esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Description
### Architecture  
The architecture of the precision agriculture scenario is divided into four layers, which are the Sensor Node Layer, the Drone Layer, the Internet Layer, and the Data Center Layer. Figure 4 shows the different layers.  
The Sensors Node Layer is the bottom layer. All the sensor nodes deployed in the crops are located in this layer. The layer immediately above is the Drone Layer. It is comprised of the remote sensing drone that is responsible for obtaining images of the crops and collecting the information stored by the nodes through its wireless interface, the location where the drone has their base station, the IT equipment where the information that the drone has collected during its flight is stored, and the network devices that allow us to connect to the Internet. The connection to the Internet is performed  
Figure 4: Architecture layers.  
by employing a Service Provider. Depending on the location of the crop fields, the technology used to connect to the Internet may vary, choosing among cable or wireless technologies to establish the connections. The following layer is the Internet. The Internet is accessed through the Operator to send all the acquired data to a remote location, where the information will be processed. The connection to the remote location is made by establishing a Virtual Private Network (VPN). This way, the data will be protected when crossing the Internet. Finally, the Data Center Layer, where we store the obtained information, is located at the remote location. This information can be processed using Artificial Intelligence (AI) to ensure that decision making is optimal.
## 2 Peculiarities of ATR Measurements
### Polarisation and Field Enhancement at the Interface of ATR Prism
Top row shows E-field enhancement when a \(\sim\)\(\lambda/20\)-diameter sphere of refractive index \(n=1.3\) (water or ice at visible), 2.2 (water or ice at THz) and gold (Au) touches the ATR prism of \(n=2.4\) (diamond); all intensity maps are calculated for the same wavelength \(\lambda=1050\) nm. Bottom row shows the evanescent field at the interface of \(\lambda=1\) μm – 0.65 μm; the interference maxima are recognisable in the \(n=2.4\) media (in the wavelength, there is \(\lambda/n\)). Calculations are carried out for the p-pol. E-field with strength \(E_{0}=1\); hence, \(E/E_{0}\) scale represents enhancement. a higher refractive index in the sample. The wavelength scaling in frustrated TIR gives insights into light tunnelling, regardless of the wavelength. Based on this [PERSON]'s scaling, it is useful to model light transmission, reflection and enhancement at the interface of the ATR prism, using finite difference time domain (FDTD) calculations (lumerical solutions). Figure 4 shows the results for the IR-visible spectra range; however, the predictions are universal as long as the refractive index is the same. To model a sub-wavelength inclusion inside a sample, a sub-wavelength diameter sphere of different refractive indices, dielectric (1.3 and 2.2), as well as metallic (Au) are chosen. Without the sphere, localisation of the evanescent field at the interface protruding into air gap for length comparable with \(\lambda/4\) is clearly recognisable. An extra enhancement of the local E-field at the interface occurs in the nano-gap regions around the sphere. This is an illustration of the E-field enhancement of the normal \(E_{n}\) component discussed above. For the ATR measurements, the local field enhancement around inclusions of difference refractive indices would cause a stronger contribution from those regions where the light intensity was larger. Overall, the volume where the light field intensity is augmented around a sub-wavelength inclusion increases (see the same intensity \(E=2.3\) markers in the top-row maps in Figure 1a).
# Analysis of Lateral Displacement of Pile Foundation Caused by Large-Diameter Shield Tunneling
## 2 Lateral Displacement of Front Pile Caused by Using a Shield Machine  
Figure 1 displays the excavation mechanical model of the large-diameter slurry shield machine within the stratum. During shield tunneling, the lateral displacement of the pile in front of the excavation face can be attributed to two primary factors: the additional thrust \(q\) exerted by the shield perpendicular to the excavation face, and the friction \(f\) created by the shield's horizontal distribution along the shield's surface.  
To calculate the lateral displacement of the pile in front of the shield caused by the shield's additional thrust and friction, the Mindlin solution can be applied to determine the soil displacement resulting from these forces [1]. From there, the Kerr foundation model can be utilized to calculate the displacement of the pile in response to the soil displacement.
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
###### Abstract  
Resolution and target detectability in ultrasound imaging are directly tied to the size of the imaging array. This is particularly important for imaging at depth, such as in the detection and diagnosis of hepatocellular carcinoma and other lesions in the liver. Swept synthetic aperture (SSA) imaging has shown promise for building large effective apertures from small physical arrays using motion but has required bulky fixtures and external motion tracking for precise positioning. This study presents an approach that constrains the transducer motion with a simple linear sliding fixture and estimates motion from the ultrasound data itself using either speckle tracking or channel correlation. This work demonstrates, through simulation and phantom experiments, the ability of both techniques to accurately estimate lateral transducer motion and form SSA images with improved resolution and target detectability. In simulation, errors were observed under 83 \(\upmu\)m across a 50 mm sweep, and improvements were found of up to 61% in resolution and up to 33% in lesion detectability experimentally even imaging through ex vivo tissue layers. This approach will increase the accessibility of SSA imaging and allow researchers to test its use in clinical settings.  
beamforming; synthetic aperture; image quality; speckle tracking; displacement estimation +
Footnote †: journal: Medical Engineering  
0
Footnote 0: 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
## 3 Experimental Setup
### Simulated Environment Setup  
The ROS platform, its packages and dependencies, and the Gazebo simulation tool were completely installed using the Ubuntu 18.04.5 packages, which is a free and open-source software operating system, and a Debian-based Linux distribution. Both were synchronized so that the logic could be executed, and the mobile robot could act in its environment. The necessary additional software, such as Windows 10 and Microsoft Office were licensed by Tecnologico de Monterrey. The main virtual tools that were used to perform the experimentation are listed in Table 1 [33; 34; 35; 36].  
The mobile robot and its environment were simulated in Gazebo. The grid environment scenario was of \(5\times 5\) m surrounded by walls and six static obstacles. Subsequently, the multiple static obstacles were simulated as cylinders and placed in random occupancy grids.  
The testing robot platform chosen for the simulation experiment was the TurtleBot 3 Waffle Pi by Robotics. A simulated accurate representation of the robot was launched in Gazebo inside the following scenarios shown in Figures 5-7. The URDF files containing the mechanical properties of the mobile robot were not modified to maintain the mobile robot's physical characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Version/Branch** & **Main Tasks** \\ \hline ROS & & Hardware abstraction, low-level device control, implementation of commonly used functionality, message passing between processes, and package management. \\ \hline Gazebo & 9.0.0 & Multi-robot and environment 3D simulation. Information acquisition and modification of the simulated world. \\ \hline RVIZ & 1.13.13 & Information visualization: sensor data, robot models, environment maps. \\ \hline Python & 2.7.17 & Interpreted, object-oriented, high level and flexible programming. \\ \hline Atom & 1.53.0 & Intuitive graphical user interface and cross-platform text-editor programming. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main tools for experimentation.  
Figure 5: Simulated environment scenario 1 with labels.  
As the dynamic obstacles can be any moving objects and/or persons, another TurtleBot 3 Waffle Pi was added to the simulation representing the dynamic obstacle, labeled as \(tb3\_1\), while the main mobile robot was marked as \(tb3\_0\). Three different scenarios, shown in Figures 5-7, were constructed to test the robustness of the proposed LSTM model  
Figure 6: Simulated environment scenario 2 with labels.  
Figure 7: Simulated environment scenario 3 with labels.  
under different conditions for the dynamic obstacle and the mobile robot. Each simulation scenario could be called through the roslaunch tool, which is the standard method for starting multiple ROS nodes, placing multiple robots, and bringing up the simulated world in Gazebo [34].  
The three scenarios are different for the starting position of the mobile robot and the dynamic obstacle; however, the position of the static obstacles remains fixed and is the same for all scenarios.
# Detection of Intelligent Intruders in Wireless Sensor Networks
## 2 System Modeling and Definitions
### Assumptions and Models  
For system modeling and problem formulation, assumptions are unavoidable. In this work, we consider a homogeneous WSN model with \(N\) uniformly- and randomly-distributed sensors in a fixed square plane \(A\) (_i.e._, FoI). The side length of \(A\) is \(L\), and the area is \(|A|=L*L\). The purpose of the WSN is to detect undesired or unexpected intruder(s) within a given application-defined time duration (\(T_{max}\)). More specifically, we assume:  
* All sensors have a sensing range of \(r_{s}\) and a communication range of \(r_{c}\), both following the Boolean sensing/communication model;
* The Base Station (BS) is located in the center of \(A\) and has a communication range of \(r_{c}\);
* Any intruder is not aware of the layout of the sensors' placement;
* Sensors deployed in the WSN are not aware of the intrusion path or strategies adopted by any intruder;
* An intruder will enter the WSN FoI from any point along the left peripheral and move toward the right peripheral, aiming to traverse the WSN FoI and arrive at its destination outside of the FoI, as illustrated in Figure 1;
* An intruder is able to detect nearby sensor(s) within its sensing range \(I_{rs}\);
* \(I_{rs}>r_{s}\), which ensures that an intelligent intruder is able to sense nearby sensor(s) and perform detection avoidance before being detected by the WSN;
* The terrain within the FoI will not affect sensor placement or the intruder's intrusion path.  
Figure 1: Illustration of an intelligent intruder detection problem formulation in a WSN. The intruder starts from a random point on the imaginary left periphery and moves toward another random point on the imaginary right periphery, which is the intruder’s destination. The intruder may adopt an AI-enabled intrusion algorithm (like \(A_{2}\)) for detection avoidance.
# A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions
## 3 Challenge: Data Quality
### Data Augmentation
#### 3.1.1 Manual Methods  
Data can be augmented with artificial samples through simulation or laboratory experiments, which are usually domain-dependent. For instance, 3D-object/CAD models are often used to mimic the imaging processes to obtain 2D images, e.g., mechanical element images for object detection [62]. [PERSON] et al. sample the sensory measurements of motor vibration for bearing fault diagnosis through accelerated lifetime tests in [63]. They simulate four different operating conditions and record 20 stream measurements of four seconds for each setting.
## 3 Results  
### Distribution and Retrieved AGB of P. australis  
As shown in Table 4, the training samples used in the present study were reliable, as the JM and TD values between _P. australis_ and other classes were consistently higher than 1.9. Table 5 shows the accuracy assessment results of the interpretation. With an overall accuracy of 92.21% and a Kappa coefficient of 0.88, the accuracy of the interpretation result was satisfactory. According to the producer's accuracy and user's accuracy, the error of interpretation mainly arose from the difficulty in identifying _N. tetragona_ and _T. orientalis_. Producer's accuracy showed that pixels of _N. tetragona_ and _T. orientalis_ were mainly misclassified as water body (165/895) and _P. australis_ (194/694), respectively. Fairly good results had been achieved for _P. australis_ interpretation, with the producer's accuracy and user's accuracy attaining 94.71% and 96.95%, respectively. The misclassified pixels were manually corrected, resulting in the distribution of _P. australis_ shown in Figure 3, with a total distribution area of 86.58 ha.  
Figure 3 shows the retrieved results and the distribution pattern of the AGB of _P. australis_ in the study area. Among the three representative months of June, July, and November, _P. australis_ in July had the highest averaged AGB (2863.59 \(\pm\) 131.86 g m\({}^{-2}\)), followed by June (1156.72 \(\pm\) 247.09 g m\({}^{-2}\)) and November (498.38 \(\pm\) 145.04 g m\({}^{-2}\)). A statistical analysis on the pixel numbers was further performed, and we defined the pixels ranged between lower and upper quartiles as the concentrated range of the AGB. Results show that AGB for June, July, and November mainly concentrated between 991.33 and 1319.43 g m\({}^{-2}\), 2774.30 and 2951.85 g m\({}^{-2}\), and 405.57 and 597.21 g m\({}^{-2}\), respectively. The above results show a consistent trend that _P. australis_ kept growing until it experienced mortality in November. Areas with high AGB values were mainly concentrated in the southern part of the study area throughout the three months. On the contrary, low AGB values were prevalent in the northern part.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **JM Value** & **TD Value** \\ \hline Water body & 1.97 & 1.99 \\ _N. tetragona_ & 1.99 & 2 \\ _T. orientalis_ & 1.99 & 2 \\ Other vegetation & 1.98 & 1.99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample separability between _P. australis_ and other classes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Producer’s Accuracy (PA)** & **User’s Accuracy (UA)** \\ \hline Water body & 87.67\% & 82.40\% \\ _P. australis_ & 94.71\% & 96.95\% \\ _T. orientalis_ & 70.89\% & 81.05\% \\ _N. tetragona_ & 75.87\% & 71.10\% \\ Other vegetation & 94.45\% & 94.78\% \\ Overall accuracy & 92.21\% \\ Kappa coefficient & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Interpretation accuracies.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 2 Methods
### Problem Statement  
The detection of building footprints and road networks in high-resolution satellite imagery is a difficult multi-class semantic segmentation problem where a model is learned to label each pixel into building, road or background classes. However, given the limited spatial resolution of S1 and S2 (10 m), multiple classes may coexist within the same pixel, drastically increasing the complexity since the separability of the classes is reduced.  
These difficulties were experienced in our previous work [24], which was focused on the extraction of buildings and roads from S1 and S2 imagery. Binary decomposition strategies, such as OVO and OVA, address multi-class problems learning multiple binary models. Nevertheless, it must be noted that that the outputs of this models must be aggregated to build up the final multi-class prediction.
## 4 Review of Existing Methods to Track Bio-Fraction of Co-Processed Fuels
### Radiocarbon Methods  
Radiocarbon methods or \"Carbon 14\" methods, quantify the bio-fraction in fuel samples by measuring the amount of biological carbon present through laboratory testing. These methods (unlike the energy/mass balance or the yield methods) allow the bio-content in the finished fuels to be measured directly. Some of the carbon in the co-processed fuel comes from fossil crude oil and is hundreds of millions of years old, while some of the carbon comes from biomass (e.g., from vegetable oil) which is at most a few years old. This newer \"bio-carbon\" includes a 14C isotope that is slightly radioactive, and which is present in biofuels and not in fossil fuels. The key to this method's usefulness in determining the bio-content of a fuel is that 14C is not stable, i.e., it decays over time. The radioactivity coming from the \"recent\" carbon in the fuel can be detected in a laboratory using 14C dating methods, thus quantifying the bio-carbon fraction in the final fuel.  
The vast majority (almost 99%) of all carbon found on Earth is 12C. Approximately 1% of all carbon on Earth is 13C, while 14C is even rarer, present in only one out of every trillion carbon atoms [35; 36]. The isotope 14C is slightly radioactive and is produced by radiation in the earth's upper atmosphere. The way in which 14C becomes incorporated into fuels is via CO\({}_{2}\); CO\({}_{2}\) in the atmosphere contains all three of these carbon isotopes in a fixed ratio and the same ratio can be found in living plants and animals [37]. However, when an organism dies, its uptake of atmospheric carbon stops and it thus stops taking in 14C. As noted 14C is unstable and it decays gradually, halving in content every 5730 years. The 14C originally present in fossil fuels has decayed completely. Biofuels on the other hand are much younger and thus the original 14C is still present.  
Three methods exist for measuring the amount of 14C in a material, two of which are applicable to fuel testing. These are: (i) Accelerator Mass Spectrometry (AMS) and (ii) Liquid Scintillation Counting (LSC) (beta ionization being the third, see also the section on EN16640). The merits and limitations of 14C testing vary depending on which of these two methods are considered; these differences are explained further in the next section, followed by an overview of two additional alternatives which are in development.
## 3 System Architecture, Software Services and Deployment
### Hierarchical Network Architecture  
Smart devices deployed for monitoring and remotely controlling water distribution are organized in a hierarchical network architecture. Since the majority of integrated third-party smart water monitoring solutions use wM-Bus communication interfaces, the specific protocol's functional requirements play a fundamental role in the design of the overall architecture. Additional communication protocols are also supported rendering the solution future-proof; however, performance optimization will be carried out in the near future.  
The proposed architecture introduces an intermediate layer of gateway devices, each one equipped with two distinct networking interfaces. The first interface uses the wM-Bus protocol for interacting with the affiliated smart devices, while the second interface delivers long-range low-rate communication based on LPWAN technology. This inevitably forms a certain type of network hierarchy in which (i) LPWAN gateways constitute the top layer connecting the IoT deployments with the Internet, (ii) gateways form an intermediate layer and, lastly, (iii) smart devices are placed at the bottom. An example of such a hierarchical topology is shown in Figure 3, while the architecture is aligned with the paradigm proposed in [26]. This scheme is considered appropriate due to its adaptability to various internal and external conditions, supporting scalable and robust communication. It only requires a limited degree of fine-tuning, and it is partially affected by the actual environmental conditions of the deployment.  
Moreover, the specific hierarchical organization structure provides a series of additional benefits such as the establishment of secure communication between sub-networks by Public Key Infrastructure (PKI) incorporation. This essentially provides an increased level of confidentiality and information integrity, while at the same time facilitating the execution of services related to data analysis at the network edge. Each transmitted message is individually encrypted, thus demanding a separate key per device to decrypt its content on the receiving end. Each device key is stored permanently on the device and cannot be changed during its lifetime (more than 10 years in most cases). The transmitted packets are collected by the gateway devices through the supported wM-Bus network interface and are forwarded via the LoRaWAN interface to the higher layers for further processing.