esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 3 Methodology Description
### Hydrodynamic Model  
Once the digital twin setup is established, the next crucial step is to estimate a hydrodynamic model, as a starting point for the optimization. There are many different methods to estimate the hydrodynamic coefficients; in some cases, they can be directly taken from similar vehicles in the literature [37]. In a more general approach, some methods are proposed in [38], including analytical methods, involving theoretical formulations and empirical correlations based on the vehicle geometry and operating conditions. Empirical methods involve conducting physical experiments, such as towing tank tests or free-decay experiments, to directly measure and observe the vehicle's response in controlled conditions. Numerical methods, particularly CFD, simulate fluid flow around the vehicle under different conditions, offering detailed insights into complex interactions between the vehicle's shape, propulsion systems, and surrounding fluid.  
Both analytical and numerical methods can become quite complex, particularly when working with open-frame vehicles that present a complex geometry with holes and different shapes and a lot of equipment introducing disturbances (sensors, lights, cables, etc.). This leads to the empirical methods being the preferred in these applications as in [39], where the parameters of the dynamic model of the BlueROV2 have been determined and validated using both computer-aided design (CAD) software and physical experiments.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications
### CPS Functional Attributes
#### 3.2.2 Conversion Level (C2)  
This level involves converting the acquired data in C1 to meaningful and useful information needed depending on the applications. This level is equipped with multiple algorithms and data processing methods that handle the conversion processes. In recent years, smart data analytical solutions have been used to obtain data needed for water quality, anomaly, and leakage monitoring in WDNs [74; 75; 76]. Elsewhere [77; 78], correlation techniques could be performed on the data to extract the information needed for decision making. Extracting information from wireless sensors used in water distribution network monitoring is crucial to analyze the collected data for the specific application. For example, in leakage detection and pipeline health monitoring using acoustic emission (AE) sensors, extracting information from remote AE sensor signals is required to infer the occurrence of a leak and its location. During leak occurrence, AE sensors positioned on the pipe can trace the AE signal as it travels along the pipe [4]. Unfortunately, signal characteristics and the variations in the environmental parameters surrounding the pipe make AE signal classification challenging [78]. Nonetheless, computational tools, such as support vector machines [74; 76], artificial neural networks [79],  
Figure 4: SC functional attributes for deploying cyber-physical systems.  
wavelet transform, and independent component analysis [80; 81], may be applied for AE signal feature extraction to detect patterns in the dataset and classify the AE signals. These tools are mostly utilized at stage C3 of the CPS functional attributes.
###### Abstract  
The advancement of topology optimization (TO) and additive manufacturing (AM) has significantly enhanced structural design flexibility and the potential for lightweight structures. However, challenges such as intermediate density, mesh dependency, checkerboard patterns, and local extrema in TO can lead to suboptimal performance. Moreover, existing AM technologies confront geometric constraints that limit their application. This study investigates minimum compliance as the objective function and volume as the constraint, employing the solid isotropic material with penalization method, density filtering, and the method of moving asymptotes. It examines how factors like mesh type, mesh size, volume fraction, material properties, initial density, filter radius, and penalty factor influence the TO results for a metallic gooseneck chain. The findings suggest that material properties primarily affect numerical variations along the TO path, with minimal impact on structural configuration. For both hexahedral and tetrahedral mesh types, a recommended mesh size is identified where the results show less than a 1% difference across varying mesh sizes. An initial density of 0.5 is advised, with a filter radius of approximately 2.3 to 2.5 times the average unit edge length for hexahedral meshes and 1.3 to 1.5 times for tetrahedral meshes. The suggested penalty factor ranges of 3-4 for hexahedral meshes and 2.5-3.5 for tetrahedral meshes. The optimal geometric reconstruction model achieves weight reductions of 23.46% and 22.22% compared to the original model while satisfying static loading requirements. This work contributes significantly to the integration of TO and AM in engineering, laying a robust foundation for future design endeavors.  
topology optimization; additive manufacturing; numerical instability; geometric reconstruction; solid isotropic material with penalization method +
Footnote †: journal: Applied Sciences  
1 School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518051, China; [EMAIL_ADDRESS]  
2 Queensland College of Art and Design, Griffith University, Brisbane 4101, Australia
## 2 Material and Methods
### Data
#### 2.1.2 Dependent Variable  
LVEF_over_50: Left Ventricular Ejection Fraction (LVEF) Is Greater or Equal with 50%  
Left ventricular ejection fraction (LVEF) is the central measure of left ventricular systolic function. LVEF is the fraction of chamber volume ejected in systole (stroke volume) in relation to the volume of the blood in the ventricle at the end of diastole (end-diastolic volume). Stroke volume (SV) is calculated as the difference between end-diastolic volume (EDV) and end-systolic volume (ESV). The simplest classification, as per the American College of Cardiology (ACC) that is used clinically, is as follows:  
Hyperdynamic = LVEF greater than 70%.  
Normal = LVEF 50% to 70% (midpoint 60%).  
Mild dysfunction = LVEF 40% to 49% (midpoint 45%).  
Moderate dysfunction = LVEF 30% to 39% (midpoint 35%).  
Severe dysfunction = LVEF less than 30%.  
The accurate measurement of LVEF is very important for managing patients with cardiovascular disease. LVEF also has a prognostic value in predicting adverse outcomes in patients with congestive heart failure, after myocardial infarction, and after revascularization. LVEF plays an important role in assessing the severity of a decrease in the systolic function of the heart and thus is helpful in directing the management of various cardiovascular diseases.
## 2 Antenna Structure
### Hybrid Coupler Design
The phase angles of \(S_{31}\) and \(S_{41}\) parameters, which are shown in Figure 3c, appear with phase difference that is approximately equal to (85-95\({}^{\circ}\)) along the range of (3.3-4.2 GHz). However, the magnitude of \(S_{31}\) and \(S_{41}\) are almost equal within this range as shown in Figure 3b, and a satisfactory isolation between port 1 and 2 is demonstrated by the \(S_{21}\) curve. Figure 2: Diagram of a 3 dB hybrid coupler.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 3 System Model
### Two Nodes Reliable Model (2n-Rm)
#### 3.2.1 Distance  
Each sensor node has certain range limit that is assigned by routing protocols. By default, in AODV, DSDV, and DSR protocols, sensor nodes' range limit is 250 m; whereas, in the VBF and DBR protocols, nodes range limit is 100 m. Consequently, destination or intermediate node must reside in this range to take data from the source node. In TBRL, nodes' range limit is set to 70 m and computes Euclidean distance between the source to the next forwarder.  
\[Distance=\sqrt{(X_{2}-X_{1})^{2}+(Y_{2}-Y_{1})^{2}}, \tag{22}\]  
\[70>Distance>0\]\(X_{2}\), \(X_{1}\), \(Y_{2}\), and \(Y_{1}\) present the position of node2 X, node1 X, node2 Y, and node1 Y, respectively. The reliability of the links is inversely proportional to the distance between the nodes. Less distance means higher reliability while higher distance means less reliability. ge limit is set to 70 m and computes Euclidean distance between source to the distance must be greater than 0 and less than 70 m to maintain ge limit is set to 70 m and computes Euclidean distance between source to a reliable link. The reliable link must fulfill the given condition. The ge limit is set to 70 m and computes Euclidean distance between source to the successful link usually means finding a resource with excellent exactitude and recall, and retrieving an authentic representation of the resource in a timely fashion, i.e., limit is set to 70 m and computes Euclidean distance with sufficiently low latency, reduced bit error rate. The parent is chosen between neighbors consistent with link estimation ways and distance to place the reliable links in routing tables instead of those supported hop count. The used metrics explore cross-layer information to boost dependableness, and also the links between neighbors are evaluated in an exceedingly probabilistic and accommodative manner to gauge even not already chosen nodes with short searching and passive link observation ways.  
```
1:All nodes from source to sinks
2:Determine each node location Initialization:
3:\(g=9.81\)// Gravity remains same in air and water
4:\(p\_time=0\)// All nodes previous time
5:for\(i=1\) to all nodesdo
6:\(x=LP[i]\)// Location Packet
7:\(y=Depth\_of\_node[i]\)
8:\(\lambda=0.17*\sqrt{gy}\)
9:\(speed=\sqrt{(((g*\lambda)/(2*\pi))*tanh(2*\pi*y/\lambda))}\)
10:\(x1=speed/4\)
11:\(time=current\_time-p\_time\)
12:\(x2=time*x1\)
13:\(node[i]\)\(((x\underline{+}x2),y)\)
14:\(p\_time=current\_time\)
15:endfor
```  
**Algorithm 1** Dynamic Topology Discovery
# Implementation of an Obstacle Recognition System for the Blind
## 3 Obstacle Recognition Model and System Architecture
### Obstacle Recognition Model
#### 3.1.2 Model  
We used the Google Object Detection API to train the obstacle recognition model. It provided several pre-trained models utilizing common objects in context (COCO) data. We trained the SSD_MobileNet_v1_COCO and Faster_R-CNN_ResNet101_COCO models with the training data we collected. We chose to use those particular models for two main reasons. First, we chose them for their ability to run on little computing power. This meant that the obstacle recognition system could be applied to portable devices in the future; for example, on small computers, such as the Raspberry Pi or a smartphone. Second, other models, such as \"You Only Look Once\" (YOLO) [15], have a relatively little ability to detect small objects, which would be counterintuitive to our study. In any case, there were also differences between the two models used in our study. The SSD_MobileNet_v1_COCO-based SSD [4] had the advantage of not using much computing power, but its obstacle recognition accuracy was not high, while the Faster_R-CNN_ResNet101_COCO-based Faster R-CNN [5] had the advantage of being able to process more image features in less time, allowing for higher accuracy with regard to detecting obstacles. We compare the performances of the two models in Section 5.  
The models could recognize 90 classes when using the current API checkpoint. Our objective was to have them recognize both bollards and crosswalk lights, so we had to train extensively with the use of our own data. For the additional training, the number of steps was 50,000. The batch size was 24, and the input image size was \(500\times 500\) pixels.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 3 Results  
### Application of Phenology Model for the Three EC Sites  
The \(R^{2}\) indicated a good fit for the phenology model in all years and sites (\(R^{2}\geq 0.8\)). The estimated parameters of the phenology model indicated that GPP differed by year and site, and that sites differed in their response to weather events (Table 2; Appendix B, Figure A2). Although the EC sites are in close proximity to one another and share a similar climatic environment [42], there were still slight differences among sites in terms of phenological characteristics derived from daily GPP. Except for 2012 and 2016, phenological responses of GPP to weather events in the sites were consistent. These years with anomalies corresponded to soil moisture-related water limitations [49], and uneven precipitation in spring [49] (non-drought-related irregular rainfall) (Appendix C, Figure A3).
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 2 Analog Predistortion MMIC Topology  
### Predistortion Circuit Topology  
The topological graph of the proposed APD circuit is shown in Figure 1, and its design aims to exhibit good adjustability and facilitates broadband applications. The input signals are separated into two components with identical gain and phase via an input power divider. The two components pass through a gain and a phase nonlinear branch, respectively. The gain nonlinear branch is designed to provide a significant gain expansion and relatively flat phase expansion, while the opposite holds for the phase nonlinear branch. After the gain and phase nonlinear branches, the two signals are combined into the output signal via an output power combiner. The gain and phase nonlinear branches are realized through Schottky diodes and a Lange coupler. Meanwhile, the NFAM is used to adjust the phase expansion difference of the APD circuit within the whole operating band.  
The APD circuits based on double-branch vector synthesis encountered in previous studies use linear and nonlinear branches for vector synthesis [12]. Schottky diodes are used for the nonlinear branches and PIN diodes for the linear ones. PIN diodes require a wide intrinsic semiconductor layer to separate the N and P regions and achieve their high resistance characteristics. However, the PIN diode process is not easily integrated with the standard commercial GaAs MMIC foundations and thus such designs are impractical. In this paper, the nonlinear gain and phase branches are designed based on the characteristics of adjustable gate number and gate width and the corresponding impedance relationships for Schottky diodes in the MMIC process. In this manner, a broadband APD circuit with independently adjustable gain and phase expansion is realized.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Free Localization
#### 4.2.1 Infrared Localization  
Infrared technology is broadly implemented for the recognition and localization of stationary devices. It is based on the radiation alterations in the series of infrared lights triggered by humans. There are apparent variations in an individual's body temperature and surrounding environment temperature utilized in the module localization scheme. An infrared-based crowd localization into the distributed wireless sensor networks [61], virtual and potent methods are used in parallel with correspondence with the angle bisector process of personal individual recognition and a scheme to group the dimension points. Positions are estimated by a filtering technique of the prime incoherent dimension  
Figure 12: RFID tag-based localization.  
point. However, the involvement of a limited number of sensor nodes deteriorates the performance of this approach. To overcome this pitfall, we increased the number of personal individual recognition sensor nodes and fixed them to the ceiling of an area of localization [62]. Formerly, localized fixtures and the real-time information of moving objects has been used for crowd localization. Tracking accuracy by this enhancement has been improved up to 90%. A study shows that among three distinct methods; personal individual recognition (PIR) sensor dimensions, Ultra-Wide Broadband and RFID, the PIR and UWB perform better than RFID for indoor localization [63]. PIR sensor nodes and filters [64] are used to improve the accuracy of the indoor localization scheme, which is based on signal strength measures. However, this technique is not feasible in real-time scenarios, and also deployment complexity is high.
# Acceleration of OCT Signal Processing with Lookup Table Method for Logarithmic Transformation
## Materials and Methods  
### OCT System  
A swept source OCT system shown in Figure 1 was built to demonstrate the capacity of the proposed technique. Output light from a 1310 nm swept source laser (87 nm bandwidth, 20 mW average power, 100 kHz A-scan rate, Santec Corporation, Japan) was split by a coupler with 90% of the light into the sample arm and 10% into the reference arm, respectively. Two circulators were used to redirect the back-reflected light to a 2 \(\times\) 2 fiber coupler for balanced detection. In the detection arm, the signal detected by the photodetectors was recorded by an analog to digital converter (ATS9360, Alazar). The entire OCT was controlled and processed on a personal computer (CPU: Intel i7-6700K@4 GHz, RAM: 32 GB, operating system: Window 7, 64 bit).
## 2 Overall Communication Solution of DSSS Transceiver
### Spreading Code  
M-sequence has an excellent autocorrelation property while it is also easy to generate, replicate and control. M-sequence also has a certain independent address number and a quite long sequence period. It is usually used as the spreading code in SPSP Communication [9]. M-sequence is the longest linear shifting register sequence, and is generated by a feedback shift register. The cycle of the \(m\) pseudorandom sequence generator used in this paper is 1023. Equation (1) is the polynomial of the M-sequence whose cycle is 1023. The M-sequence generator used in this paper is shown in Figure 1.
## 3 Design of Hardware and Software for the Data Monitoring System  
The structure of the data-logger is going to be divided into three different modules (Master module, Slave module, and Wireless module). The goal of the Master module is to collect data from all the slave modules installed in the wireless network and manage them in order to save the information inside a USB memory (in this project) or sending the information to other device such as a computer.  
The Slave module is the responsible for collecting data from the sensors and sending the information to the connected module.  
The Wireless module is able to send the information received from the connected module to other Wireless module or the opposite.  
It is designed to connect two modules and form three different kinds of data-loggers. For example if we connect a Master module with a Slave module we will have a common data-logger, but we can create a wireless network system using two Slave modules and a Master module connecting each one with a Wireless module. That way, two different data-loggers can collect data from two different places and save them into the same external memory by wireless, allowing one, for example, to easily monitor different rooms in a museum with a unique monitoring system.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 5 Experimental Setup
### State-Of-The-Art MAC Protocols Used for Comparison  
The SNW-MAC protocol described in the previous section is compared to PW-MAC [9] and the Unified Radio Power Management Architecture (UPMA)-X-MAC [48], two well-known state-of-the-art pseudo-asynchronous MAC protocols. Figure 8a illustrates a packet transmission using the transmitter-initiated UPMA-X-MAC from UPMA. The node initiates the communication by continuously sending the data packet until an ACK frame from the sink is received. On its side, the sink  
Figure 6: PowWow node equipped with an Ultra Low Power (ULP) WuRx.  
Figure 7: Power consumption of the node for different input voltages of the DC-DC converter when (i) the MCU is active and the radio is listening and when (ii) the MCU is active and the radio is sleeping.  
periodically wakes up and listens to the channel. If it detects activity, it waits until the incoming data packet is fully received and then sends the ACK frame.  
PW-MAC is a receiver-initiated protocol that focuses on energy efficiency at both the receiver and transmitter sides. A simplified version of the protocol is used in this study as only downlink transmissions are considered. The packet transmission using PW-MAC is shown in Figure 8b. At the receiver side, the sink periodically wakes up and sends a Beacon (BCN) frame. At the transmitter side, each node accurately predicts the time at which the sink will wake up. If a packet needs to be sent, the node wakes up just before the next beacon is sent by the sink. Once the beacon is acquired, the node sends the data packet and waits for the ACK frame. At each packet transmission, a prediction error is computed, and the node updates its prediction time according to this error.
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 3 Methodology
### Design and Implementation of a Long-Distance WiFi Network
#### 3.2.2 Material Descriptions  
Three main materials are used to construct the antennas: PCV, copper, and a parabolic plate:  
* PVC (Polyvinyl Chloride):
* Density: 1.38-1.42 g/cm\({}^{3}\);
* Electrical conductivity: Insulator (non-conductive);
* Dielectric constant: 3.0-3.2 (at 1 MHz);
* Loss tangent: 0.01-0.03 (at 1 MHz);
* Melting point: 100-260 \({}^{\circ}\)C (depending on the type of PVC);
* UV resistance: Moderate to good (depending on the formulation).  
* Density: 8.96 g/cm\({}^{3}\);
* Electrical conductivity: Very high (one of the most conductive metals);
* Resistivity: 0.0168 \(\mu\Omega\)-cm (Temperature Coefficient at 20 \({}^{\circ}\)C);
* Melting point: 1085 \({}^{\circ}\)C;
* Thermal conductivity: 401 W/m\(\cdot\)K;
* Corrosion resistance: Good (forms a protective oxide layer).
* Parabolic Plate (typically made of metal, such as aluminum or steel):
* Material properties would depend on the specific metal used;
* Aluminum is commonly used due to its lightweight and good conductivity;
* Density: 2.7 g/cm\({}^{3}\) (for aluminum);
* Electrical conductivity: High (for aluminum);
* Melting point: 660.3 \({}^{\circ}\)C (for aluminum);
* Reflectivity: High (parabolic shape allows efficient focusing of electromagnetic waves.)  
These properties are general characteristics and can vary depending on the specific grade, composition, or manufacturing processes of the materials used [51; 52].