esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
To enhance thermal stratification and improve energy storage efficiency in thermocline storage tanks, this study designed a system with porous obstacles installed inside the storage tank and investigated its stratification characteristics. Two types of discharge simulations were conducted based on different c-h ratios: one set involved simulations of traditional storage tanks, orifice plate obstacle storage tanks, and porous obstacle storage tanks with an initial c-h ratio of 0.38; the other set involved discharge simulations of storage tanks with different installation positions of porous obstacles with an initial c-h of 0. Through simulations analyzing thermocline thickness, exergy efficiency, and internal flow fields, the stratification effects of each storage tank were evaluated.  
The conclusions can be summarized as follows:  
1. Installing obstacles inside the tank can partially hinder the mixing of hot and cold water, thereby improving stratification. Among these, porous obstacles exhibit superior stratification performance. At a flow rate of 0.3 m/s, the thermocline thickness of traditional and orifice plate obstacle tanks is 42% and 14.3% higher, respectively, than that of tanks with porous obstacles. A porous obstacle can limit the disturbance range of the jet, ensuring that the fluid above is not subjected to intense turbulence.
2. The fluid below the porous obstacle experiences intense mixing. When the obstacle is near the inlet, it can reduce the mixing region of the fluid. Considering that the c-h ratio inside the storage tank varies in practice applications, installing the porous obstacle near the bottom is more reasonable, as it can adapt to different discharge and charge states.
3. Adding porous obstacles at both the top and bottom outlets can reduce the disturbance at the outlets, further improving stratification. At a flow rate of 0.3 m/s, the thermocline thickness is 17% higher when there is only one porous obstacle compared to when there are two porous obstacles.
4. When the flow rate is too low, axial thermal conduction within the storage tank becomes the main factor reducing energy storage efficiency. Therefore, selecting an appropriate flow rate is crucial when using the storage tank.  
Conceptualization, J.T and S.H.; methodology, J.T. and S.H.; software, J.T.; formal analysis, [PERSON]; supervision, data curation, J.T.; writing--original draft preparation, J.T.; writing--review and editing, J.T.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
This research appreciates the supervision and technical support from the relevant personnel.  
The authors declare no conflicts of interest.
# Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
## 4 Experiment
### Parameter Sensitivity Study
#### 4.5.2 Influence of the Number of Weight Matrices  
The weight matrices in the network help the model to better process and analyze the input data. Therefore, we discuss the effect of varying the number of weight matrices (denoted by the number of D) in the model weight library on the point cloud segmentation results. As shown in Table 6, the size of D increases exponentially, and when the number reaches 16, the segmentation metric mIoU appears to decrease, but FLOPs gradually increase. The test results show that when the number of weight matrices is too large, it brings redundancy and leads to heavy memory/computation overhead, which results in a rise in complexity FLOPs and a decrease in segmentation metric mIoU. Comparative analysis shows that the mIoU metric performs best when the weight matrix size is 16. As shown in Figure 6, it can be seen on the test set that the accuracy tends to increase when the weight matrix size is 16. When the weight matrix size is 32, there is a fluctuation. In comparison, it is not as stable as when the weight matrix size is 16.
## 2 Materials and Methods  
### Study Area and Measurements  
This research was conducted with the use of a multichannel GPR Stream C. The frequency of the acquired data is 600 MHz. The use of more than ten antennas with the same frequency shortens the duration of data acquisition or enables to increase the resolution of measurements by using smaller spacing between the antennas. The application of multi-antenna systems offers a possibility to obtain full 3D migration by recording a broadband signal, as outlined by authors [PERSON] and [PERSON] in [50]. Data are acquired on 32 channels, in a band of 1 m. Most of the images (23 images) are obtained from VV polarization (transverse to the direction of measurement), and nine are obtained from HH polarization (along the direction of measurement). For VV polarization, images are captured at the intervals of 4.5 cm, while for HH polarization, the interval is 10 cm. The placement of channels and the type of antennas (transmitter, receiver) together with information about polarization (VV, HH) for the Stream C ground-penetrating radar are presented in Figure 1.  
The samples recorded in a temporal window (during a specific interval) create a single ground-penetrating radar track, the so-called A-scan (Figure 1). It is a record of the changes in the amplitude of the returning electronic signal in time. Further tracks recorded along the line form the radargram referred to as the B-scan, which is the vertical cross-section of the ground medium. The process of the interpolation of several B-scan profiles enables to generate so-called C-scans that provide data about the correlations between the phenomena recorded in the B-scans. GPR data were obtained on the campus of the AGH University in Krakow, Poland.  
Figure 2 shows the location of the area where the GPR data were acquired. The orthophoto shows a horizontal cross-section (so-called C-scan) of the ground and the acquired underground utilities. In Figure 2, above the visible location of GPR data acquisition, three example C-scans generated at three different depths, i.e., 0.72 m, 0.94 m, and 1.50 m, are shown. The information contained in the C-scans is presented in the amplitude of A (V). In addition to the C-scan maps, vertical cross-sections of the ground known as B-scans are also shown.  
The GPR measurement was integrated with the GNSS-RTK receiver to ensure the accurate positioning and mapping of underground objects.
# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
###### Abstract  
In order to feed a growing global population projected to increase to 9 billion by 2050, food production will need to increase from its current level. The bulk of this growth will need to come from smallholder farmers who rely on generational knowledge in their farming practices and who live in locations where weather patterns and seasons are becoming less predictable due to climate change. The expansion of internet-connected devices is increasing opportunities to apply digital tools and services on smallholder farms, including monitoring soil and plants in horticulture, water quality in aquaculture, and ambient environments in greenhouses. In combination with other food security efforts, internet of things (IoT)-enabled precision smallholder farming has the potential to improve livelihoods and accelerate low- and middle-income countries' journey to self-reliance. Using a combination of interviews, surveys and site visits to gather information, this research presents a review of the current state of the IoT for on-farm measurement, cases of successful IoT implementation in low- and middle-income countries, challenges associated with implementing the IoT on smallholder farms, and recommendations for practitioners.  
internet of things; low-cost sensors; smallholder agriculture; global engineering +
Footnote †: journal: Journal of the Royal Society Interface B
## 2 Methods
### Performance Evaluation  
As an objective standard, the reference database is used for performance evaluation. If the signal result is positive and appears in the reference database, it is denoted as a true positive (_TP_), otherwise it is a false positive (_FP_). If the signal result is negative and appears in the reference database, it is denoted as a false negative (_FN_). Precision (_P_) is the proportion of true positive in all predicted positives, and can be defined as follows:  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
Recall (_R_), the proportion of true positive in all actual positives, is defined as follows:  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **The Target ADR** & **The Other ADRs** \\ \hline the target drug & A & B \\ the other drugs & C & D \\ \hline \hline \end{tabular}
\end{table}
Table 1: Two-by-two contingency table.  
F-Measure (\(F\)) is the arithmetic harmonic mean of Precision and Recall, is defined as follows:  
\[F=\frac{2\bullet P\bullet R}{P+R} \tag{4}\]  
The larger the \(F\) value, the higher the performance overall, and the more ideal the effect of minimizing data masking.
# Low-Profile High-Efficiency Transmitarray Antenna for Beamforming Applications
## 2 Design of 2D Partial H-Plane Waveguide Slot Array Antenna
the taper efficiency (\(\eta_{t}\)) of the partial H-plane waveguide slot array antenna can be higher than that of the conventional waveguide slot array antenna. To confirm this property, we calculate and compare their taper efficiency in the plane where the FSS is located using Equation (3), as follows:  
\[\eta_{t}=\frac{1}{S}\frac{\left|\int E(x,y)dS\right|^{2}}{\int\left|E(x,y) \right|^{2}dS} \tag{3}\]  
where \(S\) is the area of the FSS [24]. The tangential electric field of the area where the FSS is placed is calculated by the commercial ANSYS Electronics desktop software. The design area of the 8 \(\times\) 10 partial H-plane waveguide slot array antenna is nearly the same as that of the 6 \(\times\) 10 waveguide slot array antenna. The simulated taper efficiencies of the designed 8 \(\times\) 10 partial H-plane waveguide slot array antenna and the 6 \(\times\) 10 conventional waveguide slot array antenna are 77% and 71%, respectively.
# A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark
## 3 The SSiGraM Approach
### Optimizing Support Evaluation
#### 3.4.2 Pre-Search Pruning  
Because the input single large graph we consider is an undirected labeled graph, if a node and its neighbors have the same node label and edges between them also have the same label, it will bring redundant search space. This phenomenon can be common in graphs, especially when the graphs have few node labels and edge labels. For example, in Figure 5, \(G\) is the input graph and \(G_{1}\) a subgraph. The CSP search space of \(G_{1}\) is illustrated at the bottom. The assignments in dashed lines are added to the search space when iteratively building the CSP domain data of \(G_{1}\) whereas they are redundant space violating the first rule in Definition 10. Here, \(u_{1}\) is assigned twice to \(v_{1}\) and \(v_{3}\) (\(\{u_{1},u_{2},u_{1}\}\), \(\{u_{1},u_{3},u_{1}\}\), \(\{u_{1},u_{4},u_{1}\}\)). If this redundant search space is pruned before calculating the actual support, the search speed will be much accelerated.  
Let \(N\) and \(n\) be the number of nodes of input graph \(G\) and subgraph \(S\). Pre-search pruning will search for redundant space for every node of \(S\) between its neighbors in \(G\), the complexity of which is \(\mathcal{O}(n\cdot N^{3})\).
## 1 Introduction
Without the use of the epipolar constraint, the system model was directly combined with the camera model and multiple light plane equations, which avoids the error propagation due to the low calibration accuracy of the stereo vision model. Thus, the uniqueness of the grid feature retrieval is the only critical task in the calibration and reconstruction. We investigated the extraction method of grid stripe features. The calibration method, based on coplanar constraint, is proposed. The experimental setup consisted of a camera and an LED projector. Experiments were carried out to verify the accuracy of the proposed method. The remainder of this paper is organized as follows. In Section 2, we introduce the system model. And the extraction method of the grid stripe features is explained. Furthermore, the system calibration procedure is also elaborated in Section 2. Experimental results are presented in Section 3. We make a conclusion in Section 4.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 2 Related Work
### Reconstruction-Based Methods  
The reconstruction-based method trains an encoder and a decoder to reconstruct images with low dependence on pre-trained models. This method aims to train a reconstruction model that works well on positive samples but poorly on anomalous regions and achieves anomaly detection and localization by comparing the original image with the reconstructed image. Early studies used Autoencoders [13; 19; 20] for image reconstruction, whereas some methods employed a generative adversarial network [12; 14; 21] to obtain better reconstruction performance. However, there is a problem of overgeneralization, which can lead to an accurate reconstruction of anomalous regions. To address this issue, some researchers proposed a method based on image inpainting [15; 22; 23; 24], in which masks are used to remove parts of the original image, preventing the reconstruction of anomalous regions. However, for images with complex structures and irregular textures, excessive loss of the original information may limit the reconstruction ability and cause many false positives in normal regions.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 2 Methodology
### Mathematical Modeling  
Governing Equations  
The three basic principles governing fluid motion are mass, momentum, and energy conservation ([PERSON] and [PERSON] [20], [PERSON] et al. [21], and [PERSON] [22]).  
\[\text{Continuity}\ \frac{\partial\rho}{\partial t}+\
abla.(\rho.V)=0 \tag{1}\]  
\[\text{Momentum}\ \rho\frac{dv}{dt}=\
abla.\tau_{ij}-\
abla.q+\rho.F \tag{2}\]  
\[\text{Energy}\ \rho\frac{dE}{dt}+\rho(\
abla.V)=\frac{\partial Q}{\partial t}- \
abla.q+\Phi \tag{3}\]  
where:  
\(\rho\) is the density, V is the fluid velocity vector, \(\tau_{ij}\) is the viscous stress tensor, p is the pressure, F represents the body forces, q is the heat source term, e is the internal energy, t is the time, \(\Phi\) is the dissipation term, and \(\
abla.q\) is the heat loss by conductivity. [PERSON]'s law for heat transfer by conduction can be used to describe q, as follows:  
\[q=-k\
abla T \tag{4}\]  
\[\
abla.V=0 \tag{5}\]  
\[\rho\frac{dv}{dt}=\mu\
abla^{2}V-\
abla p+\rho F \tag{6}\]  
\[\rho\frac{de}{dt}=\frac{\partial Q}{\partial t}k\
abla^{2}T+\Phi \tag{7}\]  
The formula for thermal conductivity (k) involves temperature (T). One or more terms might be negligible, depending on the physics governing fluid motion.  
In the simulations, the PISO algorithm was used for pressure velocity coupling to solve the equations for the flow field variable values in each computational cell. The discretization scheme used was a first-order upwind scheme, except for the pressure field, where the second-order upwind scheme was applied. The transient formulation utilized is bounded as second-order implicit.
# Investigation on the Application of Artificial Intelligence in Prostodontics
## 2 Methodology  
With the advancement in the field of technology, AI plays an important role not only in our day-to-day life but also in the field of dentistry, as well as in enhancing prosthetic rehabilitation. The present review was conducted with the same purpose of exploring and identifying AI use in prosthodonics. This review was filed and registered in the International Database to Register Systematic Reviews (INPLASY) dated 24 December 2022. The DOI registration no. is 10.37766/inplay 2022.12.0096. It followed the PRISMA 2020 [33,34] and Cochrane criteria [35]. Figure 1 represents the flow chart depicting the methodology used in this review.  
This systematic review followed PRISMA guidelines [33]. For the PICO question, the following statements were formulated:  
1. What kinds of AI methods are currently being employed in the prosthodonic field?
2. How does AI enhance clinical decision-making and outcomes in prosthodontics?
3. What are the current clinical uses of AI in prosthodontics, and how well does it work as a diagnostic tool during the determination of the type of prosthesis required for a particular patient?  
P--population: images of patients' and simulators' faces in two and three dimensions, as well as a variety of radiographs used during the prosthodonic rehabilitation procedure (including periapical, OPG, and cone-beam computed tomography). I--intervention: machine learning, natural language processing, and robots are used for the diagnosis, management, and prognosis assessment of prosthodonic procedures. C--comparison: image analysis, testing models, and automation algorithms involved in prosthodonic steps while using the digitalized method of prosthetic rehabilitation. O--outcome: several areas of prosthetic dentistry may benefit from the use of artificial intelligence, including performance, accuracy, precision, sensitivity, and clinical decision support (CDS).  
Study design: English-language publications of both observational research (such as case-control and cohort studies) and experimental research (such as randomized controlled trials) were included in this analysis, which was basically concerned with the prosthodonic workflow.  
Figure 1: The flow of review methodology.
# Experimental Investigation of Anisotropic Diffusion Applied in Ghost Imaging Reconstruction
## 3 Experiments and Discussion
The PSNR of the AD-IPGI improved significantly while the PSNR of the IPGI has not changed much with the increased number of measurements. The PSNRs of AD-IPGI are 2.5, 2.6, 3.6 and 4.9 dB higher than those of the IPGI image results for 500, 1000, 1500, and 2000 measurements, respectively. The above experiments have shown that the method can further improve the imaging quality and both the visual effect and PSNR of the reconstructed image are better than those of IPGI; at the same time, the edge details of the image are well preserved. Figure 4: Experimental results for the imaging object “Zhong” obtained by iterative pseudo-inverse ghost imaging (IPGI) and anisotropic diffusion -IPGI (AD-IPGI) with 600, 800, 1000 and 1200 measurements. Figure 5: The PSNR curves of the image “Zhong”. Figure 6: Experimental results for the imaging object “Slits” obtained by the IPGI and AD-IPGI imaging processes, with 500, 1000, 1500 and 2000 measurements. To explain why we choose anisotropic diffusion instead of other filtering methods, the common filtering methods median filter, mean filter and Gaussian filter are combined with IPGI separately to smooth the images used to construct the noise interference term before each iterative operation in the third set of experiments. These three methods are respectively abbreviated as Mean-IPGI, Median-IPGI and Gaussian-IPGI. The experimental results are shown in Figures 8 and 9. Compared with IPGI, the reconstruction results of these four methods are improved to various degrees, which again proves the importance of image denoising before each iteration. As Figures 8 and 9 show, Mean-IPGI can partially filter the background noise, but at the same time, it makes the image details blurred. Median-IPGI can protect the sharp edges of the image, but the denoising effect is poor. Compared with Mean-IPGI and Median-IPGI, the reconstruction quality of Gaussian-IPGI is much better and the details are well preserved. However, there is always a gap between the Gaussian-IPGI and AD-IPGI on denoising capability. Moreover, the three methods above are only applicable to certain types of noise, while anisotropic diffusion has a more general usability. The PSNR curves in Figure 8 are consistent with the analysis of visual effect.
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Fuzzy Transparency
#### 6.8.3 Multidimensional Class Evaluation  
Based on Table 12, these papers focus predominantly on general concept drift, with additional emphasis on incremental and sudden concept drift. The concentration on general and incremental drift emphasizes how fuzzy logic naturally accommodates gradual shifts through linguistic rule adaptation and membership function updates. The application contexts identified are mainly IoT, healthcare, industrial processes, and general data mining. The prevalence of IoT and healthcare applications suggests fuzzy methods' strength in fields where human-readable rules and flexible, linguistic interpretability are crucial for operational understanding and decision-making. This class exhibits a balanced approach to the interpretability scope, with local explanations appearing slightly more frequently. This likely results from fuzzy systems' inherent linguistic interpretability, naturally suited to instance-level explanations that stakeholders can immediately understand in domain-specific terms. The audience consistently comprises data scientists, reflecting that, although fuzzy logic generates intuitive and understandable linguistic rules, the development, tuning, and validation of these rules typically demand expert analytical knowledge. Explicit interpretability metrics are not reported within this group. Interpretability implicitly arises from linguistic transparency and rule-based explanations, without standardized quantitative evaluations. Regarding the learning mode, online and hybrid approaches dominate, demonstrating fuzzy logic's natural adaptability to real-time environments. This aligns well with scenarios requiring continuous rule refinement and membership function updates as data streams evolve.