esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Discussion
#### 5.5.2 Limitations of the Chosen Model  
On the positive side, CNN architecture showed many advantages; on the negative side, however, certain disadvantages were detected. First, there is a practical problem of training embeddings, which depend on data from other sources and, therefore, may not necessarily reflect the Arabic hate speech in informal or emerging contexts. For instance, models mentioned in this paper, such as AraBERT, can be trained for this purpose but are not originally optimized for the specific task of hate speech detection.  
Moreover, while CNN is stronger in local features within text, these same characteristics reduce its ability to understand broader, more complex contextual dependencies. In the case of longer tweets that contain more than one idea or when the hate speech is intricately woven within the text message, sometimes the CNN model barely got it right, because it was missing specific contextual information. In similar circumstances, CNN could be outperformed by transformer-based models such as AraBERT because they incorporate attention representations directed towards global and contextual communication.  
One of the limitations involved is that of scalability of the detection system, which must be transposable at any time to accommodate for any type of instance. Although CNN offered high and efficient output during the research experiments, implementing this model in a large-scale real-time architecture can be difficult. Furthermore, handling new embeddings and managing streams of millions of tweets per day would require more enhancement and resources.  
Last, the research focus was limited to text-based discrimination; other formats, ranging from images, videos or even multimedia content, as seen on platforms such as X (formerly Twitter) were not considered. This might have restricted the model's generalizability, as much of the hate speech that occurs in different social media platforms contains other forms of media that escalate the effect of hate speech.
## 2 Materials and Methods
### Commercial Distillation Process Simulation  
A software process simulator was employed to simulate the FCCPT main fractionator operation for both cases using as input process stream characteristics those obtained by the ASTM D 1160, and the (ASTM D 7169 methods. All distillation curves were converted to True Boiling Point (TBP) curves using published in literature methods [34; 35]. ASTM D86 distillation curves were converted with API Procedure 3A1.1 to TBP Curves [34]. ASTM D1160 distillation data are entered as atmospheric pressure equivalent temperature curve (AET) and is converted to TBP using the procedure described in API Technical Data book [35]. The ASTM D 2887 (simulated distillation) curve is converted by a two-step process--first to weight % ASTM D86 curve and then to TBP in accordance with the procedure described by [PERSON] [34]. HTDS (ASTM D7169) distillation curves are entered as a weight % true boiling point curves for the lack of a correlation to convert it to TBP Curve. Standard methods were applied for the generation of the pseudo-components and their molecular weight, critical properties and physical properties: New API method  
Figure 3: A flow chart of methodology used in the commercial distillation test to validate the diesel fraction content in H-Oil VGO measured by ASTM D 7169, and ASTM D 1160 methods.  
(Riazi-Daubert extended method) for molecular weight prediction, API Method for critical properties prediction [34].  
Based on the measured flow rate of all outlet streams of the FCCPT stabilization column, the characteristics of the column inlet stream were obtained through summation. A simulation model was created for the stabilization section where the stabilization column is located. The Grayson-Streed equation [36] was used for the phase equilibrium calculations and the Lee-Kesler equation [37; 38] was used for the enthalpy calculations.  
The simulation model was fine-tuned to match the instrument readings as close as possible. The instrument readings are from a snapshot of the FCCPT Stabilization Section actual operation at the moment the product samples were collected (usually at 8:00 am). The only variable that was left free was the flowrate of the diesel fraction from the side-stripper column K-102.
# A Comprehensive Review of Computer Vision in Sports:
## 8 Research Directions in Sports Vision
### Different Challenges to Overcome in Sports Studies  
* Classification of jersey numbers in sports such as soccer and basketball is quite simple [205] as they have plain jerseys, but in the case of the sports such as hockey and American football, the jerseys are massive and have sharp contours. Therefore, jersey number recognition is quite hard. By implementing proper bounding box techniques and digit recognition methods, better performance of jersey number recognition in every sport can be achieved.
* Action recognition in sports videos [222; 223] is explicitly a non-linearity problem, which can be obtained by aligning feature vectors, by providing a massive amount of discriminative video representations. It can provide a method to capture the temporal structure of a video that is not present in the dynamic image space and analyzing salient regions of frames for action recognition.
* Provisional tactical analysis related to player formation in sports such as soccer [102], basketball, rugby, American football, and hockey, and pass prediction [89; 90; 91; 98], shot prediction [81; 301], the expectation for goals given a game's state [82] or possession of the ball, or more general game strategies, can be achieved through AI algorithms.
* Recognition of the fine-grained activity of typical badminton strokes can be performed by using off-the-shelf sensors [192], and it can be replaced with automatic detection and tagging of aspects/events in the game and use of CCTV-grade digital cameras without additional sensors.
* The identity of the player is lost when the player moves out of the frame and, to retain the identity when the players reappear in subsequent frames, the player must be recognized. The key challenges for player recognition are posing variations, i.e., change or rotation of the image in different poses on 2D or 3D perspectives [56; 302], which is the most difficult recognition challenge, especially in case of resolution effects, variable illuminations, or lighting effects and severe occlusions.
# Effect of Harmonics on Ferroresonance in Low Voltage Power Factor Correction System--A Case Study
## 5 Conclusions  
In this paper, three-phase ferroresonance in a low-voltage detuned power factor correction system is analysed by means of an experimental study and a new configuration in which ferroresonance can occur is presented. The study is based on real field measurements carried out in an industrial plant where ferroresonance occurs at the detuned reactor of the PFC system. The three-phase ferroresonance analysed in this paper is triggered by the switching on of the capacitor stage of the detuned PFC system when a high harmonic distortion is present at the low voltage busbar. In this case, the ferroresonance is supported by the medium voltage system. This study has shown the importance of limiting the total harmonic distortion level of voltage in industrial plants. The experimental results have confirmed that high harmonic distortion of voltage was a necessary condition for the occurrence of ferroresonance in the studied industrial plant. In addition to the high harmonic distortion, the system configuration, the detuning factor of the detuned reactor and the available resistance in the system also affect the occurrence of ferroresonance. When the passive devices are present in the system, any change in the system configuration or load type must be verified by simulation test to avoid harmonic related problems and dangerous events like ferroresonance.  
Three-phase ferroresonance is an extremely rare phenomenon that exposes system components to excessive stress due to overvoltage and overcurrent. This complex phenomenon should be further analysed and prevented if possible.  
Conceptualization, M.K.; methodology, M.K. and L.P.; software, L.P.; validation, M.K. and L.P.; formal analysis, M.K. and L.P.; investigation, M.K. and L.P.; resources, M.K.; data curation, L.P.; writing--original draft preparation, M.K.; writing--review and editing, M.K. and L.P.; visualization, L.P.; supervision, M.K.; project administration, M.K. Both authors have read and agreed to the published version of the manuscript.  
Funding: This work was supported by the Department of Electric Machines, Drives and Automation, University of Zagreb, Zagreb, Croatia.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 5 Implementation Results and Lab Test
The proposed receiver was also tested under various DVB-T2/C2/S2 operation modes as specified in the standards and demonstrated successful operations in all test scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Measured** & **Required Time** \\  & **Time [\(\mu\)s]** & **[\(\mu\)s]** \\ \hline P1 Processing & - & - & 224 \\  & Channel Estimation \& Equalization & 211 \\ P2 Processing & Freq. De-interleaving & 86 \\  & Sub-Total & 297 & 1904 \\  & Channel Estimation \& Equalization & 972 \\  & Freq. De-interleaving & 504 \\  & Frame De-mapping & 66 \\ Data Processing & Time De-interleaving & 402 \\  & Cell De-interleaving & 1080 \\  & Frame Decoding & 396 \\  & Sub-Total & 3420 & 7140 \\ \hline Total Processing & & 3717 & 9268 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Processing time of the frame processor operation. Figure 15: Test environment for the proposed DVB-X2 receiver.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### Gpu
#### 3.1.1 Power Flow Analysis  
As shown in Figure 2, power flow analysis is the application where GPU has been the most used ref. [6; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. Researchers have attempted to reduce the power flow convergence time by testing multiple numerical algorithms for the solution of linear equation systems, parallelizing the algorithm steps in the GPU. Depending on the iterative algorithm used to find the power flow solution, tasks, such as building the admittance and Jacobian matrices of large-scale power systems, have been parallelized taking advantage of the GPU architecture. In ref. [18], a multigrid-preconditioned conjugate gradient method was implemented in a GPU where the Direct Current (DC) analysis of power systems was accelerated, thus, improving the convergence rate of the conjugate gradient algorithm with the multigrid preconditioning method.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Application**} & \multicolumn{1}{c}{**GPU**} & \multicolumn{1}{c}{**CPU Clusters, Fog and Cloud**} & \multicolumn{1}{c}{**Total**} \\  & & & **Computing and FPGA** & \\ \hline Power Flow Analysis & [6; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31  
In ref. [28], the convergence rate of the conjugate gradient method was improved with a polynomial Chebyshev preconditioner integrated into a GPU-based conjugate gradient solver. In ref. [35], a fast decouple power flow algorithm integrated with the Inexact Newton method was solved with a GPU-based preconditioned conjugate gradient solver with a two-step preconditioner based on a diagonal Jacobi preconditioner and a polynomial Chebyshev preconditioner. In ref. [37], the Newton-Raphson and Gauss-Seidel power flow solvers were accelerated using a GPU where efficient data-oriented parallel primitives, such as map function, reduction, and scan, were used in different steps of both algorithms.
## 3 Results and Discussion
### Global Modeling  
To improve the accuracy of calibration models for grape and apple, a global model was developed using PLS with CWT preprocessing on a large calibration set that included the calibration set for strawberry and the transfer set for grape and apple. The optimal number of LVs was determined to be 13 using cross-validation, and the PLS model was established. Figure 3 compares the PLS models for strawberry only and the global model. While the global model and the PLS model for strawberry have similar trends, the relative intensity of the global model is smaller than that of the PLS model for strawberry.  
Figure 4 shows the prediction of the global model applied to the three different fruits. In comparison to the PLS model for strawberry, the global model performed significantly better when applied to grape and apple spectra, with RMSEP decreasing to 1.55 and 1.28, respectively. These values were only 3.8 and 3.1 times higher than the RMSEP of the strawberry model on the strawberry validation set. The Rp also improved, increasing to 20% and 264% for grape and apple, respectively. However, the performance of the global model decreased when applied to the validation set of strawberry, with RMSEP and Rp decreasing to 0.66 \({}^{\circ}\)Brix and 0.86 \({}^{\circ}\)Brix, respectively, in comparison to a 61% increase and a 9% increase for the PLS model for strawberry (Table 3). Therefore, the global model is a versatile model that is suitable for all three fruits, but it may not be the most optimal model for the fruit with the largest training dataset, which in this case is strawberry.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Prediction Set** & **RMSEP (\({}^{\circ}\)Brix)** & **Rp** \\ \hline Strawberry & 0.66 & 0.86 \\ Grape & 1.55 & 0.89 \\ Apple & 1.28 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction of global model on three different fruits.  
Figure 3: Comparison of PLS model for strawberry and the global model.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 2 Materials and Methods
### Methods
#### 2.3.2 Gray Distribution Matrix  
The effective monitoring range of ocean waves is limited in radar images, and so, the echo signals of ocean waves in farther regions are few or even none. It is therefore necessary to first determine the effective monitoring range of waves. The image was filtered by means of a window of 20\(\times\)160, and a gray distribution matrix was generated (Figure 10a). According to the threshold of gray intensity, the mask of the effective range of the ocean wave was established in Figure 10b. The final pre-analyzed image area is shown in Figure 10c.
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 6 Biomedical Applications of Nanocellulose
### Bone Tissue Engineering  
[PERSON] et al. investigated the long-term secretion of bone morphogenic protein (BMP) and vascular endothelial growth factor (VEGF) through the nanocellulose scaffold for the regeneration of bone tissues [168]. Proliferative and adhesive properties were improved using the nanocellulose-based scaffolds. The bone morphogenic protein-2 (BMP2) and vascular endothelial growth factor (VEGF) loaded scaffolds showed an increase in the orthopedic deformities of rat bone marrow stem cells (RBMSCs), either with or without the treatment of stem cells. Following the 7 and 14 days of the treatment, the growth factor's influence on the alkaline phosphatase protein (ALP) expression was observed (Figure 4c). The BMP2 and VEGF growth factors' loaded constructs showed an increased ALP expression compared to the control, implying that it has a greater bone repair and regeneration ability. For the human bone marrow-derived mesenchymal stem cells, gelatin/alginate scaffoldings incorporated with CNC outperformed pure gelatin/alginate scaffolds in cellular activity. CNC's presence in the polymer matrices significantly influenced cellular functions [169]. Compared to gelatin/alginate scaffold, CNC containing gelatin/alginate scaffold showed improved swelling properties and mechanical strength. For BMSCs, when cultured on CNC/Chitosan and chitosan scaffolds, the CNC/chitosan composites resulted in enhanced osteogenic potential and cellular functions when compared with scaffolds containing only chitosan. Figure 4d depicted the fluorescence microscopy images of BMSCs on the CNC/chitosan composite. [PERSON] et al. synthesized hydroxyapatite/carboxymethyl cellulose nanostructures for drug delivery and osteogenesis. To track the medication distribution in cells and tissues, carbon dots were incorporated into the hydroxyapatite/carboxymethyl nanostructures [170].
## 6 Sixth-Generation (6G) Testbeds and Platforms
### Testbeds for 6G Technologies  
To develop and validate novel 6G technologies, numerous testbeds have been proposed by various organizations. This overview provides insights into representative testbeds, covering different aspects.
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger  
### Pharmacokinetics from Oral Intake  
The amount of ginger active components that reach the bloodstream following oral intake is relatively low due to losses during gastroenter absorption. Furthermore, gingerols, like most orally administered apolar phenolic compounds, undergo glucuronide and sulfate conjugation that takes place primarily in the intestinal mucosa and secondarily in the liver and other tissues [20]. This way, plasma components of ginger are typically found in the conjugated forms rather than the free ones. This was demonstrated by a study on mice treated with 250 mg/kg of ginger extract. The study monitored the main active components of ginger in the free form, revealing very low seric levels [21]. Also remarkable are the different absorption kinetics observed for each one of the components. The 6-gingerol plasma peak was observed at roughly two hours of administration (but with a large error, 27.18 \(\pm\) 38.43 \(\upmu\)g/L), while 10-gingerol peaked at 30 min (55.95 \(\pm\) 31.61 \(\upmu\)g/L) and 8-gingerol was practically undetected; 6-shogaol peaked at 10 min with only 4.04 \(\pm\) 5.37 \(\upmu\)g/L. Pharmacokinetics of ginger in humans was investigated using an escalation study with 27 healthy subjects. A single oral dose of ginger extracts was administered, with concentrations standardised to 5% of total gingerols and ranging from 100 mg to 2.0 g. The subjects were monitored for 6-, 8-, 10-gingerols and 6-shogaol [22]. The compounds were rapidly absorbed, with a t\({}_{max}\) of 55 to 65.6 min (range: 45-120 min) and elimination half-lives (t\({}_{1\beta\beta}\)) of 75 to 120 min at the highest dose, 2.0 g. No free 6-, 8-, 10-gingerols or 6-shogaol were observed in the plasma, in agreement with the studies performed with mice. Instead, their circulating metabolites were detected, mostly glucuronides and/or sulphates (Table 1). It should be noted that only 6-gingerol conjugates were detectable in subjects taking doses below 1.0 g, and for this reason, the low doses are not listed in the Table 1. The AUC values for the doses of 250 and 500 mg of ginger presented low values, of 2.8 and 5.3 \(\upmu\)g-min-mL\({}^{-1}\) respectively. For the two
## 2 Background and Related Literature  
### Classical Methods for Aircraft Recognition  
In the realm of classical methods for aircraft recognition, many studies in the literature rely on the utilization of rotation-invariant features, often achieved by applying binarization techniques to images. Prominent examples of such features include Fourier descriptors and moment invariant features [19]. These methods typically leverage shape features extracted from either the object's contour or a binary representation of the object. They assume that accurate edge detection or region delineation can be readily obtained, which is a task that can be challenging in real-world scenarios. Additionally, these approaches often rely on threshold-based image segmentation applied to the entire shape or silhouette of the target [20]. Features related to rotation invariance, such as Zernike moments, wavelet moments, and Hu moments, are subsequently extracted for recognition purposes [19].  
For example, in [20], Zernike invariant moments were effectively combined with an independent component algorithm to facilitate aircraft recognition. Meanwhile, in [21], the authors adopted histogram equalization to identify airports in images. Subsequently, they employed segmentation to isolate the areas containing planes, followed by the generation of binary images. A Principal Component Analysis (PCA) was applied to determine the main axes of each airplane, and aircraft recognition was accomplished through template matching [21]. However, these methods encounter certain limitations highlighted by [16]: (1) The alignment of segmentations or aircraft often necessitates direction estimation, which is constrained by the methods' capacity and may result in inaccuracies in direction prediction. (2) The coarse shape representations employed by these techniques tend to overlook crucial details that are essential for discriminating between different aircraft types.
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
###### Abstract  
In addressing the multifaceted problem of multiple-input multiple-output (MIMO) detection in wireless communication systems, this work highlights the pressing need for enhanced detection reliability under variable channel conditions and MIMO antenna configurations. We propose a novel method that sets a new standard for deep unfolding in MIMO detection by integrating the iterative conjugate gradient method with Tikhonov regularization, combining the adaptability of modern deep learning techniques with the robustness of classical regularization. Unlike conventional techniques, our strategy treats the Tikhonov regularization parameter, as well as the step size and search direction coefficients of the conjugate gradient (CG) method, as trainable parameters within the deep learning framework. This enables dynamic adjustments based on varying channel conditions and MIMO antenna configurations. Detection performance is significantly improved by the proposed approach across a range of MIMO configurations and channel conditions, consistently achieving lower bit error rate (BER) and normalized minimum mean square error (NMSE) compared to well-known techniques like DetNet and CG. The proposed method has superior performance over CG and other model-based methods, especially with a small number of iterations. Consequently, the simulation results demonstrate the flexibility of the proposed approach, making it a viable choice for MIMO systems with a range of antenna configurations, modulation orders, and different channel conditions.  
MIMO detection; Tikhonov regularization; conjugate gradient; deep learning; wireless communication +
Footnote †: journal: _electronics_  
_Acticle_  
Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection.