esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 LED Modeling: The Governing Equations
### The Delphi4 LED Chip-Level Multi-Domain LED Model [31]
The \(\Delta V_{r}\) voltage of the voltage-controlled voltage source represents the temperature-dependent changes of the characteristic of this diode. The junction temperature dependence of the \(\Delta V_{r}\) generator is described with a similar bi-quadratic approximation, as seen earlier for the \(\Delta V_{e}\) generator:  
\[\Delta V_{r}=\left(a_{r}\cdot I_{r}^{2}+b_{r}\cdot I_{r}+c_{r}\right)\cdot \left(T_{I}^{2}-T_{ref}^{2}\right)+\left(d_{r}\cdot I_{r}^{2}+e_{r}\cdot I_{r }+f_{r}\right)\cdot\left(T_{I}-T_{ref}\right) \tag{5}\]  
The total emitted optical power of the LED, \(\Phi_{e}\) (also known as the total emitted radiant flux), is directly proportional to the radiative current:  
\[\Phi_{e}=I_{r}\cdot\left[\Delta V_{r}+m_{r}\cdot U_{T}\cdot\ln\left(\frac{I_{ r}}{I_{0r}}+1\right)\right] \tag{6}\]  
Figure 1: The SPICE circuit schematic representation of the Delphi4 LED multi-domain chip-level LED model: (**a**) the branch representing the heat generation of the LED, (**b**) the branch describing the electrical behavior of the whole LED as a diode, and (**c**) the branch modeling the light emission of the LED. Lines indicate how one branch influences the other branches. The circuit symbols used are those typically found in SPICE circuit schematics. With the emitted optical power, the value of the \(P_{H}\) heating power can be calculated as:  
\[P_{H}=V_{F}\cdot I_{F}-\Phi_{e} \tag{7}\]  
With this last step, the model is closed and the law of the conservation of energy is satisfied. With additional models, the spectral power distribution or another important light output property, the total emitted luminous flux can be also modeled. The Delphi4 LED multi-domain LED model contains a simple, empirical approximation for the total luminous flux, \(\Phi_{v}\), with a bi-quadratic formula as follows. The total luminous flux can be determined by:  
\[\Phi_{v}=\Phi_{e}\cdot\text{K}\big{(}I_{F},T_{J}\big{)} \tag{8}\]  
where K is the so-called efficacy of the source of the radiation (for the LED) that can be well approximated by a bi-quadratic formula of the forward current and temperature for most LEDs (in which the luminous flux is a relevant light output property), as follows:  
\[ \begin{split}\text{K}(I_{F},T_{J})=(a_{L}\cdot T_{J}^{2}+b_{L} \cdot T_{J}+c_{L})\cdot I_{F}^{2}\\ +(d_{L}\cdot T_{J}^{2}+\epsilon_{L}\cdot T_{J}+f_{L})\cdot I_{F}+ (g_{L}\cdot T_{J}^{2}+h_{L}\cdot T_{J}+l_{L}))\end{split}  \tag{9}\]  
Overall, 27 parameters (excluding the thermal impedance that is directly measured and modeled on the package level rather than being part of this model) should be fitted to the measurement data to establish a particular LED model (circuit schematic/netlist with parameters).
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### DD Network Solutions
#### 4.3.1 Accuracy of Coordinates
The positioning accuracies of GLONASS on N, E, and U components are 22.13%, 62.86%, and 38.24% worse than that of GPS, and the 3D positioning accuracy is 39.93% lower than that of GPS. The positioning accuracy of GPS+GLONASS, however, is better than that of GPS, which is 1.37%, 2.39%, and 10.89% on N, E, and U components, respectively, and the 3D accuracy increases by 9.00%. The poor positioning results of GLONASS and the improvement of GPS+GLONASS  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Component** & **System** & **G (mm)** & **R (mm)** & **G + R (mm)** & **R/G (\%)** & **(G + R)/G (\%)** \\ \hline N & 2.99 & 2.58 & 2.27 & +13.79 & +24.29 \\ E & 1.38 & 1.60 & 1.38 & \(-\)16.17 & +0.00 \\ U & 9.87 & 9.24 & 9.41 & +6.35 & +4.66 \\
3D & 10.40 & 9.73 & 9.77 & +6.50 & +6.05 \\ \hlinepositioning performance by introducing GLONASS observations are consistent with the analysis of PDOP.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### Algorithm Implementations and Setup
#### FastText (FT)  
Custom _FastText_ vectors were trained using the fasttext Python library [57] with vector dimensionality set to 100, skip-gram training mode, and other parameters left at their default settings. Combined data from all SLR studies were used for embedding training, which had been previously found superior to both using publicly available pretrained vectors or training separate embeddings for each dataset [13].
## 3 Speech Recognition Results
### Clean Transcription Generation  
KsponSpeech can contribute to the task of generating clean transcriptions because the disfluency tags can be used for the disfluent words provided by the corpus to produce a clean transcription. In spontaneous speech, disfluent words, such as filler words, self-corrections, and repeated words, reduce the readability of automatically-generated transcriptions. Therefore, we demonstrated the feasibility of approaches generating the fluent transcription directly from disfluent speech using the end-to-end ASR model.  
We can attempt two possible approaches. First, fluent transcription can be obtained by detecting all disfluent words and then removing them. In this case, the end-to-end model should be trained to output disfluent words along with their disfluency tag. Then, the disfluency-tagged words are removed through post-processing. Second, fluent transcription can be obtained directly from the end-to-end model. In this case, the model should be trained to generate fluent transcription from disfluent speech.  
To perform this experiment, we used end-to-end models trained with two types of transcriptions, as shown in Table 6: (1) disfluent transcription with disfluency tag (\"disfluent w/tag\") and (2) fluent transcription (\"fluent\"). The disfluent w/tag type is a transcription containing the disfluency tags and the fluent type is a transcription with the disfluent words completely removed.  
Table 7 below shows the performance for each transcription type. Here, we use the transcription of the fluent type as reference text in evaluation. For the disfluent w/tag model, we first removed all disfluent words from their recognition results and then measured their sWER. For the fluent model, we calculated sWER without additional processing. Table 7 shows that both models performed similarly to each other. For the fluent model, we observed that many disfluent words were automatically removed in the hypothesis text. In the predicted clean transcription, there were also words that were incorrectly inserted or deleted. The possible reason is that many people may miss some disfluency tags on the disfluent words in the process of building a large corpus.  
\begin{table}
\begin{tabular}{c c} \hline
**Types** & **Example of Transcription 1** \\ \hline (a) Original & \(\downarrow\)\(\downarrow\)\(\uparrow\)  
As a result, we demonstrated the feasibility of generating the fluent transcription from disfluent speech using the end-to-end ASR model. KsponSpeech can contribute to clean transcription generation. We think this task will be helpful for a variety of applications that require readability and clarity, such as automatic minutes generation and machine translation.
## 2 System Model
### Data Image Creation  
In the control room, the command information to be sent is generated. This information is converted into a binary data stream and stored in an array. The following steps are used to create a black and white binary data image from the incoming data bit stream.  
Step 1: Create a binary image of size 1920 \(\times\) 1080 by assigning '0' value to all the pixels. This obtained image is called a _black binary Image_.  
Step 2: The black binary image is divided into 8 \(\times\) 8 blocks.  
Step 3: Select the first 8 \(\times\) 8 block (containing 64 pixels).  
Step 4: If the incoming data bit is '1', replace all 64-pixel values in that block by '1'. Otherwise, the pixel values in that block are unchanged.  
Step 5: Select the next block in the same row and repeat Step 3 for that block. If the previous block is the last block in that row, then the next block is the first block in next row.  
Step 6: Repeat Step 4 until every 8 \(\times\) 8 block in the black binary image is modified based on the incoming binary data bit stream.  
Step 7: This process generates a black and white binary data image.  
These black and white binary data images need to be embedded within the cover image. Since the cover image consists of three planes, Red, Green, and Blue, the above process is repeated for three images, as shown in Figure 3. It is important to note that the created black and white image and the cover image must be of the same size. The Integer Wavelet Transform (IWT) is applied to the created black and white image; this gives the Low-Low (LL), Low-High (LH), High-Low (HL), and High-High (HH) subbands. The LL subband (960 \(\times\) 540) is selected; this LL subband of these three black and white binary data images is given to three different pixel coefficient readers, as shown in Figure 3. The pixel coefficient reader reads coefficients of the LL subband of black and white binary data images row-wise, from top to bottom. The output of these three-pixel coefficient readers is noted as A, B, and C respectively. The A, B, and C are binary bit streams created from the LL subband of the black and white binary data image.  
Figure 3: Black & White Binary Image Creation for Red, Green& Blue plane.
## 4 MLOps Pipeline Design
### Model Training and Evaluation Process  
Within the framework of MLOps, model training and evaluation processes include steps such as training the model on data, testing its performance, and adjusting hyperparameters. The training process of various machine learning models for 'LV ActivePower (kW)' prediction based on SCADA data was carried out. The process started with the preparation of the data and dividing the data into training and test sets. Models were developed by defining various regression models in a pipeline, including LR, DT, RF, GBM, XGBoost, LightGBM, and CatBoost. RMSE was used as the evaluation criterion for each model.  
Figure 6: MLOps architecture.  
Models were developed using the sklearn library in Python's scikit-learn library. LinearRegression works by fitting a linear model with coefficients to minimize the residual sum of squares between the observed targets in the dataset and the targets predicted by the linear approximation. The model then calculates the best-fitting coefficients for the features that provide a best-fit line for the data. In the RF model, multiple decision trees are created and combined to obtain a more accurate and stable prediction. In GBR, a model is created on a stage-by-stage basis, like other strengthening methods. However, its generalization is achieved by allowing the optimization of a differentiable loss function. Model training was carried out by adding trees sequentially, each correcting the errors made by the previously trained trees.
## 4 Discussion
### Pain Relief and Complication Rate  
Nearly all studies on third-generation PVAS devices report significant reduction in pain scores and disability, predominantly measured through the Visual Analog Scale (VAS) and the Oswestry Disability Index (ODI), as well as improvements in quality of life and lower postoperative complication rates. [PERSON] et al., for example, demonstrated a significant reduction in pain intensity (VAS), dropping from a preoperative score of 8.0 to 1.6 after, along with significant improvement in activity level (ODI) from a preoperative 79.0 to 30.5% after 12 months [21]. [PERSON] et al. treated unstable thoracolumbar burst fractures in elderly patients using titanium mesh cages combined with a transpedicular fixation system with expandable screws, resulting in significant pain reduction, maintained fracture alignment, and a low complication rate [25]. In most of the studies reviewed, follow-up imaging confirmed partial recovery of the vertebral height and stability of the treated segments, reinforcing the notion that third-generation devices provide lasting biomechanical support.  
Although the current literature indicates that third-generation VPAS are associated with a lower overall complication rate, certain issues remain. Cement leakage, while less common than in earlier techniques, can still occur, particularly in cases involving compromised vertebral endplates or suboptimal implant positioning. Adjacent vertebral fractures, likely due to altered biomechanical forces post-procedure, have also been observed, albeit at low rates. Furthermore, the success of these devices heavily depends on accurate positioning. Improper placement can result in uneven height restoration, inadequate fracture reduction, or suboptimal biomechanical outcomes.
## 1 Introduction
Other studies on the load-deflection hysteresis have mostly focused on uniform disk springs [35; 36; 37]. The advantages of linearly gradient thickness (LGT) disk springs in improving the load capacity and optimizing stress distributions have been proven theoretically [26; 27; 28; 29; 30]. However, the static behavior of slotted disk springs with linearly gradient thickness has not been explored. In this paper, a mechanical model for linearly gradient thickness slotted (LGTS) disk springs is developed by introducing the dimensionless thickness variation parameter and considering symmetric friction conditions. The slotted segment is simplified to be a number of cantilever beams with both gradually changing thicknesses and widths. The analytical formula is derived to characterize the load-deflection of LGTS disk springs based on the moment equilibrium equation. The influences of the dimensionless thickness variation parameter and the friction coefficient on the nonlinear characteristics are investigated theoretically. Then, slotted disk springs with different parameter configurations and different slot shapes were fabricated and tested. The effectiveness of the proposed model is validated with comparisons of the load-deflection characteristics between the experiments and the proposed model.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 2 Materials and Methods  
### Dataset and Imaging Techniques  
The experiment involves 632 HPPT patients subjected to parathyroid scintigraphy in the Department of Nuclea Medicine of the University Hospital of Patras, Greece. The participants were enrolled from January 2010 to December 2021. Biochemical HPPT evidence was present in 607 participants, whilst refractory secondary of tertiary HPPT was detected in 25 patients. The dataset is detailed in Table 1. The planar dual-phase method was applied to all participants. However, when judged essential, the thyroid subtraction method was applied as well (81.3% of the participants). Planar imaging was applied using a pinhole collimator, which was placed 10 cm over the neck. Half an hour after the injection of the radioactive tracer, a high-sensitivity parallel-hole collimator was used for SPECT/CT imaging of the neck and the mediastum. The present study involves only planar imaging, performed by the Hawkey-4 system (GE Healthcare). Two senior medical experts retrospectively evaluated the planar scintigraphic studies. In a few ambiguous cases, the final decision was achieved by consensus.  
The medical experts (DA, NP, TS, with more than 20 years of experience) assigned the labels of this study. Instances with at least one abnormal PG are labelled as \"Positive\", whilst the rest are labelled as \"Negative\". An abnormal PG may imply various diseases, such as primary, secondary, and tertiary HPPT. Detailed information from operated cases was available in 472 patients treated surgically at our hospital. The gender imbalance of the dataset reflects the male-female distribution of traffic in our department. Gender, other demographic, and even clinical attributes are not anticipated to dictate changes in the image. Gender and age signatures are not discovered in such images.
# Exopolysaccharides from Cyanobacteria: Strategies for Bioprocess Development
## 3 Cyanobacterial Bioprocess (Cyano-EPS) Development
### Production and Optimization
#### 3.2.3 Modes of Cultivation and PBR Design  
Cultivation modes are used as a strategy to increase biomass and EPS production [94]. The majority of Cyano-EPS are reported in batch mode followed by almost unexplored fed-batch [51; 72] and continuous mode [70]. _Anabaena_ sp. ATCC 33047 grown under different dilution rates was found optimal EPS/biomass ratio of higher than 1.5 and EPS productivity attaining 1.1 g/L/day at 0.03 hour\({}^{-1}\). In addition, EPS and biomass production phases were shown to be disassociated [70]. Other authors have claimed the same disassociation of the kinetics [76; 77; 79].  
Photobioreactor (PBR) design technology has been highly evolving due to extensive research applied to microalgal biofuels; however, their application to EPS production does not seem adequate [95]. Immobilizing cyanobacteria in cotton toweling showed the basic limitations of biomass production due to high lateral pressure [96]. A flow-lane incubator in fed-batch mode was used to grow biofilm-forming cyanobacteria. The results showed that the planktonic growth was far more productive. The same was observed for EPS production in a 25 L open bound system with _N. flagelliforme_ under batch and fed-bath cultivation mode. The comparison demonstrated higher biomass and EPS production under fed-batch mode [72]. _Arthospira platensis_ was batch-grown in 30 m\({}^{2}\) PBR located at a coal power plant in Taiwan for flue gas consumption. This novel PBR consisted of multiple 1 L stacked vessels individually controlled. The outdoor conditions were supplied with sunlight and an extra 10 h of LED illumination during the night. Temperature levels ranged from 30-34 \({}^{\circ}\)C during 15 days of experiment. The system was able to produce 2.36 g L\({}^{-1}\) and produce a CPS with immunogenic activity [92].
## 3 Results
### Mechanical Characterization
The results obtained are consistent with those obtained by other researchers in previous studies. As a synthesis for the discussion, Table 6 compiles the results obtained for flexural and compressive strength in other research studies that incorporate lightweight additions in gypsum. It can be seen that the values obtained in this study are consistent with those presented. Finally, with the aim of understanding in detail the interface between the added hemp material and the gypsum matrix, an analysis was carried out through scanning electron microscopy. In Figure 5, the images obtained for the matrix of the G0.65-30%P composite are shown. In Figure 5a, the good dispersion of hemp particles in the gypsum matrix and their perfect integration can be observed. While there are pores formed during the setting process, there is no apparent detachment of the hemp material due to the lack of cohesion between it and the gypsum base composite. In Figure 5b, a detailed view of the composite matrix reveals the characteristic acidicular morphology of the dihydrate (CaSO\({}_{4}\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Binder** & **Addition** & **Flexural Strength [MPa]** & **Compressive Strength [MPa]** \\ \hline
[54] & Plaster & 10\% wt., cork granulates (\(<\)1 cm) & 2.15 & 4.88 \\
[34] & Gypsum & 94\% gypsum, 6\% hemp fiber (15 mm) & 7.20 & 8.30 \\
[51] & Plaster & 1\% wt., rice husk & 3.54 & 7.88 \\
[55] & Gypsum & 6\% wt., jute fiber & 2.46 & — \\
[24] & Gypsum & 70\% gypsum, 30\% coir & 5.23 & — \\
[56] & Gypsum & 3.5\% wt., cellulose acetate fibers & 2.40 & 7.10 \\
[57] & Plaster & 1\% wt., end-of-life tire textile fibers & 4.96 & 7.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Critical literature review: results of flexural and compressive strength obtained in other research studies. Figure 4: Mechanical test results. (**a**) Flexural strength; and (**b**) compressive strength. the rough surface of the hemp additions promotes their adhesion to the matrix [31], with no discontinuities observed between the addition and the gypsum matrix. In Figure 5c, a closer look reveals the formation of gypsum crystals on the surface of the hemp compounds, a phenomenon previously observed by [PERSON] et al., highlighting the effective integration of these plant-based additions compared to other synthetic additions [35]. However, at 500\(\times\) magnification, some areas with a weaker bond between the addition and the matrix are noticeable, explaining the lower mechanical resistance of compounds with the addition of hemp powder. Finally, Figure 5d provides a detailed view of the hemp-gypsum interface, where the proper formation of gypsum crystals around the hemp particles can be observed.
## 5 Conclusions  
This study developed a metal foreign object detection system for coal mine crushers. A truncated region eddy current field model was established for the rectangular cross-section coaxial excitation, receiving coils, and Hall sensors interacting with the feeding metal foreign objects. The time-domain expressions for the reflected field induced voltage and magnetic induction intensity were calculated under actual square wave current signals.  
Simulation and experimental results demonstrate a close relationship between the effective range, peak value, and peak time of the receiving voltage and magnetic induction signals and the typical characteristics of the metal foreign objects. These characteristics can be used to classify and identify the dimensions, thickness, electrical conductivity, and magnetic permeability of the feeding metal foreign objects. The results of field drop experiments reveal that the horizontal width, thickness, and material of the metal foreign objects were detected with high accuracy. The relative error in detecting the vertical width was less than 5%, which meets the requirements for field detection. This can enhance the recognition rate of metal foreign objects, reduce false alarm rates, and ensure the safe, continuous operation of coal mines.  
The signal conditioning module used in this study may be insufficient to handle the strong electromagnetic radiation environment of coal mines. Future research will elaborate on signal preprocessing algorithms tailored to the metallic foreign object detection environment. The research work and findings presented in this paper hold significance for the advancement of classification and recognition methods for feeding metallic foreign objects in crushing operations within coal mines and the development of moving metal detection systems.  
Conceptualization, B.M. and S.Z.; methodology, B.M.; software, B.M.; validation, B.M., Z.Z. and J.M.; formal analysis, B.M.; investigation, B.M.; resources, B.M.; data curation, B.M.; writing--original draft preparation, B.M.; writing--review and editing, Z.Z. and J.M.; visualization, B.M.; supervision, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Figure 13: Dual \(Y\)-axis plot of PEC differential signals and time for the effective detection interval in the field experiment.