esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
## 2 Materials and Methods
### Methods
#### 2.2.3 The Dynamic Shear Rheometer  
According to AASHTO T 315-20 [45], the fiber bitumen mortar was tested by the dynamic shear rheometer (DSR) test. Testing was conducted at a frequency of 10 rad/s \(\pm\) 0.1 rad/s,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**SMA-10**} & \multicolumn{6}{c}{**Percentages of Volume Passing Through the Following Sieves (\%)**} \\ \cline{2-10}  & **13.2** & **9.5** & **4.75** & **2.36** & **1.18** & **0.6** & **0.3** & **0.15** & **0.075** \\ \hline The upper limit of gradation & 100 & 100 & 60 & 32 & 26 & 22 & 18 & 16 & 13 \\ The lower limit of gradation & 100 & 90 & 28 & 20 & 14 & 12 & 10 & 9 & 8 \\ Design gradation & 100 & 95 & 45 & 26 & 20 & 17 & 14 & 12.5 & 10.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mixture aggregate gradation range requirements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fiber Type**} & \multirow{2}{*}{**Tensile Strength (MPa)**} & \multicolumn{2}{c}{**Density**} & \multirow{2}{*}{**Purity**} & \multirow{2}{*}{**Price (RMB/kg)**} \\  & & & & (kg/m\({}^{3}\)) & & & \\ \hline Basalt fiber & 3000–3650 & 2650–3000 & & 18 \\ Polypropylene fiber & 2800 & 900–920 & \(\geq\)99\% & 15 \\ Glass fiber & 3100 & 2400–2760 & & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fiber performance index.  
with temperature scans performed at 58 \({}^{\circ}\)C, 64 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 76 \({}^{\circ}\)C. According to the Superpave specification, the rutting factor \(G^{\ast}\)/sin\(\delta\) is used to characterize the high-temperature performance of bitumen binder. The impact of various fiber types and their quantities on the high-temperature rheological properties of the bitumen mortar were investigated.
# An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
## 2 Materials and Methods  
### Neighborhood-Based Collaborative Filtering  
An RS is an algorithm that associates a set of items and users providing a ranked list of items to each user according to their preferences [28; 29]. Formally, items and users are represented in a ratings matrix, \(\textbf{R}=\{r_{u,i}\}\), where \(r_{u,i}\) is the degree of preference of user \(u\) to item \(i\). The task of the RS is to assign preference predictions, \(\hat{r}_{u,i}\), to all missing values in the (usually sparse) matrix **R**. CF recommendation is a strategy to obtain those predictions by exploiting item-item and user-user relationships under the hypothesis that similar users ought to prefer similar items.  
The most popular methods for addressing CF consist of reducing the dimensionality of **R** using matrix factorization (MF) [15], singular value decomposition (SVD) [30], and other related techniques [31; 32; 33]. Another approach consists of using the information of the \(k\)-nearest neighbor items or users to make rating predictions. The most representative method of this approach is the Item-KNN algorithm as proposed by [PERSON] [14]. This method makes use of a similarity function, \(S\), that providesa similarity score, \(s_{i,j}\), for any pair of items \(i\) and \(j\). This function, denoted by \(S_{i,\mu}^{k}\), is used to identify the set of \(k\) items rated by a user \(u\) being most similar to an item \(i\). Formally,  
\[\hat{r}_{u,i}=b_{u,i}+\frac{\sum_{j\in S_{i,u}^{k}}s_{i,j}(r_{u,j}-b_{u,j})}{ \sum_{j\in S_{i,u}^{k}}s_{i,j}}. \tag{1}\]  
This model is adjusted by baseline estimates \(b_{u,i}\) representing the rating bias of the user \(b_{u}\) that of the item \(b_{i}\), and the system overall bias \(\mu\) (i.e., the mean of the ratings in \(\mathbf{R}\)). The bias \(b_{u,i}\) is simply \(\mu+b_{u}+b_{i}\); and \(b_{u}\) and \(b_{i}\) are computed as follows:  
\[b_{i}=\frac{\sum_{u\in U_{i}}(r_{u,i}-\mu)}{\lambda_{1}+|U_{i}|};b_{u}=\frac{ \sum_{i\in I_{u}}(r_{u,i}-\mu-b_{i})}{\lambda_{2}+|I_{u}|}. \tag{2}\]  
Here, \(U_{i}\) is the set of users who rated \(i\), \(I_{u}\) is the set of items rated by \(u\), and finally \(\lambda_{1}\) and \(\lambda_{2}\) are regularization parameters determined by cross validation. In summary, \(\hat{r}_{u,i}\) is a weighted average of the ratings of the \(k\)-most similar items to \(i\) rated by \(u\) considering the item and user bias, which are their respective mean deviations from the average.  
The flexibility of this model relies on the item-similarity function \(S\), which can be built with any informational resources at hand. The natural choice for \(S\) is to obtain it from correlations between the items in \(\mathbf{R}\). We refer to this CF model as IKNN (item K-nearest neighbors). Note that this choice is particularly weak against the item cold-start problem [9]. When a particular item has not been rated by a considerable number of users, the correlations of its ratings against other items are in most cases non-statistically significant.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
## 3 Proposed Method
### Principal Component Analysis  
The application of PCA is a machine learning (ML) algorithm that aims to identify a basis for an \(N\)-dimensional vector space where the elements are uncorrelated and arranged based on their ability to explain the variance within the data [21]. By discarding components that contribute less to explaining the dataset's variance, PCA facilitates data compression through dimensionality reduction while preserving the most pertinent information. The original vector \(T\) is decomposed into a matrix of principal components \(X\) and a weights matrix \(W\), as shown in Equation (1). This maps the dataset vector \(x\) from the original space \(p\) to a new space \(\hat{p}\), where \(W\) is a \(p\)-by-\(p\) matrix of weights whose columns are the eigenvectors of Equation (2). The compression happens as only the first \(L\) eigenvectors are kept, producing the truncated transformation shown in Equation (3); the matrix \(T_{L}\) has \(n\) rows but only \(L\) columns [22].  
\[T=XW \tag{1}\] \[X^{T}X\] (2) \[T_{L}=XW_{L} \tag{3}\]  
The specific decomposition used in this paper is the singular value decomposition (SVD) in Equation (4), where \(\Sigma\) is an \(n\)-by-\(p\) rectangular diagonal matrix of positive number \(\sigma_{(k)}\) called the singular values of \(X\); \(U\) is an \(n\)-by-\(n\) matrix, the columns of which are orthogonal unit vectors of length \(n\) called the left singular vectors of \(X\); and \(W\) is a \(p\)-by-\(p\) matrix whose columns are orthogonal unit vectors of length \(p\) and called the right singular vectors of \(X\). We can then obtain a truncated \(n\times L\) matrix \(T_{L}\) through Equation (5) [23].  
\[X=U\Sigma W^{T} \tag{4}\] \[T_{L}=U_{L}\Sigma_{L}=XW_{L} \tag{5}\]  
Figure 1: PCA + DWT compression and decompression workflow.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Grading and Assessment Tools  
#### 3.6.1 Introduction to Grading and Assessment Tools  
Grading and assessment are fundamental components of the educational process, providing feedback to learners and informing instructional decisions. Traditional grading methods often involve manual evaluation of student work, which can be time consuming, subject to human bias, and inconsistent across different evaluators [91]. The advent of artificial intelligence, particularly multimodal large language models (MLLMs), has opened new avenues for automating and enhancing the grading and assessment process [92]. MLLMs can process and evaluate complex student outputs across various modalities--such as essays, short answers, presentations, code, and multimedia projects--providing timely, objective, and detailed feedback [93; 94].  
Automated grading systems leveraging MLLMs aim to improve efficiency, consistency, and fairness in assessments. By analyzing student submissions using advanced natural language processing (NLP) and computer vision techniques, these systems can assess not only the correctness but also the quality and depth of understanding demonstrated in student work [95]. Moreover, MLLMs enable formative assessments by providing personalized feedback and recommendations for improvement, supporting adaptive learning paths [96].
# Microsseismic Source Location Method and Application Based on NM-PSO Algorithm
## 7 Engineering Verification  
In order to further verify the effectiveness of the NM-PSO source location algorithm proposed in this paper, the ZF3808 working face of Shuiliangdong coal mine in Bin County, Shaanxi Province, is selected as the engineering background, and the engineering example is checked and analyzed. Figure 6 shows the mine's microseismic monitoring system.  
Figure 6: Flow chart of microseismic monitoring.  
Three known coordinate points are set and knocked to create microseismic events. Then NM simplex algorithm and NM-PSO source location algorithm are used to locate and calculate microseismic events. The positioning results are shown in Table 5; it can be seen from Table 5 that the two positioning methods mentioned in this paper have achieved ideal positioning results in all directions of microseismic events. Compared with the PSO optimization algorithm and NM algorithm, the event location calculated by the NM-PSO source location algorithm proposed in this paper is closer to the known coordinate points, and the overall error control is more stable. The final average location error in each direction is (5.65 m, 5.01 m, and 7.21 m), which is within the acceptable range and can meet the engineering needs. Therefore, the NM-PSO source location algorithm proposed in this paper can be applied to engineering practice.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 4 Shallow Underwater Acoustic Channel Model
### Deterministic Response  
The energy of an acoustic signal attenuates both with respect to distance as well as a function of the frequency. The path loss thus combines both geometric spreading and absorption. If the time variation is represented as \(e^{j\omega t}\), the expression for the frequency domain transmission loss in a positive \(z\) direction is mathematically expressed as:  
\[E(z)=E_{0}\;e^{-\gamma z}=E_{0}\;e^{-\alpha z}\;e^{-j\beta z} \tag{13}\]  
where \(E_{0}\) denotes the scaling constant. The deterministic transfer function \(H_{a}(f,d)\)[33; 34] becomes:  
\[H_{a}(f,d)=A_{d}\;e^{-\gamma(f)d} \tag{14}\]  
where \(A_{d}\) is the scaling constant, \(d\) is the distance between the transmitter and receiver, and \(\gamma\) represents the propagation constant in \(m^{-1}\):  
\[\gamma(f)=\alpha(f)+j\beta(f) \tag{15}\]  
where \(\alpha(f)\) and \(\beta(f)\) are the absorption and phase constants, respectively. The Thorp formula [31] expresses the absorption coefficient in dB/km as:  
\[\alpha_{dB}(f)=1.094\bigg{(}0.003+\frac{0.1f^{2}}{1+f^{2}}+\frac{40f^{2}}{41 00+f^{2}}+0.000275f^{2}\bigg{)}. \tag{16}\]  
The phase constant in rad/m is expressed as:  
\[\beta(f)=\frac{2\pi f}{c_{s}} \tag{17}\]  
where \(c_{s}\) represents the sound velocity in \(ms^{-1}\) and is computed using [PERSON]'s equation [35]. The signal \(c_{1}(t)\) in Figure 2 was computed through convolution as:  
\[c_{1}(t)=h_{a}(t,d)\;\otimes\;s(t) \tag{18}\]  
where \(h_{a}(t,d)\) is the IFFT of the channel transfer function.
# Towards Digital-Twin Assisted Software-Defined Quantum Satellite Networks
## 4 Proposed Architecture
The SDN Controller provides the configuration of the satellite path, handling entanglement generation and swapping in an appropriate manner, communicating with the satellites via dedicated southbound APIs. Figure 3: The proposed QSB control architecture. The DT monitors the network and calculates the best E2E temporal paths. Moreover, the SDN Controller configures the satellite paths resulting from the processing performed by the DT.
## 5 Simulation  
In order to verify the feasibility and effectiveness of the sliding-mode active disturbance rejection control algorithm, a comparative test of the effect of different trajectory tracking and the tracking effect of ordinary sliding-modes control and sliding-mode active disturbance rejection control was carried out in simulation.  
The parameters were set as: m\({}_{\mathrm{e}}\)= 0.516, k\({}_{\mathrm{m}}\)= 17.7, \(\alpha\) = 0.8, k = 9.4, b\({}_{\mathrm{o}}\)= 27.44. In the tracking step signals, \(\gamma_{1}\)= \(-\)376, \(\gamma_{2}\)= 752, \(\gamma_{3}\)= 4.7, and the observer bandwidth was 120.8. In the tracking sign signals, \(\gamma_{1}\)= \(-\)25,\(\gamma_{2}\)= 1562.5,\(\gamma_{3}\)= 0.025, and the observer bandwidth was 4.25. In the tracking square wave signals, \(\gamma_{1}\)= \(-\)7.92,\(\gamma_{2}\)= 2178,\(\gamma_{3}\)= 0.0072, and the observer bandwidth was 150.6. The control effect is shown in Figures 3-5.  
Figure 4: The tracking errors variation under the different controllers, (**a**) Tracking error of step signals, (**b**) Tracking error of sign signals, (**c**) Tracking error of square wave signals.  
Figure 3: Trajectory tracking variation under the different controllers. (**a**) Step signal trajectory tracking, (**b**) sign signal trajectory tracking, (**c**) square wave signal trajectory tracking.  
In Figures 3-5, SMC means the sliding-mode control, and SMADRC means the proposed sliding-mode active disturbance rejection control. As shown in Figure 3, under the external disturbance, the SMC showed significant jitter in both the step trajectory and the square wave trajectory, while the SMADRC tracked well and had better robustness.  
Figure 4 shows the tracking error changes of the two control algorithms. It can be seen that the tracking error of the SMADRC was lower than SMC for all three tracking tasks, and SMADRC also entered the steady state more quickly. Especially in Figure (b), the tracking error changed greatly for SMC at the beginning of simulation. Figure 5 shows the estimated perturbation curves of SMADRC. The actual outputs of the active disturbance rejection observer were all close to the set values, which indicates the effectiveness of the perturbation suppression in the control process and means that the SMADRC improves the accuracy of the control process.
Sensors_**2011**, _11_, 4231-4243; doi:10.3390/s110404231  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**SMS-Based Medical Diagnostic Telemetry Data Transmission Protocol for Medical Sensors**  
**[PERSON] 1, [PERSON] 1 and [PERSON] 2,* * 1 School of Information Technology, Deakin University, Pigdons Road, Geelong, VIC 3217, Australia; E-Mail: [EMAIL_ADDRESS] (B.T.); [EMAIL_ADDRESS] (J.A.) 2 Department of Multimedia Engineering, Hannam University, 133 Ojeong-dong, Daedeok-gu, Daejeon 306-791, Korea * Author to whom correspondence should be addressed; E-Mail: [PERSON]DDRESS]; Tel.: +82-42-629-8373; Fax: +82-42-629-8383; Mobile: +82-10-8592-4900. _Received: 10 February 2011; in revised form: 28 March 2011 / Accepted: 2 April 2011 / Published: 8 April 2011_  
**Abstract:** People with special medical monitoring needs can, these days, be sent home and remotely monitored through the use of data logging medical sensors and a transmission base-station. While this can improve quality of life by allowing the patient to spend most of their time at home, most current technologies rely on hardwired landline technology or expensive mobile data transmissions to transmit data to a medical facility. The aim of this paper is to investigate and develop an approach to increase the freedom of a monitored patient and decrease costs by utilising mobile technologies and SMS messaging to transmit data from patient to medico. To this end, we evaluated the capabilities of SMS and propose a generic communications protocol which can work within the constraints of the SMS format, but provide the necessary redundancy and robustness to be used for the transmission of non-critical medical telemetry from data logging medical sensors.  
**Keywords:** data transmission; data communications; SMS; short message service; GSM; mobile communications; information security; information privacy; telemetry; medical telemetry
## 3 Example Analysis and Experiment  
### Platform Introduction  
To verify the effectiveness of the proposed method, a rotational machinery (reducer) experimental platform was built. This platform mainly consists of an AC motor, a gear reducer, a powder brake, a coupling, and various sensors. Through the installation of speed, vibration, sound, and infrared sensors, real signals from the reducer's operation are collected. The specific types and installation positions of the sensors are shown in Figure 5.  
The experiment used the QABP100L4A model electric motor (Shanghai Yaqi Electric Co., Ltd., Shanghai, China), the MCC USB-1608 FS-Plus data acquisition card (Measurement Computing Corp (MCC), Norwood, MA, USA), and the CZ-20 magnetic powder brake (Hai'an County Aerospace Electromechanical Manufacturing Co., Ltd., Haian City, China). The reducer is a three-stage helical gear reducer with a safety factor of 0.79. The entire experiment was conducted under constant speed and rated load conditions.  
In this paper, the following seven parameters were selected as the data basis for gearbox health condition monitoring and identification. The sampling frequency, sampling interval, and sampling duration were set to 20,480 Hz, 30 s, and 1 s, respectively. The experiment was conducted using the horizontal vibration signal at the input end, the sound signal of the input shaft, and infrared images. The specific experimental data parameters and their units are listed in Table 2. The sensors used in the experiment are described in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Parameter Name** & **Unit** \\ \hline
1 & Vertical Output Vibration & m/s\({}^{2}\) \\
2 & Vertical Input Vibration & m/s\({}^{2}\) \\
3 & Horizontal Output Vibration & m/s\({}^{2}\) \\
4 & Horizontal Input Vibration & m/s\({}^{2}\) \\
5 & Voice Output & dB \\
6 & Voice Input & dB \\
7 & Infrared Image & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gearbox health condition monitoring data.  
Figure 5: Reducer experimental platform.
# A Linear-Arc Composite Beam Piezoelectric Energy Harvester Modeling and Finite Element Analysis
## 2 Structure and Theoretical Model of PEH-C  
### Structure of PEH-C  
The PEH-C, shown in Figure 1, is comprised of a composite beam, mass, piezoelectric material (PVDF), and base. The cantilever beam is made up of a combination of linear and arced structures. Under the condition of preserving the total length of the structure, the curvature of the curved part can be changed to form a different linear-arc-shaped combination beam. When the curvature is 100 m\({}^{-1}\), the radius of the arc beam corresponds to 0.01 mm and the corresponding string length is 20 mm. At this point, the curved shape is semi-circular. Earlier team research [30] has proved that this structure has an output power higher than the straight beam. The horizontal distance of the cantilever beam is L. When the curvature of the curved part is changed, the horizontal distance will change accordingly. The mass is fixed at the end of the cantilever beam. The piezoelectric material is attached to the surface of the arc part of the curve-shaped beam to realize energy conversion and the arc part remains free. If the PEH-C is excited by ambient vibrations, the piezoelectric cantilever and mass are vibrated with the base, so the oscillation of the piezoelectric cantilever would result in the deformation of PVDF. Thus, the conversion of mechanical energy from ambiance into electrical energy via the piezoelectric effect can be achieved.
## 3 Results and Discussion
The differences were found between the following pairs: control (Time 0) versus eluate (Time 0 and 28 days), control (Time 7 days) versus eluate (Time 0) at a 0.05 significance level (*) (see Table 2). The results of the MTT assay confirmed the results of the test with dissolved oxygen. The viability of the microorganisms from suspensions (aquatic solution + own particles) was decreased about 28% in comparison to the control. On the other hand, the viability of the microorganisms in the pure eluates was decreased only about 9% in comparison to the control (see Table 3). The toxicity for microorganisms was thereby confirmed by the second independent assay. The viability test seems to be more sensitive than the respiration inhibition test because the observed toxicity of the eluates was higher in the viability test (compare Figure 2 and Table 3). We can note that the toxicity in the viability tests can be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample (No. of Repetition)** & **0 Time** & **7 th Day** & **14 th Day** & **21 st Day** & **28 th Day** \\ \hline \multicolumn{6}{c}{(A) The control medium with microorganisms} \\ \hline Control (1) + inoculum & 5.5 & 5.3 & 4.6 & 3.9 & 3.7 \\ Control (2) + inoculum & 5.4 & 5.2 & 4.8 & 4.2 & 4.0 \\ Control (3) + inoculum & 5.2 & 5.1 & 4.5 & 4.3 & 4.0 \\ Mean Control & 5.4 & 5.2 & 4.6 & 4.1 & 3.9 \\ SD & 0.2 & 0.1 & 0.2 & 0.2 & 0.2 \\ Oxygen consumption by microorganisms from & - & 4 & 15 & 24 & 28 \\
0 to 28 days (\%) & - & - & - & - & - \\ \hline \multicolumn{6}{c}{(B) The eluate of crushed particles} \\ \hline Tire sample (1) + inoculum & 4.5 & 4.4 & 3.8 & 3.1 & 3.1 \\ Tire sample (2) + inoculum & 4.4 & 4.3 & 3.9 & 3.8 & 3.1 \\ Tire sample (3) + inoculum & 4.5 & 4.4 & 3.7 & 3.5 & 3.4 \\ Mean TS + inoculum & 4.5 & 4.4 & 3.8 & 3.5 & 3.2 \\ SD & 0.1 & 0.1 & 0.1 & 0.4 & 0.2 \\ Oxygen consumption by microorganisms from & - & 2 & 16 & 22 & 29 \\
0 to 28 days (\%) & - & - & - & - & - \\ \hline \multicolumn{6}{c}{(C) The suspension of crushed particles} \\ \hline Tire sample (1) + inoculum & 4.7 & 4.5 & 4.1 & 4.1 & 4.0 \\ Tire sample (2) + inoculum & 4.5 & 4.3 & 4.0 & 3.8 & 3.7 \\ Tire sample (3) + inoculum & 4.9 & 4.7 & 3.9 & 3.8 & 3.8 \\ Mean TS + inoculum & 4.6 & 4.6 & 4.0 & 4.0 & 3.8 \\ SD & 0.2 & 0.2 & 0.1 & 0.2 & 0.2 \\ Oxygen consumption by microorganisms from & - & 0 & 13 & 13 & 17 \\
0 to 28 days (\%) & - & 0 & 13 & 13 & 17 \\ \hline \multicolumn{6}{c}{(D) Tire samples without microorganisms} \\ \hline \multicolumn{6}{c}{Eluate of mesh. crushed particles} \\ \described as an acute toxic effect, at variance with the respiration test, where the chronic effect was observed after 28 days of exposure. It is also a destructive method and it is not possible to monitor the same microbial community during the different times. The respiration test is also a representative of the multi-generation microorganismal method and it is evident (Figure 2) that the microorganismal biomass was reduced over the time period, probably due to gradually released metals from the tire particles.
# Experimental Study on the Influence of Low Temperature on the Gas Permeability of Granite
## 3 Results and Discussion
### Relationship Between Pore Structure and Permeability  
#### 3.5.1 Contributing Porosity of Granite  
In practical applications, although porosity may be consistent, different pore size distributions within a rock can lead to variability in permeability. Therefore, it is crucial to consider both pore size and porosity when assessing permeability. [PERSON] et al. [46] introduced a metric called \"contributing porosity,\" defined as the product of porosity and the proportion of relevant pore size to overall porosity within a given size range. This metric quantifies the proportion of pores within a specified size range, reflecting the combined effect of pore size and porosity on gas permeability. The contributing porosities for different pore size ranges before and after freeze-thaw cycling in granite are presented in Table 5.