esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 6 Prototyping and Testing  
Assessing the performance of the proposed assistive maintenance framework first required the realization of a prototype. The employed AR device (Microsoft HoloLens) was installed on the bottom end of a safety helmet (see Figure 4a) of the kind that is usually adopted for on-field maintenance operations. The helmet was also equipped with a FLIR AX8 thermal camera, calibrated along with HoloLens' depth sensor as a two-view stereo system (see Section 5.1).  
Microsoft HoloLens embeds a ToF depth sensor with two infrared illuminators, namely _short-throw_ (up to \(\sim\)1 m) and _long-throw_ (up to \(\sim\)3 m), operating on different fields of view and acquiring depth and reflectivity information simultaneously with a resolution of 450 \(\times\) 448 pixels (see Figure 5 for sample frames).  
FLIR AX8 thermal camera acquires 80 \(\times\) 60 thermal images with a temperature range from \(-\)10 \({}^{\circ}\)C to 150 \({}^{\circ}\)C and a field of view (FOV) of 48\({}^{\circ}\)\(\times\) 37\({}^{\circ}\); it acquires standard RGB images as well with the same FOV and a resolution of 640 \(\times\) 480 pixels (see Figure 6 for sample frames).  
Figure 4: Prototype helmet equipped with AR smartglasses and thermal camera (**a**); thermal image combined with the warning hologram reprojected in the HMD display (**b**).  
Figure 5: Example frames from HoloLens’ ToF depth camera. The sensor acquires 450 \(\times\) 448 depth and reflectivity maps, in short-throw (ST), i.e., up to \(\sim\)1 m, and long-throw (LT), i.e., up to \(\sim\)3 m.
## 3 Instruments Used and Data Capturing
### Instruments Used  
Modern miniaturized and easily transportable laser scanners were used (Figure 4). The Leica BLK360 miniaturized laser scanner with a small carbon tripod and built-in camera was a good choice for the precise documentation of valuable objects. Nevertheless, even with this device, the documentation takes a relatively long time (up to several hours of measurement is typical for an object with dozens of scanner positions). BLK360 has a precision 4 mm on 10 m and a scan rate to 360,000 points per second. One scan takes (with the capturing of photos) about six minutes.  
Due to the tense itinerary and the often-complex agreement with the local citizens, the ZEB-REVO mobile personal laser scanner (PLS) was used successfully. It is a laser handheld scanner equipped with an inertial unit, which uses SLAM (Simultaneous Localization and Mapping) technology to join data and mapping [29; 30; 31]. Although the ZEB-REVO does not have the millimeter accuracy that conventional laser scanners have, it excels in speed and mobility of measurements. For buildings that are made of unfired bricks, partially damaged, very irregular, and full of corridors with poor lighting, it was an excellent choice. A completely regular covered building with an area of more than one hectare could be documented in about one hour. The device is slowly carried by the operator, starting and ending at the same place. The system keeps navigation for up to 50 min without further intervention or information. Incremental errors are divided in the point cloud during postprocessing because the measuring starts and ends at the same place. The difference found is divided into a whole trajectory. Data processing is fully automatic, with accuracy in this case sufficient, reaching 1-3 cm in a position with a range of up to 30 m (scan rate: 43 thousand points per second). The amount of data from the two above-mentioned scanners is also significantly different. A typical single scan from the BLK360 device has 600 MB; data from a 40-min walk through an object with the ZEB-REVO has about 300 MB. Sixty-eight scans with the BLK360 scanner and 21 scans with the ZEB-REVO scanner were performed on 10 objects in five days.  
A standard Canon 450 SLR or simple a smartphone was used for close-range photogrammetry in some cases (details, rooms, artifacts).
# Liquid Nanofilms' Condensation Inside a Heat Exchanger by Mixed Convection
## 5 Conclusions  
The present work focused on the numerical investigation of the condensation of falling liquid film containing different types of nanoparticles with low-volume fraction. The nanofluid film fell inside a heat exchanger by mixed convection. The heat exchanger was composed of two parallel vertical plates. One of the plates was wetted and heated, while the other plate was isothermal and dry. The impact of the volume fraction and dispersion of several types of nanoparticles in the film on the heat transfer, mass transfer, and the condensation process was examined in this work.  
The conclusions are as follows:  
1. The dispersion of the nanoparticles improved the mass and heat exchange during film condensation.
2. The dispersion of the nanoparticles improved the film condensation.
3. The mass and heat exchange were improved by using the Cu-water nanofluid compared to the Al-water nanofluid.
4. Compared to the Al nanoparticles and pure water, the liquid film condensation was considerably improved in the case of Cu nanoparticles.
5. It was observed that an increase in the inlet gas mass fraction improved the water film condensation.
6. An increase in the thermal flux cooling improved the water film condensation.
7. An increase in the inlet gas velocity worsened the water film condensation.  
**Author Contributions:** Conceptualisation, A.N.; Formal analysis, A.N.; Writing--original draft, A.N.; Conceptualisation, A.S.A.-G.; Writing--review and editing, A.S.A.-G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
c   mass fraction for water vapour  
cpv   heat capacity of water vapour [] kg\({}^{-1}\) K\({}^{-1}\)]  
cpa   heat capacity of water dry air [] kg\({}^{-1}\) K\({}^{-1}\)]  
D   mass diffusivity [m\({}^{2}\) s\({}^{-1}\)]  
D\({}_{\rm B}\)   Brownian coefficient  
D\({}_{\rm T}\)   Thermal diffusion coefficient  
L\({}_{\rm v}\)   latent heat of water evaporation [] kg\({}^{-1}\)]  
P   pressure [N m\({}^{-2}\)]  
T   temperature [K]  
g   gravitational acceleration (m s\({}^{-2}\))  
u   axial velocity [m s\({}^{-1}\)]  
v   transverse velocity [m s\({}^{-1}\)]  
x   coordinate in the axial direction [m]  
y   coordinate in the transverse direction [m]  
k\({}_{\rm b}\)   Boltzmann's constant  
Greek symbols  
d\({}_{\rm P}\)   nanoparticles diameter  
\(\varphi\)   volume fraction of nanoparticles  
\(\lambda\)   thermal conductivity [W m\({}^{-1}\) K\({}^{-1}\)]  
\(\mu\)   dynamic viscosity [kg m\({}^{-1}\) s\({}^{-1}\)]  
\(\delta\)   thickness of liquid film (m)
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## Abbreviations  
The following abbreviations are used in this manuscript:  
AJAX  
Asynchronous Javascript and XML  
AOI  
Area of Interest  
CAPF  
Customized Automatic Processing Framework  
CD  
Compact disc  
ESA  
European Space Agency  
EUMETSAT  
European Organisation for the Exploitation of Meteorological Satellites  
FTP  
File Transfer Protocol  
GISTDA  
Geo-Informatics and Space Technology Development Agency  
GloVis USGS  
Global Visualization Viewer  
GMF  
Geophysical Model Function  
GUI  
Graphical User Interface  
HDF  
Hierarchical Data Format  
HH  
Antenna that transmits and receives with an H-polarization  
HY-2A  
Hai Yang 2A Satellite  
IDL  
Interactive Data Language  
JPEG  
Joint Photographic Experts Group  
ISON  
JavaScript Object Notation  
NASA  
National Aeronautics and Space Administration  
NetCDF  
Network Common Data Format  
NSOAS  
National Satellite Ocean Application Service  
RADI  
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences  
REST  
Representational State Transfer  
SOA  
State Oceanic Administration  
TIFF  
Tagged Image File Format  
USGS  
United States Geological Survey  
UCAR  
University Corporation for Atmospheric Research  
VV  
Antenna that transmits and receives with a V-polarization  
XML  
eXtensible Markup Language
## 4 Results and Discussion
### Gradual Microscopic Contact Behaviour of the Main Skeleton
#### 4.1.3 Contribution Rate of Coarse Aggregate Particle Size to Contact Points  
The rutting resistance of asphalt mixtures is closely related to the size of coarse aggregates constituting the main skeleton. The contribution of coarse aggregates with different particle sizes to the contact points is an important factor for determining the stability of the main skeleton. The contact analysis is performed using iPas software; the coordination number of each coarse aggregate can be obtained by statistical analysis after determining the number and particle size information of each coarse aggregate. The contact distribution statistics of coarse aggregates with different sizes at different loading stages are summarised in Table 8, in which the bold italic numbers indicate the peak value positions. In this table, 2.36 mm 2.36 mm indicates that the coarse aggregates with particle sizes of 2.36-4.75 mm come into contact with 2.36-4.75 mm coarse aggregates; 2.36 mm 4.75 mm indicates that the coarse aggregates with particle sizes of 2.36-4.75 mm come into contact with 4.75-9.5 mm coarse aggregates; the rest may be deduced by analogy. The data summarised in Table 8 indicate the following. (1) In 70# asphalt AC-13C and rock asphalt AC-13C, the maximum quantities of contact points among coarse aggregates with different particle sizes are found when the particles are 2.36 4.75 mm, 4.75 mm, and 2.36 4.75 mm; and 2.36 mm; the peak value of contact points is found in coarse aggregates that are 2.36 4.75 mm, and the contribution rate of coarse aggregates with particle sizes larger than 4.75 mm to the number of contact points is less than 60%. In SMA-13, the maximum quantities of contact points among coarse aggregates are found when the particles are 4.75 mm, 4.75 mm, 4.75 9.5 mm, and 2.36 4.75 mm; the peak value of contact points is found in coarse aggregates that are 4.75 mm, and the contribution rate of coarse aggregates with particle sizes larger than 4.75 mm to the number of contact points reaches 80%. In AC-13, although aggregates with particle sizes of 2.36-4.75 mm can effectively increase the quantity of contact points, the mixture's rutting resistance is relatively poor because of the smaller particle sizes and the insufficient stability of the main skeleton. When particle sizes are larger than 4.75 mm, especially when the contribution rate of coarse aggregates larger than 9.5 mm to the number of contact points is high, the coarse aggregates can form a relatively steady main skeleton structure. Therefore, coarse aggregates with particle sizes larger than 4.75 mm provide the largest contribution to the formation of a steady main skeleton. (2) The total number of contact points in the coarse aggregates increases with the increase in loading time. The quantity of contact points that involves coarse aggregates with particle sizes less than
# Assessing Efficiency in Artificial Neural Networks
## 2 Approach
### Neural Efficiency  
Neural efficiency is defined as the utilization of state space, and can be measured by entropic efficiency. If all possible states are recorded for data fed into the network, then the probability, \(p\), of a state occurring can be used to calculate Shannon's entropy, \(E_{l}\), of network layer \(l\):  
\[E_{l}=-\sum_{i=0}^{i=N_{l}}p_{i}*log_{2}(p_{i}) \tag{1}\]  
Intuitively, \(E_{l}\) is an estimation of the minimum number of neurons required to encode the information exported by the neural layer if the output information could be perfectly encoded. The maximum theoretical entropy of the layer will occur when all states occur the same number of times, and the entropy value will be equal to the number of neurons in the layer, \(N_{l}\). Neural efficiency, \(\eta_{l}\), can then be defined as the entropy of the observed states (\(E_{l}\)) relative to the maximum entropy (\(N_{l}\)):  
\[\eta_{l}=\frac{E_{l}}{N_{l}} \tag{2}\]  
Thus, neural efficiency, \(\eta_{l}\), is defined as state space efficiency using Shannon's entropy with a range of 0-1. Neural efficiency values close to zero are likely to have more neurons than needed to process the information in the layer, while neuron layers with neural efficiency close to one are making maximum usage of the available state space. Alternatively, very high neural efficiency could also mean too few neurons are in the layer.
## 3 Proposed Modified Sine Cosine Algorithm (MSCA)  
### The Theory of Sine Cosine Algorithm Adjustment  
Here, the formation of the proposed MSCA algorithm, its implementation, and the working principles are explained in detail. For any optimization algorithm to achieve a global optimum, a proper modification should show a high convergence rate to the true global minimum even at a high number of dimensions [49]. The SCA has been tested using LHS as an efficient sampling method that is widely used in computer experiments. LHS sampling was proven in [45], in which this method improved various optimization algorithms efficiency.  
The modification involves an additional information exchange to affect the performance of an algorithm, as in the case of LHS and SCA, which resulted in a stagnation effect and convergence complexity. The iterative modification concept is used to generate the set of random solutions. In [48], LHS was generally described as a spectrum of stratified sampling designs known as a partially stratified sample design. LHS represents the extremes of the partially stratified sample spectrum. The variance of partially stratified sample estimates is derived along with some asymptotic properties. The design of the partially stratified sample demonstrates its ability to reduce variance associated with variable interactions, whereas LHS reduces variance associated with the main effects. The SCA can be enhanced through the implementation of LHS in its optimizer. Firstly, the input of the SCA in an \(n\)-dimensional hypercube with lower and upper bounds of \(X\) is initialized for a random set solution. To obtain a set of random solutions for \(H\) sampling scale as an output, there are three steps involved, which are generated by partitioning the sampling matrix and sampling point from each selected hypercube. This output is then used as the replacement for a new set of SCA solutions.  
Given enough computation, the SCA will always find the optimum, but a fast convergence cannot be guaranteed since the search relies entirely on random walks. Presented here for the first time, one modification to the method is made with the aim of increasing the convergence rate and the minimum optimal solution, thus ensuring the method becomes more practical for a wide range of applications without losing the attractive features of its original method.
## 4 Discussion
Coarse Gaussian SVM is a Gaussian radial basis function (RBF) that was used to improve SVM classification when the data were not linearly separable. The kernel approach enables SVM to find a hyperplane in the kernel space, enabling non-linear separation within the feature space to be viable. The best kernel width is determined by balancing underfitting and overfitting loss. In the remote sensing field, SVM is particularly appealing for managing limited training data effectively and having higher classification accuracy than the conventional classification [49]. The basic principle that benefits SVM comparedto conventional classification is the structural risk minimization learning process. SVM minimizes the classification error on unseen data without making prior assumptions about the probability distribution of data [50]. In contrast, conventional classification, such as maximum likelihood, usually assumes that data distribution is a priority. In recent studies, SVM was frequently used to classify healthily and _G. boninense_ infected oil palm with high accuracy of 100% [35], 94.8% [36], 91% [51], and 89% [32], which confirmed the suitability of SVM classifiers in oil palm disease classification. Furthermore, machine learning classification models typically produced better results compared to conventional classification models, such in the study conducted by [PERSON] et al. [34] that produced a net accuracy of 82% and kappa coefficient of 0.73 using maximum likelihood to classify multiple severities of _G. boninense_ infection in oil palm seedlings. Furthermore, this finding was not only consistent with previous research conducted for BSR disease in mature oil palm trees, but also with other diseases such as identifying cotton canopy infected with _Verticillium_ wilt, apple scab disease caused by _Venturia inaequalis_, and corn kernels infected with fungi.
## 4 Analysis of Different Parameters  
### Pedestrian Load Model  
During the complete gait cycle of pedestrian walking, the center of gravity fluctuates up and down, and the force acting on the evacuation platform changes constantly. The single-person continuous load model mainly includes the deterministic and random load models. The deterministic load model is commonly used in engineering to describe the walking load [45], as shown in Equation (1):  
\[F_{p}(t)=G\Bigg{[}1+\sum_{i=1}^{n}\beta_{i}\sin(2i\pi f_{p}t-\Phi_{i})\Bigg{]} \tag{1}\]  
where \(G\) is the pedestrian weight, \(n\) is the order of the function, \(t\) is the time, \(f_{p}\) is the walking frequency, \(\beta_{i}\) is the coefficient of the \(i\)th order Fourier series, and \(\Phi_{i}\) is the phase angle of the \(i\)th order load. \(\beta_{1}\) = 0.2611,\(f_{p}\) - 0.2109, \(\beta_{2}\) = 0.09, \(\beta_{3}\) = 0.077, \(\Phi_{1}\) = \(\Phi_{2}\) = \(\Phi_{3}\) = 0.  
According to relevant research results [29, 46, 47, 48], this paper selected the single-person load step frequency of slow walking as 1.0 Hz, normal walking as 2.0 Hz, fast walking or running as 3.0 Hz, and the load walking curve is shown in Figure 11.  
Figure 10: Comfort distribution of human-induced vibration.
## 4 Performance Evaluation: Results
### Real Data-Logarithmic Conversion  
The adoption of the a logarithmic conversion has the effect of significantly reducing the variability of the rewards when compared to the linear conversion analyzed in Section 4.3. This, in turn, has a dramatic impact on the performance of the MLI algorithm. Results presented in Figures 11b-13b show in fact that the MLI algorithm leads to a regret in line with the POKER and \(\varepsilon\)-decreasing algorithms, in particular for \(T_{U}/T_{M}>1\). Oppositely, algorithms that are more conservative in exploiting/using, such as UCB1 and muUCB1, are not capable of taking full advantage of the low variance in rewards; in this case as well, however, the performance of the muUCB1 algorithm improves as \(T_{U}/T_{M}\) increases.
# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 5 Conclusions  
After analyzing real-world electronic health record data, it has been revealed that a patient could be diagnosed with having more than one disease simultaneously. Therefore, to suggest a list of possible diseases, the task of classifying patients is transferred into a multi-label learning task. However, the class imbalance issue is a challenge for multi-label learning approaches. COCOA is a typical multi-label learning approach aimed at leveraging label correlation and exploring class imbalance. To improve the performance of COCOA, a regularized ensemble approach integrated into multi-class classification process of COCOA named as COCOA-RE was presented in this paper. Considering the class imbalance problem, this method leverages a regularized ensemble method to explore disease correlations and integrates the correlations among diseases in the multi-label learning process. To provide disease diagnosis, COCOA-RE learns from the available laboratory test results and essential information of patients and produces a multi-label predictive model. Experimental results validated the effectiveness of the proposed multi-label learning approach, and the proposed approach was implemented in a developed prototype system that can assist clinicians to work more efficiently. The features extracted from laboratory test reports and essential information of patients were also considered in this paper. In our further works, features selected from more sources like textual and monitoring reports will be integrated to construct a more comprehensive profile of patients. To ensure the efficiency of the decision support system for medical diagnosis, an effective feature selection method should be used to reduce the increasing number of integrated features. In addition, multi-label approaches would process large-scale clinical data in a slow rapid, which is required to develop a more efficient multi-label learning method. [PERSON] and [PERSON] conceived the algorithm, prepared the datasets, and wrote the manuscript. [PERSON], and [PERSON] designed, performed, and analyzed the experiments. [PERSON] and [PERSON] revised the manuscript. All authors read and approved the final manuscript. This research was funded by the National Natural Science Foundation of China (Grant #: 61462022 and Grant #: 71161007), Major Science and Technology Project of Hainan province (Grant #: ZDKJ2016015), Natural Science Foundation of Hainan province (Grant#:617062), and Higher Education Reform Key Project of Hainan province (Hnjg2017 ZD-1). The authors would like to thank the editor and anonymous referees for the constructive comments in improving the contents and presentation of this paper. The authors declare no conflict of interest.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 2 Related Works
### Deep Learning-Based Point Cloud Completion Methods
#### 2.2.2 Transformer-Based Methods  
Transformer-based methods can determine how a point cloud's local features and general structural characteristics relate to one another. Pointr [26] treats point cloud completion as a set-to-set transformation task by representing a point cloud as a set of unordered point proxies, employing an Encoder-Decoder structure to generate a point cloud of missing regions, and designing a geometry-aware block to explicitly model the geometric relationships. PMP-Net++ [27] introduces the Transformer network on the basis of PMP-Net [28], which makes the point moving path conform to the target structural features through the relationship between the overall structural features and local features of the point cloud, and significantly improves the performance of the point cloud complementation. However, compared to other methods, the Transformer-based method has a lot of parameters, needs expensive experimental equipment, and its attention process is hard to understand.