esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments
## 2 Material and Methods
### Research Methodology
#### 2.2.3 Analysis of Results
As we can see, the \"Fog Computing Concept\" category has the largest number of articles (38), followed by the \"Faster Responds\" (20) and \"Time, Delay or Latency Constraint\" (16) categories. Still in Table 2, we can analyze that the publishers IEEE, Elsevier, and Springer obtained more publications in the category \"Fog Computing Concept\", with 20, 5, and 5 articles published, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publisher** & **Article** & **Inproceeding** & **Total** \\ \hline IEEE & 19 & 39 & 58 \\ Elsevier & 11 & - & 11 \\ ACM & - & 8 & 8 \\ Others & 6 & 2 & 8 \\ Springer & 6 & 1 & 7 \\ Hindawi & 3 & - & 3 \\ \hline Total & 45 & 50 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Publications by type and publisher. The number of publications per year and category is shown in Figure 6. We can see that the articles belonging to the categories \"Fog Computing Concept\", \"Low Latency\", \"Time, Delay or Latency Constraint\", and \"Data Streaming Application\" had more publications in the year 2018 with 13, 6, 6, and 3 articles, respectively. On the other hand, the category \"Faster Response\" had more publications in 2017 (9 articles). Finally, we analyze the applications used by the authors to implement their proposals. Table 3 shows the number of published articles separated by categories and applications. It can be analyzed that the applications \"Health\" (18), \"Smart Vehicular\" (17), and \"Smart City\" (13) have the largest number of publications in the \"Fog Computing Concept\", \"Faster Response\", and \"Low Latency\" categories, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Publishers** & **Data Streaming Application** & **Faster Response** & **Fog Computing Concept** & **Low Latency** & **Time, Delay or Latency Constraint** & **Total** \\ \hline IEEE & 4 & 14 & 20 & 9 & 11 & 58 \\ Elsevier & 1 & 3 & 5 & 2 & - & 11 \\ ACM & 2 & - & 4 & - & 2 & 8 \\ Springer & 1 & - & 5 & - & 1 & 7 \\ Hindawi & - & 2 & - & - & 1 & 3 \\ MDPI & - & 1 & - & - & 1 & 2 \\ Chulalongkorn & & & & & & \\ University & - & - & - & 1 & - & 1 \\ CLOSER & - & - & - & 1 & - & 1 \\ IOP & - & - & 1 & - & - & 1 \\ SERSC & - & - & 1 & - & - & 1 \\ Taylor \& Francis & - & 1 & - & - & 1 \\ SAGE & - & - & 1 & - & - & 1 \\ \hline Total & 8 & 20 & 38 & 13 & 16 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Publications separated by publisher and category. Figure 6: Publications separated by year and category. The next section details the articles belonging to each of the categories.
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
## 6 Conclusions  
In this article, we experimentally demonstrated an industrial-use case of a DAS network based on RoF that can accommodate both a fifth-generation millimetre wave and sixth-generation terahertz services. The single-carrier modulated 6G signal using 256-QAM and a 5G new radio signal is experimentally broadcasted across a single mode fibre optic link of 10 km. Additionally, the 6G signal is received through a 3 m wireless medium at the 6G RAU. The limitations of the experimental setting forced the 6G THz link's wireless transmission distance to be 3 m with a net loss of 7 dBm in power. The performance is evaluated in terms of EVM and CSR. The dynamic range of the allowed RF input power for a 6G signal is 10 dB, while the dynamic range for a 5G waveform signal is 18 dB. Similarly,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **EVM [\%]** \\ \hline Proposed 5G RAU (only) & 2.5 \\ Proposed 5G RAU with 6G & 3.1 \\ Fi-Wi RoF (5G only) [16] & 7 \\ Fi-Wi RoF [40] & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary and comparison with the relevant state of the art at RF input power of 0 dBm.  
the enhancement of CSR resulted in a significant improvement in the BER. Additionally, as there are many photonic components used in this experiment, photonic integration is a crucial tool for the coexistence of the 5G and 6G services, which can be realized using silicon photonics. Although silicon photonics are an expensive solution, real-time digital signal processing-based solutions can be used to simplify some of the integration issues.  
Conceptualization, M.U.H.; methodology, M.U.H.; software, M.U.H.; validation, M.U.H. and G.M.; formal analysis, M.U.H.; investigation, M.U.H. and G.M.; resources, M.U.H.; data curation, writing--original draft preparation, M.U.H. and G.M.; supervision, M.U.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Authors would like to thank School of Engineering, Ulster University for the provision of financial support in realizing the experimental testbed.  
The authors declare no conflict of interest.
# Ultra-Wideband Compact Fractal Antenna for WiMAX, WLAN, C and X Band Applications
## 2 Antenna Design  
### Antenna Dimensions  
The suggested fractal antenna was produced by going through several iterations, using a circularly shaped patch as an initiator, and in each step square-shaped slots were introduced as shown in Figure 1. The FR4 dielectric was used as a support for the suggested antenna. The primary goal of designing the circular antenna was to make it resonate at a frequency of 3.6 GHz to provide coverage for both WiMAX and WIFI. To determine the antenna's size, Equations (1) and (2) outlined in reference [39] were utilized. Once the antenna dimensions were optimized to cover the desired frequency range, a radius of 12 mm was identified as the optimal size. The power was provided by a 50 \(\Omega\) line where the size was calculated by the transmission line model equation. The ground plane was partial with a gap line. The antenna was of a width (Ws) of 40 mm and a length (Ls) of 34 mm. Table 1 summarizes all suggested antenna sizes.  
\[\mathrm{R}=\frac{\mathrm{F}}{\sqrt{1+\left(\frac{2\mathrm{h}}{\pi\epsilon_{ \mathrm{r}}\mathrm{F}}\right)\left[\mathrm{ln}\left(\frac{\pi\mathrm{F}}{2 }\right)+1.7726\right]}} \tag{1}\]  
where:  
\[\mathrm{F}=\frac{8.791\times 10^{9}}{\mathrm{f}_{\mathrm{r}}\sqrt{\epsilon_{ \mathrm{r}}}} \tag{2}\]
# Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram
## 3 Materials and Methods
### Sinograms  
All sinograms, denoted by \(\widehat{f}(\rho,\theta)\) as in Equation (1), of the simulated IQ phantom described above were generated in MATLAB(r) R2019b (The Mathworks Inc., Natick, MA, USA). In particular, all sinograms involved had dimensions of \(119\times 180\) pixels, corresponding to 119 detectors and 180 angles (180 views over 180 degrees), respectively, with a bin size of 1.17 mm and 161 image slices. For the purposes of our Chebyshev and FBP-based simulations we created two types of sinograms: sinograms with equally spaced \(\rho\) for FBP reconstructions, and sinograms evaluated in \(\rho\) locations corresponding to the roots of the Chebyshev polynomials, as in Equation (15). The simulated sinograms were acquired for three noise levels (NL), taking into account 100% (NL1), 50% (NL2) and 20% (NL3) of the total counts, respectively. The noisy sinograms were generated by adding Poisson noise in ten realizations (\(R=10\)) at each of the three noise levels.
## 2 Preliminaries
### Elements Sorting Strategy  
In each iteration, it is essential to rank the elements for evaluation. Subsequently, we design the update strategy based on this ranking. Following the evaluation, we retain the elite elements while discarding the non-elite ones, ultimately identifying the optimal solutions.  
In MOFECO-MS, we employ two sorting methods for these scenarios. Firstly, we sort the elements within each cycle based on their forces when determining the update strategy. Secondly, we utilize fast non-dominated sorting [26] to rank the elements according to their dominance relationship after each evolutionary iteration.  
Since this paper entails two objective functions, each element possesses two distinct forces denoted as \(F_{ij,r},r=1,2\). Consequently, unlike the single objective function scenario,the element with the greatest force cannot be straightforwardly deemed the optimal element within the cycle. Herein, we adopt a sorting approach where we initially arrange the forces \(F_{ijr},i=1,2,\cdots,L,r=1,2\) in the \(j\)th cycle for each objective function in ascending order, yielding respective sequence numbers denoted as \(temp\). Subsequently, we compute the sum of sequence numbers for each element across both objective functions. By sorting these sum values, we identify the element with the highest ranking within cycle \(j\), denoted as \(\mathbf{x}_{ij,j}\). We collate all \(\mathbf{x}_{ij,j},j=1,2,\cdots,q\) into \(pop_{bc}\), structured as \(pop_{bc}=[\mathbf{x}_{ij,1},\mathbf{x}_{i,2},\cdots,\mathbf{x}_{ij,t},\cdots, \mathbf{x}_{ij,q}]\). \(pop_{bc}\) encompasses two element types: those within the same cycle as \(\mathbf{x}_{ij}\), termed \(\mathbf{x}_{bj}\), and those outside cycle \(j\), denoted as \(\mathbf{x}_{bj}\). This sorted collection is denoted as \(popsort\). The comprehensive sorting process is delineated in Algorithm 2.  
```
1:\(pop,F_{ijr},i=1,2,\cdots,L,j=1,2,\cdots,q,r=1,2\)
2:\(pop_{sort},pop_{bc}\)
3:\(temp\Leftarrow zeros(L,2)\)
4:for\(j=1\to q\)do
5:\([\sim,noF]\Leftarrow Sort(F_{ijr})\),
6:for\(r=1\to 2\)do
7:for\(i=1\to L\)do
8:\(temp(noF(i,r),r)\Leftarrow i\)
9:endfor
10:endfor
11:\([\,no]\Leftarrow Sort(sum(temp,2))\)
12:\(i_{b}\Leftarrow no(L)\)
13:\(pop_{bc}(j)\Leftarrow\mathbf{x}_{ij}\)
14:\(pop_{sort}((j-1)\times L+1:(j-1)\times L+L,:)\Leftarrow pop(no,:)\)
15:endfor
16:Return \(pop_{sort}\), \(pop_{bc}\)
```  
**Algorithm 2** The sorting process of the elements in each cycle  
Once the elements are sorted, we can identify the optimal element within each cycle based on the force order. Additionally, employing non-dominated sorting enables us to pinpoint the Pareto optimal solution, representing the solution with a non-dominant sorting rank of 1 [26] as the current optimal solution.  
Hence, elements can be categorized into different types, as illustrated in Table 3.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 5 Results and Discussion  
### Model Comparison  
The dataset is divided into two parts: training set and test set. The training set is used to train the weight parameters of the model, and the test set is used to evaluate the performance of the model. Five typical deep-object detection networks (YOLOv3, YOLOv5, YOLOv7, YOLOX and Faster R-CNN) are fully trained, using an asphalt pavement-distress dataset (100 training images) with a real-to-virtual ratio of 0:1, and the models are tested based on the test set (30 test images) to reveal the most discriminative network structure for asphalt pavement-distress detection. The loss curve of the training results and the MAP of the test results of YOLOv5 model with an input size of 640 pixels \(\times\) 640 pixels are shown in Figure 9. The loss value starts with a relatively high learning rate and gradually decreases with the increase of training epochs, and finally has reached convergence and stability at an approximately equal value after 600 epochs of training. In the fitting state, the peak value of the accuracy for the training set appears in the 960 th epoch, reaching 39.5%. Compared to the other four models, YOLOv5 has a higher average detection accuracy and a lower computational cost (single image processing time) of 0.0283 s/image. The transformer has recently shown outstanding performance in natural language processing and computer vision tasks such as object detection [72; 73; 74; 75]. The transformer-based encoder employs a self-attention mechanism to improve the detection performance. Unlike convolutional neural networks (CNNs) used in traditional backbones, the transformer can capture long-range dependencies between image features and thus provide more effective feature representation. We further replace the CNN layer of the backbone of YOLOv5 with a transformer to investigate the effectiveness of the transformer-based YOLOv5 architecture in pavement object detection tasks. The results show that the YOLOv5 network based on CNN has the best detection performance for the pavement distress dataset. The average detection accuracy of the transformer-based YOLOv5 model for the five pavement injuries is slightly lower than the that of the CNN-based YOLOv5 model, with an MAP of 38.5%. However, the training time of the model is about 40 times longer than that of the model of the CNN backbone. Because the encoders in the transformer are independent of each other and do not share parameters and the self-attentive mechanism in the encoder needs to calculate the interactions between all positions, a surge in the number of parameters and a longer training time ensues. The comparison of MAP and calculation time for different models is shown in Figure 10.  
Figure 9: MAP and Loss-epoch curves of the YOLOv5 model.
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
## 4 ETSI PoC
### B5G E2E Security Slice Reaction  
The reactive aspect of the use case is illustrated in Figure 9, showing the local and E2E reactions within the system. The reactive mitigation process follows the steps outlined in Figure 6. The STA, which monitors and analyzes the 5G core SBI, detects abnormal traffic in the AMF. After analyzing the traffic anomaly, a cryptomining alert is sent to the Decision Engine (DE), accompanied by a confidence level in the detection result. The DE subsequently updates the trust values for the STA, the affected NFs, and the domain in the Trust and Reputation Management (TRM) system. Specifically, the trust value for the STA increases, while the values for the affected version of the NF, Free5 GC, and the domain decrease.  
Upon receiving the alert, two countermeasures are generated. First, a local reaction is triggered, which redeploys a more reliable version of the vulnerable NF within the local domain. Second, an E2E reaction is initiated, which involves scanning other domains for similar vulnerabilities and applying appropriate countermeasures. This ensures end-to-end security compliance and addresses inter-slice security correlation.  
These reactive mechanisms enable the system to adapt swiftly to security threats without human intervention, maintaining compliance with SCLA requirements and ensuring continuous protection across the infrastructure.  
\begin{table}
\begin{tabular}{c c c} \hline
**Capability** & **UMU** & **TID** \\ \hline
5G core & ✓ & ✓ \\ \hline Channel protection & ✓ & ✓ \\ \hline Monitoring channel protection & ✓ & ✓ \\ \hline Monitoring and analysis 5G core & & ✓ \\ \hline Radio Access Network & ✓ & \\ \hline \end{tabular}
\end{table}
Table 1: Available domain’s capabilities.
## 3 Applications: Earthquake Early Warning in Kyrgyzstan  
Earthquake early warning systems (EEWSs) in their various forms are recognized as potentially valuable tools for mitigating against the risk associated with earthquakes. Until recently, most on-site EEWS have attempted the timely estimation of the incoming danger either through a rapid (and first order) estimation of the magnitude and location of the event, and then estimating the possible ground motion that the site will experience [15; 16], or by directly estimating the ground motion depending on the peak values of the ground displacement measured during the first few seconds of the first arriving P-waves (e.g., [17; 18]). During the last decade, in order to increase the efficiency of EEWSs, an end-to-end approach where the concept of early warning was coupled with the expected structural performance has been under development (e.g., [19]). Following this approach, early warning, structural analysis, and damage and loss analyses are combined within a performance-based framework upon which a decision-making procedure can be established (e.g., [20; 21]). Exploiting the computational power of modern sensing units, such as those used in the MPwise units, the implementation of this concept can be transferred to each unit, following a decentralized performance-based early warning scheme (see [6] Figure 4).  
The deployed instruments would thus be able to serve a number of functions, such as  
* Implementing a threshold-based on-site early warning system (OSEWS) that can also be employed following a decentralized approach for specific infrastructure;
* Monitoring the structural response, and its changes, of buildings and infrastructure in real time during an after-shock sequence;
* Assessing the expected damage to nearby structures soon after an event's occurrence;
* Estimating the overall expected damage to a target structure during the aftershock sequence;
* Validating damage forecasts determined by, e.g., probabilistic approaches, and updating the fragility curves based on recorded ground motion.  
Figure 3: The steps followed when the MPwise units are being employed for (**a**) decentralized on-site earthquake early warning [5] and (**b**) computer vision-based monitoring roles. Note the GFZ-Sentry application would be employed for the decentralized on-site early warning.  
The value of having the capacity for onsite processing is that for a particular site, a detected event may be analyzed and, if necessary, a warning issued more rapidly, than would be possible if the recordings had to be transmitted to a centralized processing center.
# Clover Dry Matter Predictor Based on Semantic Segmentation Network and Random Forest
## 2 Methods
### Dry Matter Prediction  
RF represents a potent machine learning methodology that is widely used in data analysis and prediction tasks. It is an integrated learning algorithm that makes predictions by constructing multiple decision trees and synthesizing their results.  
The basic principle of random forest is to achieve prediction by combining multiple decision trees. Each decision tree is a classification or regression model that classifies data into different categories or makes predictions of continuous values by progressively dividing the input features. Each decision tree in a random forest is constructed independently, by randomly selecting a subset of features and samples. This randomness makes each decision tree different, thus increasing the diversity of the model. In random forests, the random selection of feature subsets is achieved by randomly selecting a fraction of the features from all the features. This is carried out to reduce the correlation between features and to ensure that each decision tree is able to take into account different feature information. In terms of samples, random forest constructs the training data required for each decision tree by randomly selecting samples with put-back. Such sampling allows the model to have a certain degree of randomness while maintaining the characteristics of the data distribution, improving the generalization ability of the model.  
The configuration of the random forest model is depicted in Figure 5. In this case, the input data are the number of pixel values for each classification segmented by the DeepLabv3+ network, and the pixel values are used as input features, which are regressed by constructing different decision trees so that each one of them is regressed, and each one of them adopts a similar structure, but the diversity is guaranteed by the introduction of randomness in the training process. To construct each decision tree, we randomly select a different subset of samples and features and learn the model through a regression algorithm that maps the number of pixel values to the prediction of dry matter. Thus, each decision tree becomes adept at discerning the intricate and nonlinear correlation existing between the array of input pixel values and the outcomes related to dry matter content. After training all the decision trees, we use a combiner to summarize the prediction results of all the decision trees. The combiner uses a simple averaging strategy to determine the final dry matter prediction result.  
Figure 5: Random forest architecture.
# SPDepth: Enhancing Self-Supervised Indoor Monocular Depth Estimation via Self-Propagation
## 2 Related Work
### Self-Attention  
The self-attention mechanism has achieved great success in natural language processing. It has also been widely applied in many computer vision tasks, such as image semantic segmentation [30], image classification [31] and object detection [32]. The CNNs were frequently replaced by a self-attention-based Transformer [33] and its variant Swin Transformer [34] in monocular depth estimation [35; 36; 37; 38; 39; 40]. A Transformer was used for feature enhancement by some self-supervised works such as GasMono [41] and Lite-Mono [42]. Similar to the work [43], ADAADepth [44] and CADepth-Net [45] introduced a self-attention module after the ResNet encoder to explore global information and predict depth for noncontiguous regions. Although self-attention modules have been embedded in the mentioned works, there are few self-supervised methods directly using depth as input to the self-attention layer. Feature self-similarities of the image are not well exploited to perform a weighted optimization of depth.  
Similar to depth, optical flow field also shares high structural similarities with the image. Inspired by Transformer, a global motion aggregation module based on self-attention was introduced in [46]. The globally aggregated motion features helped propagate the flow from matched pixels to unmatched ones. GMFlow [5] added a self-attention layer after the obtained optical flow. The operation also led to an improvement for unmatched pixels. Based on GMFlow, UniMatch [6] extended the optical flow task to stereo matching and stereo depth estimation. UniMatch validated the effectiveness of self-propagation on stereo depth estimation. However, it belonged to supervised depth estimation using the posed stereo images instead of a single image. There are few works exploring the effect of depth self-propagation on a single image in a self-supervised manner.  
Inspired by the abovementioned optical flow estimation works, we propose to enhance self-supervised indoor monocular depth estimation with self-propagation. The proposed framework is called SPDepth.