esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Swarm Satellite Field Aligned Current Data  
It is important to note that the FAC data are Level 2 data, derived from the magnetic field measurements of the Swarm satellite using Ampere's law. The FAC data were archived on the Swarm Data Access Server, managed by the European Space Agency (ESA) ([[https://swarm-disse.e.esa.int/](https://swarm-disse.e.esa.int/)]([https://swarm-disse.e.esa.int/](https://swarm-disse.e.esa.int/)), accessed on 1 December 2024). FAC strength is attributed to the difference in conductivity between the ionospheric hemispheres [33]. The steady flow of the FAC is highly sensitive to small changes in the ionospheric state, and any variations in the magnetic field are reflected in the FAC data. Figure 4a shows the projection of Swarm-B tracks as red lines over the geographic map, and the corresponding FAC data along these three paths are displayed in the three rightmost panels (Figure 4b-d). The order of Swarm-B tracks 1, 2, and 3 is indicated in boxes adjacent to each path at the bottom of the geographic map. The FAC data showed perturbations along the first and third tracks, whereas the second track exhibited no significant disturbances. The most prominent perturbation occurred along the third track over the African continent, appearing later than the second track over the Pacific Ocean. Notably, the first and third tracks pass closer to the Beirut port explosion site, as marked by a green circle. FAC disturbances were observed not only in the northern hemisphere near the explosion site, but also at the conjugate point in the Southern Hemisphere, as indicated by the blue traced field line in Figure 4a.  
Figure 4: **(a)** The three Swarm-B paths (1, 2, and 3) projected onto the geographic map, **(b)** field-aligned current (FAC) along path 1 (17:25–17:50 UT), **(c)** FAC along path 2 (18:00–18:40 UT), and **(d)** FAC along path 3 (18:52–19:25 UT). The corresponding path numbers are indicated in small boxes to the right of each path on the map.  
The FAC data retrieved from Swarm-B along the three different paths and their corresponding wavelet transformations are shown in the upper and lower rows of Figure 5. However, the FAC data for paths 1 and 3 exhibited transient oscillations in the frequency range of 4-8 s, and the Northern and Southern hemispheric frequencies were not well-mirrored. In contrast, the FAC data for the second track exhibited significantly different behaviours, showing oscillations with a much longer periodic time range of approximately 64 s, as indicated by its high-power amplitude. This longer periodicity suggests a more stable, non-transient variation in the FAC along path 2.
_Article_  
**Tracking the Movements of Juvenile Chinook Salmon using an Autonomous Underwater Vehicle under Payload Control**  
**[PERSON] 1,*, [PERSON] 2(r), [PERSON] 2 and [PERSON] 3**  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Received: 30 May 2019; Accepted: 17 June 2019; Published: 20 June 2019  
**Featured Application: Using autonomous underwater vehicles equipped with telemetry-based payload control to locate marine fish tagged with acoustic transmitters.**  
**Abstract:** An autonomous underwater vehicle (AUV) under payload control (PC) was used to map the movements of juvenile Chinook salmon (_Oncorhynchus tshawtytscha_) tagged with acoustic transmitters. After detecting a tag, the AUV deviated from its pre-programmed route and performed a maneuver designed to enhance the location estimate of the fish and to move closer to collect proximal environmental data. Nineteen fish were released into marine waters of southeastern Alaska. Seven missions with concurrent AUV and vessel-based surveys were conducted with two to nine fish present in the area per mission. The AUV was able to repeatedly detect and estimate the location of the fish, even when multiple individuals were present. Although less effective at detecting the fish, location estimates from the vessel-based surveys helped verify the veracity of the AUV data. All of the fish left the area within 48 h of release. Most fish exhibited localized movements (milling behavior) before leaving the area. Dispersal rates calculated for the fish suggest that error associated with the location estimates was minimal. The average movement rate was 0.62 body length per second and was comparable to marine movement rates reported for other Chinook salmon stocks. These results suggest that AUV-based payload control can provide an effective method for mapping the movements of marine fish.  
**Keywords:** autonomous underwater vehicle; AUV; payload control; acoustic telemetry; Chinook salmon; _Oncorhynchus tshawtytscha_; fish telemetry; juvenile salmon; marine fish movements
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
## 3 Results  
The proposed architecture is implemented on DE2i-150 FPGA development board, developed by Terasic [8]. A simplified schematic of the DE2i-150 development board is shown in Figure 12 (refer to the Terasic site [8] for complete details). The board is an embedded platform with Intel N2600 Atom Dual core processor (Intel Corporation, Santa Clara, CA, USA) [9] coupled with Altera's Cyclone IV GX FPGA (Altera (Acquire by Intel), Santa Clara, CA, USA). The Atom processor has 64-bit Instruction set, 1M cache running at 1.6 GHz clock speed, and is connected to external DDR3 memory. The Atom pairs with Intel@NM10 Express Chipset through Direct Media Interface (DMI) to provide rich I/O capabilities and flexibility via high-bandwidth interfaces, such as PCI Express, Serial ATA (SATA), mSATA, and Ethernet. Cyclone IV FPGA is connected to Atom through PCI Express (PCIe) bus and NM10. The FPGA is connected to 128 MB SDRAM (32 bits width), 4 MB SSRAM and 64 MB Flash memory with 16-bit mode. Both Atom and FPGA has a VGA connector to interface with monitor  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Seed** & **Visit-Flag** & \begin{tabular}{c} **Current Round** \\ **Votes (\(V_{C}\))** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Votes (\(V_{M}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Cluster** \\ **Number (L)** \\ \end{tabular} &
\begin{tabular}{c} **Identified** \\ **Nuclei (C)** \\ \end{tabular} \\ \hline (x1, y1) & 1 & 2 & 2 & 1 & (a1, b1) \\ (x2, y2) & 1 & 2 & 3 & 2 & (a2, b2) \\ (x3, y3) & 0 & 0 & 0 &. & (a3, b3) \\ \hline \end{tabular}
\end{table}
Table 1: Illustration of data table format. Table entry numbers are randomly filled.  
Figure 11: Architecture of MS clustering.  
In this paper, the proposed architecture is implemented using only the FPGA (Atom processor is not used).  
In the implementation, the block RAMs on FPGA are used to store intermediate results instead of available 128 MB SDRAM. Because, this SDRAM has a latency of four clock cycles, with a maximum allowed clock frequency of 100 MHz, which can degrade the proposed architectures performance. Because there is a lot of intermediate date generated and it should be accessible randomly. But implementing the same architectures (with block RAM replaced by available memory) on high-end FPGA boards having lower latency and higher clock frequency for memory can give a similar performance with fewer resources.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method  
For the analysis of the literature the study was based on QFD and structured in 3 phases (Figure 2): definition of research categories in TAM, identification of commonly used TAM variables, and analysis of the strength of the relationships represented by the path coefficients.  
For the research purposes we conducted a literature review of theoretical and empirical studies within the last ten years and grouped them into seven categories according to the topic and the research perspective. Papers were selected similarly to the quantitative meta-analysis proposed by [PERSON] and [PERSON] [6] and adapted for this study, to identify commonly used external factors in TAM extensions in e-learning, agriculture and VR. In the third phase a more strict selection of papers was done in order to analyze the degree  
Figure 2: Layout of the proposed methodology.  
of importance of the research categories (What's) and the strength of the relationships between commonly used TAM variables (How's) and the two main constructs of TAM, PU and PEOU. The degree of importance is represented by the number of works belonging to each research category. The strength of the relationships between most common external variables and PU and PEOU is represented by the path coefficient. Therefore, only studies reporting the path coefficients were considered. Finally, we gathered the previous information to construct the QFD matrix proposed in Figure 3. The papers' selection and classification process is explained in detail in Figure 4.  
Figure 4: Papers selection process.  
Figure 3: Proposed QFD matrix.  
Identification of Commonly Used External TAM Variables in e-Learning, Agriculture and VR Applications  
To analyze the current state of the art of the technology acceptance model and apply QFD, we followed the selection criteria mentioned in the methodology and obtained a first sample of 67 papers. The selected works were further analyzed to identify types of applications and the recurrent variables used in TAM extensions.
## 5 Hardware and Software Simulation Implementation
The distribution of currents in the passband, which is utilized to confirm the resonant behaviour of the ultra-wideband filter, is another important hypothesis that should be  
Figure 11: (**a**) Frequency vs. group delay response. (**b**) Frequency vs. phase response of the SWB filter. explained. The simulated current distributions at a centre frequency of 9.65 GHz are shown in Figure 12. As discussed, the wideband filter is constructed on the UTL with three folded \(\lambda/4\) shunt stubs, as shown in Figure 1; therefore, it is expected that most of the current energy will be absorbed by this portion of the filter. This absorption of current energy by the UTL and stubs contributes to the creation of the passband in the filter. The simple architecture to decrease costs and the configurable bandwidth for user end selection are stated to be innovative in this work. As seen in Figure 13, the wideband filter's bandwidth changes by varying the uniform transmission line's width. To evaluate the performance of the proposed structure, a comparison is made with the recently published filters in the literature. The comparison is based on various parameters tabulated in Table 1. By presenting this comparison, the authors highlight the advantages of their proposed structure and demonstrate its competitiveness in terms of key performance metrics. This provides further support for the novelty and effectiveness of the research work. Moreover, the S-parameter plots with photographs of the proposed SWB filter and the SWB filter with dual-notched bands are shown in Figure 14a,b, respectively. The small discrepancy between the experimental and manufactured findings is caused by measurement errors made by humans that are unavoidable, as well as the effect of soldering, losses in the substrate material, and the SMA connectors. Figure 8:0 Current distribution graph of the ultra-passband at 9.65 GHz. Figure 8:1 Bandwidth control with different values of parameter W.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 2 Materials and Methods
### Dataset and Pre-Processing  
The ground vibration dataset consists of 100 data points. There are eight inputs: the MIC, SD, distance, elevation, blast longitudinal, blast latitude, measured longitudinal, and measured latitude, while the PPV is the output. Table 2 indicates the descriptive statistical analysis of the dataset, and Figure 5 illustrates the frequency histogram of the dataset. The dataset was split into training dataset (80% of the total dataset) and testing dataset (20% of the total dataset). Due to the variation in the dataset in Figure 5, normalization was applied, to avoid overfitting and to increase the model's learning performance. Equation (5) expresses the formula of normalization.  
\[x_{normalised}=\frac{(x-x_{minimum})}{(x_{maximum}-x_{minimum})} \tag{5}\]  
where \(x\) represents the initial value, \(x_{minimum}\) represents the minimum value in the dataset, \(x_{maximum}\) represents the highest value in the dataset, and \(x_{normalized}\) represents the normalized value. Normalization is typically between 0 and 1.  
Figure 4: Ground vibration monitoring instruments (**a**) laptop computer, (**b**) geophone, (**c**) data logger, and (**d**) seismograph.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 3 Design and Implementation
### Gateways  
Whilst sensors transmit data to gateways, the collected data is converted into a proper format at the gateway (or aggregation gateway as a customer edge). There are two key data which are processed at the gateway: (1) LID: location information such as the GPS coordinate or proprietary location data format being used by the DRA is converted into the A party address format in the signalling field consisting of XY (network ID) and 10 digits of numeric data for the location, as shown in Table 3, for instance; (2) sensor measurements are included in the data (payload) field, which is kept short to conserve battery power. The gateway plays a key role in performing data collection from the sensors, instructing the network to change the status to an emergency mode when triggered, forming the PAN field from data received from the sensors, and connecting to carrier or peer-to-peer networks. It also interacts with other gateways in the LPWAN for routing if required.
# Numerical Voids Detection in Bonded Metal/Composite Assemblies Using Acousto-Ultrasonic Method
###### Abstract  
This research focuses on the application of an acousto-ultrasonics (AU) technique, a combination of ultrasonic characterization and acoustic emission, to nondestructively detect defects such as voids in bonded metal/composite assemblies. Computational methods are established to examine the effects of voids on the collected signal. The position of the receiver sensor with respect to the defect is also investigated. Given a specific structure and type of actuation signal, the sensor location and probability of detection can be enhanced by the model developed in this work. The defect detection is optimal provided the receiver sensor is located around the epicenter of the defect. Moreover, this work highlights the importance of the choice of reception sensor.  
acousto-ultrasonic; acoustic emission; bonded assemblies; defects; FEM model +
Footnote †: journal: Applied sciences
## 4 Experimental Results
### Error Measurement Based on the Number of Samples  
Our proposal takes into account k nearest neighbour samples from a position using Euclidian distance (see Figure 1). This experiment gives us the optimum number of samples in order to obtain the lowest error. In order to test our approach we took 56 samples spread equitably throughout the floor of the building. The floor was split in a grid where sampling points are placed every 2 meters.  
Figure 3 shows that the error is not reduced linearly as regards the number of samples. It has an inflection point in which the error changes its trend. In other words, the error decreases until it has the three closest samples, then, the error begins to increase.  
This happens because the method obtains relative distances from the samples to the sensor and when the method begins to use measures that are not close to the sensor the error increases. Obviously,  
Figure 3: Average location estimation error as a function of the number of samples.  
Figure 2: Test bench place used in the experiments.  
the smaller the area is, where the sensor can be found, the lower the error in its location will be. More samples will give higher relative distances and therefore the error of location will be greater.  
Our first conclusion, based on the previous graph, is that given a fixed number of samples, there will be a value of number of samples where the location error will be the optimal. Then, if the number of samples used to train the system is greater, the estimated position will be more accurate because there will be closer samples.
###### Abstract  
A novel integrated system based on solid oxide fuel cells (SOFCs), a gas turbine (GT), the steam Rankine cycle (SRC), the Kalina cycle (KC), and the organic Rankine cycle (ORC) is proposed to achieve cascade energy utilization. Mathematical models are introduced and system performance is analyzed using energy and exergy methods. The first and second laws of thermodynamics are used to analyze the system thermodynamically. In addition, exergy destruction and losses of the various integrated subsystems are calculated. The energy and exergy efficiencies of the multigeneration system are estimated to be 60.4% and 57.3%, respectively. In addition, the hot water produced during the waste heat recovery process may also be used for accommodating seafarers on ships. Sequential optimization is developed to optimize the operating conditions of the integrated system to achieve the required power output. A comprehensive parametric study is conducted to investigate the effect of varying the current densities of the fuel cell and working fluid of the ORC on the overall performance of the combined system and subsystems. The working performance of five working fluids for the ORC as candidates--R134a, R600, R601, R152a, and R124--is compared. R152a, which provides 71.23 kW of power output, and energy and exergy efficiencies of 22.49% and 42.76%, respectively, is selected as the best thermodynamic performance for the ORC.  
SOFC; ammonia; combine heat and power; organic Rankine cycle system +
Footnote †: journal: Applied sciences
## 4 Engineering Application  
### Data Collection  
To validate the performance of the proposed method, a series of vibration signals with different fault locations and sizes were gathered from Bearings Data Center of Case Western Reserve University [45]. The experiment stand was mainly composed of a motor, an accelerometer, a torque sensor/encoder, and a dynamometer. The bearing was a deep groove ball bearing with model SKF6205-2 RS. By using electro-discharge machining (EDM), the experiment device simulated three fault states of the rolling bearing: The inner race fault, ball element fault, and outer race fault. The depth of faults was 0.011 inches. Vibration signals collected from the drive end (DE) were taken as the research objects. In the experiment, the sample frequency was 12,000 Hz, and the rotation speed was 1750 rpm under the rated load of 2 hp. To fully verify the validity of the proposed fault diagnosis method, 9 types of samples were used in this paper, namely, the inner race fault, ball fault, and outer race fault with diameters of 0.007, 0.014, and 0.021 inches (i.e., each of the three types of faults has three defect sizes). Further, the vibration signal of each type of fault was divided into 59 segments containing 2048 sampling points. An image of the experiment device is shown in Figure 5. The experimental signals are listed in Table 1.  
Figure 4: The flowchart of the proposed fault diagnosis method.
# Morphological Reconstruction for Variable Wing Leading Edge Based on the Node Curvature Vectors
## 3 Simulation Verifications
### Results and Discussion
#### 3.2.2 Optimization of Virtual Sensing Node Quantities Based on Particle Swarm Algorithms  
Taking the example of a leading-edge deflection angle of \(10^{\circ}\), the entire surface strain/curvature distribution data for the leading edge were reconstructed based on the simulated data from 26 strain-sensing points, as described in Section 3.2. Furthermore, utilizing the particle swarm optimization algorithm outlined in Section 2.4, the reconstruction accuracy of the leading-edge morphology was calculated after each iteration of particle quantity. This was carried out to determine the optimal number of virtual sensing points required for the morphological reconstruction of the wing leading edge, as illustrated in Figure 10.  
During the particle quantity iteration process, the root mean square error of the morphological reconstruction of the variable wing leading edge, starting from the initial 26 actual sensing points, decreased from 28.84 mm to 7.06 mm.  
As depicted in Figure 10, with the continuous increase in the number of virtual sensing points, the reconstruction error of the variable wing leading-edge morphology steadily decreased. When the sum of the virtual and actual sensing points exceeded 51, the average relative error of the leading-edge morphology reconstruction converged and no longer decreased. Therefore, uniformly distributing 51 sensing points along the circumferential direction of the wing leading edge could be considered the optimal sensor layout scheme. In this scheme, 26 strain data points were acquired from actual sensors, while the additional 25 strain data points from virtual sensing points could be calculated using the reconstruction method described in Section 2.2.
# A High-Throughput In Vitro Radiobiology Platform for Megavoltage Photon Linear Accelerator Studies
## 2 Materials and Methods  
### Plate Irradiation Setup, Scanning, and Contouring  
A tissue culture plate holder was designed and created in a radiotherapy mould room facility using Perspex for its near tissue-equivalent properties and ease of manufacture to irradiate 96-, 24-, and 6-well tissue culture plates (Figure 1A). This holding plate consists of an upper (with two halves) and lower plate with a space created in the middle to hold three types of tissue culture plates composed of polystyrene with varying well volumes (6-well, 3 mL; 24-well, 1 mL; and 96-well, 400 \(\upmu\)L) and each with dimensions of \(128\times 86\) mm, enabling easy handling of the plates without much disturbance of cultured cells during experiments. The dimensions of this holder were kept sufficient to cover full scatter conditions. The holder was able to be attached to the indexed immobilisation positioning system of both a clinical linear accelerator and computed tomography (CT) scanner with a base plate made of medium-density fibreboard (Figure 1B-D). We positioned 96-, 24-, and 6-well tissue culture plates, with wells prefilled with water representing typical amounts of tissue culture media, in a CT scanner (GE LightSpeed, GE Healthcare) with the aid of room lasers (Laser Applikationen, Luvemberg). The plates were scanned (Figure 1B) with an image slice thickness of 1.25 mm for 96- and 24-well tissue culture plates and 2.5 mm for the 6-well tissue culture plate. The images were then transferred to a radiotherapy treatment planning system (Varian Eclipse(tm), Varian Medical Systems, Inc., Palo Alto, CA, USA) and water columns of individual wells were contoured manually (Figure 2A-F).  
Figure 1: (**A**) Design of multiwell tissue culture plate holding device. The top plate (stone colour) has two halves to facilitate the handling of plates for accurate positioning. The bottom plate is shown in light green and a representative tissue culture plate is shown in red. (**B**) Multiwell tissue culture plate scanning in a computed tomography scanner. (**C,D**) Multiwell tissue culture plate irradiation in a clinical linear accelerator. Plates positioned within multiwell tissue culture plate holding device, with the use of room lasers for accurate reproducibility. Plate holder attached to the CT scanner and linear accelerator by a medium-density fibreboard plate (brown).