esa-sceva/satcom-chunk-collection huggingface dataset:
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
## 4 User-Impact on the Proposed Smartphone Antenna  
One of the human body parts that most frequently touch the smartphone is the user's hands, which usually has a negative impact on the smartphone antenna performance [28; 29; 30]. The fundamental radiation characteristics of the 5G smartphone antenna in the presence of the user's hands is studied in this section. The radiation beams of the presented phased array design for a scanning range of 0-75 degrees in the vicinity of the user's hand are represented in Figure 17. As seen, it provides good radiation beams with high directivity values at different angles. Table 3 summarizes the effect of the user's hands on the antenna performance. Compared with the antenna in free space, the overall losses of the smartphone antenna characteristics in terms of antenna gain, radiation, and total efficiencies are less than 3 dB, \(-\)2 dB, and \(-\)2.5 dB, respectively.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Reference** & **Bandwidth** & **Efficiency** & **Gain (dB)** & **Element** & **Isolation** & **Scanning** \\
**(GHz)** & **(\%)** & **Gain (dB)** & **Size (mm\({}^{2}\))** & **(dB)** & **Range** \\ \hline
[7] & 21–22 & - & 8–12 & - & 14 & 0\({}^{\circ}\)\(\sim\)75\({}^{\circ}\) \\
[8]  
The SAR function of the proposed phased array smartphone antenna is studied in Figure 18. SAR, one of the most critical issues of mobile terminal systems, is a measurement function for absorbed electromagnetic waves by the human body, which should be as low as possible [31; 32]. The distance between the 5G smartphone antenna and the user's hand in the z-axis is less than 15 mm. As illustrated in Figure 18, the antenna has sufficient SAR values for different placements. There is a bit of a difference in the SAR values for top and bottom placements of the 5G antenna, which was predictable due to different distances.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 3 Model Introduction and Problem Analysis
### SatTEG Construction  
Unlike traditional terrestrial MEC networks, which have a fixed topology, satellite MEC networks have periodic topological changes due to the high-speed movement of satellites. It makes the transmission time delay between satellites uncertain. The fundamental difficulty to be addressed in the subsequent study is how to design the satellite MEC network as a mathematical model in a reasonable manner. Like the previous works [11; 24; 25], our paper first characterizes the dynamic satellite network using time-expanded graphs. The LEO satellite network experiences periodic topological changes as it travels around the globe, separating each operational period \(T\) into \(N\) time slots and determining the length of each time slot \(\Delta t=\frac{T}{N}\). Within each time slot, the topological state of the satellite network can be considered relatively stable and constant.  
The set of all satellites in the LEO satellite network can be stated as \(V=\{v_{1},\cdots,v_{i},\cdots,v_{d}\}\), in which \(d\) is the number of LEO satellites. In each time slot, every satellite establishes a connection with the two satellites preceding and following it in the same orbit, as well as the two satellites closest to it in adjacent orbits. The resulting network of connections among satellites in time slot \(t\) can be represented by the connection status \(C(t)\).  
\[C(t)=\begin{pmatrix}C_{11}&\cdots&C_{1d}\\ \vdots&\ddots&\vdots\\ C_{d1}&\cdots&C_{dd}\end{pmatrix} \tag{1}\]  
where \(C_{ij}=1,i,j\in d\) means that the two satellites are connected, otherwise \(C_{ij}=0\).  
Further, the connectivity of LEO satellite nodes during their operation cycle can be expressed as a route table \(SatTEG\) by combining the connection status \(C(t)\) of all time slots. By using \(SatTEG\), we can obtain the connectivity path of any satellite in any time slot during the satellite network operation. In addition, we can get the number of hops transmitted between satellites, denoted as _hop_.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.2.4 Sea Level Change Projections  
NASA's Sea Level Projection Tool ([[https://sealevel.nasa.gov/ipcc-ar6-sea-level-project-tool](https://sealevel.nasa.gov/ipcc-ar6-sea-level-project-tool)]([https://sealevel.nasa.gov/ipcc-ar6-sea-level-project-tool](https://sealevel.nasa.gov/ipcc-ar6-sea-level-project-tool)), accessed on 2 May 2024) was used to measure the potential impact of Sea Level Change on the coastal regions and heritage assets in the Mediterranean Sea. The data are based on the findings of IPCC's 6 th Assessment Report for Working Group 1 [77]. The tool incorporates all five RCP or SSP (Shared Socioeconomic Pathways) and five Global Mean Surface Temperatures. All data can be downloaded for further analysis and processing in a NetCDF format with a separate file for each IPCC scenario [77; 78]. The datasets are split into different contributing components, i.e., Antarctic Ice Sheet, Greenland Ice Sheet, Glaciers,  
Figure 3: EGMS L3 mean vertical velocity in the Mediterranean Coasts in mm/year.  
Land Water Storage, Ocean Dynamics, Vertical Land Motion (non-climatic processes), and total expected Sea Level Rise (integrating all individual components) [41; 77]. The dimensions of the NetCDF file include the location where the SLR projection is calculated, the years at which projection data are available (i.e., from 2020 to 2150 using a 10-year increment), and the quantiles which refer to the quantile probability box of the SLR with 107 available values ranging from 0.0 to 1.0 in 0.01 increments. The \"Sea Level Change\" variable refers to SLR since the AR6 reference period, in millimeters. The \"Sea Level Change Rate\" variable depicts 10-year average rates in mm/year.  
The pre-processing of NASA's Sea Level Rise datasets involves reading the NetCDF files per scenario (e.g., RCP: 2.6, 4.5, 8.5) and extracting information on SLR projections for 2050 and 2100 per location point (expressed as the median value of the quantile intervals between 0.14 and 0.79), using Python Scripts in Google Colab Notebooks. The extracted data are saved in csv format and converted to a raster mosaic of 30 resolution using desktop GIS software (QGIS v.3.28) The mosaic is then re-projected from WGS84 to ETRS89-LAEA. A raster calculation is performed to convert SLR units from mm/year to m/year. The output of this process consists of six individual raster files that refer to different RCP scenarios for the two target years (2050, 2100). Each output raster is clipped to the area of interest boundaries to depict the expected SLR in meters. The process is repeated for both Total Sea Level Rise and vertical motion datasets. The two are then subtracted, and the vertical motion value is replaced by EGMS's datasets' set values, which are more accurate and precise at the local level of the Mediterranean Sea. Figure 4 provides information on SLR projection for the target year 2100 under RCP 8.5 scenario.  
Figure 4: SLR projection for 2100 under RCP 8.5 scenario.
## 6 System Development
### Apache Kafka Application Setup
#### 6.1.4 Consumer Grouping  
Apache Kafka offers a specific way of delivering messages to consumers. A consumer represents an application instance that subscribes to a Kafka topic to receive messages. Upon subscribing, the consumer will be assigned to a consumer group, indicated at runtime as a consumer group identifier. Multiple consumers can have the same consumer group ID, thus creating a consumer group. In this case, when a message is emitted on a topic, only the first available consumer in that group will receive the message (Figure 11).  
If consumer C1 becomes unavailable, the other consumers are going to pick up the incoming messages because they are part of the same consumer group. This behavior is used in places where multiple backup running instances for a microservice exist. If multiple instances are required to run in parallel, multiple partitions will be required. Each partition can be thought of as a separate \"stream\" of messages within the same topic.  
In the proposed application setup, there are instances where all the consumers need to receive the same message (broadcasting information to all subscribers). To achieve that behavior, each consumer needs to be assigned to a different consumer group (Figure 12).  
In the context of the real-time document collaboration system, Apache Kafka was used as a message broker to distribute events across the microservices. Besides distributing messages, Kafka was also used for grouping microservices as follows.  
Consumer groups: In this setup, multiple document content manipulation instances were grouped to provide a highly available set of workers ready to handle incoming editing messages from subscribed clients. This broadcasting strategy ensures that there is always a microservice instance ready to intercept a message from the Kafka stream.  
Figure 11: Group consumer.  
Broadcasting groups: For the RTC (real-time collaboration) microservices, it is crucial that each instance receives the broadcasted document editing messages emitted by a user. This ensures that all subscribed users can receive updates regarding a document. A consumer group allows only one instance to consume a message at a time. By creating multiple consumer groups, each with a uniquely identifiable consumer, it can be guaranteed that all subscribers will receive a message. The downside of this approach is the higher resource consumption of the Kafka cluster [E3].
## Materials and Methods
### Fiber Pullout Test and Matrix Fracture Test  
A single fiber pullout test was employed to measure the properties of the interface between the fiber and the matrix [21]. Figure 2b illustrates the experimental setup. Protruding PVA fiber was wrapped with an acrylic film and fixed to the grip attached to the load cell of the test equipment. The test was carried out under displacement control at the speed of 0.2 mm/min. Figure 2c shows the test setup for the matrix fracture toughness test [22]. A clip gauge for measuring the crack-mouth-opening displacement (CMOD) was installed and the CMOD was measured according to an increase in the load using the displacement control method (0.001 mm/s) to calculate the fracture toughness (\(K_{m}\)) using Equation (1).  
\[K_{m}=\frac{P_{c}}{b\sqrt{d}}F(\alpha), \tag{1}\]  
\[F(\alpha)=29.6\alpha^{0.5}-185.5\alpha^{1.5}+665.7\alpha^{2.5}-1017.0\alpha^{3.5}+638.9\alpha^{4.5}, \tag{2}\]  
where, \(P_{c}\) is the critical (peak) load, \(b\) is the thickness of a specimen, \(d\) is the length from bottom face of a specimen to roller axis, \(\alpha\) is \(a/d\), and \(a\) is the crack length.
## 2 Turbulence Modeling
### Scale-Resolving Simulation  
The present RANS results were validated against the data attained using a more sophisticated SRS method. Specifically, to generate a reference high-fidelity solution, we used the stress-blended eddy simulation (SBES) approach, which represents a hybrid RANS-LES method specially developed for industrial CFD [4]. Basically, SBES follows the same principle as detached-eddy simulation (DES), using RANS for near-wall flows and LES for bulk flows [22], with the turbulent eddy-viscosity being determined through model blending:  
\[\mu_{t}=f_{s}\mu_{t}^{\rm RANS}+(1-f_{s})\mu_{t}^{\rm LES}. \tag{5}\]  
The main difference with respect to previous hybrid formulations is that SBES employs a much stronger shielding function \(f_{s}\) by adopting a novel methodology of combining RANS and LES approaches to achieve optimal performance. In practice, SBES exhibits a faster transition between the two different solutions, resulting in lower eddy-viscosity levels and more resolved turbulent fluctuations. This makes it a suitable method for highly separated shear layer flows of aeronautical interest.  
The SBES approach provides a certain flexibility to choose different formulations for the RANS and LES components. In this study, the \(k\)-\(\omega\) SST model [7] was used for RANS in conjunction with the wall-adapting local eddy-viscosity (WALE) model [23] for LES. While the \(k\)-\(\omega\) SST model is commonly taken as the benchmark for developing data-driven methods in applied computational aerodynamics [24], the WALE approach was selected as a straightforward LES method that automatically provides vanishing turbulent viscosity for laminar shear flows.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Controlling** & **Range** & **Default** \\ \hline \(C_{\rm sep}\) & separation behavior & 0.7–2.5 & 1.75 \\ \(C_{\rm mw}\) & near wall behavior & \(-\)2.0–2.0 & 0.5 \\ \(C_{\rm mix}\) & spreading rate of free shear flows & 0.5–1.0 & \(C_{\rm mix}^{\rm opt}\) \\ \(C_{\rm jet}\) & free jet flows & 0.0–1.0 & 0.9 \\ \(C_{\rm corner}\) & corner flows & 0.0–1.5 & 1.0 \\ \(C_{\rm curv}\) & curvature correction & 0.0–1.5 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specific function and recommended range of variation for the GEXO parameters [21].  
Finally, it should be noted that at present, some important details of both the above modeling procedures are still proprietary. For instance, the exact definition of the GEKO functions \(F_{1}\), \(F_{2}\), and \(F_{3}\) in (4), as well as that of the SBES function \(f_{s}\) in (5) (containing the complexity of the model), is not publicly available.
## 2 The Test Case and the Data Sets  
The data used in the present analysis were collected from a multi-megawatt plant located in southern Italy, including of 9 turbines with 2 MW of nominal power each. The three bladed rotor has a diameter of 90 m and has a working range that spans from 4 m/s to 25 m/s of wind speed, where the nominal power is reached at about 12 m/s. This test case wind farm has been considered to be particularly interesting, as its location shows harsh environmental conditions caused by the presence of mountain ridges, gusts and frequent unsteady flow. As a consequence, the wind turbines are frequently subjected to strong solicitations that may cause faults induced by fatigue loads on gearboxes and bearings.  
When innovative practices of condition monitoring have to be applied on industrial plants, the low intrusivity of the survey methods plays an important role, as hopefully the collection of data should not influence the energy production. The wind farm object of the study is a valid test case to implement non-intrusive condition monitoring techniques, as it natively includes the measurement of gearbox and nacelle vibrations in its TCM system: the acquired signals can be remotely available for consultation and downloading.  
From the turbines a total of 30 signals were acquired, some of which have been used as selection parameters to ensure that the operative conditions were similar among the turbines. In fact, to guarantee the maximum rigorousness of the method, only the time histories at nominal power were included in the analysis.  
Other data employed in this study concern quantities providing an overall perspective on the status of the machines, in particular:  
* Temperature of the main bearing \(T_{MB}\) (K);
* Temperature of the gear bearing \(T_{GB}\) (K);
* Temperature of the generator bearing \(T_{GENB}\) (K);
* Oil temperature \(T_{oil}\) (K);
* Pressures \(P_{Ref}\) and \(P_{aft}\) before and after the filtering of lubrication and cooling oil circuit (bar).  
The mentioned quantities all belong to the SCADA system, which in its OPC-DA (Open Platform Communication-Data Access) disposal to the end user have 10 min of sampling time.  
To apply a more detailed focus on some significant elements of the turbines, in the present analysis the SCADA data were coupled with TCM ones where vibrations are measured on the bearings and tower with up to 12 KHz frequency. A total of 8 high time resolution signals were acquired that are comprehensive of:  
* Nacelle accelerations in the Fore-Aft,Side-Side and vertical directions (NacXdir, NaClYdir, NacZdir);
* Main bearing rear and front side (MnBrgRr,MnBrgFr);
* Gearbox high speed shaft rear and front (GbxHssRr,GbxHssFr);
* Intermediate shaft speed (GbxIss).  
The general gearbox layout is represented in Figure 1: this is the typical three stages gear used in multi-MW machines with the input planetary stage and two parallel stages.
## 3 Results and Discussions
### Basic Benchmarking of the Imaging System
We note that the experimental protocol of interleaved irradiation acquisition  
Figure 5: The response of the imaging system to the luminescence of the sample was measured from reflectance standards differing in reflectivity, under the same illuminance, and acquired at the minimal exposure time (1 us) possible of the CCD imager. (**A**) shows the image acquired from a reflectance standard of 99% reflectivity. The vertical strip was an artifact that resulted from iterative specular reflection of the diffused light between the surface of the reflectance standard and the imaging optics. The intensity was assessed over two localized regions of approximately 21 pixels each way (horizontally and vertically). One spot was local to the brightest section of the vertical strip, and another spot was beyond the upper tip of the vertical strip, corresponding to the projected position of the illuminating fiber. (**B**). The intensity assessed at the zone marked by the dashed circle in (**A**), as a function of the reflectivity of the reflectance standards, is plotted. The inset in (**B**) shows the focus on the sample surface. The right-pointing arrow in (**A**) indicates the central spot around which the data corresponding to the plot of (**B**) were processed. involved changing the time of acquisition over a range of 9 orders of magnitude, from 1 us to 1000 s. The linearity of the acquisition to the length of the duration of acquisition ranging over the 9 orders of magnitude, however, was not assessable due to the lack of a calibrated stable light source that would fit for this task of benchmarking.
# Experimental Study on Anisotropic Mechanical Characteristics of Shale under Triaxial Loading
## 4 Analysis of Anisotropic Mechanical Characteristics of Shale
### Anisotropic Index of Shale's Mechanical Parameters
\tag{6}\]  
According to Figure 13, the anisotropic indexes of shale are obviously affected by confining pressure \(\sigma_{3}\), and the anisotropic indexes of elastic modulus \(E_{\theta}\) are positively correlated with \(\sigma_{3}\), while the anisotropic indexes of compressive strength \(\sigma_{\text{cs}}\) are negatively correlated with \(\sigma_{3}\) in the range of 0 to 35.314 MPa, and when the \(\sigma_{3}\) is greater than 35.314 MPa, the indexes of compressive strength \(\sigma_{\text{cs}}\) are positively correlated with \(\sigma_{3}\). The reason is that the compressive strength of shale samples with a \(90^{\circ}\) bedding angle will increase rapidly with \(\sigma_{3}\), and its growth rate is greater than that of the samples with a \(0^{\circ}\) bedding angle. When the \(\sigma_{3}\) increases from 30 MPa to 40 MPa, the compressive strength of shale samples with a \(90^{\circ}\) bedding angle exceeds that of samples with a \(0^{\circ}\) bedding angle, which becomes the maximum compressive strength of shale with different bedding angles. As a result, the anisotropic index of compressive strength has a transition value between the \(\sigma_{3}\) of 30 MPa and 40 MPa, that is, the minimum value. The anisotropy of strength and deformation parameters of shale samples is closely related to the obvious differences in layered structure and mechanical properties of constituent materials, and the macro failure mode of shale samples is also deeply related to the anisotropic characteristics.
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 2 Methods
### Susceptible-Infected-Recovered (SIR) Model  
The SIR model has been extensively utilized in literature and by policymakers to predict the spread of many diseases. The basic SIR model divides the population (N) into three compartments as:  
S: Susceptible healthy population that is at risk of getting infected.  
I: Infected population who have symptoms whether mild or severe  
R: Recovered population who has recovered from the disease and has gained immunity  
Thus, the total population can be written as:  
\[N=S+I+R \tag{7}\]  
In the basic SIR model, the population is assumed to be constant; no births during the prediction of the epidemic stages and people are equal in terms of recovery and infection rates. There exist numerous variations of the SIR model that include many other parameters to model the spread such as the death rate, birth rates, departure rates and vital dynamics [10; 16]. The model estimates the rate of change of the susceptible, infected and recovered population by differential equations respectively as:  
\[\frac{dS}{dt}=-\frac{\beta}{N}IS \tag{8}\]  
\[\frac{dl}{dt}=\frac{\beta}{N}IS-\gamma I \tag{9}\]  
\[\frac{dR}{dt}=\gamma I \tag{10}\]  
With the parameters defined as:  
\(\beta\) : The infection rate and defined as the proportion of the infected population per day.  
\(\gamma\) : The recovery rate and defined as the proportion of the recovered population per day.  
Also, the reproduction number of the disease is an important indicator of the severity of the spread and is defined as the total number of days that an infected person can spread the disease to others. Using the infection and recovery rate parameters, the SIR model estimates the reproduction number of the disease \(R_{o}\) as the ratio:  
\[R_{o}=\frac{\beta}{\gamma} \tag{11}\]  
WHO estimates the reproduction number of the COVID-19 to be within the range of 2-2.5 [17; 18]. The reproduction number is an estimated number that depends on infection and recovery rates of each region and used as an indicator of the severity of the disease spread. To fit the model to the actual data values, a stochastic SIR-individual contact model (SIR-ICM) is utilized with multiple iterations to minimize the fitting deviations. In addition, the stochastic SIR-ICM is used to simulate the time-varying number of infected and susceptible individuals. The Residual Sum of Squares (RSS) approach is utilized to find optimal parameters of the infection and recovery rates as:  
\[RSS(\beta,\gamma)=\sum_{l_{o}}^{t_{end}}\bigl{(}I_{actual}(t)-I_{predictal}(t )\bigr{)}^{2} \tag{12}\]  
The models were simulated using Matlab (MathWorks, Natick, MA, USA) and RStudio (RStudio PBC, Boston, MA) environments using combined hardcoded parts as well as open-source packages of SIR and logistic models found in [15; 19; 20]. In the following section, the results are presented to illustrate the current situation in Kuwait and the forecast of the upcoming repatriation phase with intervention scenarios in the discussion.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 5 Experimental Results and Discussion
### Comparison of Proposed Technique with Existing Techniques
#### 5.3.1 Pairwise Canonical Correlation Analysis
Figure 10: Comparison of Proposed Technique (PCCA-SVM) with Existing Techniques on PU dataset. (**a**) Ground Truth, (**b**) Segmentation map after SVM, (**c**) Segmentation map after RF, (**d**) Segmentation map after Naive Bayes, (**e**) Segmentation map after KNN.