esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Comparison of ML Classifiers  
In general, our experiments exhibited that the ML classifiers integrated with the G-SMOTE method yielded sustainably better results in different landscapes. With balanced datasets, the coastal and desert were two experiment sites that showed the highest accuracies; in contrast, the lowest classification accuracy belonged to the semi-arid landscape. Comparing the classifiers revealed that different classifiers show dissimilar performances in diverse landscapes. For example, although the highest accuracy (OA and G-mean) belonged to the SVM-G-SMOTE classifier in three landscapes (coastal, cropland, and mountain), the RF-G-SMOTE provided better results in three remaining landscapes (plain, semi-arid, and desert). On the other hand, ELM-G-SMOTE had the worst outcome in five sites (in common with the SVM-G-SMOTE in two cases), whereas it provided the same leadership position as RF-G-SMOTE in the plain landscape.  
Since our methodological approach in image classification was the same in all experiments, this diversity in results might be attributed to the landscape circumstances and the LC class distribution of reference datasets. That diversity in results among classifiers agrees with the previous studies where scholars and scientists have tried to find the best classifier for LC classification, but their conclusions were contradictory. For instance, [PERSON] and others [7], Fusing Sentinel-1, and Sentinel-2 data, introduced the SVM algorithm as the most accurate one (OA = 88.75%). [PERSON] and others [36] claimed that RF shows higher accuracy than SVM in a heterogeneous coastal landscape. Our results suggest a comparable performance by both the SVM-G-SMOTE and RF-G-SMOTE methods. While the time and experimentation required to select the user-defined parameters of RF are pretty small compared to SVM. The SVM implementation involves choosing a suitable kernel and some kernel specific parameters like cost and gamma [26]. On the other hand, the RF only needs a selection of two parameters, including \(n\) tree and \(m\) try. The value of 500 for \(n\) tree and the square root of the number of input variables for \(m\) try had already been proven as valid values [28].
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
## 1 Introduction
### Conditions That Induce VBNC State  
Bacterial cells commonly respond to stressful conditions by losing their ability to form colonies in standard culture media, although cells can remain viable for long periods of time [5]. Cells may enter into the VBNC state in response to natural stresses, such as starvation, extreme temperatures, high osmotic or oxygen concentrations, or exposure to white light [1]. In general, extreme environmental conditions can be lethal unless they adopt a latency status; for example, it is known that one of the most critical factors for bacterial survival is the availability of nutrients in the surrounding environment. When bacterial cells are subject to nutrient starvation conditions, they may reduce their size and become more resistant to adverse environmental conditions; alternatively, they could induce a latent estate, forming viable but non-culturable cells or structures resembling spores [3]. However, spore-forming bacteria are typically not classified within the VBNC state literature [3]. It is interesting that not only environmental stress can induce the VBNC state, but various processes and bactericidal substances are able to induce this state as well. For example, milk pasteurization [6], wastewater chlorination [2], and the use of food preservatives such as potassium sorbate and sodium benzoate [7] have been documented as inducers of the VBNC state.
## 1 Introduction  
Sensors have been applied to a variety of fields such as environmental quality monitoring [1], non-invasive disease diagnosis [2], food quality control [3], and industrial process analysis [4], because of the following advantages: (1) ability to function in a harsh environment, (2) ability to operate continuously and automatically, and (3) high accuracy and sensitivity. In general, developing a sensor depends on two major components: analytical instruments and data analysis techniques. Novelanalytical instruments allow producing a great amount of information (data) and also permit the exploration of new fields.  
However, these generated sensor data may contain irrelevant information and moreover the principles of the new fields could be very complex and even totally unknown, so reliable sensor systems are becoming increasingly reliant on sophisticated data processing techniques.  
As a powerful tool for advanced data processing, machine learning has become a core technique for novel sensor development, aiming to discover the hidden rules that control complex systems. As shown in Figure 1, a complete machine learning process is composed of three steps: data pre-processing, feature extraction and dimension reduction, and system modeling. Data pre-processing comprises noise filtering, data normalization, signal alignment, and other related data treatments. Sensor signals are usually composed of a large number of variables, so in the second step feature extracting methods are used to transfer sensor signals from their original high dimensional space to a low dimensional feature space or to select \"representative\" (pertinent) variables to characterize an entire system. Given a good feature expression, the last step of machine learning is to establish system models either for classification problems such as pollutant detection and disease diagnosis or quantitative estimation problems like chemical concentration prediction.  
Any of these three steps can play a key role in controlling machine learning effects. There have been some good review papers and books about pattern recognition techniques for either general industrial applications or specific fields like food science [5, 6]. This paper will provide a review of the algorithms that are currently used in each step, compare their individual properties, and discuss future perspectives of machine learning methods for sensor development, covering the applications to both classification and quantitative estimation.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### NLP for Electronic Health Records  
EHRs are vital in healthcare, storing patient clinical tests, records, and treatments, encompassing both structured (symptomatic codes, lab results) and unstructured data (clinician observations). NLP plays a crucial role in analyzing unstructured data and extracting meaningful information from it [50]. Several critical NLP tasks aim to render narrative, ambiguous, and highly unstructured language data comprehensible and analyzable for computers, such as morphological analysis, semantic analysis, named-entity recognition, and word-sense disambiguation [80]. Pre-trained language models extract representations to compute disease contributions, maximizing EHR data utilization and effectively leveraging healthcare domain knowledge [81].  
In practice, NLP has been applied to various EHR-related tasks. For example, the MIMIC-III dataset has been widely used to train and evaluate NLP models that can automatically extract and classify symptoms and diagnostic information from medical records, thereby improving healthcare service efficiency. Specific applications include automatically identifying and tagging drug allergies in medical records using NLP, which enhances patient safety, and using named-entity recognition techniques to extract patients' medical history and surgical records from medical records, aiding physicians in diagnosis and treatment decisions [82].  
However, excessive or inaccurate NLP outputs in EHRs may cause users to disregard its intelligence, reducing productivity. Therefore, healthcare NLP software should prioritize actionable insights with minimal noise for providers. Accurate predictions depend on the quality and completeness of training data, making data quality a critical concern. Additionally, protecting patient data security and privacy is crucial, and AI usage in EHRs must comply with laws such as HIPAA in the US [10].
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 4 Simulation Analysis
### Influence of the Number and Station Layout of RISs on Localization Accuracy  
Firstly, the influence of the number of RISs on the localization accuracy is discussed. A 2-D radar located at [0, 0, 0] km is assisted by several RISs to locate one target with its location as [20, 40, 9] km. Up to four RISs are used here, and their locations are km, [1.34, 2.68, 1.3] km, [1.34, 2.68, 2.5] km and [1.34, 2.68, 3.7] km. When the first \(m\) RISs are used to assist the 2-D radar, the localization accuracy can be simulated as shown in Figure 5, where three curves give the simulated GDOP with different numbers of RISs as \(m=2,3,4\). The GDOP is obtained with 5000 Monte Carlo simulation experiments.  
In addition to the number of RISs, the station layout of RISs will also have an impact on the target localization accuracy. In the simulation scene of locating the target at [20, 40, 9] km, three RISs are applied to assist the 2-D radar, and three different station  
Figure 4: Simulated GDOP and CRLB versus the target location.  
layouts of the RISs are considered. Under different geometry conditions, the GDOP is statistically obtained and compared. The station locations of the RISs are as follows.  
In the above three geometry conditions, the baseline of the RISs becomes longer in the pitch dimension. Figure 6 depicts the simulated GDOP varying with the SNR under three geometry conditions.  
Figure 5: Simulated GDOP versus the target SNR with different number of RISs.  
Figure 6: Simulated GDOP versus the target SNR under different geometry conditions.  
By comparing three curves in Figure 5, it is shown that the GDOP is lower with more assisted RISs. The localization accuracy is improved with the increase of RISs, since more redundant measurements in the auxiliary channels are used for target localization. As shown in Figure 6, the GDOP in Geometry 3 with the longest baseline is lowest. The localization accuracy of the proposed RIS-assisted 2-D radar becomes higher with the longer baseline of three RISs. The baseline influences the localization accuracy by its effect on the difference in the observation perspective of the target in the pitch dimension. The perspective difference becomes larger with a longer baseline, which will enlarge the observability of the target location and improve the target localization accuracy.  
In this study, detailed Monte Carlo simulations are conducted to investigate the effect of the number and the station layout of RISs on the localization accuracy in the RIS-assisted 2-D radar. Simulation results show that increasing the number and the baseline of the assisted RISs can improve the localization accuracy, especially in low SNR conditions. The conclusions are important for optimizing the design of the radar system to improve its localization performance. It is shown that the localization accuracy of the proposed RIS-assisted 2-D radar can be significantly improved by properly configuring the number and location of RISs.
## 4 Conclusions  
We investigated the impact of the scattering effect of a spherical particle on the orbital angular spectrum of a vector Bessel-Gaussian vortex beam. We considered y polarization, right circular polarization, and radial polarization. The generalized Lorenz-Mie theory and the angular spectrum decomposition method were used to describe the scattered and external fields of vector Bessel-Gaussian vortex beam in the far-field region. The spiral spectrum expansion method was then combined to simulate the orbital angular momentum spectra of scattered and external fields. The calculation algorithm of the field does not cause the spiral spectrum of the beam change with the position of the measurement plane. Our simulation results further suggested that sampling along the circumference cannot fully reflect the phase information of the field, and different sampling radii lead to different sampling results. The spiral spectra of the scattered and external fields of y polarized, right circular polarized, and radial polarized Bessel-Gaussian vortex beams are sensitive to the changes in the particle characteristic parameters, such as its complex refractive index and size. The sampling results are inconsistent in different directions. Therefore, the polarization state of the beam and sampling direction of the field can be used as calculation dimensions to build a database for retrieving the parameters of scattered particles. Similarly, the characteristics of the incident beam can be retrieved by using the particle parameters. Studying the field distribution and spiral spectrum of the vector Bessel-Gaussian vortex beam scattered by spherical particles provides a theoretical basis for potential applications  
Figure 17: The same as in Figure 16, but the radial polarization is considered.  
of vortex beam in remote sensing, such as the real-time monitoring of rain droplet size in rainy environments.  
Conceptualization, M.C., L.G., M.P.J.L., P.W. and S.L.; methodology, C.S., M.C., M.P.J.L., R.L. and S.L.; software, C.S. and R.L.; investigation; C.S., M.C. and S.L.; resources, L.G., J.L. and P.W.; writing--original draft preparation; C.S. and M.C.; writing--review and editing, M.C., L.G., M.P.J.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. U20B2059, 61627901, 61901336, 61905186), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 61621005), the Fundamental Research Funds for the Central Universities (Grant No. YJS2208, QTZX2037).  
Not applicable.  
The authors would like to thank the editor and anonymous reviewers who handled our paper.  
The authors declare no conflict of interest.
## 3 AMO Model Construction  
To better simulate the decision-making process of residents under the TRS, this study established the following fundamental assumptions:  
1. Timing of Decision-Making: It is assumed that travelers will decide whether to participate in the TRS within 30 min before their initially scheduled departure time.
2. Adherence to Scheduled Departures: This study focuses on the impact of the reservation mechanism on residents' travel decisions. Therefore, it is assumed that users who have successfully made a reservation will strictly adhere to the scheduled departure time, while those without a reservation will depart as planned initially.  
Based on these assumptions, the model constructed in this paper will be able to capture and analyze the impact of the reserved travel strategy on residents' travel behavior more accurately. This approach allows for a detailed understanding of how reservation systems influence travel patterns, ensuring that the model reflects realistic decision-making processes. The framework of this model is shown in Figure 2. Table 1 illustrates the parameters and symbols employed in this study.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Sets:** & \\ \hline \(N\) & The set of travelers, \(n\in N\). \\ \(A\) & The set of directed links, \(a\in A\). \\ \(M\) & The set of travel modes, \(m\in M\). \\ \(A_{m}\) & The set of directed links for travel mode \(m\). \\ \(K\) & The set of paths, \(k\in K\). \\ \hline \hline \multicolumn{2}{l}{**Parameters:**} \\ \hline \(v_{e}\), \(v_{l}\), \(v_{t}\) & The traveler’s early arrival time value, late arrival time value, and on-route time value, respectively. \\ \(l\) & The unit fuel consumption cost. \\ \(d_{a}\) & The length of link \(a\). \\ \(\lambda\) & The weight of objective. \\ \(\theta\) & The choosing discrete coefficient. \\ \(S\), \(F\) & Represents the maximum incentive and penalty. \\ \(t_{w}\) & Working hours. \\ \(f_{bus}\) & Bus fare. \\ \(f_{metro}\) & Metro fare. \\ \(P_{a}\) & Parking fee. \\ \(\tilde{t}\) & Original departure time. \\ \(h\) & The reservation time window in the TRS (every 10 min is regarded as a time window). \\ \(u_{r}\) & Incentive for each time window changed. \\ \(Q^{b}\), \(Q^{metro}\) & Limits on the number of passengers per bus and metro. \\ \(Q^{park}\) & Parking capacity. \\ \hline \multicolumn{2}{l}{**Calculated variable:**} \\ \hline \(R_{n}^{m}\) & The transportation system’s revenue for traveler \(n\) choosing travel mode \(m\). \\ \(S_{n}^{m}\) & The incentive for traveler \(n\) choosing travel mode \(m\). \\ \(C_{n,t}^{m}\) & The generalized travel cost for traveler \(n\) choosing travel mode \(m\) at time \(t\). \\ \(C_{n}^{m}\) & The travel cost of traveler \(n\) choosing travel mode \(m\). \\ \(t_{n}\) & The travel time of traveler \(n\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter and symbol description.  
Figure 2: The model framework.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 5 Conclusions  
In this paper, we propose a novel grasp detection network called ODGNet. This network can efficiently and accurately predict the optimal grasping configuration from input RGB-D images. Benefiting from the ODConv and GRFEF modules, the ODGNet achieves a better grasp detection performance with the Cornell dataset and multi-object dataset. In the simulated grasping experiments, our method achieved a grasp success rate of 95.2% for single objects and 90.4% for multiple objects. In conclusion, our method exhibits a higher detection accuracy and faster detection speed for potential application industries such as tidying up the home environment and grasping various workpieces. Future work will focus on improving the grasp detection of multiple objects in complex environments, as well as studying the extension of the algorithm to three-finger grippers and multi-finger grippers.  
Conceptualization, X.K. and B.T.; methodology, B.T.; software, B.T.; validation, X.K. and B.T.; formal analysis, B.T.; investigation, B.T.; resources, X.K.; data curation, B.T.; writing--original draft preparation, B.T.; writing--review and editing, X.K.; visualization, B.T.; supervision, X.K.; project administration, X.K.; funding acquisition, X.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China: Design and Development of High Performance Marine Electric Field Sensor, grant number 2022 YFC3104001.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ODConv & Omni-dimensional dynamic convolution ODGNet & Omni-dimensional dynamic convolution grasp detection network SA & Shuffle Attention GRFEF & Grasping region feature enhancement and fusion GR-ConvNet & Generative Residual Convolutional Neural Network GAP & Global Average Pooling FC Fully connected  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**GR-ConvNet [(20)]**} & \multicolumn{2}{c}{**Our Model**} \\ \cline{2-5}  & **Grasp** & **Success Rate (\%)** & **Grasp** & **Success Rate (\%)** \\ \hline Single-object & 232/250 & 92.8 & 238/250 & 95.2 \\ Multi-object & 215/250 & 86.0 & 226/250 & 90.4 \\ \hline \end{tabular}
\end{table}
Table 4: Grasping results in the simulation environment.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## Appendix A  
### Kriging (KRG)  
The basic assumption of KRG is the estimation of the response in the form of:  
\[f\left(x\right)=p\left(x\right)+z\left(x\right) \tag{1}\]  
where \(f(x)\) is the response value of the function, \(p(x)\) is a known polynomial that globally approximates the response, and \(z(x)\) is the stochastic component that generates deviations such that the Kriging model interpolates the sampled response data. \(z(x)\) has a mean value of zero and covariance as:  
\[\text{cov}[x_{i},x_{j}]=\sigma^{2}\mathbf{R}(x_{i},x_{j}) \tag{2}\]  
\(\mathbf{R}(x_{i},x_{j})\) is a correlation function between the data points \(x_{i}\) and \(x_{j}\), when choosing Gaussian; it is represented as:  
\[R(x_{i},x_{j})=\exp[-\theta\big{|}x_{i}-x_{j}\big{|}_{2}],\,\theta>0 \tag{3}\]  
Once the correlation function vector has been established, the response can be predicted as:  
\[\begin{array}{l}\hat{f}(x)=\beta+r^{\mathsf{T}}(x)\mathbf{R}^{-1}(\mathbf{f}- \mathbf{1}\beta)\\ \hat{\beta}=\frac{1\mathbf{R}^{-1}\mathbf{f}}{1^{2}\mathbf{R}^{-1}\mathbf{1}}\end{array} \tag{4}\]  
where the matrix \(\mathbf{R}^{-1}\) is the inverse of the correlation matrix \(\mathbf{R}\) whose elements \(R_{ij}\) are computed by (3), \(\mathbf{f}\) is the vector of the sample responses, and \(\mathbf{1}\) is an \(n\times 1\) vector of ones. \(\mathbf{r}(x)\) is calculated by:  
\[r^{\mathsf{T}}(x)=\left(\text{cov}[x,x_{1}],\text{cov}[x,x_{2}],\cdots,\text{ cov}[x,x_{n}]\right) \tag{5}\]  
Using parameter estimation methods, \(\gamma=[\beta,\,\sigma^{2},\,\theta]\) can be calculated by maximum likelihood estimation, and the detailed derivation of Kriging can be found in [5]. In this work, the MATLAB Kriging toolbox DACE [48] was used.
## 5 Cutter Spacing Optimization and Bit Rock Breaking Simulation
### Full-Size Bit Rock Breaking Simulation
The variance and standard deviation measure the dispersion degree of drill bit ROP, further quantifying the stability of rock-breaking performance. In particular, PDC drill bits utilizing axe cutters showcased the highest ROP dispersion degree, while PDC drill bits with a mixed cutter arrangement showcased the lowest ROP dispersion degree. Compared to drill bits with axe cutters, the standard deviation of the ROP for drill bits with triangular cutters decreased by 18.5%, whereas drill bits with a mixed cutter arrangement showcased a decrease in the standard deviation of the ROP by 53.6% and 43.08%, respectively, relative to drill bits with axe cutters and triangular prism cutters. This  
Figure 22: Bit penetration rate and displacement comparison: (**a**) Bit footage comparison; (**b**) Comparison of instantaneous ROP. illustrates that a mixed cutter arrangement can maintain the stability of rock breaking, consequently sustaining the WOB stability, which ultimately contributes to the improved efficiency and increased lifespan of the drill bit.