esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Biocompatibility and Biodegradability
Other advantageous properties include: acceptable stability of 3D networks, elasticity, flexibility, and superior biocompatibility due to controllable drug release [45]. The amount of cross-linking agent directly influences DDS properties. High amounts might lead to significant toxicity, while low quantities lead to the fast degradation and burst release of the desired drug [45]. Other biodegradable/biocompatible materials that are extensively used for molecular imprinting include PEG, lactic acid and poly-lactic glycolic-acid [45]. Fully degradable PLGA-based MIPs were designed by [PERSON] et. al. for the recognition and delivery of biotin. Their good biodegradability not only provides adjustable release kinetics and ensures complete drug release upon hydrolysis, but also lead to superior biocompatibility as a consequence of their lack of accumulation [6]. PEG is another synthetic macromolecule frequently used as a biocompatible scaffold for drug delivery. However, conflicting data about the impact PEG coating on the biocompatibility of the NPs is found in the literature. For example, one study showed that PEG1100-coated NPs induced mitochondrial toxicity, most likely due to its different conformation compared to PEG4000, which has proven to be very biocompatible [51]. Another topic that has recently been studied in connection to MIPs' biocompatibility is the influence of PC. It has been recurrently shown that the in vivo fate and biological function of the nanocarri relies on PC formation. The in vivo applications of MIPs should always consider PC formations [7]. According to a recent study, the formation and composition of PC is a key factor in the clearance of NPs from the organisms, rendering such studies highly recommended in the future [7]. The PC can coat and shield the nanocarri; therefore, its release profile could be severely altered. PC could drastically reduce the burst effect that comes from desorption of surface-adsorbed drugs. However, some reports related to polymeric nanoparticles mentioned that PC only slightly altered their drug release profile [52].
## 3 Description of the Maritime Sensor Network in the Gulf of Guinea
One-hour difference is due to the difference between local time and UTC time used in the creation of the file name and should be neglected. The important information lies in difference between minutes and seconds. In this case it is 86.58 s, which is a time needed to completely collect, process and transmit the data at the HFSWR level. Of course, this is just an example and actual times can vary, but it provides an explanation regarding time stamps which are discussed in the following sections. The C2 center performs fusion on HFSWR data received from both HFSWR locations. Bandwidth assigned for the HFSWR data transmission is 128 kb/s, where 64 kb/s is assigned for control commands, and the other 64 kb/s is assigned for the transmission of detection files. Naturally, the file size impacts the file transfer time. Thus, detailed distribution of the file size analyzed over the period of one year is shown in a form of histogram in Figure 6. Please note that file size depends on various factors such as density of traffic in the HFSWR coverage area, and number of false detections, which depends on weather conditions, and ionospheric interference. Given the distribution of the detection file sizes shown in Figure 6, the total transfer of detection file can take up to several seconds regarding that the assigned link capacity for detection file transfer is 64 kb/s. It should be noted that transfer duration is not as critical as reliability of transfer, since the lag of several seconds means nothing to the overall data fusion process at the C2 center (please see [15]). However, data missing or long lag due to the link failure or bad weather conditions may put the data fusion process in a difficult position. For this reason, TCP protocol is used for the detection file transfer, and TCP flow and error control can affect the file transfer time. In case of bad satellite link conditions frequent packet retransmissions can occur, which lead to an increase of the file transfer time. Henceforth, this paper will examine performance of the IoT OTH maritime surveillance service in various meteorological conditions. In Figure 6, it can be seen that the majority (over 80%) of the detection files are between 5 and 35 kB in size. For this reason, in the remainder of the paper we have provided the analysis for the two most frequent detection file size ranges: 15-25 kB and 25-35 kB. The conclusions derived for these two ranges apply to other detection file size ranges as well. Figure 6: Distribution of detection (cfar_reg) file sizes.
## 4 Multi-Objective Optimization Framework
### Algorithmic Process of EDMOA  
The EDMOA algorithm initializes population parameters, including population size \(N_{\text{pop}}\), maximum iteration count \(T_{\text{max}}\), and key operational parameters. It initializes the output power \(P_{\text{Grid}}\) of the high-speed railway power grid across 608 consecutive 10 min intervals, along with the operational power \(P_{\text{ESS}}\) and capacity \(E_{\text{ESS}}\) of the energy storage system. Upon execution, an initial population \(P_{0}\) is generated via an enhanced logistic circle composite chaotic mapping, where each individual \(X_{i}\) is systematically distributed within the solution space. A pre-computed random number pool \(R_{\text{pool}}\) is established to optimize computational efficiency throughout the optimization process. During initialization, an elite archive \(E\) containing the top 5% of the population is created, and the fitness \(f(X_{i})\) of each candidate is evaluated to identify the global optimum \(X^{*}\) and its fitness \(f^{*}\). Within the main iteration loop, dynamic parameter adjustments are first executed per generation: the learning rate \(\alpha(t)\) decays exponentially, while the crossover rate \(P_{c}(t)\) adaptively adjusts according to real-time population diversity \(\sigma(t)\) using predefined formulas. The core evolutionary phase integrates hierarchical pursuit mechanism of GWO with spiral encircling strategy of WOA. The GWO component generates dynamic search vectors, while the WOA constructs spiral trajectories. Intermediate solutions are produced through differential mutation and subsequently fused with the original DMOA strategy via a time-varying hybrid coefficient \(\eta(t)\), forming new candidate solutions.  
Following hybrid evolution, the algorithm initiates trust-region local search, conducting precision probes along five symmetric directions centered on \(X^{*}\). The trust-region radius \(\Delta(t)\) is dynamically adjusted: it is expanded by 1.2\(\times\) upon success or contracted by 0.8\(\times\) upon failure. Every 100 generations, chaotic population renewal is executed using time-varying parameter \(r\) to reorganize the worst-performing 10% of individuals, continuously injecting diversity. The elite archive \(E\) persistently retains high-quality individuals, while roulette wheel selection is applied to the hybrid population to maintain exploration-exploitation equilibrium. Convergence is monitored continuously; the algorithm terminates early if the global fitness variation \(|f^{*}_{t}-f^{*}_{t-50}|<\epsilon\) (\(\epsilon<10^{-8}\)) persists for 50 consecutive generations, or when reaching \(T_{\text{max}}\). Final outputs include \(X^{*}\) and semi-logarithmic convergence curves. A parallel evaluation framework accelerates fitness computation, achieving optimal balance between search efficiency and solution quality. The flowchart is shown in the Figure 5 below.
## 5 Experimental Methodology
### Pipeline Leak Detection
#### 5.1.2 Methodology  
The computer-generated data are split into training and test data, with the training data consisting of 75% of the observations, all occurring before the leak is introduced. The test set, on the other hand, is 50% normal data and 50% leaks. We created three different versions of the computer-generated data, with leaks amounting to 2%, 5%, and 30% of the flow being simulated.  
The lab-generated data are first split as follows: the training dataset consists of 75% of the \"normal\" observations (converted to delay embeddings and pooled across all of the experimental conditions), selected in chronological order beginning with the earliest observations. The out-of-sample test sets combine either the \"2 mm Leak\" or \"3 mm Leak\" observations with the remaining normal examples (similarly embedded and pooled). A validation set consisting of 10% of the training data is split off for use in parameter exploration for the normal model. Once the best parameter set is found, the normal model is re-trained using those parameters on the full training set. The anomaly detector is then trained on the full training set (again, consisting only of normal data), and its performance is finally measured using the out-of-sample test sets.  
Based on the existing literature, the candidate deep architectures for the normal model were narrowed down to 1-dimensional Convolutional Neural Networks (1-D CNNs) and the Long Short-Term Memory (LSTM) architecture. These are well-known as highly accurate models in time-series forecasting. The 1-D CNN model consists of two Conv1D layers with 32 and 64 filters, strides equal to 1, and the same padding. BatchNormalization and Maxpooling1D are performed after each layer; dropout is performed on the last layer before the fully-connected output layer. Regarding the LSTM model, it includes 2 LSTM layers each containing 20 units and a fully connected output layer. The validation set error is measured in terms of the Root-Mean-Square-Error (RMSE).  
The Isolation Forest ([PERSON], 2007) and One-Class SVM ([PERSON], 2007) are our candidate Anomaly Detector components. Parameter exploration was again performed, but this time, the training classification error was used to determine the best parameterization (the goal being to classify the training set as normal, with no errors). The AD's performance based on the final out-of-sample dataset is measured in terms of the True Positive Rate (\(TPR\)) and the False Positive Rate (\(FPR\)). Define True Positives (TPs) as the number of leaking observations correctly classified as leaks, False Positives (FPs) as the number of no-leak observations erroneously classified as leaks, True Negatives (TNs) as the number of no-leak observations correctly classified, and False Negatives (FNs) as the number of leaky observations erroneously classified as no-leak. \(TPR\) and \(FPR\) are given by the following:  
\[TPR=\frac{TP}{TP+FN} \tag{3}\]  
\[FPR=\frac{FP}{FP+TN} \tag{4}\]
## 4 Case Study and Applications
### A Regression Problem: Prediction of the Electrical Consumption of Institution Buildings  
In order to achieve the performance required for the application to make meaningful predictions about energy consumption, we propose two Spark-based model implementations for useful EC predictions over a given time horizon.These include, for example, hourly power consumption during a month, as the one presented here, which can be run on both CPUs and GPUs to leverage the full potential of the implementation in terms of performance.  
Several studies have provided solutions to the problem of predicting EC in buildings using EAs and ANN. Nevertheless, the principal disadvantage of these techniques is that they exhibit an unreliable response time. To date, a number of approaches have been proposed in a effort to address this challenge, as evidenced by the work presented in [27]. Consequently, there is a significant scope for further research in this field. The paper by [1] presents a number of GPU-based EA implementations, exploring the impact of different data structures, configurations, data sizes, and complexities on the problem solving process.  
The two variants DSPSO and DAPSO, implemented in Scala/Spark in this study were employed to address a 24-h EC prediction problem in buildings, which represents one of the fundamental objectives of this research. To evaluate their performance, we used the PSO algorithm as a baseline and the Spark tool to train a perceptron-type neural network to predict the EC of a set of buildings (Figures 5 and 6) at the University of Granada (UGr), Spain. The performance of both implementations of the algorithm was evaluated by measuring the execution time required to run each of them. To this end, an abstraction was employed for each benchmark, whereby a monotonic clock was used for all measurements. This approach ensured that the time was always recorded in a forward direction and was not affected by hardware issues, such as time skew.  
Figure 5: Prediction with DSPSO implementation of the hourly power consumed during the month of January 2024 by a group of UGr buildings.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Hybrid Protocols  
This category corresponds to protocols whose MAC mechanisms are based on a mix of protocols belonging to the other categories previously discussed in this section. The ones already mentioned and that also match this category are SS-CRDSA, MF-CRDSA, FC-TDMA, and R-Aloha; all of them employ a mix of fixed allocation techniques in conjunction with random access mechanisms.  
Figure 12: Example of operation of enhanced spread spectrum Aloha (E-SSA) protocol. The sliding window of length \(W\) is shifted in \(\Delta W\) after performing the iterative process of interference cancellation (ISIC) in the current window. Figure adapted from [47].  
Aloha-LoRa  
Despite not being reported in satellite environments, another protocol that is worth mentioning due to its wide use in LPWA technologies is the one employed in LoRa. In this technology, the bandwidth is divided into several channels, where the number is dependent on the regulation (e.g., 13 channels for the industrial, scientific and medical (ISM) 902-928 MHz band under federal communications commission (FCC) regulation). In these channels, nodes transmit modulating the signal with the chirp spread spectrum (CSS) and make use of different spreading factors (SF). The channels are further divided into 6 subchannels (from SF = 7 to SF = 12). The MAC behavior depends on the LoRa class of device:  
* Class A: The lowest power consumption type of device. Transmits data when is necessary using Pure Aloha. To receive messages from the gateway, a listening window is open after each transmission.
* Class B: For these devices there is a schedule to transmit, which is defined through beacons.
* Class C: These nodes are always in reception mode, except during transmission.  
In the case of Class A nodes, the MAC protocol performs a fixed allocation of resources (in bandwidth and code) together with a random access scheme [14].  
To summarize our review, Figure 13 presents a taxonomy elaborated with the MAC protocols under study. Furthermore, the main characteristics and performance metrics when the MAC protocols are evaluated in a satellite environment are presented in Table 2.  
Figure 13: Taxonomy of random access protocols evaluated for satellite systems based on nanosatellites for IoT connectivity. Protocols with * indicate no time synchronization is needed.
###### Abstract  
The study of peatland is challenging due to the water saturation and evergreen mixed vegetation that ranges from simple forms of plants such as chondrites, grasses, etc. The changing water level through the growing season makes the peatland vegetation very dynamic. In this work, we have used ground-level remote-sensing signals to understand the dynamic nature of peatland vegetation. We have also estimated the leaf area index (LAI) and Sun-Induced fluorescence (SIF) through the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model. The estimated LAI and SIF were compared with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-Infrared Reflectance of vegetation (NIRv), and measured SIF. The modeled LAI was observed to be significantly correlated with NDVI, EVI, and NIRv, whereas a good correlation was observed between measured and modeled SIF. Along with showing the dynamic behavior of peatland vegetation, the study indicates that SCOPE in its inverted form can be used to estimate reflectance-based LAI for peatland, which can be more reliable to present biomass and productivity of peatland ecosystem in comparison to transmittance-based LAI measurement for such ecosystem. The good correlation between measured and modeled SIF at 760 nm indicates that a reliable SIF value can be estimated through the SCOPE model for a complex ecosystem such as peatland, which can be very helpful in the absence of high-resolution hyperspectral data (usually used for SIF measurements).  
sun-induced fluorescence; leaf area index; peatland; radiative transfer model; vegetation indices Article Exploring the Potential of SCOPE Model for Detection of Leaf Area Index and Sun-Induced Fluorescence of Peatland Canopy [PERSON]  
1 Laboratory of Biochemistry, Department of Ecology and Environmental Protection,  
Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences,  
Piatkowska 94, 60-649 Poznan, Poland 1
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Space Exploration
#### 3.4.4 Example  
The primary goal is to design a swarm robotic system to efficiently and autonomously explore planetary surfaces. The primary goals are ensuring comprehensive terrain mapping, resource discovery, and infrastructure deployment while maintaining energy efficiency and reliability. A swarm of robots is deployed on a planetary surface to perform tasks such as terrain mapping, resource discovery, and infrastructure construction. The environment is unknown and dynamic, with potential obstacles and varying environmental conditions.  
* Field Coverage: The exploration area must be covered without significant overlaps or gaps.
* Obstacle Avoidance: Robots must navigate around rocks, craters, and other obstacles.
* Energy Efficiency: Each robot has limited energy resources, requiring efficient path planning and task allocation.
* Communication: Reliable communication must be maintained among the robots and the base station.
* Data Accuracy: Data collected on terrain, resources, and environmental conditions must be accurate and reliable.  
The objective is to maximize exploration coverage and resource discovery while minimizing energy consumption and ensuring robust communication. The objective function \(F\) can be formulated as:  
\[F=\alpha\left(\frac{1}{A}\sum_{i=1}^{N}F_{i}\right)+\beta\left(\frac{1}{N}\sum _{i=1}^{N}R_{i}\right)+e\left(\frac{1}{N}\sum_{i=1}^{N}C_{i}\right)-\delta \left(\frac{1}{N}\sum_{i=1}^{N}E_{i}\right)-\gamma\left(\frac{1}{N}\sum_{i=1}^ {N}T_{i}\right) \tag{25}\]  
where all variables are shown in Table 3.
# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 2 Materials and Methods
### Proposed Methods
#### 2.1.5 1D-CNN-LSTM
As for the 1D-CNN method, it consisted of two convolutional layers  
Figure 5: The suggested (a–c) model’s architecture (in case Time and Frequency domain). with 16 and 32 filters for each layer, two MaxPooling layers, and a dropout of 0.3 applied between each layer to prevent overfitting. A fully connected layer with 32 neurons and a SoftMax activation function were included, with a kernel size of 3 and a MaxPooling pool size of 3. The learning rate for this architecture was set to 0.0001. On the other hand, the LSTM model involved two connected LSTM units, which had been shown to be the most efficient design for detecting both arcing and non-arcing faults. The first layer had 64 LSTM units, followed by another layer with 64 units, and a dropout of 0.5 was applied after each unit to reduce overfitting. The architecture also included a fully connected layer with 32 neurons and a SoftMax activation function, with a learning rate of 0.0001. Additionally, Figure 5 illustrates the 1D-CNN, LSTM, and 1D-CNN-LSTM architecture for the frequency domain. Table 3 provides detailed information about the design of deep learning algorithms for the frequency domain.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 7 Conclusions  
This research presents a novel and innovative approach for accelerating BEC in hardware-constrained applications. Significant improvements in speed and resource utilization are achieved by combining the fixed window algorithm, Montgomery radix-4 multiplier, and two-stage pipelining. The proposed design addresses the challenges of low-resource environments by offering higher throughput and lower hardware requirements. The research also highlights the effectiveness of the proposed architecture through comprehensive security analysis and extensive evaluations of various hardware platforms. These evaluations include the performance metrics presented in Table 6, such as throughput comparisons, hardware resource utilization, and execution times.  
Looking ahead, there are several potential avenues for future work in this field. Firstly, further optimizations can be explored to enhance the performance of the proposed architecture, such as investigating advanced pipelining techniques or exploring alternative hardware optimization methods. Additionally, the security analysis can be extended to evaluate the resilience of the design against other potential attacks or side-channel vulnerabilities.  
Conceptualization, A.S. and M.R.; methodology, M.R. and A.S.; validation, O.S.S., A.R.J., M.A. and M.Y.I.Z.; formal analysis, M.A., M.Y.I.Z. and M.R.; investigation, A.S., A.R.J. and M.R.; resources, O.S.S., M.R. and M.Y.I.Z.; data curation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, M.R.; visualization, O.S.S.; supervision, M.R.; project administration, M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number: IFP22 UQU4320199 DSR102.