esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Fuzzy Inference Systems  
Computing using inference based on fuzzy logic is a popular method of computing, with many applications in areas such as control classification, expert systems, robotics, and pattern recognition adopting this method. The fuzzy inference system (FIS) is known by many names, such as the fuzzy expert system, the fuzzy model, fuzzy logic associative memory, and the fuzzy controller (also known as fuzzy rule-based systems). The FIS represents the major unit of a logic system. It can formulate adequate rules, based on which the decision is made. Figure 1 depicts the FIS structure.  
An important element of the FIS is fuzzy sets, whereby knowledge is partitioned into particular situations; the fuzzy set is defined by its vague and ambiguous features, and limits may also be specified ambiguously. Crisp sets are those without ambiguity in their membership functions. The fuzzy set theory can deal powerfully with the presented ambiguity.  
The work in [18; 19] proposed the concept of Type-1 and Type-2 fuzzy sets. Type-1 fuzzy sets are described by membership functions that are aligned with numeric values between \([0,1]\), whereas Type-2 fuzzy sets are described by membership functions that are themselves fuzzy, with linguistic and subjective values. Type-1 fuzzy sets are certain. Type-2 fuzzy sets are useful  
Figure 1: Fuzzy inference system.  
in scenarios where it is complicated to determine exact membership functions due to the uncertainty present; accordingly, they can be used to handle rule uncertainties and measure uncertainties. For these reasons, we chose to work with Type-2 FIS, because it allowed for the handling of arising uncertainties; additionally, the case study reported is a dynamic context where uncertainty exists all of the time and at every moment.
# The Impact of Space-Based AIS Antenna Orientation on In-Orbit AIS Detection Performance
## 3 Results and Discussion
### AIS Messages Comparison
Excluding the Long Range AIS, the total number of messages received by making 45\({}^{\circ}\)-maneuvered increases around 31.11% to 239.29% compared to before and after the maneuver. One might have assumed that the increasing AIS message reception during the 45\({}^{\circ}\)-maneuvered and 90\({}^{\circ}\)-maneuvered is influenced by the different satellite ground track produced during the 11 h of maneuver. To deal with this assumption, the visualization of all ground tracks produced by the satellite during the observation is provided in Figures 11 and 12. Figure 10: Comparison of AIS message acquired under the 45\({}^{\circ}\)-maneuvered flight configuration. The ground tracks provided in Figures 11 and 12 were calculated during the 90\({}^{\circ}\)-maneuvered and 45-maneuvered observation period using the SGP4 algorithm [21]. It is shown that the ground tracks before the maneuvering, during the maneuvering, and after the maneuvering are quite similar; hence, it is assumed that the AIS message reception should be quite similar. This phenomenon eliminates the assumption and confirms that the increasing AIS message reception is purely influenced by satellite maneuvering. Figure 11: The LAPAN-A2 satellite ground track produced during the 90\({}^{\circ}\)-maneuvered observation period. Figure 12: The LAPAN-A2 satellite ground track produced during the 45\({}^{\circ}\)-maneuvered observation period.
## 3 Results
### Transplanting Performance Experiment  
Under the optimal combination parameters, the transplanting performance of the robotic transplanting workcell was further evaluated in a laboratory. Taking tomato, cucumber and pepper seedlings as the transplanting objects, various seedlings were transplanted into the 50-cell growing trays. The transplanting process was observed and recorded on-site using a CCD camera. The corresponding results are shown in Table 6. The maximum success ratio for automatic transplanting was 95.47% for 128-cell trays of tomato seedlings, and the minimum success ratio was 91.67% for 128-cell trays of pepper seedlings. In general, the success ratio of pick-up seedlings at 128-cell trays was higher than that at 72-cell trays. The successful possibilities of transplanting seedlings increased as the tray cells were relatively small. The reason might be that these small tray cells tend to produce sturdy seedling plugs with well-developed roots.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multicolumn{3}{c}{**Factor (Level)**} & \multirow{2}{*}{**Integrity Rate (\%)**} \\ \cline{2-2} \cline{4-5}  & **A (3)** & **B (3)** & **C (3)** & \(\mathbf{D^{a}}\) **(3)** \\ \hline
1 & 1 (15 plant/min) & 1 (0.9 N) & 1 (30 mm) & 1 & 90.79 \\
2 & 1 & 2 (1.2 N) & 2 (35 mm) & 2 & 93.63 \\
3 & 1 & 3 (1.5 N) & 3 (40 mm) & 3 & 92.11 \\
4 & 2 (20 plant/min) & 1 & 2 & 3 & 90.57 \\
5 & 2 & 2 & 3 & 1 & 90.24 \\
6 & 2 & 3 & 1 & 2 & 88.36 \\
7 & 3 (25 plant/min) & 1 & 3 & 2 & 88.75 \\
8 & 3 & 2 & 1 & 3 & 88.46 \\
9 & 3 & 3 & 2 & 1 & 89.21 \\ \hline K\({}_{1}\) & 92.18 & 90.04 & 89.20 & & \\ K\({}_{2}\) & 89.72 & 90.78 & 91.14 & & \\ K\({}_{3}\) & 88.81 & 89.89 & 90.37 & & \\ R & 3.37 & 0.88 & 1.93 & & \\ Optimal level & A1 & B2 & C2 & & \\ \hline \hline \end{tabular} Note: \(\mathbf{D^{a}}\) is Error.  
\end{table}
Table 4: Results of the multi-factor optimization experiment.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Source**} & **Sum** & **DOF** & **Mean Square** & **F-Value** & \(p\)**-Value** & **Significance** \\ \hline A: Transplanting & 18.216 & 2 & 9.1080 & 134.3805 & 0.0074 & **\(\ast\)** \\ B: Grasping force & 1.3484 & 2 & 0.6742 & 9.9474 & 0.0913 & ns \\ C: Penetration depth & 5.6840 & 2 & 2.8420 & 41.9313 & 0.0233 & \(\ast\) \\ D: Error \(\ast\) & 0.1356 & 2 & 0.0678 & & \\ Sum & 25.384 & & & & \\ \hline \hline \end{tabular} Note: ns, no significant effect; \(\ast\)\(\ast\) significant level at 0.01 \(<\)\(p\) \(<\) 0.05, \(p\) \(<\) 0.01, respectively.  
\end{table}
Table 5: Analysis of variance (ANOVA) for the multi-factor optimization experiment.
## 3 Design of Maritime Data Security Management Platform
### The Architecture of Maritime Data Security Management Platform  
The Marine data security management platform uses the J2 EE technology architecture on the server. This method can be highly compatible with the operating system and database system and can be transplanted to become cross-platform. Applications deployed on the J2 EE platform have the advantages of high availability, security, scalability and reliability. This method uses Service-Oriented Architectures (SOA) to encapsulate the data interface of each subsystem for information exchange into a Web Service, which is registered in the OSB through the Web Services Description Language (WSDL) file to provide data services, then adapts to the changing environment and shields the technical differences between different systems.  
This paper proposes a four-layer system architecture, as shown in Figure 1, which includes the application layer, information service layer, data layer and hardware layer. The design of the system architecture aims to realize the unified collection and management of multi-source maritime data, use methods of big data analysis and mining to fuse and analyze the data and then provide users with diverse, professional and accurate data services. The application layer is composed of a shore-based application system and berth application system, which runs on the client machine and provides visualization and an  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Num** & **The Service Content** \\ \hline MSP1 & VTS information service (IS) \\ MSP2 & VTS navigation assistance service (NAS) \\ MSP3 & VTS traffic organization service (TOS) \\ MSP4 & Local port service (LPS) \\ MSP5 & Maritime safety Information service (MSI) \\ MSP6 & Pilotage service \\ MSP7 & Tugs service \\ MSP8 & Vessel shore reporting \\ MSP9 & Telemedical maritime assistance services (TMAS) \\ MSP10 & Maritime assistance services (MAS) \\ MSP11 & Nautical chart service \\ MSP12 & Nautical Publications service \\ MSP13 & Ice navigation service \\ MSP14 & Meteorological Information service \\ MSP15 & Real-time hydrological and environmental information services \\ MSP16 & Search and rescue service (SAR) \\ \hline \hline \end{tabular}
\end{table}
Table 1: MSP content.  
interactive application system for different users. The information service layer is composed of a big data analysis and mining system and an information service system, which runs on a J2 EE Web server and provides a professional data service module for the application layer and performs data fusion and analysis. The data layer consists of all kinds of special databases, which run on the J2 EE database server, providing the required data for the resource service layer and managing the data. The hardware layer is composed of the network hardware, data platform, data acquisition sensor and so on. It runs on acquisition equipment such as the ship, shore, surface and underwater, and provides original data support for the data layer.
## 4 Results
### \(Z_{sd}\) Estimation Scheme for AHSI Imagery
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**\(Z_{sd}\) Range (m)**} & \multirow{2}{*}{**N**} & \multicolumn{2}{c}{**MAE (m)**} & \multirow{2}{*}{**MRE**} \\  & & & \(QAA_{V5}\) & \(QAA_{M14}\) & \(QAA_{V5}\) & \(QAA_{M14}\) \\ \hline
0.3–1.0 & 23 & **0.22** & 0.39 & **33.1\%** & 47.4\% \\
1.0–3.0 & 43 & **0.46** & 0.98 & **29.1\%** & 60.9\% \\ \textgreater{}3.0 & 18 & **0.24** & 2.94 & **6.3\%** & 75.6\% \\ \hline Total & 84 & **0.35** & 1.24 & **25.3\%** & 60.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean absolute errors (MAEs) and mean relative errors (MREs) of the estimated Advanced HyperSpectral Imager (AHSI) image \(Z_{sd}\) based on semi-analytical methods using various Quasi-Analytical Algorithm (QAA) algorithms. Optimal results listed in bold. All of the above analyses indicate that only the SAM with the \(QAA_{V5}\) can predict desirable \(Z_{sd}\) values with an approximate remote sensing reflectance from the AHSI imagery. Therefore, based on the semi-analytical method with the \(QAA_{V5}\), \(Z_{sd}\) images were produced for the AHSI images of four study areas (see Figure 5). Retrieved \(Z_{sd}\) from the study regions have consistent distributions with ground surveys: (1) in Guanting Reservoir, southwestern areas have higher water clarity than northeastern areas; (2) Lake Baiyangdian contains numerous small fish ponds, which have relative turbid waters in its most areas; and (3) Panjiakou Reservoir has better water quality than Dhaheiting Reservoir, such that it has higher water clarities.
## 2 Data and Methodology
### Data Sets
#### 2.2.2 Sentinel-2  
Sentinel-2 (S2) contains two satellites (Sentinel-2A and Sentinel-2B), both carrying a multispectral imager (MSI) with a bandwidth of 290 km. The S-2A satellite carries an innovative wide-swath high-resolution multispectral imager with 13 spectral bands. These working bands have three different geometric resolutions ranging from 10 to 60 m, specifically, high-resolution visible images (red, green, blue, and near-infrared bands), with a 10 m ground sample distance (GSD), and red-edge, narrow near-infrared, and shortwave infrared bands with a 20 m GSD. With the launch of Satellite S-2B, the two sensors in orbit have significantly shortened the revisit time, resulting in a 5-day interval for S2 images. Its superiority in spatiotemporal resolution provides adequate and high-quality data in areas, facilitating the broad application of satellite images. S2 images are provided freely for users through the United States Geological Survey (USGS) ([[https://glovis.usgs.gov/](https://glovis.usgs.gov/)]([https://glovis.usgs.gov/](https://glovis.usgs.gov/))). Optical RS images of the study site were obtained from Sentinel-2A on dates 25 April 2019 and 27 April 2021, which are close to those of the S1 images acquired (28 April 2019 and 29 April 2021). In theory, it is possible to obtain one S1 image and one S2 image with a difference in time of less than one week because, in agricultural areas, the vegetation does not change much within one week [36]. The obtained S2 images need to be processed before being analyzed. S2 Level 1C images (S2 L1C) were selected, to which geometric correction and radiometric correction were made. The Sentinel Application Platform (SNAP) was used to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **Incident Angle (\({}^{\circ}\))** & **Spatial Resolution** & **Width (km)** \\ \hline MW & 22–38 & 5 m \(\times\) 5 m & 20 \(\times\) 20 \\ EW & 19–47 & 20 m \(\times\) 40 m & 400 \\ IW & 29–45 & 5 m \(\times\) 20 m & 250 \\ SM & 20–45 & 5 m \(\times\) 5 m & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the various imaging modes of S1 image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & \
\begin{tabular}{c} **Data of** \\ **Acquisition** \\ **(D/M/V)** \\ \end{tabular} &
\begin{tabular}{c} **Mean Incidence** \\ **Angle (\({}^{\circ}\)C)** \\ \end{tabular} & **Imaging Model** & **Polarization** \\ \hline D1 & 28 April 2019 & 38.85 & IW & VV/VH \\ D2 & 29 April 2021 & 38.85 & IW & VV/VH \\ \hline \hline \end{tabular}
\end{table}
Table 2: S1 images used in this research.  
treat it as an L2A product. Based on the S2 images, image registration was performed on S1 and S2 data after pretreatment.
# On Cross-Layer Interactions for Congestion Control in the Internet
###### Abstract
Despite the considerable amount of research on TCP congestion control algorithms and AQM performance, the interdependence of those two mechanisms is yet to be efficiently studied and addressed. The unique characteristics of any congestion avoidance component require a comprehensive analysis under various network conditions. In this paper, the interactions between the most commonly used TCP versions and state-of-the-art Active Queue Management algorithms are evaluated. We are trying to answer the following questions:  
* To what extent an AQM influences the end-to-end congestion control mechanism? * What are the effects of network parameters, such as RTT variation and congestion level, on the combined TCP and AQM performance? * Can cross-layer interaction between TCP and AQM overcome the challenges of network heterogeneity and variability? * Is there one universal TCP-AQM combination for all cases? The rest of the paper is organized as follows. Section 2 reviews the related research. In Sections 3 and 4, the evaluated TCP and AQM approaches are described in detail. The performance evaluation of different AQM and TCP combinations in different scenarios is presented in Section 5. Finally, conclusions are provided in Section 6.
## 5 Experiments and Results  
To evaluate the models presented in Section 4.4, we ran a repeated ten-fold cross-validation [97], executing 50 cross-validation runs. We conducted four experiments: two for regression models and two for classification models. Either for regression and classification, the experiments consisted of training the model only with historical data of the debutanizer unit we aim to predict for (Experiment 1), and to enrich the model validation with the data available from another debutanizer unit (Experiment 2). We present the corresponding cross-validation setting in Figure 10. We ensure results from both experiments are comparable by preserving the same cross-validation test sets among both experiments. We also assessed if the differences in results obtained for the different models were statisticallysignificant. To that end, we executed the Wilcoxon signed-rank test [98] and tested for significance at a 95% level.
## 2 Related Works
### Interpretable Machine Learning Approach  
Instead of treating a deep learning model as a black box, some authors have pursued an approach where deep learning algorithms are only employed as extractors of relevant information about the DME. They utilize these extracted features to conduct DME grading, employing a conventional machine learning classifier. This method offers some degree of interpretability since the DME grade assignment is based on the retinal anatomical features extracted in the first stage.  
For example, in [14], the authors advocated the utilization of a deep learning model to extract features from fundus images. The training process involved incorporating these multi-scale features from the image, such as hard exudate masks, macular masks, and macular images. Subsequently, XGBoost is used for DME grading, which introduces these data. This work can be considered partially interpretable because input data, such as the positional relationship between HaExs and macula, is interpretable data, which is generally used by ophthalmologists to determine the severity of the DME. However, the decision-making performed by XGBoost is still a black box.  
In reference [17], the authors developed an end-to-end deep fusion model based on EfficientNet as a backbone network. This network is used not only for binary classification, DME or non-DME but also HaExs localization, combined with a bi-directional feature pyramid network (BiFPN). This fusion model was trained using a loss function, which is a linear combination of classification loss and detection loss. Although the decision about the presence or absence of DME was taken by a black box like CNN, the fact that a part of the same CNN is used for the detection of HaExs increases the interpretability of results. Additionally, the authors detected optic discs and macula using YOLOv3 to provide additional information for ophthalmologists, although the system does not provide the three grades of the DME shown in Table 1.
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Administrative Applications  
ChatGPT can help create precise and exhaustive patients' documentation. By entering patient data, clinical findings, and treatment plans, specialists can utilize ChatGPT's capabilities for text generation to formulate comprehensive reports [68]. This method boosts the quality of documentation, ensuring that critical information is effectively recorded and may support seamless communication among healthcare providers, possibly overcoming language barriers in doctor-patient interaction. While no specific result regarding administrative application in dentistry resulted from the research selection, [PERSON] et al. underlined the potential of LLMs in supporting health care professionals in routine written communications and record keeping, potentially enhancing the quality of patient care and saving time, thus reducing costs [25]. However, the input of sensitive health-related information raises privacy and cybersecurity concerns.
## 1 Introduction  
The era of big data has arrived with the advent of massive data. Semi-structured data [1, 2] plays a crucial role in massive data with the non-strict structure feature. Most data with a complicated structure, including semi-structured data, can be represented by a tree structure. Data mining methods are used to find hidden relationships among massive data [3, 4]. Frequent subtree mining has become an important field of data mining research [5, 6, 7]. It is the process of mining a subtree set from a given data set that satisfies insertion (support or frequent degree). Frequent subtree mining can be applied in many fields. For example, RNA molecule structure can be represented by a tree structure where, in order to obtain information about a new RNA molecule, the new one must be compared to the known RNA structures. The function information of new RNA can be obtained by looking for the same topology [8].  
CFMIS (compressed frequent maximal induced subtrees) [9] is an efficient method for the frequent subtree mining we proposed earlier. The CFMIS algorithm can find all frequent induced subtrees without throwing solutions in less time. Parallel frequent subtree mining processing is essential for mining massive volumes of data in a timely manner. MapReduce is an ideal software framework to support distributed computing on large data sets on clusters of computers [10, 11]. However, not all algorithms could be transplanted to the MapReduce framework, in fact, only a few algorithms could. Assigning data into appropriate blocks is crucial for paralleling algorithms in MapReduce [12]. In this paper, three parallel CFMIS (PCFMIS) algorithms, PCFMIS1, PCFMIS2 and PCFMIS3, are proposed. PCFMIS1 parallels CFMIS, transplanting CFMIS to MapReduce framework by GS. Furthermore, PCFMIS2 is proposed by optimizing the compression to reduce the running time on each slave node. Based on PCFMIS1 and PCFMIS2, PCFMIS3 is proposed to achieve load balancing by using an effective EDS.  
In summary, the contributions of our work are as follows:  
1. A grouping strategy is proposed to achieve effective data partitioning in order to parallel the frequent subtrees mining method.
2. The compression is optimized by avoiding all candidate subtrees to reduce the mining time on nodes.
3. An effective edge division strategy is proposed to achieve load balancing.
4. The proposed algorithm PCFMIS3 is outstanding on load balancing and running time.  
The rest of the paper is organized as follows: in Section 2, related work is reviewed; the proposed PCFMIS1, PCFMIS2 and PCFMIS3 are presented in Section 3; in Section 4, experimental results are displayed and discussed; conclusions are made in Section 5.
# Experimental and Numerical Investigation of the Mesoscale Size Effect in Notched Woven Composites
## 2 Materials and Methods
### Data-Driven Calibration of Material Properties
#### 2.3.2 Failure Properties  
Failure stresses were retrieved from the stresses in the FEM at the frame corresponding to a load equal to the failure load of the unnotched specimen. Following the material model, *MAT_54 of LS-Dyna, failure was modeled with the Hashin criterion, each tow being a unidirectional carbon fiber reinforced composite. Given the specific load case and the separation of materials, several hypotheses were made:  
1. The stresses causing failure are longitudinal stress \(\sigma_{yy}\) and shear stress \(\tau_{xy}\);
2. In vertical tows (\"fiber direction\"), failure occurs by fiber breakage. The failure criterion is thus: \[e_{f}^{2}=\left(\frac{\sigma_{yy}}{S_{1}}\right)^{2}-1\ \ \square\ \ \ e_{f}^{2}\geq 0\ \overset{\square}{\Leftrightarrow}\text{ failure}\] (2) where \(\sigma_{yy}\) is the longitudinal stress, \(S_{1}\) is the longitudinal tensile strength of the material (fiber direction), and \(e_{f}\) is the failure index in the fiber direction.
3. In horizontal tows (\"matrix direction\"), failure occurs by matrix failure due to tensile stresses combined with shear stresses. The failure criterion is thus: \[e_{m}^{2}=\left(\frac{\sigma_{yy}}{S_{2}}\right)^{2}+\left(\frac{\tau_{xy}}{ S_{12}}\right)^{2}-1\ \ \square\ \ \ e_{m}^{2}\geq 0\ \overset{\square}{ \Leftrightarrow}\text{ failure}\] (3) where \(\sigma_{yy}\) and \(\tau_{xy}\) are longitudinal and shear stresses, \(S_{2}\) is the transverse tensile strength of the material (matrix direction), \(S_{12}\) is the shear strength, and \(e_{m}\) is the failure index in the matrix direction.  
Three material strength parameters are thus needed: \(S_{1}\), \(S_{2}\), and \(S_{12}\). They were estimated by observation of the element-by-element, ply-by-ply maximum stress state in the numerical model.  
With a similar approach, the stress state of notched specimens was investigated in correspondence with the failure load. The stress concentration, the location of most loaded elements, and the material volume subjected to intensification were recovered through the validated mesoscale FEM. The size effect due to stress intensification and localization was therefore investigated.