esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 5 Conclusions and Future Work  
In this paper, we conducted a wide review of the recent hierarchical approaches based on CI or ML. To achieve this, we classified these algorithms in terms of the CI used which can be FL, GA, NN, RL or SI. In order to evaluate and to compare them, we consider 10 parameters: CI used; the data delivery rate (depend of the amount of data received by the BS); data aggregation; the energy consumption that characterize the network lifetime; the scalability of the algorithm when increasing the number of nodes; the approach of the algorithm which can be centralized or either distributed; homogeneity or the heterogeneity of the network that considers whether sensors have or not the same performance; the radio model used by the optimized algorithms which represent the model of energy; multihop to identify if an optimized solution considers or not multihop communications; multipath for the fault tolerance of algorithms. The tables of comparison show that optimized clustering based on SI are more recent and outperforms the other algorithms in terms of scalability, energy consumption and the amount of data delivered to the BS.  
As future works, we plan to classify the optimized clustering in terms of WSN types. We would talk about clustering parameters used to WSNs which locations are non-terrestrial or not above the ground but rather under the water or under the ground. In addition to future work, we would discuss about the spectrum-awareness or spectrum-efficient clustering studies in practical large wireless sensor networks.  
The presented work in this paper is the collaborative development of all authors. [PERSON] conducted the review and the evaluation of the algorithms. [PERSON] and [PERSON] help at designing and comparing in terms of clustering and Machine Learning/Computational Intelligence the presented algorithms. [PERSON] gave a formal overview and the organization of the paper.  
This research received no external funding. The APC is funded by the University of Bremen in partnership with the University of Ngaoundere.  
The authors would like to thank the editor and the anonymous reviewers for their insightful comments and remarks that lead to improvement of the content and presentation of the paper.  
The authors declare no conflict of interest.
# Effective Adsorption of Phenoxyacetic Herbicides by Tomato Stem-Derived Activated Carbons
## 2 Experimental Section
### Synthesis and Characterization of Activated Carbons from Tomato Stems (TS-AC)
Thermogravimetric analysis (TGA) was performed using a thermal analysis test stand (STA 449 F5 Jupiter-QMS, NETZSCH-Gruppe, Burlington, MA, USA) to characterize the surface chemistry of the TS-AC further. Samples (15 \(\pm\) 1 mg) were heated from 25 \({}^{\circ}\)C to 950 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min under a helium flow of 25 cm\({}^{3}\)/min. Mass losses were quantified within the temperature ranges of 200-500 \({}^{\circ}\)C, 500-700 \({}^{\circ}\)C, and 700-950 \({}^{\circ}\)C for each TS-AC sample. The TS-AC's surface morphology was examined using a scanning electron microscope (SEM) and Electron Dispersive Spectroscopy (EDS) (Zeiss EVO 10, Carl Zeiss Microscopy GmbH, Jena, Germany). Samples were mounted on carbon tape-coated SEM stubs and subsequently sputter-coated with gold (Quorum Q150R Plus, Quorum Technologies Ltd., Laughton, UK). Nitrogen adsorption/desorption isotherms, measured at 77 K using an adsorption analyzer (ASAP 2020, Micromeritics Instrument Corp., Norcross, GA, USA), were employed to evaluate the porous structure parameters of the TS-AC. The carbon samples underwent degassing at 300 \({}^{\circ}\)C for 24 h before isotherm acquisition. The iodine number to assess the adsorption capacity of selected TS-AC was determined (redox titration method) according to standardized test methods: ASTM D1510-24B [23] and ASTM D4607-14 [24]. When calculating the iodine value (IV), the following relationship was used:  
\[\rm IV=\frac{\left(V_{0}-V_{p}\right)c_{tio}\cdot 126.92}{M_{AC}} \tag{3}\]  
where  
\(V_{0}\)--the volume of sodium thiosulfate solution used in the blank (mL);  
\(V_{p}\)--the volume of sodium thiosulfate solution used in the actual determination (mL);  
\(c_{\rmtio}\)--titer of sodium thiosulfate solution (mol/L);  
\(M_{\rm AC}\)--mass of the activated carbons (TS-AC) (g);  
126.92--the mass of 0.5 moles of iodine (g).
# LEO Precise Orbit Determination with Inter-satellite Links
## 4 Results and Analyses
### Test with Different Link Topologies  
Two ISL link topologies, named \"all-connected\" and \"4-connected\", have been introduced in the Section 3.2. It is obvious that link topology has a great impact on the orbit determination performance. For test, the 60-LEO constellation detailed in the Table 1 is selected, then the ISLs based on both link topologies are simulated. It is noted that average numbers of links per satellite are 16.7 with \"all-connected\" link topology. For the sake of simplicity, we take a rough orbital solution for each satellite in the constellation. Assuming that the coordinates of the satellites linked to each satellite are known, the coordinates of each satellite are determined by precise positioning. Average orbital errors are shown in the Table 2. It can be concluded that except for the errors along the z-axis, orbital errors are much smaller with the \"all-connected\" link topology than with the \"4-connected\" link topology. Numerical analysis shows that when adopting \"all-connected\" link topology, there is 38.4% reduction of average 3D orbital errors compared to adopting \"4-connected\" link topology.  
According to the above brief analysis, \"all-connected\" link topology shows better orbit determination performance than \"4-connected\" link topology. In order to better demonstrate the effectiveness of ISL POD, \"all-connected\" link topology is used in the following study.
# The Microverse: A Task-Oriented Edge-Scale Metaverese
## 3 The Microverse: Rationale and Architecture
### The Microverse: A Task-Oriented Metaverse  
The microverse, as an application- or task-oriented metaverse, marks a significant shift in virtual environments, moving beyond traditional digital spaces to prioritize goal-driven interactions and efficiency [57]. The microverse approach redefines the metaverse into a dynamic, purpose-built ecosystem that streamlines task completion, enhances collaboration, and offers immersive professional settings. Diverging from the typical entertainment-centric virtual worlds, the task-oriented metaverse focuses on developing specialized functionalities tailored to specific goals, including professional and educational pursuits. It features virtual workspaces meticulously designed to mirror real-life scenarios, seamlessly incorporating the necessary tools and resources to support a broad spectrum of activities.  
Collaboration plays a fundamental role in the task-oriented metaverse [58], enabling users to participate in real-time and spatially aware interactions. The microverse enables significant interaction by reproducing the intricacies of the actual world, fostering a more immersive and authentic collaboration experience across many settings, such as virtual conference rooms, collaborative creative studios, and educational environments. The microverse exhibits a broad scope that extends beyond a specific industry, demonstrating its ability to transcend borders and find practical applications in many disciplines, including but not limited to remote work, education, healthcare, and several others.  
As illustrated by the top layers in Figure 1, each microverse instance mirrors an application task, such as overseeing the daily operations on a smart farm, monitoring the safety and security of a smart grid [59], or enabling remote border surveillance to reduce the risk for law enforcement officers. Each microverse instance is the digital twin of the task space, including the network slice and the application's working environment. For example, a microverse instance created for a smart healthcare service that monitors the safety and health of a senior resident living alone in her home mirrors wearable medical sensors and networking devices along with the layout of the rooms and the pieces of furniture [60]. Real-time sensing data are transferred to the microverse, where the senior's position, gesture, and health condition are displayed, recorded, and analyzed. When signs of dangerous actions are identified, i.e., falling, an alert is generated and sent to service providers instantly [61; 62].  
In addition, the microverse utilizes technologies such as augmented reality (AR) and virtual reality (VR) to enrich users' immersive experiences in a 3D virtual-physical interwoven space. These technologies enhance the perception of presence and immersion, making the perceptual experience of tasks more palpable and participatory. For instance, in the microverse, the healthcare service provider can \"see\", \"hear\", and \"sense\" the senior's status just as they are physically in the same room.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 5 Experiments and Results Analysis
### Evaluation Metrics  
To objectively evaluate the algorithm, we selected Entropy, the Standard Deviation, and the Structural Similarity Measure (SSIM) as objective evaluation metrics to assess the amount of edge, texture, and contrast information in the fused images. Mutual Information, Feature Mutual Information using Discrete Cosine Transform (FMIdct), Feature Mutual Information using Wavelet Transform (FMIw), and Visual Information Fidelity were used to evaluate the distortion, noise, and artifacts caused by fusion, similarity, and the transfer of complementary information between the fused and source images. We also used the \"params\" metric to evaluate the model's size.  
EN is used to assess the information content of fused images, with higher values indicating richer content, and is crucial for evaluating fusion effectiveness. The SD measures pixel dispersion, reflecting image contrast, which is important for enhancing visibility and details. The SSIM evaluates the structural similarity between the fused and original images, with high values showing the effective preservation of visual features. MI assesses the degree of information correlation between the fusion result and original images, indicating the preservation of original data. FMIdct and FMIw assess the Mutual Information of discrete cosine and wavelet features, respectively, reflecting the algorithm's ability to retain significant original features. VIF, which assesses the visual quality of the fusion result, shows that higher values indicate greater fidelity to human visual perception, representing better quality. The model's size and computational complexity are critical for practical applications, with models having fewer parameters being easier to deploy in resource-limited environments, reducing energy and operational costs.  
In comparative experiments, we selected six classical image fusion algorithms as benchmarks: DeepFuse (CNN-based fusion), DenseFuse (DenseNet-based fusion with autoencoders), NestFuse (fusion with nested connections and spatial/channel attention models), FusionGAN (GAN-based fusion), U2 Fusion (end-to-end unsupervised fusion), and IFCNN (fusion using multiple fusion strategies).
###### Abstract
Enhancements in composite performance are not limited to matrix modification, as the interleaving of nanofiber mats within composite laminates has also been widely explored [33; 34; 35]. Electrospinning is considered one of the most frequently employed techniques for generating nanofiber mats in recent years [33; 34; 35], and the integration of polyacrylamide (PAN)-based electrospun nanofiber mats interlayered into the fiber-reinforced polymer composites has shown significant potential in improving interlaminar fracture toughness and energy absorption capacity, owing to PAN's high carbon content, excellent mechanical properties, and thermal stability up to 300 \({}^{\circ}\)C [36; 37; 38]. PAN-based nanofiber mats are typically produced using the electrospinning technique, which involves the ejection of a polymer solution under a high-voltage electric field toward a conductive collector [39; 40]. The resulting nanofibers exhibit exceptional surface area and porosity, which make them highly suitable for a wide range of engineering applications due to their large specific surface area [41] and strong mechanical interlocking capability, functioning as nanoscale bridging elements that hinder crack propagation and thereby improve the overall toughness and mechanical performance of the laminate [38; 42; 43; 44; 45; 46; 47]. The synergistic integration of self-healing thermoplastics and mechanically reinforcing nanofibers offers a promising approach for the development of next-generation glass-fiber-reinforced polymer (GFRP) composites with enhanced damage tolerance and extended service life. The aforementioned studies demonstrate that both thermoplastic modifiers such as high-density polyethylene and PAN-based nanofiber mats, when used independently, can significantly improve the mechanical performance of laminated composites. However, the simultaneous incorporation of these two functional components may further augment the structural integrity and multifunctional capabilities of the composite system. Therefore, in this study, both PE powder as self-healing material and PAN nanofiber mats were electrospun and then interleaved into glass fiber/epoxy to manufacture self-healing nanofiber-modified GFRP composite structures. Low-velocity impact tests conducted on the specimens. Comparisons were made to investigate the effects of the modifications on impact resistance, energy absorption, and tensile strength capabilities of the specimens.
# Normalized Weighting Schemes for Image Interpolation Algorithms
## 2 Materials and Methods
### Normalized Weighting Schemes
#### 2.2.4 Preliminary Results  
Figure 5 shows the preliminary interpolation results by the traditional bilinear or tetragon-based algorithm (TB), minimum side-based diameter algorithm (MD), and hypotenuse-based radius algorithm (HR). As can be seen, the basic structures and features, in the red square test image, were relatively recovered after image interpolation. Here, the scaling ratio was equal to 4. In this example, TB produced more blurriness artefacts than both MD and HR. However, HR produced more heavy jagged artefacts than both TB and MD. In all cases, HR produced a higher contrast image, with smoother edges, than both TB and HR algorithms cases. This means that the image interpolation quality-based performance, for each weighting scheme-based algorithm mentioned, is dependent on the spatial distribution of the input image.  
Figure 3: Example showing the minimum side diameter for the weighting scheme—this is the MD weighting scheme.
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 1 Introduction
Essentially, NODE describes the input to output variable transformation by a trajectory through a vector field, i.e., mapping the input to latent space. By using a black box numerical ODE solver, the vector field is defined by a neural network and the trajectory is solved. The most vital difference between NODE and other neural networks is that NODE has continuous layers and parameters. Compared with common neural networks with discrete layers and parameters, NODE is more dynamic and flexible. This special dynamism and continuity is suitable for time series forecasting because meaningful information will be learned from local signals of time series by using dynamism and continuity. Therefore, we can apply NODE to address challenge 1 with its high dynamic and flexibility. In addition, through the ODE solver, NODE can continuously extrapolate input series to approach the expected target series in latent space and capture continuous internal series information, which addresses challenge 2. Therefore, we solve the above challenges at the same time by using NODE. To apply powerful NODE to STLF field, except for LSTM, feasible methods include combining NODE with backward propagation neural network (BPNN) or convolutional neural network (CNN). Compared with these two methods, the combination of NODE and LSTM enables to capture continuous series information step by step in chronological order, which is more promising in the electricity load forecasting field. When adding an NODE block ahead, continuous series information can be passed to the series learner, which addresses the challenge of the LSTM-based model we mentioned above, and this can achieve higher prediction accuracy in the electricity load forecasting field. Therefore, in this paper, we are interested in the combination of LSTM and NODE. [PERSON] et al. [13] showed that the stacked bidirectional LSTM models and stacked LSTM models achieved superior prediction performance. In addition, as a feature processor, CNN is also considered in this paper. After attempting different combinations of LSTM and BiLSTM with different depths and considering CNN at the same time, we selected three models with the best performance, which are CNN-BiLSTM-LSTM (CBL), BiLSTM-stacked LSTM (BLL), and stacked BiLSTM-stacked LSTM (BBLL), respectively. Then, NODE is applied to these models to build three new models, which are named ODE-CNN-BiLSTM-LSTM (OCBL),ODE-BiLSTM-stacked LSTM (OBLL), and ODE-stacked BiLSTM-stacked LSTM (OBBLL). Finally, we compare the prediction results of electricity load between models without NODE and with NODE, based on data collected from 2 May to 4 July (2019-2021) in Queensland. The rest of this paper is organized as follows:  
Section 2 introduces the related work in the electricity load forecasting field. Section 3 introduces the structure of LSTM, BiLSTM, and NODE. In Section 4, data and methods of designing our experiments are presented. The detailed experimental results and analysis are given in Section 5.
# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 3 Dataset
### Data Collection
#### 3.1.1 Media Outlets and Label Collection  
Similar to the CheckThat! 2023 [21; 62] and MBFC datasets [18; 19], we collect media outlets in different languages and their factuality and political bias labels.  
Firstly, we collect 5398 media outlets from MBFC to obtain a multilingual media outlets list. We filter the list of possible multilingual media outlets by country, sifting out English-only countries, which are the United States, the United Kingdom, Canada, and Australia, and end up with 627 possible multilingual media outlets. Secondly, we use three language detection methods to further filter the multilingual news media list, which are the tags of HTML and two Python packages: langid (v1.1.6) ([[https://github.com/saffsd/langid.py](https://github.com/saffsd/langid.py)]([https://github.com/saffsd/langid.py](https://github.com/saffsd/langid.py)), accessed on 15 December 2023.) and langdetect (v1.0.9) ([[https://github.com/Mimino666](https://github.com/Mimino666)]([https://github.com/Mimino666](https://github.com/Mimino666)) /langdetect, accessed on 15 December 2023.), and we find that HTML tags are the most accurate. Thus, we utilize the HTML tags and obtain 123 multilingual media outlets. After that, we expand the multilingual media outlets list by finding multilingual versions of media outlets in the English media outlets list, and we finally obtain a multilingual media outlets list containing 291 media outlets. The remaining English news media sites make up our English media outlets list.  
In terms of labeling the articles, we used distant supervision based on the media outlet labels from the MBFC website. This method assumes that the overall bias and factuality of a media outlet reflect the articles published by that outlet, a practice commonly adopted in prior research. Therefore, we directly applied the MBFC labels for factuality (ranging from \"Very Low\" to \"Very High\") and political bias (ranging from \"Extreme Left\" to \"Extreme Right\") to the articles collected from these outlets, without modifying this methodology.
## 1 Introduction
It is flexible enough to accommodate any language combination, even those involving three or more languages. In this article, we present the design of an advanced mixed-language ASR system optimized for Mandarin-English speech. Our system achieves an impressive average error rate of less than 13% across all tested datasets, where the lowest recorded average error rate achieved in one of the datasets was 7.56%. This is primarily due to the integration of threshold adjustments and recognition result compensation mechanisms. This approach provides a robust, adaptable solution for addressing the challenges of mixed-language speech recognition. The remainder of the article is structured as follows: Section 2 reviews the literature on mixed-language speech recognition, with a particular emphasis on bilingual ASR systems and existing approaches to addressing the challenges of recognizing speech that alternates between languages. The proposed self-evaluated bilingual ASR (SEB-ASR) system is described in Section 3, outlining the evaluation metrics employed, including word error rate (WER), character error rate (CER), and mixed error rate (MER), along with the workflow for MER calculation. Section 4 gives the results and discussion. Section 4.1 presents the performance evaluation of the VOSK monolingual ASR system and explains its integration into our mixed-language ASR framework, while Section 4.2 provides an in-depth description of the implementation of the system, operational workflow, and the enhancements it brings to the baseline VOSK system in handling mixed-language speech. Finally, Section 5 concludes the article by summarizing the key findings and discussing future directions for advancing multilingual ASR systems.