esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Analysis of Extreme Events Using the Univariate Beta Family Probability Distributions
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Distr.**} & \multicolumn{10}{c}{**Methods of Parameter Estimation**} \\ \cline{2-13}  & \multicolumn{10}{c}{**MOM**} & \multicolumn{10}{c}{**L-Moments**} \\ \cline{2-13}  & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\mathbf{\gamma}\) & \(\mathbf{\lambda}\) & \(\mathbf{\delta}\) & \(\mathbf{\alpha}\) & \(\mathbf{\varsigma}\) & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\mathbf{\gamma}\) & \(\mathbf{\lambda}\) & \(\mathbf{\delta}\) & \(\mathbf{\alpha}\) & \(\mathbf{\varsigma}\) \\ \hline PE3 & 0.766 & 24.1 & 9.2 & - & - & - & - & 0.694 & 26.9 & 9.0 & - & - & - & - \\ \hline BG5 & 55.4 & 17,088 & 1.1675 & 0.1375 & 83.9 & - & - & 30.49 & 3558 & 6.51 & 0.1193 & 28.73 & - & - \\ \hline BG4 & 0.404 & 5.59 & - & - & - & 12.7 & 235 & 0.645 & 3937 & - & - & - & 9.43 & 11,125 \\ \hline KUM4 & 0.88 & 59.4 & 8.72 & 1853 & - & - & - & 0.799 & 42.3 & 9.03 & 1853 & - & - & - \\ \hline PYII & 0.203 & 121 & 15.38 & - & - & - & - & 0.354 & 95.9 & 10.7 & - & - & - & - \\ \hline BPG5 & 0.0227 & 141 & 16.2 & 6.3042 & 11.1 & - & - & 0.0632 & 103 & \(-102\) & \(-104.68\) & 560.3 & - & - \\ \hline PYI & 2.7164 & 75.8 & - & 8.446 & - & - & - & 10.31 & 6.29 & - & 3.34 & - & - \\ \hline LMX & - & 454.42 & 7.49 & 23.556 & - & - & - & - & 120.9 & 7.95 & 7.137 & - & - & - \\ \hline LL & 5.230 & 52.8 & \(-28.5\) & - & - & - & - & 2.51 & 20.3 & 0.90 & - & - & - & - \\ \hline DG & 4.34 & 46.1 & 0.24 & - & - & - & - & 2.53 & 19.84 & 1.13 & - & - & - & - \\ \hline BR4 & 57.95 & 7.641 & \(-83.5\) & 59.7 & - & - & - & 0.226 & 2.760 & 8.54 & 36.1 & - & - & - \\ \hline PR & 2.2531 & 44.95 & \(-5.11\) & - & - & - & - & 1.6775 & 24.39 & 4.545 & - & - & - & - \\ \hline IPR & 6.9615 & 66.76 & \(-69.1\) & - & - & - & - & 2.7342 & 17.22 & \(-6.092\) & - & - & - & - \\ \hline BEG & 0.0648 & 1.45 & 12.7 & 0.1236 & - & - & - & 28.17 & 790 & 9.43 & 0.645 & - & - & - \\ \hline EE & - & 22.8 & 9.68 & 0.703 & - & - & - & 24.5 & 9.164 & 0.662 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Estimated parameter values. Figure 4: The fitting distributions. method, the selection criterion is represented by the values and the L-skewness-L-kurtosis diagram. \[RME=\frac{1}{n}\cdot\sqrt{\sum_{i=1}^{n}\biggl{(}\frac{x_{i}-x(p)}{x_{i}}\biggr{)} ^{2}} \tag{116}\]  
\[RAE=\frac{1}{n}\cdot\sum_{i=1}^{n}\biggl{|}\frac{x_{i}-x(p)}{x_{i}}\biggr{|} \tag{117}\]  
where \(n,x_{i},x(p)\) represent sample size, observed value, and estimated value for a given probability, respectively. The distributions' performance values are presented in Table 8.
# Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification
## 1 Introduction
A distributed computing system consists of a set of connected computers that perform tasks in parallel and communicate with one another as needed. Distributed machines are intended to improve performance and accuracy while also scaling to larger data sets. By expanding the input data amount, we may dramatically minimize the learning error for many algorithms [9]. In this paper, we present a precision-based weighted blending distributed ensemble model for emotion categorization. The proposed ensemble model uses a distributed approach by inheriting the main concepts of Spark's resilient distributed datasets, which allow fast in-memory processing and can conduct effectively iterative computations. Weights are assigned to the different classifiers in the ensemble model depending on their precision value using the model validation set. Each weight specifies the importance of each classifier in making a prediction, and a new model is built using the resultant weights. The final prediction on the test dataset is made using the model that was created. The prediction results show that the proposed ensemble model is capable of distinguishing between primary (such as sadness, fear, and rage) and secondary emotions. The main contributions of the paper are the following:  
* A precision-based weighted blending distributed ensemble model for emotion classification is proposed and tested on three datasets, as well as on a combination of them. * The suggested ensemble model can work in a distributed manner using the concepts of Spark's resilient distributed datasets, which provide quick in-memory processing capabilities and also perform iterative computations in an effective way. * The proposed ensemble model outperforms other approaches because not only does it consider the probabilities of each class, but also the precision value of each classifier, when generating the final prediction, thus giving greater weight to the classifier that performs well throughout each run. The paper is structured as follows. In Section 2, we present related research on the use of machine learning and ensemble models for emotion detection and classification. In Section 3, we describe our proposed precision-based weighted blending ensemble model for emotion classification. The description emphasizes particularly on the explainability issues of the proposed ensemble model. In Section 4, we present the results produced after testing the model on some available datasets. In the last section, conclusions and future work are included.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 4 Design
### Underlay Network Architecture  
Our system builds on the Edge-ICN architecture defined in [5]. In particular, our system considers _edge_ nodes interconnected using the SDN-based infrastructure described in Section 2.2. Each edge node is identified by an _identifier_, and each IoT device is attached to one of these edge nodes. Moreover, our system assumes that each edge node knows a \"forwarding identifier\" toward any other edge node. Finally, SDN switches are configured with a \"special\" forwarding identifier used for broadcasting messages; hence, we will refer to this forwarding identifier as the _broadcast forwarding identifier_. In our system, the core network is oblivious to the application-layer identifiers used by endpoints; therefore, the SDN controllers does not have to be aware of the \(Resource_{\textit{URL}}\)s used by IoT endpoints and client applications.
## 7 Results and Discussion  
### Dry Period and Wet Period Stability Analyses  
Pore-pressure distributions and critical factor of safety for stability at dry and wet periods are presented in Figure 15a,b. FoS was calculated as 1.74 for the dry period and 1.32 for the wet period. The analyses are important to reveal how stability is affected by changes in the soil matric suction profile and positive pore water pressure, depending on seasonal conditions in the landslide region.  
A factor of safety below 1.50 can create a critical situation in terms of stability. The fact that the obtained factor of safety was 1.32 in the analysis considering the wet period conditions shows that slope safety can be critical in a region similar to the examined section and in the case of rainfall close to the critical threshold, the factor of safety can decrease to 1.00. Wet period conditions create a critical situation here very close to pre-failure.  
Figure 15: Factor of safety and pore pressure distributions. (**a**) Dry period, (**b**) Wet period.  
Seasonal variations in matric suction and groundwater level play an important role in slope instability. [PERSON] et al. [8] highlighted the significance of in-situ conditions at the time before a landslide is triggered. According to [PERSON] et al. [8], when the degree of saturation in a slope is relatively high, indicating a low initial in-situ suction, it becomes more vulnerable to slope failure triggered by rainfall. [PERSON] et al. [69] discussed the relationships among rainfall conditions, in-situ soil suction and induced slope instability. The authors proposed four periods in a year for auctions based on in-situ soil suction measurement in the pyroclastic deposits of the Campania region (southern Italy): low (<10 kPa) in January-May; very high (>30 kPa) in June-August; high (20-30 kPa) suction in September-October; and medium (10-20 kPa) in November-December. They showed that slope instability and triggering strictly depend on these suction periods and rainfall patterns. Similar results were confirmed by us for this particular landslide area.
## 4 Experiments and Analysis
### Experiment 3: Robustness of Joint Classification Network  
In the acquisition of sonar images, the object may not have shadows due to angle or other reasons. In order to verify the classification performance of the proposed network, classification experiments are conducted on simulated and real data. The network training uses a pair of the complete highlight-shadow image. The test process only uses a single highlight/shadow image, and the missing image is recorded as an empty array. The classification results of this experiment are shown in Table 6.  
Under the absence of the highlight region, the joint classification method proposed in this paper only uses a shadow image to realize the classification. The accuracy of a simulated and real image is approximately 83.6% and 80%. Under the absence of a shadow region, the classification accuracy of the proposed joint classification network is 86.7 and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Simulation Images/\%** & **Real Images/\%** \\ \hline Shadow image Only & 83.6 & 80.0 \\ Highlight image Only & 86.7 & 83.1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy of test sets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Simulated Images/\%** & **Real Images/\%** \\ \hline Original Image & 78.7 & 67.1 \\ Shadow Image & 86.7 & 81.3 \\ Highlight Image & 90.1 & 85.7 \\ Joint of Both & 93.1 & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy of simulation and real data.  
83.1%, respectively. Compared with the results of the experiment on normal data, the above classification accuracy of using only one kind of image is relatively reduced by using the proposed joint classification network. This may be due to the certain interference of the empty array input to the network. Nevertheless, the lowest accuracy of the network can still reach about 80%, which has certain robustness for the underwater object classification task compared with the single network that loses the classification effectiveness under such circumstances.
## 4 Conclusions  
We have fabricated low-loss (high-Q) TiTe\({}_{3}\)O\({}_{8}\) thick films directly on Al\({}_{2}\)O\({}_{3}\) substrates by EPD and reported for the first time their microwave dielectric properties. Nineteen micrometre-thick TiTe\({}_{3}\)O\({}_{8}\) films on Al\({}_{2}\)O\({}_{3}\) sintered at 700 \({}^{\circ}\)C have the relative permittivity of 32 and Q \(\times\) f > 21,000 GHz at a frequency of 20 GHz. Our results confirm the crucial role of a sacrificial carbon conductive coating on non-conductive Al\({}_{2}\)O\({}_{3}\) substrate for the direct utilization of insulating substrates in EPD. During the sintering of TiTe\({}_{3}\)O\({}_{8}\) films on Al\({}_{2}\)O\({}_{3}\) at temperatures above 700 \({}^{\circ}\)C, an interfacial reaction between Al\({}_{2}\)O\({}_{3}\) and TiTe\({}_{3}\)O\({}_{8}\) takes place resulting in the formation of a stable aluminium tellurite compound, Al\({}_{2}\)TeO\({}_{6}\). However, our findings also suggest that this thin (100-200 nm) interlayer of low-loss aluminium tellurite has no significant negative effect on the final properties of TiTe\({}_{3}\)O\({}_{8}\) films. Finally, our results provide relevant information for the use of TiTe\({}_{3}\)O\({}_{8}\) thick films on Al\({}_{2}\)O\({}_{3}\) substrates as microlayer microstrip arrays in antennas, filters and transceivers for microwave telecommunications, satellite broadcasting and intelligent transport systems.  
Conceptualization, P.M.V.; investigation, X.S., A.T. and J.K.; methodology, X.S., A.T., J.K. and P.M.V.; writing--original draft preparation, P.M.V.; writing--review and editing, A.T. and P.M.V.; supervision, P.M.V. All authors have read and agreed to the published version of the manuscript.  
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 and UIDP/50011/2020, financed by national funds through the Portuguese Foundation for Science and Technology/MCTES.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**TiTe\({}_{3}\)O\({}_{8}\)**} & \multicolumn{4}{c}{**Dielectric Properties**} \\ \hline
**Type** & **Sintering** & **Measurement** & **E\({}_{\text{r}}\)** & **Q \(\times\) f, GHz** & **Ref.** \\  & **Conditions** & **Frequency, GHz** & **E\({}_{\text{r}}\)** & **Q \(\times\) f, GHz** & **Ref.** \\ \hline
21-\(\mu\)m-thick TiTe\({}_{3}\)O\({}_{8}\) films on Al\({}_{2}\)O\({}_{3}\) & 680 \({}^{\circ}\)C/5 h & 20 & 28 & 21,978 & This work \\
19-\(\mu\)m thick TiTe\({}_{3}\)O\({}_{8}\) films on Al\({}_{2}\)O\({}_{3}\) & 700 \({}^{\circ}\)C/5 h & 20 & 32 & 21,053 & This work \\ TiTe\({}_{3}\)O\({}_{8}\) ceramics & 700 \({}^{\circ}\)C/5 h & 4 & 36 & 13,600 & [14] \\ TiTe\({}_{3}\)O\({}_{8}\) ceramics & 720 \({}^{\circ}\)C & 5 & 50 & 30,600 & [15] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relative dielectric permittivity, quality factor–frequency product and losses of TiTe\({}_{3}\)O\({}_{8}\) thick films and ceramics. Sintering conditions are also indicated.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## SONAR
### Summary and Future Direction  
RS data has been playing an essential role in the environmental monitoring and management of AV and CR ecosystems. Accelerated research of global change has provided a strong impetus to the development of RS technology for CR and AV monitoring in recent years. However, the present technology is still insufficient to monitor complex CR and AV ecosystems. Based on the conducted literature review, future development tendencies would focus on (1) investigating the impact of various input data (e.g., photo transects, underwater camera videos, bathymetry, salinity or temperature measurements) on the accuracy of the models; (2) accurate CR spectral measurements and establishing a spectral database for different CR and AV; (3) integrating applications of multi-source RS data and techniques; (4) establishing advanced models using ensemble and DL models; (5) employing web-based cloud computing platforms.
## 4 Methodology
### Convolutional Neural Network  
Convolutional neural network (CNN) is a type of feedforward neural network inspired by the biological visual cognitive mechanism [51]. It is one of the most popular deep learning approaches in the field of graphic processing as CNN performs well in image processing and directly deals with raw images. CNN extracts image features and compresses the data volume by operations, such as convolution, pooling, etc. The model is trained through gradient descent and back propagation algorithms [23], so that it can achieve functions like image classification. Generally, five layers constitute the main architecture of CNN: The input layer, convolution layer, activation layer, pooling layer, and fully connected layer. The operation procedure of CNNs is shown in Figure 3, and related descriptions are as follows:  
1. **Input Layer.** This is the entrance for raw image data. In this layer, images can be preprocessed using operations, including normalization, principal component analysis, and whitening. Preprocessing makes images normative, which helps to speed up the training of network models and thus elevates model performance.
2. **Convolution Layer.** This is the main layer of a CNN, which performs convolution on inputted images to extract image features. Generally, a convolution layer contains multiple convolution kernels as filters so that it can obtain multiple image feature results.
3. **Activation Layer.** This layer is used for the nonlinear mapping of convolution results so that the multi-layer network can be nonlinear and has a better expression ability. Commonly used activation functions are the Relu function and the Sigmoid function.
4. **Pooling Layer.** This is also known as the down-sampling layer, and is the part that conducts dimensionality reduction for extracted feature and data compression, so that overfitting can be reduced and fault tolerance of the model can be improved. Pooling methods include MaxPooling and AveragePooling, and MaxPooling is commonly used now.
## 4 Performance Evaluation
### Numerical Simulations and Results
In order to derive the number of signaling molecules S that should be arrived at the node's periphery, we used the following equation:  
\[MAX[C_{S}(x)=\frac{M_{S}}{(4\pi D_{S}t)^{3/2})}exp(-\frac{|X_{n}-X|^{2}}{4D_{S }t})] \tag{4}\]  
Figure 7: Speed of convergence for the LA. where:  
* \(D_{S}\) is the diffusion coefficient of the signaling molecules;
* \(M_{S}\) is the rate of release of signaling molecules;
* \(X_{n}-X\) is the distance between node and BS. (While it would also be practical to measure the maximum number of bacteria that had reached the node's periphery and accordingly set the thresholds for each case, we opted for this solution so as to give to the fixed threshold solution the ability to adapt to the channel changes. Correspondingly, this demands the establishment of a mechanism that provides guaranteed delivery of the initial messages, but this was out of the scope of this work, and we manually set the appropriate initial parameters for Equation (4)). One should notice that the value in the forth scenario (the one having the distribution (1000, 200)) seems to have caused a slightly better performance than the other cases, but we believe this was due to the high deviation and the number of iterations (100 messages for this series of simulations). In all the cases, the LA surpassed the performance of the predefined threshold solution, whereas at the level of 250, due to the high deviation, it minimized its performance margins, as it tries to find the best possible value for the parameters based on the previous channel condition, though the next message may have a significantly different number of molecules, and the previous values become outdated. In the final scenario, the speed of convergence of the learning-automaton was under investigation. As shown in Figure 9, starting from a high number of required ascensions demands a great amount of time to reach a stable state. Slow though it may seem, there are two reasons responsible for this behavior. Firstly, this was caused by the small number of corrective changes, as after each erroneous iteration, the LA amended the number of required ascensions by 100 and 50 for ups and downs, respectively. Secondly, the distance between receiver and transmitter was 300 \(\upmu\)m, which is a relatively high distance, and for every iteration, almost 250 s was required. The initial number of 2200 ascensions was used to show that even in cases where the algorithm starts from a large number of ascensions, it has the ability to find the best possible value. In order to provide a possible relation between the number of ascensions and the time of convergence, the best fitting trend line was added to the Figure 9, which was based on a third-degree polynomial function. The point of convergence for this setup is around 700 ascensions, but this is relevant to the specific simulation parameters. Figure 8: BER for the proposed solution versus a fixed threshold algorithm. The normal distribution refers to the number of bacteria per pulse.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Agriculture  
Typical applications of the satellite RS observation on agriculture are precision agriculture and viticulture.  
Crop monitoring is a core part of the precision agriculture management, whose goal is optimizing returns on inputs while preserving resources. It uses multispectral or hyper-spectral observations from visible and near infrared spectroradiometers; monitoring crops provides the capability to respond to the crop dynamics on particular field zone. The related information enables the agriculture companies to manage crop vegetation control, forecast crop yield, and optimize management decisions. Based on this information, investors and investment analysts can carry out potential estimation, make sustainable forecasts, and investment decisions; insurance brokers can calculate scale of rates and insurance premiums, and evaluate clients' claims verification. More importantly, some extremity of the information may be related to food security or ecological issues in the eyes of state or federal government [100]. The information of land use history may be beneficial by providing the best timing and place for a specific crop to grow, and help with inter-annual planning such as rotation and fallow.  
Similarly, in the context of the grape and wine industry, precision viticulture may be defined as monitoring and managing spatial variation in any physical, chemical, and biological variables related to \"productivity\" (e.g., grape yield, Baume, pH, and total phenolics) within a single vineyard [101; 102]. The information includes environmental conditions, such as microclimate, topography, and soil, which have significant effects on wine quality, though their importance may differ at regional scales and the scale of individual vineyard blocks. Ultimately, yield and quality information may be estimated from monitoring of these factors using space remote sensing techniques and resulted CDRs and CIRs.
# Application of Machine Learning Techniques to
## 3 Methodology
### Climate Zoning
#### 3.1.1 Seasonal Data Resampling  
The daily precipitation data in the APHRODITE time series, at 138 Grid stations in the study area from 1975-2005, were resampled as hydrological seasons: warm-wet (\"July, August and September\"), cold-dry (October, November and December), cold-wet (January, February and March) and warm-dry (April, May and June). The daily precipitation data of 35 years were resampled in a seasonal cycle using simple Python code.