esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Time-Varying Parameters of the Degradation Model  
When the initial fault appears in a bearing, according to the Paris-Erdogan theorem, the relationship between crack propagation and the number of stress cycles can be derived as [31]  
\[\frac{dx}{dL}=C(\Delta K)^{m} \tag{16}\]  
where \(x\) is the crack length, \(L\) is the number of stress cycles (fatigue life), \(C\) and \(m\) are constants related to materials, and \(\Delta K\) is the amplitude of stress intensity factor, which changes with the propagation of bearing cracks.  
To obtain the relationship between the stress intensity factor and the crack length in the actual degradation process of bearing, a finite element method (FEM) is used to analyze the crack of rolling bearing, as shown in Figure 1.  
Firstly, an experimental bearing FEM is established, in which the crack length and depth information are set to simulate the fatigue crack propagation. Constraints and loads are imposed according to experimental conditions. Because the shape function of ordinary element cannot represent the singularity of stress and strain at the crack front, a 12-node singular element is used for the tetrahedral mesh. A singular element is a kind of quadratic element whose nodes are located at the quarter, with its shape function given by  
\[u=2\Big{(}\sqrt{v}/\sqrt{l}\Big{)}[(1-\eta)u_{2}+0.5u_{3}] \tag{17}\]  
\[\frac{\partial u}{\partial v}=\frac{1}{\sqrt{v}}*\frac{1}{l}[-2\eta u_{2}+(0. 5+\eta)u_{3}] \tag{18}\]  
where \(u\) and \(v\) are the node coordinate, \(u_{2}\) and \(u_{3}\) are the corresponding node coordinate, \(\eta\) is a constant, and \(l\) is the unit side length.  
The finite element analysis is used to simulate the crack propagation, and a set of stress intensity factors varying with the crack length are obtained by J-integral analysis to simulate the fatigue crack propagation. The mathematical relationship of \(\Delta K\) with the square root of the crack length is obtained by numerical interpolation fitting. The time-varying physical parameters obtained from the Paris-Erdogan theorem and FEM are transferred to the degradation model of the bearing. The stress intensity factor is taken as the time-varying physical parameter in the bearing degradation model.  
Figure 1: Time-varying parameter degradation model of bearings.
## 2 Problem Statement of Renal Localization  
The current classification of WHO kidney tumors from 2004, and the International Society of Urological Pathology (ISUP) from 2013, distinguishes over 60 histological units [4; 7].  
The basic factor influencing the further management of the patient is the assessment of the nature of the identified pathology and its clinical advancement. An essential element of the diagnostic process is the differentiation of benign lesions that can be monitored from malignant lesions that should be treated surgically. To determine the proper treatment method: organ-sparing surgery, radical nephrectomy, or thrombectomy, the correct assessment of the local tumor advancement is necessary.  
Contemporary computer tomographs (CT scanner) perform up to 2,000,000 projections of high resolution, even up to tens of micrometers. Multi-row computed tomography becomes the standard. The number of receiving elements has been increased to 64. Companies have already introduced 128 and even 256-row scanners. The introduction of two X-ray tubes is the next stage of development. The examination time is significantly shortening, decreases reduction of X-rays and at the same time much more data, allowing for more accurate imaging of the examined organs are obtained for the better spatial projection of the image (3D).  
Digital images obtained as a result of computed tomography, used in our experiments, have the dimensions of \(512\times 512\) pixels. Exemplary CT images are shown in Figure 1.  
The visual assessment of the kidney condition is done manually by a doctor who must look at hundreds of scans and carry out a detailed description. From computer methods of supporting medical diagnostics, it is very important to develop modern techniques and tools for automatic or semi-automatic kidney location. The real-time computer software will reduce the time of image analysis and allow for more effective diagnostics. With many images, the likelihood of human error increases. Thanks to algorithms for the automatic location of the kidneys, we can give doctors a very useful tool. In this paper, we focus on techniques for an automatic location of kidneys along with finding the outline of the kidney on each slice, regardless of its location in the abdominal region.
## 1 Introduction
It is evident that the configuration of the stiffener must be adjusted in accordance with the specific experimental conditions, with the exception of the dimensions of the stiffener itself. Consequently, geometric distortion presents notable challenges when attempting to simulate prototype behaviors using scaled models. These phenomena are not prone to scaling, thereby impeding the accurate replication of the prototype in geometrically distorted scaled model experiments. Although existing methods [10; 11; 12; 13; 14; 15; 21; 22; 23; 24; 25; 26] for addressing geometrically distorted scaled models provide a means of identifying similarities, they primarily focus on individual geometric distortions and require iterative processes to identify key factors. This makes them complex to utilize in practical applications. To address these nonscaling phenomena, this study introduces a systematic procedure that modifies the load cases in a rational manner, primarily aimed at compensating for geometric distortions relative to the prototype. It is imperative to emphasize that the similitude methodology is based on the assumption of thin plate theory focusing on ductile material, requiring the prototype to be composed of thin, ductile plates. Figure 1: Schematic of common distortions between prototype and scaled model. The following section outlines the structure of the remainder of this paper. Section 2 investigates the dynamic behavior of a stiffened plate under impact loading using the finite element method. It provides valuable guidance for establishing suitable criteria for altering the stiffener configuration in distorted models. Section 3 outlines the similarity procedure employed to capture the dynamic response of a prototype stiffened plate using distorted scaled models, neglecting the strain rate effect based on numerical observations, and focusing on correcting double input parameters involving the load case and geometry. Section 4 presents numerical simulations that validate the reliability of the similarity procedure and distortion criterion for shipbuilding structures. Section 5 discusses the limitations of the proposed method, emphasizing large stiffener heights and introducing an approximate approach to discern dominant behaviors during impact. Finally, Section 6 summarizes the key findings and contributions of this study.
# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 4 Experimental Setup for Simulations
### Performance Evaluation  
The performance is evaluated to ascertain the efficiency of the proposed technique using the simulation toolkit fogs. Analysis and comparisons are carried out with the existing algorithm EEHFC [10] and LBS [30], considering the following qualitative performance metrics: the usage of energy and response time within the IoT-fog-cloud environment. To evaluate the performance of the proposed technique, the following metrics are calculated and utilized:  
1. Network lifetime: It represents the lifespan of devices in the network. When an average number of devices are not functional, network partitioning occurs [13]. Network lifetime can be computed as follows: \[Node\ Lifetime=\frac{Node^{\prime}s\ Remaining\ Energy}{Drain\ Rate}\] (7)
2. Total energy consumption: It represents the overall power consumed in the IoT-fog-cloud environment. The overall energy [35] usage of all devices can be computed using Equation (4). Due to the increased energy of fog nodes, the suggested technique saves energy and increases network longevity, as shown in Figure 3.
3. Average Delay: It consists of both transmission and Computational delay in IoT-fog-cloud operation [36]. It can be calculated using Equations (2) and (3). \[Average\ Delay=\frac{\tau_{D}+CD_{k}^{\prime fog}}{Total\ No.\ of\ devices}\] (8) iv. Response time: It represents the response time of all tasks (\(T\)) up to the present interval [37]. The average response time can be computed as follows: \[ResTime\ (T_{1})=\frac{\sum_{d_{t}^{i}\in T}ResponseTime\left(d_{t}^{j}\right)}{ ResponseTime\left(d_{j}^{S_{c}}\right)}\] (9) where \(T\) is a set of tasks, and \(d_{t}\) is the host device at time \(t\).
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 1 Introduction
In Section 4.2, the block diagram of the proposed loop is given and illustrated in detail. In Section 5, some simulations are undertaken to demonstrate the performance of the proposed method. The performance of the LM method and KF is tested in Sections 5.1 and 5.2, respectively. Monte Carlo (MC) simulations are made to calculate the tracking probability, tracking accuracy and bit error rate. A conventional FLL-assisted PLL is simulated too in order to make a comparison. Section 5.4 designs an optimal loop which combines the proposed loop and FLL-assisted PLL to achieve the optimal performance in weak and strong signal environments.
## 2 Materials and Methods
### Removal of Bacterial Biofilm  
The apical foramen of instrumented teeth (35/.06 or 50/.06) was sealed with self-cure glass ionomer, and the root surfaces were covered with two layers of nail varnish. To initiate the experiment, previously sterilized teeth had their root canals filled up to the orifice level with a suspension of _E. faecalis_ ATCC 29212 (ca. 10\({}^{8}\) cells/mL). These teeth were individually submerged in tubes containing brain heart infusion broth (BHI, Liofilchem, Roseto degli Abruzzi (TE), Italy) and incubated at 37 \({}^{\circ}\)C for 21 days, with regular renewal of the culture medium every two days. After incubation, the BHI broth in the canal space was aspirated, and the canal was washed with phosphate buffer saline (PBS, Sigma Aldrich, Darmstadt, Germany). Subsequently, the teeth, for each apical size, were randomly gathered into three groups (_n_ = 10/group), and root canal irrigations (i.e., Manual, Ultrasonic, Sonic) were performed. An additional control group (C) was created in which the root canals were not subjected to any irrigation procedure.  
To determine the remaining metabolic active bacteria in the root canals after each procedure, the resazurin assay was conducted. Briefly, root canals were filled with BHI broth with 10% resazurin (0.1 mg/mL, Sigma-Aldrich, Darmstadt, Germany) and incubated for 3 h at 37 \({}^{\circ}\)C. The medium in the canal space was then collected, and its fluorescence intensity (excitation: 530 nm; emission: 590 nm) was measured in a microplate reader (Synergy HT, BioTek, Santa Clara, CA, USA). Results were presented as relative fluorescence units (RFU).  
Additionally, an assessment of the biofilm remaining on the root canal after each irrigation procedure was accessed by SEM. Briefly, teeth were fixed with 1.5% glutaraldehyde (Sigma-Aldrich) for 30 min, longitudinally split, and dehydrated in sequential graded ethanol solutions. Lastly, teeth were sputter-coated with Au-Pd alloy and visualized by SEM.
# Fabrication of Silicon Carbide from Recycled Silicon Wafer Cutting Sludge and Its Purification
###### Abstract  
Around the world, silicon carbide (SiC) is used as a raw material in several engineering applications because of its various beneficial properties. Currently, though the Acheson method is one of the most emblematic to manufacture SiC, the direct carbonization of metallic silicon is simple and beneficial. In this reaction, silicon wafer cutting sludge can be used as an alternative silicon source material. The silicon wafer sludge contains silicon, ethylene glycol, cooling water, and a small amount of impurities. In this study, SiC was synthesized using silicon wafer sludge by a carbothermal process. In a typical experiment, the silicon sludge was mixed with carbon at different molar ratios. Then, the mixture was turned into pellets, which were placed in alumina crucibles and heat-treated at a temperature from 1400 \({}^{\circ}\)C to 1600 \({}^{\circ}\)C to fabricate SiC. To deduce the optimum condition for the synthesis of SiC, an investigation was carried out on the effects of different mixing ratios, temperatures, and heating times. To ensure sufficient carbonization, excess carbon was mixed, and the synthesized SiC was characterized by X-ray diffraction (XRD). Subsequently, purification of the synthesized SiC products by oxidation of excess carbon was performed. The removal of extra carbon could be confirmed by XRD and attenuated total reflectance (ATR) spectroscopy. This process can give basic information for the development of a technology to produce SiC using recycling Si wafer cutting sludge waste.  
silicon carbide; Si wafer cutting sludge waste; recycling; carbothermal; oxidation Article
### Case Study No. 2: CODESYS Linked with Node-RED and Microsoft Azure
#### 6.2.5. Node-RED Dashboard in Microsoft Azure Cloud  
In the first case study, we implemented a dashboard for system monitoring and emergency intervention using Azure IoT Central aPaaS service. In the second case study, we opted for a different approach. Node-RED also provides the possibility of implementing a dashboard, so as suitable option turned out to be to deploy Node-RED also in the cloud and create a dashboard using it.  
Deploying Node-RED to the cloud is relatively easy, as it is actually an application running on the Node-JS runtime. So we used a virtual machine, specifically the Azure Virtual Machine IaaS service [37]. Azure Virtual Machine is one of several types of scalable on-demand compute resources that Azure offers. Before we create one, we need to think about a few things such as the application name, where the resources are stored, virtual machine size, operating memory, maximum number of virtual machines, operating system,  
Figure 21: Local Node-RED—OPC UA client.  
configuration, and related resources. Further, it gives us the flexibility of virtualisation without having to buy and maintain the physical hardware on which it runs. We used a Linux-based operating system, specifically Ubuntu Server 20.04 (Focal).  
Thus, we send data from the local Node-RED to the Node-RED in the cloud using the MQTT protocol. Here, we read the data, i.e., receive it using the MQTT protocol, and then display it in the dashboard. We use the _node-reddashboard 3.1.6_ library and _node-red-contrib-ui-led 0.4.11_ to display the data in the dashboard, since the nodes that allow us to do this are not part of the base installation and need to be installed separately. Table 2 gives us a more detailed description of Figure 22, where we can display data in the dashboard using these nodes.  
Our final dashboard is shown in Figure 23. It is important to note that we can not only monitor the variables, but again we can also intervene in the production process in an emergency.
## 2 Materials and Methods
### Experimental Procedure  
Due to the variability of MT levels in ambient air (see Table 1), it is important to select proper concentration levels for human olfaction tests. Two types of olfactory experiments were designed and conducted. In our first experiment (Exp 1), measurements of target gases were carried out using individual standard gases of MT, wherein the detection threshold values of each MT were measured. In this study, the detection threshold was defined as the MT concentration at which 50% of participants could distinguish their presence from the background. This approach was adopted by following the odor threshold procedures from the US EPA (1990) [33]. A questionnaire was used to investigate the relative preference among the three MTs by each participant because of their various mixing ratios in ambient air (Table 1). Information regarding the relative preference was then used to determine a mixing ratio for the three target MTs in the next experiment (Exp 2). To this end, a mixture of the three MTs (\(\alpha\)-pinene, \(\beta\)-pinene, and \(d\)-limonene = 1:0.5:0.5) was generated. Hence, during Exp 2, each participant inhaled the mixture over 2.5 min to quantitatively assess the therapeutic potential of the MT gases.  
3.1 Assessment of the Human Olfactory Response to the Odors of \(\alpha\)- Pinene, \(\beta\)- Pinene, \(d\)-Limonene (Exp 1)  
Exp 1 was conducted to determine the human olfactory response at given concentration levels of each MT. Each participant was led into a clean testing room (L \(\times\) W \(\times\) H = 7 \(\times\) 7 \(\times\) 3 (147) m\({}^{3}\)) and inhaled a test MT mixture dispensed from a funnel close to the nose for 2.5 min (Figure 1). The exposure time was 2.5 min because it was sufficient to observe physiological changes [29].  
Sequentially, each participant inhaled each MT standard prepared at four different concentration levels (i.e., 2, 3, 5, and 7 ppbv). The different concentration were presented randomly. To this end, the permeation tubes for standard MT gases of \(\alpha\)-pinene, \(\beta\)-pinene, and \(d\)- limonene were used at permeation rates of 201, 252, and 253 ng/min, respectively. For every single MT tested, each participant inhaled samples at a certain concentration level for 2.5 min. To avoid contamination of the room during the experiment [34], a 15 min break was taken by all participants before the inhalation of a mixture at a different concentration levels. The testing room was ventilated with fresh air during this time, at a flow rate of approximately 30 m\({}^{3}\)/min. The participant seated in the room during break time. The inhalation testing room was maintained at a temperature of \(25\pm 1\)\({}^{\circ}\)C and a relative humidity of \(50\pm 5\%\). Before and after each experiment where one kind of MT was tested, the room was ventilated with fresh air for one hour using fans. After inhaling the MTs at all concentration levels, the participants were asked to answer a questionnaire (see Table 2).  
Figure 1: Experimental scheme for monoterpene (MT) exposure in Exps 1 and 2.
# A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor
## 4 Conclusions  
A low-profile HF meandered dipole antenna with a ferrite-loaded AMC was proposed in this paper. By implementing ferrite given its high permeability on the HF band to the unit cell of the AMC, the unit cell structure can operate on the HF band while maintaining a compact size. The meandered dipole antenna was designed through optimization of a binary GA and combined with the \(8\times 8\) AMC. The VSWR bandwidth (<3) of the proposed antenna with the AMC covers the range of 20-30 MHz, which is within the HF band. In addition, due to the influence of the AMC, the proposed antenna has directional radiation  
Figure 11: Receiving power measurement setup.  
Figure 12: Measured receiving power levels of the proposed antenna with the AMC and the reference monopole antenna.  
patterns and maintains its performance capabilities despite the distance between the antenna and the AMC being closer than that of the antenna and a general reflector. As a result, the proposed antenna with the AMC is compact in the size of \(0.06\times 0.06\times 0.002\,\lambda_{L}^{3}\) (\(\lambda_{L}\) is the wavelength of the lowest operating frequency in free space). The performance of the proposed antenna with the AMC is confirmed by measurements. From these results, it can be indicated that the designed antenna can be used in applications operating on the HF band that require a low profile and a compact antenna, such as ground vehicle.  
Conceptualization, O.H.K. and K.C.H.; methodology, O.H.K.; software, O.H.K.; validation, O.H.K., W.B.P., H.J.L. and J.Y.; formal analysis, O.H.K.; investigation, O.H.K., H.J.L.; resources, O.H.K., W.B.P.; data curation, O.H.K., J.Y.; writing--original draft preparation, O.H.K.; writing--review and editing, O.H.K., W.B.P., H.J.L. J.Y. and K.C.H.; visualization, O.H.K.; supervision, K.C.H.; project administration, K.C.H.; funding acquisition, K.C.H. All authors have read and agreed to the published version of the manuscript.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
# Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing
###### Abstract  
Despite the increasing demand of updated and detailed indoor models, indoor reconstruction from point clouds is still in an early stage in comparison with the reconstruction of outdoor scenes. Specific challenges are related to the complex building layouts and the high presence of elements such as pieces of furniture causing clutter and occlusions. This work proposes an automatic method for modelling Manhattan-World indoors acquired with a mobile laser scanner in the presence of highly occluded walls. The core of the methodology is the transformation of indoor reconstruction into a labelling problem of structural cells in a 2D floor plan. Assuming the prevalence of orthogonal intersections between walls, indoor completion is formulated as an energy minimization problem using graph cuts. Doors and windows are detected from occlusions by implementing a ray-tracing algorithm. The methodology is tested in a real case study. Except for one window partially covered by a curtain, all building elements were successfully reconstructed.  
Keywords: indoor modelling; BIM; energy minimization; graph cut; regularization; terrestrial laser scanning +
Footnote †: journal: Applied
# PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications
## 2 Synchronization Protocol for UANets  
### Underwater Acoustic Networks (UANets)  
Figure 1 shows the target cluster-oriented underwater acoustic network model, which comprises one command ship (CS) and multiple underwater nodes (UN). CS is positioned on the ocean surface and moves along the ocean current; it controls the entire network. CS is connected to a GPS and performs clock synchronization, thus functioning as a reference clock for UN. CS is equipped with an underwater acoustic communication modem (UAC) for communicating with UN, and periodically broadcasts a beacon containing transmission time information to UN for synchronization.  
UN, which is a mobile node similar to an autonomous underwater vehicle (AUV) or a submarine, is self-propelled. UN collects underwater data or performs underwater missions delivered from CS. All UNs are within the acoustic communication range of CS and communicate with CS. UN renews the position information by obtaining its own moving speed and moving direction information through mounted sensors. UN synchronizes the CS and UN clock times by using the transmission and reception times of CS that have been periodically received.