esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation of Optimal Testing Conditions
The distributions of BRDFss, BRDFsp, and BRDFps (corresponding to (i), (ii), and (iii) in Figures 5 and 6, respectively) for dirty particles above the surface and SSBD defects do not change significantly for different incidence angles, and all of them show symmetric distributions. On the other hand, BRDFpp (iv) exhibits an asymmetric distribution. The reason for this, as can be seen from Equations (12) and (14), is that the three polarization factors Jss, Jsp, and Jps in the Jones matrix have the same function term \(\phi_{s}\), so the scattering of the two kinds of particles will be superimposed in the scattering component measurement, resulting in the pseudo-color maps of the three polarization factors, Jss, Jsp, and Jps, which are almost the same. In contrast, the polarization factor Jpp lacks the common functional term \(\phi_{s}\) as described above, and its respective expression is significantly different. Therefore, it is theoretically possible to characterize the optimal detection conditions for dirty particles and SSBD above the surface of an optical element by using the relationship between the BRDFpp and the refractive index, the angle of incidence, the scattering angle, and the azimuthal angle. Following this, the BRDFpp term was selected to solve for the optimal detection conditions [18]. The simulation results of the optimal detection conditions are shown in Figure 7 when the size of the dirty particles \(a=5\) nm, the refractive index of the particles \(n_{1}=1.457\), and the refractive index of the material \(n_{2}=1.5151\), and \(\lambda=633\) nm. As \(\theta_{i}\) increases, the maximum BRDFpp corresponds to \(\theta_{s}\), which gradually increases until the maximum value is obtained when \(\theta_{s}\) is about \(70^{\circ}\) and \(\phi_{s}=180^{\circ}\), as shown in Figure 7a. Based on Figure 7b, the amplitude of BRDFpp at \(\theta_{i}=70^{\circ}\) is changed with an increase in particle size, but the trend of the scattering distribution remains unaffected, and the maximum BRDFpp value is reached at \(\theta_{i}=\theta_{s}=70^{\circ}\). Similarly, analyzing surface roughness, pockmarks, and SSBD determined the optimal detection conditions for all types of defects on the optical element surface as \(\theta_{i}=\theta_{s}=70^{\circ},\phi_{s}=\pi\).
## 2 Experimental Section  
Figure 1 shows the experimental setup. A part of the NIR pulse emerging from a Ti:sapphire regenerative amplifier (800 nm, 35 fs, 4 mJ, 1 kHz, Legend Elite-USP, Coherent Inc., Santa Clara, CA, USA) was used as the pump source for an optical parametric amplifier (OPA, OPerASolo, Coherent Inc., Santa Clara, CA, USA). The OPA generated a NIR pulse at 1200 nm, whose frequency separation from the Ti:sapphire amplifier (800 nm) was adjusted to the vibrational Raman shift frequency of molecular hydrogen (4155 cm\({}^{-1}\)). The output of the OPA (pump 1; P1) was spatially combined with the remaining part of the output beam of the regenerative amplifier at 800 nm (pump 2; P2). The beam was focused with an off-axis parabolic mirror (with a focal length of 655 mm) into a gas cell filled with hydrogen or a fused-silica hollow fiber (core diameter, 320 \(\upmu\)m; length, 600 mm) placed inside a gas cell (length, 1 m) filled with hydrogen (hereafter denoted as a hollow fiber chamber). The core diameter of the hollow fiber of 320 \(\upmu\)m was chosen for keeping the same focusing condition as for the gas cell, and the fiber length of 600 mm was the maximum possible length for the hollow fiber chamber. The gas cell and the hollow fiber chamber were equipped with 0.5-mm-thick windows made of fused silica. The time delay between the two pulses (P1 and P2) was optimized to obtain the highest energy in the highest-order anti-Stokes Raman emission generated at a gas pressure of 0.4 atm. The spectra of the output beams from the gas cell and hollow fiber chamber were measured using a multichannel spectrometer (Maya2000 pro, Ocean Optics, Dunedin, FL, USA). The spectral response was calibrated using a deuterium-halogen light source over a wavelength range of 220-900 nm.
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 3 System Design
### System Model  
We follow the existing works on the combination of edge computing and sensor networks. The system model is shown in Figure 1. In the heterogeneous sensor networks, various different kinds of applications and computational tasks co-exist. In the area of the sensor networks, a number of edge computing servers are deployed to provide computing services for the underlying sensor nodes. The communication range of the edge servers is much larger than the communication range of sensor nodes as the edge servers have no battery limit. The radios on edge servers can be set to the highest power level while the sensor nodes need to reduce the power level to save energy.  
**Task model.** We assume a one-to-many model of data transmissions in sensor networks [30]. In sensor networks, maintenance is one of the most important problems, where information needs to be distributed from one node to a number of different other nodes. We focus on such scenarios as shown in Figure 1. Node \(s\) intends to transmit controlling packets to all destination nodes, which are marked as \(d\).  
Figure 1: The system model of edge-aided sensor networks.  
**Data transmission model.** In traditional sensor networks, node \(s\) will deliver the packets in a multi-hop manner and eventually cover all the destination nodes. In the edge-aided sensor networks, when the packets propagate to the area of edge servers, we may employ the edge servers for data transmissions such that many hops can be saved. For example, in the figure, if we use the edge server (in the middle) to forward the packet, four destination nodes are covered immediately and at least four relay transmissions are saved for each packet.  
**Retransmission model.** We use the Acknowledgment-based (ACK-based) retransmission mechanism. When a sensor node receives a packet, it will return a sequenced ACK to its sender. The sender will retransmit the packet if the ACK is timed out. For poor wireless links, the edge servers will transmit packets to the target sensors. The key challenge is that the communication range of sensor networks is highly limited compared to the communication range of edge servers. The edge servers may not be aware of the poor links with multiple retransmissions. We detail our solution to this problem in Section 3.4.
# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 3 Detection of CISs Based on Traditional Machine Learning Methods
### Feature Extraction  
CT sign recognition approaches based on traditional machine learning methods mainly adopt manual design of features or descriptors. The manually designed features are defined by experts using professional medical knowledge to describe lung tissues and are more commonly used, such as histogram of oriented gradients (HOG), local binary pattern (LBP), local intensity order pattern (LIOP), dense scale-invariant feature transform (SIFT), global context feature, graphics feature, texture feature, wavelet feature, and geometric shape feature.  
Due of the diversity of CISs in lung diseases, previous proposed feature extraction approaches incorporated various aspects to characterize the area of interest. In 2012, [PERSON] [21] combined the visual word bags of HOG and LBP to depict the suspected lesion locations in lung CT images for sign classification and identification, and they conducted five-fold cross-validation on the LISS dataset with an average recognition accuracy of 98.8%. This approach confirms that combining HOG and LBP characteristics for sign identification is superior to employing individual HOG (95.7%) or LBP (90.4%) features. In view of the efficiency of this technique, some researchers adapted this notion to the field of image retrieval and achieved the retrieval of nine frequent indicators of lung diseases with good performance [22; 23].  
Although fusing numerous features can increase the performance of the model to a certain amount, it is unnecessary to blindly add features, which may not improve the performance of the model much and will cause a waste of computational resources. In 2014, [PERSON] [24] added CT value histogram (CVH) characteristics on the basis of [21]. However, the identification accuracy was only 80.26% and the specificity was just 70.22%. Therefore, the key to enhancing the performance of the model is to analyze the association between the characteristics and to adopt various features in an informed manner.  
A comparison of the above methods reveals that whether using a single feature or fusing multiple features, the above methods mainly extract low-level semantic features such as shape and grayscale, which are not able to describe the key information of a large number of sophisticated clinical images, and thus cannot meet the requirements of high accuracy, a variety of sign recognition, and classification in clinical applications.
# Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis
## 5 Profiling MPI Applications
### Load Imbalance Estimation  
We have found several approaches to estimating the load imbalance of the MPI application: heuristic, analytic, and pattern-based.
## 2 Materials and Methods
### Predicting Injuries  
This section presents our approach to predict injuries based on a dataset containing both external load and internal load features. As external load features (\(GPS\)) have already shown good results for injury prediction in soccer [19], this study aims to unveil the predictive power of internal load features (questionnaires) relatively to external load ones. Several classifiers were optimised and compared in terms of predictive performance. Two prediction perspectives (horizons) were considered in this study: injury at 1 week and injury at 1 month.  
The models thus constructed can therefore serve as an alert for any new training session for which the model would predict an injury and can be used as an aid to training planning and adjustment.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Type of Data** & **Name** & **Definition** \\ \hline \multirow{4}{*}{personal features} & age & age at the beginning of the 2017 season \\  & weight & weight at the beginning of the 2017 season \\  & height & height at the beginning of the 2017 season \\  & \(BMI\) & Body Mass Index \\  & role & role \\ \hline \multirow{4}{*}{_GPS_} & tot\_dur & total duration \\  & tot\_dist & total distance covered \\  & tot\_PL & total Player Load \\  & vel\_B1 & distance covered between 0 and 1 km/h \\  & vel\_B2 & distance covered between 0 and 6 km/h \\  & vel\_B3 & distance covered between 6 and 15 km/h \\  & vel\_B4 & distance covered between 15 and 20 km/h \\  & vel\_B5 & distance covered between 20 and 25 km/h \\  & vel\_B6 & distance covered at more than 25 km/h \\  & acc\_B2 & number of accelerations above 2 m/s\({}^{2}\) \\  & acc\_B3 & number of decelerations above \(-\)2m/s\({}^{2}\) \\  & max\_vel & maximum speed in m/s \\ \hline \multirow{4}{*}{pre-session questionnaire} & sleep\_qual (VAS10) & sleep quality \\  & fatigue (VAS10) & fatigue state of the player \\  & shape (VAS10) & being in good shape \\ \cline{1-1}  & mood (VAS10) & actual mood of the player \\ \cline{1-1}  & pain (yes or no) & perceived pain \\ \cline{1-1}  & inj\_worry (if pain, VAS10) & worry in relation to pain \\ \cline{1-1}  & ill & sick or not \\ \hline \multirow{4}{*}{post-session questionnaire} & \(RPE\) (VAS10) & rating of perceived exertion of the session \\ \cline{1-1}  & satisfaction (VAS10) & satisfaction during his performance \\ \cline{1-1}  & pleasure (VAS10) & pleasure during the session \\ \hline \hline \end{tabular}
\end{table}
Table 1: Set of features used in the study. GPS is corresponding to features of external loads while questionnaires (based on a 10 cm visual analog scale, VAS10) are dealing with internal loads.  
Moreover, the models interpretation can provide knowledge for expert in order to have a better understanding of when, how and why injuries happen.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 3 Test Analysis
### Test of Orbit Determination for BDS-3 Satellites and LEO Satellites
#### 3.2.2 Test Scenarios for Orbit Determination with Simulated Data
The results for Scenario 5, Scenario 6 and Scenario 7 were, respectively, 0.077 m, 0.043 m and 0.043 m, meaning that accuracy could be improved with the addition of the satellite-based links. Nevertheless, the results could not be further improved by adding more LEO satellites. The evaluation results of the orbit determination over 7 days are shown in the Table 5. It can be seen that:  
1. The results in the R direction were, respectively, 0.050 m, 0.020 m, 0.013 m and 0.008 m in Scenario 1 to Scenario 4. Compared with Scenario 1, the improvement rates were, respectively, 60%, 74% and 84%. Similarly, the improvement rates were, respectively, 69%, 75% and 77% in the T direction and 67%, 70% and 78% in the N direction. The improvement in the different directions was basically the same with the support of LEO satellites. 2. The results in the R direction were, respectively, 0.005 m, 0.006 m and 0.007 m in Scenario 5 to Scenario 7. The decrease rates were, respectively, 20% and 40% compared with Scenario 5. Nevertheless, results in the T direction were, respectively, 0.033 m, 0.031 m and 0.030 m, meaning that there were slight increases in the T direction. The improvement rates were, respectively, 6% and 9%. The results in the N direction showed slight decreases with the addition of six LEO satellites and could be slightly improved when more LEO satellites are added. This might be accompanied by more errors in the R direction when more satellite-based observation links are added. 3. The results in the 3D direction were, respectively, 0.191 m, 0.063 m, 0.041 m and 0.059 m in Scenario 1, Scenario 2, Scenario 4 and Scenario 5. Compared with Scenario 1, the improvement rates were, respectively, 67%, 78% and 69%, meaning that the augmentation performance of the LEO constellation might be better than the inter-satellite links when the LEO constellation consists of a certain number of LEO satellites. 4. The results in the 3D direction were, respectively, 0.059 m, 0.059 m and 0.057 m in Scenario 5 to Scenario 7, meaning that the augmentation performance of the multi-source observation links was not better than the results obtained with the support of the satellite-to-ground and inter-satellite links. For the observation equations, the weights of the satellite-to-ground, inter-satellite and satellite-based observation links were set the same. Satellite-to-ground and satellite-based links both contain pseudo ranging and phase observation data, while inter-satellite links only contain pseudo ranging data, with more systematic errors. This might be the reason for the decrease in accuracy observed with the addition of more observation links. Figure 13: Statistical results of the three-dimensional position errors in Scenarios 1 to 7 (one day).
## 1 Introduction
The inference mechanism of the ontology, as well as the open characteristics of the Web, are strong enough arguments to motivate and justify the design of an ontology for any land use classification standard and in particular, the design of a LBCS ontology for the Semantic Web. It is modular and can be easily extended, it can be shared and reused, information can be inferred and data consistency maintained, and it is ready for integration, thereby fostering interoperability between distributed urban knowledge and different applications. This article describes the development of the LBCS-OWL2 ontology using the Protege ontology authoring tool [6], and how it can be used for classifying georeferenced spatial units that are stored in a shapefile, for instance. The Web Ontology Language (OWL) is the W3C standard language for modelling ontologies in the Semantic Web. OWL2 [7] is an extension of the first OWL version and contains several new features, some of which are used in this research, such as punning meta-modelling. Punning techniques are described in Section 4.1. The ontology design issues in question are not specific to the LBCS but can serve as useful guidelines for the development of an OWL ontology for any other land use classification standard, particularly if it has a hierarchical structure similar to the LBCS. The article is organised as follows: related work is discussed in Section 2; the LBCS land use standard is described in Section 3; Section 4 is dedicated to the presentation of LBCS-OWL2, which constitutes the backbone of the CIM, enabling semantic and computer-readable descriptions of geo-referenced spatial data to be developed; Section 5 exemplifies the use of the LBCS-OWL ontology, together with an illustration of the inference and querying capabilities provided by OWL2 formal semantics; Section 6 describes how data can be transferred from LBCS relational databases, or from other land use systems such as the NLUD standard, to the LBCS ontology proposed here; and Sections 7 and 8 summarise the results and present the conclusion. A shorter version of this paper was initially published in [8].
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Evaluation and Comparison of Surface Albedo's Methods
#### 4.3.2 Evaluation and Comparison with SURFRAD
An additional investigation of the heterogeneity of the land cover of the data selected for this project was carried out via the NDVI calculated from Landsat 8 current images. Finally, it was concluded that the PSU has a heterogeneous environment (Figure 3), so the conclusions of Table 4 are correct. On the contrary, it is found that the rest of the stations have a homogeneous environment; therefore, it is shown that the data from the FPK station can be used for seasonal periods with both leaves and no leaves. Therefore, the FPK station is used when examining the point comparison with SURFRAD data, and the PSU station is discarded. Taking into account the recommendations on the homogeneity of the land cover of the SURFRAD stations to compare with satellite data, five of the case studies of the PSU station for seasonal periods without leaves were discarded. In view of this, the results of the V02 methodology were closer to the surface albedo data of the SURFRAD station in 91% of the study cases, regardless of the value of the NDVI. The second-best results were achieved with the V01 methodology. Methodologies V03 and V04 provided the worst results in all of their cases, despite being methodologies based on albedoeters similar to those of the SURFRAD network. This may be due to the fact that these methodologies were developed empirically from urban observations (asphalt, concrete, etc.) at static stations, as opposed to SURFRAD stations which obtain observations from rural areas (pasture, crops, etc.). The results of the various existing methods for surface albedo computation examined with Landsat 8 images compared to the measurements of the corresponding SURFRAD stations for the same date were as follows (Table 5). Figure 3: RGB display of Google Satellite vs NDVI of Landsat 8 from SURFRAD (**a**) BND, (**b**) FPK, (**c**) PSU, and (**d**) SXF stations. The circle at each station corresponds to the albedometer footprint. The image has North orientation (the top of the image is oriented to the North).
## 3 Results and Discussion
### Critical Roughness Parameters for Leakage Detection
Figure 15: The value of leakage \(Q\) as a function of the difference in the value of the surface topography parameter \(Sa\) for a constant compressive force of 2280 N for the base surface with the value of \(Sa=0.33\)\(\mu\)m.  
When analyzing the sets in which one of the samples had surface parameters from code group C, while the other part of the pair belonged to one of the other groups, no correlation was observed between the results of the tightness tests and the surface topography parameters _Sq_, _Sp_, _Sa_, _Sz_, and _Spc_. The minimum compressive forces needed to obtain the tightness of the analyzed sets were significantly higher than the forces obtained for the other tested sets. Moreover, the leakage test was not carried out for three pairs of samples, as it was not possible to stabilize the set pressure in the chamber between the samples. The surfaces of the samples classified and assigned to code group C were created in the milling process on a numerical milling machine. Comparing the parameters of the surface topography of code group C with the other groups, a discrepancy in the value of the spatial parameter of the surface topography was found between the samples, in which the surface was generated in the turning process and also in produced in the milling process. For the samples belonging to code groups A, B, D, and F, the parameter _Std_ had a value in the range of 86-93\({}^{\circ}\), while for code groups C and E, this parameter had a value greater or lesser, respectively. This is in line with the expectations, as the turning process is characterized by a greater surface anisotropy than the milling process. The research showed the dependence of the surface directivity influence on the tightness of the entire system. The milling process on the macro scale is also characterized by anisotropy; however, the juxtaposition of surfaces with smaller dimensions results in significantly lower anisotropy values, which was confirmed by the values of the _Std_ parameter. When comparing the obtained results regardless of the samples of the set, it was found that when the difference in the _Sv_ parameter was greater than 5 \(\upmu\)m, the leakage value increased. This relationship is shown in Figure 17.  
When the limit value of the difference in the depth of the valleys of the surfaces is exceeded, the leakage value increases, due to the inability of the surfaces to adapt to each other. Since it is difficult to ensure sufficient contact between the surfaces, different valley geometries are characterized. The influence of the skewness, which has an effect on the work of [PERSON] et al., does not have a significant impact on the samples. This could be due to the different materials and various working conditions. Figure 12.0 The value of leakage \(Q\) as a function of the difference in the value of the surface topography parameter Sa for a constant compressive force of 2280 N for the base surface with a value of _Sa_ = 0.187 \(\upmu\)m.