esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Improvement of Prediction Performance by Data Augmentation  
To predict the peak frequency--an indicator of implant stability in cadaveric bone--using machine learning-based analysis, the accuracy was improved by adding data other than those obtained by L-RFA using artificial bone to the teaching data. As shown in Figures 3 and 4, it is possible that the difference in foundation between the artificial and cadaveric bones potentially influenced the L-RFA results. Using this experimental finding as a guide, the artificial bone-teaching dataset was augmented using a small amount of cadaveric bone data. Furthermore, bone density was introduced as a parameter obtained in clinical practice, and an explanatory variable was obtained outside the L-RFA assessment. As a validation method, a three-fold cross validation was performed by combining 31 datasets from artificial bones with L-RFA evaluation and 17 datasets from cadaveric bones. Each fold was created equally, such that the peak torque was unbiased, and the percentage of data from the donated bones was approximately 35%. Table 2 summarizes the results of this three-fold validation of the machine learning performance with and without the introduction of bone density.  
Figure 5: Analysis result of (**a**) relationship between measured and predicted insertion torques and (**b**) Bland–Altman plot by limiting the peak torque range to the same as that of the cadaveric bone dataset.
## 3 Methodology
### Post-Processing Method  
In this study, the generator-based post-processing (GPP) method [36] was employed to correct EPFs. The GPP method is specifically designed for statistical post-process meteorological ensemble forecasts and has proven effective in previous research [45; 46]. This method integrates meteorological forecast information into a weather generator, allowing for the regeneration of ensemble forecast outcomes that are consistent with both the ensemble meteorological forecasts and historical observational characteristics. The statistical distribution parameters for forecast variables within the weather generator are estimated from historical observational data. Firstly, the probability distribution function (PDF) for precipitation parameters is defined as follows:  
\[y|x_{1},\ldots,x_{M}\sim g(y|x_{1},\ldots,x_{k}) \tag{1}\]  
where \(y\) represents the precipitation variable, \(x_{1},\ldots,x_{k}\) corresponds to an ensemble of precipitation forecasts composed of \(K\) members. The PDF characterizes a mixed discrete/continuous distribution where the probability of zero precipitation is positive, and the probability of positive precipitation follows a continuous skewed distribution. The precipitation mixed distribution model proposed by [47] is as follows:  
\[g(y|f_{k})=P(y=0|f_{k})\cdot I(y=0)+P(y>0|f_{k})\cdot g_{k}(y|f_{k})\cdot I(y>0) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Sources** & **Ensemble Members (Perturbed)** & **Temporal Resolution** & **Spatial Resolution** & **Temporal Span** \\ \hline Observed meteorological data & 175 meteorological stations & - & Daily & Station & 2014–2020 \\ \hline Ensemble precipitation forecasts (EPFs) & ECMWF (European Centre for Medium-Range Weather Forecasts) & 50 & 7 lead days & 0.5 grid & 2014–2020 \\ \hline Observed discharge data & Shuibuya hydropower station & - & Daily & Station & 2014–2020 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the datasets used in this study.  
where \(g(y|f_{k})\) denotes the probability distribution given the member forecasts; \(I()\) is the indicator function, which equals 1 if the expression inside the parenthesis holds true, and, otherwise, it equals 0; \(P(y=0|f_{k})\) and \(g(y>0|f_{k})\) represent the probabilities of no precipitation and precipitation, respectively; and \(g_{k}(y|f_{k})\) signifies a two-parameter gamma distribution. When applying the GPP method to correct precipitation, the probability density functions for precipitation in different seasons or of different intensities are independently calibrated to match observational data. Subsequently, using the forecast information from the original EPF, samples are redrawn from these calibrated probability density functions to generate the post-processed EPFs. For a detailed methodology, refer to [PERSON], [PERSON] [36].
## 6 Limitations and Future Work  
Our solution has a number of limitations. Below, we describe some of these and suggest directions for future work. We were able to achieve 42% F1 and 39% EM for our test dataset due to the challenging nature of zero-shot open-book problems. The performance of the solution proposed in this article is fair if tested against technical software documentation. However, it needs to be improved before finding use in real-world software products. Additionally, more testing and evaluation is needed if we want to thoroughly assess the applicability of this solution in other domains (e.g., medical, financial, or laws and regulations). We are planning to experiment and evaluate this solution in multiple domains and compare it to fine-tuned (not zero-shot) models in our future work.  
Furthermore, the solution performs better if the answer can be extracted from a continuous block of text from the document. The performance drops if the answer is extracted from several different locations in a document. Moreover, all questions had a clear answer in the AWS documentation dataset, which is not always the case in the real world. As our proposed solution always returns an answer to any question, it fails to recognize if a question cannot be answered.  
For future work, we plan to experiment with custom-built retrievers similar to DPR [34], as well as generative models, such as GPT-2 [35] and GPT-3 [5], with a wider variety of text in pretraining to improve the F1 and EM scores presented in this article.
# An Algorithm to Generate a Weighted Network Voronoi Diagram Based on Improved PCNN
## 4 Network Voronoi Diagram Construction Based on the Improved PCNN
### Transmission of the Auto-Wave
#### 4.2.1 Speed Calculation  
In real traffic networks, some roads are one-way and some roads are two-way, which play different roles in the transportation system. Similarly, the facilities themselves also have different importance with respect to different functions, which can affect the impacting regions of facilities. Therefore, these factors should be considered while computing transmission speed. For instance, the auto-wave will transmit quicker to compete for a larger growth area if it is generated from an important point and expand along an important road. These impact factors are illustrated specifically as follows:  
* Direction of the roads The auto-wave can only expand along the road if its expanding direction is the same as the direction of the road. Otherwise, the transmission of the auto-wave should be terminated. For example, if the road merely permits moving from west to east, the auto-wave expanding from east to west must stop on this road. This can be expressed by Formula (13): \[D_{R}=F(N_{1},N_{2})\] (13) where \(D_{R}\) is the direction factor of the current road segment. \(F\) (\(N_{1}\), \(N_{2}\)) is a direction function which equals to 1 if the auto-wave is the same as the direction of the road and equals to 0 otherwise.
* Grades of the roads As we all know, the traffic capacities of roads are different. Generally, a road of a higher grade has a higher traffic capacity, and the auto-wave can transmit quicker along these kinds of roads correspondingly. Formula (14) is defined to represent the differences: \[G_{R}=W(R)\] (14) where \(G_{R}\) is grade factor of the current road segment, and function \(W(R)\) denotes the grade function of the road segment.
* Grades of the points The higher the grade of a point is, the larger its impact range, which can be represented as Formula (15): \[G_{P}  
As shown in Figure 6, if the unit speed of the auto-wave (generated by neurons of weight 1 and expanding on the road segment of weight 1) is set as 10 m per iteration, the speed of the auto-wave generated from point \(P\) is 20 m per iteration (\(V_{p1}\)) when expanding along road segment \(PN_{2}\), and 0 m per iteration (\(V_{p2}\)) when expanding along road segment \(PN_{1}\). \(V_{Q1}\) (the speed of the auto-wave generated from point \(Q\) and expanding along road segment \(QN_{6}\)) is 10 m per iteration, and \(VQ_{2}\), which represents the speed on road segment \(N_{6}N_{5}\), is 20 m per iteration.
# GNSS Based Low-Cost Magnetometer Calibration
###### Abstract  
With the development of MEMS sensors, the magnetometer has increasingly become a part of various wearable devices. The magnetometer measures the intensity of the magnetic field in all three axes, resulting in a 3D vector--direction and power. Calibration must be done before using a magnetometer, especially in wearable electronics, due to the low quality of the sensor and high proximity to other electromagnetic emission sources. Several magnetometer calibration algorithms exist in the literature, with most of them requiring multi-sided rotation. However, such calibration is highly impractical when the sensor is mounted on larger objects, e.g., vehicles, which cannot easily be rotated. Vehicles contain a large amount of ferromagnetic soft and hard material that affects the measured magnetic field. A magnetometer can be useful for an INS system in a car as long as it does not drift over time. This article describes how to calibrate a magnetometer using the GNSS motion vector. The calibration is performed using data from the initial section of the vehicle's trajectory. The quality of the calibration is then validated using the remaining section of the trajectory, comparing the deviation between the azimuth obtained by GNSS and by the calibrated magnetometer. Based on the azimuth and speed of the vehicle, we predicted the position of the vehicle and plotted the prediction on the map. The experiment showed that such calibration is functional. The uncalibrated data were unusable due to the strong effect of ferromagnetic soft and hard materials in the vehicle.  
magnetometer; data acquisition unit; calibration; GNSS +
Footnote †: journal: Sensors  
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Footnote 1: institutetext: Department of Control and Information Systems, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia  
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Footnote 2: institutetext: Department of Control and Information Systems, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia  
3
Footnote 3: institutetext: Department of Control and Information Systems, Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia  
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Footnote 4: institutetext: Correspondence: [EMAIL_ADDRESS]
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
###### Abstract  
During shield construction in underground spaces, synchronous grouting slurry is poured between the surrounding rock and tunnel lining to ensure stability. For synchronous grouting slurries, few studies have investigated the relationship between the rheological parameters and physical properties, grout-segregation mechanism, and anti-segregation performance. Therefore, we explored the relationships between the slurry rheological parameters, segregation rate, and bleeding rate. Cement, sand, fly ash, and bentonite were used to prepare the slurry, and the effects of different polycarbonate water-reducing agents and dispersible latex powder dosages were studied. The rheological parameters of 16 groups of uniformly designed slurries were tested, and the data were fit using the Herschel-Bulkley model. The optimal mix ratio lowered the slurry segregation rate, and its rheological behaviour was consistent with the Herschel-Bulkley fluid characteristics. High-yield-shear-stress synchronous grouting slurries with high and low viscosity coefficients were less likely to bleed and segregate, respectively. The optimised slurry fluidity, 3 h bleeding rate, 24 h bleeding rate, segregation rate, coagulation time, and 28 days compressive strength were 257.5 mm, 0.71%, 0.36%, 3.1%, 6.7 h, and 2.61 MPa, respectively, which meet the requirements of a synchronous grouting slurry of shield tunnels for sufficiently preventing soil disturbance and deformation in areas surrounding underground construction sites.  
shield tunnel; rheological properties; synchronous grouting; segregation rate; uniform design; mix proportion +
Footnote †: journal: Applied Sciences
# Ergonomic Design of a Workplace Using Virtual Reality and a Motion Capture Suit
## 2 Reference Workplace for Validation  
For validation and study of the pilot method, we chose a standardised workplace of an assembly line for the production of door parts in the automotive industry (Figure 1). This workplace was selected by the expert group from an ergonomic point of view. CAD data and workflow were provided for the workstation and is given below. The description also includes a time standard.  
The working procedure for the workplace:  
* The work is performed standing up, with occasional walking.
* The worker grasps the part with both upper limbs, carries it to the workbench and places it in the jig (standard time 10 s).
* The worker then uses their right upper limb to remove the first part from the KLT box (80 mm \(\times\) 80 mm, weight 1.5 kg), which is placed on a shelf behind the workbench. They place the part in a predefined place according to the instructions (standard time 13 s).
* Then, again with the right upper limb, they remove the second part (small part 10 mm \(\times\) 10 mm, weight 3 g) which is on the left part of the shelf. They place the part again according to the instructions (standard time 15 s).
* After placing the parts, the worker presses the control button, which is on the right side of the workbench (standard time 2 s).
* The worker then removes the entire part (1200 mm \(\times\) 1000 mm, weight 8 kg) and moves it to the work line, which is behind the worker's back (standard time 10 s).
* They then remove the trolley rail with their right upper limb and then place it in the stack of completed parts on the production line (standard time 8 s).
* The final step is to confirmed using the save button--the final-part departure button (standard time 2 s).  
This process is repeated throughout the production shift. The shift lasts 450 min, with both women and men working on the job. The production standard is 450 pieces/shift. A diagram of the workflow is shown in Figure 2.  
Figure 1: Reference workplace.
# Experimental Study on the Mechanical Properties of Deep Granite under Gradient-Confining Pressure
## 5 Conclusions  
Using conventional triaxial compression tests and the finite element software Abaqus, this paper studied the mechanical properties of deep granite under gradient confining pressure, and the following conclusions were drawn:  
* Owing to its peculiar and complex geological environment, under gradient-confining pressure, the elastic modulus, Poisson's ratio, and peak strength of the deep granite present a greater degree of dispersion than those of the shallow granite.
* The characteristic stress of the deep granite exhibits an evident exponential function distribution under the gradient-confining pressure, which indicates that the greater the confining pressure is, the greater the characteristic stress and the faster the growth rate.
* The results of the numerical simulation indicate that in the conventional triaxial compression test, there is a marked shear stress concentration in the radial direction of the specimen, and with increasing confining pressure, the range of the shear stress concentration correspondingly expands. The axial deformation of the specimen decreases from both ends to the middle, there is a distinct layered distribution, and the layered distribution effect becomes weaker with increasing confining pressure.  
Figure 11: Cloud diagram of the axial displacement distribution of the numerical model under low confining pressure and high confining pressure: (**a**) \(\sigma_{3}=8\) MPa, (**b**) \(\sigma_{3}=40\) MPa.  
Author Contributions: Funding acquisition, X.Z.; Writing--review and editing, S.Q.; Test, S.Q., W.Y., J.S. and T.W. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Key Projects of the National Natural Science Foundation of China (52130403) and the Ministry of Education's Major Needs Program for Basic Research Operating Expenses (N2301027). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Acknowledgments: Extremely thankful to the editorial board and reviewers of this paper. Conflicts of Interest: The authors declare no conflict of interest. References
## 4 Experiments and Results
### Statistical Analysis  
We have analyzed the results statistically to compare our approach with the result achieved using sentence transformers (Figure 8). We used the ranking-based non-parametric Wilcoxon test. The improvement in accuracy was statistically significant for decision Tree (\(p<0.001\)), FFNN (\(p<0.001\)), KNN (\(p<0.01\)), and Random Forest (\(p<0.001\)) classifiers, however, there was no significant difference for Log regression and Naive Bayes classifiers. The results of the Wilcoxon test show that the performance of the sentence transformers and the proposed two-stage semi-supervised methodology are statistically different.  
Figure 9 shows the critical distance diagram from the post hoc Nemenyi test for the two-class and three-class classification scenarios. The best performance across four emotion subsets was demonstrated by FFNN (the mean rank is 1.33) and Histogram Gradient Boosting classifier (the mean rank is 2.88), although the performance of other machine learning classifiers (excluding Bagging regressor and AdaBoost regressor) was not significantly different (within a critical distance of 10.534 for the 2-class classification scenario, and within a critical distance of 9.123 for the 3-class classification scenario).  
Figure 8: Results of statistical significance testing using non-parametric Wilcoxon test. Boxplots show an accuracy of classification using sentence transformers (Sent. Trans.) and the proposed method.
# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
## 4 Experiments and Results
### Lexicon Generation
After that, SentiWordNet, TextBlob, VADER, UMLS, and statistical techniques were used to develop a specialized vocabulary (medical domain) that determined the polarity of each sentence. Various sizes of the lexicon (number of terms) were also examined to test this method's reliability [57]. The lexicon's capacities were also evaluated with different sizes of lexicons to investigate whether the largest or smallest lexicon of sentiment can produce better results [58]. The largest lexicon produced the highest performance, and the smallest lexicon resulted poorly. Figure 6 compares the obtained results relying on the number of words in each lexicon, indicating the values of lexicons 1,2, 3, 4, 5, 6, and 7 are 10.000, 20.000, 30.000, 40.000, 50.000, 60.000, and 70.000, respectively. After creating the lexicons, the ratio of the sentiments in assigning the polarity was non-equivalent, indicating the model over-fit on the unbalanced dataset. To solve these issues, SMOTE-OSS-CNN (One-Side-Selection) and (Condensed Nearest Neighbors) were proposed for the imbalanced datasets following the balance of the number of samples for all classes in the dataset. Balancing was accomplished by generating the synthetic minority samples so that the number of samples for the minority class was almost equal to that of the majority class. Consequently, the models achieved a high accuracy using the SMOTE-OSS-CNN approach, producing synthetic samples of the minority class [59]. In brief, the proposed models achieved a high level of accuracy when the SMOTE-OSS-CNN approach was employed to produce the artificial samples of the minority class. The binary classification was performed as the first experiment of the proposed method, which was achieved for four cases of illness: asthma with neutral and positive labels, asthma with neutral and negative labels, obesity with neutral and positive labels, and obesity with neutral and negative labels. Figure 5: The steps of the proposed solution. Figure 6: The size-dependent performance of each sentiment lexicon.
# A 13.44-Bit Low-Power SAR ADC for Brain-Computer Interface Applications
## 2 Circuit Topology
### Capacitive DAC Array Design
The maximum standard deviation of integral nonlinearity (INL) in the ADC is given by the following: \[\sigma_{\mathrm{INL,max}}=\sqrt{2^{\mathrm{N-1}}}\frac{\sigma_{u}}{\mathrm{ Cu}}\mathrm{LSB}.\] (14)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \(\mathbf{C_{13}}\) & \(\mathbf{C_{12}}\) & \(\mathbf{C_{11}}\) & \(\mathbf{C_{10}}\) & \(\mathbf{C_{9}}\) & \(\mathbf{C_{8}}\) & \(\mathbf{C_{7}}\) & \(\mathbf{C_{6}}\) & \(\mathbf{C_{5}}\) & \(\mathbf{C_{4}}\) & \(\mathbf{C_{3}}\)The maximum allowable standard deviation of integral nonlinearity (\(\sigma_{\rm INL,max}\)) in an ADC must satisfy the following:  
\[3\sigma_{INL,max}=3\sqrt{2^{N-1}}\bigg{(}\frac{\sigma_{u}}{C_{u}}\bigg{)}LSB<0.5 LSB. \tag{15}\]  
Simplification yields:  
\[\frac{\sigma_{u}}{C_{u}}<\frac{1}{6\sqrt{2^{N}-1}}. \tag{16}\]  
Solving the equation yields:  
\[\frac{\sigma_{u}}{C_{u}}<0.37\%. \tag{17}\]  
The process library's MIM (Metal-Insulator-Metal) capacitor with a minimum value of 127.36 fF demonstrates a 0.22% mismatch (\(\Delta\)C/C) under Monte Carlo simulation, as illustrated in Figure 11, comfortably meeting the matching requirement for high-precision analog design.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### Drone Mounters for the Water Quality Monitoring System
To estimate the effects of the external system on the availability of the Global Navigation Satellite System (GNSS)-assisted flight control system, the Global Positioning System (GPS) accuracy indicator [18] of the drone was used. The indicator was associated with restricted flight in GPS mode [18]. For example, in the SplashDroneTM 4, <1 m of location error in the GNSS corresponds to the maximum value (10) of the indicator [18], which allows flight in GPS mode. The upper monuter was placed on the GNSS antenna of each drone. Legs for the upper monuter were used to maintain the GNSS accuracy as <1 m. The maximum elevation angle for the GNSS with the upper monuter was approximately 40\({}^{\circ}\). Figure 4: _Cont._