esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Training and Test Dataset  
In this experiment, the size of the training dataset using the proposed method was fixed to show that the small training dataset from which noise has been properly removed is more effective than the large training dataset containing noise data. For the entire dataset, including 225,711 entries, the proposed method uses 20,000 (about 8.9%) and 40,000 (about 17.7%), respectively, as the training dataset in the two experiments. To maintain the time series characteristics of the dataset, each training dataset uses the first part of the entire dataset, and the proposed method uses the remaining 91.1% to 82.3% of the entire dataset as the test dataset. At this time, the test dataset in each experiment is divided into 100 folds of the same size, and each fold is sequentially input to the model. The model used classifies the sequentially input test dataset folds, regenerates the new training dataset using filtered classification results, and repeats the process of retraining the model. On the other hand, the ideal experiment to compare the performance of the proposed experiment uses all data up to the starting index of each fold. For example, in the first experiment with 20,000 training datasets (the size of each fold is 2057), the proposed method uses a model trained with 20,000 training datasets to measure the performance for the tenth fold. However, in the ideal experiment, classification is performed with a model trained on all data from 0 to 40,570. This difference in the size of the training dataset was established to show that the proposed model can obtain a sufficient level of classification accuracy even with a model trained quickly using a small-scale training dataset.
## 4 Engineering Application and Analysis
### Characteristics of Rock Mass and Lining Deformation
#### 4.2.3 Concrete Lining Deformation Characteristics  
To visually present the deformation characteristics of concrete linings under varying rock pressures \(p_{\rm i}\), calculations were performed for linings cast with concrete of different strength grades to determine the radial displacement \(u_{\rm b}\) of the lining's outer wall under different \(p_{\rm i}\) conditions. Within the elastic operational phase of the lining and excluding damage scenarios from the consideration, the linear relationship between the lining's support pressure \(p_{\rm i}\) and the radial displacement of its outer radius is plotted in a Cartesian coordinate system, as depicted in Figure 11.  
Figure 11 clearly demonstrates the effect of varying concrete strength grades on the equivalent resistance coefficient \(k\) of the lining. As the concrete strength grade increases, so does the equivalent resistance coefficient \(k\) (i.e., the slope of the curve). The data points and curves in Figure 11 not only reveal the impact of material properties on structural response but also provide crucial guidance for selecting an appropriate concrete strength grade. This analysis is instrumental in ensuring that tunnel linings remain within a safe operational range when subjected to anticipated rock pressures, thus preventing transition into nonlinear stages that could lead to damage.  
Figure 11: Radial displacement of lining and surrounding rock pressure in the fault section.  
Figure 10: Fitting analysis of deformation in grade IV surrounding rock tunnel walls.
## 3 Methods
### Crop Yield Forecasting  
Crop yield forecasting proposed in this study employs a machine learning technique i.e., eXtreme Gradient Boosting (XGBoost) algorithm [38] implemented in R environment [32; 39] to predict crop yield residuals from the Theil-Sen monotonic trend using a variety of predictors derived from satellite and agrometeorological data. To train the XGBoost method, an extensive input table was constructed for each administrative unit (LAU or NUTS-2) consisting of \(r\) rows and \(c\) columns, where \(r\) denotes a number of years for which predictors and reference crop yields were available, and \(c\) indicates a number of predictors. The following predictors were calculated for each of eight GDD levels (150 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 450 \({}^{\circ}\)C,..., 1200 \({}^{\circ}\)C):  
* Minimum, maximum and mean air temperature;
* Surface radiation;
* Accumulated surface radiation since 1 April;
* Soil moisture at 0-7 cm and 7-28 cm levels;
* Precipitation;
* Accumulated precipitation since 1 April;
* NDVI\({}_{\rm GDD}\);
* \({\rm VCI_{GDD}}\);
* \({\rm LST_{GDD}}\);
* \({\rm TCI_{GDD}}\);
* Annual maximum NDVI (which does not correspond to the GDD levels).  
In total, there were 170 predictors (\(c\)) but the number of years (\(r\)) varied between crop types and administrative units due to the reference data availability. For LAUs, the number of years is presented in Figure 3, whereas for NUTS-2, it equaled 17 for winter wheat and 22 for winter rapeseed and maize.  
All predictors were linearly scaled to the range between zero and one. Then, highly correlated predictors (above 0.75) were removed. Further, the feature selection procedure was applied based on the recursive feature elimination [40] employing the XGBoost method. The optimized XGBoost algorithm was ultimately trained based on selected predictors and crop yield residuals as a dependent variable. The application of the prediction model resulted in the forecasted crop yield residuals. The final absolute yield forecast was then calculated as a sum of this value and the crop yield estimated from the Theil-Sen monotonic trend.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Materials
#### 2.2.2 Permittivity Measurements of Solid Polymers  
Table 1 shows the dielectric properties of the different polymers for the three frequencies. The materials show very slight differences in frequency, indicating that within the microwave bandwidth, the dielectric properties are stable. As expected, materials containing silica in the resin (Rigid 10K and Composite X) present a higher dielectric constant, and materials made from polymer powder (UPP and PA11) present a lower one. The same behavior is observed in the material density, which, in the end, will be translated to the weight of the final printed item.
## 2 Microspeaker  
A schematic diagram of the microspeaker used in this study is shown in Figure 1a; the corresponding front side view, rear side view (without ventilation cloth), and rear side view (with ventilation cloth) are shown in Figure 1b-d, respectively. A typical microspeaker mainly consists of six parts: front cover, under yoke (UY), diaphragm, voice coil (VC), polar piece (PP), and magnet. The diaphragm is the crucial part of the loudspeaker for sound production and is generally made of paper, polymer, metal, fabric, or composite, etc. The microspeaker does not have a spider due to the space and size constraints; hence, the suspension is be taken care of by the diaphragm itself, resulting in the crucial role of diaphragm design. The magnetic loop is comprised of a magnet, an upper PP, and a lower PP; its primary function is to generate a uniform and fixed magnetic field to enable the VC to operate. The vibration thus generated by the diaphragm pushes the surrounding air to generate sound waves. Thus, the sound can be transmitted towards the front and rear sides of the diaphragm. There are 11 vents in the microspeaker casing which allow sound from the rear side of the diaphragm to travel to the rear side of the microspeaker (Figure 1c) via the rear chamber.  
Figure 1: (**a**) Microspeaker schematic, (**b**) Front view of microspeaker, (**c**) Rear view (without ventilation cloth) of microspeaker, and (**d**) Rear view (with ventilation cloth) of microspeaker.  
The microspeker investigated in this work was a 40 mm diameter unit (DSH742-005) from Merry Electronic Co., Ltd., Taichung, Taiwan (ROC), with 11 acoustic holes (vents) in the casing towards the rear side of the microspeker. Table 1 shows the main specifications of this microspeker. In addition, its fundamental frequency was 80 Hz \(\pm\) 20 Hz.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Based Insight and Predictive Analytics for Educators
#### 3.5.3 Examples of Applications  
The use of MLLMs in providing AI-based insights and predictive analytics for educators is demonstrated in several applications. Early warning systems analyze multimodal data to identify students at risk of underperforming or dropping out. For instance, text analysis of student messages combined with engagement metrics can provide early indicators of disengagement [82]. Sentiment and emotion analysis processes textual and visual data to gauge student sentiments and emotions, helping educators understand the classroom climate and address issues promptly [83].  
Adaptive feedback generation is another application where MLLMs analyze student submissions to provide personalized feedback, highlighting areas of strength and improvement [55]. By aggregating and analyzing data on student interactions with course materials, educators gain curriculum and instructional design insights that inform adjustments and teaching strategies [84]. Additionally, analyzing multimodal data from group projects, discussions, and peer interactions allows for the assessment of collaborative skills and group dynamics [85]. Evaluating student presentations using audio and visual data provides feedback on delivery, clarity, and engagement [86].
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 2 Small Molecules
### Immunosuppressants
#### 2.2.6 Ionic Liquids  
Ionic liquids (ILs) are \"liquid salts\" created by combining acids and bases, serving roles such as solubilizers, antimicrobial agents, and permeability enhancers in various biomedical applications. When one component of the ionic pair is an active pharmaceutical ingredient, the resulting ILs are referred to as API-ILs. Previously, several choline-based ILs have been observed to enhance the skin delivery of a range of biological macromolecules, such as choline malate, choline-producing acid choline, and several choline fatty acids. As a new osmotic accelerator, ILs have the advantages of biocompatibility, degradability, customization, stability, and safety [70]. [PERSON] et al. use ILs as carrier systems and osmotic accelerators to promote CyA solubility. The potential of choline ricinoleate ([Ch] [Ra]), choline sorbate ([Ch] [So]), choline geranate ([Ch] [Ge]), and choline citrate ([Ch] [Ci]) in the cutaneous delivery of cyclosporine was evaluated. In three of the four ILs, the solubility of CyA increased to 95.5 mg/mL, while its solubility was only 16.4 mg/mL in [Ch] [Ci]. The content of water in ILs significantly influences the permeation efficacy; the maximum efficiency is achieved at a 30% water content. In pharmacodynamics studies, the ILs presented around a 70% reduction in TNF-\(\alpha\) and IL-22. Moreover, [Ch] [So] demonstrated no significant cytotoxicity and irritation compared with azone following 7 days of administration [71]. To solubilize and facilitate the infiltrating properties of cyclosporine, [PERSON] et al. prepared a cyclosporine-loaded system by combining choline/geranic acid ionic liquid with Pluronic F127. Compared with free cyclosporine, cyclosporine ionic liquids and ionic liquid gel improved the penetration of cyclospor in porcine skin models. In rat pharmacokinetic models, the Cmax and AUC values of gel were increased 2.6 and 1.9 times, respectively. In the imiquimmod model, the psoriasis lesion area and PASI score were significantly reduced, and changes in skin thickness, blood flow, and percutaneous water loss were reversed [72].
## 4 Discussion
### F10.7 and TEC
TEC enhancement in the southern EIA crest obtained by us is comparable to that presented by [31] for TSE2019 (winter hemisphere) over South America, and by [28] for the 2019 December 26 annular solar eclipse (winter hemisphere). Also, we can note that all three eclipses occurred during autumn or winter in their respective hemispheres. Therefore, we demonstrate that our findings of ionospheric behavior in the southern EIA crest for the eclipses of 2018 and 2022 are validated by earlier studies. Moreover, previous research suggests that solar eclipses can cause strong oscillations in the zonal electric field and in the neutral wind circulation pattern, causing a pronounced reduction or marked enhancement in the EIA crests [25; 28; 29; 71]. As a possible explanation for these phenomena, we must consider that, in the summer hemisphere, the Sun's rays arrive perpendicular to the surface, generating a temperature gradient that produces the summer-to-winter neutral wind. This neutral wind transports the plasma by ion-drag effect along the magnetic field lines, in the upward/equatorward direction in the summer hemisphere and in the downward/poleward direction in the winter hemisphere. On the other hand, the vertical E \(\times\) B drift at the equator carries plasma to high altitudes, wherein the chemical recombination loss rate is lower and the ambipolar diffusion occurs on both sides along the magnetic field lines (fountain effect) carrying to magnetic latitudes where the chemical recombination loss rate is more significant than the production rate [72]. At the summer EIA crest, an accumulation of plasma occurs due to the opposite directions of the neutral wind and ambipolar diffusion (as modeled by [73]); on the contrary, at the winter EIA crest, the neutral wind and the ambipolar diffusion have the same direction, so there is no accumulation of plasma. Since the solar eclipse is only one day in the entire season, the weakening of the neutral wind due to the shadow of the eclipse should decrease summer EIA crest accumulation and increase winter EIA crest accumulation [74]. This process is observed in the southern EIA crest over South America in PSE2018 (summer), PSE2019 (winter), PSE2020 (summer), and PSE2022 (autumn, close to winter).
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 5 Conclusions  
This paper proposes a comparative analysis of statistical and machine learning methods along with their combination to two days ahead short-term wind speed point and interval forecasting. Various techniques that have been used for diverse forms of forecasting and forecast combinations along with uncertainty measures were explored. The use of GAM for other forms of forecasting but not for wind speed forecasting is the main gap filled by this work. Different mathematical underpinnings of these models and accuracy metrics along with uncertainty metrics serve as the methods and materialsemployed in this paper. Accuracy measures and uncertainty metrics were used to select the best models. In most of the discussions, it was found that the model AQRA, which is the additive quantile regression average method, is the best model for wind speed forecasting using both the point forecasts accuracy metrics (such as MAE, RMSE and MAPE) and the prediction intervals uncertainty measures involving PIW, PINC, PICP, PICAW, PINAW and PINAD. Hence, forecasts combination tends to improve short-term wind speed forecasting. Quantifying uncertainty in forecasts can assist the grid's proper planning for scheduling of resultant power incorporation and dispatch. Future areas of study involve exploring other machine learning or deep learning algorithms for point forecasting, forecasts combination, prediction interval construction, and combination in wind speed/power forecasting. Conceptualisation, L.O.D. and C.S. ; methodology, L.O.D., and C.S. ; software, L.O.D., C.S. and R.M. ; validation, L.O.D., C.S., C.C. and R.M. ; formal analysis, L.O.D. and C.S. ; investigation, L.O.D., C.S., C.C., and R.M. ; resources, L.O.D., and C.C. ; data curation, L.O.D. ; writing--original draft preparation, L.O.D. and C.S. ; writing--review and editing, C.S., C.C. and R.M. ; visualisation, L.O.D. and C.S. ; supervision, C.S., C.C. and R.M. ; project administration, C.S., C.C. ; and funding acquisition, L.O.D., and C.C. All authors have read and agreed to the published version of the manuscript. The research was funded by the DST-CSIR National e-Science Postgraduate Teaching and Training Platform (NEPTTP) (www.science.ac.za). The support of the DST-CSIR National e-Science Postgraduate Teaching and Training Platform (NEPTTP) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the NEPTTP. In addition, we are grateful to the Wind Atlas South Africa (WASA) for providing the data ([[http://wasadata.csir.co.za/wasal/WASAData](http://wasadata.csir.co.za/wasal/WASAData)]([http://wasadata.csir.co.za/wasal/WASAData](http://wasadata.csir.co.za/wasal/WASAData))). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript:
## 4 Results and Analysis
### Analysis of InSAR Deformation Results
Among the four selected points mentioned, P1 exhibits the highest cumulative deformation, approximately \(-170\) mm, with an average deformation rate of \(-70\) mm/yr (Figure 6a). Deformation results from the descending images indicate two significant acceleration periods at points P1 and P2 from January 2022 (Figure 6b). Overall, deformation at P3 is minimal, showing an indistinct trend in deformation rate change.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 4 Experimental Verification
### Fixed-Frequency Mode Experiment  
In fixed-frequency mode, a fixed coil spacing of 24 cm is used. A phase diagram of the voltage and current at the emitter when the load \(R_{\mathrm{L}}\) is 5 \(\Omega\) and 15 \(\Omega\), respectively, is shown in Figure 13. At \(R_{\mathrm{L}}\) = 5 \(\Omega\), the voltage lags behind the current by 12.12\({}^{\circ}\), and the system is capacitive at this time. At \(R_{\mathrm{L}}\) = 15 \(\Omega\), the voltage leads the current by 19.04\({}^{\circ}\), and the system is inductive at this time.  
Figure 14 shows the phase diagram of the voltage and current at the emitter when the load \(R_{\mathrm{L}}\) = 7 \(\Omega\). When the coil distance is 18 cm, the voltage lags behind the current by 10.38\({}^{\circ}\), and the system is weakly capacitive. When the coil distance is 28 cm, the voltage leads the current by 15.65\({}^{\circ}\), and the system is inductive.  
From the amount of frequency detuning and the analysis of the waveforms in Figures 13 and 14 above, the actual resonant frequency of the system inferred should be less than 48 kHz.
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Cellular Network Architecture
#### 2.1.1 Global System for Mobile Communications  
The GSM is a digital wireless network specified by standardization committees from major European telecommunications operators and manufacturers. Through the standard, all mobile users worldwide can use a common set of compatible services. Figure 1 illustrates the architecture of GSM [16; 17; 18]. The network system of GSM at least includes three sections: an MS (Figure 1a), a Base Station Subsystem (BSS, Figure 1b), and a Network and Switch Subsystem (NSS, Figure 1c). Moreover, it has to build the standard communication interface between any two components in GSM to transmit information and control command. Through the standard interface, the components in GSM can communicate with each other and complete the capability of communication. BSS consists of the Base Transceiver Station (BTS, Figure 1d) and the Base Station Controller (BSC, Figure 1e). NSS consists of the Mobile Switching Center (MSC, Figure 1f), Visitor Location Register (VLR, Figure 1g) and Home Location Register (HLR, Figure 1h). The work of BTS is listening to the order from BSC to communicate with the MS via the radio interface, while the BSC communicates with the MSC via the A interface (Figure 1i). MSC is a special switch that executes the capability of circuit switch and is responsible for recording to the billing system. HLR is responsible for recoding the information of user subscribed our system, including what service user subscribed and the location of MS. VLR is responsible for recording the information of user inside its domain, including the state and location of user.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Data Collection Modalities
#### 3.1.2 Auditory Modalities  
Auditory data provides insights into discourse patterns, collaboration, and emotional tone through voice and paralinguistic signals. It is typically gathered using microphones in classroom or online settings [43; 44; 45]. Audio features such as Mel Frequency Cepstral Coefficients (MFCCs) and Voice Activity Detection (VAD) are used to analyse speech quality and emotional engagement [46; 47]. In some cases, speech is synchronized with video to investigate participation and discussion quality [9; 48]. Mobile setups like AirPods or lapel mimics have also been applied to record in situ conversations [49].  
Figure 1: Key methodological components of multimodal learning analytics (MMLA), from data collection to tools and frameworks.