esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 4 Conclusions  
In this paper, we propose a Stacked RBM-AE model to compress WSNs data by using RBM and AE. In order to improve compression performance, we design a model parameter adjustment strategy, which includes two parts: pre-training and retraining. We test the efficiency of the number of iterations of pre-training and retraining on the performance of the model by experiment studies. We also offer a solution to use the model for WSNs and discuss the computational efficiency of the model. Considering the calculation and communication energy consumption, we use the method of prune parameters to further optimize the energy consumption of our algorithm. Our experimental results show that our algorithm has better transfer learning ability, and has better reconstruction accuracy than the traditional algorithms under the same _CR_. The data communication energy consumption can be reduced by 90%.  
The sensor node usually be equipped with multiple sensors to collect different environmental monitoring data. This method can be extended to perform joint compression and reconstruction of multi-stream data. Since Gibbs Sampling and k-step divergence are used to estimate the probability distribution of the reconstructed data in this method, the theoretical system error is inevitably introduced. Exploring how to reduce the systematic error of the model is the focus of our next research work. VAE, GAN or other mixed deep learning models can also be used in future research work, which is expected to reduce the systematic error caused by the model assumptions and further reduce the energy consumption for WSNs. We will also look at how to use deep neural networks for WSNs.  
Conceptualization, J.L. and F.C.; methodology, J.L. and F.C.; project administration, F.C. and D.W.; supervision, D.W.; writing, original draft, J.L.  
This work was supported partly by the National Natural Science Foundation of China (Grant No.61771436, 61271274), key project of Natural Science Foundation of Hubei Province of China (2011 CDB339), Key Science and Technology of Hubei Province of China (2012 BAA02003), and Key Science and Technology of Wuhan City (2017010201010134, 2013075001)  
The authors declare no conflict of interest.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 6 Example of Application of the Energy-Efficiency Protocol
### Results
#### 6.3.4. Energy Saving Protocol: Application Guidelines and the Comparison of the Three Approaches
Furthermore, when compared with the first method, the third approach seems to be more flexible, allowing to operate on distinct energy saving working modes, among which there are some with better \(\mathcal{H}_{\infty}\) performance indexes. Figure 9: **(a)**\(\mathcal{H}_{\infty}\) norm of the closed-loop system and **(b)** The global energy consumption (\(E_{GE}\)) of the network using: the classical approach assuming a full-reliable (\(L=\infty\)) network (in blue); \(A1\) with \(L=5\) (\(A1_{L=5}\) in dashed black); \(A1\) with \(L=8\) (\(A1_{L=8}\) in dotted black); \(A2\) with \(L\in\{\infty,5\}\) (\(A2\) in red); \(A3\) with \(L\in\{8,5\}\) (\(A3\) in green).
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 3 Proposed EDR Protocol
### Number of Subspaces  
According to the EDR, the network field is divided into a number of predefined subspaces in different levels \(l\). The number of subspaces \(k\) formation is defined in Equation (1):  
\[SubSL=\frac{SD}{l\sqrt{3}},\ k=\ \frac{V}{SubSL^{3}}, \tag{1}\]  
where \(SubSL\) is the edge length of a unit cube 3D subspace, \(l\) denotes a number of levels and \(SD\) is the space diagonal length of the network field.  
As the nodes are uniformly distributed, each subspace contains an equal number of nodes (\(n/k\)). A cluster is formed among the constituted nodes in a subspace and the size of the cluster is fixed.
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
## 3 Results and Discussion
### Topology Optimization Result
#### Effect of Mass Constraint  
Figure 10 shows the optimization result for several mass constraints from 30% to 60% mass constraint. It can be seen that each variation gives different topological results from one another. In a more specific discussion, the hole patterns created from the minimization process are proportional to the increment of mass constraint percentage. As the mass percentage increases, the holes created on the arm reduces. It can be seen at the result of the sample with a 30% mass constraint which shows a very large hole at the center of the crank arm and several holes around the pedal holes with only a thin or narrow mass remaining at the top and bottom of the crank arm. These two conditions make a high-stress concentration level at those particular areas due to the narrowing geometry.  
Meanwhile, the result from the sample with 40% and 50% mass constraint shows a bit similar in terms of the hole pattern. Generally, one large hole is close to the crankshaft hole and the other several holes are close to the pedal hole. However, the hole size from the sample with a 50% mass constraint is considerably smaller than that of a 40% mass constraint. On the other hand, the sample with a 40% mass constraint has more narrow areas with higher stress levels. Lastly, the result from 60% mass constraint only creates two small holes close to the pedal hole and one hole close to the crankshaft hole, respectively. Both holes are considerably smaller compared to the previous mass constraints.  
From the optimized mass result, the sample with a 30% mass constraint gives 0.09 kg final mass and 127.5 MPa Von Mises stress. The sample with a 40% mass constraint gives 0.12 kg final mass and 105.8 MPa Von Mises stress. The sample with a 50% mass constraint gives 0.15 kg final mass and 91.01 MPa Von Mises stress. Lastly, the sample with a 60% mass constraint gives 0.2 kg final mass and  
Figure 10: Effect of mass constraint in compliance minimization: (**a**) 30%, (**b**) 40%, (**c**) 50%, (**d**) 60%.  
85.01 MPa Von Mises stress. Based on the result above, in terms of mass reduction, the optimization method seems to successfully work with the mass or volume constraints.
## 4 Results and Discussion
### Performance Analysis and Comparison of Different CBRTs  
For the courtyards of different vernacular buildings, temperature and humidity monitoring was carried out for one year. The results showed that the temperature of T-CBRT was higher than that of the open courtyard in summer, and the maximum temperature difference could reach 2 \({}^{\circ}\)C, suggesting that T-CBRT may cause overheating in summer (Figure 6a). NT-CBRT did not appear to be hotter than the open courtyard in summer, and even had a slightly lower temperature than the latter. This is because there are generally operable windows around the NT-CBRT, and these windows are often open in summer. The risk of summer overheating caused by light-transmitting roofs has been mentioned many times in previous studies, which well validates the monitoring results of this study [49; 50; 51; 52].  
However, T-CBRT showed a better performance to the NT-CBRT and open courtyard in winter (Figure 6c). In sunny winter days, T-CBRT can increase the courtyard temperature by more than 5 \({}^{\circ}\)C, while NT-CBRT can only raise the courtyard temperature by about 4 \({}^{\circ}\)C. This is consistent with previous research findings, where [PERSON] and [PERSON] [8] found that converting south-facing open spaces into closed interior spaces can increase temperatures by 6 \({}^{\circ}\)C. [PERSON], [PERSON] [43] also reported that adding a light-permeable roof could raise the temperature of a previously open pati by as much as 5.5 \({}^{\circ}\)C. Furthermore, the open courtyard, T-CBRT, and NT-CBRT performed comparably in the transition season, and their courtyard temperatures were almost identical (Figure 6b).  
Figure 6: _Cont._
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 3 Results and Discussion
### Size of Routes  
The last verification checks the route sizes utilised by the HRAN and DSR protocols throughout the simulation. Figure 10 shows the findings, which show that the HRAN protocol's lengthier routes may be progressively reduced to values close to those of the DSR thanks to the route improvement mechanism being engaged every 20 s. These findings verify the efficacy of this approach in addressing a concern raised by HRAN users about the protocol's partly randomised route search.  
Figure 10: Average size of routes used.  
Figure 9: Performance with increasing NoN.  
Improved network scalability, decreased resource consumption, efficient content retrieval, and adaptive routing are just some of the concrete advantages that result from using the suggested Hybrid Routing Algorithm in real-world mobile networks. There are benefits and difficulties associated with all different kinds of routing protocols. However, non-geographic protocols may have trouble scaling and may not be as efficient in retrieving material. Geographic protocols exploit location information but might be sensitive to movement. Content-centric protocols have scalability issues but favour efficient content retrieval.
## 2 Synthesis and Characterization of the Nanoparticles  
### Properties and Applications  
Silica nanoparticles KCC-1 have a unique spherical morphology described as fibrous, nanoflowers, radial wrinkle, or dendritic structure, characterized by a high surface area with a large pore volume and narrow pore size distribution.  
Nanofibers are organized along the center-radial direction with gradually-decreasing material density and gradually increasing pore size from particle interior to surface. That peculiar morphology and the high surface area are responsible for the exceptional performances when applied in many fields ranging from catalysis to biomedical applications. In addition, the silica KCC-1 exhibits thermal and hydrothermal stability and relatively low cytotoxicity. KCC-1, as the catalyst support, increases the dispersion of metals, giving superior activity and stability [33]. In the biomedical field, the structural KCC-1 features drive the adsorption and release of large guest molecules and macromolecules (protein, DNA) [34]. KCC-1 has been exploited also in the field of sensoring as a multifunction organic/inorganic hybrid material, by the immobilization of the Rhodamine-based receptor (RB-Si), exhibiting high efficiency for Hg\({}^{2+}\) detection [35].  
The silica KCC-1 exhibits excellent physical and chemical properties, very important for practical applications. The features of dendritic nanofibers mainly determine some of these properties. Indeed, by regulating the length, thickness, and density of fibers, particle size and pore size can be adjusted. The evaluation of the particle resistance to mechanical stress or manipulation appears very important, in order to preserve the KCC-1 properties across time. As an example, in the catalytic processes, it is important to avoid the breaking of the nanofibers, leading to catalyst deactivation. As a consequence, the dependence of mechanical stability from the density and thickness of the external fiber layer should be studied. Some authors reported an indirect mechanical stability evaluation by High-Resolution Transmission Electron Microscopy (HRTEM), where comparing particles before and after compression stress up to 216 MPa pressure showed that the fibrous morphology of KCC-1 remained unaffected [36]. To the best of our knowledge, the mechanical resistance toward compression or manipulation of KCC-1 has not yet been sufficiently tested.
# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 4 Harmony Search Applications in Japan
### Engineering  
#### 4.1.1 Electrical/Electronic  
In Ref. [42], three forms of meta-heuristic optimization methods evaluated the position and scale of the Static VAR Compensator (SVC): PSO, Improved HS (IHS) and Cuckoo Search Algorithm (CSA). Multi-objectives were power failure, voltage divergence and SVC operating costs. The three forms of IEEE power systems, including 9-bus, 30-bus and 57-bus, were all checked with all methodologies mentioned. The optimization tests showed that the CSA not only has stronger strategies than some, but also has higher performance, while the IHS was equivalent to the PSO.  
To solve the Combined Heat and Power Economic Dispatch (CHPED) problem a new hybridized algorithm was developed [43]. Due to the non-convex nature of the CHPED problem, the global optimum is difficult to achieve. Their approach combined the algorithms of HS and Nelder-Mead (NM), and the hybridized algorithm was called the NM-HS algorithm. This algorithm improved the efficiency of the two standard algorithms. Two benchmark examples demonstrated that the NM-HS algorithm performed better than its standard versions.
# Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
## 3 Asynchronous Detection Algorithm
### Mimo-SCMA Scheme  
Because the multi-user grouping model increases the number of accessed users, the number of interference terms will also increase, resulting in a high bit error rate in the system. In order to solve this problem, the multi-antenna-diversity effect can be used to increase transmission reliability and accuracy.  
In the multi-antenna system, each antenna in the base station receives the same superimposed signal from the users. The joint factor graph is constructed, as in Figure 8. Each antenna can be converted into a virtual function node [27] and there are \(R\times K\) function nodes in one group, which corresponded to \(R\times K\) rows. Each resource block accommodates \(2\times J\) users, which corresponded to \(2\times J\) columns. The corresponding joint factor matrix is in Figure 9. It can be seen from the factor graph that the amount of information propagation increases as the number of antennas increases, which can improve system performance.
## 5 Conclusions  
Communication technology in the maritime domain has seen slower evolution than terrestrial communication. However, the advent of various marine applications has triggered a growing need for communication that can ensure stability and high transmission rates in marine environments. In this paper, we introduce the mARF technique, which is designed to enhance transmission rates while maintaining reliable communication by effectively addressing the challenges of the marine communication environment. The mARF technique handles the deep null phenomenon, which is a prevalent problem in marine communication, through the implementation of fast drop-out and fast recovery mechanisms. Furthermore, we demonstrate that the threshold for a fast recovery operation can be dynamically updated by leveraging existing communication success records. Through simulations, we provide empirical evidence of the ability of this technique to efficiently regulate transmission rates to meet the demands of marine communication environments. However, mARF has a limitation whereby the communication history is not updated when the VDES enters sleep status and does not transmit data. mARF operates based on previous communication history, which is not representative of the current channel state in VDES sleep status. To address these limitations, the mARF technique can be combined with intelligent algorithms in the future. The proposed mARF technology is not only implementable and operational within a real VDES, but also possesses the potential for expansion into an intelligent algorithm. This algorithm can collaborate with the VDES internal scheduler to control the transmission cycles by considering data transmission delays, thereby further enhancing its adaptability to real-world maritime communication scenarios. Additionally, innovative technologies such as stacked intelligent metasurface (SIM)-aided communications will also be applied to maritime communications in the future. When technologies such as SIM are applied in the future, research on channel capacity optimization in the maritime environment is essential and needs to be conducted.  
W.S. devised the basic concept of the proposed scheme; B.K. performed implementation of the simulation code; E.-J.K. validated the results; D.K. supervised the whole procedure related to this paper. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Normalized throughput comparison between mARF and existing technique.  
**Funding:** This research was supported by a grant from the Republic of Korea National R&D Project, \"Development of VDES for shipborne mobile station with satellite and base station with net-work management\", funded by the Ministry of Oceans and Fisheries (RS-2023-00256110).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# MixDiff-TTS: Mixture Alignment and Diffusion Model for Text-to-Speech
## 3 Results
### Feature Visualization  
We explore the prediction of mel-spectrograms and the visualization of attention alignment. In speech synthesis, mel-spectrograms featuring clear harmonic structures and broad frequency coverage directly signify superior phonetic clarity and prosodic naturalness in synthesized speech. Thus, we perform a comparative analysis of mel-spectrograms generated by all tested models across three key dimensions: adjacent harmonic structures, unvoiced frame distributions, and low-frequency region characteristics. In mel-spectrograms, adjacent harmonics typically manifest as parallel bright bands, which correspond to the frequency distribution of the fundamental frequency and its harmonic components. A well-defined harmonic structure generally indicates pitch stability. Unvoiced frames usually appear as dark regions in the mel-spectrogram, where no prominent harmonics are present. The low-frequency region is typically located in the lower part of the mel-spectrogram. Figure 5 shows the mel-spectrograms generated by all tested models. The horizontal axis denotes the frame number, with each frame typically corresponding to an audio segment of approximately 10 ms. The vertical axis denotes the frequency in the mel-spectrogram, with values ranging from 0 to 80 indicating 80 mel-frequency bins. The spectrograms within the black boxes show that the mel-spectrograms synthesized by MixDiff-TTS exhibit a clearer harmonic structure, more accurate unvoiced frame prediction, and a more stable low-frequency region. Experimental results demonstrate that the MixDiff-TTS model has significant advantages in generating detailed mel-spectrogram characteristics.  
Figure 5: Cont.  
Figure 5f illustrates the attention alignment visualization of MixDiff-TTS. The horizontal axis denotes audio time steps, with each step corresponding to an audio segment of approximately 10 ms. The vertical axis denotes text time steps, with each unit corresponding to a token (e.g., a phoneme) in the sequence. In the attention alignment map, dots denote attention weights, with brighter points indicating higher values. The dot-formed diagonal represents the alignment of phoneme and mel-spectrogram sequences, where a clearer diagonal signifies higher alignment accuracy. In Figure 5f, the dots exhibit distinct brightness, and the diagonal line is clear and smooth, demonstrating the significant capability of MixDiff-TTS in aligning phoneme sequences with mel-spectrograms.
## 1 Introduction
### Literature Review
#### 1.2.2 Path and Trajectory Planning Algorithm
Choosing the trajectory's final point (during re-planning) is also an difficult issue in emergency situations.
# ChatGPT in Education: A Systematic Review on Opportunities, Challenges, and Future Directions
## 6 Result and Analysis
### Governance and Policy Challenges
#### 6.2.2 Data Privacy and Security  
ChatGPT operates on vast datasets, often processing user-generated content that may contain sensitive information. This raises concerns about data privacy, user anonymity, and compliance with education regulations [38]. Without clear policies, institutions risk exposing student data to unauthorized access and misuse. Future governance efforts must focus on transparent AI usage policies and secure data management frameworks [20].
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Evaluation Metrics
#### 3.6.2 Precision  
Precision is a very popular method for evaluation of machine learning models. Its calculation is based on the concepts of TP and FP explained in the preceding subsection. In the context of ontology alignment, precision is defined as the ratio of the number of the correct alignments predicted by the proposed model as true positives to the total number of all correct alignments [56]. _Precision_ is generally calculated according to Equation (10).  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
Given \(R\) as the reference alignment, and A as the prediction of the alignment system (the proposed method), the precision of A is defined with Equation (11) [56]:  
\[Precision(A)=\frac{|R\cap A|}{|A|} \tag{11}\]  
where \(|R\cap A|\) is the number of predictions that are correct matches according to the reference alignment, and \(|A|\) is to total number of all pairs of classes of ontologies that are predicted as correct matches by the alignment system (proposed method).
# Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools
## 4 Relationship to CogInfoCom  
The complexity of the term cognitive InfoCommunications (CogInfoCom) closely reflects its multidisciplinary characteristics, since acquiring information by observing geovisualized spatial data can support different cognitive processes. The key factor is demonstrated in the process of geovisualization, during which data were transformed into a special form enhancing the co-evolvement of cognitive processes with infocommunication devices. Human perception as a cognitive process can interact with certain artificial cognitive systems and the process itself can also be supported in the context of cognitive infocommunications. Although we have not tested directly the efficiency of our visualization, but we relied on other studies whose findings are about how 3D visualization enhances human perception [63; 64; 65]. Considering the current study, the following cognitive considerations related to the field of CogInfoCom are of high importance:  
* The most important advantage of using geovisualization tools is the visualized spatial environmental data, which makes valuable information understandable both for the public and for decision-makers.
* Revealing relationships between the investigated wells and the location became easier after the geovisualization.
* Geovisualization facilitates capturing the spatial pattern of nitrate and phosphate distribution at different times.
* The general cognitive perception of digital data is supported. The more data sources are used, the greater need can be identified for supporting the appropriate interpretation of the data.