esa-sceva/satcom-chunk-collection huggingface dataset:
# Kriging Interpolation in Modelling Tropospheric Wet Delay
## 3 Experiments in Sparse and Dense Network
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **User Station** & **RMSE of Sparse Network** & **Average** & **RMSE of Dense Network** & **Average** \\ \hline \multirow{4}{*}{Inside} & LITH & 0.83 & & 0.77 & \\  & LELY & 0.74 & & 0.75 & \\  & LWRD & 0.74 & 0.74 & 0.67 & 0.69 \\  & HOOG & 0.70 & & 0.66 & \\  & ADR2 & 0.67 & & 0.61 & \\ \hline \multirow{4}{*}{On the edge} & WARM & 2.26 & & 1.90 & \\  & TERS & 1.31 & & 1.22 & \\ \cline{1-1}  & DZYL & 0.96 & 1.12 & 0.80 & 0.98 \\ \cline{1-1}  & OLDE & 0.54 & & 0.51 & \\ \cline{1-1}  & HHOL & 0.53 & & 0.48 & \\ \hline \multirow{4}{*}{Outside} & KERK & 1.35 & & 1.21 & \\ \cline{1-1}  & MSTR & 1.28 & & 1.14 & \\ \cline{1-1}  & SASG & 1.13 & 1.17 & 1.13 & \\ \cline{1-1}  & VLIS & 1.12 & & 1.12 & \\ \cline{1-1}  & RIL2 & 0.99 & & 0.85 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average RMSE of each user station. The unit is cm. Figure 12: Violin plot of the RMSEs of each user station in (**a**) the sparse network and (**b**) the dense network. Stations from the left to the right are sequential 5 inside the network, 5 on the edge of the network and 5 outside the network.
# Spatial and Temporal Variation of GPP and Its Response to Urban Environmental Changes in Beijing
## Data and Methods
### Data Sets  
In this paper, we choose the dataset of annual gross primary productivity in China's terrestrial ecosystems during 2000-2020, published by [PERSON] et al. [25]. This dataset was constructed based on the annual GPP observation dataset obtained from ChinaFLUX [26] long-term observation data and publicly available data, and an annual GPP(AGPP) assessment model constructed by simulating annual GPP per unit leaf area using a random forest regression tree model, thereby accurately depicting the geographical and temporal fluctuations in GPP across China. We utilized the CNLUCC dataset, a high-precision national-scale multi-temporal land use/land cover database derived from manual interpretation of Landsat imagery, as our source for surface coverage data. Additionally, we incorporated Land Surface Temperature (LST) data, monthly precipitation (PRE) and temperature records (T), and Chinese air pollutant data. Table 1 delineates the specifics of these data sources, including their spatial and temporal resolutions. Due to the restricted period for which some pollutant data are available, this paper only examines the effects of seven environmental factors on AGPP from 2014 to 2020.  
The AGPP data and LST data underwent preliminary processing in the ArcGIS 10.2 software environment, through the process including band extraction, reprojection, splicing, cropping, invalid value removal, unit conversion, etc., to finally obtain the annual average data of GPP and surface temperature in Beijing. At the same time, annual data were obtained for temperature and precipitation data using the image element statistics tool, and annual air pollutant data were obtained for air pollutant data in Beijing using the mask extraction and raster calculator tools.  
To resolve the discrepancies in data resolution within our study, we implemented a systematic approach to standardize the data. Initially, we utilized ArcGIS to resample the data for the seven influential factors to a 500-m resolution, aligning it with the resolution of the AGPP data. Next, we determined the dataset's minimum spatial resolution, finding it to be 10 km, and adopted this as our sampling unit. For each unit, we identified the pixel location with the median AGPP value and extracted the corresponding data for the seven influencing factors from that pixel. These values were then used as the representative dataset for each unit, enabling subsequent analysis and processing to be conducted at the unit level. This approach effectively addressed the issue of inconsistent data resolutions and minimized the potential distortions in GWR and GTWR analyses that can arise from resampling.
# A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems
## 4 Discussion
### Impact on TRISO Fuel Performance  
Neodymium and cerium are present in the TAF-ID database as they are important lanthanoid fission products for fuel safety simulations [36]. However, their carbide binary systems are absent [36]. As a result, a Gibbs energy minimization of the binary systemwould first need to be assessed and then validated using experimental data to accurately model their behavior. The results from this work directly address this knowledge gap in the open literature, permitting validation of Nd-C and Ce-C models to assist with TRISO fuel fission product simulations [36]. Binary carbide system data in the TAF-ID is significantly lacking in general. Therefore, data reported here further improve the efficacy of the database to support TRISO fuel modeling. Additionally, the SiC layer of the TRISO particle is the primary barrier to non-gaseous fission product release and provides crucial structural integrity [1]. Therefore, these results further understanding of the interactions of Nd-C and Ce-C with SiC in a ternary system.
###### Abstract  
With an increase in network-connected devices, the existing Radio Frequency (RF) spectrum is getting highly saturated. Non-RF-based communication systems have recently garnered attention as they can be considered an alternative to RF-based systems for some applications. The availability of efficient and low-cost electronic components like Light Emitting Diode (LED), photodiode, and cameras have been pivotal in building communications systems using visible light. High-speed communication using visible light can be achieved with customized hardware and software. Visible Light Communication (VLC) uses various properties of light to encode digital data, which is then modulated and transmitted over a short distance to the receiver. Photodiodes are inexpensive and provide low complexity implementation, but their adoption requires modifying existing devices to house dedicated sensors. On the other hand, in Optical Camera Communication (OCC), existing camera-based receivers are used to extract encoded data using properties of light like color, blink frequency, intensity, and polarity. In this paper, a novel OCC technique to achieve improved robustness using a Hybrid Frequency Shift Pulse Width Modulation (HFSPDM) is proposed, implemented, and evaluated. The performance of the proposed technique is compared for a short distance with On-Off Keying (OOK) and Binary Frequency Shift-OOK (BPSOOK) due to similar computational requirements. It was observed that the proposed technique used a 17% lesser number of frames than BPSOOK and provided 8% better BER than OOK under a test environment. It also supports longer distance communication than OOK as it is less sensitive to external noise.  
optical camera communication; visible light communication; Li-Fi; testbed +
Footnote †: journal: _Compputation_  
_Acticle_  
An Optical Camera Communication Using Novel Hybrid Frequency Shift and Pulse Width Modulation for Li-Fi
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
###### Abstract  
Current computation capabilities normal, Delay and Sum (DAS) and Total Focusing Method (TFM) allow new definitions for beamformers. In this paper, a new beamforming method is proposed. It exploits diversity across pixel data after focusing process. The method is based on statistical analysis and sparse array concept. It avoids common aberrations introduced by beamforming process without loosing the original image texture, producing a better quality image. We evaluate the proposed method through simulation on standard phantoms. Images resulting from our novel method display better quality and provide localised estimations on image noise.  
sparse arrays; Rician beamformer; ultrasonic imaging; space diversity 202020202020202  
improvement to transduction systems [14] and ultrasound frontend massive data management have contributed to the popularity of these chained techniques in recent years. Image quality can be improved while increasing complexity of acquisition systems. This is not only due to this cascade of techniques, but to the ability to extract disaggregated data related to each transmitter-receiver pair. Additionally, after applying the Synthetic Aperture Focusing Technique (SAFT), disaggregated data of each point within the region of interest (ROI) can also be extracted. This allows for new strategies to mitigate the effect of secondary lobes and low directivity of the main lobe that translate into speckle noise in the resulting ultrasonic images [15].  
In light of this, we describe a new postprocessing approach: we propose to use virtual sparse arrays to exploit spatial diversity on disaggregated data from SAFT. In this way, robust estimates can be obtained, beyond standard limitations of traditional beamformers lower resolution and speckle effect [8]. The resulting stochastic beamformer produces better contrasted images with better defined tissue interfaces, as well as better estimates on structures spatial reflectivity.  
This new methodology contributes to a reliable noise characterisation at specific positions in the analysis area. Modern speckle reduction techniques based on homomorphic filtering [16], diffusion models [17], or total variation estimation [18], could take advantage of this informed new feature.  
The remainder of this paper is organised as follows. Section 2 introduces the physical and mathematical background emphasising on signals projection modelling onto the coarray, as well as the stochastic behaviour of the envelope in tissue onto this same domain. Section 3 details the proposed beamformer: the Rician Beamformer (RiB). Section 4 describes the materials used and methods followed along the different tests described in Section 5. Finally, Section 6 presents the conclusions of this work.
###### Abstract
Deep formalized representations related to emotional features are thus extracted from speech, and a supervised learning method is employed to achieve the automated evaluation (scoring) of emotional expression in the English spoken language. In this study, for the first time in the context of voice communication scenarios, the automated evaluation of emotional expression in spoken English is realized. This enables this study to be specifically applicable in the context of Chinese students' English oral practice. This advancement aids Chinese students in enhancing their ability to express emotions in spoken English. The main contributions of this paper are as follows:  
1. Combining an LLM with the deep representation of features in the emotional space, this study successfully builds a deep learning framework for the evaluation of emotions in spoken English based on the transformer attention mechanism. This is a first in the academic community, achieving the automated evaluation of emotions in spoken English. 2. This study integrates emotional speech data generated by an LLM with existing public emotion datasets. Using frequency domain transformations and methods based on the transformer, combined with the membership function method in fuzzy mathematics, it effectively achieves the decoupling of emotional features from other sound features in spoken English and establishes the corresponding deep learning representation of emotional features. 3. Compared to current multimodal emotion recognition and evaluation methods, this research focuses on training and evaluating vocal emotional expression in spoken English. This not only provides rich emotional expression practice materials for learning spoken English but also automatically provides learners with feedback and evaluation on emotional expression during the learning process, opening a new path for Chinese students to enhance their spoken English abilities. Section 2 will introduce relevant work related to this paper, Section 3 will introduce the dataset, models, and methods used in this paper, Section 4 will describe the experimental methods and results of this paper, and finally Section 5 will summarize and discuss this paper.
## 3 Signal Analysis
### Bispectrum Analysis  
Higher-order spectrum and higher-order cumulants are useful tools in describing the dynamic features and processing signals [44]. Bispectrum is the two-dimensional Fourier transform of third-order cumulants and is the simplest higher-order spectrum. It is widely used in describing the nonlinear coupling, restraining Gaussian noise and preserving the phase information of random signals. At present, there are many methods to estimate the bispectrum. A parametric method based on the AR model is introduced to study arc faults in this section.
## 3 Validation of the Calculation Method
### Comparison with the Experimental Data in Undrained Heating Test of [PERSON] et al. (2017)  
To investigate the influence of \(OCR\) on thermal pore water pressure under undrained conditions, [PERSON] et al. [26] used a temperature-controlled GDS triaxial testing apparatus to conduct undrained heating tests on normally consolidated and overconsolidated kaolin clays. During the test, saturated kaolin clay was consolidated at a pressure of 300 kPa. After consolidation was completed, the consolidation pressure was unloaded to 150 kPa, 100 kPa, 75 kPa, 37.5 kPa, 30 kPa, and 10 kPa to obtain soil samples with \(OCR\) of 1, 2, 3, 4, 8, 10, and 30. Subsequently, the obtained soil samples were used to carry out undrained heating tests. Furthermore, the physical parameters of kaolin clay are shown in Table 1.  
Through calculation, it has been found that when the clay is in slightly overconsolidated state (1 < \(OCR\) \(\leq\) 4.3), the \(\Lambda\) obtained by Equations (10) and (11) is larger than that of normally consolidated clay. In this case, the value of \(\Lambda\) adopts that of normally consolidated clay. However, for highly overconsolidated clays (\(OCR\) > 4.3), the values of \(\Lambda\) are different from those of normally consolidated clays, and when \(OCR\) of soil is 8, 10, and 30, the calculated values of \(\Lambda\) are 5.4, 5.0, and 3.8, respectively. Thus, it can be seen  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{\theta}\) (\%) & \(\mathbf{S_{r}}\) & \(\mathbf{G_{s}}\) & \(\mathbf{I_{P}}\) & \(\mathbf{\Lambda}\) & \(\mathbf{\kappa}\) & \(\mathbf{n_{0}}\) \\ \hline
59–61 & 98 & 2.72 & 27 & 0.17 & 0.026 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of kaolin clay obtained from the test of [PERSON] et al. (2017).  
that for saturated clays, there is a critical threshold of \(OCR\) that determines whether the stress history will change the calculated value of \(\Lambda\), thereby reducing the thermal pore water pressure. In this paper, the critical threshold of \(OCR\) is taken as 4.3. Subsequently, \(\Lambda\) is brought into Equation (12) to predict the thermal pore water pressure. The predicted results and their comparison with the experimental results of [PERSON] et al. [26] are presented in Figure 1.  
It can be seen from Figure 1 that all experimental data points are basically on the predicting lines. Meanwhile, through calculation, it has been found that except for the predicted results with \(OCR\) equal to 30, the coefficients of determination (\(R^{2}\)) of other predicted results are all greater than or equal to 0.89. Moreover, it should be noted that when \(OCR\) is equal to 30, although the coefficient of determination of the predicted results is only 0.44, the maximum difference between the predicted results and the experimental data is within 1 kPa, which is acceptable in engineering. Therefore, it can be concluded that the calculation method established in this paper can accurately predict the undrained thermal pore water pressure for both normally consolidated and overconsolidated clays.
# Quantitative Characterization of _Geotrichum candidum_ Growth in Milk
## 3 Results
### Secondary Modelling of G. candidum Growth in Milk
candidum_ and reported a similar trend, where kinetic growth over the entire temperature range (from 8 to 37 \({}^{\circ}\)C) was positively influenced by 1% NaCl content in the growth medium. However, in Figure 4, it can also be noticed that, in the temperature interval from 25 to 34 \({}^{\circ}\)C, increased osmotic stress resulted in decreased growth potential, which was already indicated by the results of the primary modelling. In addition, both \(T_{\text{max}}\) and \(\mu_{\text{opt}}\) were lower for lower \(a_{w}\) values. The predicted growth kinetics of _G. candidum_ J, which were influenced by temperature in Figure 4, demonstrated a similar growth response framework to that reported by [PERSON] et al. [50]. However, they observed slightly higher \(\mu_{\text{opt}}\) values associated with the temperature of the _G. candidum_ isolate A (0.535 \(\pm\) 0.011 h\({}^{-1}\)). Since they used the same experimental research design, the differences between the estimated \(\mu_{\text{opt}}\) of _G. candidum_ may be related to the use of different isolates. This is in accordance with the polymorphism, as well as morphological and phenotypic variability, of _G. candidum_ observed by some authors [4; 39]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**0\% NaCl**} & \multicolumn{2}{c}{**1\% NaCl**} \\ \hline
**Model** & **CM\({}_{\text{J/A}}\)** & **CM\({}_{\text{H}}\)** & **CM\({}_{\text{J/A}}\)** & **CM\({}_{\text{H}}\)** \\ \hline \(T_{\text{min}}\) (\({}^{\circ}\)C) & \(-\)0.81 \(\pm\) 0.01 & 0 (fixed) & \(-\)3.75 \(\pm\) 0.05 & 0 (fixed) \\ \(T_{\text{opt}}\) (\({}^{\circ}\)C) & 30.32 \(\pm\) 0.27 & 28.00 \(\pm\) 0.06 & 34.63 \(\pm\) 0.44 & 28.27 \(\pm\) 0.14 \\ \(T_{\text{max}}\) (\({}^{\circ}\)C) & 35.21 \(\pm\) 0.03 & 35.20 \(\pm\) 0.05 & 37.17 \(\pm\) 0.12 & 36.62 \(\pm\) 0.12 \\ \(1/\lambda_{\text{opt}}\) (h\({}^{-1}\)) & 0.229 \(\pm\) 0.022 & - & 0.202 \(\pm\) 0.029 & - \\ \(\mu_{\text{opt}}\) (h\({}^{-1}\)) & - & 0.358 \(\pm\) 0.008 & - & 0.325 \(\pm\) 0.016 \\ \hline \hline \end{tabular} \(T_{\text{min}}\)—minimum temperature (theoretical value); \(T_{\text{opt}}\)—optimal temperature; \(T_{\text{max}}\)—maximum temperature; \(1/\lambda_{\text{opt}}\)—reciprocal lag phase duration at the optimal temperature; \(\mu_{\text{opt}}\)—maximum specific growth rate at the optimal temperature; CM\({}_{\text{J/A}}\)—cardinal model applied to its lag phase data; CM\({}_{\text{H}}\)—cardinal model applied to its specific growth rate.  
\end{table} 
Table 2: Cardinal parameters of the growth of _G. candidum_ J in milk depending on NaCl presence as a result of CM applied to the specific growth rate (CM\({}_{\text{H}}\)) and lag phase (CM\({}_{\text{J/A}}\)).
# A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas
## 4 Applications in Energy Field
### Petroleum Supply Chain
#### 4.1.1 Refinery Production Planning  
Refinery production planning is the precondition of all the operations, as it defines the number of end products to be produced and predicts the total profits of the whole production process. The ML model based on game theory was investigated to deal with refinery production planning problems.  
[PERSON] et al. [98] presented a structure based on game theory according to a Cournot oligopoly-type game to solve strategic refinery production planning problem, as the model involves multiple refineries and markets. The results show rational and robust decisions and are mutual best responses in the competitive planning game. [PERSON] [99] built a Cournot oligopoly model embedding a stochastic algorithm to cope with product profits problem for refineries. This model with an oligopolistic market structure takes into account key characteristics of refineries and yields a solution concerning investment decisions under demand shock. [PERSON] [100] also constructed a Cournot oligopoly model for refinery operation problems in multiple markets competition, which is an optimal strategic production planning problem. The model takes into account both economic objectives and process constraints to tackle such MILP. On the aspect of economy, [PERSON] et al. [101] used a three-phase Stackelberg game theory approach based on a multi-agent method to analyze the gasoline industry investment by the government under massive consumption and shortage of production in developing countries. The results show that profit is maximized for investors under the management of production volume.  
From producing point of view, the ML approach based on game theory regards each refinery as a single rational agent, where each considers both their profits and integrated profits in the game to achieve Nash equilibrium. Therefore, with cooperation, i.e., transporting products to each other refinery, the whole supply system maximizes profit. However, transportation costs cannot be easily considered in this game model since none of the agents would be willing to take the risk. Therefore, dealing with transportation costs in the ML approach based on game theory would be another challenge.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 4 Typical Hardware-Based LQEs: A Survey
### LQI Based LQEs and Their Mapping Models  
According to Figure 3, the Cubic model, LR model, and piecewise linear model are usually used as mapping models between LQI and \(PRR\) for LQI based LQEs. In [17], the mapping model between averaged LQI (defined as \(\mu_{|qi}\)) and \(PRR\) is obtained using the Cubic model. Using the measured data in the runway, a mapping model was obtained, as shown in Equation (12).  
\[\begin{split} PRR=\left\{-0.0000066147\times\mu_{|qi}{}^{3}+0.001 0661\times\mu_{|qi}{}^{2}-0.0063\times\mu_{|qi}{}-2.3975,& 68\times\mu_{|qi}{}\leq 98\\ 0,&\mu_{|qi}{}\leq 68\end{split}\right.\end{split} \tag{12}\]  
In [18], the mapping model between \(\mu_{|qi}\) and \(PRR\) is obtained based on the piecewise linear model. Using the measured data in the runway, a mapping model was obtained, as shown in Equation (13).  
\[\begin{split} PRR=\left\{\begin{array}{ccc}1,&\mu_{|qi}>96\\ 0.02492\times\mu_{|qi}-1.392,&80<\mu_{|qi}\leq 96\\ 0.04986\times\mu_{|qi}-3.389,&68<\mu_{|qi}\leq 80\\ 0.00008222\times\mu_{|qi}-0.004111,&50\leq\mu_{|qi}\leq 68\end{array}\right.\end{split} \tag{13}\]  
In [26], the mapping model between \(\mu_{|qi}\) and \(PRR\) is obtained based on logistic regression. Using the measured data in the runway, a mapping model was obtained, as shown in Equation (14).  
\[PRR=\frac{1}{1+e^{16.9491-0.2125\times\mu_{|qi}}} \tag{14}\]
# The Impact of Antenna Height on 3D Channel:
## 3 Results
### Scenario B: Forked Road in the Center of Paris
(**a**) The description of the AS of EAoD; and (**b**) the description of the AS of EAoA. Figure 9: The DS under the forked road scenario with various antenna heights (4 m, 6 m, 8 m, 10 m, 12 m, 14 m, 16 m, and 18 m). \[\sigma_{DS}=\left\{ \begin{array}{cc}-0.5647m+32.5588,&m\leq 12.5\\ 0.4364m+20.0455,&12.5<\ m\leq 25\end{array} \right. \tag{14}\]