esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Sushi Rice: Preparation Techniques, Physicochemical Properties and Quality Attributes
## 3 Results and Discussion
### Investigating the Sushi Rice Production Process
For longer soaking durations (6-15 min), the weight remained unchanged, suggesting that the grains reached a saturation point and can no longer absorb additional water under these conditions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Washing**} & \multicolumn{6}{c}{**Initial Weight After S soaking (\%)**} \\  & & & **CV** & **30 min** & **CV** & **45 min** & **CV** & **60 min** & **CV** & **75 min** & **CV** \\  & & & (\%) & & (\%) & & (\%) & & (\%) & (\%) \\ \hline W1 & 200 & 112  
The findings demonstrate that extending soaking time beyond 3 min does not provide additional benefits in terms of water absorption. This information can be utilized during industrial rice preparation, in which time efficiency is important. Knowing that rice reaches near-maximum absorption by 3 min can help in planning cooking processes, ensuring the rice is adequately hydrated without unnecessary delay. The impact of water temperature on weight gain during rice soaking was investigated by soaking rice in water at temperatures ranging from 10 to 50 \({}^{\circ}\)C for 30 min. As illustrated in Figure 3, the initial rice weights after soaking were consistent across all temperature conditions. The uniformity of the results demonstrates that temperature had minimal impact on water absorption. Recent research by [PERSON] et al. [34] examined water absorption in _Indica rice_ when soaked at various temperatures (10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C) for different time periods (ranging from 10 to 360 min). Their results demonstrated that higher temperatures accelerated water absorption. For instance, after just 10 min of soaking, water absorption increased from 17.45% at 10 \({}^{\circ}\)C to 20.75% at 40 \({}^{\circ}\)C. This indicates that water absorption rates in rice grains increase with temperature until reaching a saturation point. Interestingly, our findings contradict these results, as our data showed no significant differences in water absorption between different temperatures, despite the expectation that warmer water would enhance absorption. However, water absorption during rice soaking is influenced not only by temperature but also by rice variety and compositional characteristics [35]. The practical implication of this finding is that rice can be soaked in regular tap water without temperature adjustment, as the absorption process reaches equilibrium regardless of water temperature within this range. Figure 2: Effect of soaking duration on rice weight gain (kg). Values are presented as means with standard error bars. Bars labelled with different superscript letters indicate significant differences between treatments (\(p<0.05\)).
## 3 Numerical Modeling of the Filling Phase
### Rheological Models
#### 3.2.5 Bird-Carreau  
The Bird-Carreau model represents a comprehensive mathematical framework for the characterization of the viscosity behavior of polymers as a function of shear rate [52; 53]. The model is expressed in the following equation:  
\[\frac{\eta-\eta_{\infty}}{\eta_{0}-\eta_{\infty}}=\left[1+(\lambda\dot{\gamma })^{2}\right]^{\frac{\eta-1}{2}} \tag{20}\]  
In this Equation, \(\eta_{0}\) represents the viscosity at zero shear rate, \(\lambda\) denotes a characteristic time constant, \(\dot{\gamma}\) is the shear rate, \(n\) represents the Power-Law index and \(\eta_{\infty}\) is the infinite shear rate viscosity.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## 4 Methodology and the Proposed Algorithms
### Heuristic Algorithm  
This algorithm is an adjustment of the heuristic proposed by [PERSON] et al. An additional variable \(H_{interval}\) is defined as the sum of all heights in one charging interval \([s_{b},s_{b}+l_{b}]\):  
\[H_{interval}=\sum_{t=s_{b}}^{s_{b}+l_{b}}h_{t}. \tag{5}\]  
Before executing the algorithm, the following steps are necessary.  
1. Schedule all preconditioning jobs (these jobs are fixed and cannot be rescheduled).
2. Calculate all possible charging intervals for all buses and write them into tuples \(P_{b}\).
3. Organize all buses ascending by their shifting time \(\delta_{b}\).  
The algorithm flowchart for a scenario of \(n\) buses is presented in Figure 7. The greatest \(H_{max}\) resulting from the presented logic is the minimum peak demand.  
Figure 7: Flowchart representing the heuristic algorithm.  
A crucial difference compared with the greedy algorithm is the preparation Step 3, organizing the buses ascending by their shifting time \(\delta_{b}\). The algorithm will first schedule the buses with no \(\delta_{b}\). These jobs are fixed in time and cannot be shifted. In this way, the algorithm leaves the jobs, which are more flexible for later scheduling. The buses with big \(\delta_{b}\) are scheduled last. Because of their flexibility, there is a high probability of scheduling without further increasing the height \(H_{max}\).
# Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation
## 4 Experimental Results and Analysis
### PDF Fitting
Therefore, as mentioned before, we can not determine the best model by only a visual inspection. Finally, we evaluate numerically the quality of fit by the Root Mean Square Error (RMSE). Results showed in Table 2 indicates the S\(\alpha\)S model has the best fit for all scenarios. The GMM reaches a better fit than the Gaussian model in the hall and outdoor scenarios, where the impulsiveness exists. In the indoor scenario, due to the absence of impulsiveness, the Gaussian model has no difficulty to fit data, although the S\(\alpha\)S has slightly better RMSE. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gaussian** & **GMM** & **S\(\alpha\)-S** \\ \hline
**Indoor** & 1.1936 & 1.1988 & 1.1925 \\
**Hall** & 1.0221 & 0.6757 & 0.5817 \\
**Outdoor** & 1.4969 & 0.2820 & 0.2072 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of data probability density function fitting by the root mean square error. Figure 14: Indoor scenario: Visual comparison among Gaussian, GMM and S\(\alpha\)S PDF fitting. Figure 15: Hall scenario: Visual comparison among Gaussian, GMM and S\(\alpha\)S PDF fitting. As expected, the simplest Gaussian model has not succeeded to fit well the data with impulsiveness due to its unbounded variance. We conclude that Gaussian is not a suitable model for this data, whereas the GMM has better performance. Finally, the SaS model has the best fitting performance, as a result of accurate modeling of the heavy tails. Figure 16 confirms this thought by showing the comparison of the cumulative distribution of data and the Cumulative Distribution Function (CDF) of Gaussian and SaS models (with the estimated parameters of the Table 1). The plots are for the outdoor scenario where the low value of \(\alpha\) suggests high impulsiveness and a non-Gaussian behavior [8].
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 3 Preliminaries  
### Fast Walsh-Hadamard Transformation Technique  
The Hadamard transform is also known as the Walsh-Hadamard transform, Hadamard-Rademacher-Walsh transform, fast Walsh-Hadamard transform, or Walsh-Fourier transform and it comes under a summed-up class of Fourier transformation. It has the benefits of simple computational complexity \(O(n^{2})\) among all similar existing transforms [48]. It is orthogonal, symmetric, and has only two values (\(-1\), \(+1\)), which results in isomorphism with no data redundancy. The FWHT has been extensively utilized in many image processing algorithms, digital steganography, and compression because it provides fast coefficient computation and fast reconstruction advantages. The fast Walsh-Hadamard transform is represented as follows:  
\[F(U,V)=\frac{1}{N}\times\ [H_{N}]\ \times\ f(x,y) \tag{1}\]  
The \(F(U,V)\) refers to the FWHT of \(f(x,y)\), while the \([H_{N}]\) refers to the Hadamard matrix of squares.  
The 4 \(\times\) 4 Hadamard matrix is represented in the form  
\[[H4]=\left[\begin{array}{cc}H2&H2\\ H2&-H2\end{array}\right]=\left[\begin{array}{ccc}1&1&11\\ 1&-1&1&-1\\ 1&1&-1&-1\\ 1&-1&-1&1\end{array}\right] \tag{2}\]  
The main formula of the FWHT transformation is represented as  
\[H_{2^{p}}=\left[\begin{array}{cc}H_{2^{p}}-1&H_{2^{p}}-1\\ H_{2^{p}}-1&-H_{2^{p}}-1\end{array}\right]for\ p=1,2,3,4,5,\ldots\ldots\ldots. \tag{3}\]  
where \(p\) values are based on the values of \(N\), where \(N=2^{p}\).  
The inverse of the fast Walsh-Hadamard transform is represented as  
\[f(x,y)=\ [H_{N}]\ \times\ F(U,V) \tag{4}\]  
where \(F(U,V)\) is the FWHT of the function, \(f(x,y)\).
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Grading and Assessment Tools
#### 3.6.2 Technology Used: MLLMs in Grading and Assessment  
The implementation of MLLMs in grading and assessment tools involves several key technologies. Natural language processing (NLP) and understanding (NLU) are employed in MLLMs like GPT-4 [9] and BERT [19] to analyze and understand student-written text, such as essays and short answers, code comments, and other textual data. These models can evaluate grammatical correctness, coherence, argumentation quality, and adherence to rubrics.  
For short-answer scoring (SAS), models need to understand concise responses and compare them against expected answers or rubrics. Techniques such as semantic similarity assessment and entailment recognition are crucial in accurately evaluating short answers [97].  
Computer vision and image recognition are utilized for assignments involving visual components, such as diagrams, handwritten responses, or design projects. Models like CLIP [14] and ViT [20] process and assess visual data.  
Speech and audio processing is applied in assessments requiring oral presentations or language proficiency. Models like Wav2 Vec 2.0 [65] and Speech Transformers [98] transcribe and analyze spoken language for fluency, pronunciation, and content accuracy.  
Multimodal data integration allows MLLMs to integrate data from multiple modalities to assess complex assignments that combine text, visuals, and audio [99]. This integration enables a holistic evaluation of student work.  
Rubric-based and criterion-referenced assessment involve training AI models to align with specific grading rubrics and criteria, ensuring that evaluations are aligned with learning objectives [100]. For SAS, aligning models with rubrics is particularly important due to the variability in acceptable short answers.  
Feedback generation is another critical aspect, where MLLMs generate personalized feedback, highlighting strengths and areas for improvement, and suggesting resources for further learning [97].
Therefore, the selection of a robust P300 detector which can provide robust P300 detection in noisy EEG signals (_i.e._, EEG averaged over a few trials) is one of the critical issues in the BCI community. Previously, various classifiers have been proposed and tested. A boosting approach was applied to the orthogonal least square classifier in the work of [PERSON] _et al._[27]. Linear discriminant analysis (LDA) method was applied to classify P300 and non-P300 potentials in [26]. The Bayesian LDA (BLDA)-based P300 detector was further used in the different P300 BCIs [28; 29]. In addition to LDA, the support vector machine (SVM) has also gained wild acceptance in P300 BCIs [24; 25; 30; 31]. In addition to being the classifier, feature extraction also plays a key role in EEG recognition. Principal component analysis (PCA) has been commonly used as a feature extraction and dimensionality reduction tool. LDA has also been used for dimensionality reduction in various pattern recognition problems, such as face recognition [32; 33] and electromyography (EMG) classification [34]. In the BCI community, LDA is often employed as a classifier [20; 26; 35; 36; 37]. Recently, LDA has also been introduced to EEG-based emotion recognition as a dimension reduction method [17]. However, EEG has the property of nonlinearity. Features extracted by PCA or LDA may not provide satisfactory representations due to their linear natures. In this paper, we chose the kernel Fisher's discriminant analysis (Kernel FDA) method [38] as the feature extraction. Kernel FDA is a nonlinear version of LDA. It maps a set of data into a higher dimensional feature space where the mapped data distribution becomes linear, and then performs the regular LDA in this space. Kernel FDA has shown successful results in various applications, such as face recognition [32] and EEG-based emotion recognition [17]. To achieve high target function accuracy, we developed a novel P300 detector by combining Kernel FDA and SVM in this paper. The rest of this paper is organized as follows. In Section 2, the overview of the proposed healthcare BCI system is provided. Settings and apparatus, such as EEG recoding sites and preprocessing, are given in Section 3. In Section 3, we introduce the switching and option selection modes in details, including the proposed SSVEP and P300 detection strategies in the two modes, respectively. Experimental results and discussions are provided in Section 4. Conclusions are drawn in Section 5.
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
## 3 Results and Discussion
### Slump  
Figure 6 shows the variation trend of recycled aggregate content on workability. When fresh concrete is mixed, the RBCA is in a water retaining state and the aggregates are surface-wet. Compared with the slump of RB-0, the slump of RB-25, RB-50, RB-75, and RB-100 decreased by 29%, 71.3%, 86.2%, and 90%, respectively. The slump decreases significantly when the recycled aggregate replacement rate is less than 50%. In addition, when the replacement rate exceeds 50% and the slump is less than 5 cm, the concrete presents a dry and hard state with poor workability.  
According to standard code GB50164-92, RBCA concrete is a plastic and fluid concrete with an RBCA content of less than 50%. However, the performance of concrete is poor when the content of RBCA is more than 50%. Similarly, according to code JGJ/T10-2019, the slump of pumped concrete is required to be greater than 100 mm. Therefore, when the content of RBCA is less than 50%, the application range of recycled concrete is more extensive. The high water absorption, large inclination, and rough surface of RBCA are the main reasons for the decrease in a slump. However, previous studies have found that adding RBCA leads to reduced workability [14; 15]. The solution to this problem is pre-soaked CBP or superplasticizers [40; 41].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material Phase** & **RBCA [39]** & **NCA [28]** & **Mortar Matrix [29]** & **Interfacial Transition Zone [39]** \\ \hline Poisson’s ratio \(v\) & 0.20 & 0.16 & 0.2 & 0.2 \\ dilatancy angle \(\psi\) (\({}^{\circ}\)) & 30 & / & 30 & 15 \\ \(f_{b0}/f_{c0}\) & 1.16 & / & 1.16 & 1.16 \\ K & 0.66667 & / & 0.66667 & 0.66667 \\ Viscosity coefficient & \(1\times 10^{-5}\) & / & \(1\times 10^{-5}\) & \(1\times 10^{-5}\) \\ Eccentricity (\%) & 0.1 & / & 0.1 & 0.1 \\ Compressive strength (MPa) & 18 & 144 & 28.96 & 16 \\ Tensile strength (MPa) & 5.1 & 9.6 & 1.988 & 1.6 \\ Elastic modulus (MPa) & 16,000 & 70,000 & 28,960 & 26,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters.
## 4 Problem Formulation
### Quality of Service (QoS) Constraints
#### 4.1.2 Bit Error Rate (BER)  
We consider a noisy channel such that the Bit Error Rate (BER) is the percentage of bits of a data packet that are corrupted due to either noise alone or noise together with interference. Let \(S_{N}\) be the Signal-to-Noise-Ratio on a noisy channel and \(S_{I}\) be the Signal-to-Noise-Ratio on a noisy channel with interference (Signal-to-Interference-Noise-Ratio (SINR)). Considering a Rayleigh fading channel with a Rayleigh fading component of \(\rho\), the probabilities of \(S_{N}\) and \(S_{I}\) follow a Chi-square distribution. The expected values of \(S_{N}\) and \(S_{I}\) are given by  
\[\hat{S}_{N}=\frac{E(\rho^{2})P_{s}}{N_{0}R} \tag{13}\]  
and  
\[\hat{S}_{I}=\frac{E(\rho^{2})P_{s}}{(N_{0}+P_{p})R} \tag{14}\]  
respectively. \(P_{s}\) is the received SU power, \(P_{p}\) the received PU power and \(N_{0}\) is the noise spectral density. The received power \(P_{s}\) and \(P_{p}\) are estimated using the Log-normal shadowing model with variance due to shadowing \(\sigma_{s}^{2}\), path-loss exponent \(l\), reference distance of \(1\) m, distance between two CRSN nodes \(d_{s}\), distance between CRSN node and PU \(d_{p}\) and a signal of wavelength \(\lambda\) operating at a frequency \(f\).  
Using Frequency Shift Keying (FSK) modulation on a Rayleigh fading channel, the BER of an SU is given by [4]:  
\[P_{BER}=\frac{(2+\hat{S}_{I})+\Sigma(\hat{S}_{N}-\hat{S}_{I})}{(2+\hat{S}_{I}) (2+\hat{S}_{N})}, \tag{15}\]  
where  
\[\Sigma=\frac{pm\left(p_{on}+p_{off}e^{-\frac{L_{S}}{R_{plan}}}\right)+p_{co}+p _{on}p_{col}}{1-(p_{d}+p_{f})}. \tag{16}\]
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Influence of Particle Damper Materials and Structural Parameters
#### 3.4.2 Particle Size  
Particle size is one of the most important parameters for the energy dissipated by a damper. With all the other parameters being equal, the steel particles with particle sizes of 1 mm, 1.5 mm, 2 mm, 2.5 mm, 3 mm, 3.5 mm, and 4 mm were tested at three filling rates of 20%, 60%, and 80%. The filling effect of the different particle sizes in the damper is shown in Figure 7.  
As shown in Figures 8 and 9, at the same particle filling rate, the total energy dissipated by the particle system decreased as the particle size increased. Due to the spatial environment in which the particles were located, the larger the particle size, the fewer particles there were in the damper cavity. This resulted in fewer particle collisions and, therefore, less total energy dissipation in the particle system. At the three particle filling rates, it was observed that the total energy dissipated in the particle system was greatest at the 60% filling rate, followed by the 80% filling rate, and the least dissipation at the 20% filling rate.  
Figure 6: Total energy dissipated by the system with different particle filling rate.  
Figure 7: Filling effect of different particle sizes in the damper.
## 2 Theory of the SPH Wind and Sand Flow Modeling Approach
### Basic Equations of SPH for Wind-Sand Flow  
#### 2.2.1 Kernel Function Interpolation  
The construction of SPH equations is usually carried out in two main steps, the first is integral representation, and the second is particle approximation. The arbitrary function and the smooth function are integrated step by step, and then the summation approximation is performed by the values of the nearest neighboring particles.  
In the SPH method, the core is the difference; for a continuous smooth function \(f(x)\), the value of the function at a point on the domain of definition is  
\[f(x)=\int\limits_{\Omega}f(x^{\prime})\delta(x-x^{\prime})dx^{\prime} \tag{1}\]Replacing the Dirac function in (1) with a smooth function \(w(x-x^{\prime})\), the integral representation of the function can be written as  
\[\langle f(x)\rangle=\int\limits_{\Omega}f(x^{\prime})W(x-x^{\prime},h)dx^{\prime} \tag{2}\]  
where \(h\) in \(Wx-x^{\prime}\), \(h\) is the smooth length used to define the range of influence of the function, and the formula is the kernel approximation formula.  
For the spatial derivative \(\
abla f(x)\) of the function \(f(x)\), it follows that  
\[\langle\
abla\cdot f(x)\rangle=\int\limits_{\Omega}\left[\
abla\cdot f(x^{ \prime})\right]w(x-x^{\prime},h)dx^{\prime} \tag{3}\]
## 6 Conclusions  
(1) Taking into account the inadequacy of the Knothe time function model to predict the dynamic surface subsidence, an improved Knothe time function model was established. The new model can accurately predict the dynamic surface subsidence caused by underground mining.  
(2) The time influence parameter \(C\) and the model order \(n\) do not affect the initial and final surface subsidence values but only affect the time to reach the final subsidence value. The larger \(C\) and \(n\) are, the less time it takes to reach the final subsidence value. When the surface subsidence monitoring data is lacking, the time influence parameter \(C\) and the model order \(n\) can be determined according to the probability integral and the dual-medium methods. When a significant amount of monitoring data exists, the time influence parameter \(C\) and the model order \(n\) can be determined according to the least-squares methods.  
(3) Four surface movement observation station monitoring data sets on the mine subsidence basin's main profile in the Guotun coal mine were randomly selected. The monitoring data indicated that the dynamic surface subsidence curve is approximately \"S\"-type. Therefore, the proposed model in the manuscript can be used to predict the vertical surface deformation. By comparing the curve of improved Knothe time function model, Knothe time function model, and monitoring data, the results showed that the improved Knothe time function model surface subsidence value agrees well with the monitoring value, having better accuracy than the Knothe time function model. The average relative standard deviation between predicted and monitoring values was only 4.8%, much lower than the 23% of the Knothe time function model. The results verify the accuracy and reliability of the improved Knothe time function model.  
Conceptualization, L.Z. and H.C.; validation, X.W.; formal analysis, L.Z.; data curation, Z.Y.; writing--original draft preparation, L.Z.; writing--review and editing, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51874005).  
The authors declare no conflict of interest.
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 1 Introduction
### Overview  
Commercial solutions focus on a specific technology and do not allow a multi-layer analysis to be performed, where each implementation layer is not based on a single vendor, and some layers are even specific to the operator's internal processes. Therefore, we found it necessary to extend Fiberhost's existing system with dedicated modules that would enable the use of machine learning methods to create multi-layer QoE learning specifically tailored to the needs of the operator. In addition, our aim was to compare the QoE from various angles, such as QoS metrics, geographic location, technology, human interactions, etc. On account of such a wide scope, Fiberhost realized that there were no ready-to-use third party systems to be implemented and decided to develop this system in-house.  
This article is divided into seven parts. In Section 2, the operator network architecture used for data collection is described. Section 3 presents the impact of the infrastructure and the different service layers of the carrier network on QoE. Section 3.1 outlines the problems in the optical access network infrastructure and their impact on QoE. Section 3.2 discusses potential problems that may arise at the ISP level. Sections 3.3 and 3.4 present the sources of QoE degradation at the TV service layer and the telephony service layer, respectively. Section 4 presents a proposal for an aggregate metric that Fiberhost uses to assess QoE. This section also presents its rationale and the assessment of the network quality measured by the proposed metric. Section 5 presents the proposed implementation of unsupervised machine learning methods for assessing QoE in a carrier network, while Section 6 proposes the use of supervised learning in the estimation of the parameters used to determine QoE in a carrier network. The paper concludes with a brief summary.