esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Feasibility and Evaluation
### Context Awareness  
The term context has been defined differently by various authors. However, a more general definition is given by [PERSON] _et al._[PERSON] et al. (2015) as any information that can be used to characterize and recognize the situation of an object, person or place. Context provides vital information regarding the who, what, where, when and why of a situation [PERSON] et al. (2015). In a computing environment, this context or information is usually offered by the system- or application-generated events. It can be seen in Figure 8 that the information in the primitive events precisely provides essential attributes necessary to qualify both definitions stated. Hence, EDAS captures the fundamental unit of a system change, _i.e._, the event, to set a clear and distinct ground for context-aware risk analysis.  
Furthermore, in real-world scenarios, a compromise is usually a combination of different attack vectors, modifications, tools and targets, which may trigger a series of different events originating from different sources as the compromise progresses. EDAS addresses such situations through event correlation. Using a correlation method, such as a rule-based OSSIM correlation directive, an analyst could define a particular compromise context defined as a rule set. The context captures all of the potential events that signify a potential compromise and can accurately qualify whether a risk is involved or not. Correlating events from different sources provides a broad view of understanding the context and holistic information. Thus, it reduces any false alarms that may be caused by analyzing events independently. Nonetheless, if events are seen as a series of steps towards a particular compromise situation (context), then by exploiting the precautionary principle [PERSON] et al. (2015); [PERSON] et al. (2015), EDAS can predict these steps and can respond to corresponding threats (events) before they are realized as actual attacks.
## 1 Introduction
### Structural Design  
Regarding structural design, [PERSON] et al. [14] proposed a novel type of anhydrous photovoltaic panel cleaning robot, developed a negative pressure adsorption wheel walking system and a rolling brush negative pressure dust removal system to ensure the stability of the robot's operation, and verified the effectiveness of its design through experiments. [PERSON] et al. [15] investigated an unmanned photovoltaic cleaning robot without a guide rail. The robot walked autonomously in a half-track manner, employing alternating rotating independent spiral brushes to clean the photovoltaic panel surface while using ultrasonic sensors to detect the spatial position of the robot in real time. The reliability of its design was confirmed experimentally. [PERSON] et al. [16] explored the structure of the dust removal port of the photovoltaic panel cleaning robot, theoretically analyzed the gas-solid two-phase flow equation, the correlation between the pressure distribution and the velocity of the dust particles during the dust removal, simulated and analyzed the flow field, and optimized the structural parameters of the dust removal port based on the orthogonal experimental method. [PERSON] et al. [17] proposed an automatic self-cleaning photovoltaic panel device comprising installation structure, cleaning wipes and relevant mechanical components. The pulley mechanism was developed to drive the cleaning wipes to dust the photovoltaic panel and improve the power generation efficiency of the photovoltaic panel. [PERSON] et al. [18] developed a novel type of autonomous path planning and anhydrous cleaning robot for photovoltaic panels. After a flexible photovoltaic panel was installed on the robot as a power source, a rubber wheel was adopted to achieve omnidirectional rotation, and the dust particles on the photovoltaic panel surface were integrated and then stored through vacuum suction. With the cross-arm structure of the photovoltaic panel cleaning robot as the research object, [PERSON] et al. [19] adopted topology optimization for the lightweight design of the cross-arm structure based on the power correlation analysis, which increased the safety and practicality of the cross arm. [PERSON] et al. [20] investigated the rubber tracked photovoltaic panel cleaning robot while exploring the problem of this type of robot slipping on wet photovoltaic panel surfaces in depth. They built a theoretical model and analyzed it. The accuracy of the theoretical model was verified through experiments. [PERSON] et al. [21] proposed a walking cleaning robot attached to the surface of photovoltaic panels which uses a driving unit composed of three driving lines to clean the photovoltaic panel surface. The reliability of the developed cleaning robot was confirmed experimentally. [PERSON] et al. [22] developed a novel type of guide rail photovoltaic cleaning device using silicone rubber foam brush to clean the photovoltaic panel surface. On that basis, the damage to the photovoltaic panel surface could be reduced, and the cleaning performance could be enhanced.
## 3 Methods
### Dimensionality Analysis of MT Data
#### 3.2.4 Induction Vectors  
Induction vectors or tipper vectors are calculated from the relation between the vertical and the horizontal components of magnetic field data, where the vertical magnetic field is generated by lateral conductivity gradient. They are used to determine the presence or absence of lateral conductivity variations at the corresponding depth. The Parkinson convention was adopted in this study, where the vectors point towards anomalous internal concentrations of current. The presence of high magnitude induction vectors can indicate the presence of large conductors and where the lateral changes in conductivity are important. Since tipper vectors are computed from the frequency-domain tensor components, the mean direction/strike inferred from the real component of tipper vector pointing toward a conductor can help to identify the existence of lateral conductivity contrast at the corresponding periods. If the length of the induction vectors is close to zero, a 1D resistivity structure at depth can be suggested; otherwise, a 2D or 3D structure should be considered. Furthermore, if the regional conductivity structure is 2D, the orientation of the tipper vector is mainly orthogonal or parallel to the geoelectric strike direction and this could be helpful in order to eliminate the 90\({}^{\circ}\) ambiguity of geoelectric strike (because there is prior available information for which axe of ellipse correspond to the maximum or the minimum phase). In the case of inconsistent orthogonality between strike direction and orientation of tipper, a 3D conductivity structure may be more appropriate.
## 6 Future Research Areas
### Possible Solutions for Reliability and Latency Requirements  
Edge communications will play an essential role in future networks; researchers have provided a number of solutions to overcome the strict QoS requirements in URLLC. Current cellular networks follow a centralized approach, while edge communications bring resources close to the UE. Despite some issues, the edge communications based solutions, that is, mobile-edge computing (MEC) [57; 58], local area communication [59; 60], and wide-area large-scale communications [61; 62], are very promising, as shown in Table 7.  
The mentioned communication methods in Figure 12 can probably be the key point in improving overall efficiency for URLLC. A number of researchers have further proposed methods to improve the communication methods in edge computing.  
In the papers explained in Table 8 and numerous other papers on edge computing, it is highlighted that the next generation networks should have a cloud-native approach in order to achieve high reliability and low latency. Although some challenges still exist in implementing the edge-computing systems (ECS) based approach for URLLC, ECS can further be improved and can provide better QoS for 5G URLLC networks.  
AI can be integrated with 5G networks to improve the efficiency of resource and network management. The network architecture and user requirements for 5G networks, the traffic management  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Communication Type** & **Current Issue** & **Possible Solution** \\ \hline
**Local-Area Communication** & Shadowing, channel estimation overhead & Multi-connectivity, 5G NR, grand-free access \\
**Mobile Edge Computing** & E2E delay and reliability, optimizing communication Reliable and precise communication between slave and master controller & Optimizing scheduling methods in computing system and communication \\ \hline \hline \end{tabular}
\end{table}
Table 7: Possible solutions to improve existing issues in communication methods [63].  
Figure 12: Three communication solutions for URLLC quality of service (QoS) requirements.  
will be a challenge [68]. AI including ML and deep learning can assist 5G networks in predicting and managing the unpredictable network traffic. AI can analyze and cope with the unpredictable requirements of 5G networks traffic [69]. Additionally, ML can play an integral part to support the MEC architecture [70]. However, the current AI-based research has some limitations [71]. Most of the existing studies focuses on utilizing AI/ML to solve core network and routing related issues. Only a few researchers have worked on applying AI to the semantic and application layer to propose traffic management solutions.
## 3 Time-Restricted Attention CTC/Attention Encoder-Decoder
### SA CTC/Attention  
In this section, we investigate another attention-based paradigm known as self-attention. In self-attention, the weights are computed from the hidden features only. It does not use any past output predictions, which is more parallelizable than location-aware attention.  
First, the hidden features are converted into input embeddings using the embedding matrix \(\mathbf{W}_{embd}\) as:  
\[\mathbf{b}_{\mathbf{t}}=\mathbf{W}_{embd}\mathbf{h}_{\mathbf{t}},t=u-\tau,\cdot\cdot\cdot,u+\tau. \tag{21}\]  
Second, use linear projections of the embedding vector \(\mathbf{b}_{\mathbf{t}}\) to compute three kinds of vectors, keys, values and query, in the self-attention block:  
\[\begin{split}\mathbf{q}_{\mathbf{t}}&=\mathbf{Q}\mathbf{b}_{\mathbf{t} },t=u,\\ \mathbf{k}_{\mathbf{t}}&=\mathbf{K}\mathbf{b}_{\mathbf{t}},t=u-\tau, \cdot\cdot\cdot,u+\tau,\\ \mathbf{v}_{\mathbf{t}}&=\mathbf{V}\mathbf{b}_{\mathbf{t}},t=u-\tau, \cdot\cdot\cdot,u+\tau,\end{split} \tag{22}\]  
where \(\mathbf{Q},\mathbf{K},\mathbf{V}\) are the query, key and value matrices, respectively.  
Third, the attention weight \(\mathbf{a}_{\mathbf{u}}\) and attention result \(\mathbf{c}_{\mathbf{u}}\) are derived by:  
\[\begin{split} e_{ut}&=\frac{\mathbf{q}_{\mathbf{u}}^{T}\bm {k}_{\mathbf{t}}}{\sqrt{d_{\mathbf{k}}}},\\ a_{ut}&=\frac{exp(e_{ut})}{\sum_{t^{\prime}=1}^{T} exp(e_{ut^{\prime}})},\\ \mathbf{c}_{\mathbf{u}}&=\sum_{t=u-\tau}^{u+\tau}\mathbf{a}_\mathbf{{u}t}\mathbf{h}_{\mathbf{t}}.\end{split} \tag{23}\]  
Finally, the weighted hidden feature \(\mathbf{c}_{\mathbf{u}}\) is projected by the CTC projection layer as described in Equation (17). Then, the projected information is optimized by Equation (14).  
As noted above, a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas the location-aware attention requires \(O(n)\) sequential operations. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies. Therefore, self-attention can better model the long-range dependencies than the location-aware attention.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 3 Methodology
### Problem Formulation  
The objective of video temporal grounding is to identify the optimal time segment \(M\), consistent with the query's semantics, given an untrimmed video \(V\) of duration \(L\) and a natural language query \(Q\). In this task, the video \(V\) is denoted by a frame sequence \(V=\{f_{i}\}_{i=1}^{T}\), where \(f_{i}\) represents the \(i\)-th frame of the video and \(T\) is the total number of frames; the natural language query \(Q=\{q_{j}\}_{j=1}^{W}\) comprises the \(j\)-th word of the query, with W being the total number of words. Presently, mainstream approaches to video temporal grounding typically employ a multimodal fusion paradigm, mapping both the video \(V\) and the query \(Q\) into a unified shared feature space \(X\). Within the feature space \(X\), the model learns the probability distribution of the time range, predicting the start and end times \([t_{s},t_{e}]\) of the target segment to ensure high semantic consistency with the query.
# A Feasibility Study of Utilizing Nuclear Energy for an Existing MED-TVC Desalination Plant
## 5 Carbon Emission Considerations  
The carbon emission was calculated using crude oil with a heating value of 47 MJ/kg [27]. The carbon emission of the existing MED-TVC desalination plant was calculated, and it equals 543,235 tons per annum. Figure 10 shows a rough comparison of the estimated GHG emissions when utilizing crude oil, natural gas, or nuclear energy as different energy sources to operate the existing MED-TVC desalination plant. Therefore, since nuclear energy does not generate any direct GHG emissions, the amount of GHG emissions resulting from burning crude oil could be halted by implementing the option of nuclear energy. Increasing the role of nuclear energy within the global energy mix will play a crucial part in meeting the net-zero target set forth by many countries before 2060 or even before 2050.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 9 Conclusions  
In this paper, we focused on the problem of adding fault-tolerance to an existing program which consists of some actions that are unchangeable. These unchangeable actions arise due to interaction with the environment, inability to change parts of the existing program, constraints on physical components in a cyber-physical system and so on. We presented algorithms for adding stabilization and fault-tolerance. These algorithms are sound and complete and run in polynomial time (in the state space).  
We considered the cases where (1) all fault-free behaviors are preserved in the fault-tolerant program, or (2) only a nonempty subset of fault-free behaviors are preserved in the fault-tolerant program. We also considered the cases where (1) environment actions can execute with any frequency for an initial duration and (2) environment actions can execute more frequently than programs. In all these cases, we demonstrated that our algorithm can be extended while preserving soundness and completeness. We provided a Java package including the implementation of our algorithm using BDDs. We presented some of our experimental results of adding stabilization and fault-tolerance using JavaRepair.  
There are several future extensions to this work. One of the extensions has to deal with partial observability in distributed or embedded systems. Algorithms in this paper need to be revised by taking into account scenarios where, due to partial observability, one transition added or removed corresponds to a group of transitions [28]. Another extension is to extend the JavaRepair package so that it can be used efficiently and with a reduced learning curve. Yet another extension is to deal with the issue of time in embedded and cyber-physical systems. Specifically, in a CPS, we expect physical components to execute at a certain speed. In other words, an action of the physical component can be thought of to take some time between \(e_{1}\) and \(e_{2}\). In turn, this affects the number of steps a computational component can execute in the middle. Note that this maps to the value of \(k\) used in our algorithms. Also, our definition of computation requires that the environment cannot execute too frequently. We also intend to extend to cases where a similar restriction also applies to computational components.  
**Author Contributions:** Supervision, S.K.; Writing--original draft, M.R.  
**Funding:** This work is supported in part by NSF CPS 1329807, NSF SaTC 1318678, and NSF XPS 1533802.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Segmentation of Intracranial Hemorrhage Using Semi-Supervised Multi-Task Attention-Based U-Net
## 2 Semi-Supervised Learning  
As has been mentioned before, in the field of automatic medical imaging, where it may involve the task of segmentation or classification, properly labeled data are difficult to obtain. For example, the RSNA Intracranial Hemorrhage Detection Dataset [35] required the collaboration of over four universities and more than 60 volunteers to label CT scans manually. Medical datasets set up for semantic segmentation training require even more resources, because professionally trained pathologists and radiologists need to draw the boundaries of hemorrhage regions manually for thousands of samples.  
The difficulty in obtaining datasets large enough to take advantage of the innovations in the field of deep learning has inspired a new way of training, semi-supervised learning. Semi-supervised learning is able to use a large repertoire of unlabeled data along with a small number of labeled samples to create robust classifiers [36]. This is especially relevant to tasks involving semantic segmentation of medical images, where CT scan machines generate unlabeled data in the order of hundreds every hour. A properly trained classifier can label a sequence of CT images in a fraction of a second, while it may take a professionally trained radiologist a few minutes to label a single sample [37].  
Formally, the goal of semi-supervised learning is to learn a function \(f:X\to Y\) using a (potentially noisy) set of labeled data, \((x_{1},y_{1}),...,(x_{n},y_{n})\) and a set of unlabeled data \((x_{n+1},...,x_{n+k})\) subject to \(n\ll k\) with the assumption that \(\forall x_{i}\in X\). The principle advantage to semi-supervised learning boils down to improving generalization and reducing overfitting on the training set. By forcing the network to learn features corresponding to a larger dataset, we effectively regularized our network against non-generalizing local minima that existed within the training process. In this paper, we solved this problem by using an autoencoder with a modified u-net initially proposed by [PERSON] et al. [34] on two separate, independent sources of data detailed in Section 5.1.
# Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks
## 6 Numerical and Simulation Results
Note that the average performance had a gradually decreasing trend with a lower path loss exponent, and more packets were left in the queue. As a result, the average throughput was even lower, and the average delay and dropping probability were much higher. The results indicated that higher path loss reduced the influence of interference links to the D2D receiver and improved the probability of successful transmission. Figure 3: The D2D performance parameters versus packet arrival rate under different path loss factors \(\alpha\). Then, we evaluated the impact of D2D density on the performance parameters in Figure 4. We set \(\alpha\)= 4. We can see that the analytical and simulation results basically matched. In Figure 4a, with the increase in the mean arrival rate, the average queue length was much quicker to reach the maximum capacity as more D2D links accessed the network. This was because when the received interference increased, more packets returned to the queue for retransmission. Figure 4b,d shows that a high D2D density \(\lambda_{D}=30\) users/km\({}^{2}\) resulted in lower average throughput and higher average delay compared with the performance for \(\lambda_{D}=10\) users/km\({}^{2}\) and \(\lambda_{D}=20\) users/km\({}^{2}\). The reason was that higher interference received by the D2D transmitter resulted in a longer waiting time for the packets. The throughput became lower, and packets needed to wait longer to be transmitted. Figure 4c illustrates the dropping probability as the packet arrival rate varied for different D2D density. As discussed below, the dropping probability included two parts: (1) the arrival of packets caused by the overflow, which was related to the mean arrival rate and buffer size; (2) according to the retransmission strategy, the dropping probability was generated by the failure of data transmission in the \(K^{\text{th}}\) sending attempt. When the arrival rate was comparatively low, the number of packets in the buffer was quite small. Thus, the dropped packets were mainly caused by unsuccessful sending, which was determined by the instantaneous channel situation. When the arrival rate increased, the dropping probability increased markedly and continued to grow. The dropping probability was mainly influenced by a heavy traffic load. Figure 4: The D2D performance parameters versus packet arrival rate for different D2D densities \(\lambda_{D}\).
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 4 Methodology
### Rationale for the Hybrid Architecture: A Synergistic Approach to Feature Extraction  
The selection of a hybrid architecture combining EfficientNet-B0 and DenseNet-121 is a deliberate design choice motivated by the principle of complementary feature extraction. Malware, as a complex form of software, exhibits malicious patterns at multiple scales. Some threats are identifiable through small, localized code sequences (e.g., a specific API call chain or an obfuscation snippet), while others are revealed by the global structural properties of the program (e.g., anomalous control flow or unusual section dependencies). A single CNN architecture often excels at one scale but may be suboptimal at another. Our hybrid model is designed to overcome this limitation by creating a synergistic system where each component specializes in a different level of analysis.  
1. EfficientNet-B0 for Local and Fine-Grained Feature Detection: EfficientNet-B0 is renowned for its compound scaling method, which optimizes network depth, width, and resolution to achieve high accuracy with remarkable computational efficiency. In the context of our Assembly-to-RGB images, this efficiency makes it exceptionally well-suited for identifying local, high-frequency patterns. These correspond to short, contiguous sequences of opcodes or specific operand combinations that form the micro-architectural signatures of malicious behavior. EfficientNet-B0 thus acts as our \"local pattern specialist\", adept at detecting the subtle, low-level artifacts that signify a threat.  
2. DenseNet-121 for Global and Structural Feature Learning: In contrast, DenseNet-121 is characterized by its dense connectivity, where each layer receives direct inputs from all preceding layers and passes on its own feature maps to all subsequent layers. This architecture promotes extensive feature reuse and strengthens feature propagation throughout the network. For our task, this is critical for learning global, long-range dependencies within the assembly code's visual representation. DenseNet-121 can effectively model the overall program structure, control flow graph, and relationships between distant code blocks. It functions as our \"global architecture analyst\", capable of recognizing macro-level anomalies that would be missed by a locally focused model.  
3. By concatenating the feature vectors from both models, our framework synthesizes a rich, multi-scale feature representation. This dual-pathway approach ensures that the final classifier is informed by both the \"trees\" (fine-grained local details from EfficientNet-B0) and the \"forest\" (holistic structural context from DenseNet-121), leading to a more robust and generalizable malware detection system.