esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 4 Results
### Method Comparisons  
Theoretically, the convergence error of the cost function decreased in each iteration of model training, as detailed in Figure 15. As presented in Figure 15, the convergence error gradually converged and decreased with the increasing number of iterations (epochs) when three of the prediction models (TCN, LSTM, and GRU) were applied; the RNN model convergence error did not converge, and the prediction error did not decrease. RNNs are thus not suitable for wind power forecasting from large amounts of temporal-spatial data series inputs. The convergence error of the TCN model decreased more than that of the LSTM close to the twentieth epoch, and continued to decrease steadily with the increasing number of iterations. The prediction error of the TCN model decreased most steadily among the four models, followed by LSTM and then GRU.  
The performance of the four prediction models was affected by the varying amounts of input training data. Therefore, performance analysis must be assessed with different amounts of historical data for long-term prediction. In our experiment, different amounts of historical data were used for model pretraining and the output results of the modules were sorted; the stability comparison of the convergence errors is detailed in Table 10.  
Figure 14: 72-h wind power forecasting (Experiment II).  
Figure 15: Convergence error of the cost function.  
With increasing amounts of historical data, the prediction error (MAPE) of the TCN-based model decreased significantly, and the 72-h forecast error of the one-week, one-month, and one-year training datasets was 66.43%, 10.93%, and 5.13%, respectively. In the experiment, the TCN model exhibited consistent, stable and good prediction results by using selected parameters from the DE algorithm.  
In summary, the wind power forecast error (MRE) of the proposed TCN-based model was near 5.13% based on one-year historical data in different climatic scenarios. Compared to the accuracy of other projects in wind power forecasting, the European team's SafeWind project in 2011 achieved a forecasting error of 17%. In 2017, the predicted error improved to 11%; a project of the BSI electric power company reached within 10% in 2019. As shown in Table 11, the proposed TCN-based approach provides a lower prediction error with higher prediction accuracy than those of real projects in studies of wind power forecasting [12; 13; 31].
## 2 Overview
### Wireless Communication Technologies  
WSN physical topology and density are entirely dependent on the applications [32], so the design and deployment of a WSN should consider its environment and application. A number of sensor nodes are densely deployed to improve data accuracy and achieve better system connectivity. However, a dense deployment of sensor nodes has some disadvantages: high energy consumption, data collisions, interferences, _etc._[33]. WSN nodes normally have three typical kinds of network topologies: star topology, cluster/tree topology and mesh topology, as shown in Figure 3.  
(1) _Star topology:_ A star topology is a point-to-point single-hop architecture in which each sensor node connects directly to a sink node. It potentially uses the least amount of power among the three topology architectures.  
(2) _Mesh topology:_ A mesh topology is a one-to-many multi-hopping architecture in which each router node connects to multiple nodes. Its advantages over a star topology include a longer range distance of transmission, decreased loss of data, and a higher self-healing communication ability. However, its disadvantages are at the cost of higher latency and higher power consumptions.  
(3) _Cluster/tree topology:_ A cluster/tree topology is a hybrid star-mesh architecture. It takes advantage of the low power consumptions and simple architecture of a star topology, as well as the extended range and fault tolerance of a mesh one. However, there probably exists some latency.  
The right and reasonable choice of network topology depends on the amount and frequency of data to be transmitted, transmission distance, battery life requirements and mobility of the sensor node [34]. It should be noted that a WSN physical topology may change due to available energy, position variations of nodes, malfunction, reachability (due to noise, severe weathers, moving obstacles, _etc._), and task details of sensor nodes [35].  
A sensor node normally incorporates a radio module for wireless communication. The transmitted distance of wireless communication can be anywhere between a few meters (Bluetooth, ZigBee, WiFi, _etc._) and thousands of kilometers (GSM or GPRS radio communication). Wireless communication has various standards and technologies including Bluetooth, ZigBee, WiFi, GSM, GPRS and WiMAX.  
Figure 3: General WSN network topologies.  
Table 2 provides a summary and brief comparison of these communication technologies. Usually, two or more wireless communication technologies are used in a real wireless sensor network. In particular, underwater acoustic communication technologies can be a good choice for data collection and exchange among underwater sensors [36; 37; 38].  
Generally, the longer the range a radio module must transmit, the more energy consumption a radio module will have. The choice of a wireless communication technology depends on the amount and frequency of the transmitted data, transmission distance, and amount of available energy.
## 5 Performance Evaluation
### Impact of the Number of \(\alpha\)-Rounds of Data Transmissions  
The saturation throughput and energy efficiency of the evaluated mechanisms as a function of the number of \(\alpha\)-rounds of data transmissions from \(\alpha=1\) to \(\alpha=10\) are presented in Figure 5a and Figure 5b, respectively. As it can be seen in Figure 5a, the throughput of the mechanisms increases as the value of \(\alpha\) increases, since more data frames can be sent in each channel access attempt. The maximum throughput is achieved for \(\alpha=10\), although the throughput increases slightly after \(\alpha=5\). This occurs because the contribution of the reduced channel access overhead to the total transmission time becomes marginal as compared to that of the data transmission time. For this reason, the maximum throughput improvement of DCF, PSM, and TXOP PSM between \(\alpha=1\) and \(\alpha=10\) is up to 48%. In addition, an increase of up to 44% in energy efficiency for DCF and PSM between \(\alpha=1\) and \(\alpha=10\) can be seen in Figure 5b.  
As it can also be seen in Figure 5b, the energy efficiency of TXOP PSM significantly increases between \(\alpha=1\) and \(\alpha=10\) and as compared to DCF and PSM. The increase of TXOP PSM with \(\alpha=10\) when compared to TXOP PSM using \(\alpha=1\) is up to 483%. The energy efficiency of TXOP PSM can also increase by up to 306% from \(\alpha=1\) to \(\alpha=10\) when compared to DCF and PSM. The reason for these results is that, as more data frames can be sent in a single channel access attempt, the STAs can sleep for a longer period and save more energy.
## 3 Results
### The Effect of DE on the Hydration Process of Cement Mortar
#### 3.5.4 Effect of DE on the Pore Structure of Cement Mortar  
Figure 12 and Table 3 present the pore structure test results of internally cured mortar with DE. As can be seen from Figure 12a, when the water-binder ratio is 0.35, the pores of each sample are mainly distributed in gel pores below 0.1 \(\upmu\)m and macropores above 100 \(\upmu\)m. The presence of macropores may be caused by air pores introduced during the mixing process of the mortar or residual DE pores after water release.  
Figure 12: Effect of diatomic internal curing on pore structure of mortar. (**a**) Pore size distribution integral curves; (**b**) pore size distribution differential curves; (**c**) integral curves of pore size distribution at different ages; (**d**) differential curves of pore size distribution at different ages.  
As can be seen from Table 3, with the increase in DE content, the porosity of cement paste at 28 d age increases, but when the amount of DE is relatively large, the average pore size is relatively low. This indicates that the introduction of DE into cement mortar can increase the porosity within the cement paste, but it can also refine the pore structure. Combining Figure 12 with Table 3, it can be seen that as age increases, there is a decreasing trend in both the porosity and average pore size of the slurry. As shown in Figure 12d, the pore structure within the range of 10-100 nm gradually decreases with age. The reason is that, during the hydration process of the mortar, the relative humidity inside the slurry decreases, and the water pre-stored in DE is slowly released, enabling the hydration reaction to continue. The produced hydration products gradually fill the capillaries, refining the pore structure [38].
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Chaotic Encryption  
[PERSON] et al. [250] proposed an encryption algorithm using key-controlled neural networks, where key generation is based on the chaotic Lorenz system. The algorithm is designed for digital traffic encryption, and cryptographic analysis demonstrated that it performs on par with or better than existing algorithms.
## 4 Proposed Architecture
### Cloud Layer  
The research took advantage of cloud services by allowing all the public management and access to the implemented microservices resources to be managed. Main message hub services were fully managed in the cloud to control data transmission through queuing discipline. Database servers were deployed to keep persistent data for further usage. Overall web services to allow data access and user permissions at different user services were also managed at this layer. Finally, the data analytics were performed over the cloud to use cloud infrastructures that were limited at the edge node.
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 2 Materials and Methods  
### Research Objectives and Research Questions  
The primary research objectives of this study were as follows:  
* **RO1**: Systematically identify and analyze existing taxonomies and systematic literature reviews (SLRs) on cyber ranges published between 2014 and 2024.
* **RO2**: Evaluate convergences and divergences within existing cyber range taxonomies.
* **RO3**: Assess the impact of the recent literature (2019 to 2024) on existing cyber range taxonomies, determining whether an updated taxonomy is needed. These objectives guided the formulation of the following research questions:
* **RQ1**: What existing taxonomies and systematic literature reviews (SLRs) on cyber ranges were conducted between 2014 and 2024?
* **RQ2**: Where do existing cyber range taxonomies converge and diverge?
* **RQ3**: What influence have recent papers (2019 to 2024) had on current cyber range taxonomies, and is there a need for an updated taxonomy?
## 2 The Model
### Description of the ME-ANTS Algorithm
```
1 FunctionME-ANTS(\(G(V,E,w)\), \(\alpha\), \(\beta\), \(K\), \(\rho\), \(T_{d}\), \(T_{max}\), \(n\), \(\beta\));
2 P pheromone matrix of size \(|E|\times|E|\); each entry \(P_{ij}\) is initialized to 1 ;
3\(I\) Boolean matrix of size \(|E|\times|E|\); each entry \(I_{ij}\) is the information used to compute the desirability \(\eta_{ij}\) (Equation (6)) and it is initialized to 0 ;
4\(num\_exited\gets 0\);
5\(path\_costs\gets 0\);
6 For each ant (\(a_{k}\)) create an empty list: visited edge \(L_{\alpha_{2}}\);
7\(\Delta=\{S_{1},\ldots,S_{\delta}\}\) partition into groups of the ants of the population;
8\(t=0\) /* Global time variable * / ;
9\(h=1\);
10 while\(t\leq T_{max}\)do
11 if\(mod\ |V|==0\)then
12 apply the global updating rule in Equation (2);
13 foreach ant \(a_{1}\) in the group \(S_{\delta}\)do
14\(c\_e\leftarrow\texttt{Ant-Movement-Async}(k,S_{b},D)\);
15\(path\_costs\gets path\_costs\cup\{c\}\) ;
16\(num\_exited\gets num\_exited+e\);
17
18
19 end if
20\(h=h+1\)
21 end while
22\(t=t+1\)
23
24 end while
25 returnnum,exited,mean(path_costs);
26 FunctionAnt-Movement-Async(\(k\), \(S_{b}\), \(tm\));
27 /* The \(k\)-th ant in the group \(S_{\delta}\) enters in the network from the node \(v_{1}\) at global time \(tm\) */ ;
28\(\pi\leftarrow\emptyset\);
29\(v_{i}=v_{1}\);
30 while\(v_{i}\
eq v_{m}\) is not the exit node and \(t\leq T_{max}\)do
31 Select a node \(v_{i}\) adjacent to \(v_{i}\) using Equation (1) by taking into account P and \(I\) ;
32 Move the k-th ant from \(v_{i}\) to \(v_{j}\) ;
33\(\pi\leftarrow\pi\cup\{(v_{i},v_{j})\}\);
34\(\tau_{ij}\leftarrow\tau_{ij}+K\);
35\(l_{ij}\gets 1\) ;
36\(v_{i}\leftarrow\tau_{ij}\);
37 Wait on the node \(v_{i}\) until \(t=tm+10*w_{ij}\);
38\(tm=t\)
39
40 end if
41\(exited\gets 0\);
42 ifthe k-th ant is on the exit node \(v_{m}\)then
43\(exited\gets 1\);
44
45 end if
46
47 Calculate the cost of the path \(\pi\) using Equation (8);
48 returnpath_cost,exited;
```  
**Algorithm 1**ME-ANTS Pseudocode
## 2 Materials and Methods
### Observation Array Design  
To increase exploration depth and improve field data acquisition efficiency, we designed a large-spacing multi-channel observation array, as shown in Figure 2.  
During field operations, the measurement electrode array \(P_{1},P_{2},\ldots,P_{n-1},P_{n}\) of the multi-channel receiving system is first deployed along the survey line. The current electrode pair \(AB\) is progressively expanded outward from the center of the measurement array, with all adjacent electrode pairs simultaneously receiving signals during each current injection. As the \(AB\) pair moves from the inner to outer sections of the array, the electrode spacing increases arithmetically within the measurement array to acquire shallow geoelectric information, while outside the measurement array, the current electrode spacing increases by multiples of the measurement electrode interval. For longer survey profiles, multiple measurement arrays need to be deployed sequentially to complete the survey.  
During the first measurement array deployment, the current electrode spacing \(AB\) is gradually increased from minimum to maximum (up to \(A_{n}B_{n}\)), completing the first array survey. Subsequently, the entire measurement array is advanced forward by one array length, and the current electrode spacing \(AB\) is progressively decreased from maximum to minimum (down to \(A_{-i}B_{-i}\)) to complete the second array survey. This alternating process continues until the entire profile is surveyed. The actual key configuration parameters of the exploration array are shown in Table 3.  
By systematically increasing the current electrode spacing, measurement results reflecting the electrical property variations from shallow to deep subsurface media are obtained. The resistivity amplitude spectrum and chargeability are then inverted to yield 2D inversion sections depicting the subsurface distribution of electrical properties and induced polarization characteristics.
# A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
## 1 Introduction  
The central nervous system, in particular the cerebrum, plays a critical role in regulating numerous physiological functions such as memory, movement, cognition, and speech, as well as autonomic control of the operation of vital organs. However, severe disruptions or pathologies affecting the brain can have fatal consequences. A stroke is an acute medical condition that requires prompt attention [1; 2]. According to the Global Stroke Organization [3], 5.5 M will die from strokes, and 13 M will suffer from a stroke annually. Stroke is the leading cause of death and functional impairment globally, so it significantly affects all facets of life [4]. Anyone at any age, regardless of gender or physical condition, can be affected by stroke [5]. There are two types of cerebral strokes: ischemic and hemorrhagic, where the damage can be temporary or extreme, ranging from mild to severe. Hemorrhages are rare and entail a brain hemorrhage occurring by the blood vessel rupture. The most frequent kind of stroke, an ischemic stroke, occurs when an artery becomes narrowed or blocked, preventing blood from reaching a specific brain part. Cerebral strokes are the sixth most common cause of mortality in the United States and the fourth most common cause of death in India. The 795,000 cases/year frequently result in permanent disability in the United States [6; 7]. The India Collaborative Acute StrokeStudy (ICASS) statistical report indicates that more than two thousand persons in India had strokes in 2004 [8]. According to statistics from the Stroke Association, five out of every 100,000 children in the United Kingdom experienced a stroke in 2012 [9].  
On the other hand, the overall stroke mortality rate in Canada was more than 15 thousand in 2000 [10]. Myocardial infarction increases the risk of stroke, such as heart conditions that increase the probability of stroke cases [11; 12; 13]. A healthy and balanced lifestyle can help avoid stroke by keeping an appropriate weight index, an optimum glucose level, and good heart and kidney function. Additional good practices include abstaining from alcohol and tobacco, maintaining a healthy weight, and exercising regularly. Prompt detection and proper treatment are necessary to prevent brain injury and associated consequences in other body areas. Magnetic resonance imaging (MRI) and computed tomography (CT) investigations are frequently used in early stroke identification.  
The paper contains five sections, including the introduction section. Section 2 summarizes the recent studies of cerebral stroke with their results. The proposed methodology with machine learning algorithms is explained in Section 3. Section 4 provides the implementation of the methodology and the tuning ensemble for cerebral stroke prediction with the experimental results. Finally, the paper concludes with an explanation of the results in Section 5.
## 5 Performance Comparison and Validation  
In this section, we compare and analyze the performance of the proposed Double Q-value Adaptive Aggregation Routing Protocol (DQAAR) in terms of energy consumption, network lifetime, transmission delay, and data retransmission energy loss. At present, there is little research on multi-objective optimization of high-speed railway wireless sensor network delay and life cycle. Due to MATLAB's powerful computational capabilities and rich reinforcement learning toolbox, the improved Q-learning algorithm proposed in this paper can be easily implemented. Therefore, this paper uses MATLAB R2018b to realize the simulation environment and compares it with the other three excellent routing protocols [38]. They are MTeck, AUMRP, and MVECR.
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 2 Materials and Methods
### Validation Study  
The graph-based segmentation algorithm was applied to a de-identified dataset comprising 25 macular OCT images healthy subjects. The algorithm segmented 7 retinal boundaries and determined the thickness of 6 layers. Gaussian filters were used for initial denoising. Manual segmentation was performed by an expert clinician using a custom designed graphical user interface (GUI). The layer thickness values obtained from manual segmentation were used to validate the efficacy of the automated segmentation by comparing the mean differences between the thickness values obtained from automated and manual segmentations, respectively, for each layer across the entire dataset. There were three primary steps in the validation study, as described in detail in the subsequent sections below.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 5 Conclusions  
In this paper, we constructed a new prediction underwater acoustic transmission loss method based on the DBN neural network. The new method solves the problem that the traditional underwater acoustic transmission loss prediction cannot accurately and adaptively select the underwater acoustic transmission loss model in the complex and changeable marine environment.  
First of all, based on the characteristics of the DBN network, we designed a DBN model according to the marine environment and signal characteristics. We studied the applicable boundary conditions of various underwater acoustic transmission loss models ray model, normal model, fast field program model, parabolic equation model). Based on these conditions, we constructed rules to generate datasets for training and testing DBN neural networks. Then, we evaluated the accuracy of the DBN neural network model and obtained the accuracy of the model adaptation, which was 94.86%. It shows that the recognition rate was already very high. Finally, the new method was verified by a certain sea trial data in 2020. The results showed that the new method achieved accurate prediction of the underwater acoustic propagation model in a complex ocean environment, and the average RMSE error of the prediction results under different array elements was less than 6.5 dB. The new method reduces the difficulty in matching underwater acoustic transmission loss models in complex marine environments and improves the speed and accuracy of underwater acoustic transmission loss calculations. The new method can accurately calculate the transmission loss of multi-band signals under different marine environmental conditions and has the generalization ability for the calculation of transmission loss in a wide range of sea areas.  
Conceptualization, Y.Z. and M.W.; Data curation, H.X. and B.Q.; Investigation, Y.Z. and B.Q.; Methodology, Y.Z. and Y.G.; Project administration, Y.Z. and H.X.; Resources, M.W., Y.G., and H.X.; Software: Y.Z., H.X., and B.Q.; Supervision, Y.Z. and H.X.; Validation, Y.G., M.W., and H.X.; Writing--original draft, Y.Z. and B.Q.; Writing--review & editing, Y.G. and M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.