esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Seismic activity prediction has been a challenging research domain: in this regard, accurate prediction using historical data is an intricate task. Numerous machine learning and traditional approaches have been presented lately for seismic activity prediction; however, no generalizable model exists. In this work, we consider seismic activity prediction as a binary classification problem, and propose a deep neural network architecture for the classification problem, using historical data from Chile, Hindukush, and Southern California. After obtaining the data for the three regions, a data cleaning process was used, which was followed by a feature engineering step, to create multiple new features based on various seismic laws. Afterwards, the proposed model was trained on the data, for improved prediction of the seismic activity. The performance of the proposed model was evaluated and compared with extant techniques, such as random forest, support vector machine, and logistic regression. The proposed model achieved accuracy scores of 98.28%, 95.13%, and 99.29% on the Chile, Hindukush, and Southern California datasets, respectively, which were higher than the current benchmark model and classifiers. In addition, we also conducted out-sample testing, where the evaluation metrics confirmed the generality of our proposed approach.  
seismic activity prediction; earthquake prediction; deep neural network; deep learning; feature engineering
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 3 Pseudo-Identity Based Encryption Framework
### Distributed CA  
We now discuss a strong motivation on why a \(k\)-threshold cryptosystem is considered in this paper. As we mentioned before, it is desirable to use a public-key cryptosystem with the flexibility of not having a constant connection with the CA. \(k\)-threshold cryptographic scheme is a candidate that satisfies this requirement. In a \(k\)-threshold cryptosystem, at least \(k\) parties can collaborate with each other to perform the role of a CA. Thus, a single point attack is not possible.  
While setting up the system, the network administrator chooses the system-wide parameter \(k\). The integer \(k\) is chosen in such a way that an adversary can break-in at most (\(k\)-1) nodes, but not \(k\) nodes. The administrator then configures \(k\) or more specific nodes in the network (such as servers and switches) to serve as a decentralized-CA. We call these nodes as prime-nodes. These nodes are responsible for generating new keys and hash-polynomials. Among these prime nodes, one node is chosen as the \"leader\" node and another one is chosen as a secondary-leader. The leader node coordinates with other prime nodes in generating the keys and hash polynomial.  
These prime nodes may use the sub-tree of the proposed CBT tree to communicate securely between them. They may use a different session key as well as tag their frames as key-maintenance frames, so that non-prime nodes may be unable to decrypt them.
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 3 Green Underwater Sensor Network Framework using Adapted Whale and Wolf Optimization
### Underwater Relay Node Optimization Model  
Here, as the adapted whale and wolf optimization algorithm is used for picking the optimal relay set required for continuous data packet transmission in W-GUN. In our proposed framework W-GUN, the traditional whale optimization is adapted for green underwater networking. The exploitation and exploration of whale optimization is further improved via wolf optimization. This adaptation works towards harnessing the benefits of underwater whale movement characteristics in reducing energy consumption and delay. The adaptation of whale movement characteristics is crucial for underwater communication environments. The aim of the objective function of the optimization scheme is to avail the maximum number of underwater forwarding routes as possible reachable paths with a minimum number of underwater relay nodes. So, the position of the underwater relay nodes is varied and checked for optimal deployment, so as to reduce the number of underwater relay nodes (RNs) needed for deployment. Therefore, the objective function is adapted for underwater scenarios, as expressed in Equation (1).  
\[max(\mathcal{P}_{n})=min\Bigg{(}R_{n}^{b},\sum_{j=1}^{N}R_{n}^{j}\Bigg{)} \tag{1}\]  
where \(max(\mathcal{P}_{n})\) represents the maximum number of underwater forwarding paths that can be formed between the base station and all sender sensor nodes, \(R_{n}^{b}\) represents the number of underwater relay nodes nearby to the base station; \(R_{n}^{j}\) the number of underwater relay nodes close to \(j\)th sensor node and \(j=\{1,2,\ldots N\}\) denotes the number of underwater sensor nodes. To achieve the above objective, the two major constraints that must be satisfied are as given below in Equations (2) and (3):  
\[R_{n}^{j}\geq\mathcal{P}_{n}^{j} \tag{2}\]  
\[R_{n}^{b}\geq\sum_{j=1}^{N}R_{n}^{j}\geq(\sum_{j=1}^{N}\mathcal{P}_{n}^{j}= \mathcal{P}_{n}) \tag{3}\]  
where \(\mathcal{P}_{n}^{j}\) denotes the number of underwater forwarding paths between the base station and \(j\)th underwater relay sensor nodes. During each iteration, based on the maximum number of underwater forwarding paths obtained for different solutions, the set of underwater relay nodes (i.e., the finest solution) and their position is selected. The base of the objective function of the adapted whale and wolf algorithm is discussed in detail, with related mathematical derivations, in the following sections.
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 4 The Proposed ADDP Framework
### Device Intracluster Feature Map Division
#### 4.3.1 Overlap Area Calculation  
In DNN architectures, multiple convolutional layers are usually stacked together as the feature extraction part. In such DNNs, each data element of the feature map in the intermediate layers depends on a local region in the input feature map. Therefore, we can decompose the DNN computation task by partitioning the region of the input feature map. In recently popular backbone networks such as Resnet, DNNs are usually divided into blocks composed of multiple convolutional layers and connected by a skip-connection. Due to the branching structure, the mapping relationship of regions connected across blocks in Resnet is more complicated than that of traditional backbone networks.  
Unlike the grid decomposition in Deepthings [25], this paper used a feature-map-partitioning method in W dimensions, i.e., strip partitioning. The strip segmentation is easy to scale and can reduce the overlapping area. The local area size is related to the receptive field of each output data element, and the receptive field (RF) can be calculated by the following equation  
\[RF_{l+1}=RF_{l}+(K_{l+1}-1)\cdot\prod_{i=0}^{l}stride_{i}\cdot dilation_{l+1}. \tag{4}\]  
The \(K_{l+1}\) denotes the convolution kernel size of the \((l+1)\)th layer and \(RF_{0}=1\). After calculating the receptive field, the mapping area of the output feature map region in the input feature map can be calculated based on the receptive field. For any selected column data \(X_{out}\) in the output feature map, its corresponding input column \(X_{in}\) can be written as:  
\[X_{in}\triangleq\prod_{i=0}^{l}stride_{i}\cdot X_{out}\pm\lfloor\frac{RF_{l+1 }}{2}\rfloor. \tag{5}\]  
When the cross-layer connections are deeper, the RF is more extensive, and there is considerable overlap between the input regions required for each region of the output feature map. There are two ways to cope with these overlaps. The first is to communicate and fuse each layer to avoid overlaps. Unfortunately, this approach introduces a significant communication overhead and can significantly reduce the parallelism of collaborative inference because of the waiting for each other. The second approach is to cut the data directly based on the computed overlap region size, which introduces additional computational overhead but improves parallelism. We adopted the second approach and propose an improvement of the padding approach.
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 7 Conclusions  
D2D is a promising technology that opens the doors for different use cases mainly during emergency situations, where direct communication is enabled by telecom operators. Using simulations, it was possible to define the required UE density level that guarantees  
Figure 24: Outbound traffic generated by notification messages sent by the Application Server.  
Figure 25: Inbound traffic generated by update messages sent to the Application Server.  
global connectivity for all participating users. Both the \"optimized tree\" and \"less cost\" approaches were evaluated in terms of the number of network updates and the level of the average CQI. It was concluded that the use of a CQI threshold equal to 1 combined with an optimization interval equal to 2 s can considerably reduce the number of optimization updates (by 35%) while the average CQI is kept close to the optimal values. The deployed approaches in this work are exclusively based on the CQI of the links between the participants. However, in an \"energy-aware\" context, a device with limited remaining autonomy should be placed as a leaf node by handling a minimal number of connections with its neighbors. Further work should therefore be conducted to create a more customized topology that takes into consideration user preferences and terminal capabilities.  
The traffic generated by the control messages was measured with simulations. The proactive approach was used to transmit notification messages from the server to all participants as soon as a topology update was reported. However, it might be possible to reduce this control traffic by opting for a reactive approach where the transmission of notification messages is carried out, on demand, by responding to a user's request. At the same time, update message traffic generated by users could be optimized by reducing the volume of the payload message. The partial update method can be used by describing changes in user neighborhoods (additions and removals) instead of sending the total description structure of all directly connected users.  
This research received no external funding.  
The source code of the simulator program used in this paper is openly available at: [[https://github.com/wajdielleuch/D2D_SIM](https://github.com/wajdielleuch/D2D_SIM)]([https://github.com/wajdielleuch/D2D_SIM](https://github.com/wajdielleuch/D2D_SIM)) (accessed on 1 November 2023). Upon execution, the simulation results are then generated in text format.  
The author declares no conflicts of interest.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 1 Introduction
Compared to previous methods [18, 19], the proposed FreANet demonstrates substantial restoration capacity with fewer parameters, achieving state-of-the-art performance on various datasets. Our main contributions are summarized as follows:  
* We propose a multi-domain feature extractor (MDFE) that selectively integrates spatial and frequency information for different types of degradation and is designed to operate appropriately in the corresponding domain. * To efficiently capture global information, we propose a multi-scale pooling attention (MPA) mechanism, which utilizes unidirectional pooling functions and convolutions with multiple strip kernels, thereby reducing the computational complexity of vanilla attention from \(\mathcal{O}(h^{2}w^{2})\) to \(\mathcal{O}(hw)\). * The proposed FreANet is evaluated on 11 benchmark datasets for various restoration tasks, demonstrating remarkable performance compared to other algorithms.
## 3 Materials and Methods
### Encoder-Decoder Architectures
#### 3.3.1 Encoder Architectures  
The encoder learns gradually during the feature-extraction process and reduces the spatial relationship with gradual convolution operations. The features learned by the encoder include low-level features such as edge, contour, and light, while high-level features have more semantic category information.  
**VGGNet as Backbone.** VGG is a deep learning network that combines the ability to perform 3 \(\times\) 3 convolution with less computational cost and the level of extracting complex features. It has gained this generalization ability thanks to the network in network architecture [28]. Therefore, it is considered a backbone option in segmentation applications.  
**ResNet as Backbone.** ResNet has a deep network architecture and requires a 7 \(\times\) 7 convolution kernel. It does not have a fully connected layer and performs average pooling operations that prevent overfitting [29]. This provides increased performance and improves precision. Therefore, Resnet is a priority architecture in backbone preferences.  
**MobileNet** as Backbone. To reduce the computational complexity of the VGGs, the Mobilenet network model was developed; it improves the real-time performance of computer vision tasks [30]. The Mobilenet generates six feature maps with different dimensions for the backend detection, which makes the network precise and prior to semantic segmentation tasks.
# Edge and Cloud Computing in Smart Cities
## 5 Challenges, Open Issues, and Future Research Directions
### Security, Privacy, and Trust Management  
The distributed nature of edge-cloud infrastructures introduces substantial security risks, as decentralized computing resources are exposed to a wide range of cyberthreats. Unlike traditional cloud environments that operate within tightly controlled data centers, edge computing infrastructures are inherently more vulnerable to attacks such as data breaches, man-in-the-middle interceptions, and distributed denial-of-service (DDoS) assaults. The integration of diverse computing nodes across public, private, and hybrid domains complicates the enforcement of uniform security policies and access control mechanisms [221; 222; 223].  
Data privacy concerns further exacerbate the security landscape, particularly in applications involving sensitive information such as healthcare records, financial transactions, and industrial control systems. The necessity to process user-generated data at the edge raises critical questions regarding data ownership, confidentiality preservation, and compliance with regulatory frameworks such as the General Data Protection Regulation (GDPR). Existing cryptographic techniques often introduce computational overhead that may be impractical for resource-constrained edge devices, necessitating lightweight encryption schemes and privacy-preserving FL models [224; 225; 226].  
The establishment of trust within heterogeneous edge-cloud environments remains an open challenge, as devices from different vendors, network operators, and service providers must interact within shared computational spaces. Blockchain and distributed ledger technologies have emerged as potential solutions for enhancing trust and transparency in edge-cloud transactions, yet scalability issues and consensus latency hinder their widespread adoption. Addressing these security and trust challenges requires a holistic approach that combines intrusion detection systems, access control frameworks, and secure multi-party computation techniques to fortify edge-cloud infrastructures against evolving cyberthreats [227; 228; 229; 230].  
To address growing security threats, future research should focus on blockchain-based security models, which provide decentralized trust mechanisms with verifiable immutability. Additionally, FL can be used for privacy-preserving analytics, enabling AI models to train on distributed data without exposing sensitive information. Lightweight encryption techniques should be optimized to enhance security with minimal computational overhead [231; 232; 233].
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
## 3 Methodology
### Operators  
This section shortly describes operators used to modify a solution, i.e., _change realization operator_, _change path operator_, _crossover operator_.  
Operator _change realization operator_ calculates a new solution for a given demand. For each demand we precalculate four sets of transponders capable of satisfying them. After choosing one of the precalculated sets, for each transponder \(t\in\mathcal{T}\) of the set one of the predefined paths is randomly chosen.  
Operator _change path operator_ changes a path assigned to a transponder. A new path is randomly chosen from a set of predefined paths. Thus _change path operator_ changes the selected path identifier allocated to the specific transponder to a random allowed value. Thus, if \(k\) paths are available, then _change path operator_ changes the path identifier to a randomly selected value taken from a set of \(\{1,2,\ldots,k\}\). In this study for each demand, \(k=3\) was assumed.  
For implementation of _crossover operator_, a uniform crossover [15] was used. In this application offspring chromosomes are created as a concatenation of demand vectors picked up randomly from parents. In this variant of crossover, the \(C\) genotype is formed from the parents' genes on the basis of the following scheme:  
\[C_{i}=\begin{cases}A_{i}&\zeta_{i}<p_{e}\\ B_{i}&\text{otherwise},\end{cases}\]  
where \(\zeta_{i}\) means the execution of a random variable with a \(U(0,1)\) uniform distribution, drawn for each \(i\) item in the genotype separately, and \(p_{e}\) is the probability of gene replacement. A typical \(p_{e}\) value is: \(p_{e}=\frac{1}{2}\). An example of the crossover, based on Polish network is depicted in Figure 2. In this specific example, the demand-two vector is subject to crossover, i.e., demand two for offspring has been swapped when compared to parents.  
Figure 1: Structure of a model. Each demand consists of \(k\) 3-element vectors; \(k\) may be different for different demands.
## 4 Discussion
### Limitations  
There are some limitations in our review. First, as this is only a narrative umbrella review, no solid conclusions can be drawn on the argument. Moreover, the study protocol was not published prior to conducting the study. Secondly, our search strategy led to a small number of articles with different methodological quality that might invalidate our conclusion. We only included studies written in English, so some relevant studies in other different languages may have been missed. More articles with a stronger methodological selection process are needed; moreover, the reviews often analyzed the same RCTs.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 4 Performance Evaluation of ISLT  
Nine BeiDou-3 medium earth orbit (MEO) satellites, which operated normally without phase or frequency modulation during the analysis period, were selected for joint timekeeping analysis. The nine satellites are equipped with five rubidium (Rb) clocks and four passive hydrogen masers (PHMs), as illustrated in Table 1.
## 5 Implementation of the \"Muting\" Safety Function in a Dual Architecture Based on sPLC
### Software Implementation in Dual Architecture  
After the software blocks are generated by the Matlab Stateflow tool, they can be imported into the TIA portal software tool. TIA portal is a tool for configuring and programming Siemens hardware components (the manufacturer of the used CPU is Siemens; more information about software TIA portal can be found in ref. [26]). After importing, these blocks must be called in the main program and assigned a specific physical and memory interface. Figure 8 shows the function blocks of the comparator and the \"Muting\" safety function in sPLC A. The same blocks are also implemented in sPLC B. The blocks in sPLC A and sPLC B differ only in the assigned interface.  
Figure 7: Block diagram of dual architecture based on sPLC.  
For sPLC A and sPLC B to work together, communication between them is necessary. The condition is that communication is two-way. Different types of communication come into consideration and the choice depends primarily on the sPLCs used. In the given application example, communication using the Profinet bus was used, and the communication mode was set to IO controller I device.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 3 Preliminary
### Attribute-Based Encryption Scheme
#### 3.1.2 ABE.Enc  
For a message \(m\), \(\omega\) as attribute set, and \(ABE.params\), ABE.ENC(\(m\), \(\omega\), \(ABE.params\)) returns the ciphertext \(CM\) as follows:  
* On input of \(k\in Z_{q}^{*}\), then output the key \(K=k\cdot PK\).
* Compute \(C=Enc_{K}(m)\).
* For \(i\in\omega\), compute \(W_{i}=k\cdot P_{i}\), respectively.
* Output the ciphertext \(CM=\langle\omega,C,\{W_{i}\mid i\in\omega\}\rangle\).
## 3 Experiments and Results
### Results in Summary  
In this study, we aimed to detect traffic-related objects from video footage, extract their behavioral features, and classify the frames by using data mining techniques such as k-means clustering and decision trees. We applied our system using a large video dataset from Osan city, automatically extracted object trajectories and frame-level features, determined the optimal number of frame clusters using the elbow method with the sum of squared errors, and evaluated the result by using a decision tree. As a result, the accuracy of our model is at 92.43% when the optimal \(K\) is 6 clusters.  
We then performed a boxplot analysis for single feature by cluster, scatter analysis for two features, and rule analysis derived from decision tree. While some clusters could be distinguished by boxplot, scatter plots provided clearer distinctions between the clusters. Rule analysis allowed us to interpret each cluster, and although three of the six clusters could be clearly identified as safe or dangerous situations, the remaining three clusters were more ambiguous in their potential risk to pedestrians. We also found that of our five initial features, only vehicle velocity, vehicle-pedestrian distance, and pedestrian velocity were needed to classify situations at a high level.  
Figure 15 illustrates an example situation from each cluster, and the three behavioral features used to classify them.