esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Spatial Autocorrelation Model  
A spatial weight matrix of 13 districts in Wuhan is constructed to represent the proximity relationship between each administrative district in Wuhan. 0 means that the two administrative districts are not adjacent in space, 1 means that the two administrative districts are adjacent in space.  
\[\text{c}w_{i,j}=\left\{\begin{array}{cc}0,&i\text{ and }j\text{ are not adjacent}\\ 1,&i\text{ and }j\text{ are adjacent}\end{array}\right.. \tag{17}\]  
1. Moran's I test  
(1) Global Moran's I test  
The global Moran's I is calculated using the following formula:  
\[I=\frac{n}{S_{0}}\times\frac{\sum_{i=1}^{n}\sum_{j=1}^{n}(y_{i}-\overline{y}) \left(y_{j}-\overline{y}\right)}{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}, \tag{18}\]  
\[S_{0}=\sum_{i=1}^{n}\sum_{j=1}^{n}w_{ij}, \tag{19}\]  
where \(n\) is the total number of administrative districts in Wuhan, which are the attribute values of the \(i\) and \(j\) administrative district units in Wuhan, \(I\) is the mean value of the unit attributes of all administrative districts in Wuhan, and \(S\) is the spatial weight value.  
(2) Local Moran's I test  
\[I_{i}=\frac{z_{i}}{s^{2}}\times\sum_{j\
eq i}^{n}w_{ij}z_{i}, \tag{20}\]  
where \(z_{i}=y_{i}-\overline{y}\), \(z_{j}=y_{j}-\overline{y}\), \(s^{2}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}\), and \(w_{ij}\) is the value of spatial weight.  
For the local Moran's I test, four situations may occur: high/high region (HH), low/low region (LL), high/low region (HL), and low/high region (LH). The corresponding test value ranges are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(z_{i}\) & \(\sum\limits_{j\
eq i}^{n}w_{ij}z_{j}\) & \(I_{i}\) & **Partition** & **Description** \\ \hline \(>\)0 & \(>\)0 & \(>\)0 & HH & The ecological level of area \(i\) is high, and the ecological level of the surrounding areas is high \\ \(<\)0 & \(<\)0 & \(>\)0 & LL & The ecological level of area \(i\) is low, and the ecological level of the surrounding areas is low \\ \(<\)0 & \(>\)0 & \(<\)0 & LH & The ecological level of area \(i\) is low, while that of the surrounding areas is high \\ \(>\)0 & \(<\)0 & \(<\)0 & HL & The ecological level of area \(i\) is high, and the ecological level of the surrounding areas is low \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of local Moran’s I test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Decoupling Type** & \(\Delta\)**X** & \(\Delta\)**Y** & **Decoupling Index** \\ \hline Declining connection & \(<\)0 & \(<\)0 & (0.8,1.2) \\ Growth connectivity & \(>\)0 & \(>\)0 & (0.8,1.2) \\ recessive decoupling & \(<\)0 & \(<\)0 & (1.2,+\(\infty\)) \\ Strong decoupling & \(<\)0 & \(>\)0 & (\(-\infty\)0) \\ Weak decoupling & \(>\)0 & \(>\)0 & (0.0,8) \\ Weak negative decoupling & \(<\)0 & \(<\)0 & (0.0,8) \\ Strong negative decoupling & \(>\)0 & \(<\)0 & (\(-\infty\),0) \\ Negative decoupling of growth & \(>\)0 & \(>\)0 & (1.2,+\(\infty\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Index and types of decoupling elasticity between Tapio urban economy and ecological environment.
# Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load
## 2 Dynamic Triaxial Test of Subgrade Soil under Traffic Load
### Testing Procedure  
1. The on-site soil samples were put into the oven to dry for 24 h. Then, the dried soil samples were crushed into powder by the pulverizer and passed through a 2 mm screen.
2. The soil sample with an optimum moisture content of 17.01% was prepared by adding distilled water into the screened soil sample. After mixing evenly, the soil sample was put into a plastic bag and sealed for 24 h to make the water thoroughly and evenly mixed with the soil.
3. Before making the triaxial specimen, the moisture content of the soil sample was measured again to ensure that the error between the measured value and the optimal moisture content was not more than 1%. First, a layer of Vaseline was applied on the inner wall of the sample cylinder (50 mm \(\times\) 100 mm), and then the prepared soil sample was smashed into the compacting instrument in five layers.
4. The prepared triaxial specimens were put into a vacuum saturator for vacuum saturation and soaked in water for three days.
5. The gas in the water pipe of the triaxial test device was discharged by the needle tube, and the sample was installed on the base of the triaxial tester.
6. The saturation of triaxial specimens was tested. The ratio of pore-pressure increment to confining-pressure increment, after confining pressure, was used as the evaluation index of saturation. If the saturation was above 0.95, the triaxial test would be started directly. If the saturation was less than 0.95, back pressure would be applied at the top of the sample until the saturation reached 0.95.
7. The specimens were consolidated. In this test, isobaric consolidation (\(\sigma_{1}=\sigma_{3}\)) was adopted to apply equal lateral and axial pressures to the specimens. The standard completed in the consolidation stage was wrong with the code for the soil test. The reference source was not found. For reference, the change of consolidation displacement within 1 h is not more than 0.1 cm\({}^{3}\).
8. After the sample reached the consolidation standard, the dynamic load was applied according to the test-design condition, and soil deformation, pore pressure, and other parameters are monitored.
# Dealing with Deadlocks in Industrial Multi Agent Systems
## 1 Introduction
### Formal Methods in AMS  
Formal methods for modeling, simulation, supervisory control, performance evaluation and fault diagnosis of AMS are very important part of global understanding of AMS. They yield an efficient help and knowledge at constructing and real implementation of AMS. A suitable review of such methods is presented in the e-book [22]. As to modeling AMS there are presented also models based on place/transition Petri nets (P/T PN), timed Petri nets (TPN), and hybrid Petri nets (HPN).  
The supervisory control of AMS determined for the deadlock prevention and avoidance are presented by means of: (i) finite state automata (FSA); (ii) the Petri net view on AMS which are perceived as resource allocation systems (RAS); (iii) HPN-based inventory control systems; (iv) stochastic flow models (SFM); (v) the infinitesimal perturbation analysis (IPA); and (vi) the max-plus algebra.  
The performance evaluation is watched by colored TPN, by continuous PN (CPN), by the timed process algebra, and by the Petri net-based complex system scheduling.  
The fault diagnosis of AMS is analyzed by means of FSA, by the fault diagnosis of PN and by the control reconfiguration of discrete-event manufacturing systems modeled by non-deterministic input/output (I/O) automata.  
Along with the development of information technologies, such as cloud computing, mobile Internet, information acquisition technology, and big data technology, traditional engineering knowledge and formal methods for knowledge-based software engineering undergo fundamental changes. Hence, networks also play an increasingly important role.  
Within this context, it is necessary to develop new methodologies as well as technical tools for network-based approaches. The term \"network\" may have different meanings in different contexts. To resolve bottleneck problems in AMS, deadlock prevention and avoidance by means of Petri nets is crucial.  
Although introduced topics cover a large set of formal methods in AMS it cannot be said that this set is complete. New methods are under development.
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Comparison of the Results Obtained by MLP, Two Other ML Models, and the Weather Service
#### 4.6.4 Mean Daily Wind Speed Prediction  
Table 10 in Appendix E contains example values of the mean daily wind speed as measured, predicted by the MLP, and predicted by the weather service. Figure 6 illustrates a comparison of the effectiveness of the MLP model and weather service in forecasting the mean daily wind speed.  
The effectiveness in the wind speed forecasting was higher for the MLP model than for the weather service as, in the graph, the values predicted by the MLP show much smaller deviations from the observed values compared with the values predicted by the weather service. Table 12 presents the quantitative values of the MLP, two other predictive model effectiveness measures, and the weather service in forecasting the mean daily wind speed. The MAE value was lower for MLP by 0.3731, and the MSE value was lower for MLP by 1.2726 compared with for the weather service; therefore, there was a higher accuracy of the MLP model compared to the weather service results.  
Figure 5: Comparison between the observed values and those predicted by the MLP and the weather service mean atmospheric pressure.  
Figure 6: Comparison between the observed values and those predicted by the MLP and the weather service mean wind speed.  
The value of the determination factor R\({}^{2}\) score also showed a higher efficiency ofor the MLP model compared with the weather service for forecasting the mean daily wind speed as this value was higher by 0.3757 for MLP compared with for the weather service. However, this is lower compared to the value of the R\({}^{2}\) score calculated for the MLP model for temperature and atmospheric pressure prediction, which indicates a lower effectiveness of the MLP for wind speed prediction. For the prediction of the daily wind speed, the two other prediction models obtained higher prediction accuracy. The best results were obtained using the LSTM model, and the SVR model also showed a small advantage over the MLP model.
# Prosody-Based Measures for Automatic Severity Assessment of Dysarthrie Speech
## 3 Methods  
### Dysarthria Severity Assessment  
A visualization of the assessment process is shown in Figure 1. The process of the experiments is as follows: extract acoustic features from all utterances, select optimal features via feature selection algorithms, split data into train and test sets, and lastly, feed features to machine learning classifiers. The classifiers are random forest (RF), support vector machine (SVM), and neural networks (NN). The RF algorithm is a robust method for classification with small datasets. An RF classifier is less influenced by outliers and handles noisy data well. SVMs are commonly used classifiers in machine learning, and in particular for impaired speech detection [23; 26; 27; 29]. The success of SVMs is not limited to early studies but continues to show good performance even in recent studies as they consistently perform well, even with small datasets [18; 19; 20]. Lastly, we include NNs, as they have shown state-of-the-art results for many speech and audio classification tasks.  
We use an SVM with a radial basis kernel function and optimized the C and gamma parameters via a grid search using values between \(10^{-4}\) and \(10^{4}\). For the RF classifier, the number of trees and the depth of trees are optimized. A forest with 100 trees led to the highest accuracy, while the optimal depth of trees is 30. Lastly, we test the performance of different NN classifiers with different parameters as shown in Table 2. After hyperparameter optimization, the NN classifier which led to the highest accuracy was one that contained two hidden layers and a ReLU activation function. The neural network is trained with an Adam optimizer which yields better results than standard stochastic gradient descent or limited-memory BFGS solvers. An adaptive learning rate is initialized with a value of 0.001.  
Figure 1: Block diagram of dysarthria severity assessment using acoustic features.  
Along with training our classifiers using the features selected from the feature selection algorithms, we also train on individual acoustic groups. Furthermore, results are compared with baseline MFCC and voice quality features. Choosing the right set of features is an important aspect when training machine learning models. In order to select the optimal set of prosodic features, we conduct several feature selection methods and compare the performance of each method. Section 3.2 describes the different methods used for the current study.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
###### Abstract  
IoT data exchange is supported today by different communication protocols and different protocol frameworks, each of which with its own advantages and disadvantages, and often coexisting in a way that is mandated by vendor policies. Although different protocols are relevant in different domains, there is not a protocol that provides better performance (jitter, latency, energy consumption) across different scenarios. The focus of this work is two-fold. First, to provide a comparison of the different available solutions in terms of protocol features such as type of transport, type of communication pattern support, security aspects, including Named-data networking as relevant example of an Information-centric networking architecture. Secondly, the work focuses on evaluating three of the most popular protocols used both in Consumer as well as in Industrial IoT environments: MQTT, CoAP, and OPC UA. The experimentation has been carried out first on a local testbed for MQTT, COAP and OPC UA. Then, larger experiments have been carried out for MQTT and CoAP, based on the large-scale FIT-IoT testbed. Results show that CoAP is the protocol that achieves across all scenarios lowest time-to-completion, while OPC UA, albeit exhibiting less variability, resulted in higher time-to-completion in comparison to CoAP or MQTT.  
Internet of Things; networking protocols; networking architectures; performance evaluation 2021  
11 May 2021  
18 May 2021  
1
## 3 Materials and Methods  
This study was based on the integration of data from in situ monitoring networks, field campaigns and satellite-derived products covering the area of the Lido inlet for a period of three days (15-17 September 2020).  
Long-term in situ data from field surveys were considered for calibration, intercalibration and validation purposes. Time series from in situ networks were used to investigate the trend of water and sediment fluxes. Tidal level, wind direction and speed were obtained from the time series recorded from the Lido Sud (LS) meteo-marine station (cf. Figure 1) managed by the Municipality of Venice (Centro Previsione e Segnalazione Maree).  
Figure 1: Copernicus Sentinel-2 true-colour composite of the study area at the two tidal channels (San Nicolo—SN, Treporti—TRP) originating from the Lido inlet in the Lagoon of Venice (Northern Adriatic Sea, Italy). In situ data were collected in the 8 sampling stations (pink dots) and the two boat-mounted ADCPs transects (yellow lines). The location of the fixed monitoring stations is also indicated: bottom-mounted ADCPs (red); tidal and meteo gauge Lido Sud station (LS, yellow); and OBS turbidimeters (brown).
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
## 3 Results  
### Simulated Data and Axial Rebinning  
Figure 5 shows resolution recovery and noise of four point sources located at different radial and axial positions. Off-axis sources in SSRB reconstructions exhibit much worse resolution than the other two rebinning methods studied. FORE provided the best axial resolution and less noise, while PINV, on the other hand, provided a more uniform resolution and noise level across the FOV, clearly improving SSRB for off-axis values, where it approached the best results of FORE. Indeed, with a proper regularization parameter (around the equivalent to 8 Landweber iterations), similar results in terms of resolution and noise were obtained for the sources placed at 10 cm radially from the center of the scanner using PINV and FORE.  
Figure 5: Noise vs. resolution recovery for different rebinning methods (SSRB, FORE, and PINVz). Point sources were simulated in a scanner with a geometry similar to Biograph TPTV with MRD 38 and SPAN 11. Numbers in brackets at the FORE and SSRB points indicate the distance (R,Z) to the center of the scanner in cm, for example, (10,0) represents a source at a radial distance of 10 cm from the center of the scanner at the axis. Several points for PINVz show the axial resolution vs. recovery for a different Landweber iterations, from right to left 5, 8, 10, 15, and 20 iterations. Outside the axis of the scanner, SSRB was not accurate, while FORE provided the best results overall, although with some sensitivity to distance to the scanner axis. PINVz keeps a more uniform resolution across the whole FOV.  
In Figure 6, we can see a coronal view of the reconstruction of a Defrise disk phantom where an improvement in the pseudoinverse rebinning can be appreciated when compared to SSRB. We can see that FORE and PINV\({}_{\mathrm{z}}\) also provided similar detail.
# An Analysis of Bubble Migration in Horizontal Thermo-Capillarity Using the VOF Modeling
## 4 Results and Discussion
### Bubble Sizes from \(R=0.01\) m to \(0.05\) m
#### 4.2.1 Effect of Temperature Gradients  
Figure 3a-f show the bubble migration under gravity for bubble sizes of \(R=0.01\) m, 0.015 m, 0.025 m, 0.035 m, and 0.05 m under various temperature gradients of 0 K, 20 K, 40 K, 60 K, 80 K, and 100 K, respectively. Since the temperature gradient is applied to thedirection perpendicular to the gravitational force, we are only interested in the bubble movement in the \(x\)-direction. The \(x\)-axis represents the centroid bubble position, and the \(y\)-axis represents the time. The surface tension is more significant in a lower temperature than higher temperature, hence, a net force on the vapor bubble is induced to move the bubble. If there is no temperature difference \(\Delta T=0\) K, as shown in Figure 3a, the bubbles with different sizes almost stay near the center (\(x=0.06\) m). With a larger temperature gradient, it is much easier for the vapor bubble to reach the left wall in a shorter time. It is also noted that the bubble with a smaller size arrives at the left wall much faster than a larger bubble under the same temperature gradient. The bubble with a radius of 0.05 m only oscillates around its initial position, which is the center of the domain. We believe that the bubbles oscillate because of the Kelvin-Helmholtz instability. The Kelvin-Helmholtz instability is due to the perturbation of the velocity field, and the larger bubble will induce larger perturbation and hence larger oscillation.
# Classroom Expression Recognition Based on Deep Learning
## 3 Theoretical Framework of Facial Expression Recognition
### Multi-Attention Fusion Network for Expression Recognition
#### 3.4.1 Feature Extraction Network  
In a classroom setting with a large number of individuals, the complex background and dense faces can hinder the neural network's ability to extract facial expression features, which may adversely affect the accuracy of expression recognition. To enhance the algorithm's robustness in such interference-prone environments, this study selects the ResNet-34 network as the foundational architecture and designs a facial expression feature extraction network that integrates both channel attention and spatial attention mechanisms, as illustrated in Figure 9.  
Figure 8: Enhanced StarGAN network structure.  
The network comprises 33 convolutional layers and 3 fully connected layers, organized into 6 modules, as shown in Figure 9. Initially, the channel attention and spatial attention mechanisms are introduced after the fifth module to suppress background noise and irrelevant features in both the channel and spatial dimensions. This reduces interference and enables the network to focus more effectively on extracting pertinent feature information. Subsequently, in the final module, an average pooling layer is employed to downsample the features, further strengthening the relationships among relevant channels, filtering out extraneous information, and extracting facial expression features.  
Finally, expression classification is accomplished by incorporating a Softmax regression function into the last layer of the neural network. During network output, the model predicts the probabilities of different categories for each sample, selecting the category with the highest probability as the final classification result.
## 4 Discussion  
The maximum standard deviation of the system, \(\max(\sigma_{\gamma 22})=1.1\times 10^{-14}\) m/V, which can be regarded as the sensitivity of the system, suggests that the method presented in the current study has achieved high measurement sensitivity and repeatability. The measurement accuracy and sensitivity can be improved further, if the determinations of the LN plate lengths and the DC voltage are more precise. In addition, the measurement speed of the system in the experiments is set at 198.9 ms per data point, which can be further improved if appropriate reduction of the measurement sensitivity is allowed.  
In this scheme, the LN EO coefficients were measured in the range of 0.42 to 0.8 \(\upmu\)m in visible light. However, the PEM, the core device used in this scheme, can be used in a wide spectral range. Thus, the method can be used to measure the EO coefficients and to study their dispersion in other wavelength bands by simply choosing a suitable light source. The wavelength range of the grating of the monochromator can be extended, and the dispersion of the EO coefficient in near-infrared, mid-infrared and even far-infrared light bands can be further measured.  
In addition, the measurement method for the EO coefficients proposed in this paper is based on a simple polarization analysis scheme using a PEM, which can be well applied to establish an ellipsometry device. Furthermore, this study provides a better method for measuring and studying the spectral dispersion of the EO coefficients of non-crystalline materials, such as thin films and two-dimensional materials.