esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 5 Experimental Results and Discussion
### Results
#### 5.2.1 Analysis of the Effectiveness of Active Learning in Rumor Detection  
Table 5 provides all of the baseline accuracy scores for the experimental models along with the best-performing accuracy scores among all query strategies.  
For the PHEME dataset, several models using active learning achieved performance surpassing that of the baseline. Even LDA, the model with the lowest performance among them, reached 87.3% of the baseline performance. Figure 3a illustrates that its performance can be further improved to 0.741 with the addition of query datapoints, reaching up to 96% of the baseline performance. This demonstrates that by carefully selecting an optimal number of query instances, machine learning models can be trained effectively with a smaller dataset compared to the entire training set while achieving the desired performance levels.  
For the RUMDECT dataset, all models using active learning exceeded the baseline performance achieved by the models trained using the entire dataset, achieving a performance level of 95%. Likewise, several models, including DTC (Gini), DTC (Entropy), GP, LDA, and Ada, outperformed the baseline. Under appropriate query strategies, these models can converge quickly relative to baseline performance. For example, as shown in the Figure 3b, the RFC model uses the Unc(Margin) strategy to quickly converge within just 100 query instances.  
In summary, the results in Table 5 demonstrate that all models can approach or achieve baseline performance using no more than 200 data instances when paired with appropriate active learning query strategies. These data instances represent only 5% and 6% of the training sets for the PHEME and RUMDECT datasets, respectively. This highlights the effectiveness of active learning in significantly reducing labeling costs while maintaining high-performance results in the context of rumor detection.
## 1 Introduction
Particularly, the existing limitation in performance of CF is due to the approximation of the channel by an integer contributing to additional self-noise apart from receiver noise. In this paper, we propose a precoder, which removes the self-noise completely and, therefore, improves the performance of CF. Further, in most of the existent works, the performance of CF is studied at the relay node, assuming the transmission from relay-to-destination to be perfect point-to-point transmission. As an extension of our work in [10], we consider a noisy transmission from relay-to-destination and analyze the end-to-end performance of CF.
# An Ultra-Thin Composite Metasurface with Hybrid-Damping Modes for Broadband Sound Absorption
## 5 Experiment Validation  
The samples of the HR structure were fabricated by 3D printing technology with ABS plastic, as shown in Figure 7a. The sample is composed of two basic units, as shown in Figure 1a, and has a dimension of 89 mm \(\times\) 70 mm \(\times\) 21 mm. The metasurface sample is achieved by placing the foams inside the HR structure. The impedance tube has a side length of 100 mm, in which only the plane wave can propagate below 1600 Hz, by which the test frequency range is limited and cannot cover the whole target band here. Due to the smaller size of the sample area compared to the tube area, the sample is positioned within a testing frame to fill the gap between the tube and the sample. The assembly's incident surface is kept flat, and the surrounding area of the sample is ensured as a rigid wall. Consequently, the sample's specific surface impedance can be obtained, and the sound absorption coefficient is calculated accordingly. To ensure that the foam remains firmly in the metasurface sample during the testing process, it has been adhered to the frame using glue applied along its perimeter.  
The experimental results of the HR structure and metasurface are shown in Figure 8a,b. It can be observed that the HR structure obtains a smoother spectrum with 82% average absorption in the experimental result, while 78% average absorption is achieved in the simulation result but with obvious fluctuations. This error of absorption performance can mainly be attributed to the manufacturing errors of small hole diameters and the energy dissipation of air itself. Considering that the precision of 3D printing technology is \(\pm\)0.1 mm, the sample may obtain smaller holes with stronger damping, by which the absorption can be enhanced. Furthermore, owing to the inherent viscosity, the air medium itself can still cause some attenuation during the propagation of sound waves, which can broaden the peak bandwidth and lead to a smoother spectrum. In Figure 8b, with the assistance of the porous layer, the metasurface achieves a much flatter absorption spectrum. Both experimental and simulation results exhibit 81% average sound absorption, albeit with minor fluctuations observed solely in the simulation results. The absorption fluctuations are further suppressed by incorporating the porous layer. It is evident that  
Figure 7: The samples of (**a**) the broadband HR structure and (**b**) the metasurface. (**c**) The impedance tube testing system.  
the experimental results can verify the effectiveness of simulation works, so the predicted absorption performance is also credible across the range of 1600-10,000 Hz.
## 2 Data Analysis and Features Extraction
### Health Features Extraction
#### 2.2.1 Time Health Features Extraction  
Existing studies have shown a close correlation between the changes in the charging voltage curve and the battery capacity degradation. As shown in Figure 2, as the number of charge-discharge cycles increases, both the overall charging efficiency and duration decrease, resulting in a deterioration of the battery's SOH. The constant current charging time directly reflects the charging speed during the constant current phase. As the battery undergoes aging, its capacity diminishes, resulting in a reduction in the duration of constant  
Figure 1: SOH degradation curves of different batteries.  
current charging. This feature is directly related to the degree of battery aging and is therefore chosen as the first health feature (HF1) to reflect SOH changes. During the constant voltage charging phase, the battery voltage is kept at a predetermined level. Under the same charge-discharge cycle, as the internal resistance of the battery rises, the charging current decreases at a slower rate, resulting in a longer constant voltage charging time compared to the constant current charging time. Therefore, the constant voltage charging time is selected as the second health feature (HF2) to reflect SOH changes. As shown in the zoomed-in section of Figure 2, the charging voltage curve exhibits the greatest variation within the range of 3.8-4.1 V [28]. Considering practical application scenarios such as range anxiety of electric vehicle drivers and charging habits, the battery charging time within the 3.8-4.1 V range is selected as the third health feature (HF3) to reflect SOH variations. This selection has important practical significance.
_Appl. Sci._**2015**, \(5\), 62-76; doi:10.3390/app5020062  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Numerical Models for Exact Description of _in-situ_ Digital In-Line Holography Experiments with Irregularly-Shaped Arbitrarily-Located Particles**  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
CORIA-UMR6614, Normandie Universite, CNRS, Universite et INSA de Rouen,  
Avenue de l'Universite, BP 12, 76801 Saint-Etienne du Rouvray cedex, France;  
E-Mails: [EMAIL_ADDRESS] (W.W.); [PERSON]RESS] (S.C.);  
[EMAIL_ADDRESS] (A.M.); [PERSON]_ADDRESS] (D.L.);  
[PERSON]_ADDRESS] (G.G.); [EMAIL_ADDRESS] (G.E.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS],  
Tel.: +33-232-953-600; Fax: +33-232-910-485.  
Academic Editor: [PERSON]  
_Received: 12 February 2015 / Accepted: 15 April 2015 / Published: 22 April 2015_  
We present the development of a numerical simulator for digital in-line holography applications. In-line holograms of arbitrarily shaped and arbitrarily located objects are calculated using generalized Huygens-Fresnel integrals. The objects are 2D opaque or phase objects. The optical set-up is described by its optical transfer matrix. A wide variety of optical systems, involving windows, spherical or cylindrical lenses, can thus be taken into account. It makes the simulator applicable for design and description of _in situ_ experiments. We discuss future applications of this simulator for detection of nanoparticles in droplets, or calibration of airborne instruments that detect and measure ice crystals in the atmosphere.  
digital holography; transfer matrices; phase contrast; ice crystals; nanoparticles
## 2 Materials and Methods
### Feature Extraction  
The spectral power density of each electrode's signal was extracted using discrete Fourier transform (DFT), and the band power (BP) of each frequency band was then calculated. The frequency bands of interest were delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). For each participant and each electrode, we averaged power in each band across the 27 EEG epochs of the resting and emotion-induction states, separately. For each state and each participant, a total of 29 BP feature values (from 29 electrodes) in each frequency band were obtained. The BPs were subsequently used to calculate relative power features.  
Different types of relative powers have been used in various EEG studies. In this study, we extracted the three types of relative power and compared their classification performances (Figure 5). Type-I relative power (RP-I) is calculated with the following formula [33]:  
\[\mathrm{RP-I}=\frac{BP(A)-BP(B)}{BP(A)+BP(B)} \tag{1}\]  
where \(BP(A)\) and \(BP(B)\) denote the BPs of two different electrodes \(A\) and \(B\) in the same frequency band. Type-II relative power (RP-II) is given by [11] the following:  
\[\mathrm{RP-II}=\frac{W(A)-W(B)}{W(A)+W(B)} \tag{2}\]  
where \(W\) is the power within a specific band of interest (e.g., alpha) divided by the total power within the entire band of 1-45 Hz. Type-III relative power (RP-III) corresponds to the difference between the natural log-transformed BPs of two different electrodes as follows [8]:  
\[\mathrm{RP-III}=\log(BP(A))-\log(BP(B)). \tag{3}\]  
For each frequency band and each type of relative power, a total of 406 (\(29\times 28/2\)) values were extracted for each participant. These values carry information about regional inter-hemispheric (e.g., FP1-FP2), cross-regional inter-hemispheric (e.g., FP1-T4), and intra-hemispheric (e.g., FP1-O1) asymmetries. Because there are five bands, the number of relative power features of each type (RP-I, RP-II, RP-III) increases to 2030 (\(406\times 5\)) when all bands are considered. For each kind of feature (e.g., RP-I in a specific frequency band), we obtained one \(N\)-dimensional feature vector from each participant. A feature vector is called a data point or a datum in this paper.  
Figure 5: Illustration of the calculation of three types of relative powers in a specific frequency band between a pair of electrodes A and B. DFT = discrete Fourier transform; PSD = power spectrum density, and BP = band power.
# Analysis of Numerical Artifacts Using Tetrahedral Meshes in Low Frequency Numerical Dosimetry
## 1 Introduction
The solution obtained with the smallest voxels (edge length of 0.25 mm) was taken as reference. Homogeneous and localized exposure were considered and, although tetrahedral meshes improved the representation of the tissue boundaries, numerical artifacts were registered and filtering techniques were still necessary. The authors conclude that low quality elements in the dosimetric domain are the reason for the failure of tetrahedral meshes in removing numerical artifacts. In this paper, we analyze the problem from another point of view. A specific methodology is used to identify the source of numerical artifacts produced by tetrahedral meshes to provide additional details about their role in low-frequency numerical dosimetry. Simplified and realistic 3D human models are used in numerical simulations. In particular, simulations are performed on: (1) simple 3D models that also have an analytical solution,  
Figure 1: No stair-casing effect on curved boundaries thanks to tetrahedral discretization (**a**). Staircasing effect on curved boundaries due to the use of a voxel-based mesh (**b**). (2) 3D models that can be studied with a 2D equivalent simulation used as reference solution, (3) anatomical 3D models that make it possible to investigate the results in real exposure conditions. It is shown that in real exposure scenarios, similarly to the conclusions in [15], numerical artifacts still require filtering techniques to be removed. However, taking advantage of specific exposure scenarios where the unique source of numerical artifacts is the stair-casing, it is shown that tetrahedral meshes are able to completely remove the errors related to geometrical modeling of the computational domain. Furthermore, it is shown that numerical artifacts can also occur in tetrahedra with very high quality if they belong to a boundary characterized by high contrast of the conductivity value. Therefore, in this paper we provide further clarifications about the role of tetrahedral meshes in numerical dosimetry with special attention to the artifacts originating from stair-casing approximation of curved boundaries of human models.
## 4 Discussion
### Effect of the Replacement of Micronutrients with Pre-Treated Effluent  
Micronutrients are an important ingredient in microbial culture media influencing several biochemical processes. Although research on the effect of micronutrients on thraustochytrids is limited, it has been reported that under combined Fe, Cu, Zn and Mn limiting conditions, biomass productivity is impeded in several _Schizochytrium_ and fungi species [66; 67; 68]. Consequently, the increase in biomass and lipid productivity with increasing concentrations of effluent in our study could be attributed to the increasing concentrations of trace elements, since nitrogen, phosphorous and potassium content was almost identical with negligible differences among the treatments. However, the decline in biomass at high effluent concentrations (48% _v/v_) could be due to high concentrations of copper (5.6 mg L\({}^{-1}\)) and zinc (12 mg L\({}^{-1}\)), since it has been reported that concentrations of copper above 0.5 mg L\({}^{-1}\) can induce growth inhibition in several _Schizochytrium_ sp. [69]. Furthermore, the increased lipid content at moderate and high effluent concentrations was probably induced by the increasing concentration of Fe. To the best of our knowledge, no research has been conducted in _Schizochytrium_ sp. and Fe limited conditions; however, several studies on oleaginous fungi indicate a positive relationship between Fe and lipogenesis [70; 71] due to its role in the synthesis of citrate, which serves as substrate for acetyl-CoA a precursor for fatty acid synthesis [64; 68].
## 6 Experimental Analysis and Results
### Security and Privacy Comparison  
To summarize, Table 4 compares the proposed AUT protocol to two previously existing schemes from [PERSON] et al. [6], [PERSON] et al. [7] and [PERSON] et al. [30]. We used the words \"Yes\" and \"No\" to indicate whether or not a scheme fulfilled the security and privacy  
Figure 4: Comparison of the transactional message encryption times.  
Figure 5: Comparison of the transactional message decryption times.  
requirements that were evaluated. As can be seen in the Table, none of the schemes in [6; 7] ensured the privacy of a participant's data throughout the data transfers.  
Similarly, the table illustrates that the schemes in [6; 7; 30] failed to ensure the confidentiality of participant data in blockchain network applications. Finally, as shown before in this work, the schemes in [6; 7; 30] could not guarantee participant anonymity or untraceability. Additionally, recent studies have revealed flaws in the approaches used in these schemes [1]. Nonetheless, the proposed AUT protocol passed all of the considered security and privacy criteria in this work.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 6 Discussion and Conclusions
### Issues and Challenges Need to Be Addressed  
_Lack of 3-Dimensional Data Acquisition Ability:_ All the systems presented in Section 4 are only able to monitor the air pollution situation of urban surface or roadside while the necessities and importance of the 3-Dimensional air pollution information are highlighted [120; 121; 122]. Current LIDARs or satellites based 3-Dimensional monitoring systems face the same issues as the conventional monitoring systems. We anticipate that 3-Dimensional air pollution information with high spatio-temporal resolution can be acquired in real-time by mounting the portable sensor nodes on the multi-rotors Unmanned Aerial Vehicles (UAVs).  
Figure 11: Grading result of the six major comparison properties in Static Sensor Network (SSN), Community Sensor Network (CSN) and Vehicle Sensor Network (VSN) (’0’ means ’None’; ‘1’ means ’Low/Short/Inconvenient’; ‘2’ means ’Medium’ and ‘3’ means ’High/Long/Convenient’).  
_Infeasibility of Active Monitoring:_ The sensor nodes in SSN, CSN and VSN systems presented are all passive monitoring sensor nodes (sensor nodes periodically update data). We believe that active monitoring (users can fully control the sensor network including the formation and routes of the sensor nodes) provides higher flexibility and QoS.  
_Uncontrolled or Semi-Controlled Carriers:_ The carriers in SSN, CSN and VSN systems are uncontrolled or semi-controlled because they are either stationary or with pre-determined routes. We anticipate that fully controlled carriers have higher mobility and make active monitoring possible. By utilizing the fully controlled carriers (_i.e._, the multi-rotors UAVs), feasibility in deployment, cost efficiency of systems and convenience in maintenance can be achieved.
###### Abstract  
Abundantly available SiO2 (silica) has great potential as an anode material for lithium-ion batteries because it is inexpensive and flexible. However, silicon oxide-based anode material preparation usually requires many complex steps. In this article, we report a facile method for preparing a SiO2/C composite derived from coal combustion fly ash as an anode material for Li-ion batteries. SiO2 was obtained by caustic extraction and HCI precipitation. Then, the SiO2/C composite was successfully obtained by mechanical milling followed by heat treatment. The samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Electrochemical properties were tested using an 18650 cylindrical cell utilizing LiNi0.8 Co0.15 Al0.05O2 (NCA) as the counter electrode. Based on the obtained results, the physiochemical characteristics and electrochemical performance, it was determined that SiO2/C composites were greatly affected by the temperature of heat treatment. The best result was obtained with the SiO2 content of 10% w/w, heating temperature of 500 \({}^{\circ}\)C, initial specific discharge capacity of 586 mAh g\({}^{-1}\) at 0.1 C (1 C = 378 mAh g\({}^{-1}\)), and reversible capacity of 87% after 20 cycles. These results confirmed that the obtained materials had good initial discharge capacity, cyclability, high performance, and exhibited great potential as an anode material for LIBs.  
**Keywords:** anode material; composite material; lithium-ion batteries; SiO2/C
## 1 Introduction  
Machine learning is pivotal in enhancing 5G network slicing and orchestration, offering innovative solutions to complex challenges, and unlocking new opportunities for network optimisation [1]. Fifth-generation (5G) cellular networks aim to transform mobile broadband communication by delivering unprecedented data rates, quality of service, connectivity, and flexibility in mobile networks [2]. Network slicing introduced a paradigm shift in mobile broadband communications, creating multiple virtual verticals from end to end while sharing the available physical infrastructure [3]. With network sub-slicing, these slices are further divided into granular sub-slices dedicated to a specific service, tenant, or application instance. This available network resource is managed and allocated as required to each service or user demanding a network slice, helping to reduce capital expenditure, a reputed challenge in current 5G network slice orchestration. Some of the standard network slices include ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) [4]. As the demand for these slices continues to grow, existing methods for resource allocation will struggle to meet performance expectations.  
This paper addresses these challenges by proposing a novel ONSSO framework to autonomously orchestrate 5G network slices optimally over a mobile network. This is conducted in real time, thereby improving the quality of service (QoS) and quality of experience (QoE). A key component of this framework is the Enhanced Sub-Slice (eSS) model, a machine-learning-based model that facilitates more granular resource allocation within the overall network slicing architecture. Figure 1 shows the isolated end-to-end sub-sliced networks and how they can make up a single slice-sharing network resource pool.  
The proposed ONSSO utilises LazyPredict, AutoRL, and Ray Tune to dynamically select the most effective machine learning algorithm for classification, prediction, and finetuning, respectively. Together, these pipelines culminate into a Company Network as a Service (CNaaS) model, designed to enhance operational efficiency for mobile network operators (MNOs). During the evaluation, the performance of the proposed framework demonstrated significant improvement in key performance metrics such as latency, bandwidth, throughput, jitter, packet loss, and resource utilisation compared to conventional methods.