esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 3 Methodology
### Generation of Source Test Cases  
The first step is to generate source test cases [38]. Test case selection strategies are developed to reveal better faults detection. Some of the traditional test-case generation techniques are random test generation through random model or Boolean model, structural or program-based test generation techniques, behavioral or specification-based techniques, the symbolic evaluation method, combinatorial techniques, and fault-based test generation techniques, etc. After studying the relevant literature work, it became clearer that the random test generation method is widely used because of its unbiased nature for the generation of test cases [6; 10; 41; 42]. Though random testing is unbiased and easy to implement, there are some limitations of this technique. For example, many of the test cases are redundant and unrealistic with the unfair distribution of parametric values.  
In the proposed framework, a dataset of brain MRI is used for the testing of the edge detection algorithm. Generally, the images in the datasets have identical parametric values such as horizontal and vertical dimension, bit depth and resolution. The dataset is selected where these parametric values are genuinely diverse. For the selection of source test cases, we have defined black-box testing and white-box testing techniques as our selection criteria. Equivalence class testing is a black-box testing technique where a domain is divided into distinct sub-domains (classes) and is further divided into weak equivalence class testing and strong equivalence class testing [43]. To generate source test cases, we have selected random test cases by using strong equivalence class testing to select images with truly diverse parametric values. We have defined following five domains based on the image characteristics: image horizontal dimension, image vertical dimension, image bit depth, image resolution, and type of image. Each domain is further divided into distinct sub-domains.  
Figure 2: Proposed methodology.  
After the generation of source test cases, we checked the adequacy of test cases using the white-box testing technique to test the thoroughness of the code coverage which includes all three types of images (T1, T2, flair) accumulatively accounting for 100 percent the branch/statement coverage. If the test suite does not fulfill 100 percent branch coverage, then new test cases are required to improve the coverage criteria not achieved through the cases of the previous test suite.
# Study of Diagnostic Accuracy: Fundus Photography vs. Optical Coherence Tomography
## 2 Materials and Methods
### Test Procedure
#### 2.2.2 Statistical Analysis  
After completing the evaluations, the group of ophthalmologists and optometrists analyzed and compared the results; the group of ophthalmologists represents the gold standard.  
All statistical analyses were performed using SPSS version 27. Differences between groups were analyzed using Pearson-Chi-square tests for categorical variables. Logistic regression models were used to analyze the odds ratios of the outcomes with 95% confidence intervals.  
To carry out this stage, archives were used with the evaluations of the two groups of evaluators collected in tables. In these tables, the information of the patients collected at the beginning was the number assigned to the patient, right or left eye, age, sex, and CDVA. Secondly, the assessment in stage I (FP) and stage II (FP + OCT) by ophthalmologists and optometrists took place, with numerical assessments. The meanings of the numerical evaluations are as follows: 0: not evaluable, 1: healthy, 2: referable, and 3: preferential referable.  
This information was used to perform statistical calculations.  
During this step, the diagnostic precision, sensitivity, specificity, likelihood ratios, and kappa index calculations were performed, in order to quantify the improvement resulting from the joint use of the FP and the OCT. At the same time, we aimed to determine whether or not the degree of agreement between the ophthalmologist and the optometrist improved when using both techniques.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing
### Data Preprocessing
#### 3.2.3 Filtering Anomalous GPS Tracks  
Abnormal GPS trajectory refers to when the data that the position recorded by the GPS device deviate from the actual position, and research shows that data drift is more likely to occur when the positioning object is in a stationary or low-speed moving state and the key pick-up and drop-off points are almost all in the taxi when it is stationary or moving at a low speed, so the drift data cleaning work is very important. In this paper, the DP (Douglas-Peucker) algorithm is used to clean the drifted data, and its basic principle is to connect the three trajectory points A, B, and C into a triangle; then, the area of the triangle is  
\[S=\sqrt{l(l-a)(l-b)(l-c)} \tag{1}\]  
where \(a\), \(b\), and \(c\) are the lengths of AB, BC, and AC, respectively, and \(l\) is half the perimeter of triangle ABC. The drift data points can be judged according to the following equation:  
\[h=2s/c\geq error \tag{2}\]  
where \(h\) indicates the distance from point B to AC, i.e., the offset distance between the trajectory point B and the two trajectory points A and C before and after. When \(h\) is less than the offset threshold error, point B is judged to be within the error allowance; on the contrary, point B is judged to be drifting data and needs to be eliminated. The offset threshold error can be determined according to the accuracy of the equipment and the width of the road.
## 3 Implementation
### Storage Layer  
As part of the F3 BFS architecture, the storage layer is deployed on the controller, secondary controller, and storage nodes, containing the core components of F3 BFS. Unlike other nodes in the cluster, the controller node does not actually store the user data, but instead stores the cluster's metadata. As a redundant copy of the main controller, the secondary controller can be used for high availability purposes. If the main controller node goes down for whatever reason, the secondary controller will step in and continue to serve the cluster until the main controller is fixed.  
The rest of the nodes in the cluster, apart from the controller and secondary controller, serve as the storage nodes, and are thus responsible for storing the chunked user data. To be noted, in order to keep the simplicity of the overall architecture, although F3 BFS provides high availability of the controller node, it does not have a mechanism to guarantee the data safety in the storage node. As a consequence, it is recommended that users create RAID volumes and use those volumes as the datastore of the storage node.
## 6 Estimating UCS Using Hoek-Brown Criterion Equation  
The database in our lab showed that the appropriate fitting equation for Iddejord granite would be non-linear equations. Therefore, in addition to the parabolic equation to fit the test data, the Hoek-Brown criterion equation for intact rock can also be used to fit the test data for estimating the corresponding UCS. The Hoek-Brown criterion equation for intact rock is non-linear and is presented as follows [41]:  
\[\sigma_{1}=\sigma_{3}+\sigma_{ci}\left(m_{i}\frac{\sigma_{3}}{\sigma_{ci}}+1 \right)^{1/2} \tag{3}\]  
where  
* \(\sigma_{1}\) and \(\sigma_{3}\) are major and minor principal stresses, respectively;
* \(\sigma_{ci}\) is the uniaxial compressive strength of the intact rock;
* \(m_{i}\) is a material constant for the intact rock.  
From the test results for each sample, as shown in Table 4, the five data points from each sample were fitted with the best-fit Hoek-Brown criterion equation. It is noted that the data at zero confinement are not included for this fitting process. The fitting work was conducted using the Rocdata program version 5.007 [44]. Results of fitting the Hoek-Brown criterion for the tested data of samples A, B, and C are presented in Figures 12-14. The results of \"Estimated-UCS\" with this method are 201, 215, and 217 MPa for samples A, B, and C, respectively. Results of the calculation of the \"Estimated-UCS\" and its error are presented in Table 6.  
Figure 12: Sample A—fitting the tested data with Hoek–Brown failure criterion and the resulting UCS. The coefficient of determination (R\({}^{2}\)) is 0.9966.  
Figure 14: Sample C—fitting the tested data with Hoek–Brown failure criterion and the resulting UCS. The coefficient of determination (R\({}^{2}\)) is 0.9987.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**“Estimated-UCS”**} \\ \cline{3-4}
**Sample** & \multicolumn{2}{c}{**“True-UCS”**} & \multicolumn{2}{c}{**“Heok–Brown Fitting)**} \\ \cline{3-4}  & **MPa** & **MPa** & **Error to the** \\  & & & **“True-UCS”** \\ \hline A & 197.7 & 201.0 & 1.7\% \\ B & 205.8 & 215.6 & 4.8\% \\ C & 212.8 & 217.2 & 2.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The “Estimated-UCS” using the Hoek–Brown criterion equation compared to the “True-UCS” from the tests.  
Figure 13: Sample B—fitting the tested data with Hoek–Brown failure criterion and the resulting UCS. The coefficient of determination (R\({}^{2}\)) is 0.9962.
## 5 Applications Across Diverse Sectors
### Recent Innovations
#### 5.1.1 Multi-Material AM  
Advancements in multi-material 3D printing technology have made it possible to deposit many materials simultaneously, resulting in the production of intricate objects with a variety of mechanical, electrical, and optical characteristics. This development finds applications in electronics, aircraft, and healthcare [53, 54]. At the voxel level, additive manufacturing offers precise control of material composition and characteristics, providing unmatched complexity and functionality. MIT researchers have created a device that prints at a precision of 50 \(\upmu\)m per voxel using up to eight different materials. This system uses tiny nozzles to deposit photopolymer resin drops, which are then hardened with ultraviolet light, allowing the creation of objects with varied hardness, clarity, color, and electrical conductivity in one session [55, 56].  
This capacity creates new opportunities in areas including electronics, Internet of Things (IoT), autonomous manufacturing, and space exploration. For instance, Nano Dimension's DragonFly system exemplifies successful multi-material additive manufacturing. This technology specializes in producing electronic devices, enabling the creation of electronic circuits and parts, like multi-layer circuit boards, by printing them layer by layer. With the DragonFly technology, complicated electrical designs, quick prototype creation, versatility in design, and the direct integration of electronic components into 3D-printed things are all made possible by printers that deposit both conductive and non-conductive inks [57]. It is utilized in research and development, aerospace, defense, and the automotive industry, showcasing the extensive applications and potential of multi-material 3D printing in high-tech electronics production [46, 58].  
Nano Dimension's DragonFly system supports printing with multiple materials by using various specialized inks, each designed for specific applications and the needs of different circuits [58]:  
* _Conductive inks_: Conductive inks containing metallic particles like silver or copper are used for printing conductive traces and interconnects;
* _Dielectric and insulation inks_: Dielectric inks are used to form insulating layers that are essential for separating the parts of a circuit, whereas insulating inks offer the structural support and strength needed for the printed item;
* _Functional inks_: Functional inks, such as magnetic or optical variants, add specific functionalities like sensors or antennas.  
This technology makes it possible to build complex electronic circuits with multiple layers and various functions, providing versatility in both design and operation tailored to the specific needs of the electronic device being created [46].
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 4 The New Clustering Routing Approach
### Configuration Step
Under this scheme, the residual energy of a generic node \(n_{i}\) is determined by Equation (13). \[E_{r}^{t}(n_{i})=\left\{ \begin{array}{cc}E_{r}^{t-1}(n_{i})-E_{TX}(l,d),&E_{ r}^{t-1}(n_{i})>0\\ 0,&E_{r}^{t-1}(n_{i})\leq 0\end{array} \right. \tag{15}\]  
On the other hand, the residual energy consumed by a cluster head \(CH_{j}\) is computed by the formulation defined in Equation (14). \[E_{r}^{t}\big{(}CH_{j}\big{)}=\left\{ \begin{array}{cc}E_{r}^{t-1}\big{(}CH_{ j}\big{)}-E\big{(}CH_{j}\big{)},&E_{r}^{t-1}\big{(}CH_{j}\big{)}>0\\ 0,&E_{r}^{t-1}\big{(}CH_{j}\big{)}\leq 0\end{array} \right. \tag{16}\]  
In the configuration step, the LS-II is used to identify the initial set of CHs. Likewise, it is generated the initial network structure according to the energy consumption model. Once the LS-II has selected the cluster head, the rest of all nodes are assigned to the nearest CH. However, if the distance of the generical node to its nearest CH is longer than the distance to the base station, then the generical node should not be considered as part of this cluster. Instead, its information will be transmitted directly to the base station.
## 3 FEM Analysis and Results
### Simulation Approach to Detect the AE Source Location  
The simulation of the AE signals propagation in a steel plate was carried out in accordance with the experimental scheme (see Figure 1). The optimal design of AE sensors and the acoustic source was implemented to simulate broadband signals, the spectral characteristics of which were close to the VS150-RIC transducers used in experiments with a steel plate.  
At the initial stage, the dependence of the calculated coordinate \(X\) on the amplitude of the recorded signals \(u_{m}\) was investigated using a linear location technique. As an example, the dependence of the calculated coordinate on the amplitude of the simulated signals \(X(u_{m})\) when \(X=\) 100, 200, and 300 mm is graphically represented in Figure 9.  
Figure 8: The frequency response of (**a**) AE source and (**b**) AE sensor.  
As follows from the scatter plots in Figure 9, the largest variability of \(X\) values was established when the AE source was located at a distance of \(X=100\) mm from AE sensor #1. The minimum error of the AE source location was noted when the signal amplitude constituted at least 80 dB. It was defined without reference to the AE simulator distance to the AE sensor #1. When the AE source was removed at \(X=300\) mm, an increase in the positioning error was indicated for signals with a maximum amplitude level of \(u_{m}\leq 52\) dB. It is also worth mentioning that the scatter of the \(X\) coordinate values is associated with the threshold discrimination of the AE signals oscillating shape. The latter ones have been recorded in the framework of modeling the propagation process of elastic waves in the isotropic metal plate.  
Figure 9: The dependence of the calculated coordinate \(X\) on the amplitude \(u_{m}\) of the simulated signals at the distance between the AE source and AE sensor #1 (a) 100 mm, (b) 200 mm and (c) 300 mm.
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
## 3 Results: Phase Shifter Prototype Performance  
The design has been validated through a prototype. Figure 6.a yields some details of the shifter prototype, and the TRL (transmission-reflection-line) kit for the calibration prior to the measuring process. The measurement results are provided in the rest of the figures in Figure 6, in order to validate its proper functioning and performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\mathbf{L_{x1}}\) & \(\mathbf{L_{x2}}\) & \(\mathbf{L_{x3}}\) & \(\mathbf{L_{x  
Figure 6: _Cont._  
As it can be noticed, there is a good agreement between the design performance and the prototype measurements. The insertion loss levels of the prototype are higher than the expected ones in the design. There exists a variety of possible causes for that: parasitic components of the varactor, conductive epoxy resistance, and roughness of the shifter printed line limits. For the sake of completeness, Figure 7 compares the simulation phase results with the experimental ones (Figure 7a), and provides an insight of the linearity of the device in terms of phase/voltage relation (Figure 7b).  
Figure 6: Prototype results of the 18 GHz phase shifter: (**a**) prototype and TRL kit (central image corresponds to a microscope zoom view); (**b**) \(\arg(S_{21})\); (**c**) \(\Delta\arg(S_{21})\) (related to \(\min\arg(S_{21})\) value, 0 v); (**d**) \(S_{21}\); (**e**) \(S_{11}\)].  
Figure 7: Phase shifting performance: (**a**) comparison between simulation and measurement phase results of the 18 GHz phase shifter; (**b**) phase/voltage relation (the dashed line is the linear reference and the grey zone is the one that provides at least 360\({}^{\circ}\) variation).  
Notice that the simulation results in Figure 7a are in terms of capacitance and the experimental ones are in terms of voltage. It must be noticed that there is a non-negligible difference between pairs of simulation and experimental curves. However, the phase range of variation is preserved and the phase shifter prototype is fully functional, only needing a voltage/phase conversion table. In addition, it must be pointed out that, although the phase/capacitance relation is not linear, it is compensated by additional non-linear behavior of the capacitance/voltage relation. It is worth mentioning that, although the device provides a quasi linear phase/voltage response at the central frequency, this linearity is strongly degraded at the band limits, as it can be seen in Figure 7b. However, since the prototype provides a complete phase shift of around 600\({}^{\circ}\), part of this possible variation can be sacrificed in order to increase the linearity of the device, as detailed in Figure 7b, where the grey region is the one that provides at least 360\({}^{\circ}\) phase variation.  
For the sake of completeness, the next table (Table 2) provides a comparison of the performance of our device with the previously referenced literature works [10; 11; 12; 13; 14; 15].
## 2 Power System Network  
The power system network is comprised of generation, transmission, and distribution. In generation, all the power generation plants, like thermal, hydro, nuclear etc., are connected in parallel. The generated power is stepped up and transmitted to reduce transmission loss. Finally, in the distribution stage, the power is supplied to different types of consumers with respect to their requirements. Figure 1 shows the schematic structure of the power system network. There are two thermal units, two hydro, and two nuclear power generation units that are connected in parallel, which will be connected to the transmission network. In the second stage of the transmission network, the co-generation plants like sugar plants and cement plants are connected to the distribution bus, and finally, in the distribution network, the voltage is stepped down, and it will supply power to different consumers like industrial, commercial, and domestic consumers. The power fluctuation severely affects consumers in the distribution stage, which is limited to some extent through the use of the microgrid, which is supplied through renewable energy sources like wind, PV, fuel cells, and diesel power. The main advantage of the microgrid is that it is able to instantaneously meet any increase in load that cannot be reciprocated by thermal, hydro, and nuclear generating units. The liability of the system will improve, and the microgrid is a better solution for supplying power to a remote village community.  
Figure 1: Power system network.  
_Proposed DC Microgrid Architecture for Rural Communities_  
The proposed DC microgrid architecture is for remote villages. In a village, the various loads such as commercial loads, agricultural vehicle loads, and priority load (street lighting) are considered. The power generation capacity of each sourceis 5 kW, as shown in Figure 2. The power generated in the fuel cell and PV is DC, but for wind it is AC, so the AC is converted into DC by means of a converter.  
To reduce voltage variation, a converter is used in all DG sources. The power produced from non-conventional energy sources areinconsistent to supply load continuously due to its intermittent. Therefore, batteries are utilized to ensure power supply to consumers without any interruption. Here, two batteries are used. One is interfaced to the load side, and the other is on the DC bus. The DC bus battery is only used for the priority load, and the load side battery is used for the commercial and agricultural vehicle loads. The diesel generator is also connected to the DC distribution network to deliver the load when available power in the DC microgrid is not sufficient. AC power generated by the diesel generator is converted to DC power by a converter.  
Figure 2: Proposed DC microgrid architecture.
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
### Resource Management Layer  
The resource management layer abstracts all smart healthcare elements as reachable resources that can be managed remotely by the network management system. This layer consists of the network management functional areas, including Fault, Configuration, Accounting, Performance, and Security (FCAPS), mobility, and identity management [100]. The structure of this layer aligns with the Telecommunications Management Network (TMN) reference model, which provides a hierarchical framework for managing open systems. As illustrated in Figure 6, the TMN's layered approach enables efficient resource monitoring and control, ensuring the seamless operation of smart healthcare networks.
## 2 Materials and Methods
### Validation and Interpretation of SAR Shorelines and Change Rates
#### 2.5.2 Salinas Beach
As for the storm surge, the Global Ocean Surges dataset is a hindcast storm surge dataset generated with the Regional Ocean Model System, which is a three-dimensional oceanic model that solves the Reynolds-averaged Navier-Stokes equations using the Boussinesq approximation. In the study area, the dataset spans from 1985 to 2020 with a temporal resolution of one hour and a spatial resolution of 0.08 degrees. Additionally, to compare the results that we can obtain from SAR and optical sensors, satellite-derived shorelines (WLs) from S2 images are obtained with the CoastSat tool [42]. The water lines represent the water-sand interface at the moment in which the image was taken. After a visual inspection to remove lines located outside the inter-tidal area, 169 WLs were kept between 2015 and 2022. The position of the shorelines from optical images was obtained as the distance from the baseline in the 61 beach profiles in Figure 7. CoastSat WLs were transformed into datum-based SLs using the total water level (TWL) at the moment in which the image was taken and the beach slope from the topographic surveys to project the WL horizontal position onto the mean low water (MLW) datum (84 cm above the chart datum). Figure 8 shows the windows where CoastSat WLs and datum-based CoastSat SLs are located across beach profile No. 6 as an example. Finally, the ability of these optical CoastSat WLs and SLs to detect morphological changes in the beach profile at the same two timescales (inter-annual and seasonal) is assessed. However, here, the correction to derive SLs from CoastSat WLs could only be applied before the year 2021 due to the available hydrodynamic data (TWL).