esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### General Operating Conditions  
Figure 1 shows the operating diagram of biogas engines in the WWTP of Burgos (Spain). The WWTP treats domestic wastewater from the surrounding area. The treatment capacity is 156,504 m\({}^{3}\)/d and 1,056,692 e.i. (equivalent inhabitant). The plant has the following sections: (1) pretreatment, where greater size objects, sand and grease are removed, (2) primary decanters, (3) secondary treatment consisting of the conventional activated sludge system, and (4) tertiary treatment consisting of lamellar decanters, ozone and ultraviolet rays.  
The primary sludge is sent to a gravity thickener to increase solid concentration. Waste activated sludge (WAS) is concentrated by air flotation thickeners. The sludge line is composed of the thickening, anaerobic digestion, conditioning and digestate dewatering section. Biogas is produced from mesophilic digesters. The sludge is a mixture of primary sludge and WAS. Sludge is preheated by thermal hydrolysis (165 \({}^{\circ}\)C) and then cool to 50 \({}^{\circ}\)C. This temperature takes into account heat losses by thermal transmission in pipeword and digesters. The plant uses Cambi thermal pre-treatment technology, which is a batch-type process using flash steam preheating [21]. The hydraulic retention time (HRT) of the digestion reactor is 17 days and the temperature is 41 \({}^{\circ}\)C. Four rotary vane biogas compressors are used for mixing the digester content, with a flow capacity of 448 m\({}^{5}\)/h each (measured at standard temperature and pressure conditions). Four gasometers with a capacity of 703 m\({}^{3}\)/each are available for biogas storage and four biogas engines for electricity production.  
The mixed recovery boiler also consumes a small quantity of biogas to generate steam for the thermal hydrolysis unit. The exhaust gas from the engines at the turbocharger's outlet is sent to the silencers, then to the recovery boiler and finally, to the stack. The thermal energy from the auxiliary cooling circuit is dissipated in cooling towers. A fraction of the energy from the main circuit is sent to the boiler heat exchanger to preheat the feeding water. Energy surplus is dissipated in cooling towers. The thermal energy of the main circuit is only used for heating the digesters when thermal hydrolysis is not operating. Gasometers are used to store biogas, maintain the system pressure and supply biogas to the boiler--when steam injection stage takes place--and to the gas engines.
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 5 Conclusions  
This study presented an improved A* obstacle avoidance algorithm for UAVs to avoid obstacles in unstructured plantation forests. Improved the traditional A* obstacle avoidance algorithm through segmented cost function of weighted processing with the dynamic heuristic function of steering cost, turning points optimization, and smoothing optimization. In performance experiments, the improved algorithm outperforms the traditional A* algorithm with a reduction of 64.87% in traversed nodes on average, 49.64% in search time on average, and 12.52% in path length on average. Additionally, the improved algorithm exhibits no obvious turning point and a 54.53% reduction in total turning angle on average. In comparison with the RRT algorithm and APF algorithm, the improved algorithm significantly reduces path search time and total turning angle by at least 90%. Moreover, the path length is reduced by 30.69% on average compared with the RRT algorithm and 1.58% on average compared with the APF algorithm. Compared with the traditional A* algorithm, the improved algorithm reduces the path search time by 33.53% on average, the path length by 6.00% on average, and the total turning angle by 92.17% on average. Compared with the improved A* algorithm proposed in the literature [38], the path search time is reduced by 20.89% on average, the path length is reduced by 3.98% on average, and the total turning angle is reduced by 90.90% on average. Based on these results, the algorithm presented in this article can achieve a better balance between path search efficiency and trajectory quality while improving obstacle avoidance safety of UAVs and reducing energy consumption, providing a new solution for the autonomous flight of UAVs in plantation forest environments.  
Conceptualization, F.K., Y.W. and J.L.; methodology, C.C., S.T., C.Z. and J.L.; investigation, F.K. and Y.W.; resources, F.K., Y.J. and Y.W.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, C.C., S.T. and J.L.; supervision, F.K. and Y.W.; funding acquisition, F.K. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (No. BLX201820).  
Not applicable.  
The raw data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
Thanks to the Key Lab of State Forestry and Grassland Administration on Forestry Equipment and Automation for providing the equipment for algorithm validation and data analysis.  
The authors declare no conflict of interest.
## 3 Results and Discussion
According to the geometrical calculations [37], variation in each of the abovementioned parameters may add an error to the final microcantilever deflection of up to 10% of the measured value. In the former studies, fluids were mostly Newtonian. However, the interpretation of microcantilever deflection is difficult in the case of an unknown fluid where the changes in dynamic viscosity and density are unknown. Moreover, if the fluid is non-Newtonian or multi-phasic, interpretation of experimental results and changes in microsystem deflection will be unclear. This, as well as the formula in the previous section, clearly states how viscosity and density of fluids are related when it comes to detecting changes in a fluid through an external force or effect. Thus, the new SPMF [3] which is designed for kinematic viscosity measurement addresses the interconnectivity of density and viscosity.
## 5 Results and Analysis of Experiment
### Rainfall Intensity Level Retrieval Results

Table 4: The statistical results of the rainfall intensity level. Figure 15: The actual rainfall intensity. (**a**) The actual rainfall intensity measured by the rain gauge; (**b**) The actual rainfall intensity after classification. In addition, due to the interference of the ocean wave echo in the wave area, the rainfall intensity fitting function cannot be determined by statistical analysis method. Therefore, the RZE method for rainfall intensity level retrieval in the wave area needs to be further studied in future work.
## 3 Dual-Stack Single-Radio Communication Architecture (DSSRCA)
### Experimental Evaluation
#### 3.4.2 Reliable Protocol Experimentation
Multiple occurrences of such events increases the collision probability, which occasionally results in transmission losses. The experiment was repeated using a link with a 40% probability of loss. It is apparent in Figure 10 that the data rate has behavior opposite to that of the previous experiment. The more the message size increases, the smaller the data rate becomes. This happens because bigger message blocks increase the probability of losing a single frame, which results in discarding all other frames already received by the 6 LoWPAN. The same drop from 500 bytes to 600 bytes observed in Figure 9 is again observed here. It should also be observed that if there is no loss in the network, it is desirable for the application to send bigger blocks because fewer acknowledgment messages at the application level are required. However, when there are losses in the network, it is more advantageous for the application to send messages of a smaller size, because the loss of a single frame does not discard the frames already received. In the next experiment, the message size was fixed at 75 bytes and the size of sliding window was changed with a link of 20% loss probability, in a network with 50 ms of latency. The results obtained are shown in Figure 11. It can be observed that increasing the window size up to the limit of seven, the data rate increases. Thus, it allows us to conclude that it is always advantageous to use a window size larger than one when there is latency in the network; otherwise, the number of collisions causes the data rate to decrease. Figure 10: Data rate in the communication between the Sink and MDC, in a link with 40% loss probability. Figure 9: Data flow between mobile data collectors (MDC) and Sink.
# Exploratory Data Analysis and Searching Cliques in Graphs
Multidimensional scaling is a commonly applied technique to find the essential dimension of the data set. The multidimensional scaling procedure tries to assign points of a \(d\)-dimensional space to each object such that the distances between the points in the space provide a good approximation of the entries in the distance matrix. If the agreement between the computed and given distances is satisfactory, then we have successfully identified the essential dimension of the row space in the data set. In this paper we will work with graphs with finite nodes and finite edges. We assume that the graphs do not have multiple edges and do not have loops. It is customary to refer to this class of graphs as finite simple graphs. Let \(G=(V,E)\) be a finite simple graph. Here, \(V\) is the set of vertices in the graph \(G\) and \(E\) is the set of edges in \(G\). A subset \(C\) of \(V\) is called a clique if two distinct nodes of \(C\) are always adjacent in \(G\). The clique \(C\) of \(G\) is called a \(k\) clique if \(C\) has exactly \(k\) elements. For each finite simple graph \(G\) there is a uniquely determined integer \(k\), such that \(G\) admits a \(k\) clique but \(G\) does not admit any \((k+1)\) cliques. This uniquely determined \(k\) is called the clique number of \(G\) and is denoted by \(\omega(G)\). It is a well-known fact from the complexity theory of algorithms that computing \(\omega(G)\) is an NP hard optimization problem (See [9]). The main result of this work is that locating approximately 1-dimensional linear manifolds in a data set can be reduced to locating a clique in a tactically constructed auxiliary graph. Typically, the more nodes the clique has the more objects the approximately 1-dimensional linear manifold consists of. At this juncture we have to point out that the connection between the number of nodes in the clique and the number of objects in the approximately 1-dimensional linear manifold is more subtle. It may be the case that a smaller clique helps to locate a larger 1-dimensional manifold. Therefore, the problem of finding 1-dimensional linear manifolds with an optimal number of objects is computationally more demanding than determining the clique number of the auxiliary graph. As we have seen, computing the clique number is a computationally demanding task. In practical computations we do not look for cliques with an optimal size. We will be satisfied with finding large enough cliques. Decomposing the data set into a (not necessarily disjoint) union of approximately 1-dimensional linear manifolds reduces to the problem of covering the nodes of the auxiliary graph by (not necessarily disjoint) cliques.
# PrimaVera: Synergising Predictive Maintenance
## 4 Predictive Maintenance Process Model
### PrimaVera Approach
#### 4.1.3 Prognostics  
A key innovation of the PrimaVera project is to leverage the two most prominent classes of prognostic algorithms: data-driven and model-based methods.  
Data-driven approaches, based on advanced data analytics [106; 107], are fast and fully automatic. However, they require large amounts of data and, as black box methods, their outcomes are difficult to understand and interpret.  
Model-based approaches are based on knowledge on the physics of failure [108; 109]. They are created using domain knowledge, which typically is rather time consuming. Their main advantage is that they give insight in why systems fail, and how these failures are related to (changes in) operating conditions. By combining data-driven and model based approaches, PrimaVera aims to obtain the best of both worlds: automatic, versatile and understandable prognostic methods.  
In particular, we plan to develop model learning techniques to automatically derive appropriate models from empirical data sets--initial results for learning fault tree models appeared in [110; 111]. Further, as data are often incomplete, domain knowledge can be incorporated to define the initial structure of the model. Further, data analytics will be utilised to determine the model parameters in traditional physical prognostic models, like fatigue crack growth and corrosion models, from data collected on real systems. The other way around, simulations with physical models will be used to generate additional data in cases where real data sets are too limited for machine learning applications. These hybrid prognostic methods will result in considerably more accurate and efficient methods.  
As many failures of today's systems are caused by software rather than hardware, the project will also work on predicting software failures. Again, a hybrid approach will be adopted, leveraging model-based software reliability techniques (e.g., reliability growth models [112], software metrics [113]) with data-driven techniques (e.g., observed failure times [114], mining the characteristics of software failures [115]).
## 4 CNQF Prototype and Testbed Implementation
### CNQF Prototype Development  
#### 4.1.1 RMS Implementation  
A resource broker (RB) has been built where resource management _decision logic_ are implemented. High level policies entered into the GUI policy editor are mapped into a set of _commands_ within the decision logic in the RB (the logic implementation will be different for CN-, WA-, and FARBs functionalities). These commands are Java code segments that invoke the services of instances of other CNQF entities (such as network monitors and resource controllers) that are installed and running at the PEPs in the network. For instance, a high level CNQF policy that has an action part: _Configure Edge Router_ will be mapped to the following RB Java code:  
_new ResCon_  
_ResCon.ConfigureEdge()_  
This creates an instance of the Resource Controller class and calls its _ConfigureEdge()_ method that provides edge router configuration services for the RB policies. The RC instances are installed on the PEPs (Linux-based routers on our testbed). The installed RC opens an RMI port (Remote Method Invocation, TCP port 1099) to listen for incoming RB commands. RC is implemented as a _ResConImpl_class. _ResConImpl_ implements methods such as _ConfigureEdge(), ConfigureCore()_ and a host of others that can be invoked by the RB via the RMI communication interface. Details of the methods' implementation will depend on the PEP type _i.e._, whether it is a router, gateway or an access point and also the specific APIs available for interacting with the internal mechanisms. Thus, the _ResConImpl_ class is the _wrapper class_ which can be customized to wrap the functionality of specific QoS mechanisms within the heterogeneous PEPs thus _exposing a homogenous interface that enables technology independent Resource Brokers to be achieved_.
# An Ensemble Model for Multi-Level Speech Emotion Recognition
###### Abstract  
Speech emotion recognition is a challenging and widely examined research topic in the field of speech processing. The accuracy of existing models in speech emotion recognition tasks is not high, and the generalization ability is not strong. Since the feature set and model design of effective speech directly affect the accuracy of speech emotion recognition, research on features and models is important. Because emotional expression is often correlated with the global features, local features, and model design of speech, it is often difficult to find a universal solution for effective speech emotion recognition. Based on this, the main research purpose of this paper is to generate general emotion features in speech signals from different angles, and use the ensemble learning model to perform emotion recognition tasks. It is divided into the following aspects: (1) Three expert roles of speech emotion recognition are designed. Expert 1 focuses on three-dimensional feature extraction of local signals; expert 2 focuses on extraction of comprehensive information in local data; and expert 3 emphasizes global features: acoustic feature descriptors (low-level descriptors (LLDs)), high-level statistics functionals (HSFs), and local features and their timing relationships. A single-multiple-level deep learning model that meets expert characteristics is designed for each expert, including convolutional neural network (CNN), bi-directional long short-term memory (BLSTM), and gated recurrent unit (GRU). Convolutional recurrent neural network (CRNN), based on a combination of an attention mechanism, is used for internal training of experts. (2) By designing an ensemble learning model, each expert can play to its own advantages and evaluate speech emotions from different focuses. (3) Through experiments, the performance of various experts and ensemble learning models in emotion recognition is compared in the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus and the validity of the proposed model is verified.  
**Keywords:** ensemble learning; multi-level technology; acoustic features; speech emotion recognition; deep learning model +
Footnote †: journal: Accepted: 23 December 2019; Published: 26 December 2019
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 2 Geologic and Production Overview  
### Geologic Setting of the Oil Field  
The oil field under investigation is located in the west-central part of the Sultanate of Oman on the eastern flank of the Rub Al Khali Basin, near the international border with Saudi Arabia. The field was discovered in 1978. Since then, a total of 32 wells have been drilled in the Haushi reservoirs of the Middle and Lower Gharif. The Haushi Group, deposited during the Late Carboniferous to Early Permian period, consists of the glaciogenic Al Khalta Formation and the marginal marine to fluvial Gharif Formation overlaid by the Khuff Formation [13]. As the deposits are located close to the marine basin, the facies are transitional from marine to the west to basinal to the east. The Gharif Formation exhibits complex lithological architecture due to these reasons. The Lower Haushi reservoir of the Lower Gharif (LHS), which represents the first post-glacial Permian transgression, is the most prolific of the reservoirs, producing 45\({}^{\circ}\) API oil. The sands are mostly of glacio-lacustrine or deltaic origin, almost entirely composed of silica with intensive quartz cementation, which increases with the depth and degrades permeability. Porosity is up to 23%. Fissures were detected in core samples, resulting in permeabilities of up to 200 mD. More details about the Gharif Formation and the Haushi reservoirs in this field can be found in [14; 15].
# Research on Grooved Concrete pavement Based on the Durability of Its Anti-Skid Performance
## 3 Results and Conclusions
### Results of Anti-Skid Tests
#### 3.1.3 Comparative Analysis of Test Results for Different Groove Forms  
Table 14 shows results for the two best-performing T and LT grooved samples in the above anti-skid tests.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Level** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline k1 & 0.605 & 0.760 & 0.917 & 0.636 & 0.709 & 0.886 \\ k2 & 0.718 & 0.752 & 0.764 & 0.728 & 0.705 & 0.792 \\ k3 & 0.751 & 0.750 & 0.677 & 0.710 & 0.763 & 0.791 \\ k4 & 0.846 & 0.775 & 0.729 & 0.793 & 0.728 & 0.637 \\ k5 & 0.605 & 0.760 & 0.917 & 0.636 & 0.709 & 0.886 \\ Range & 0.241 & 0.088 & 0.281 & 0.253 & 0.112 & 0.269 \\ Factors in Primary and Secondary Order & C\textgreater{} F\textgreater{} D\textgreater{} A\textgreater{} E\textgreater{} B & & & & & \\ Optimal Scheme & C1F1D5A4E5B4 & & & & & & \\ \hline \end{tabular}  
* 2 Where A is T width, B is T depth, C is T spacing, D is L width, E is L depth, and F is L spacing.  
\end{table}
Table 13: Range analysis of LT samples 2.  
Figure 8: Columnnar analysis diagram of the range analysis of LT samples.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sample No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \(\mu\) & 0.758 & 0.623 & 0.573 & 0.544 & 0.528 & 0.574 & 0.919 & 0.863 & 0.505 & 0.727 \\
**Sample No.** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** \\ \(\mu\) & 0.696 & 0.492 & 0.756 & 1.039 & 0.772 & 0.949 & 0.567 & 0.904 & 1.200 & 0.609 \\
**Sample No.** & **21** & **22** & **23** & **24** & **25** & & & & \\ \(\mu\) & 0.824 & 1.157 & 0.652 & 0.586 & 0.797 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Skid-resistant performance of LT samples.  
As can be seen in Table 14, \(\mu\) of LT samples (5, 5, 30, 6, 4, 10) is approximately 46.2% greater than that of T samples (6, 4, 20). The anti-skid performance of the LT samples used in this experiment is significantly better than that of the T samples.  
It can be seen that groove forms have a very important influence on anti-skid performance, and that LT schemes can effectively improve the anti-skid performance of pavement. It can be inferred that the embedded squeeze effect of the tire-road interface, the effective contact area, and the resistance to change all increase as the groove number increases; therefore, the sliding resistance increases.
###### Abstract  
In the present study, nickel phosphorous alloys (Ni-P) and Ni-P/ silicon carbide (SiC) nanocomposite coatings were deposited by electrodeposition on steel substrates in order for their microstructural properties to be assessed while using SEM, XRD, and three-dimensional (3D) profilometry as well as nanoindentation. The amorphisation of the as-plated coatings was observed in all cases, whereas subsequent heat treatment induced crystallization and Ni3P intermetallic phase precipitation. Examination of the surface topology revealed that the surface roughness follows the deposition characteristics and heat treatment induced microstructural changes. Additionally, substantial improvements in mechanical properties, including hardness, yield stress, and elasticity modulus, were obtained for the Ni-P, Ni-P/SiC nanocomposites when heat treated as seen from the nanoindentation results. A Finite Element Analysis (FEA) was developed to simulate the nanoindentation tests that enable the precise extraction of the Ni-P and Ni-P/SiC nanocomposite coatings' stress-strain behavior. It is shown that the correlation between the nanoindentation tests and the computational models was satisfactory, while the stress-strain curves revealed higher yield points for the heat-treated samples.  
Keywords:Nanocomposite coatings; Electrodeposition; Nickel-phosphorus; SiC nanoparticles; Hardness; Finite Element Analysis +
Footnote †: journal: Appl. Sci. 2019, 9, 2901; doi:10.3390/appr9142901