esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The cutting tools are the key factors for achieving efficient and high-quality processing, as the condition of the tool directly affects the cutting performance, processing accuracy, and production efficiency during the machining process [1]. Research has shown that machine downtime caused by tool failure accounts for 7 to 20%, and processing costs increased due to tool changes account for 3 to 12% [2, 3]. The conventional periodic replacement strategy may lead to premature or delayed tool changes, resulting in a series of issues, including increased costs, downtime, and decreased processing quality. Therefore, real-time tool condition monitoring is critical for reducing costs, minimizing downtime, and improving processing quality.  
Tool condition monitoring includes direct and indirect methods [4]. The direct method is to obtain the dimensional changes in the cutting tool with a microscope or surface profiler and use machine vision to monitor tool wear. However, the direct method relies on expensive visual systems, and the optical system has high requirements for the field environment. The indirect method is to observe tool wear using signals of vibration, cutting force, and acoustic emission, having the advantages of easy installation and real-time monitoring. In addition, the indirect method can be transferred across different machining conditions and equipment.
## 2 Related Works
### Sensor Node Position Estimation  
Existing efforts in sensor node position estimation can be organized into two categories: range-based and range-free. Range-based approaches [12; 13] assume that sensor nodes can measure the range or distance between them. Range-free approaches [14; 15] use nondeterministic attributes of the network, such as closeness and hop count, to perform position estimation. Range-free approaches usually show lower accuracy than range-based solutions.  
An adaptive range-based trilateration localization algorithm (ARBL) is proposed in [12] in order to improve the accuracy of range-based trilateration localization for WSN nodes in outdoor conditions. The algorithm is based on reference node selection and aims to find the best reference nodes at a given time for computing the final location estimate of a node, taking into account the prevailing localization geometry and ranging errors. [PERSON] and [PERSON] [13] proposed a cooperative range-based localization system, where they modified a range-based algorithm in order to save energy by reducing collisions and retransmission of range packets.  
[PERSON] et al. [14] proposed an approach that uses a bounding box method to identify the possible location area of unknown nodes, and a Kalman filter to refine the localization process and improve accuracy. The focus is on decreasing the number of anchors and reducing costs. The range-free method is used to estimate inter-distance via the calculation of the lowest number of hops, and the mobile anchor node trajectory could be random or predetermined. [PERSON] et al. [15] proposed ZBLM and EZBLM, two range-free localization methods that utilize only two anchor nodes, placed on the bottom-left and bottom-right corners of a square region of the WSN, and use bilateration to estimate node positions by counting the minimum number of hops to the anchor nodes.
# Unveiling Accurate Seismic Imaging through the Advanced Target-Oriented Kirchhoff Migration Method
## 4 Conclusions  
To satisfy the requirements of target-oriented imaging, we proposed a workflow for target-oriented Kirchhoff migration based on a seismic redatuming method. This approach aimed to reduce both the turnaround time and the associated migration costs. The outcomes from both synthetic and field datasets affirmed the feasibility of the proposed workflow for target-oriented imaging. Its capability to generate better quality and instant migrated images within the designated region was demonstrated. Most importantly, it significantly accelerated the seismic-imaging process.  
We would like to emphasize the advantages of the proposed workflow: the seismic redatuming enhanced the quality of the input gathers for Kirchhoff migration and led to the better accuracy of target-oriented Kirchhoff migration imaging below the overburden; the flexibility of the Kirchhoff algorithm was maximized by repositioning the source and receiver locations to a virtual depth for target-oriented migration, followed by the implementation of patch-based migration. Furthermore, the computational costs in computing the travel-time for Kirchhoff migration was reduced significantly by focusing on the targeted area compared to the conventional full-scale migration.  
Moreover, there are many opportunities for further exploration of this methodology, particularly in the realms of reservoir characterization and seismic inversion. The present study focused solely on assessing the kinematic accuracy of the proposed target-oriented migration, without addressing the amplitude accuracy of the migrated data. Therefore, this study can be expanded to evaluate the dynamic accuracy of the proposed target-oriented migration method, with the aim of achieving true-amplitude target-oriented migration.  
Conceptualization, F.S.D. and H.R.; methodology, F.S.D. and H.S.H.; validation, F.S.D.; formal analysis, F.S.D. and H.S.H.; investigation, F.S.D.; data curation, F.S.D., H.S.H. and A.M.M.; writing--original draft preparation, F.S.D.; writing--review and editing, A.H.A.L., H.R. and J.L.; supervision, A.H.A.L. and H.R.; project administration, A.H.A.L. and H.R.; All authors have read and agreed to the published version of the manuscript.  
The work is supported by PETRONAS Research Sdn Bhd and Universiti Teknologi PETRONAS under grant 015 LC0-309.  
Not applicable.  
I would like to express my special thanks of gratitude to [PERSON] from the University of Calgary, [PERSON] from TU Delft, and [PERSON] from King Fahd University of Petroleum and Minerals, who helped me during the conceptual understanding, providing extraordinary cooperation and invaluable guidance.  
The authors declare no conflict of interest.
# Real-Time Monitoring of Passenger's Psychological Stress
## Materials and Methods
### Feature Computation  
Following this first step, 24 statistical features were computed from the processed signals, which are displayed in Table 3. Each one of them has been calculated over successive 60-s time windows, with no overlapping. They have been selected on the basis of previous work on experimental databases [12; 13], and for their suitability for real-time implementation.  
Regarding the electrodermal activity, with respect to the four different features computed from the local maxima in the SC curve (SCRs), previous filtering allows setting minimal constraints on the detection parameters, without time delay between two peaks and with a threshold prominence set to \(10^{-3}\) uS. For a given peak, the prominence is defined as its height, relatively to the lowest point located between this peak and the nearest higher peak (for more detailed information see [14]). The peak width is defined as the number of samples, within this peak, which the ordinate exceeds its half prominence.  
Regarding cardiac activity, given that the IBI signal is not steadily sampled, the frequency domain features have been derived from its Lomb-Scargle power spectral density estimate on each time window. When no IBI data could be found, the time window was discarded from the whole analysis. After feature extraction, the cleaned feature matrix included 84 non-stress and 137 stress examples, which represent 78.4% of the initial database.  
Since real-time computations were intended, no artefact rejection was performed on the remaining dataset. Together with the expected poor signal-to-noise ratio in ambulatory conditions, this motivated the use of the whole feature set to fully take advantage of information content from all the recorded signals.  
\begin{table}
\begin{tabular}{r} \hline \hline
**Description** \\ \hline Mean interbeat interval (IBI) \\ IBI standard deviation \\ Root mean square of the successive differences in IBI \\ Mean heart rate (HR) \\ HR standard deviation \\ Ratio of the HR mean over standard deviation \\ Root mean square of the successive differences in HR \\ Total power in the IBI signal \\ IBI signal power in the Low Frequency (LF) band [0.04 Hz 0.15 Hz] \\ Previous value normalized by the total power in IBI \\ IBI signal power in the High Frequency (HF) band [0.15 Hz 0.5 Hz] \\ Previous value normalized by the total power \\ Ratio of the previous LF and HF frequency components \\ Mean of the SCL signal \\ Mean of the 1 st temporal derivative on the SCL signal \\ Standard deviation of the SC signal \\ Number of local maxima (SCRs) on the SC curve \\ Mean prominence of the SCRs \\ Mean width of the SCRs \\ Sum of all SCRs’ width-prominence products \\ Mean Skin Temperature \\ Mean of the 1 st temporal derivative of the Skin Temperature \\ Total power of the Acceleration norm \\ Mean absolute deviation of Acceleration norm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feature set.
## 4 Propagation Loss Calculation Using the Improved TWPE Model  
In this section, we simulate intercity link ducting propagation with turbulence. The terrain relief on links were extracted from real DEM data. The LC types are classified based on the IGBP standards from MODIS data. Some dielectric permittivities of LC types relative to the intercity links are referenced from different literature studies, and measured or modeled on empirical formulae. The frequency was 3 GHz. The antenna height was 60 m, the antenna beam width was 3\({}^{\circ}\), and the antenna elevation angle was 0\({}^{\circ}\). The surface-based duct with turbulence in the upper boundary of duct is assumed here. We describe the surface-based duct with the tri-fold four-parameter model, of which the four-parameter combination is [40 40 0.12 \(-\)0.2].  
In this intercity link from Hangzhou to Wuxi, the propagation distance was as far as 136 km. The relief from Hangzhou to Wuxi reached more than 200 m, which exceeded the radio-wave elevation angle limits of WPE (so was the intercity link from Nanjing to Wuxi). As the results show in Figures 13 and 14, the obstacles greatly increased the propagation loss. The radio-wave could not travel further with the presence of very high obstacles.  
The effects of ducting and turbulence were cut off. Therefore, no CCI would occur on the intercity link when there is high topography.  
Taking into account the above analysis, the Hangzhou-Wuxi and Nanjing-Wuxi intercity links had large obstacles regarding cutting-off the ducting propagation. Therefore, the following selected the relatively gentle Shanghai-Wuxi and Jiaxing-Wuxi links to the intercity links. When the duct occurred on the intercity link, it was simulated whether to consider the non-uniform tropospheric turbulence or the influence of the PL distribution--of the distribution of real land covers--on the possible intercity CCI.
## 5 Results and Discussion
### Application in a Real Poultry-Processing Setting  
In real poultry processing environments, the automated classification of chicken parts, such as white wings, yellow thighs, and white thighs, offers significant potential for enhancing operational efficiency and product quality. This approach enables the precise identification of different chicken parts through an analysis of images captured by cameras installed along processing lines. Artificial intelligence (AI) models, particularly CNNs, trained on diverse patterns of chicken appearance, can improve accuracy compared to manual inspection by workers. These systems can continuously analyze images of poultry products, automatically detecting deviations from desired standards. Automated chicken-part detection streamlines production efficiency by assisting in prioritizing processing based  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Method** & **Features** & **Acc. (\%) (C1)** & **Acc. (\%) (C2)** & **Acc. (\%) (C3)** & **Acc. (\%) (C4)** \\ \hline CNN & - & 100 & 100 & 100 & 100 \\ CNN + HOG & HOG & 97.14 & 95.62 & 96.30 & 91.92 \\ CNN + LBP & LBP & 82.35 & 79.03 & 92.65 & 79.03 \\ CNN + Saliency & Saliency & 99.04 & 97.60 & 99.08 & 96.29 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Summary of results obtained in this study.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Reference** & **Method** & **Acc. (\%)** \\ \hline [PERSON] et al. [57] & CNN-based method for red meat & \\  & freshness classification & 92.5 \\ \hline [PERSON] et al. [58] & Computer vision and machine & \\  & learning for tuna and salmon & \\  & meat classification & 88.3 \\ \hline [PERSON] and [PERSON] [59] & Hybrid CNN and SVM approach for & \\  & shrimp freshness diagnosis & 95.1 \\ \hline [PERSON] et al. [60] & Deep machine learning for fish flesh & \\  & identification using multispectral imaging & 91.7 \\ \hline [PERSON] et al. [61] & Detection of red meat adulteration & \\  & by deep spectral-spatial features & 89.6 \\ \hline [PERSON] et al. [62] & Potential of deep learning and & \\  & snapshot hyperspectral imaging for meat & \\  & species classification & 93.2 \\ \hline [PERSON] and [PERSON] [63] & Computer vision detection of salmon & \\  & muscle gaping using CNN features & 90.4 \\ \hline [PERSON] et al. [64] & Smart deep learning-based approach & \\  & for non-destructive carp freshness diagnosis & 94.8 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison with other studies.  
on the specific needs of each part. However, integrating AI-based chicken-part classification requires addressing challenges such as ensuring robust validation and regulatory approvals to ensure food safety and regulatory compliance. Collaboration among poultry industry professionals, data scientists, and regulatory authorities is essential for the successful and safe implementation of AI technologies in poultry processing. These efforts contribute to optimizing the efficiency of poultry processing operations and ensure the quality and safety of poultry products offered to end consumers.
# Enhancing the Content of Hesperidin and Nobiletin in Citrus Wines through Multi-Strain Fermentation
Consequently, the amalgamation of non-_Saccharomyces_ yeast with Sc to replicate the advantageous outcomes of natural fermentation has the potential to harness the unique functional attributes of individual strains for the production of premium-quality goods. Identifying combinations of fermentation methods that enhance fruit wine quality represents a promising avenue of research. Our previous research identified non-_Saccharomyces_ yeast with favorable characteristics in naturally fermented Nanfeng tangerine fruit vinegar, laying a solid foundation for this study. Furthermore, it was observed that co-fermentation of these non-_Saccharomyces_ yeasts and Sc could enhance the flavor and total flavonoid content of fruit wine, although the levels of hesperidin and nobitetin did not show an increase [17]. It has been reported that multi-strain fermentation can elevate the flavonoid content in Goji Juice [18]. The aim of this research is to utilize three non-_Saccharomyces_ yeast varieties (_Candida ethanolica_ Ce, _Hansenispora guillermondii_ Hg, and _Hansenispora thailandica_ Hf) obtained from the spontaneous fermentation of Nanfeng tangerine wine, known for their impressive fermentation abilities, along with commercial wine yeast, in various mixtures to ferment Nanfeng tangerines. The analysis of nobitetin and hesperidin content, as well as aromatic components, in fruit wines produced through various fermentation combinations will be conducted to establish a foundation for the creation of functional fruit wines with high levels of bioactive compounds.
## 5 Triple-Frequency Medium RTK Performance Analysis
### Positioning Accuracy and Fixing Rate  
Using the three schemes described in Section 3, we performed the RTK algorithm described in Section 2 for positioning. We calculated the positioning RMSE in both the  
Figure 5: The time series of float WL ambiguities for the baseline HZDQ-HZLA. Each colored line represents a satellite.  
Figure 6: (**a**) The satellite number and PDOP of Scheme 2; (**b**) The satellite number and PDOP of Scheme 3.  
horizontal and vertical directions, as well as the fixing rate, which represents the proportion of correctly fixed solutions out of the total epochs, as shown Figure 7. A correctly fixed solution is defined as a solution with a ratio greater than 3.0 and a 3D positioning error less than 10 cm.  
Overall, the horizontal RMSE for the six baselines using the three positioning schemes is better than 1 cm, and the vertical RMSE is better than 2.5 cm. The horizontal and vertical RMSE difference for three schemes is less than 2 mm, which can be ignored for RTK  
Figure 7: **(a)** Horizontal positioning RMSE of three schemes; **(b)** Vertical positioning RMSE of three schemes; **(c)** Fixing rate of three schemes.  
positioning. In terms of fixing rate, Scheme 2 and Scheme 3 are comparable and are both better than Scheme 1. Taking the baseline WHJX-WHJA as an example, Figure 8 shows the positioning error series using Scheme 1 and Scheme 2. It can be seen that, in the initial convergence stage, the triple-frequency scheme achieves instantaneous ambiguity fixing compared to the dual-frequency scheme, indicating that triple-frequency observations can accelerate convergence for medium baseline RTK. In addition, around 4 h and 8 h, the dual-frequency scheme has float solutions, while the triple-frequency-only scheme mostly has fixed solutions, indicating that the triple-frequency scheme is more likely to achieve ambiguity fixing.
# Real-Time Tension Distribution Design for Cable-Driven Parallel Robot
## 6 Simulation Studies  
In order to verify the efficacy of our strategy, a simulation study of a cable-driven robot with six degrees of freedom and eight cables was conducted. This robot's geometric structure and configuration model are depicted in Figure 3.  
The generalized robot's state can be represented as follows: \(\big{[}x,y,z,\phi,\theta,\psi\big{]}^{T}\). The position of the eight motors in the original coordinate system is set as  
\[\begin{split} A_{1}&=\begin{bmatrix}1.5\\ -1.5\\ 2\end{bmatrix},A_{2}=\begin{bmatrix}1.5\\ 1.5\\ 2\end{bmatrix},A_{3}=\begin{bmatrix}-1.5\\ 1.5\\ 2\end{bmatrix},A_{4}=\begin{bmatrix}-1.5\\ -1.5\\ 2\end{bmatrix},\\ A_{5}&=\begin{bmatrix}1.5\\ -1.5\\ -2\end{bmatrix},A_{6}=\begin{bmatrix}1.5\\ 1.5\\ -2\end{bmatrix},A_{7}=\begin{bmatrix}-1.5\\ 1.5\\ -2\end{bmatrix},A_{8}=\begin{bmatrix}-1.5\\ -1.5\\ -2\end{bmatrix}\end{split} \tag{59}\]  
with the angular parameters \((\phi,\theta,\psi)\) all set to zero. The tension constraint is set as \(f_{\mathtt{min}}=0\) N, \(f_{\mathtt{max}}=20\) N.  
This robot's dynamics can be shown as follows:  
\[\begin{bmatrix}m_{3\times 3}&0\\ 0&I_{3\times 3}\end{bmatrix}\tilde{q}+G=A^{T}f \tag{60}\]  
where \(m_{3\times 3}\) and \(I_{3\times 3}\) represent a diagonal matrix with diagonal elements set as the mass \(m\) and the inertia \(I\) of the end-effector, respectively. In the simulation, \(m\) and \(I\) are selected as \(0.5\) and \(0.3\), respectively.  
Using a conventional PID controller, the simulated robot tracks the desired pose \(q_{d}=[0.6,-0.6,0.6,0,0,0]^{T}\) from its initial position \([0,0,0,0,0,0]\). We performed simulations with two proposed strategies, and the next two subsections detail the simulation results for each strategy.
# Data Imputation Based on Retrieval-Augmented Generation
## 4 Experimental Verification
### Evaluation of Retriever Effectiveness
#### 4.2.2 Baseline Model  
**Retriever Baseline**: We compare our retriever with several baselines. The first two methods have zero-shot capabilities and are, thus, applied directly. For the third method, we retrain it using the same training data for a fair comparison. The baselines are introduced as follows:  
* BM25: BM25 is the most commonly used sparse retrieval method and demonstrates very robust performance.
* Contriever: Contriever is an unsupervised retriever that performs well in few-shot and zero-shot settings for paragraph retrieval.
* BERT [103] with Language Modeling (LM) Task: Previous work on table data representation has adopted pre-training tasks centered around language modeling and tabular data structures. For example, TabReformer and TURL utilize masked language modeling tasks, with the latter adding masked entity recovery. To demonstrate that contrastive learning methods are crucial for the success of tuple embeddings in retrieval, we trained a tuple encoder using the BERT-base model. This model shares the same basic structure as our encoder but was trained using a language modeling task. Specifically, for a given tuple (t), we randomly mask one cell, then input t into the encoder, asking it to predict the value of the masked cell. Additionally, we apply two enhancement operators--shuffle and delete--to tuple t to enrich our training data. Due to the structural nature of tables and their inherent permutation invariance, these operators are commonly used for tabular data.
## 4 Discussion
### Storms  
The formation of beach ridges on coarse grained, gravel, or shingle beaches is generally attributed to storm events [5]. This is because coarse-grained, shingle beaches have a high permeability, enabling water to drain into the shingle, reducing backwash and effectively trapping coarse grains on the ridge crest. Landward dips indicative of overtopping and washover are rare in the beach ridges at Dungeness, possibly only occurring when the beach becomes saturated, and infiltration is reduced. Coastal flood frequencies in Britain since the 1780s have been compiled by [34] in three sectors, one of which, the south and southwest sector, includes Dungeness. A relatively low number of floods is recorded on the south coast for the 19 th century, from the 1890s to the 1940s flood frequencies increased [34]. The 1960s show a decrease in the number of reported floods, which is associated with a negative NAO (Figure 8). Recorded floods increased in the 1970s and decrease towards 2000 [34]. It should be noted that the number of reported floods can be affected by cultural factors, particularly expansion of coastal towns, creating an increased flood risk, while later improvements in flood defenses reduced vulnerability and flooding [34]. A reduction in coastal flooding from southwesterly storms provides an alternative explanation for a potential decrease in erosion from the southwest shore and consequent decrease in sediment accumulation on the east facing shore. In addition, the rates of sediment accumulation since 1990 have returned to values that are similar to those that pertained between 1940 and 1960, suggesting that there is no obvious long-term trend in the annual rates of accretion before and after the construction of the nuclear power stations at Dungeness. If anything, the rates of accretion appear to have increased slightly during the past century (Figure 8), possibly a result of increased erosion on the updrift, southwest-facing shore. However, the duration of the time gaps between freely available aerial survey data, typically 20 to 30 years, precludes more detailed analysis of the rates of change. It is suggested here that analysis of the erosion rates along the southwest-facing shore would help to resolve this question because that might provide an independent assessment of the potential sediment supply due to erosion.  
Figure 8: **(A)** Flood frequencies for southwest England since the 1780s, compiled by [34]. **(B)** Rates of progradation determined in this study. **(C)** Hurrell North Atlantic Oscillation (NAO) winter (December–March) station-based index from [35].