esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Findings and Discussion, Contribution, Limitations and Future Work of The Study
### Contribution  
Network science has been commonly applied as a quantitative tool to contribute to a better understanding of the various layers of the aviation system. The existing literature usually considers the network of an operating carrier as a single layer, but leaves the codeshare system remaining almost unexplored. The codeshare network is definitely worthy of deeper investigative analysis, as it is the result of airline aggregations and alliances decisions, which are crucial to airlines' strategical resource optimization. Hence, this study fills in knowledge gaps by uniquely taking the codeshare network into account, and addressing its effects on airline topology and transport geography. More importantly, the rarely discussed industry-specific issues are explored, based on the reality of the airline networks in the commercial world, rather than defining communities algorithmically.  
Affecting the network aspect spatially and impacting the passenger rerouting choices, the traffic en route is as important as the topological characters for airlines and airports. However, there is very limited literature that has investigated the aviation network as a weighted system. This paper contributes to the existing literature by considering the dynamics of weights along the routes. This research increases the rationale and accuracy of the analysis by taking flight frequency into account, and expands the research scope from topological structure to the real world.  
Last, but not least, this study examines the applicability and the robustness of the weighted clique percolation method with a case study, testing ten major airlines with different business models. The results argue that the clique-based analysis is quite distinct from what the existing literature found with low-order methods, which reveals new insights in air transport geographical and topological patterns. First, the result suggests that the high-order communities detected in the airline systems are separated based on geographical information, which has not been achieved by other techniques. Second, the topological hub-shifting phenomenon is observed, revealing that the topological gateway airports are not always consistent with the actual hub airports of the airlines. Third, unlike previous studies on low-order communities [20], no clear patterns in the high-order network topology and community structures are identified between legacy carriers and low-cost airlines. In contrast, this research reflects the possibility of airlines with different business models and network sizes sharing an identical topology profile. This overall ensemble of unique inputs that were applied in this study separates it from other studies.
# Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism
## 2 Methods
### Fan-Bd  
The overall structure of the proposed convolutional vision neural network method is shown in Figure 2. This method aims to achieve precise bearing information recognition through the effective processing of current and vibration signals.  
Since the original VIT image feature extraction method focuses on the global characteristics of targets while neglecting local characteristics, we used small convolution kernels and small strides to extract more detailed local information, and used the CBMA attention mechanism for feature calibration and fusion to reach our conclusions. The specific steps are as follows.  
This process first extracts features from the input vibration signal grayscale image through convolutional and activation layers, then adds position encoding information, as shown in Equation (2), for both vibration and current signal two-dimensional grayscale images. Next, the Vision Transformer (ViT) is used to extract features from current signals (3) and vibration signals (4). These two types of signal features are then fused through the CBMA module (5), generating queries, keys, and values (6), and calculating channel attention through the attention mechanism (7). The multi-head attention mechanism further processes these features, enhancing feature representation through concatenation and linear transformation (8) and (9). Subsequently, the fused features undergo nonlinear transformation through a multilayer perceptron (MLP) (10), and finally, classification results are obtained through layer normalization and linear layers (11). This model structure can effectively process and analyze multi-modal data, extract key information, and achieve accurate classification.  
\[z_{0}=\left[x_{\text{class}};x_{P}^{1}E_{i};x_{P}^{2}E_{i};\cdots;x_{P}^{n}E \right]+E_{pes} \tag{2}\]  
\[X_{current}=\text{ViT}(I_{current}) \tag{3}\]  
\[X_{gibration}=\text{ViT}(I_{gibration}) \tag{4}\]  
Figure 2: Flow chart of vibration signal and current signal data preprocessing.  
\[X_{combined}=Concat(X_{current},X_{vibration}) \tag{5}\]  
\[Q=X_{combined}W_{Q},K=X_{combined}W_{K},V=X_{combined}W_{V} \tag{6}\]  
\[Attention(Q,K,V)=softmax\left(\frac{Q_{K}{}^{T}}{\sqrt{d_{k}}}\right)V \tag{7}\]  
\[\text{MultiHead}(Q,K,V)=\text{Concat}(\text{head}_{1}\ldots\text{Head}_{h})W ^{0} \tag{8}\]  
\[\text{Head}_{i}=\text{Attention}(Q_{i}^{e},K_{i}^{e},V_{i}^{v}) \tag{9}\]  
\[z\delta=MLP(LN(MultiHead)) \tag{10}\]  
\[y=LN(zt) \tag{11}\]  
The detailed layer information is shown in Table 1:
## 4 Activation of PKA by G-Proteins  
The second messenger, cyclic AMAP (cAMP), is produced by adenylyl cyclase, which converts adenosine triphosphate (ATP) to cAMP. PKA is activated by the binding of cAMP to the PKA-R subunits, which releases PKA-C. cAMP levels are reduced by phosphodiesterase (PDEs), which convert cAMP into AMP [26]. Adenylyl cyclases are commonly regulated by G-protein signaling. Heterotrimeric G-proteins consist of an \(\alpha\) and a \(\beta\gamma\) subunit, which, when activated by an upstream G-protein coupled receptor (GPCR) or G-protein regulator (GPR), dissociate and independently activate signaling cascades [27; 28; 29]. Upon ligand binding, the GPCR acts as a guanine nucleotide exchange factor (GEF), exchanging GDP for GTP on the \(\alpha\) subunit, and activating the heterotrimeric G-protein. Heterotrimeric G-proteins can also be activated via a receptor-independent mechanism facilitated by G-protein regulator proteins (GPRs) (Figure 2) [30; 31].  
Upon activation, the GTP-bound \(\alpha\) subunit disassociates from the \(\beta\gamma\) subunit, and both the \(\alpha\) and \(\beta\gamma\) subunits can initiate downstream signaling pathways [32]. _C. elegans_ expresses 21 G\(\alpha\) subunits [27] of the Gs, Gi/o, Gq, and G12 families, including only one ortholog of Ga\({}_{\text{s}}\) (GSA-1) and Ga\({}_{\text{i/o}}\) (GOA-1) [33]; these G\(\alpha\) subunits are typically upstream of adenylyl cyclase. _C. elegans_ express two G\(\beta\) subunits, GPB-1 and GPB-2 and two \(\gamma\) subunits, GPC-1 and GPC-2. GPB-1 shares 86% homology with mammalian \(\beta\) subunits and interacts with all G\(\alpha\) subunits in _C. elegans_[34; 35]. GPC-1/\(\gamma\) is expressed in sensory neurons, while GPC-2/\(\gamma\) is expressed more broadly [36]. G\(\beta\gamma\) subunits can regulate ion channels [37; 38] including Ca\({}^{2+}\) channels [39], as well as activate or inhibit adenylyl cyclase [40]. GPB-2 acts downstream of the G\(\alpha\) subunit GOA-1 in pharyngeal pumping [41], and is required for egg-laying and locomotion [42; 43], and works with GPB-1 and GSA-1 to regulate Ca\({}^{2+}\) signaling and contractility in the spermathe  
Figure 2: **Schematic representation of G-protein activation of PKA.** PKA is activated when the regulatory subunit binds to cAMP, releasing the catalytic subunit. cAMP is produced by adenylyl cyclase, which is either activated by G\(\alpha\)s, or inhibited by G\(\alpha\)i/o. Adapted from “Activation of Protein Kinase A (PKA)”, by BioRender.com (2022). Retrieved from [[https://app.biorender.com/biorender-templates](https://app.biorender.com/biorender-templates)]([https://app.biorender.com/biorender-templates](https://app.biorender.com/biorender-templates)) (accessed on 27 October 2022).
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 4 Experiments and Discussion
### Ablation Study  
In the SWM network designed in this study, in addition to the conventional backbone and head, the proposed modules include the context feature branch, spatial feature branch, and fusion block. To verify their effectiveness, the combination of backbone and head is used as the baseline network.  
According to the comparison of various methods, the results of the ablation study are obtained (Table 1). Compared with the single module class and baseline network, Method A uses the MobileNet block structure for the dual-branch part of the baseline network, Method B adds the Spatial feature branch, and Method C adds the Context feature branch. Compared with Method A, Methods B and C show significant improvement in all four indicators, demonstrating better performance. This indicates that both the spatial feature branch and contextual branch can effectively improve the model's feature extraction ability. Among them, Method C has a 2.351% increase in F1 score, which is higher than the 2.078% increase of Method B. The two methods are close in recall rate, and the main difference in performance improvement is that Method C has a larger increase in precision, which may be due to the fact that contextual features are more helpful in distinguishing solid waste from the background. Compared with the single module method group and double module method, both Methods B and C only add single modules, while Method D adds double modules. From Table 1, it can be found that Method D performs better than the single module group in comprehensive performance F1 score, with a recall rate increase of 1.57% and 1.75%, respectively. However, not all indicators have increased, and the precision indicator has decreased by 0.22% and 0.98%, respectively. This may be due to the fact that the dual-branch structure allows the model to obtain more information, which helps improve its ability to detect solid waste, but the interaction between the information reduces precision. Compared with the double module method and double module method with fusion block, Method D directly superimposes the information of the two modules in the channel dimension, while Method E designs two feature fusions. The comparison shows that Method E has better overall indicators than Method D. This indicates that a simple feature superimposition method cannot fully utilize the features extracted by the dual branch. By designing a feature fusion mechanism, Method E can make a combined use of the two types of information by interacting and cooperating between the dual branches, providing more comprehensive and accurate data representations and effectively improving the model's prediction ability and accuracy.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 4 Experiments  
We conducted experiments at two different sites. One experiment was run in a wave tank simulator where the environment was controllable. The other was run in an actual wave power harvesting plant for five days, which showed a wide enough range of output power to classify waves into three levels of power generation.  
The wave tank simulator was located at Chonnam University, Yeosu, South Korea. The purpose of this experiment was to evaluate the feasibility of the proposed method before it was deployed in the actual site. The wave tank simulator was 50 m wide, 50 m long and 1.5 m tall, and it had the capacity to generate waves with periods ranging from 0.5-4 s and a maximum height of 60 cm. A WEC reduced to 1/20 of its original size was used at the site, as shown in Figure 6. In the experiment, we also used a smaller buoy, which had a radius of 1 m and a height of 0.5 m.  
Figure 5: Schematic diagram of the proposed system.  
In the wave tank experiment, we attached the data collection module to the top of the buoy. For the simulation, we generated three types of regular waves: weak, mild and strong. Since waves generated in the tank are smaller than actual offshore waves, we scaled down the wave height and the period by a factor of 20. Table 1 shows the values for actual waves and the adjusted values for the waves in the wave tank. The three cases in the left column of the table represent the wave patterns for strong, mild and weak, respectively. The average powers in the table correspond to power generated for each case. The experiment for each case was conducted for 5 min without altering the experimental setup.  
The second experiment site was located at Bukchon in Jejudo, the largest island in South Korea. We installed the data collection module on top of the yellow buoy and the receiver inside the power plant building located on the shoreline (see Figure 7). The distance between the buoy and the building was approximately 80 m. The incoming data were continuously monitored and recorded throughout the experiment. We segmented the data with several sizes and observed how the performance varied.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Experimental Cases**} & \multicolumn{2}{c}{**Actual Values**} & \multicolumn{2}{c}{**Scaled Values**} & \multirow{2}{*}{**Scaled Average**} \\ \cline{2-2} \cline{5-6}  & **Height** & **Period** & **Height** & **Period** & **Power (W)** \\ \hline Case 1 & 4 m & 12 s & 20 cm & 2.68 s & 9.36 \\ Case 2 & 3 m & 10 s & 15 cm & 2.24 s & 5.82 \\ Case 3 & 2.5 m & 10 s & 12.5 cm & 2.24 s & 3.22 \\ \hline \end{tabular}
\end{table}
Table 1: Three cases for the experiment.  
Figure 6: Scene from the wave tank experimental site.  
Figure 7: INGINE wave energy harvesting station. (**a**) Scene from the experimental site located at Bukchon in Jejudo. (**b**) Simulated arrangement of the connecting ropes of the buoy.
## 2 Traditional Encryption Techniques  
Amid the rapid advancements in quantum computing, conventional encryption techniques grounded in computational complexity are increasingly susceptible to compromise. This concern is particularly significant for agrotechnical monitoring systems that utilize UAVs, as the data transmitted often include sensitive information related to agricultural production, soil properties, weather conditions, and other critical parameters [1; 7]. Consequently, ensuring the security of data transmission in these systems is becoming ever more urgent, given that breaches could result in substantial economic losses and pose a threat to food security.
## 4 Results
### Evaluation of Satellite Precipitation Products
#### 4.1.1 Spatial Patterns  
Figure 3a,b shows the spatial patterns of 3B42V7 and IMERG precipitation estimates from 1 January to 31 December 2015 in the Chindwin River basin. Both products represented a similar spatial pattern: precipitation tended to decrease from north to south. The amount of maximum annual precipitation in 2015 appeared in the source region of the basin, reaching 3345.6 mm for IMERG and 3167.6 mm for 3B42V7. The lowest amount generally occurred in the grid cells near the watershed outlet, reaching 715.2 mm for IMERG and 892.3 mm for 3B42V7. Figure 3c illustrates that the observed precipitation is in a remarkable decreasing trend from the upper to the lower reach for both periods (1 April-31 December 2014 and 1 January-31 December 2015). Both IMERG and 3B42V7 manifested a similar trend but generally underestimated the precipitation in most grid boxes where the rain gauges are located, particularly at the upstream gauges. For instance, IMERG and 3B42V7 underestimated the precipitation at the Hkamti station by 44.7% and 19.4% from 1 January to 31 December 2015. Given that IMERG estimates precipitation on a much finer resolution than 3B42V7, the former can provide more detailed spatial features of precipitation than the latter (Figure 3a,b). Overall, 3B42V7 provided considerably higher precipitation estimates than IMERG. From 1 January to 31 December 2015, 3B42V7 estimated the amount of basin-averaged precipitation to be 2014.0 mm, whereas IMERG provided an estimation of 1720.1 mm, which is 14.6% less than that of the 3B42V7 product.
## 3 Results
### Data Integration: Regularised Generalised Canonical Correlation Analysis (RGCCA) Approach  
Once the relationships between the sensory attributes and the metabolite composition of wines were independently assessed (GC-sensory and LC-sensory), regularized generalized canonical correlation analysis (RGCCA) was performed to integrate the three datasets. The connection of the three blocks was set to a link of 1 to maximise their correlations. As stated in the previous section, a total of 74 and 247 metabolites for GC and LC platforms were, respectively, selected as relevant and were used to built the RGCCA model. Using this approach, all the sensory attributes fell between the two circumferences of the correlation circle plot, showing important correlations with several metabolites (Figure 9). As stated in the PLSR models, citrus fruit, balance, acidity, tree fruit and alcohol descriptors were highly correlated with 3-isopropyl-1-pentanol (A5). In addition, other compounds such as ethyl 5-hexenoate (E3), hexyl acetate (E7), linalyl anthranilate (T3), proline betaine (AA11) and alanine (AA2) were found to be also related with these sensory descriptors. In this approach, scent intensity was allocated between the two circumferences,showing correlations with 2-pyrimidine acetic acid (OA1), 2-phenylglycine (AA4) and trans-3-hexen-1-ol (A7), as well as with complexity, black fruit, red fruit and taste intensity. Meanwhile, the sensory descriptor persistence was negatively correlated with most of the sensory descriptors and positively correlated with imidazole acetic acid (OA4), L-alanine, N-propyl-/L-alloisoleucine/L-isoleucine/L-norleucine (OA6), uracil (M2), 1-undecanol (A3), isopentyl hexanoate (E8), ethyl heptanoate (E1), pentyl-cyclopropane (M1), 1-nonanol (A2) and ethyl hexanoate (E5). The rest of volatile and non-volatile metabolites were allocated within the inner circumference and therefore, no correlations were established.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 4 Methodology
### Scenarios and Discussion  
The specific routing protocol and its design choices are carefully considered to address the properties of mobile nodes with a random-waypoint mobility model and achieve efficient and reliable data delivery in the given routing scenario. The results for each scenario were analyzed and visualized using various types of charts to enhance clarity and comprehension.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 2 Threat Reasons in Cloud Computing
### Cloud Computing Fundamentals  
Cloud computing represents a transformative approach to delivering computing services and resources to end-users over the internet. It is a paradigm shift in how IT infrastructure is managed and utilized. In this model, various hardware and software components collaborate seamlessly to provide a wide array of services. The essence of cloud computing lies in the on-demand accessibility of computing resources, including data storage and processing power, obviating the need for users to manage their dedicated infrastructure. The core concept of cloud computing is the on-demand availability of computing resources. This means that users can access the computing power and data storage they need without the burden of managing their dedicated infrastructure. The benefits of cloud computing are numerous and include cost-efficiency, scalability, improved speed and efficiency, enhanced performance, and robust security measures [3; 14]. Cloud computing can be implemented in different settings, such as public, private, or hybrid models. Public cloud providers offer services over the internet to a broad range of clients, while private cloud firms limit their offerings to specific clients or organizations. Hybridmodels combine elements of both public and private cloud deployments. Cloud computing is a versatile approach for distributing valuable information through the internet, and it is typically categorized into three primary service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
### Control Packets  
In this section, we analyze the number of control packets in the network. In GTRF, the control messages are especially used in three conditions:  
1. The cluster head periodically sends the control packets to check the validity of each cluster member.
2. In the forwarding process, when the void region is detected, the feedback packets will be sent to inform the upstream node that there are selfish nodes located downstream.
3. When the sink node receives the packets, it will notify the source that the packets have been successfully received by the sink.  
In Figure 12, we analyze the relationship between the radius of void region and number of control packets. We record the number of control packets by transmitting 1000, 2000, 5000 and 10,000 packets, respectively. All the selfish nodes will be considered as faulty nodes in all the protocols except GTRF. Due to the increasing size of the void region (the radius of the void region is within [0,5]), packet loss appears frequently, which increases the number of controlling packets in SPEED, SPEED-T, SPEED-S and MMSPEED. Whereas in FTSPEED, DMRF and GTRF, the number of the control packets changes relatively smooth. GTRF is slightly lower than DMRF and FTSPEED, because the GTRF utilizes the cluster structure; only the cluster head is capable of sending the control messages. When the radius exceeds above five, the result of each protocol displays a slow growth tendency which results from the fewer nodes working in the network. Since GTRF utilizes a cluster structure combined with the jumping transmission manner, the void region does not affect GTRF much, so the number thereby is not perturbed a lot.  
Figure 11: Lifetime of the network without selfish nodes.