standing up, calling someone, etc. These features are also the fine-grained features for identifying important regions of an image, such as the region of the face in an image. These features only appear in certain regions of a certain time-series in a video generally. If we do global pooling to all video frames, then the ability to capture local information will decrease. The spatial attention mechanism enables the model to acquire local features. [PERSON] (2018) proposed the idea of applying a spatial attention mechanism in the video description.  
### Spatial Attention  
After an image is processed with the CNN, each layer of the CNN will produce different numbers of feature graphs, i.e., channel features. The experiment done by [PERSON] (2018) showed that the feature map of different layers of the CNN can show different semantic information. Specifically, low-level feature maps show low-level visual features, such as texture and color, whereas the high-level feature maps show high-level semantic features such as objects with different spatial features. Because the CNN contains many convolution kernels that can detect different features, we can obtain different feature maps. Figure 1 is the visualization result of a part of low-level and high-level feature maps after an image entered the CNN. In this figure, the low-level features focus on some edge contour information of the image (Figure 1b), whereas the high-level features prefer to express semantic information of the image (Figure 1c). As shown in Figure 1, the brightness of the feature represents the response value to the original image. While it is brighter, the response value is higher. We can see that the Figure 1c shows the response of the feature channel in the same layer after the image is input into the CNN network. It can be seen from the figure that the response positions of different feature maps are different and that these different response positions express different semantic information prominently. If the response position of some feature maps is on the person, then the region with people will have a larger regional response value. Some feature maps are responses to other objects. Inspired by this visualization result, for obtaining video feature information consistent with description words of a video in video descriptions more effectively, this paper proposes a channel attention mechanism. We can enhance the ability of the model to focus on the feature maps in need by using channel attention mechanism and then improve the description capability of the model.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Feature Typeset** & **Features** & **Accuracy** \\ \hline [PERSON] et al. [8] & Full Features & 48 & 96.17\% \\  & Baseline & 10 & 94.60\% \\  & Full Features HEFS & 30 & 92.40\% \\  & Baseline HEFS & 5 & 93.22\% \\ [PERSON] et al. [17] & Full Features & 48 & 97.87\% \\  & Using PCA & 30 & 94.90\% \\ [PERSON] et al. [18] & RF & 30 & 93.7\% \\ Ours & Full features & 48 & 98.1\% \\  & PPS Correlation Only & 38 & 98.16\% \\  & PPS+RFE & 30 & 97.96\% \\  & PPS+RFE & 20 & 97.63\% \\  & PPS+RFE & 10 & 96.96\% \\  & MICE TICE Correlation Only & 38 & 97.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The studies feature set and accuracy comparison on [PERSON]’s dataset. Figure 6: Comparison of all feature subset scenarios for dataset 2. Table 17 shows the achievement of our proposed approaches compared to the existing work. Refs. [8; 17] utilize the [PERSON]'s dataset [15] with similar proportion of training and testing at 70% and 30%. There is only one study on this topic utilize the second dataset, namely the [PERSON] and Yahiouche [16] dataset. The results of comparing the accuracy values with the number of features extracted for each study's methodology are presented in Table 18. According to the Table 18, our method outperformed the accuracy value from another method that uses the same dataset. Table 18 shows the achievement of our proposed approaches compared to the existing work. [PERSON] and [PERSON] [16] utilize dataset 2 with similar proportion of training and testing at 70% and 30%. The accuracy of RF classification of 10 selected features slightly better than the result reported in [16] with more features considered in their approach. After feature selection, we learn about what is important: redundant features on both datasets. Although both datasets have different set of data, they share some high rank features such as length of the host name, path level and the number of hyperlink. Those high ranking features remain in the 10 most important features in the final feature selection. The first 10 removed features are redundant and it was proven removing them lead to improvement of the classification performance. For example, both \"nb_dots\" (number of dots) and \"nb_subdomains\" (number of sub domain) are highly correlated since number dots (.) are the separators between domain and subdomains and therefore removing one of them will not suffer the quality of the model, since they represent the same thing.
## 5 Simulation Setting and Results
### Total Delay of the Network  
The comparison of the total delay of the network is plotted in Figure 12. The reason for producing higher delay in the CoDBR scheme is the cooperation of two RLNs with the DSN, which consumes extra time during best forwarder nodes selection and packet transmission. The CoEERD scheme uses single RLN with the DSN, which consumes less than CoDBR. In addition, the calculation of function parameters in EERD and CoEERD has more computation time that shoots up their delay than ODBR. The ODBR scheme considers only depth information for the best relay selection, which lowers delay in proceeding information. At round 400, the ODBR scheme produces lower delay than the rest of the other schemes and its value is \(6\times 10^{8}\) seconds. The EERD and CoEERD provide the second and third smallest delay values with magnitude of \(8\times 10^{8}\) and \(10\times 10^{8}\) seconds, respectively. At this time, the value of delay of CoDBR scheme is \(16\times 10^{8}\) seconds.  
Figure 11: RODP.  
Figure 12: Total delay of the network.
###### Abstract  
Viral diseases affecting both humans and animals are a serious public problem. Chemical modifications of the structure of compounds of natural origin, e.g., betulin, seem to be a promising model in the search for new antiviral agents. The subject of our work was to conduct preliminary tests on the antiviral activity of phosphonic derivatives of betulin and betulinic acid and to assess the pharmacokinetic profile of target compounds. Human (HHV-1, HAdV-5) and animal viruses (BEV, VSV) were used in the in vitro tests. Additionally, this paper presents the results of research using in silico methods (ADMET and molecular docking). Two compounds (betulin 29-phosphonate **3** and 3-(3\({}^{\prime}\),3\({}^{\prime}\)-dimethylsucciny)betulin acid 29-phosphonate **8a**) showed antiviral activity against BEV, and compound **3** was also active against HAdV-5. For compound **3**, which showed advantageous pharmacokinetic parameters, molecular docking was performed to determine possible interactions with the cellular target HAdV-5 endopeptidase, which plays an important role in various functions of the virus. Selecting the most active derivatives makes it possible to plan tests on an animal model.  
Keywords: antiviral activity; phosphonate derivatives; betulin; betulinic acid; ADME +
Footnote †: journal: Applied Sciences
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 3 Collaboration between Seaports and Dry Ports  
### Cooperative Performance  
The cooperative performance of the cooperation between seaports and dry ports involves multiple aspects, including the scale, functionality, objectives, and more; for both the seaport and the dry port, we can elaborate on the following aspects:  
1. The cooperation between seaports and dry ports should be coordinated internally. For example, the site area, supporting facilities, and formalities of the dry port should match the size, cargo throughput, and staffing of the seaport.
2. In cooperation, seaports and dry ports need to work together for win-win results. Dry ports should give play to the advantages of facility sharing, attract goods sources with high-quality and low-cost services, maximize the aggregation effect of goods, accelerate the integration process of \"two ports\", complete some of the functions of seaports from dry ports, and form a cooperative mechanism of mutual benefit.
3. The cooperation between seaport and dry port needs a mature and perfect normative system. The cooperation between seaports and dry ports can only ensure lasting and efficient cooperation between the two sides under a fixed and standardized mode and reaching a consensus of common development.
4. The system platform of the cooperation between seaports and dry ports needs to be open and transparent, and the closed and independent system is not conducive to the real-time transmission of information. In fact, the process of interaction and cooperation between the seaports and dry ports is that the internal systems are unified and adapt to each other, and ultimately maintain a dynamic balance. Only in this way can we ensure smooth and convenient communication between the two sides.
5. The interaction and cooperation between seaports and dry ports are constantly changing and developing. There may be a time difference between the seaports and dry ports in the process of information transmission, resulting in delayed information transmission. In the case of information inconsistency, both parties need to communicate and improve in time to avoid information inconsistency. The two sides faced the problems together, reached a coordinated pace in a dynamic manner, and finally formed the common progress and development of the cooperation between seaports and dry ports.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 4 Experiments and Results
### QoE Measurement Metrics  
In order to measure the QoE, the delivered video on each client was recorded and matched with the original video sequence using the MSU video quality measurement tool [35]. The proposed model was validated using both objective and subjective metrics of QoE. There are two types of objective metrics used in this study: the structural similarity index metric (SSIM) [36] and the video multimethod assessment fusion (VMAF) [37]. While we used the mean opinion score (MOS) [38] as the subjective metric, a total of 15 users assessed the quality test cases. The following section describes the performed experiments and presents the obtained results.
# A Data-Driven Deep Learning Framework for Prediction of Traffic Crashes at Road Intersections
## 4 Model Architecture
### Phase I in RoadInTCP  
For traffic crash prediction, existing works consider the road network structure as the spatial features and predict traffic crashes in grid regions. However, the coarse-grained division may not reflect the inherent attributes of geography well. Moreover, they only focus on road segments and ignore the characteristics of intersections and the relationships between intersections and road segments, i.e., they do not fully explore the inherent geographical information on road networks. Therefore, in this paper, we consider both intersections and road segments in road networks. More specifically, we extract road networks and user-generated tags associated with intersections and road segments from the OpenStreetMap (OSM), a publicly accessible map service. We download the raw OSM data of New York City to generate the road networks. Based on our analysis of New York City's historical traffic crash data, we find that the number of traffic accidents is closely related to (i) whether it is a one-way road segment and (ii) traffic lanes. In Figure 4a,b, we observe that the number of crashes on a one-way road segment is much higher than on bi-directional traffic lanes. On the other hand, when the number of traffic lanes is 2-4, the number of traffic crashes is the highest. Moreover, with the increase in the number of lanes, the number of traffic crashes decreases obviously. However, these two characteristics are ignored in existing works.  
To exploit the features discussed above, we extend RN2 Vec, a neural network model we proposed in [30], to learn embeddings of road intersections that encode rich information in a road network by exploring geo-locality and intrinsic properties of intersections, moving behaviors of road users, one-way road segments, traffic lanes, and the number of traffic crashes. Specifically, we extract road networks and user-generated tags associated with intersections and road segments from the raw OSM data of New York City. According to Definition 1, consider a road network \(G=(V,E,\Psi,\Omega)\), we use the aforementioned information on intersections \(v\in V\) and road segments \(e\in E\) as input of the RN2 Vec model to learn latent vectors of the intersections, which are referred to as the _topological-relational features_ (_tr-features_) of road intersections that encode topological information of a road network by exploring a great diversity of properties.  
Figure 4: (**a**) The relationship between the number of traffic crashes and one-way road segments. (**b**) The relationship between the number of traffic crashes and traffic lanes.
## 4 A Hybrid-ISL System with Ranging and LoS Support  
### CCSDS' Proximity-1 Network Stack  
Before presenting our recommendation for close-range (proximity) communications, we first provide a brief overview of the CCSDS Proximity communication suite to provide readers with relevant context for comparing the two protocol stacks. Additionally, we included a brief comparison between Proximity-1 and the proposed communication stack to offer the reader a quick overview of their differences. The Proximity-1 protocol stack, developed by the Consultative Committee for Space Data Systems (CCSDS), is intended to support reliable data exchange in space missions. The CCSDS has issued four recommendations pertaining to the Proximity-1 protocol [5; 6; 7; 8]. The Proximity-1 protocol stack consists of two communication layers, as illustrated in Figure 3, adapted from [6].  
* Physical Layer (PHY): at the lowest level, the PHY defines the hardware interfaces and signaling characteristics for transmitting data over space communication links. It specifies parameters such as modulation, coding, and frequency allocation to ensure reliable transmission in the harsh space environment.
* Data Link Layer (DLL): it provides mechanisms for framing, error control, and flow control to ensure the integrity and efficiency of data transmission. It ensures reliable delivery of data using Automatic Repeat reQuest (ARQ) and provides services for both acknowledged and unacknowledged data transfers. It includes protocols for packetization, error detection and correction, and management of data link connections. This layer furthermore consists of a Medium Access Control (MAC) and Coding and Synchronization sublayers.  
Let us provide a comparison between CCSDS's Proximity-1 suite and our proposed hybrid-ISL system protocol stack in advance of presenting the letter. The comparison is summarized in Table 1.  
Figure 3: Simplified diagram of Proximity-1 layers.
# An Improvement to the 2-Opt Heuristic Algorithm for Approximation of Optimal TSP Tour
## 5 Discussion  
After analysing the results of 2-Opt++ with seven other algorithms in similar settings, we assert that the 2-Opt++ outperforms all other well-known algorithms in terms of (i) error variance; (ii) convergence; and (iii) computation time. In this section, we compare the average error and the average time taken by the 2-Opt++ and the \(R\&R\) algorithm. For the 35 problems extracted from the TSPLIB library for the small category, the average error variance for \(R\&R\) stood at 6.79%, while our 2-Opt++ produced 2.33%, which is a quite significant difference. Even for those cases where \(R\&R\) produced a better result than our proposed algorithm, the difference was minor. The average time consumed by \(R\&R\) and 2-Opt++ is 64.63 s and 2.41 s, respectively. These results show that our 2-Opt++ is efficient for almost all types of symmetric TSP problem instances.  
We are also able to conclude that the NN algorithm is the fastest, followed by the 2-Opt++ algorithm, TPO, and GA. Based on the problem size, different algorithms show different patterns in time consumption. Tabu search and ACO depict exponential time  
Figure 19: pla85,900 magnified view of solution graph [11].  
increases as the problem size increases, while others show a slight increase in time with the problem size. In the average error comparison, 2-Opt++ performed outstandingly, followed by Tabu search, TPO, and GA. Tabu search's better performance in terms of the average error could be due to its diversification mechanisms, such as swap, reversion, and insertion techniques, and the Tabu list, which ensures the best selection of the solution candidate. TS still shows up to a 64% error margin in a problem of 442 nodes. This problem can be addressed by using some other algorithms in the Tabu list mechanism. In terms of accuracy, GA shows consistent results, with error margins of less than 15% in all selected problems. It uses a mutation process, which drives it towards a better solution. If we compare the individual results, 2-Opt++ performs best, followed by TS, TPO, and GA.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 4 Bluetooth Low Energy  
The original design of Bluetooth wireless technology, which is now referred to as Bluetooth Classic, was intended for short-range communication applications like audio streaming, mobile printing, file transfers, etc. Bluetooth Classic transmits data over 79 channelsthat are 1 MHz wide in the 2.4 GHz unlicensed ISM band and only supports point-to-point device communication.  
Bluetooth low energy was later introduced as part of the Bluetooth 4.0 specification optimized for low power consumption while supporting the same transmission power [22]. Low power consumption is achieved by turning off the radio when it is not in use and by reducing data transmission rates. In addition to the features supported by Bluetooth Classic, BLE supports positioning features and multiple topologies for device-to-device communication. These desired features and its ease of integration with other devices made BLE one of the most popular modes of wireless communication among IoT applications that require low power consumption [23, 24].  
BLE signals are also transmitted over the 2.4 GHz radio band but consist of 40 channels that are 2 MHz wide, as shown in Figure 1. Among the 40 channels, three widely spaced channels numbered 37, 38, and 39 at 2402 MHz, 2426 MHz, and 2480 MHz, respectively, are used for device discovery and are known as primary advertising channels. They are intentionally spaced to avoid interference with Wi-Fi channels, which are also on the same 2.4 GHz ISM band. The remaining 37 BLE channels are used for bi-directional data transfer after the connection is established with another device and are also occasionally used as advertising channels (specifically named secondary advertising channels). During typical BLE device communication, the device discovery state starts by sending an advertising packet on one or three of the primary advertising channels. The advertising packet is sent periodically with a preset advertising interval ranging from 20 ms to 10.24 s with a step size of 0.625 ms until it is discovered by another device and a connection is established. The continuous broadcast of advertisement packets in the discovery state before advancing to the connection state is used in positioning applications. The positioning applications run on a scanner device to read the RSS measurements from the advertising packets and derive the proximity or location of the devices. These implementations are called Bluetooth beacons or simply beacons. In this paper, we present the implementation of a Beacon by referring to some of the existing and popular implementations like iBeacons and Eddystone beacons. The BLE device used for the implementation of our beacon supports Bluetooth v5.0. The RSS of the advertising packets broadcasted by beacon will serve as the input data of a localization algorithm using a single scanner device such as a smartphone, as discussed in detail in Section 5.3.