esa-sceva/satcom-chunk-collection huggingface dataset:
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
## 4 Methodology
### Generating Dataset for Sentiment Cue Extraction
#### 4.2.4 Process of Selecting Pseudo Label for \(X\)  
Algorithm 3 details the comprehensive process for generating a pseudo label for an instance \(X\). This process involves evaluating multiple candidate sequences generated under various sampling ratios, each with the aim of identifying the sequence that best represents the sentiment cues within \(X\). The algorithm utilizes a well-trained sentiment classification model \(f_{\text{sc}}\) to calculate the MSIS for each candidate sequence, ultimately selecting the sequence with the highest MSIS as the pseudo label for \(X\).  
It should be noted that in order to ensure the RCT of the candidate sequences obtained through sampling is as uniform as possible, covering different instances, we will uniformly select several decimals between \(0\) and \(1\) to serve as sampling ratios.  
By employing this algorithm for all instances in the dataset, a collection of pseudo labels is generated, forming a dataset that can be used to train the SCE model.  
```
0: instance \(X\), the sentiment category of the instance \(C\), length of the instance \(n\), well trained sentiment classification model \(f_{\text{sc}}\), set of sampling ratios \(\{p_{1},p_{2},\dots,p_{k}\}\), sampling number \(m\)
1:\(Y\leftarrow\emptyset\)\(\triangleright\) Initialize the pseudo label ad a empty set
2:\(MSIS_{Y}\gets 0\)\(\triangleright\) Initialize \(MSIS\) of \(Y\) with 0
3:for\(p\in\{p_{1},p_{2},\dots,p_{k}\}\)do
4:for\(i\in[1,2,\dots,m]\)do
5:\(Y^{c}\leftarrow\) Generate a candidate sequence with \(X\) and \(p\)\(\triangleright\) Refer to Algorithm 1
6:\(MSIS_{Y^{c}}\leftarrow\) Evaluate \(Y^{c}\)\(\triangleright\) Refer to Algorithm 2
7:if\(MSIS_{Y^{c}}>MSIS_{Y}\)then
8:\(Y\gets Y^{c}\)\(\triangleright\) Update Pseudo Label \(Y\) if a higher score is achieved
9:\(MSIS_{Y}\gets MSIS_{Y^{c}}\)\(\triangleright\) Update the score accordingly
10:endif
11:endfor
12:endfor
13:return\(Y\)
```  
**Algorithm 3** Process of selecting pseudo label for \(X\)
## 5 Conclusions  
Polymer gel dosimeters PAGAT and MAGAT-f were investigated to measure FWHM, spot width, Bragg peak range, and scanning positions of the pencil beam. The 3D volume rendering of the Bragg peak was reconstructed to measure the spot size at different depths. Obvious saturation was seen at the high dose region over 20 Gy. Bragg peak range and DDF were measured with both gel dosimeters and compared with MC simulation. Our results demonstrated that Bragg peak range errors within 0.5 mm can be achieved with a polymer gel dosimeter. The spot size deviations between the gel measurement and MC simulation were \(-\)0.56% and \(-\)14.23% for MAGAT-f and PAGAT gels, respectively. The discrepancies between the spot spacing measurement and the theoretical value were 7.79% and 4.51% for MAGAT-f and PAGAT, respectively. The results ascertained the feasibility of the polymer gel dosimeter to measure the spot size, spot positions, and Bragg peak range of the proton pencil beam. In conclusion, polymer gel dosimeter is a valuable tool to investigate the geometric properties of a proton pencil beam with high spatial resolution.  
Data curation, L.Z., M.Z. and X.W.; formal analysis, L.Z. and X.W.; funding acquisition, X.X. and X.W.; investigation, L.Z. and X.X.; methodology, L.Z.; supervision, M.Z., X.X. and X.W.; writing--original draft, L.Z.; writing--review and editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by the National Key R&D Program of China (Ministry of Science and Technology) grant number No.2016 YC0105406; National Natural Science Foundation of China grant number No.61571262, No.11575095, No.61171115 And The APC was funded by No.2016 YFC0105406.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
The authors would like to thank SAPT staff for the experiments support and [PERSON] for the language corrections.  
The authors declare that there is no conflict of interest.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 2 Literature Review  
### Data Sources for OD Estimation  
Traffic counts are the earliest and most fundamental data in OD estimation [21; 22]. Nevertheless, much of the information about trips, including route choice, departure, and arrival time, is not directly reflected in traffic count data. With the development of big data in the era of ITS, much additional data are being used to improve the accuracy of OD estimation, including data from automatic vehicle identification (AVI) [3; 23; 24], Bluetooth MAC scanner [13], mobile device and GPS [25; 26], floating cars [10; 27], and smart card records [28].  
AVI and Bluetooth-scanning data gather the vehicle identification information and corresponding timestamps. By re-identifying the same vehicle at another location after the initial identification, complete or partial travel path information of the vehicle can be obtained. Recent work has pointed to the existence of a minimum sampling rate that guarantees estimation accuracy using AVI data [24]. The sampling rate is restricted by deployment of roadside AVI devices, the penetration rate of vehicle-side devices, and the accuracy of identification, making it challenging to achieve the desired accuracy in many cities.  
Mobile device data includes cellular signaling token [26] and mobile positioning data [29]. It is frequently employed in crowd mobility modeling thanks to the high coverage of cellphones [30]. However, when applying mobile device data to the OD estimation, there are challenges of matching cellphone users with specified roads and traffic modes, and trade-offs between coarse spatial-temporal resolution and privacy protection.  
Floating Car Data (FCD) contains detailed spatio-temporal trajectories of a subset of vehicles in the road network, typically from taxis equipped with positioning systems. As a sample of the complete traffic flows, the OD matrix from FCD can be scaled up in a statistical sense into the OD matrix for the full traffic volume. Additionally, FCD can be availed to obtain traffic status, including road travel times and turning rates at intersections. Nevertheless, FCD might be a biased sampling of the entire traffic flow in time and space, influenced by low penetration rates and operating characteristics [16] (e.g., tendency to gravitate to areas with higher demand; higher nighttime share relative to private cars).  
Integrating multiple sources of data in OD estimation is undoubtedly a promising future direction. However, in different cities and application scenarios, the availability and usability of additional data sources are not identical, and integrating multi-source data at different levels of completeness is still challenging. Traffic-count-based OD estimation provides a basic usable solution for different scenarios.
# V2X Wireless Technology Identification Using Time-Frequency Analysis and Random Forest Classifier
## 5 Performance Evaluation
### Simulation Results  
#### 5.2.1 Data Analysis  
To better understand the resulting datasets, we need to visualize the feature vectors of the three considered signal types in two-dimensional space. For ease of plotting, the t-Distributed Stochastic Neighbour Embedding (t-SNE) technique is used. It is a method for visualizing high-dimensional data by giving each sample a location in a two or three-dimensional space, whilst preserving distances between samples [38].  
As can be seen in Figure 3, the t-sne representation of both datasets clusters the three signal types into distinct groups in space. However, by comparing the two graphs, we can first observe that the SCF feature makes the separation harder and will consequently require a more complex classifier such as SVM. Moreover, the samples not belonging to any of the formed clusters or those superimposed on each other may increase the confusion among signals, unlike the IF feature samples in which almost no confusion can be seen. Therefore, the proposed feature vector allows us to address the first issue of signal identification, which consists of reducing the confusion between signals sharing the same PHY layer parameters. More in-depth analysis is required to explore these preliminary results in greater detail, which will be performed in the next subsection through confusion matrices and the previously defined classification metrics.
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
## 3 Optimal Guidance Laws
### Spacecraft Dynamics in Terms of Classical Orbital Elements
\begin{bmatrix}N_{11}\lambda_{a}+N_{21}\lambda_ {e}+N_{51}\lambda_{\omega}+N_{61}\lambda_{v}\\ N_{12}\lambda_{a}+N_{22}\lambda_{e}+N_{52}\lambda_{\omega}+N_{62}\lambda_{v}\\ N_{43}\lambda_{\Omega}+N_{33}\lambda_{i}+N_{53}\lambda_{\omega}+N_{63}\lambda_{v }\end{bmatrix}  \tag{52}\]  
where \(N_{ij}\) is the entry of matrix \(\mathbb{N}\) at the \(i\)th row and \(j\)th column, with  
\[\lambda_{o}\triangleq\|\boldsymbol{\lambda}_{o}\|\qquad\text{and}\qquad \boldsymbol{\lambda}_{o}=\frac{\boldsymbol{\lambda}_{o}}{\lambda_{o}} \tag{53}\]  
Using Equations (50) and (52), the function \(\mathcal{H}_{c}\) is  
\[\mathcal{H}_{c}=\boldsymbol{a}\cdot\boldsymbol{\lambda}_{o} \tag{54}\]  
so that, according again to Equations (21)-(23), the optimal guidance law is  
\[\boldsymbol{\hat{t}}^{\star}= \begin{cases}\boldsymbol{\hat{\lambda}}_{o}& \text{if}\quad\boldsymbol{\alpha}_{\lambda_{o}}\leq\alpha_{\text{max}}\\ \frac{\sin(\boldsymbol{\alpha}_{\lambda_{o}}-\alpha_{\text{max}})}{\sin \boldsymbol{\alpha}_{\lambda_{o}}}\,\boldsymbol{\hat{r}}+\frac{\sin\boldsymbol {\alpha}_{\text{max}}}{\sin\boldsymbol{\alpha}_{\lambda_{o}}}\,\boldsymbol{ \hat{\lambda}}_{o}&\text{if}\quad\boldsymbol{\alpha}_{\lambda_{o}}\in(\alpha_ {\text{max}},\,180\,\text{deg})\end{cases}  \tag{55}\]  
where \(\boldsymbol{\alpha}_{\lambda_{o}}\in[0,\,180)\,\text{deg}\) is given by  
\[\boldsymbol{\alpha}_{\lambda_{o}}=\arccos\left(\boldsymbol{\hat{\lambda}}_{o} \cdot\boldsymbol{\hat{r}}\right) \tag{56}\]
# Effect of High-Temperature Pavement Paving on Fatigue Durability of Bearing-Supported Steel Decks
## 7 Conclusions  
The influence of high-temperature pavement paving on the fatigue performance of fatigable welds was focused in this study. Both in-situ field monitoring and numerical analysis were carried out to accomplish the objectives. The following conclusions can be drawn.  
(1) The temperature of an OSD exhibits spatiotemporal variations during high-temperature pavement paving. Nonuniform temperature variation leads to uplift and expanded deformation of OSD, the displacements of bearings and subsequently the temperature stress at fatigable details.  
(2) Residual displacement of bearings and temperature stress at fatigable details are observed after high-temperature pavement paving. The maximum residual temperature stress appears at the rib-deck weld, indicating that the rib-deck weld is most sensitive to the high-temperature pavement paving. It is recommended to monitor the stresses at fatigable details (especially rib-deck welds) in similar projects.  
(3) The stress ratio (R) increases after considering the residual temperature stress, which has considerable effect on the fatigue performance of rib-deck weld. For involving the effect of residual temperature stress, a fatigue damage amplification factor of 1.1 is suggested for similar structures.  
As the monitoring data in this paper is limited, it could just represent some certain conditions. Due to some limitations, the weld stress was not monitored during high-temperature pavement paving, thus the temperature stress cannot be directly validated by the monitored data. In the future, the stress at fatigable details should also be monitored together with the temperature field.  
Conceptualization, Q.W. and B.J.; methodology, Q.W.; software, Q.W. and H.W.; validation, Q.W.; investigation, Q.W.; data curation, Q.W. and H.W.; writing--original draft preparation, Q.W.; writing--review and editing, B.J. and Z.F.; project administration, B.J.; funding acquisition, B.J. All authors have read and agreed to the published version of the manuscript. This research was sponsored by the National Key Research and Development Project, grant number 2017 YFE0128700. We would like to thank the Nanjing Municipal Public Works Construction Centre for providing the in-situ monitored data.  
The authors declare no conflict of interest.  
Figure 21: Flowchart of fatigue damage evaluation procedure.
# An Enhanced Belief Propagation Flipping Decoder for Polar Codes with Stepping Strategy
## 2 Preliminary
### BP Flipping Decoding Algorithm
The BLER performance and the average number of iterations for the existing algorithms are shown in Figure 4. It can be seen that the BLER performance of the BPF-\(\Omega\) and GBPF-\(\Omega\) algorithms is competitive. However, the average number of iterations for the BPF and GBPF algorithms increases exponentially with the rise of \(\Omega\). The average number  
Figure 3: An example of Rate-1 with \(P(16,8)\). White node means that all its leaf nodes are frozen bits, blue node means that all its leaf nodes are information bits, striped blue node denotes the first information bit of the blue node and grey node denotes that its leaf nodes include both information and frozen bits. of iterations of the BPF-2 algorithm is more than two thousand at \(E_{b}/N_{0}=1\). Therefore, we propose a step-flipping strategy to reduce the average number of iterations in Section 3.
# Data-Completion and Model Correction by Means of Evanescent Regularization
## 2 Correction of the Available Model
### Methods
#### 2.1.1 Dynamical Model
\begin{array}{c}\mathbf{y}_{i}^{s, m}\\ \mathbf{y}_{i}^{s,mm}\end{array} \right)+\] \[\Delta t^{2}\left( \begin{array}{cc}B^{m,m}B^{m,m}+B^{m,m}B^{ mm,m}\\ B^{mm,m}B^{mm,m}+B^{mm,mm}B^{mm,mm}\end{array} \right)\left( \begin{array}{c} \mathbf{y}_{i}^{s,m}\\ \mathbf{y}_{i}^{s,mm}\end{array} \right)\]  
From Equation (6), one can note that after a second iteration of the integration scheme, all the non-measurable quantities \(\mathbf{y}^{s,nm}\), as well as the parts of the modeling matrix \(\mathbf{B}\) multiplying them, appear in the equation of the measurable quantities \(\mathbf{y}^{s,m}\). Thus, an optimization problem can be formulated on the measurable quantities only, and calculating a correction of the model matrix \(\mathbf{B}\) for both the measurable and non-measurable quantities. This is only possible by taking into consideration the history of the measurable quantities \(\mathbf{y}^{s,m}\). The optimization problem can be formulated under the following form:  
\[\left(\mathbf{B},\mathbf{y}_{0}^{s,nm}\right)= \begin{array}{c}\underset{ \begin{subarray}{c}\mathbf{B}\in\mathbb{N}^{2}\\ \mathbf{y}_{0}^{s,nm}\in\mathbb{N}\end{subarray}}{arg\min}\ \ \left\{\sum\limits_{i=1}^{n_{t}}\left(\sum\limits_{j=1}^{N_{m}}\left(y_{i}^{s, j}-y_{i}^{s,j}\right)^{2}\right)\right\},\end{array}  \tag{7}\]  
where \(\mathbf{y}_{0}^{s,nm}\) is the initial condition of the non-measurable variables, and \(n_{t}\) is the number of time steps performed in the integration scheme. Selecting several values of \(n_{t}\) can generate as much equations as required for the problem's solution. However, the loss function defined in Equation (7) is highly non-linear and involves high degrees of the problem's unknowns. To improve the convergence of the optimization problem using a gradient descent algorithm, a regularization is required. Our proposal is making use of the evanescent regularization, revisited in Section 2.1.2.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.7 Fast Fourier Transfom (FFT)  
The modulated symbols pass through the fast Fourier transform (FFT) block, which performs the single-carrier frequency-division multiple access (SC-FDMA) modulation technique to reduce the peak to average power ratio (PAPR), which correspondingly reduces the power consumption [16]. The FFT block has to support the different values of the number of subcarriers, which can be 1, 3, 6, or 12 subcarriers, as stated in [9].  
To cope with the continuous stream of QPSK or BPSK modulated symbols, the choice of a pipelined implementation is necessary to avoid buffering before the FFT block, which can cost a lot in leakage power and area [17; 18; 19]. In addition, with the continuous stream at the input, the FFT unit also has to calculate the different points (3, 6, 12), which necessitates that the pipeline shares resources efficiently.  
The algorithm is mainly based on two ideas: the first one is to reuse a single 3-point FFT pipeline in calculating the 6- and 12-point FFTs, and to reuse the 6-point FFT butterfly stage pipeline to calculate the 12-point FFT as is illustrated in Figure 5. This can be possible by initially reordering the input samples. The second idea is to increase the time utilization of the 3-point FFT pipeline by freezing the inputs for 3 clock cycles so that all inputs are available, and hence the calculations can be serialized without adding delay. This results in a hardware reduction by half.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 3 Proposed Method
### The Overall Framework of Our Method  
In order to overcome the drawback of the current low-light image enhancement algorithms, in this paper, we propose a method based on the color model transformation and the convolution neural network. At first, the low-light image is converted from RGB to HSI color space. Then the hue component (\(H\)) is kept constant, the saturation component (\(S\)) is enhanced by using the segmentation exponential method, and the intensity component (\(I\)) is improved by the convolution neural network. Finally, the output is transformed into the RGB space to obtain the enhanced image as well. The flow chart of our algorithm is shown in the Figure 4.
# Mechanism Study of Combustion Dynamics of GO@CL-20 Composite
## 3 Results and Discussion
### Thermal Behaviors of Composites
#### 3.1.3 Ignition Delay Time (IDT) Experiment  
To further investigate the thermodynamic properties of GO@CL-20 complexes, the ignition delay time of GO@CL-20 was measured using a single-pulse excitation tube. In this experiment, the pressure was maintained at 10.0 bar and the temperature range was set from 2000 to 2600 K. A 100 mg sample of the powder was placed on the carrier platform within the tube. High-temperature and high-pressure shocks were generated by rupturing a diaphragm and the resulting data were collected via sensors to determine the ignition delay time for the relevant tests. The results are shown in Figure 6.  
As illustrated in the experimental results graph, the ignition delay time progressively increases with the rising concentration of graphene oxide (GO) in the sample. A detailed examination of the data points indicates that an increase of merely 0.5% in GO content leads to an approximate 20% extension in ignition delay time. This phenomenon can be attributed to the role of GO within the material's overall composition. The incorporation of GO effectively lowers the energy threshold necessary for the composite system to achieve ignition. Consequently, the GO@CL-20 composite system exhibits reduced sensitivity and enhanced resistance to ignition, thereby significantly prolonging the ignition delay time. This effect is especially pronounced in propellant combustion scenarios. In practical applications, such as rocket propulsion systems, the presence of GO can enhance safety and control by delaying the onset of combustion. This characteristic is crucial for ensuring propellant reliability and performance, as it facilitates better management of the combustion process, mitigates the risk of premature ignition, and improves overall system stability.
# The Influence of Substrate and Strain on Protein Quality of _Pleurotus ostreatus_
## 1 Introduction
However, there is still limited information on the extraction of mushroom proteins and thus its effects on the proteins' functional and nutritional properties [21]. Moreover, in the framework of a circular and sustainable economy, mushrooms can be cultivated on several types of agro-industrial residues and wastes; however, this can impact their protein quality. Thus, our aim was to assess the protein quality of _P. ostreatus_ flours and protein concentrates and investigate the effect of the cultivation substrate and strain on their nutritional value and functional properties. For this purpose, two strains of _P. ostreatus_ (AMRL150 and AMRL144) were cultivated on two different substrates, namely, wheat straw (WS) and a mixture of barley with oat straw (BOS). The amino acid composition of flours was used to estimate their nutritional value and the protein quality index (PSI) was used to evaluate the protein quality of the flours and PCs. Mid-infrared spectroscopy was chosen in the present study because it is a rapid and efficient technique for determining the type of functional group as well as the effect of the chemical environment on the structure of the molecules. The water (OAC) and oil absorption capacity (OAC), the foam capacity (FC) and stability (FS), and the emulsion stability (ES) of FLs and PCs of _P. ostreatus_ were determined to correlate them with the different strains and/or substrates.
## 3 Results
### Dermatology-Specific Burden  
The overall median DLQI score was 12.0 (IQR: 7.0-17.0; mean: 12.6), with 81.0% (149/184) of the patients reporting at least a moderate effect of AD (score of \(\geq\)6) and 60.9% (112/184) reporting a very large to extremely large effect (score of \(\geq\)11). The effect on QoL was significantly higher in s-AD patients compared with m-AD patients (\(p\) < 0.001), with a median DLQI score of 14.0 (IQR: 12.0-19.0) versus 10.0 (IQR: 5.0-16.0) and a score of \(\geq\)11 reported in 83.6% (56/67) versus 47.9% (56/117), respectively. The most affected DLQI domain was'symptoms and feelings', followed by 'daily activities' (Figure 1). The overall median POEM score was 17.0 (IQR: 12.0-21.0; mean: 16.7). Median POEM scores in the m-AD and s-AD subpopulations were 16.0 (IQR: 11.0-20.0) and 20.0 (IQR: 15.0-24.0), with 41.9% and 68.7% reporting severe eczema or very severe eczema, respectively (Figure 2a). Across all POEM items, the proportion of patients experiencing eczema symptoms for \(\geq\)3 days in the last week (score \(\geq\) 2) was higher among s-AD patients (Figure 2b). In both the m-AD and s-AD subgroups, the symptom experienced most frequently over the last week was 'dryness or roughness', followed by 'itch' and 'cracked skin'. Notably, about 20% in both groups experienced daily sleep disturbances (Figure 2b). AD-related pain, which is not included in the symptoms explored by the POEM, was explored independently. It was reported by 80.4% (148/184) of the overall population, with a median pain intensity of 5.0 (IQR: 3.0-7.0; mean: 5.1) on a scale of up to 10. Although the proportion of patients experiencing AD-related pain was slightly higher in m-AD patients (82.9%; 97/117) versus s-AD patients (76.1%; 51/67), pain frequency and intensity were worse among s-AD patients. Specifically, among m-AD and s-AD patients experiencing pain, 25.8% (25/97) and 37.3% (19/51) experienced pain every day, while the median pain intensity was 4.0 (IQR: 3.0-7.0) and 6.0 (IQR: 4.0-8.0), with 29.9% (29/97) and 49.0% (25/51)