esa-sceva/satcom-chunk-collection huggingface dataset:
# Q-MeaMetaVC: An MVC Solver of a Large-Scale Graph Based on Membrane Evolutionary Algorithms
## 3 A MVC Solver Based on MEA--Q-MaeMetaVC
### Bipartite Graph Solving Algorithm
#### 3.3.1 Create a Membrane Structure
```
Input: M membrane C\({}_{i}\).M\({}_{j}\), vertex extraction times _bms_v_; Output: M membrane;
1\(S=|C_{i}\).M\({}_{j}|>\) k? A set composed of randomly selected _bms_v_ vertexes from C\({}_{i}\).M\({}_{j}\): C\({}_{i}\).M\({}_{j}\);
2 bestv = an element with the lowest fitness in S, select the element with the older age when the fitness is the same;
3 C\({}_{i}\).M\({}_{j}\)-bestv;
4 C\({}_{i}\).M\({}_{j}^{\prime}\) = bestv;
5 return C\({}_{i}\).M\({}_{j}\) membrane;
```
**Algorithm 10: Division**

The fusion operator is shown in Algorithm 11: first, select the membrane element with the highest fitness in the membrane population MP\({}^{\prime}\) (if the fitness is the same, select the older one) (line 2); if its fitness is greater than the most recently split element, the M membrane will add this element (lines 3-4); otherwise, it will add an element through the quik_AddOne operator (line 5, Algorithm 12). ```
Input: M membrane C\({}_{i}\).M\({}_{j}\), edge extraction times _bms_e_; Output: M membrane;
1 droppedv = element in C\({}_{i}\).M\({}_{j}^{\prime}\);
2 bestv = C\({}_{i}\).MP\({}^{\prime}\) is the element in the membrane with the highest fitness; when the fitness is the same, select the element with older age;
3 if fit(bestv, C\({}_{i}\).M\({}_{j}\))-fit(droppedv, C\({}_{i}\).M\({}_{j}\)) then
4 C\({}_{i}\).M\({}_{j}\)+bestv;
5 else quick_AddOne(C\({}_{i}\).M\({}_{j}\), bms_e);
6 return C\({}_{i}\).M\({}_{j}\) membrane;
```
**Algorithm 11: Fusion/Membrane fusion operator**

Considering that the research problem is to solve the vertex cover of a large-scale sparse graph, the division and fusion operator are called frequently to ensure that the M is characterized by the number of elements that is one less than the size of the currently found MVC during the division and fusion process. To improve the efficiency of the Q-MeMetaVC algorithm, we specifically designed the quick_AddOne algorithm, as shown in Algorithm 12. ``` Input: M membrane, edge extraction times _bms_e_; Output: M membrane;
1 S\({}_{e}\) = \(|\) UCE(M)\(|>\) _bms_e_? Randomly extract a set composed of _bms_e_ times from UCE (M):UCE(M);
2 beste = the oldest edge in S\({}_{e}\);
3 v = beste is the endpoint with higher fitness, and the endpoint with the same fitness is the older endpoint;
4 M + v;
5 return M membrane;When the number of elements in the M membrane is less than _bms_e_, the candidate edge set S_e is all the edges in UCE(M). Otherwise, it is a set composed of extracting _bms_e_ edges from S_e (line 1). M membrane adds an endpoint with a higher adaptation of the oldest edge, and the older endpoint is selected when the adaptation is the same (lines 2-4).
## 1 Introduction
It is customary to apply improvement heuristics iteratively. Metaheuristics are general optimization frameworks that can be appropriately modified in their individual characteristics in order to generate efficient methods for solving specific classes of optimization problems. The main contribution of this paper is the application of the novel extension of general variable neighborhood search (GVNS for short) metaheuristic to a GPS problem. We achieve efficient solutions in a short period of time for a GPS application used for garbage trucks, which is modeled as an instance of the TSP. The proposed method guarantees optimal or near-optimal solutions for a real life routing application. GPS applications typically use the nearest neighbor (NN) or some of its variations in order to achieve good results for the routing section. The routing section is a critical part of a typical GPS application because, if the provided routes are optimal or near-optimal, then the end result will also be near-optimal. The routing solutions produced by the GVNS were compared to the ones obtained by the well known NN algorithm and some of its most widely used variations. The computational results reveal that both GVNS's first and best improvement solutions provide efficient routes that are either optimal or near-optimal and outperform with a wide margin classic, widely used methods, such as NN and its modifications. This novel GVNS is a quantum-inspired expansion of the conventional GVNS in which the shaking function is based on complex unit vectors. This paper is organized as follows. In Section 2 we present related works, in Section 3 we explain metaheuristics and the variable neighborhood search, and we describe our algorithm in detail. A GPS application and the specific problem we tackle with our algorithm is presented in Section 4. In Section 5, the experimental results of our implementation are presented in a series of matrices and figures that clearly demonstrate that our method outperforms standard approaches like the NN algorithm. Finally, conclusions and ideas for future work are described in Section 6.
## 3 Methods
### Real-Time Video Oil Sheen Prediction (VOS-Net)  
Based on the OS-Net, we developed a real-time video-based oil sheen prediction system (VOS-Net). The input of the VOS-Net is sheen formation video and the output is the real-time prediction result. Given a video input, the VOS-Net classifies the images every few frames based on image extraction frequency parameter.  
The latency of the VOS-Net may delay the prediction results if the model predicts frame by frame. To achieve real-time prediction, an appropriate image extraction frequency will have to be set to offset the delay. When the interval between image extractions is greater than the model inference time, predictions would not be delayed.  
While recall is 100% and false negatives are not likely, the precision is not 100% so some false-positive results could occur and a method was developed to address this potential.  
We combined oil sheen domain knowledge with our machine learning algorithm to improve the prediction accuracy. When oil leaks from the sediment in OS-SS, the oil will spread out rapidly on the water surface to form sheen. The appearance of oil sheen can sometimes be fleeting, disappearing after a few seconds, or it can persist for a longer period [22]. Thus, if the sheen only appears for one frame (the video includes 60 frames per second), the prediction may be a false positive. We set a filter with kernel size k, where k represents the threshold of positive prediction. We suppressed the positive prediction until k successive positive predictions occurred. The kernel size k can be adjusted to control the sensitivity of VOS-Net to avoid false positives and increase predictive accuracy.  
Figure 4 shows how the VOS-Net filter works. The VOS-Net predicts the oil sheen every 60 frames (\(\sim\)1 s) and records the result. Figure 4a shows the ground truth (b) and (c) show the VOS-Net prediction result with the different filters applied. The red line represents the model's prediction of \"with sheen\", while the green line depicts a model result of \"no sheen\". The ground truth is when no oil appears in the following frames. Without applying a filter, (b), VOS-Net could have a false positive (red lines). After applying a filter, (c), the VOS-Net can reduce the false-positive incidence and achieve higher accuracy.
# A Review of Transformer-Based Approaches for Image Captioning
## 4 Transformers in Captioning
### X-Linear Attention Networks  
[PERSON] et al. [87] employ a method based on bilinear pooling [88]. Bilinear pooling entails getting the outer product of two input vectors. It is employed in a bid to more fully capture the relationships between two vectors. However, it is computationally expensive since the resulting number of parameters is \(O(n^{2})\). The approach can be made compact by dimensionality reduction. Bilinear pooling is an option to combine two vectors instead of other approaches such as concatenation, element-wise vector summing or element-wise vector multiplication.  
A problem of conventional attention mechanisms that [PERSON] et al. [87] try to overcome is the fact that the attention weights are essentially derived from the linear combination of the query and the key via an element-wise summation. This only exploits the first-order feature interactions between the textual domain and the visual domain. Whereas typical attention approaches are additive attention or dot-product attention, they propose a spatial and channel-wise attention mechanism based on bilinear pooling to exploit the higher order feature interactions. They package their attention mechanism into a block that they name X-linear attention block. The X-linear attention block uses a feature extraction backbone based on the Squeeze-and-Excitation Networks (SENet) of [PERSON] et al. [89]. The final model is the X-Linear Attention Network (X-LAN) shown in Figure 7. It incorporates the X-linear attention block into the encoding and decoding operations.  
Similar to AoANet, the authors confirm that improving attention measurement is an effective way of improving the interactions between the visual and textual domains. Exploiting rich semantic information in images (such as scene graphs and visual relations) leads to improved performance.  
Figure 6: Architecture of the meshed-memory transformer. Multi-level encodings are connected to the decoder through a meshed and learnable connectivity. Each decoder layer inputs are controlled by a gating mechanism (\(\sigma\)) that weights the multilevel contributions at each stage.
# Hybrid Deep Learning-Based Intrusion Detection System for RPL IoT Networks
## 5 Experiment Setup and Evaluation
### Evaluation Results
#### 5.3.2 Supervised Binary Classification  
For the binary classification, loss, and Area Under Curve (AUC) performance, there are plots based on the training process. Although the training iterations (number of epochs) was initially set to 1000, the training stopped, and the loss AUC achieved was very good, even much earlier (converged). The dropout and callback techniques were used to speed up the training process and avoid overfitting. Figure 9 shows that the loss and AUC converge to the best performance with 70 epochs in the DIS dataset. For the rank dataset, 140 training epochs were needed to reach a higher performance rate (Figure 10), and 200 training epochs were needed in the wormhole case (Figure 11).  
The performance detail results for DIS, rank, and wormhole attacks binary classification are reported in Tables 11-13, respectively. The model classification accuracy rates are 99, 98, and 97% for the
## 4 Case Study
### Analysis of the Prediction Results
#### 4.2.1 Results of Parameter Estimations and Tamping Cycle Prediction  
The estimation results for \(\beta_{k}\), \(\sigma_{k}\), and \(\theta_{k}\) for the tamping cycle prediction models for the 2171 track segments are shown in Figure 4a-c, respectively. The different line colors in the figure represent the results obtained using different parameter estimation methods. Taking \(X_{lim}\) = 3 mm as an example, the tamping maintenance cycle of each track segment was predicted using Equations (4) and (5), as shown in Figure 4d.  
As Figure 4 shows, regardless of the parameter estimation method, the parameters of the longitudinal-level degradation model for each track segment were heterogeneous, and the prediction results of the tamping maintenance cycle were heterogeneous, reflecting the heterogeneity of the deterioration process of the track geometry at different spatial locations. In addition, although the estimation results obtained with the four parameter estimation methods were not significantly different, the estimation results for the tamping maintenance cycle were significantly different, as shown in the marked examples of several representative track segments in Figure 4e. This illustrates the necessity of selecting an accurate and effective parameter estimation method. The next section presents a comparison  
Figure 3: Study area along the Lanxin railway line in northwest China.  
of the prediction errors of the four methods conducted to identify the parameter estimation method with the highest accuracy.  
Figure 4: Results of parameter estimation and tamping maintenance cycle prediction for the 2171 track segments in the downlink line of the Lanxin railway: (**a**) estimation results for parameter \(\beta_{k}\), (**b**) estimation results for parameter \(\sigma_{k}\), (**c**) estimation results for parameter \(\theta_{k}\), (**d**) tamping maintenance cycle prediction results, and (**e**) difference in the prediction results between different methods.
# Numerical Study on Track-Bridge Interaction of Integral Railway Rigid-Frame Bridge
## 1 Introduction
[20] explored the distribution and regularity of the longitudinal forces of CWRs on a railway suspension bridge with a length exceeding 1000 m.  
The integral rigid-frame bridge is an innovative bridge structure [21; 22]. With no bearings throughout the whole bridge, the side piers and middle piers are consolidated with the main beam. The outstanding advantages of this new type of bridge are as follows. (1) Due to the elimination of bearings and expansion joints, much less maintenance is required during the bridge operation stage. (2) The buckling risk of piers is reduced and the stiffness distribution between the superstructure and substructure is more reasonable on account of the consolidation of the pier and beam. (3) Solid sections without internal templates are widely applied for piers, which obviously reduces the construction cost and makes inspection and maintenance more convenient. (4) The transfer path of longitudinal forces may be more explicit and direct after the consolidation of the abutment and beam end. (5) Finally, by adopting a slender and graceful main beam, this new type of bridge exhibits much more impressive mechanical properties, such as excellent antiseismic performance, beautiful appearance, competitive economic effectiveness and high durability. Due to the above advantages, the integral rigid-frame bridge without bearings has been widely used in German railways in recent years [21; 22]. In China, the continuous rigid-frame bridge system without bearings has been adopted in the elevated part of Guangzhou Metro Line 6, Line 14 and Line 21 [23]. However, there is no precedent application case in China's existing HSR lines so far. In order to realize a span arrangement consistent with adjacent highway bridges and acquire better structural performances, a series of 3 \(\times\) 70 m integral rigid-frame bridges without bearings were adopted for the approach bridge of Quanzhou Bay Bridge in the Fuzhou-Xiamen HSR, which is the first exploration and application of this new type of high-speed railway bridge in China [24]. Though a few scholars have carried out studies on the interaction between CWRs and rigid-frame bridges [21; 25; 26], the stress characteristics of the rails on the novel integral rigid-frame bridge still remain unclear and the main influencing factors have not been fully studied. In order to guarantee the running safety and reliability of HSR lines, the 3 \(\times\) 70 m integral rigid-frame bridge in the Fuzhou-Xiamen HSR was taken as a research project and the adaptability of the bridge-track system and the influence of different design parameters are investigated and discussed in this paper. In addition, for long-span integral rigid-frame railway bridges, the suitable rail fasteners, the optimal layout of adjacent bridges and the reasonable stiffness of piers were also studied.
# I-Shaped Metamaterial Using SRR for Multi-Band Wireless Communication
## 4 Design Methodology  
In microwave communication application, various factors must be considered to provide multi-band operating frequencies. To increase the efficiency and effectiveness of the design for the proposed ISMeTM structure, the configuration is optimized using CST microwave studio. An iterative approach is used to obtain the response of the ISMeTM unit cell. Hence, modifying the resonance frequency is feasible by adjusting the dimensions and thickness of the substrate. However, because of the capacitance and inductance variation, some major parameters such as the length and width of the ring resonator, the ring gap between the rings, spilt gap and the ring length are responsible for producing the multi-band operating frequencies. The design steps for the proposed ISMeTM unit cell are shown in Figure 3. The reflection coefficient, transmission coefficient and permittivity for various design steps are shown in Figure 4a-c, respectively. In design step-a, double copper wire cut was introduced into the outer ring. From the S21 graph, it can be observed that the resonant frequencies are 6.4, 10.02 and 11.82 GHz. A second copper ring is introduced to the unit cell and four resonances at 6.23, 7.81, 10.17, 11.98 and 14.68 GHz are realized for design step-b. A third copper ring is introduced into the structure in design step-c. This design produces six resonances at 6.32, 7.81, 9.98, 11.46, 12.21 and 15.67 GHz. In design step-d (ISMeTM), an I-shaped metallic structure is introduced into the internal ring of the unit cell. Furthermore, seven resonances are obtained at 6.31, 7.79, 9.98, 10.82, 11.86, 13.36 and 15.5 GHz. The parameters for various design steps are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Layout** & **Frequency (GHz)** & **S21 (dB)** \\ \hline Design a & 6.4, 10.02, 11.82 & \(-21.99\), \(-8.23\), \(-22.96\) \\ Design b & 6.23, 7.81, 10.17, 11.98, 14.68 & \(-23.12\), \(-19.53\), \(-16.81\), \(-19.97\), \\  & & \(-16.41\) \\ Design c & 6.32, 7.81, 9.98, 11.46, 12.21, 15.67 & \(-18.15\), \(-16.14\), \(-11.75\), \(-7.34\), \\  & & \(-19.13\), \(8.32\) \\ Proposed design (ISMeTM) & 6.31, 7.79, 9.98, 10.82, 11.86, 13.36, 15.5 & \(-22.96\), \(-18.89\), \(-13.47\), \(-8.64\) \\  & & \(-15.49\), \(-6.87\), \(-6.45\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of various design steps.  
Figure 3: Design steps for the proposed ISMeTM unit cell: (**a**) design step-a; (**b**) design step-b; (**c**) design step-c; (**d**) design step-d (ISMeTM).
## 6 Intra-Cellular Transport of TIA Intermediates and End Products
How do two important precursors secolognain and tryptamine are transported into the vacuole for their condensation into strictosidine by vacuole-localized STR? Are they transported by MATE transporters? What is it necessary for SGD localization in the nucleus, as reported, since aglycon is still need to be transported out of the nucleus? How the product of T3O/T3R is transported into the chloroplast for methylation by the granule-localized NMT? All these questions need to be answered to give a clear scenario of intermediate transport or trafficking during the TIA biosynthesis. Abbreviations are: SGD, strictosidine \(\beta\)-D-glucosidase; STR, strictosidine synthase; TDC, tryptophan decarboxylase; DXS, 1-deoxy-D-xylulose 5-phosphate synthase; DXR, 1-deoxy-D-xylulose 5-phosphate reductio isomerase; MCT, MEP cytidyltransferase; CMK, 4-(cytidine 5’-diphospho)-2-C-methyl-D-erythitol kinase; MECS, 2-C-methylerythnitol 2,4-cyclodiphosphate synthase; HDS, hydroxymethylbutenyl 4-diphosphate synthase; HDR, hydroxymethylbutenyl 4-diphosphate reductase; IDI, IPP isomerase; GPPS, geranyldiphosphate synthase; GES, geraniol synthase; G10H, geraniol 10-hydroxylase; 10 HGO, 10-hydroxygeraniol dehydrogenase; IDS, iridoid synthase; 7 DLS, 7-deoxyloganetic acid synthase; DLGT, 7-deoxyloganetic acid glucosyltransferase; DL7H, 7-deoxyloganic acid 7-hydroxylase; LAMT, loganic acid methyltransferase; SLS, secologanin synthase; T16H2, tabersonine 16-hydroxylase 2; 16 OMT, 16-hydroxylasonine-16-O-methyltransferase; T3O, tabersonine 3-oxygenase; T3R, tabersonine 3-reductase; NMT, \(N\)-methyltransferase; D4H, desacetoxyindoline 4-hydroxylase; DAT, acetyl CoA: deacetyllyundoline 4-O-acetyltransferase. Laticifers and diidoblasts are the cells located in stems and leaves where tabersonine derivative converts into vindoline. The last two vindoline synthetic enzymes DAT and D4H are localized in cytoplasm and nucleus of the laticifers and idioblasts cells [74]. Vindoline is formed in the cytosol of laticifers and idioblasts and transported into the vacuole by a specific proton antiporter, which is energized by the V-H\({}^{+}\)-ATPase. Catharanthine and AVLB are also taken up into the vacuole of epidermal cells by an uncharacterized antiporter through an H\({}^{+}\)-dependent mechanism (Figure 4) [78]. Since catharanthine and vindoline are not in the vacuole of the same cells in _C. roseus_ leaves, they are coupled to form bisindole alkaloids vinblastine and vincristine, only because stimulation from external environment could break down the spatial separation. However, as long as bisindole alkaloid forms, they are transported and accumulated in the vacuoles.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 3 Type of Attacks on IoT and IIoT Devices
### Data Attacks  
Cloud computing plays an important role in maintaining and providing resources such as virtual servers, databases, etc. Whenever a cloud server provides these services, data security becomes a concern, and it may be improved by using software updates, firmware, and proper authentication mechanisms. Some examples of data attacks are Data Inconsistency, Unauthorized Access, and Data Breaches. Most common insider attacks are due to weak access policy or weak login credentials. Examples of data attacks were seen in 2018, when 50 million Facebook users' data were breached by Cambridge Analytica, and in 2021, when the AIRINDIA data server SITA was compromised, and due to that, the details of users, including their travel history, payment card details, and date of birth, were compromised.  
**Protection against Data Attacks**: The protection of users' data is the utmost significant task. The following countermeasures are proposed against data attacks. The integrity may be protected by a digital signature or message authentication code. However, there are challenges in signature implementation in smartphones, i.e., Android OS, iOS, and IoT sensors because these devices are computationally intensive and difficult to implement.  
The model proposed by [23] used symmetric key cryptography. The model is based on a message authentication protocol and chaos-based privacy protection to ensure the integrity and secure data communication of smart home application devices. The author proposed a blockchain-based solution to ensure integrity in [24]. The proposed approach is based on three layers, namely concepts of trust, a trust space-time protocol, and the verification of data availability and integrity. Blockchain-based architecture for ABE (Attribute-Based Encryption) also ensures the nonrepudiation, integrity, confidentiality, and privacy of data transactions. The sender sends the encrypted data, and only validated users who satisfy the access control mechanism can decrypt and use the data. Another security measure is the Improved Secure Directed Diffusion protocol (ISDDP) for end-to-end data protection in the IoT domain. In this protocol, first, a shared key is generated using the bilinear pairing method; later, that key is used for the generation of multiple layers of encryption. This protocol ensures privacy protection on multiple layers, and this makes it impossible to retrieve the original data from malicious hackers. Some of these attacks and mitigation solutions are presented concisely in Table 1.