esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 4 Semi-Supervised ML Solution  
### Determination of Sampling Fraction  
The designed semi-supervised ML solution is based on the building sampling investigation in the predicted city. In detail, the sampling building is randomly selected, and then the structural types of sampling buildings are determined by consulting the relevant engineering drawings from the urban archive administration, so that the sampled structural types are accurate. The building data obtained by the sampling investigation were used to predict the structural type of all buildings in the city. In the sampling investigation, deficient building data may cause an inaccurate prediction result, while excessive building data incur extensive manual work; therefore, the number of buildings to be investigated (i.e., sampling fraction) is a critical question.  
Figure 6: Prediction accuracies for different cities.  
According to the existing building data of the two cities (i.e., Taiyuan and Tangshan), the predictions were performed based on different sampling fractions to determine the optimal fraction. In detail, six sampling fractions (i.e., 0.05%, 0.1%, 0.5%, 1%, 5%, and 10%) were performed for the prediction of two cities. The predictions employ the decision forest model. The sample data were used to train the model, while all the remaining building data in the city except the sample data were used for assessing the accuracy of the prediction.  
For Taiyuan and Tangshan, the data were randomly sampled 50 times at each sampling fraction, and the corresponding 50 times prediction were performed according to the sampled data. The accuracies of the predictions are shown in Figures 7 and 8. The curves of prediction accuracies and variance with the sampling fractions are illustrated in Figures 9 and 10, respectively.  
According to the prediction results of Taiyuan and Tangshan (shown in Figures 9 and 10, respectively), when the sampling fraction is greater than 1%, the prediction is highly accurate (i.e., above 97% in two cases) and the variance of the prediction accuracies decreases. Therefore, the sampling fraction of 1% is recommended for predicting the structural type of a city.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 1 Introduction
For the fraction of absorbed photosynthetically active radiation (fPAR), the original CASA model only established a linear relationship with the Ratio Vegetation Index (SR), which did not properly represent the internal relationship between fPAR and vegetation. In addition, the original CASA model used the soil-moisture model to calculate the water stress factor which was based on the light use efficiency, but the parameters of the soil-moisture model were difficult to obtain, and complex to calculate--making this approach unsuitable for application over a small region. Thus, with the change of environment and climatic conditions and plant characteristics, the original CASA model is not suitable to estimate the NPP of vegetation. It is vital to improve the original CASA model based on actual production and applications, which may be done by incorporating high-quality remote sensing data. Given this motivation, the major objectives of this study are (1) to optimize the important parameters of the original CASA model in order to fit the growth of winter wheat in the study area; (2) to obtain the high temporal and spatial distribution of winter wheat NPP at five-day cycle intervals in the growing season based on the improved CASA model; (3) to analyze the distribution of winter wheat yield in 2009 combined with the NPP-yield conversion model. Therefore, two multispectral data: HJ-1A/B remote sensing images and the MOD17A2H data products, as data sources in this study. In addition, the accuracy of this estimation was verified by the measured yield data.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## Appendix A Abel Integral Algorithms  
The forward model (FM) consists of the Abel integral methods and interpolation methods. Before we explain the principles of the four Abel integral algorithms, it is necessary to understand how the Abel integral is formed and its important assumption of a spherically symmetric atmosphere.
## 5 Discussion of the Modelling of SPDR Loaded with Semiconductor Samples  
It is possible to claim the effects due to nonlinearities of the semiconductors on the resonant peak are minimal for the homogeneously doped silicon wafer. However, this developed coupled solver would also enable us to simulate non-homogeneous doping on the wafer (possible junctions, devices, etc.). In order to improve the resolution of TD-FEM based solvers, one can run even longer simulations that might require more computational power.  
For the accurate simulation of the SPDR with semiconductor samples, it is important to consider the possible effects due to nonlinearities of the semiconductors, and one needs to consider PDD equations set for accurate modelling. Especially for the lower conductivity of the samples, the effects due to nonlinear nature of the semiconductors start to affect the analysis. These effects are still not so big, and one could avoid costly EM-PDD coupled simulations if the doping of the semiconductor sample is homogeneous. However, if the doping changes from region to region, in order to be able to model the depletion regions that are formed between the junctions of different doping levels, it is mandatory to employ a PDD solver. Here in this work, our focus was to analyze the possible effects of the semiconductor samples with homogeneous doping, and it is shown that for the fundamental mode with the \(\phi\)-oriented electric field fundamental mode, the semiconductor can easily be modelled without the coupled solver as a bulk lossy dielectric, and for the higher order modes with r- and  
Figure 4: (**a**) Source and probe locations shown on the axisymmetric representation of the resonator; (**b**) spectrum obtained through EM solver and EM-PDD coupled solvers for a Si sample with n-type doping \(10^{15}\) cm\({}^{-3}\); (**c**) spectrum obtained through EM solver and EM-PDD coupled solvers for a Si sample with n-type doping of \(10^{16}\) cm\({}^{-3}\).  
z-oriented electric field components, one should be careful about the doping of the sample, and the physical model can be chosen based on the required accuracy of the simulation.
## 4 Conclusions  
Measurement of reverberation time is not an easy procedure to perform and requires the use of a space exclusively. It is in fact necessary to use instruments that reproduce a sound source and instruments to record the impulse response. In this work, an automatic procedure for estimating reverberation time based on the use of artificial neural networks was developed. At first some sounds were selected to be reproduced in the rooms, having the foresight to choose sounds that covered all the frequency ranges, with diversified characteristics. These sounds were reproduced, and joint recordings were made. The reverberation times of the rooms under observation were then measured using the integrated impulse response method. Toy balloons inflated with air were used as the sound source to produce the impulsive sound.  
The recorded sounds were processed with the feature's extraction, then they were labeled by associating to each sound the value of the reverberation time in octave bands of that specific room. The feature used was the magnitude-squared coherence estimation for the signals recorded in twelve rooms. The obtained dataset was used as input for the training of an algorithm based on artificial neural networks. Subsequently, previously unused observations were used for model validation and testing. The model returned excellent performance (R = 0.979), suggesting the use of the procedure for estimating the reverberation time of the rooms.  
The evaluation of the reverberation time with the methodology described in this work is easy to carry out both in terms of instruments and in terms of people to use. On the other hand, it requires careful selection of where to place the sound source and where to place the recorder.  
Conceptualization, G.C. and G.I.; methodology, G.C.; investigation, G.C.; measurements, G.C. and G.I.; software, G.C.; post-processing data, G.C. and G.I.; data curation, G.C. and G.I.; writing--original draft preparation, G.C. and G.I.; writing--review and editing, G.C. and G.I.; visualization, G.C.; supervision, G.C. and G.I.; references study, G.C. and G.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical.  
The authors declare no conflict of interest.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 3 Numerical Study of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Numerical Model and Simulation Method  
#### 3.2.1 Coupled SPH-FEM Modeling Method  
In dealing with the large deformation problem of rock blasting, the traditional FEM is prone to mesh distortion. It results in a significant reduction in computational accuracy and efficiency, and even sometimes, computations are unsustainable. The SPH modeling  
Figure 1: Conceptual model of multiple-hole blasting under in situ stress.  
method is a meshless Lagrangian approach that treats materials as arbitrarily distributed particles. These particles carry material information such as position, mass, velocity, and energy. Then the behavior of materials can be obtained by tracking the trajectory and computing the dynamic equation of each particle [46]. Because the SPH approach is meshless and avoids mesh tangling, it is especially suitable for modeling the large deformation problem of rock blasting. This hydrodynamics method was originally proposed for modeling astrophysical problems with infinite boundaries. A major limitation of applying this method in other fields is that finite boundary conditions cannot be enforced [47]. Therefore, the pure SPH approach is powerless when applying in situ stress on the model boundaries for simulating the deep rock blasting. In addition, the SPH method is computationally expensive, and its computational efficiency is inferior to the FEM in handling small deformation issues such as seismic wave propagation.  
Combining the strengths of each of these two modeling methods, a coupled SPH-FEM approach is adopted in this study to address the rock fracture and seismic wave propagation caused by blasting in stressed rock masses. When using the coupled method, explosives and the rock surrounding blastholes are represented by SPH particles to accommodate large distortions and deformations. Outside the fractured zone, the rock is represented by FEM elements to realize the application of in situ stress on the model boundaries and improve the computational efficiency of seismic wave propagation. The coupled interactions between the SPH particles and the FEM elements are realized via a constraint interface [48], as presented in Figure 2. In this way, the stress transmission and displacement coordination between the SPH particles and the FEM elements are achieved.
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 4 Experiment
### Analysis
#### 4.5.1 Combine with HateBERT  
By replacing the encoder with HateBERT, we explored the ability of our method to combine with HateBERT. We guaranteed that all parameters were set in accordance with Table 2 and we used macro F1 as the metric. The overall results are shown in Figure 3. As we can see, multitask learning improved the performance on all datasets, which means that our multitask learning framework is easy to expand and effective. However, for OffEval, StormW, and Davids, the improvement was not large. Although we did not conduct further parameter adjustment according to HateBERT, it can also be seen that compared with BERT, HateBERT's ability to combine with the task of emotion classification was not strong. This may be due to the fact that HateBERT had been pretrained by data from abuse detection domain, which made it less sensitive to external multitask adjustment. That is why we chose BERT as our base encoder instead of HateBERT, although HateBERT is theoretically more expressive. We will also explore HateBERT's performance further in future work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models/Datasets** & **HatEval** & **Davids** & **OffEval** & **FNUC** & **StormW** & **Average** \\ \hline Word-level BiSLTM & 60.81 & 89.57 & 78.26 & 71.71 & 90.28 & 78.126 \\ Word-level BiSLTM Attention & 59.73 & 91.22 & 80.12 & 72.37 & 90.50 & 78.788 \\ Character-level BiLSTM & 52.05 & 93.77 & 78.37 & 72.36 & 90.37 & 77.384 \\ Hyper-RNN & 58.21 & 95.54 & 80.47 & 73.03 & 90.96 & 79.642 \\ \hline Word-level CNN & 60.20 & 91.59 & 79.53 & 73.68 & 90.69 & 79.138 \\ Character-level CNN & 53.73 & 93.24 & 77.79 & 73.02 & 89.95 & 77.546 \\ Hyper CNN & 57.27 & 93.64 & 78.83 & 72.37 & 90.73 & 78.568 \\ \hline Character-level Transformer [50] & 63.43 & 92.41 & 80.47 & 73.03 & 90.50 & 79.968 \\ \hline MTL [1] & & & & & & \\ MTL\({}_{Hard}\) & 62.53 & 93.12 & 81.27 & 74.34 & 90.73 & 80.398 \\ MTL\({}_{DEnorder}\) & 62.39 & 92.69 & 79.53 & 71.71 & 90.78 & 79.42 \\ MTL\({}_{GatchDEncoder}\) & 59.87 & 92.59 & 80.81 & 71.70 & 90.23 & 79.04 \\ \hline BERT [9] & 61.12 & 95.84 & 84.30 & 73.03 & 91.14 & 81.086 \\ MFAE & **63.55** & **96.25** & **84.89** & **75.10** & **91.79** & **82.316** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy score (%) for different models on five datasets. The optimal results are indicated in bold.
## 5 Performance Evaluation
### Evaluation Platform  
To evaluate the performance of RCU-style synchronization methods, we installed sv6 variant with ported RCU-style synchronization schemes on a 36-core machine and conducted benchmark experiments. The user- and kernel-level benchmark experiments were conducted on sv6 variant. These two benchmark experiments were completed on a 36-core (hyperthreaded 72-core) machine equipped with two NUMA sockets for two Intel Xeon Processor E5-2697 v4 2.4 GHz CPUs (each processor has 18 physical cores) and 256 GB of memory.
# A Multiprotocol Wireless Sensor Network for High Performance Sport Applications
## 2 Materials and Methods
### Choice of User Communication Protocol  
The project specifications require communication between user and system and data download to be carried out from any kind of platform (smartphone, tablet, laptop PC) with any kind of OS (Windows, MAC, Android). While nRF24L01+ is arguably the best choice for data transmission between master and slave nodes, it is not featured in smartphones or laptop computers, so adopting it for user interaction would mean having to design a plug-in module and a different app for every OS. Wi-Fi modules, on the other hand, are installed by default on virtually every mobile device. Furthermore, a Wi-Fi interface can be designed as a webpage. This means it is readable with any device and OS, as long as a web browser is present. No other app needs to be installed. Finally, data download via Wi-Fi prevents all problems related to connection via cable (USB or similar) in the wet, windy, or snowy environments which are typical of outdoor sports.  
The firmware installed on the master node microcontroller and Wi-Fi module generates a purpose-made WLAN. Several users can connect to the WLAN and load the communication webpage (Figure 4). This includes, on the left side of the page, commands for acquisition start-stop, data download, system status (battery level, memory status, number of connected nodes) and, on the right side of the page, a subset of parameters, for instance, speed and stroke frequency, which are refreshed at 5 Hz and shown on a graph.  
Figure 3: The nRF24L01P+PA+LNA module.  
Thus, in a typical situation, the athlete can have immediate feedback on his training on his smartphone placed in the cockpit of the canoe, while the coach, who usually follows the kayak on a motor boat during training sessions, can manage the acquisition and download data on a PC. Several e-kayak systems can operate at the same time without interference. Each one creates its own WLAN, identified by a serial number and, if necessary, password protected. A single coach can therefore supervise multiple boats. Data download speed is about 300 kBps (i.e., data from a typical 90 min training session for a K1 boat can be downloaded in less than a minute). Moreover, data download is also possible, if it is required, using a USB cable (up to 6 Mbit/s).
## 3 Results
### Main Outcomes of the Studies  
All 52 articles [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68] included concluded that 62% GP was partially found in six anterior teeth of the population with an aesthetically pleasing smile. Nine studies [14; 15; 18; 22; 37; 42; 45; 47; 48] found that GP only existed between apparent widths of LI and CIs. Five studies [16; 17; 23; 32; 50] concluded that although GP was present in some patients, statistically reliable value explained in the form of range would be more clinically applicable for dental practitioners to support the existing theories on aesthetic parameters. Three studies [21; 24; 25] reported variability in GP values in the left and right arch quadrant. In addition, four studies [17; 18; 19; 20] claimed that the incidence of GP might be different for various ethnic populations as inter-arch tooth size relationships are population and gender-specific. Among all study participants, the mean dental ratios between the successive widths of maxillary anterior teeth were either larger or smaller than GP (Table 1). GP, as observed in the included studies for maxillary anterior teeth, was identified. The ratio between right CI-to-LI; right Ca-to-LI was 18.514 \(\pm\) 14.138 and 12.180 \(\pm\) 12.545, respectively. The mean ratio between the left CI-to- LI; left Ca-to-LI was 18.259 \(\pm\) 13.958 and 11.788 \(\pm\) 12.333, respectively (Table 4). The observed proportion in the population between right CI-to- LI; right Ca-to-LI was 54.526 \(\pm\) 14.684 and 63.975 \(\pm\) 13.121, respectively. The local proportions in left CI-to- LI; were left Ca-to-LI 55.024 \(\pm\) 13.072 and 63.9 \(\pm\) 13.121, respectively (Tables 4 and 5). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Golden Proportion 62\%** & \(N\) & **Mean Percentage \({}^{\alpha}\)** & **Standard** \\  & & & **Deviation** \\ \hline Right central and lateral incisor & 26 & 18.514 & 14.138 \\ Right lateral incisor and canine & 26 & 12.180 & 12.545 \\ Left central and lateral incisor & 26 & 18.259 & 13.958 \\ Left lateral incisor and canine & 26 & 11.788 & 12.333 \\ \hline \hline \end{tabular} \(N\), number of articles; \({}^{\alpha}\) Mean percentage, presence of golden proportion in articles included.  
\end{table} 
Table 4: Mean values of golden proportion found in included articles (\(n\) = 26).
## 1 Introduction
As it can be noted by the literature, even if PPP usually uses DF GNSS observations, different authors propose the development of the SF-PPP model to be adopted when low-cost SF GNSS receivers are used [23; 24; 25]. On the other hand, the development of four GNSS constellations is showed also in [26]. In [27], different PPP models were developed using SF measurements from GPS, GLONASS, BeiDou, and Galileo; the un-differenced SF GNSS PPP model, the un-differenced IF code, and the carrier phase model; and the Between-Satellite-Single-Difference model (BSSD) and the Between-Satellite-Single-Difference IF (BSSDIF) models. For both un-differenced and BSSD models, the IGS final GIM model was used to correct the ionospheric delay, while the IGS final precise products were used in all the considered PPP models. Furthermore, in this work, it was highlighted that the benefit of the additional GLONASS and BeiDou observations is significant. It was also shown that the positioning accuracy obtained using IF-PPP model was significantly better than the GIM-based PPP. After 2 h of data processing, the 3D positioning accuracy was enhanced by 13 cm when the IF-PPP was used with respect to the GIM-based technique. Furthermore, comparable results were obtained from both the GPS/Galileo and GPS/BeiDou combinations. For the GPS/GLONASS configuration, the mean value of 3D positioning solutions was enhanced by 6 cm using IF-PPP technique with respect to the GIM-based model. The aim of current study is to assess the performance of PPP positioning, using multi-frequency and multi-constellation GNSS. In detail, the adopted GNSS systems are GPS, GLONASS, and Galileo, and particularly interesting is the contribution of the latter. The considered GNSS configurations are GPS only, GLONASS only, Galileo only, GPS+GLONASS, GPS+GLONASS, GPS+G Galileo, GLONASS+Galileo, and GPS+GLONASS+Galileo. Large sets of data, collected in southern Italy (Sicily), are processed in order to study the PPP performance: 31 days from a permanent station and 10 days from a static receiver placed on a pillar. Conversely to the literature, in which mainly customized PPP software are used, in the current research, the datasets are processed with the open source software RTKLib and different tropospheric models are considered: Saastamoinen and Estimate ZTD (Zenith Troposhperic Delay). The use of an open source software has the main objective of guarantying the repeatability of the experiment for scientific community interested in precise survey applications; in addition, the large amount of processed data allows one to provide significant information about the performance of the multi-GNSS PPP. The paper is organized as follows. In Section 2, the experiment is described, providing details about the equipment and the location, and RTKLib software and its working are explained. In Section 3, the results, obtained with the considered configurations, are shown and clarified. In Section 4, the conclusions of the work are exposed.