esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Lubrication Performance of Journal Bearing with Multiple Texture Distributions
## 2 Simulation Model
### Numerical Solution  
For a journal bearing under stable working conditions, the two-dimensional Reynolds equation can be expressed in the following form [16]:  
\[\frac{\partial}{\partial X}(\frac{h^{3}}{\eta}\frac{\partial p}{\partial X})+ \frac{\partial}{\partial Z}(\frac{h^{3}}{\eta}\frac{\partial p}{\partial Z})= 6U\frac{\partial h}{\partial X}, \tag{4}\]  
where \(p\) is the oil film pressure at a specific point on the bearing surface, \(U\) is the linear sliding velocity of the shaft relative to the bearing and \(\eta\) is the dynamic viscosity of the lubricant.  
* Solution of the Reynolds equation
* The Reynolds boundary is applied in the bearing modelling process. The effect of cavitation is not included in the analysis, either in terms of the single-phase analysis used or in the application of the boundary conditions, which is considered in a thermo-hydrodynamic analysis of the bearing with the CFD approach [17; 18].
* In the \(XOZ\) plane, the journal bearing is meshed into \(m\) and \(n\) grids along the \(X\) and \(Z\) directions, where \(m\) is \(10\times M\) and \(n\) is \(10\times N\).
* The five-points difference method is used to discretize Equation (4). The symmetric successive over relaxation (SSOR) method is used to solve the discrete algebraic system of equations and the pressure \(p\) is obtained.
* Solution of lubrication parameters of the journal bearing
* Load capacity of the journal bearing *  
Figure 2: Journal bearing model with spherical texture: (**a**) schematic of the bearing model and (**b**) grid details of different textures.  
The calculated oil film pressure \(p\) is numerically integrated in the whole fluid domain along the \(X\) and \(Y\) directions, and the load capacities \(W_{X}\) and \(W_{Y}\) can be obtained as follows:  
\[\begin{array}{l}W_{X}=\int_{-\frac{L}{2}}^{\frac{L}{2}}(\int_{0}^{2\pi R}p\cos \psi dX)dZ\\ W_{Y}=\int_{-\frac{L}{2}}^{\frac{L}{2}}(\int_{0}^{2\pi R}p\sin\psi dX)dZ\end{array}, \tag{5}\]  
In both cases, the total load capacity \(W\) and the attitude angle can be calculated as:  
\[W=\sqrt{W_{X}{}^{2}+W_{Y}{}^{2}}, \tag{6}\]  
\[\psi=\frac{X}{R}, \tag{7}\]  
(b) Friction coefficient  
The shear stress \(\tau\) of the slip plane between the friction pairs can be given as [19]:  
\[\tau=\frac{h}{2}\frac{\partial p}{\partial X}+\frac{\eta}{h}U, \tag{8}\]  
The friction force \(F\) can be expressed as the integral of the shear stress on the slip plane:  
\[F=\iint\left(\frac{h}{2}\frac{\partial p}{\partial X}+\frac{\eta}{h}U\right) dXdZ, \tag{9}\]  
According to Equations (6) and (9), the expression of the friction coefficient \(\mu\) can be given as:  
\[\mu=\frac{F}{W}. \tag{10}\]
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 4 The Network Layer
### The Configuration Layer  
The Configuration Layer deals with protocols running on top of the IoT monitoring platform allowing the exchange of data specified by its context regarding what is described, what was measured, when, where, by what, the time of validity, ownership, and others. The main objective of this layer is to curate, harmonize and aggregate the collected raw data, so that it can be published as context information, or supplied to upstream data processing algorithms or analytics. It is also capable of sending operational commands to the Middleware Layer. Context information is a term widely used in IoT research and characterizes data that may come from existing systems, users, through mobile applications, IoT smart devices, agricultural machinery or public geo-services. The Configuration layer uses an IoT broker to guarantee a common interpretation of information produced by heterogeneous data sources that typically employ different data formats and ontologies, and therefore are unable to directly share information among them. An IoT broker enables the forwarding of lower-level device information to higher-level Thing Information.
* Regarding precision agriculture applications, a popular context broker is implemented by the FIWARE NGSI technology, named Orion Context Broker. FIWARE is a framework of open-source platform components towards the deployment of the IoT paradigm. FIWARE NGSI is the FIWARE version of the OMA NGSI, an API based on HTTP that enables the integration of components and provides the basis for the interoperability and portability of IoT-enabled Smart Agriculture applications [34]. NGSI is an information model developed by OMA SpecWorks to manage context information with a meta-model based on entities, attributes and metadata. This protocol manages data concerning context entities, such as the lifetime and quality of information.
* The implementation of a smart farming monitoring system is greatly depended upon geo-services, location detection tools and mapping technologies. In such systems, the exchange of geographical information should be effortlessly accomplished between the involved network entities across the web. The Open Geospatial Consortium-Web Feature Service (OGC-WFS) [35] constitutes a desirable asset in formulating geographic information and offering direct fine-grained access at feature property level of the data to IoT sensor nodes in precision agriculture applications. OGC offers various standards that can ease the way location data is exchanged and stored in a smart farming system that is based on drone monitoring. Furthermore, the OpenGIS Web Map Service Interface Standard (WMS) [35] can be efficiently used by UAVs, since it provides a simple HTTP interface for requesting geo-registered map images from one or more distributed geospatial databases.
## 4 Results and Discussion
### Visualization of Augmentation Using DCGAN with Transfer Learning
#### 4.5.2 Generalization Performance Study  
The generalization performance of ShuffleNetV2-1.0-SE trained with data augmentation using transfer learning and DCGAN was evaluated on a publicly available dataset containing tunnel lining cracks [3]. Figure 15 shows some test results for different types of cracks (disconnected, high luminance, interference background and edge cracks), where (a)-(d) show the test results achieved after training ShuffleNetV2-1.0-SE on Dataset C, while (e)-(h) show the results obtained after training the network on Dataset A. The results obtained after training on Dataset A, which contains DCGAN-augmented samples, are consistently correct, while those obtained after training on Dataset C are not. The results of ShuffleNetV2-1.0-SE trained on Dataset C, which correctly classifies crack images misclassified as backgrounds, demonstrate that DCGAN can effectively enhance the generalization ability of ShuffleNetV2-1.0-SE for crack detection.  
Figure 14: Comparison of the predictions of ShuffleNetV2-1.0-SE trained on different datasets: (**a–e**) represent interference background image; (**f–j**) represent crack image.
# Design and Application of a Smart Lighting System Based on Distributed Wireless Sensor Networks
###### Abstract  
Buildings have been an important energy consuming sector, and inefficient controlling of lights can result in wastage of energy in buildings. The aim of the study is to reduce energy consumption by implementing a smart lighting system that integrates sensor technologies, a distributed wireless sensor network (WSN) using ZigBee protocol, and illumination control rules. A sensing module consists of occupancy sensors, including passive infrared (PIR) sensors and microwave Doppler sensors, an ambient light sensor, and lighting control rules. The dimming level of each luminaire is controlled by rules taking into consideration occupancy and daylight harvesting. The performance of the proposed system is evaluated in two scenarios, a metro station and an office room, and the average energy savings are about 45% and 36%, respectively. The effects of different factors on energy savings are analyzed, including people flow density, weather, desired illuminance, and the number of people in a room. Experimental results demonstrate the robustness of the proposed system and its ability to save energy consumption. The study can benefit the development of intelligent and sustainable buildings.  
s +
Footnote †: journal: Applied Sciences  
article
## 4 Experimental Test Results
### Load Resistance and Failure Modes  
The cracking resistance \(P_{cr}\), resistance corresponding to steel yielding \(P_{y}\), and ultimate resistance \(P_{u}\) of the tested T-section beams could be determined from P-\(\delta\) curves of beams as shown in Figure 3. Table 1 lists the \(P_{cr}\), \(P_{y}\), and \(P_{u}\) values and the corresponding failure mode for each of the tested 8 T-section beam specimens. Based on the P-\(\delta\) curves and load-strain curves of the main steel reinforcement and CFRP, two distinct failure modes were observed. The first is a flexural mode that occurred in beams B1-B3, which was characterized by: (1) strength hardening plateau of the P-\(\delta\) curves; (2) yielding of the bottom steel reinforcement at final failure (Figure 4a); and (3) flexural cracks at the pure bending region in the T-section beam (Figure 5). The combined flexural failure and CFRP-concrete debonding failure occurred in beams C1\(\sim\)C5 at final failure stage. This was accompanied by: (1) much smaller strains of about 6000\(\mu\) in the CFRP than failure strain of 13090\(\mu\), which implies that the CFRP did not fail in tension; (2) yielding of the bottom steel reinforcement at final failure (Figure 4a); and (3) step drops in the P-\(\delta\) curves of specimens C1\(\sim\)C5. \(P_{cr}\) and \(P_{y}\) increased significantly when bonding layers increased (C4) or the reinforcement ratio increased (C3). However, peeling failure often occurred when there was excessive bonding of FRP.The peeling of the CFRP from the bottom of the T-section beam specimens was evident in C4 (Figure 5).
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Machine Learning (ML) Localization Algorithms  
The machine learning (ML) localization algorithms have emerged as a powerful framework for solving many challenging practical problems and addressing the limitations of the conventional techniques, especially for the indoor localization systems [27]. ML, in general, describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve the associated tasks. It utilizes real-world data for training and capturing the complex relations between the input data (features) and output values (labels) [24]. For localization, the output variables can consist of discrete values (categories), such as \"within zone\" or \"outside zone\", or a range of continuous values, such as the RSS of the signal over time. Both classification and regression problems can be applied, such as finding a point on a finite grid that best fits the observed input data and estimating the coordinates of the device. Numerous algorithms exist for supervised, unsupervised, and reinforcement learning, such as the \(k\)-nearest neighbour (\(k\)-NN), artificial neural networks, Bayesian inference, support vector machines (SVMs), or their combination [22; 24].  
More recently, deep learning (DL) approaches including recurrent neural networks (RNNs) have also been investigated for building positioning systems [56]. In [57], a mobile positioning system was built with a mobile station, a mobile positioning server, a database server, and a model server. The mobile positioning server handled the collection and normalization, mobile positioning method, de-normalization, and estimation. The database server included the network signal and GPS coordinates, while the model sever included the recurrent neural networks (RNNs). The model server could save the trained RNN models from the positioning layer (base stations and Wi-Fi APs normalized RSSIs), a recurrent hidden layer, and the output layer (estimated normalized longitude and latitude). For the optimization of recurrent neural networks, the learning rate and the gradient descent method is applied to update each weight and bias. On the other hand, deep learning requires large scale annotation of the models training, which can be challenging [58]. Nevertheless, the deep learning techniques may be more adopted for indoor positioning in the near future [59].
# Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar-Communication System
###### Abstract  
In this paper, we try to design the joint waveform and passive beamforming within the context of dual-function radar-communication (DFRC) systems. Focusing on the intricate trade-off between stringent radar beampattern constraints and their desired performance, we introduce a novel manifold idea based on the alternating direction method of multipliers (ADMM) framework. Specifically, our proposed method, named DFRC-MA, could address the challenge of constant modulus waveform design in a multiple-input-multiple-output (MIMO) DFRC system. Firstly, our methodology begins by formulating the reference waveform to achieve an optimal radar beamforming pattern. Subsequently, we define the DFRC optimization problem to mitigate the multi-user interference (MUI) under the constant modulus constraint. Through a series of simulations, we evaluate the efficacy of DFRC-MA, where the integrated waveform designed by DFRC-MA exhibits superior performance over some prevalent ones.  
Keywords:dual-function radar-communication system; waveform design; manifold optimization; constant modulus; alternating direction multiplier method +
Footnote †: journal:
## 4 Numerical Case Study--Assessment of SSI Effects on a Steel Building
### Moment Resisting Frame
#### 4.1.2 Moment Frame with SSI Effects  
To incorporate SSI effects, shallow single foundations were designed by following the provisions of Eurocode 7 (EC7) [48] and the Croatian national annex [49]. According to the national annex for EC7 in Croatia, design approach number three should be used for the design.  
The soil properties were taken from the local river sand. Sand was determined to be uniform when it was tested in geotechnical laboratory, detailed results can be found in [50]. The properties that are used for this research are for the compacted sand case: density 1550 kg/m \({}^{3}\), shear wave velocity 135 m/s, and Poisson ratio 0.3.  
Under each column, single foundations with layout dimensions 3.60 \(\times\) 3.60 m and a depth of 1.0 m were considered. Foundations are numerically modelled as reinforced concrete shell elements under each column connected with a foundation frame beam 0.80 m wide, 0.50 m high, and 5.40 m long.  
Horizontal stiffness of the soil around the foundation were represented by linear horizontal springs determined according to [PERSON] [37] and [PERSON] et al. [38]. Every single foundation was modeled with two horizontal springs for each direction (Table 2). Vertical soil stiffness was represented by nonlinear link elements which were assigned soil properties determined for local river sand following the steps described in Section 3. There were nine vertical links total under every single foundation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Cross-Section** & **Story** & \(M_{y}\) **(kNim)/\(M_{p}\) (kNim)** & \(\theta_{y}\)/\(\theta_{p}\) \\ \hline HEB 340 & 1, 2, 3, 4, and 5 & 593/662 & 0.004/0.026 \\ \hline HEB 450 & 1, 2, 3, 4, and 5 & 976/1095 & 0.003/0.019 \\ \hline IPE 500 & 1, 2, 3, 4, and 5 & 530/603 & 0.009/0.05 \\ \hline \end{tabular}
\end{table}
Table 1: Calculated plastic hinge properties.  
Figure 8: Comparison of the capacity curves.  
Furthermore, two different lateral load distributions were used for the numerical models founded on the soil. Firstly, a lateral load distribution in the analysis corresponding to the first mode of oscillation and second, uniform lateral load distribution. Modal lateral load distribution is encouraged to be used for SSI systems as shown in [51]. In Table 3 one can find information regarding the forces applied on the model. The comparison of the capacity curves for each case will benefit explaining the effects of the lateral load distributions on the results.  
Figure 9 presents capacity curves obtained within the numerical calculations for the moment resisting frame system on the soil. Differences in the capacity and stiffness are noticeable but further conclusions will be made when the effects of the load distribution will be studied on the structure elements separately.
## 5 Conclusions  
A novel combined VSG control with a simplified CVB method has been presented for grid-connected MMC systems control. The proposed control structure integrates VSG for power sharing and inertia emulation, resulting in improved dynamic response, system stability, and a simplified CVB method for estimating and balancing the SM capacitor voltages.  
Simulations and experimental tests were conducted to validate the effectiveness of the proposed control strategy, comparing it with the conventional PQ decoupled control approach. The results highlight the suitability of CVB-VSG for controlling MMC-based grid-connected systems. Additionally, the proposed control technique minimizes circulating current, reduces DC-side disturbances, eliminates the need for extensive sensor deployment, and reduces computational requirements.  
Conceptualization, H.B.; Methodology, H.B., F.K., A.B. and M.B.; Validation, F.K., A.B. and M.B.; Formal analysis, H.B., F.K., A.B., L.B. and M.B.; Investigation, H.B.; Data curation, H.B.; Writing--original draft, H.B.; Writing--review & editing, F.K., A.B., L.B. and M.B.; Visualization, F.K., A.B. and M.B.; Supervision, F.K. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control** & \(\mathbf{\Delta}\)**Idc** & \(\mathbf{\Delta}\)**Icirc** & \(\mathbf{\Delta}\)**Vc** & **Vcref–Vcstm** \\ \hline PQ & 1.6 A & 3.6 A & 12 V & 100 V \\ VSG & 1.4 A & 3.4 A & 9 V & 100 V \\ CVB-PQ & 1 A & 1.6 A & 1.5 V & 7 V \\ CVB-VSG & 0.9 A & 1.4 A & 1.5 V & 3 V \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical data from practical validations for PQ, VSG, PQ-CVB, and VSG-CVB control.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 4 Paradigm Shift: Bulk Hydrogels to Hydrogel Micro/Nanoparticles
### Designing Stimuli-Responsive Properties
#### 4.1.2 Temperature Sensitivity  
Temperature-responsive hydrogels could undergo phase transition induced by temperature changes across certain thresholds, more commonly referred to as the lower critical solution temperature (LCST) and upper critical solution temperature (UCST). In the simplest form, hydrogels can be described as a polymeric network comprising the skeletal structure of a gel and an aqueous phase filling this structure driven by the hydrophilicity of the gel material [21; 64; 193]. Each critical temperature describes a thermodynamic point where the phase behavior transitions between hydrated (soluble) and collapsed (insoluble) states. LCST refers to the temperature below which a polymeric phase remains soluble with a solvent, hence hydrated, and above which it becomes insoluble, and the gel becomes collapsed [63; 64; 196].  
Above the LCST, the hydrogel is dominated by hydrophobic interactions within the polymeric network, causing the hydrogel to expel water molecules and a steep reduction in its volume [193]. This volumetric contraction is a primary mechanism for the selective onset of drug release, and it is particularly useful when the LCST is within the range of body temperatures such as PNIPAM. The concept of UCST is the opposite of LCST, and the polymeric phase stays insoluble with a solvent and becomes soluble below and above this threshold, respectively. At high temperatures, thermal energy becomes sufficient to disrupt hydrogen bonding or ionic coupling, and this mechanism is often dominated by types of polymer-polymer interactions including hydrogen bonding and ionic interactions over polymer-solvent interactions [197; 198].  
The thermo-responsive behavior of hydrogels is largely determined by the chemical properties and compositions of hydrogel material, but it is also affected by other parameters such as crosslinking density, polymer architecture, or types of incorporated additives [197; 198; 199]. Conventionally, LCST-type hydrogels are more common in both synthetic and natural materials. PNIPAM, its derivatives, and poly(vinyl methyl ether) exhibit an LCST of body temperature, and various copolymers including Pluronic(r) series exhibit a wider range of LCST. For natural hydrogels, polysaccharide-based types are known as LCST type; for example, methylcellulose and hydroxypropyl cellulose exhibit an LCST of approximately 60 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, respectively [200,201]. UCST characteristics can be found in copolymer-based or ionically crosslinked systems, to achieve strong polymer-polymer interactions such as poly(acrylamide-co-acrylonitrile) or chitosan/alginate blend. In addition, gelatin is the most common UCST-type hydrogel material that exhibits an LCST of 40 \({}^{\circ}\)C [146,202,203].
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### Creation of the Head Models
#### 2.2.1 Reference Model Creation  
The goal of the reference model creation task is to reconstruct the mannequin's head with the highest precision. This 3D model should have the lowest level of semantic noise and the lowest level of reconstruction errors. Semantic noise (any points belonging to the non-head class) may be reduced by removing background information from the images. Possible reconstruction errors may be reduced by making and selecting the highest quality images. The creation of the reference head model does not have time or tool selection constraints or any manual work quota. After the photos were taken, they were manually edited to remove the background. The background is removed approximately by trying to select as much as possible of it without damaging parts of the head. The photos were also reviewed to avoid poor-quality photos with poor focus and motion blur distortions. A total of 187 photos were selected for reference model reconstruction. Three-dimensional photomodeling was performed using Meshroom software (version 2021.1.0) [57]. The default pipeline of Meshroom photogrammetry with the default parameters was used, except the Describer Density preset from Feature Extraction node was changed from _normal_ to _high_, and the Describer Type was changed from _sift_ to _sift_upright_, forcing orientation of all features the same. The reconstructed reference head model with camera positions is shown in Figure 5. In the evaluation of the automatic reconstruction algorithms, the result of the final reconstruction step, i.e., the mesh (refer to Figure 1, step 17. _Texturing_ of the reconstruction pipeline), is used.  
Figure 4: Method for selecting frames of the highest quality: in (**a**) frames are ordered by their similarity in 2D (for visualization purposes) using the t-SNE dimensionality-reduction technique, and the spanned area is subdivided into equal parts where only one image with the highest sharpness (quality metric) in each grid cell is kept, resulting in a set of diverse images of the highest quality (**b**).