esa-sceva/satcom-chunk-collection huggingface dataset:
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 2 Preliminaries
### Fuzzy Logic  
The fuzzy logic concept was introduced in 1965 by the renowned mathematician [PERSON] [35]. Fuzzy logic is an approach used to describe fuzziness by employing a set of mathematical principles. It is based on the idea that everything can be described by a degree, and it allows for the inclusion of approximate reasoning to handle uncertainty in a subject. In contrast to Boolean logic, fuzzy logic is intended to compute the degree of truth to be between \"completely true\" and \"completely false\" or the degree of membership between 0 and 1 [35]. For more clarification, we briefly explain the terms associated with fuzzy logic as follows:  
* Fuzzy set: This is set A and is defined by the membership function MA (Equation (1)), and each element x in the set has a certain degree of membership between 0 and 1. \[MA(x):X\rightarrow[0,1]\;\textit{where}\;MA(x)=\left\{\begin{array}{c}1\; \textit{if}\;\textit{x}\;\textit{is totally in}\;A\\ 0\;\textit{if}\;\textit{x}\;\textit{is totally not in}\;A\\ 0<MA\;(x)<1\;\textit{if}\;\textit{x}\;\textit{is partly in}\;A\end{array}\right.\] (1)
* Membership function: This function computes how each element in the fuzzy sets is mapped to its degree of membership, which is a value from a range within [0,1].  
There are several kinds of membership functions, and they are selected depending on the condition of the problem. In general, the most commonly used functions are trapezoidal, Gaussian, and triangular functions.  
The following sequential steps are usually followed when implementing fuzzy logic in a real application [35; 36]:  
* Fuzzification: This step uses a membership function to transform a crisp value into a fuzzy value that expresses the degree of membership of an element to different fuzzy sets.
* Fuzzy inference: This step applies some of the if-then rules on the results of the membership functions to obtain the fuzzy output.
* Defuzzification: This step converts the fuzzy output into a crisp value.
# A Simple and Robust Equalization Algorithm for Variable Modulation Systems
###### Abstract
In [8], an improved CMA blind equalization algorithm was proposed, which modifies the updated equation of the filter coefficients and proposes a new weight calculation equation, thus improving the convergence speed and improving the BER performance. However, it is insensitive to the phase, and it needs to specify the modulation and modify the correction parameters. A blind equalizer composed of a modified algorithm (MA) cascaded with multi-step prediction error filters (MA-MSPEF) was proposed to minimize the mean square error (MSE) in [9]. However, it increases the implementation complexity for the VM system. In the VM system, the conventional blind equalization algorithm needs to be dynamically adjusted according to the current modulation, but the current modulation method cannot be determined. Therefore, the conventional blind equalization algorithms are not suitable for the VM system. In this study, the advantages and disadvantages of the three conventional blind equalization algorithms of CMA, MCMA, and DDLMS were analyzed, and the data-aided equalization algorithm (VM-DDLMS) was proposed for the VM system. The proposed algorithm utilizes the characteristics of a QPSK-like modulation in the modulated frame header and modulated pilot from the PL frame to perform the equalization decision, updates the filter coefficients using the deviation between the known information and the corresponding received frame, and corrects data through an equalization filter. The VM-DDLMS algorithm only makes QPSK symbol decisions; it is not necessary to adjust the correction parameters and switch the symbol decision method according to the modulation method--this is conducive to the stability of the VM system. The proposed algorithm has the advantages of an easy implementation and good practicability.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 3 Detailed Description of the Proposed Method
### Point Cloud Tracking and Pose Optimization
#### 3.3.3 Pose Determination  
Because of the necessity to provide a reference to the navigation system, according to our previous work, the docking ring of non-cooperative spacecraft was identified in the visual images to complete the absolute pose of the first frame. The reference absolute pose has five degrees of freedom. The detection results of the docking ring are shown in Figure 10.  
The pose transformation matrix of the current i-th frame can be represented as:  
\[\mathbf{H}_{i}^{\rm w}=\begin{bmatrix}\mathbf{R}_{i}^{\rm w}&\mathbf{T}_{i}^{\rm w}\\ 0&1\end{bmatrix} \tag{21}\]  
where \(\mathbf{R}_{i}^{\rm w}\) and \(\mathbf{T}_{i}^{\rm w}\) represent the rotation matrix and the translation vector of the current frame, respectively. For the non-first frame, the pose transformation matrix of the \(j\)-th frame can be expressed as:  
\[\mathbf{H}_{j}^{\rm w}=\mathbf{H}_{j}^{i}\mathbf{H}_{i}^{i-1}\cdots\mathbf{H}_{1}^{\rm w} \tag{22}\]  
where \(\mathbf{H}_{j}^{i}\) represents the transformation matrix from the \(i\)-th frame point cloud to the \(j\)-th frame point cloud. A pose correction method based on key frames is proposed in this study for the purpose of eliminating the cumulative error in the process of point cloud registration and tracking. The key frames selection criteria is as follows:  
(a) The frame count difference between the current frame and the previous key frame is greater than the threshold \(T_{c}\) (it was set to 15 in this text), and the current frame is added to the key frame set;  
Figure 10: The image of the docking ring detection.  
(b) The relative movement distance between the current frame and the previous key frame is greater than the threshold \(T_{d}\) (it was set to 100 mm in this text) or the relative angle greater than the threshold \(T_{a}\) (it was set to 50 degree in this text); then, the current frame is added to the key frame set.  
Inspired by the work in Reference [29], the pose objective function to be optimized in the key frame set can be defined as:  
\[\text{argmin}\underset{i,j}{\sum}\frac{1}{3}\|\mathbf{E}-(\mathbf{R}_{j}^{\text{w}})^{ -1}(\mathbf{R}_{j}^{\text{w}}\mathbf{R}_{i}^{\text{w}})\|_{F}^{2}+\left\|\mathbf{T}_{j}^{ \text{w}}-\mathbf{T}_{i}^{\text{w}}-\mathbf{R}_{i}^{\text{w}}\mathbf{T}_{j}^{i}\right\|_{2} ^{2} \tag{23}\]  
where \(\mathbf{R}_{j}^{i}\), \(\mathbf{T}_{j}^{i}\) represent the rotation matrix and translation vector from the i-th frame point cloud to the j-th frame point cloud, respectively. \(\left\|\cdot\right\|_{F}\) represents the Frobenius norm of the matrix. The abovementioned nonlinear optimization problem is solved based on the Ceres library [30] in its specific implementation.
## 2 Results
### Catalytic Functions of SmMTPSLs  
To investigate the catalytic activity of SmMTPSLs, all cloned _SmMTPSL_ genes were heterologously expressed in _E. coli_. Recombinant SmMTPSLs proteins were tested with the potential substrates GPP or FPP. The eight SmMTPSs were all able to accept GPP as the substrate to produce monoterpenes (Figure 4A). SmMTPSL5, SmMTPSL19, and SmMTPSL33 catalyzed the formation of geraniol as a single product, while SmMTPSL15 yielded lawanduldol as a major product. In contrast, the other three SmMTPSLs each produced multiple monoterpenes. SmMTPSL23 and SmMTPSL37 converted GPP to \(\beta\)-linaload and geraniol, when using GPP as the substrate. SmMTPSL46 produced three monoterpenes, including geraniol, \(\beta\)-linalool, and \(\alpha\)-cyclogeraniol; whereas SmMTPSL47 could convert GPP into geraniol and lavandulol (Figure 4A).  
Additionally, eight SmMTPSs all showed sesquiterpenes synthase activities with FPP as the substrate in vitro (Figure 5A). SmMTPSL5 produced two sesquiterpenes, including famesol and aromadendrene, with the two products also produced by SmMTPSL33. Incubation of SmMTPSL15 with FPP catalyzed the formation of six sesquiterpenes predominantly aromadendrene along with \(\beta\)-farnesene, nerolidol, germacrene, farnesol, and patchouli alcohol. Upon incubation with FPP, SmMTPSL19 led to the production of six sesquiterpenes, including \(\beta\)-farnesene as a major product along with \(\beta\)-elemene, nerolidol, germacrene, farnesol, and aromadendrene while the SmMTPSL23 reactions with FPP resulted in the productions of five sesquiterpenes viz., \(\beta\)-farnesene, nerolidol, germacrene, farnesol, and aromadendrene, with the four of them also produced by SmMTPSL37. When SmMTPSL46 incubated with FPP, five sesquiterpene products were detected, viz., \(\beta\)-farnesene, germacrene, farnesol, patchouli alcohol, and shyobunone. When SmMTPSL47 incubated with FPP, five sesquiterpene products also were detected, including germacrene as a major product along with \(\beta\)-farnesene, \(\beta\)-elemene, aromadendrene, and isoshyobunone (Figure 5A).
## 2 Capacity Influence Analysis and Index Systems Construction
### Evaluation Index Construction  
The analysis in Section 2.1 shows that the average length of failure duration at the stop and the forced lane-changing rate of transit significantly impact the capacity. Therefore, this paper selects FD and FLR of buses as the evaluation indicators of traffic operation efficiency in the bus stop area.  
In the case of a bus stop failure occurring, FD is defined as the time a bus waits for service after arriving at the stop. FD can be determined by examining the arrival and departure times of the buses, which the following formula can calculate:  
\[FD=t_{f(1)}+t_{f(2)}+\cdots+t_{f(N)} \tag{1}\]  
Figure 2: Bus lane-changing area and waiting area.  
where \(t_{f(1)}\) is the failure duration of the first time in one-unit time, and \(N\) is the number of failure times of stops per unit time.  
The critical position of lane change is recorded as LP. A vehicle must start changing lanes before the critical position. As explained in Section 2.1, forced lane change is defined as changing lanes after the critical position of the lane change. FLR is defined as the ratio of forced lane change times to total lane change times. The FLR can be calculated by:  
\[FLR=\frac{C_{m}}{\sum\left(C_{f}+C_{m}\right)} \tag{2}\]  
\(C_{f}\) represents the number of free lane changes for buses, and \(C_{m}\) indicates the number of forced lane changes for buses.
## 4 Experimental Validation and Discussion
### Discussion of Excitation Frequency on Vibration Characteristics  
In order to verify the influence of excitation frequency on the performance of the piezoelectric energy harvester, we first set the excitation amplitude to 14 m/s\({}^{2}\), and measured the response output characteristics under the conditions of \(f=6\) Hz, \(f=9\) Hz, and \(f=12\) Hz.  
Figure 17a-c shows the simulation results of time-domain velocity versus the displacement when \(A=14\) m/s\({}^{2}\) and the excitation frequency is 6 Hz, 9 Hz, and 12 Hz, respectively. Figure 18a-c shows the voltage-time diagrams of \(A=14\) m/s\({}^{2}\) and excitation frequency of 6 Hz, 9 Hz, and 12 Hz, respectively. It can be seen from Figure 17 combined with Figure 18 that the IBPEH system exhibits chaotic motion under the condition of an excitation frequency of 6 Hz, and the RMS voltage produced by IBPEH is 15.42 V. BPEH is small-amplitude intrawell motion state, and the RMS voltage is only 2.32 V. When the excitation frequency is increased to 9 Hz, the IBPEH system can adapt to this frequency and perform large-amplitude intrawell motion, and the RMS voltage is 22.32 V, while the BPEH still maintains small-amplitude intrawell motion, and the RMS voltage is only 3.52 V. The RMS voltage of IBPEH is 6.64 times and 6.34 times that of BPEH RMS voltage in turn. Continuing to increase the excitation frequency to 12 Hz, both BPEH and IBPEH show a state of large-amplitude intrawell periodic motion, the RMS voltage produced by BPEH is 20.81 V, and the IBPEH is 27.02 V. Under this excitation condition, the RMS voltage produced by IBPEH is 29.84% higher than that of BPEH, and the experimental results are in agreement with the theoretical analysis results.
## 2 Theory
### Spectrum Information Entropy  
In 1948, [PERSON] proposed the concept of \"information entropy\", which solved the problem of quantifying information. The more orderly a system, the lower the information entropy; conversely, the more chaotic a system, the higher the information entropy. Therefore, information entropy is an important indicator to measure the order of the system [20; 21; 22].  
Considering the characteristics of the signal, the power spectrum of the HIFU echo signal is more uncertain, and its power spectrum information entropy is higher than that of the noise signal. Therefore, power spectrum information entropy can be used as a judgment index.  
The judgment process based on power spectrum information entropy is as follows:  
1. AR model is used to estimate the power spectrum of the sampled signal, and the power spectrum amplitude is normalized.
2. The amplitude range of the power spectrum is segmented. For example, 0-0.1 is the first segment, 0.1-0.2 is the second segment, and so on.
3. The amount of data in each segment number is counted and the probability that the segment appears in the whole data point is calculated.
4. According to the Equation (10), the spectrum information entropy is calculated, and the HIFU echo signal is judged according to the appropriate threshold.  
\[H(x)=-\sum_{i=1}^{N}p_{i}log(p_{i}) \tag{10}\]  
where \(H(x)\) is spectrum information entropy; \(p_{i}\) is the probability of the \(i\)-th segment after the power spectrum segmentation; \(N\) is the segment number.
###### Abstract  
Multi-focus image fusion (MIF) uses fusion rules to combine two or more images of the same scene with various focus values into a fully focused image. An all-in-focus image refers to a fully focused image that is more informative and useful for visual perception. A fused image with high quality is essential for maintaining shift-invariant and directional selectivity characteristics of the image. Traditional wavelet-based fusion methods, in turn, create ringing distortions in the fused image due to a lack of directional selectivity and shift-invariance. In this paper, a classical MIF system based on quarter shift dual-tree complex wavelet transform (qshiftN DTCWT) and modified principal component analysis (MPCA) in the laplacian pyramid (LP) domain is proposed to extract the focused image from multiple source images. In the proposed fusion approach, the LP first decomposes the multi-focus source images into low-frequency (LF) components and high-frequency (HF) components. Then, qshiftN DTCWT is used to fuse low and high-frequency components to produce a fused image. Finally, to improve the effectiveness of the qshiftN DTCWT and LP-based method, the MPCA algorithm is utilized to generate an all-in-focus image. Due to its directionality, and its shift-invariance, this transform can provide high-quality information in a fused image. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques in terms of visual and quantitative evaluations.  
image quality; quarter shift dual-tree complex wavelet transform; laplacian pyramid; modified principal component analysis; multi-focus image fusion; quality evaluation metrics +
Footnote †: journal: Applied Sciences
## 2 Chromatographic Techniques for Carotenoids Analysis
### Gas Chromatography (GC)  
Gas chromatography (GC) is not commonly used for carotenoid analysis due to these compounds' volatility and thermal stability limitations [33]. Unlike liquid chromatography (LC), which is well-suited for carotenoids in their native forms, GC is primarily used to analyze volatile carotenoid-derived compounds (CPs), particularly apocarotonoids and short-chain degradation products [23; 33]. The efficiency of GC for these compounds depends on extraction, pretreatment (derivatization), and instrumental conditions.  
Extraction methods for volatile CPs often involve solvent extraction, steam distillation, or solid phase microextraction (SPME), depending on the sample matrix [33]. Derivatization is often necessary to enhance volatility and thermal stability; for instance, N-methyl-N-(tert-butyldimethylsilyl)trifluoroacetamide and the Sweeley reagent (10% \(\sigma\)/\(v\) hexamethyldisilazane-trimethylchlorosilane, 2:1 in pyridine, heated at 45 \({}^{\circ}\)C for 30 min) are frequently used in CP analysis [23]. However, some compounds, such as \(\beta\)-ionone (\(\beta\)-IO), \(\beta\)-cyclocitral (\(\beta\)-CC), dihydroactinidiolide, and epoxy-\(\beta\)-ionone, can be analyzed without derivatization [23] GC analysis is performed using capillary columns such as BPX5 from SGE Analytical Science, Zebron ZB-1 from Phenomenex, DB-5 MS and CP Wax 58 CB from Agilent Thencologies, and STABILWAX-DA from Restek Corporation, with temperature gradients ranging from 40 \({}^{\circ}\)C to 310 \({}^{\circ}\)C, depending on the sample composition [23; 33; 34].  
These optimized extractions, pretreatment, and instrumental techniques allow GC to be a viable method for analyzing volatile CPs despite its limitations for intact carotenoids.
## 3 QoS Correlation Analysis
#### Correlation Analysis of Empirical Measurements
One thing to keep in mind is that the delay times provided here represent Round Trip Time (RTT), or the total time it takes a packet to travel to and from the server. To emulate end-to-end delay (either downstream or upstream), we halve the latency times, assuming the packet takes the same amount of time to travel to and from the server. At the moment, there is no way around this assumption because there are no existing empirical solutions for determining one-way latency. The mean end-to-end delay for all four operators obtained are shown in Table 1. The characteristic parameters; mu (\(\mu\)) and sigma (\(\sigma\)) of log-normal distribution were found for each operator. These parameterised log-normal distributions were later implemented in NetEm to provide realistic packet delay distribution for our QoE experiments. To get a better context of the delay distribution throughout the day, a box-plot representation for each hour packet delay was plotted, as shown in Figure 2. The outliers were not included in the calculation because they were limited in number and more than \begin{table}
\begin{tabular}{c c} \hline \hline
**Operator** & **Mean Delay (ms)** \\ \hline
1 & 51.48 \\ \hline
2 & 57.41 \\ \hline
3 & 59.36 \\ \hline
4 & 48.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean end-to-end delay for four UK operators. 1000 milliseconds, which is an enormous amount in networking terms. As predicted by the log-normal distribution nature of packet delay, the medians were considerably closer to the First Quartile than the Third Quartile. Figure 2 demonstrates that operator 4 had a more constant average hourly delay than the other operators. Even though the average delay values for operator 1 and operator 4 were nearly identical (51 ms and 49 ms, respectively), the hourly distributions were substantially different. While the average hourly delay for operator 4 remained between 40 ms and 60 ms throughout the day, the average hourly delay for operator 1 fluctuated significantly more, reaching a peak of nearly 120 ms between 8 a.m. and 10 a.m. This might be considered operator 1's busiest hour. Similar results were applicable for operators 2 and 3. Even though their mean latencies, over a 24-h period, were quite comparable, their hourly averages were vastly different. These results could be utilised to emulate alternative off-peak and peak distributions for QoS-QoE evaluation. Depending on the type of application and time of day, consumers are able to determine which operator best meets their requirements. The operators could also use this information to efficiently reallocate network resources. The findings of different mean hourly delay and maximum points were later used in the QoE evaluation experiments, to emulate different off-peak and on-peak scenarios.
# Big-Delay Estimation for Speech Separation in Assisted Living Environments
###### Abstract  
Phase wraparound due to large inter-sensor spacings in multi-channel demixing renders the DUCT and AdRess source separation algorithms--known for their low computational complexity and effective speech demixing performance--unsuitable for hearing-assisted living applications, where such configurations are needed. DUCT is limited to relative delays of up to 7 samples, given a sampling rate of \(F_{s}=16\) kHz in anechoic scenarios, while the AdRess algorithm is constrained to instantaneous mixing problems. The task of this paper is to improve the performance of DUCT-type time-frequency (TF) masks when microphones are placed far apart. A significant challenge in assistive hearing scenarios is phase wraparound caused by large relative delays. We evaluate the performance of a large relative delay estimation method, called the Elevatogram, in the presence of significant phase wraparound. We present extensions of DUCT and AdRess, termed Elevato-DUET and Elevato-AdRess, which are effective in scenarios with relative delays of up to 200 samples. The findings demonstrate that Elevato-AdRess not only outperforms Elevato-DUET in terms of objective separation quality metrics--BSS_Eval and PEASS--but also achieves higher intelligibility scores, as measured by the Perceptual Evaluation of Speech Quality (PESQ) Mean Opinion Score (MOS) scores. These findings suggest that the phase wraparound limitations of DUCT and AdRess algorithms in assistive hearing scenarios involving large inter-microphone spacing can be addressed by introducing the Elevatogram-based Elevato-DUET and Elevato-AdRess algorithms. These algorithms improve separation quality and intelligibility, with Elevato-AdRess demonstrating the best overall performance.  
Keywords:assisted living (AL); relative delay estimation; source separation (SS); time-frequency (TF); binary mask; interaural phase difference (IPD); interaural intensity difference (IID); remote microphone (RM); windowed-disjoint orthogonal (WDO); relative transfer function (RTF); single source point (SSP) +
Footnote †: journal: