esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation
### Machine Learning Implementation  
The model was implemented and tested using the Weka 3.8 tool. Weka 3.8 is an open-source machine learning tool that can be used to test and implement data mining tasks, such as prediction, analysis, and classification. Weka 3.8 offers wide flexibility in applying and listing predicted values. It enables the implementation of nearly all algorithms of data mining functions, such as clustering, association rules, regression, classification, and data pre-processing. The C4.5 DT algorithm is one of the commonly used algorithms for prediction and classification. C4.5 (J48) is used in Weka 3.8 for prediction and has proven accurate in different disciplines, such as education, medicine, sports, and economics. In our model, C4.5 was used to predict the optimal node in a 5G environment to help in congestion control. The first step in the model evaluation was DT/ML evaluation to determine if it covered all the training datasets and if the reader could visually determine the optimal node. Figure 5 shows the optimum alternatives whereas Figure 6 shows the result of the DT of congestion control based on the training dataset with predefined optimal nodes. The tree graph can provide the optimal node based on four factors (cwnd, que, throu, and pkt loss).  
Figure 4: Simulation setup. mmWave: millimeter wave; PGW: core gateway; SCTP: stream control transmission protocol; TCP: transmission control protocol.
# An Optimal and Stable Algorithm for Clustering Numerical Data
## 1 Introduction
From an experimental perspective, clustering algorithms should produce optimal and stable results in every run; however, the seeding selection mechanism may vary, which may occasionally lead to different clustering results. Therefore, we herein introduces a new algorithm based on the k-AMH framework and associated with a simple but novel seeding mechanism, with the objective of achieving cluster optimality and stability. Clustering algorithms are made of a clear distinction between clustering method (objective function) and clustering algorithm (the framework) ([PERSON] and [PERSON], 2017; 2017); therefore, the proposed algorithm is based on the k-means objective function, combined with the k-AMH clustering framework and worked with a simple and novel seeding selection mechanism known as zero-point multidimensional spaces. The aim is to achieve optimality clustering along with stability. Cluster optimality and stability are defined as the capability of an algorithm to produce optimal results and stable results for every seeding and run. Therefore, the proposed algorithm, which has the advantages of optimality and stability, can serve as an alternative method for the repeated k-means approach.
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 2 Method Development  
### Proposed Methodology  
The proposed method considers the previously discussed skylight effect and aims to improve the color correction in shaded regions by performing a statistical pixel-by-pixel local color transfer algorithm. The proposed method consists of three main blocks: input data, which consists of the image capturing process and shadow mask selection as input data; the color transfer algorithm, in which the proposed color correction method is executed to obtain an image with color correction at shadowed areas; and lastly, the color tuning process that executes a final color correction to improve the output image. The proposed methodology is schematized in Figure 1.
## 5 Summary and Conclusions  
Simulated STECs along small-satellite constellation-AIS signal rays are applied for CIT over sea areas to reconstruct the 3D electron density. This can be used to analyze the variation of electron density over sea areas, which is important for maritime communication and can be applied as a complement to global ionospheric electron density data.  
In this paper, we have designed a small-satellite constellation consisting of four small satellites, deployed the AIS in the ship-dense area, and used the simulated STEC of the signals between the small-satellite constellations and AIS installed on ships to perform CIT. For the CIT process, a method is proposed to evaluate the initial electron density and improve the accuracy of CIT by improving the iterative initial value of the electron density, i.e., the iterative initial value is optimized by utilizing the ratio of the measured STEC of the simulated STEC to the one obtained from the initial value and the parameter matrix, which is denoted as \(\delta\). The optimized initial electron density is brought to the MART for iteration to obtain the reconstructed 3D electron density. The use of MART1 can obtain better iterative results than those of MART0 when there is a large difference between MART0 and MART1. When the difference between MART1 and 1 is not large, it means that the initial value is close to the real value of the background, and better iteration results can often be obtained. Therefore, our proposed improvement of the iterative initial value can obtain better CIT electron density reconstruction results. More work needs to be conducted in order to achieve ionospheric observation and obtain reconstruction data in the sea area.  
Conceptualization, B.X. and H.L.; methodology, H.L. and C.W.; funding acquisition, R.J. and H.Z. ([PERSON]); validation, C.W., F.W. and H.Z. ([PERSON]); formal analysis, F.W. and H.Z. ([PERSON]); investigation, B.X. and H.L.; writing--original draft preparation, H.L.; writing--review and editing, C.W.; visualization, F.W.; supervision, B.X.; project administration, C.W.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 62171355, 42074225, and 52371354), and National Key Laboratory Foundation of Electromagnetic Environment (No. 6142403220302, and 6142403210302).  
Not applicable.  
The data presented in this study are openly available in Ionospheric-Electron-Density-reconstruction at [[https://doi.org/10.5281/zenodo.11639020](https://doi.org/10.5281/zenodo.11639020)]([https://doi.org/10.5281/zenodo.11639020](https://doi.org/10.5281/zenodo.11639020)), reference number [31].  
Thanks are due for the electron density data provided by IRI model.  
The authors declare no conflicts of interest.
## 5 Analysis of Factors Influencing the Digital Service Trade Network of RCEP Member Countries
### QAP Regression Analysis
#### 5.1.1 Model Building  
Through the analysis of the characteristics of the digital service trade network among RCEP member countries, it is found that the digital service trade between member countries is greatly influenced by factors such as the level of economic and digital development of each country. Based on the above characteristic analysis, in order to further identify the influencing factors of the digital service trade network of RCEP member countries and draw on the research of relevant scholars [31,32], an indicator system for its influencing factors is attempted to be constructed. The model is set as follows:  
\[\text{DST}_{\text{Gj}}=\text{f}(\text{GDP}_{\text{Gj}},\text{INF}_{\text{Gj}}, \text{SYS}_{\text{Gj}},\text{ LAN}_{\text{Gj}},\text{ DIS}_{\text{Gj}}) \tag{1}\]
# Antimicrobials in Dentistry
## 2 Antimicrobials Used in Dentistry
### Eugenyl Methacrylate in Dental Adhesive Systems
It is well known that Gram-positive bacteria are more sensitive to essential oils such as eugenol, as the hydrophilic cell wall structures of Gram-negative bacteria block the penetration of hydrophobic components into the cell membrane [91]. The respective virulence of each species and the differences in the chemical composition and structure of the bacteria cell walls resulted in different bacterial sensitivities towards EgMA. _E. faecalis_ is the most resistant bacterium to a wide range of antibiotics; however, the effectiveness of EgMA-modified adhesives against _E. faecalis_ offers a considerable advantage over the commercial dental adhesives. It is also known that eugenol at high concentrations can exert some toxic effects on the dental pulp [80]. However, EgMA-modified adhesives showed good biocompatibility and a high percentage of cell viability. The cytocompatibility of EgMA-modified adhesives exhibited human gingival fibroblast viability matching that of their commercial non-antibacterial controls, as well as that of the negative control without any resin eluent. Finally, the incorporation of EgMA did not adversely affect the root dentine bonding ability of both parent adhesive systems evaluated by push-out bond strength testing. The EgMA-modified adhesives presented bond strength values similar to their corresponding controls. In addition, confocal laser scanning microscopy assessment of the resin-dentine interface showed that EgMA-modified self-etch and total-etch adhesives were able to create a resin diffusion zone within the coronal and middle root dentine, forming a clear, continuous, gap-free hybrid layer located underneath a thick adhesive layer. The presence of resin tags was also evident with both adhesives. The incorporation of EgMA produced an effective bond to root canal dentin and high compatibility in vitro, indicating a potential application to achieve successful post-endodontic restorations [101].
# Fatty Acid Composition and Volatile Profile of _M. longissimus thoracic_ from Commercial Lambs
## 2 Materials and Methods
### Volatile Compounds Analysis  
The samples for volatile analysis were analysed in daily batches with 10 samples in each batch. One loin from each production system was randomly chosen for each daily batch. From each loin, duplicate samples of 4 g of frozen lean meat were removed as 5 mm cores and placed in the bottom of 20 mL solid-phase microextraction (SPME) vials. Samples were kept at 4 \({}^{\circ}\)C overnight until analysis. Internal standard (50 \(\upmu\)L, 1.25 mg/L fenchol in water, 99 % purity, Sigma-Aldrich, Madrid, Spain) was added to a 250 \(\upmu\)L insert vial sitting next to the meat within the SPME vials. To minimise the chance of volatile compound changes, samples were held in the auto sampler tray for no longer than 2.5 h. To minimise analysis sequence effects, samples were analysed using a balanced random order, and blanks (empty vials) were also run every day to check background signals.  
The vial was equilibrated at 37 \({}^{\circ}\)C for 5 min in the automated sample preparation unit. Following equilibration, a 50/30 \(\upmu\)m Divinylbenzene-Carboxen-Polydimethylsiloxane (DVB/CAR/PDMS) fibre of the auto sampler (PAL RSI 85, Switzerland) was exposed to the headspace of the unstirred sample for 30 min at 37 \({}^{\circ}\)C and then desorbed directly in the injection port of a 6890N gas chromatograph coupled to a 5975B VL mass spectrometric detection system (Agilent Technologies, Inc., Santa Clara, CA, USA). Desorption occurred for 5 min at 240 \({}^{\circ}\)C, and 2 min in splitless mode followed by 3 min with a purge flow of 60 mL/min. Helium was the carrier gas at a flow rate of 1.2 mL/min. A ZB-WAXcapillary column (Phenomenex, Torrance, CA, USA) of 60 m \(\times\) 0.32 mm i.d. \(\times\) 0.5 \(\upmu\)m film thickness was used for the separation. The oven temperature was initially 50 \({}^{\circ}\)C for 2 min, then raised by 10 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C and held at this temperature for 10 min. After desorption, the fibre was cleaned for 2 min at 270 \({}^{\circ}\)C. For mass spectrometry, the transfer line temperature was 230 \({}^{\circ}\)C and the trap temperature was 150 \({}^{\circ}\)C with an emission current of 34.61 \(\upmu\)A. The global run time was recorded in full scan mode (_m/z_ 29-300 mass range) and the chromatographic data were analysed by MSD Chemstation (version F01.012317, Agilent Technologies, Inc., Santa Clara, CA, USA).  
Compound identification was carried out by spectra comparison using the Wiley7 Nist05 Library (Wiley & Sons Inc., Weinheim, Germany) supported by linear retention indexes (LRI) relative to a series of alkanes (C7-C26) compared to 11 th edition National Institutes of Standards and Technology Mass Spectral Library. Due to some selected analytes coeluting with other compounds, the selected masses were used for integration. The integrated areas of the selected compound ions were divided by the area of the internal standard ion (_m/z_ 81) to give relative abundances.
# Statistical Analysis and Modeling for Optical Networks
## 2 Literature Review on Statistical Analysis and Modeling for Networks
### Statistics for Networks
By refining and validating these models, engineers can optimize resource allocation, improve network efficiency, and enhance overall system performance, ensuring that modern optical networks meet the growing demands of high-speed optical communication [47; 48; 49; 50; 51; 52; 53]. Early optical network design necessitates estimating link-dependent parameters, a challenge when network topology details are incomplete [53]. Consequently, estimation methods relying on partial network configuration knowledge are crucial. This demonstrates that employing a link length statistical model significantly enhances the accuracy of estimating key link-dependent network parameters compared to methods using average link length [31]. By leveraging statistical distributionsof link lengths, more realistic and precise parameter estimations are achieved [31; 32; 33]. This approach addresses the inherent uncertainty in early-stage design, providing a robust foundation for subsequent network planning and optimization, particularly when detailed topology information is scarce [54]. Shortest path lengths are critical in communication network applications, like traffic management and routing, and for early-stage planning [33]. Realistic models of these lengths enable the estimation of CAPEX, OPEX, and MANEX, which are vital for network dimensioning [3; 4; 31; 32; 33]. In dynamic optical networks, fluctuating traffic necessitates statistical shortest path length models [55; 56; 57]. When complete network information is unavailable, as is often the case, these models facilitate parameter estimation [28; 58]. They address the challenges of modern optical network complexities, demonstrating the utility of statistical models developed for shortest path length analysis. This approach is essential for effective network management and planning, especially in evolving network environments. Shortest path length calculations are fundamental in optical networks, driving several critical utilities and applications [33]. Primarily, they enable efficient routing of optical signals, minimizing latency and maximizing network throughput [59; 60; 61; 62; 63; 64; 65; 66; 67; 68]. This is vital for time-sensitive applications, like high-frequency trading and real-time data streaming. Furthermore, these calculations aid in network design and optimization, allowing engineers to determine optimal fiber cable layouts and node placements [33]. They also support network resilience by enabling the identification of alternate routes in case of failures, ensuring uninterrupted service [60; 61; 62; 63]. In addition, shortest path data is used in network management for traffic engineering, balancing loads, and preventing congestion [33; 59; 60]. In essence, it is a core tool for ensuring optical networks operate reliably and efficiently.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 4 Simulation Results and Discussions
### Analysis of Time Complexity  
Wireless sensor networks comprise many small devices, each with a limited battery, and operate under stringent constraints in terms of capacity and energy consumption. Implementing complex algorithms in WSNs presents a considerable challenge in many fields. Due to these considerations, it is essential to conduct an evaluation of the time complexity of the proposed algorithms. This is particularly important when considering the NP-hard nature of the WSN localization problem. This scenario considered a WSN consisting of \(n\) sensor nodes with unknown locations and \(k\) anchors; the generation maximum for CSO and PSO were \(MaxG1\) and \(MaxG2\), respectively, the population size for CSO and PSO was \(NP1\) and \(NP2\), respectively, and the time interval for status updates in the chicken swarm was \(T\). We employed the complexity to evaluate the time to completion required by an algorithm or, in the worst-case scenario, its overall execution time.  
The time complexity of all presented algorithms was examined. During the first stage, all algorithms similarly create a hop count matrix; the time complexity of this stage is \(O(k^{2})\). In the second step, the complexity for DV-Hop and PSODV-Hop is \(O(k^{2})\) due to the way in which they calculate the average hop size. The W-DV-Hop, W-DV-HopCSO, HW-DV-Hop, HW-DV-HopCSO, and MDV-Hop algorithms all apply different weighted formulas to calculate the average hop size; despite these differences, the time complexity remains \(O(k^{2})\). In the third step, the complexity for DV-Hop, PSODV-Hop, and DV-HopCSO is \(O(k*(n-K))\), as these algorithms all use the least squares approach to calculate the nodes' location. The time complexity for W-DV-Hop, W-DV-HopCSO, HW-DV-Hop, HW-DV-HopCSO, and MDV-Hop is \(O(k*(n-K))\) in this stage, which is due to their reliance on the hyperbolic 2D integration technique rather than the trilateration approach.  
In the fourth phase, the time complexity of PSODV-Hop is \(O(MaxG1*NP1*(n-k))\), while for W-DV-HopCSO and HW-DV-HopCSO, which use CSO, it is \(O(MaxG2*NP2*(n-k))\). An extra time cost of \(O(k*NP1)\) is required due to the fitness function calculation for the PSO-based algorithms, while \(O(k*NP2)\) is needed for the CSO-based proposed algorithms. In addition, \(O(MaxG1*NP1)\) and \(O(Max21*NP2)\) are required to update the locations, resulting in the time complexity of the W-DV-HopCSO and HW-DV-HopCSO algorithms, which use CSO, being \(O(MaxG2*NP2*(n-k))\). Table 5 provides a summary of the time complexity for the tested algorithms.
## 4 Proposed Algorithm
### Ebba
#### 4.3.1 Levy Flight-Based Global Search Method  
Feature selection problems also are large-scale optimization problems since the dimension of some datasets is relatively large. In this case, the exploration ability of the optimization algorithm should be sufficient. However, the update of other bats in the swarm are determined by the best bat, and this mechanism will undoubtedly decrease the exploration ability of the algorithm. Thus, we introduce the Levy flight to propose a global search method to improve the exploration ability of the algorithm. Specifically, a Levy flight is a random walk in which the step-lengths have a Levy distribution, a probability distribution that is heavy-tailed. By using the short-distance or long-distance searching alternately, the search scope can be extended.  
First, mathematically, in each iteration, we generate a new bat according to the best bat \(X^{*}\) and Levy flight, which can be expressed as follows:  
\[X^{new}=X^{*}+\alpha\oplus\text{ Levy }(\lambda), \tag{12}\]  
where \(\alpha\) is a parameter and its value is often assigned according to applications. Moreover, Levy flight is taken from the Levy distribution, which can be expressed as follows:  
\[\text{ Levy}(\lambda)\sim u=t^{-\lambda}(1<\lambda<3), \tag{13}\]  
Second, the newly generated bat \(X^{new}\) is evaluated to obtain its fitness value, and then we compare the \(X^{new}\) with the best bat \(X^{*}\) based on their fitness function values. If the \(X^{new}\) outmatches \(X^{*}\), then \(X^{*}=X^{new}\). By using this method, the best bat of the swarm is easy to jump out of local optima, thereby enhancing the exploration ability of EBBA.
## Appendix C RL Algorithm Posterior Updates and Posterior Sampling Action Selection
### Posterior Updates to the RL Algorithm at Update Time
#### c.1.1 Bayesian Linear Regression Model  
Suppose we are selecting actions for decision time \(t\). Let \(\phi(S_{i,t},A_{i,t})=[m(S_{i,t}),\)\(\pi_{i,t}f(S_{i,t}),\)\((A_{i,t}-\pi_{i,t})f(S_{i,t})]\) be the joint feature vector and \(\theta_{i}=[\alpha_{i,0},\alpha_{i,1},\beta_{i}]\) be the joint weight vector. Notice that Equation (10) can be vectorized in the form: \(R_{i,t}=\phi(S_{i,t},A_{i,t})^{T}\theta_{i}+\varepsilon_{i,t}\). Now let \(\Phi_{i,1:t-1}\) be the matrix of all stacked vectors \(\{\phi(S_{i,s^{\prime}},A_{i,s})\}_{s=1}^{t-1}\), and \(\mathbf{R}_{i,1:t-1}\) be a vector of stacked rewards \(\{R_{i,s}\}_{s=1}^{t-1}\), where we have batch data of the \(t-1\) decision times before the current update time.  
Recall that we have normal priors on \(\theta_{i}\) where \(\theta_{i}\sim\mathcal{N}(\mu_{\text{prior}},\Sigma_{\text{prior}})\), where \(\mu_{\text{prior}}=\mathbf{0}\in\mathbb{R}^{3+3+4}\) and \(\Sigma_{\text{prior}}=\text{diag}(\sigma_{prior}^{2}I_{3},\sigma_{prior}^{2}I _{3},\sigma_{prior}^{2}I_{4})\). The posterior distribution of the weights given current history \(H_{i,t-1}\), \(p(\theta_{i}|H_{i,t-1})\) is conjugate and is also normal.  
\[\theta_{i}|H_{i,t-1}\sim\mathcal{N}(\mu_{i,t-1}^{posterior},\Sigma_{i,t-1}^{ posterior})\]  
\[\Sigma_{i,t-1}^{posterior}=\left(\frac{1}{\eta^{2}}\Phi_{i,1:t-1}^{T}\Phi_{i,1:t-1}+\Sigma_{prior}^{-1}\right)^{-1}\]  
\[\mu_{i,t-1}^{posterior}=\Sigma_{i,t-1}^{posterior}\left(\frac{1}{\eta^{2}} \Phi_{i,1:t-1}^{T}\mathbf{R}_{i,1:t-1}+\Sigma_{prior}^{-1}\mu_{prior}\right)\]  
Note that we fit \(\eta^{2}\) to the ROBAS 2 dataset and fixed it for all of our experiments. For the real study, we are considering assigning a conjugate prior on \(\eta^{2}\) and updating it at update times.
## 3 M2M Information and Services
### M2M Information and Service Technologies
Open Geospatial Consortium (OGC) Sensor Web Enablement (OGC SWE) initiative is focused on developing an infrastructure which enables an interoperable usage of sensor resources by enabling their discovery, access, tasking, as well as events and alerting on the Web from the application level. It hides the underlying layers, the network communication details, and heterogeneous sensor hardware, from the applications built on top of it, but does not provide sensor network management functionality [54; 55; 56].