esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Case Studies
#### 3.2.2 Multi-Stage Centrifugal Pump-as-Turbine (PAT)  
Running a standard PAT is often a cost-efficient solution to recover the extra available energy of the water supply systems, and not only--[PERSON] and [PERSON] (2014); [PERSON] (2015); [PERSON] (2016); [PERSON] et al. (2017) and [PERSON] et al. (2018). However, prior to the installation, its operation and control in turbine mode must be investigated, [PERSON] (2019). Indeed, if the hydraulic characteristics may be derived using, for example, the experimental data provided by [PERSON] et al. (2010), or the method presented by [PERSON] and [PERSON] (2018), the hydrodynamic stability mainly outside the normal operating range remains difficult or even impossible to predict without experimental tests.  
In this context, a second case study dedicated to the energy recovery in water supply networks, consisting of an 11 kW multi-stage centrifugal pump used as turbine (Ebara EVMG32 5-0F5/11, Tokyo, Japan) has been considered (see Figure 4). Its five radial-axial runners (with 5 blades each) have an inlet diameter of \(\phi\)136 mm and an outlet diameter of \(\phi\)67.5 mm. The PAT is regulated only with the help of the variable rotational speed of the runners. The permanent magnet synchronous generator, Leroy-Sommer LSRPM 132 M (Angouleme, French), with a nominal power of 15.8 kW, has been driven, in this case as well, by a frequency converter able to keep constant the desired rotational speed value, whatever the sign of the mechanical torque or the rotational speed. Concerning the instrumentation, the machine is equipped only with an incremental encoder, necessary for the rotational speed driving. Therefore, for this case, only the electrical power of the generator has been measured.  
Figure 4: Experimental setup of the counter-rotating microturbine and of the pump-as-turbine (PAT) for hydraulic performance measurements on the test rig.  
One may state here that the PAT has been tested in a hydraulic configuration similar to the one of the pilot site, where it has been already installed. The release valve installed on the by-pass branch, which plays the role of an existing release valve on a drinking water supply network, ensures the extra flow passage when the requested discharge is larger than the maximum flowrate of the turbine. The second release valve, placed downstream the turbine, limits the pressure at the outlet of the main branch, and therefore secures the network, whatever the operating conditions of the turbine. Moreover, in the case of discharges lower than the minimum value allowed for the PAT, or in the case of a failure, this release valve is able to cut completely the main branch, the whole discharge passing automatically through the by-pass one. Anyway, during the performance tests on the PAT, the by-pass release valve remained closed all the time, while the one of the turbine branch has been kept completely opened.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 4 Research Challenges
### UAV Operations in LTE  
Long Term Evolution (LTE) is well-suited to serve aerial vehicles, such as UAVs. Field tests where LTE systems are used to connect UAVs to networks are becoming more common. UAVs are expected to rapidly grow, providing modern and exciting trade opportunities for new LTE telecommunication companies. LTE network enhancements can be made in the near future to better prepare for the expected increase in data traffic from aerial vehicles. Radio propagation parameters encountered by an airborne UE, for instance, are likely to differ from those encountered by a ground-based UE. The behavior of an aerial vehicle is normal as long as it flies at low altitudes to the BS radio line. After flying well above the BS antenna height, the UL signal from an aerial vehicle becomes more visible to many cells due to LoS propagation conditions. An aerial aircraft's UL signal can block the cells around it. Increased interruptions are harmful to UEs on the ground, such as smartphones and IoT devices. To maintain normal throughput performance for the UE, the network may impose restrictions on the admission of aerial vehicles within the organization. UAVs also have administrative authorizations that are unique to them. There are two types of \"UAV UE\" in the field. The first is the UAV with a cellular module that is approved for use in the air. The second is the UAV with a cellular module that is only authorized for terrestrial use. Not all districts allow their usage due to administrative concerns since the UL signal from a UE can block nearby cells. Another point to emphasize here is that the processing time in LTE systems is high, and the mobile station is moving quickly, which may raise the possibility that LTE will not be a supportive network for UAVs, particularly in high-speed scenarios. In addition, the HO delay will be extended beyond the standard 30 ms execution time. This may cause the UAV to fly outside of its coverage area without performing the necessary HO, disrupting the connection and reducing communication efficiency. Because LTE networks have a limited capacity and BW when compared to 5G and 6G networks, they may not be suitable for supporting UAV communication due to the massive growth of mobile devices connected to the network.
## 1 Introduction  
Optoelectronic devices based on the semiconductors CdS and PbSe have been widely used throughout the technological revolution. The main devices, in the case of PbSe, are infrared detectors used for different applications [1, 2, 3, 4, 5, 6]. CdS, meanwhile, is used in transistors, visible light detectors and, particularly, as a window material for solar cells [7, 8, 9, 10, 11, 12, 13, 14]. These materials are produced by several methods with a wide range of costs; of these, being used mainly, CBD is the lowest cost method for CdS. However, the methods used to deposit PbSe are expensive, since they mainly use physical techniques [15, 16, 17]. These materials are also compatible to produce heterojunctions and could open a new path in the manufacture of optoelectronic devices for different applications [18, 19, 20]. Usually, heterojunctions are not synthesized using methods in aqueous solutions; such methods involve a great difficulty in optimization, which has led to little use despite their low cost and the good quality of the films that they generate [21, 22, 23, 24]. [PERSON] et al. [18] developed a CdS/PbSe heterojunction for high-temperature mid-infrared photovoltaic detector applications. They deposited CdS via CBD and PbSe via Molecular Beam Epitaxy (MBE) techniques. [PERSON] et al. [19]reported a PbSe/CdS/nitrogen-doped TiO\({}_{2}\) nanorod array photoelectrode, in which CdS and PbSe were deposited by the Chemical Vapor Deposition (CVD) technique, using N\({}_{2}\) and H\({}_{2}\) atmospheres, respectively. [PERSON] et al. [20] reported a PbSe-QDs/CdS/CdSe:In/Si heterojunction photodiode. They used quantum dots of PbSe instead of a film; however, the response of their photodiode had promise for the production of infrared devices. Most of the articles used the CVD technique to create the heterojunction, due to the difficulty of depositing a bilayer by chemical methods. In this sense, the goal of this work is to synthesize a CdS/PbSe heterojunction through chemical methods to develop low-cost and scalable devices. The characterizations carried out for each material were by X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and UV-Vis techniques, for structural, morphological and chemical composition, and optical properties, respectively. Finally, the response of the CdS/PbSe heterojunction was analyzed by current-voltage characterization using the modified Shockley model.
## 1 Introduction  
Cameras have been used for swimming kinematic analysis for many years, since a single camera can obtain the bi-dimensional coordinates of a point on a calibrated plane in its field of view. In this way, a group of selected points of interest for a swimmer can be used to describe movement. However, none of the swimmer's movements are truly planar, generating uncertainty in the bi-dimensional data [1, 2]. Besides this limitation, 2D kinematics remains a viable alternative for some parameters due to its lower cost and easy implementation and analysis when compared with 3D methods [3, 4].  
Another problem with the use of a single camera is the length of the field of view. Front crawl swimmers usually have a stroke length longer than 1.5 m [5], being necessary a large field of view to analyze consecutive stroke cycles [6, 7, 8]. The further the swimmers are away from the camera, the longer the field of view is in the swimming plane, but it also reduces sensitivity and makes the digitalization process harder. Moreover, larger calibratedvolumes present larger errors when compared with those of smaller volumes [9, 10], and extrapolations beyond the calibrated volume also increase the reconstruction error as a function of distance from the initial frame position [11, 12]. These limitations make the kinematical analysis of an entire swimming trial (25 or 50 m) almost impracticable with a single static camera.  
The usage of a single moving camera could solve this problem, since it is possible to use a relatively small camera field of view with a small calibrated area that follows the swimmer's displacement, allowing for the analysis of an entire swimming trial. Only one study evaluated 2D kinematics variables using moving cameras, and their calibration process was limited to using a rope with marks at every 100 cm to scale the images [13]. Moreover, the accuracy, sensitivity, and resolution were not reported.  
The challenge of using a single moving camera is knowing the extrinsic parameters (i.e., position and orientation) at any time in the pool global reference, in addition to the intrinsic parameters. The aim of the current study was to verify the applicability of a single moving camera for underwater kinematical analysis by comparing its accuracy and precision with a static camera. One traditional extrinsic calibration method (where the extrinsic matrix was obtained based on the 3D real coordinates of a set of point in the camera field of view) and a method based on the real coordinates of the principal point (when the camera movement degrees of freedom are constrained) were tested.
## 3 Mathematical Modelling, Theoretical Aspects
### Indirect Variational Method for the Study of the Conditions and Nature of Equilibrium
\[\Delta\pi=\frac{1}{2}\cdot\delta^{2}U=\frac{1}{2!}\sum_{i}\sum_{j}\frac{ \partial^{2}U(v,\lambda)}{\partial v_{i}\partial v_{j}}\partial v_{i}\partial v _{j} \tag{52}\]  
Expression (52) allows the study of the nature of equilibrium, as presented in \[\pi=U-V \tag{46}\]0  
In the case of neutral equilibrium, it is necessary to determine and study the higher-order derivative of the total potential energy according to Expression (50). The nature of equilibrium is indicated by the fourth-order derivative of the total potential energy, which has to be positive-definite, positive definiteness of \(\Delta\pi\) guarantees stability [20]. Based on the first theorem of [PERSON] the second-order derivative of the total potential energy with respect to the displacements represents the stiffness coefficient \(k_{ij}\), which indicates a force at \(i\) due to unit displacement at \(j\). According to this remark, Expression (52) can be expressed as in Expression (53) [23]. \[\pi=U-V \tag{46}\]1  
Expression (53) is a quadratic from in displacements, which in a matrix form is presented in Expression (54). \[\pi=U-V \tag{46}\]2 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **The State of Equilibrium in Mathematical Terms** & **A Short Description of the State of Equilibrium** \\ \hline
1. & \(sign\left(\delta^{2}U\right)<0\) & In stable equilibrium, the second-order derivative of the strain energy is positive-definite, with respect to the Lagrange-Dirichlet theorem. \\
2. & \(sign\left(\delta^{2}U\right)>0\) & In unstable equilibrium, the second-order derivative of the strain energy is not positive-definite, according to the Liapunov stability theorem. \\
3. & \(\delta^{2}U=0\) & Neutral equilibrium, corresponds to a limit state immediately before the loss of equilibrium. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Study of the nature of equilibrium according to the sign of \(\delta^{2}U\)[20]. In Expression (54), \(\left\{v\right\}_{1\leq i\leq n}\) is the displacement vector, \([K]\) is the elastic stiffness matrix of the studied structural element. Based on the condition that the variation of the total potential energy at the limit equilibrium state must be zero, respectively \([K]\) is obtained through the assembly of the material stiffness matrix and the geometric stiffness matrix, the equation of stability can be determined by an energetic method, which assumes an eigenvalue problem [23]. Continuous structures are analyzed through an indirect variational method, in which the structure is not discretized. The differential equations are obtained from the minimizing condition for the potential energy [20]. In contrast to this approach, the stability analysis, in which the structure is discrete or discretized and the expression of the second variation of potential energy is reduced to a quadratic form, is the direct variational approach [20].
## 3 Point Cloud Alignment Methods  
### ISS Feature Point Extraction  
The point cloud data obtained by downsampling the point cloud still has a large amount of data, and direct point cloud calculation is not only complex but also time-consuming, which affects the speed of data processing. Therefore, representative feature points can be extracted from the source and target point clouds before point cloud alignment. This ensures that the spatial structure of the original data features remain unchanged on the premise of the extracted feature points for the later point cloud alignment work. This reduces the amount of data processed in the point cloud and improves the efficiency of point cloud alignment. Considering the geometric characteristics of point cloud data, ISS feature points are selected as the extraction method. If the
## 4 Experimental Results and Discussion
### Experimental Setup and Evaluation Metrics
#### 4.1.3 Compared Methods  
To comprehensively evaluate the performance of our OCC network in the following sections, we have compared it with three methods that are commonly used in color correction of the TCM tongue images, including the polynomial-based correction method (POLY) [11], the SVR-based correction method (SVR) [12], and the Kernel Partial Least Squares Regression (K-PLSR) based color correction method [16]. In the experiments, the regression model of 3 elements x: [R, G, B] used by [6] is selected in the polynomial-based method. The parameters of the SVR-based method are selected the same as [12], that is, using RBF (Radial Basis Function) kernel and \(C=infinity\), \(\delta=5\), \(\epsilon=0.025\). For the K-PLSR-based method, the nonlinear kernel function selected is also RBF, which is \(K(x,y)=\exp(-\|x-y\|^{2}/\delta^{2})\), the same as [8]. We have implemented the polynomial-based method. The source codes of the SVR and the K-PLSR based methods are provided by the authors. We have trained the models using our training set.  
Figure 5: The color area of interest.  
Figure 6: The captured color checkers (upper row) and their corrected results (bottom row) under five different illuminations. From left to right, they are captured under ILL1 to ILL5.
## 3 Diagnosis System
### System Construction  
The voiceprint recognition system to diagnose insulation defects of GIS based on ultrasonic PD signals is composed of the following three main parts: signal preprocessing, feature extraction and defect recognition. Four kinds of common defects of GIS are designed in the system, i.e., free metal particles, floating electrode, metal protrusions, and creeping discharge. Therefore, six binary classifiers are demanded to constitute the four-class classifier in the system. The whole flow chart of diagnosis is shown in Figure 2.  
Figure 2: The flowchart for constructing the voiceprint recognition system for insulation defects identification of gas-insulated switchgear (GIS).  
The whole process of diagnosing the type of insulation defects of GIS based on the ultrasonic PD signals can be divided into three steps. Firstly, the collected signals go through denoising, frequency shifting, framing, and windowing to make the features extracted from them more distinguishable. Secondly, MFCC feature extraction method is conducted to the preprocessed signals to acquire the static MFCC features and calculate the delta MFCC features. Both constitute the acoustic features of GIS. Finally, the SMO-SVM multi-classifier recognizes the type of the insulation defects according to the acoustic features.
# Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services +
## 2 Related Work
### Edge Technologies
#### 2.1.2 Coordinated Fog-to-Cloud Resources  
The extension of cloud services is not the only operation at the edge computing. There is also the Fog-to-cloud (F2C) concept [26], which has been merged into a collaborative model to facilitate an optimal match between cloud and fog resources. With the inception of the F2C, many works have discussed the advantages of putting together cloud and fog resources. For example, in [27], a new workload allocation solution proposed highlighting how the cooperation of F2C could enhance the performance of cloud platforms regarding reducing communication latency. Similarly, in [28], a second fog layer was proposed to enhance service allocation and communication of combined fog-cloud infrastructures. The mF2C [29] is another example of the innovative edge projects that aim to build a global stack of cloud-fog resources to optimize services execution in real-time. Furthermore, interesting work in [30] introduced a Fog-to-Fog communication algorithm to reduce the overall end-to-end delay for F2C by allowing fogs to communicate with each other. The authors highlighted how the edge technologies could play two roles at the same time, which are: middle layers and collaborators for cloud computing.  
As one can see, many existing edge solutions are seeking to enhance the processing of IoT data, since they are close to the original sources. Also, they focus on improving the quality of edgeservices by collaborating with centralized cloud platforms and exploiting the advancement of network technologies. However, the advantages of edge computing (i.e., low latency) are not the only criteria to pick edge services as the best solutions. Notably, in the fog-to-cloud environment, the IoT clients have the choice to consume services from clouds and edge servers. Yet, this requires a deep understanding of the selection service problem to determine which ones perform the best.
## 2 Cloud-Native Network Architecture in Future Networks
### Convergence of Networking and Cloud/Edge Computing  
The cloud-native network design adopts the virtualization and service-oriented architectural principles in networking essentially in the same way as the principles being applied in cloud/edge computing. Therefore, cloud-native network design enables network systems to be realized based on cloud technologies and network services to be provisioning in the cloud service model, which facilitates convergence between networking and cloud/edge computing. Such convergence calls for a holistic vision across the networking and computing domains that supports integrated management of networking-computing functionalities and unified provisioning of network and cloud/edge services [10].  
The NFV architecture provides a unified virtualization layer for network-compute infrastructures and common management of virtual network/compute functions, thus forming the basis for network-cloud/edge convergence as reflected in the MEC-in-NFV reference architecture. Through its embrace of cloud-native design in the SBA, the 5 Gnetwork introduces the very same characteristics about network function interactions, system operations, and service provisioning shared with cloud and edge computing [29]. Therefore, network-cloud/edge convergence with integrated network-compute infrastructures for end-to-end provisioning of composite network-cloud/edge services is expected to be a key feature of future networks.  
The holistic architecture design, resource management, system operations, and service provisioning enabled by network-cloud/edge convergence may significantly improve resource utilization and service performance, lower capital/operational costs, and introduce opportunities for technical and business innovations. On the other hand, all these benefits can be realized only if the converged network-cloud/edge systems in future networks are properly managed. Standardization of the management architecture for cloud-native future networks plays a significant role in order to fully leverage various cognition techniques such as AI/ML to manage heterogeneous systems and integrate diverse management domains for end-to-end service provisioning. The following sections of this paper will review the representative standardization work on management architecture that addresses the challenges of intelligent and autonomous management in future networks.
# Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads
###### Abstract  
An innovative method is proposed to analyze the coupled vibration between random traffic and large-span bridges under the combined action of wind loads. The dynamic behavior of bridges subjected to these multifactorial influences is investigated through a comprehensive bridge dynamics model. Specifically, a refined full-bridge finite element model is developed to simulate the traffic-bridge coupled vibration, with wind forces applied as external dynamic loads. The effects of wind speed and vehicle speed on the coupled system are systematically evaluated using the finite element software ABAQUS 2023. To ensure computational accuracy and efficiency, the large-span nonlinear dynamic solution method is employed, integrating the Newmark-\(\beta\) time integration method with the Newton-Raphson iterative technique. The proposed method is validated through experimental measurements, demonstrating its effectiveness in capturing the synergistic impacts of wind and traffic on bridge dynamics. By incorporating the stochastic nature of traffic flow and combined wind forces, this approach provides a detailed analysis of bridge responses under complex loading conditions. The study establishes a theoretical foundation and practical reference for the safety assessment of large-span bridges.  
large-span bridge; coupled vibration analysis; finite element model; nonlinear dynamic response; safety assessment Article