esa-sceva/satcom-chunk-collection huggingface dataset:
# Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor
## 3 Optimization Algorithms
### Grey Wolf Optimizer (GWO)
#### 3.2.4. Search for Prey (Exploration)  
In this stage, the grey wolves diverge from each other to search for prey and converge to attack prey. Some random values are selected to model the divergence to force the search agent to diverge from the prey. The \(\vec{C}\) vector contains random values in \([0,2]\). This is mainly performed to increase the range of exploration and avoid stagnation, perhaps finding a better target. Considering this scheme, the search hyperparameters are selected, and in this case, the prey acts as the vector of engine parameters. The algorithm evaluates each search agent and selects the one with the lowest fitness as the alpha wolf. The algorithm will repeat the process until the selected number of iterations is completed.  
The flowchart in Figure 4 is explained as follows. First, the program initializes the population to search for the constants to optimize, within the upper and lower limits. The main program receives the parameters through Simulink in order to calculate the motor current and velocity responses and compare with the actual responses using the fitnessfunction. The algorithm chooses the three best solutions closest to the actual response which become the alpha, beta, and gamma agents, thus emulating the hunting behavior of wolves. The program is initialized again with a new iteration, now creating the agents within the range of solutions of alpha, beta, and gamma agents of the last iteration; in this way, the same cycle is repeated until reaching the maximum iteration, obtaining the best solution.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Search agents & 100 & Number of random solutions proposed of each metaheuristic algorithm. \\ Upper limits & R \(\leq\) 1, L \(\leq\) 0.1, K \(\leq\) 0.1, B \(\leq\) 0.001, J \(\leq\) 0.001 & Upper search limit for each DC motor parameter \\ Lower limits & R \(\geq\) 0.1, L \(\geq\) 0.001, K \(\geq\) 0.005, B \(\geq\) 0.00001, & Lower search limit for each DC motor parameter \\ Iterations & 100 & Repetitions of each metaheuristic algorithm. \\ Fitness function & \(\mathit{Fitness}=\frac{1}{\sqrt{\sum(I-I_{k})^{2}+\sum(\omega-\omega_{k})^{2}}}\) & Function used to evaluate the performance of each random solution (\(f:i\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: General parameters applied in metaheuristic algorithms.  
Figure 4: The flowchart of the GWO algorithm.  
This algorithm has been used in multiple optimization works to estimate the parameters of DC motors. It offers good optimization in robust analyses, as in [35], where it is sought to optimize the fractional-order PID controller in motor speed control.
## 7 Conclusions and Future Directions  
To overcome the problems with the existing algorithms in channel state inference in WSNs, such as high complexity, poor generalization, impracticability and so on, we develop a new channel estimation method with variational tempering for MIMO-OFDM scenario. A ground truth information extraction algorithm based on variational tempering is proposed and implemented. Our CEVTI provides insights that account for multiple factors in inferring truth and can generalize to higher dimensions and finds better local optimum. As a variation of SVI, CEVTI can find out the optimal hyper-parameters of channels with fast convergence rate, and can be applied to the case of CDMA and uplink massive MIMO easily. As can be seen in Section 5, CEVTI can iteratively minimize the objective function with multiple dimensions. We demonstrate the performance of CEVTI through numerical simulation. The BER, convergence rate, and mutual information comparisons with the five existing CE algorithms show that CEVTI outperforms others under different noise variance and signal-to-noise ratio. Furthermore, the results show that the more parameters that are considered in each iteration, the faster the convergence rate and the lower the non-degenerate bit error rate with CEVTI. Analysis shows that CEVTI has satisfying computational complexity, and guarantees better local optimum. Therefore, this paper has contributed to the quest for developing efficient algorithms in artificial advanced sensor networks. Possible future research directions include investigation of how the graph structure can impact the performance of CEVTI, and constructing inference algorithms that can suit more complex situations.  
Conceptualization, J.L. and Y.C.; methodology, J.L. and Y.C.; software, J.L.; validation, M.L and N.C., and S.M.N.I.; writing-original draft preparation, J.L.; writing-review and editing, S.M.N.I.; supervision, Y.C. and N.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61802097), the Project of Qianjiang Talent (Grant No. QJD1802020), and the Key Research & Development Plan of Zhejiang Province (Grant No. 2019C01012).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
WSNs  
Wireless Sensor Networks  
V  
Variational Inference  
CEVTI  
Channel Estimation  
C  
Channel Estimation  
Compressive Sensing  
Channel State Information  
Message Passing  
Evidence Lower Bound  
Monte Carlo Markov Chain
# Gridshells in Recent Research--A Systematic Mapping Study
### Focus and Terms  
The following section presents the focus of the article and defines the main terms applied. See also Figure 1. A shell structure is a curved surface structure that derives its strength from its geometry, typically constructed from timber, metal, concrete or masonry. Shells are generally thin in the direction perpendicular to the surface, and work through a combination of membrane (mainly) and bending action [10] (p. 31). A Gridshell, lattice shell or reticulated shell is a general term for a shell constructed from some kind of grid of linear members rather than a surface. The terms are used inconsequently. Gridshell is most often used to describe those built with timber and bent during erection (bending active), while the term reticulated shell is more typically used for metal structures, often barrel vaults, spherical caps, or braced domes, for industrial purposes.  
Bending active gridshell, post formed, kinematic, elastic, active-bent or strained is a gridshell where the members are initially flat and bent into shape. The grid is typically quadrilateral, with one or several layers in each direction. After bending, the members are locked from further moving, typically by tightening connections and adding bracing as diagonal members or cables for providing in-plane shear stiffness. The most well-known example is probably the Mannheim Multihalle by [PERSON] [11]. A gridshell is often termed discrete gridshell or rigid gridshell when it consists of straight members connected at a node. As opposed to a bending active gridshell, the nodes typically cater for the geometrical changes between members, horizontal angle, tilt angle and torsion, depending on the global shape and topology. When discussing shape, global shape or just shape describe the overall geometry of the entire shell, representing both the building envelope as an architectural element and the structural system.  
Topology, network, or grid pattern are terms for the logic and order of connectivity between the members of a gridshell. The most common topologies are quadrilateral and triangular; however, numerous topologies are possible. The topology contains information about the members and the nodes that connect them, and in a mesh representation, this corresponds to edges and vertices. A member is the strip of material that can be either continuous, like in a bending active gridshell, or shorter pieces between two nodes, like in a discrete gridshell. The node, joint, connection or knot is the physical object connecting two or more members. Whereas several members meet at the same point in a digital model, the node is designed to create the connection away from this point to make it physically possible. Nodes are typically constructed in metal regardless of member material because of the strong forces, especially bending moments, it needs to transfer.  
Figure 1: Gridshell terminology.
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 4 Experimental Setup  
Although widely applicable, the proposed processing comes in favor of man-made structures monitoring, which usually correspond to urban areas. The dynamics of the built up area are given by the economic development of that particular region. Nevertheless, there are situations when scene displacements are influencing the land cover evolution. Landslides, earthquakes, or subsidence are the most common phenomena. In this context, we consider Buzau and Constanta cities in Romania as the test areas (Figure 3), as they are both located near important epicenters in East Europe. The most important aspect from the point of view of the proposed experiment is that both test areas contain urban regions, which are usually characterized by a high density of PS due to the presence of built-in structures. Constanta city is located in the south-east part of Romania, in Dobrogea Plain, on the Black Sea coast. It has an area of 125 km\({}^{2}\), while its port extends across 30 km, being the largest harbor of Black Sea. Buzau city is located in Munteria region, in the Baragan Plain, close to the south-east curvature of the Carpathian Mountains. Its surface equals 81 km\({}^{2}\). Since both cities are located in flat areas, similar classes are expected to be identified across both test regions. The surrounding areas of both cities are dominated by agricultural fields. Buzau city is crossed by the homonymous river; therefore water areas are present in both regions (in different proportions). Forest regions are also present, but not dominant, in both test areas. Considering the main objective of this work (direct comparison between detected PS and the LDA classification output), it was essential that the test scenes contain urban areas, since the stable scatterers are expected to be detected mostly in such regions. The presence of the aforementioned structures (river and sea) makes these two cities an interesting case study.  
Figure 3: Optical view of Buzü (**A**) and Constanta (**B**) test regions (© Google Earth).  
The goal is to perform a temporal analysis to identify specific categories of evolution and try to test if it is possible to provide a classification for the PS points. The process will emphasize the impact of regular low intensity earthquakes over the stability of built-up area. In order to demonstrate the efficiency of the proposed methodology, the experiments were made on data acquired with two different sensors.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Autonomous Vehicles
The system intends to facilitate the exchange of data obtained from video files between train wagons and train stations to monitor passenger movements and traffic. As a result, passenger movements are constantly tracked and monitored. In [87], the authors leveraged QoS settings in DDS middleware to avoid regular communication interruptions in underground environments as a train moves from one station to another. The system uses the middleware for data exchange between trains and stations during stops despite being disconnected from the global networks. However, finding the optimal QoS settings is non-trivial.
## 3 The Applications of the ZMS EOS
### The Impact of Impurities (CH\({}_{4}\) and N\({}_{2}\)) on the CO\({}_{2}\) Storage Capacity  
CH\({}_{4}\) and N\({}_{2}\) are non-condensable impurities, which reduce the CO\({}_{2}\) storage capacity in geological formations. Based on the EOS of the CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) fluid mixtures, the impact of CH\({}_{4}\) and N\({}_{2}\) on CO\({}_{2}\) storage capacity can be calculated quantitatively by the normalized storage capacity proposed by [PERSON] et al. [9]. For a certain reservoir with a certain volume, the normalized storage capacity of the CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) gas mixtures is shown as  
\[\frac{A}{A_{\text{CO}_{2}}}=\frac{\rho}{\rho_{\text{CO}_{2}}(1+\frac{M_{\text{ CH}_{4}}}{M_{\text{CO}_{2}}}+\frac{M_{N_{2}}}{M_{\text{CO}_{2}}})} \tag{18}\]  
where \(A\) and \(A_{\text{CO}_{2}}\) are the mass of CO\({}_{2}\) in the mixtures and the mass of pure CO\({}_{2}\) under the same volume, respectively. \(\rho_{\text{CO}_{2}}\) and \(\rho\) are the density of the pure CO\({}_{2}\) and CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) gas mixtures, respectively; \(M_{\text{CH}_{4}}\), \(M_{N_{2}}\) and \(M_{\text{CO}_{2}}\) are the mass of CH\({}_{4}\), N\({}_{2}\), and CO\({}_{2}\) in the mixture, respectively. \(A/A_{\text{CO}_{2}}\) is the ratio of the mass of CO\({}_{2}\) per unit volume in the mixture to that in the pure state. The value of \(A/A_{\text{CO}_{2}}\) can be viewed as the normalized storage capacity for CO\({}_{2}\) (i.e., the storage capacity for structural trapping of CO\({}_{2}\)). For pure CO\({}_{2}\), the normalized storage capacity (\(A/A_{\text{CO}_{2}}\)) is 1. For the CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) gas mixtures of a given composition, \(M_{\text{CH}_{4}}\), \(M_{N_{2}}\) and \(M_{\text{CO}_{2}}\) are also known. \(\rho_{\text{CO}_{2}}\) can be calculated by the above-mentioned equation of pure CO\({}_{2}\) fluid and \(\rho\) can be calculated by the ZMS EOS.  
The normalized storage capacity of the CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) fluid mixture calculated by the ZMS EOS is plotted in Figure 12. Figure 12a shows the normalized storage capacity of a given composition at different temperatures. With the increase in pressure, the normalized storage capacity decreases at first and then increases, and there is a minimum point. When the temperature changes, the pressure corresponding to the lowest point of the normalized storage also changes. Figure 12b shows the normalized storage capacity of different compositions at the same temperature, which indicates that the content of impurities is much larger and the normalized storage capacity is much smaller.
# Evaluation of a Newly Developed Device for Resonance Disorders: A Clinical Pilot Study
## 2 Materials and Methods
### Enrollment Procedures  
(1) Prior to assessment, the therapist thoroughly disinfected two nasometers (Nasometer II 6450, KayPENTAX, Lincoln Park, NJ and Smart Naso(tm), Humanopia, Co., Ltd., Pohang, Republic of Korea) with ethanol. The therapist turned on the PC and calibrated the Nasometer II 6450 device using the associated program according to the manufacturer's instructions. Additionally, the therapist activated the power of Smart Naso(tm), connected it to a laptop (LG Electronics Gram 15-inch, 15Z95N-GA56K) via Bluetooth, and subsequently checked Smart Naso(tm).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Normal Group (\(n=9\))** & **Hypernasality Group (\(n=9\))** \\ \hline Age, years & 13.56 (\(\pm\)5.88) & 14.44 (\(\pm\)7.00) \\ Sex (male/female) & 2:7 & 4:5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Participant characteristics.  
(2) On the day of the participant's visit, the velopharyngeal insufficiency (VPI) screening test was performed to evaluate nasalance while conducting an auditory perceptual evaluation using the VPI articulation differential inspection scale (Table 1) [17].  
The VPI articulation differential inspection scale ranged from 0 to 4 points (0 points: normal, 1 point: minimal, 2 points: mild, 3 points: moderate, and 4 points: severe). Patients scoring 0 points were placed in the normal group, and those scoring \(\geq\)1 point were placed in the hypernasality group.  
(3) After the screening test, participant nasalance was measured using the newly developed Smart Naso(tm) or Nasometer II 6450 according to a randomized order table. Following a 5 min interval, nasalance was reassessed using Smart Naso(tm) or Nasometer II 6450, following the same procedure.  
The inspection words were derived from the K SNAP Test, which was developed by the Dental Treatment Center at Jeonbuk National University Hospital through translation of the SNAP Test-R [5; 20].
## 3 Results and Discussion
### Groundwater Quality and Assessment for Drinking
Sulfate concentrations in Mornag shallow groundwater samples, which range from 65 to 1174.96 mg/L in 2013, from 61.94 to 1621.7 mg/L in 2016, and from 172.604 to 1480.166 mg/L in 2019, derive from the potential dissolution of sulfate minerals and/or the leaching of gypsum-rich marls from the Triassic and upper Oligocene formations. The lowest values are recorded in the southwest of the basin and increase to the northeast according to the direction of groundwater flow (Figure 7). Most groundwater samples are distinguished by SO\({}_{4}\) concentrations higher than the [43] standard, which is about 250 mg/L. This can result in an unpleasant taste and gastrointestinal alteration manifested by diarrhea [43]. Figure 6: (**a**) Spatial distribution maps of cations for 2013; (**b**) spatial distribution maps of cations for 2016; (**c**) spatial distribution maps of cations for 2019. The spatial distribution of HCO\({}_{3}\)\({}^{-}\) displays concentrations ranging between 109.8 and 480 mg/L in 2013, from 244 to 616.1 mg/L in 2016, and from 160 to 600 mg/L in 2019. This can be related to the water-rock interaction in relation to the dissolution of relatively abundant carbonate minerals at the foot of the surrounding hills. The HCO\({}_{3}\)\({}^{-}\) content in Mornag shallow groundwater is often higher than the standard WHO drinking water level (380 mg/L), which makes the groundwater of the study area unsuitable for consumption.
## 3 Result
### Spatiotemporal Changes in FPAR  
#### 3.2.1 Spatial Changes  
According to results from the analysis of the new dataset, the terrestrial ecosystems in China had a mean FPAR of 0.37 during the period of 1980 to 2018 (Figure 1a). Higher FPAR measures, from 0.39 to 0.59, were found over forest areas. South China (SC) had the highest FPAR (0.59) followed by Southeast China (SE), Central China (CC), Southwest China (SW), and Northeast China (NE) with regional means of 0.57, 0.52, 0.50, and 0.39, respectively. Lower FPAR measures were found over grasslands and desert areas with lower values from 0.20 in Northwest China (NW) and the Tibetan Plateau (TP) to 0.25 in Inner Mongolia (IM) and 0.36 in North China (NC). Meanwhile, evergreen broadleaf forests (EBF) had the highest FPAR (0.60) followed by other forests, croplands, shrubland and grasslands (Figure 1c).
## 5 Case Study
### Simulation Results
#### 5.3.3 Effect of on-Body Path Loss Model  
Given the heavy attenuation of the human body to communication at 2.4 GHz, node placement is expected to play a significant role in the PER performance. Moreover, on-body path loss model differentiates depending on the body size of BSN wearer. To investigate the effect of on-body path loss model we compare the inter-user interference under three different node placement. The path loss model parameters are listed in Table 4. Figure 5 shows the average PER over different node placement. Thus on-body path loss model influences the inter-user interference obviously. In order to increase probability of successful packets, node placement on the same side of body is preferred.  
Figure 4: Average PER for different transmission power, where PS represents the transmission power.
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 4 Nephele Project as Enabler for SAR Operations
### The Search and Rescue Use Case in NEPHELe
**Application tier**: the inputs and requests coming from the presentation tier are collected and the application components are activated to execute mission tasks. All application components for supporting the application logic are foreseen and run on the different layers of the continuum. As an example, localization functions and camera streaming will run directly on the robots/drones. Three-dimensional SLAM solutions and video analysis will run on the edge in cases where the IoT devices (i.e., robots and drones) do not have the required resources. Other more advanced and computationally demanding functions and components will instead run on the cloud (or edge) through VO-supportive functions. Examples of these are AI algorithms for mission control, risk assessment, danger prediction, optimization problems for path planning, and so on. In all of these components, new data can be produced, and old data can be accessed from the data tier. **Data tier**: this foresees a storage element for storing processed images or historical data about the SAR mission. The data produced by the IoT devices (drones, robots, sensors) will be compressed, downsampled and/or secured before being stored for future use by the application tier. These functions on the data will be running, if possible, on the drones and robots themselves, to reduce the data transmissions. Data analysis and complex information extraction will be offered by support functions from the VO. The data can be either stored on the VO data storage or on remotely distributed storage.