esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Guided Manufacturing of Transducer Arrays Based on Single-Fibre Piezocomposites
## 3 Results
### Quality Control  
Analysing the EMI properties can now be used to monitor the quality of the assembly. Two possible defects, which cannot be detected optically, are air inclusions in front and behind the PZT fibre. This would either cause low emission due to high reflections or low damping resulting in a decreased bandwidth.  
The phase angle at thickness resonance \(\varphi_{\rm t}\) seems the most suitable property for monitoring the production and identifying these two defects. The other three EMI properties listed in Table 1 exhibit high overlapping between the measured distributions. Only \(\varphi_{\rm t}\) shows a consistent change after each assembly step. However, due to the high standard deviation after attaching the PCB and the matching layer, unambiguous classification may not be possible.  
To visualise all possible classifications for detecting the backing and the matching layer attachment, a receiver operating characteristic curve (ROC) can be used. There, the effect of potential classifiers on false and true positive decisions are visualised [28]. Figure 12(a) shows the ROC curves after attaching the matching layer and the backing for \(-80^{\circ}\leq\varphi_{\rm t}\leq 0^{\circ}\). The matching layer ROC exhibits a lower trend compared to the backing ROC, which indicates poorer classifying performance.  
When allowing a maximum of 5% false positive decisions, detecting a successful attachment of the matching layer results in the classifier T1 at \(-50^{\circ}\). Likewise, detecting if the backing is applied correctly gives T2 at \(-62.6^{\circ}\). With a resulting true positive detection rate of 94% for T2, it is possible to find faults in the backing reliably. This is not given for T2, as correct matching layer attachments can be identified with only 46% accuracy. All measured phase angles \(\varphi_{\rm t}\) and the two classifiers are visualised in Figure 12(b).  
Figure 13: Quality control using the measured phase angle at thickness resonance \(\varphi_{\rm t}\) as classifier to detect correct matching layer and backing application. ROC curves for \(-80^{\circ}\leq\varphi_{\rm t}\leq 0^{\circ}\) and classifiers T1 and T2 at maximum 5% false positive decisions (**a**). T2 indicates good performance for backing detection. T1 shows less than 50% sensitivity for detecting correct matching layer attachment (**b**).
## 1 Introduction
### Scope and Limitations  
This review encompasses a wide range of topics related to the seismic behavior of structures with supplementary dampers, spanning from fundamental principles to cutting-edge applications. The scope of the review is comprehensive, covering various types of supplementary damping devices, including but not limited to viscous, viscoelastic, friction, and metallic yield dampers, as well as tuned mass dampers and base isolation systems. It explores both passive and semi-active damping technologies, with a brief discussion of active control systems. The review examines applications in building structures of different heights and configurations, as well as bridge structures, addressing both new construction and retrofit applications of supplementary damping systems. Furthermore, it delves into analytical, numerical, and experimental methods for studying the behavior of damped structures, providing a holistic view of the field. This wide-ranging approach ensures that the review offers valuable insights into the diverse aspects of supplementary damping in seismic engineering.  
While this review aims for comprehensiveness, it is important to acknowledge certain limitations. The evolving nature of the field suggests that some of the latest developments may not be fully captured. The review primarily concentrates on literature published in English, which could lead to a partial representation of research conducted in other languages. Detailed mathematical derivations and complex technical aspects of control theory are not included in this review. Additionally, extensive economic assessments and cost analyses of various damping systems are not thoroughly explored. The review also does not provide an in-depth analysis of non-structural components and their relationships with damping systems. Despite these constraints, this review strives to offer a balanced and comprehensive assessment of the current understanding of the seismic performance of structures with supplementary dampers. In this way, it aims to deepen the comprehension of these technologies and encourage further research and innovation in this critical area of earthquake engineering.  
To provide a clear overview of the supplementary damping systems discussed in this review, Figure 1 presents the general framework of seismic protection strategies, highlighting the position of supplementary damping systems within the broader context of structural control methods. The detailed classification of various damper types and their relationships will be presented in Section 2.1, along with a comprehensive comparative analysis of their characteristics.
# Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey
## 2 Basics of Complex Systems and Emergence
### Power Law and Heavy-Tailed Distributions  
In contrast to Gaussian, exponential, and other thin-tailed distributions that have a well-defined scale, a power law distribution does not have a scale. It has been observed in various kinds of physical, biological, technological, and and social systems. Well-known examples include the distribution of word frequency, web hits, citations of scientific papers, telephone calls, copies of books sold, diameter of moon craters, intensity of solar flares, intensity of wars, magnitude of earthquakes, wealth of the richest people, and population of cities [37].  
A power law distribution can be expressed by its probability density function (PDF) [38]  
\[f(x)\sim x^{-\alpha-1},\ \ \ x\to\infty, \tag{1}\]  
or equivalently by the complementary cumulative distribution function (CCDF) [38]  
\[P[X\geq x]\sim x^{-\alpha},\ \ \ x\to\infty. \tag{2}\]  
Notice here the emphasis that \(x\to\infty\). An interesting property of the power law distribution is that for a given \(\alpha\), its moments with order higher than \(\alpha\) do not exist. Therefore, when \(0<\alpha<2\), the variance and all moments higher than the second order do not exist, and when \(0<\alpha\leq 1\), even the mean is infinite. When the power law relation extends to the entire range of the allowable \(x\), we have the Pareto distribution [39]:  
\[P[X\geq x]=\left(\frac{b}{x}\right)^{\alpha},\ \ x\geq b>0,\ \ \alpha>0, \tag{3}\]  
Here, \(\alpha\) is the shape parameter, and \(b\) the location parameter. In the discrete case, the Pareto distribution is called the Zipf distribution, which provides an excellent description between the frequency of any word in a corpus of natural language and its rank in the frequency table.  
Somewhat related to the Zipf distribution is another distribution called Benford's law [40], which is about the probability of occurrence of leading digits \(d\in\{1,2,\cdots,9\}\),  
\[P(d)=\log_{10}(d+1)-\log_{10}(d)=\log_{10}\left(1+\frac{1}{d}\right) \tag{4}\]  
A good mechanism for explaining the uneven distributions stipulated by [PERSON]'s law has been proposed in [41].  
[PERSON]'s law has been used for evaluating possible fraud in accounting data [42], legal status [43], election data [44; 45; 46], macroeconomic data [47], price data [48], etc. From Equation (4), we observe that beyond the small digits, the probability approximately approaches the Zipf distribution with \(\alpha=1\),  
\[P(d)=\log_{10}\left(1+\frac{1}{d}\right)\sim d^{-1}/\ln(10),\ \ \ \ d=3,4,\cdots,9 \tag{5}\]
## 8 Issues and Recommendations for Future Research
### Deployment and Orchestration  
The IoV network entities, including vehicles, RSUs, and BSs, have heterogeneous storage, computing, routing, and forwarding capabilities. The ideal edge intelligence deployment technique to support as many vehicles as possible to meet the coverage, connectivity, and support of various intelligent services is still a research problem. Thus, the placement of edge intelligence is of paramount importance to improve communication, computing, and caching performance in terms of optimizing the resource allocation with minimal deployment costs and message exchange overheads. Given the large scale of physical IoV networks, we need to determine the minimum number of edge nodes, their location placement, network topology, and their association with BS and MBS to achieve an optimal allocation of resources. An intelligent optimization solution that considers the types, amount, and update frequency of information and available underlying network elements is needed. A clean-slate deployment requires all the network entities to support name-based routing and intelligent forwarding strategies.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks  
This section presents the analytical model for WiMAX network dimensioning, based on Markov chain theory, which is designed for Best Effort (BE) services. The model incorporates various scheduling algorithms, and the underlying system assumptions are discussed to derive the key performance parameters. The objective is to explore the interactions between traffic, channel conditions, and resource allocation strategies to effectively evaluate the network's performance.  
To start, key parameters of the WiMAX network used throughout this study are defined.  
* \(N_{S}\): The total number of slots available for data transmission in the downlink part of a Time Division Duplex (TDD) frame.
* \(T_{F}\): The duration of a TDD frame (\(T_{F}\) = 5 \(ms\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Focus** & **Key Contributions** & **Limitations** & **Current Work Advantage** \\ \hline \multirow{4}{*}{[21]} & \multirow{4}{*}{Performance analysis of a smart trash bin using Markov models} & Evaluated system reliability, unreliability, and Mean Time to Failure (MTTF); identified critical components through sensitivity analysis. & & Employs a Markovian framework for optimizing resource allocation in OFDMA networks, adapting to fluctuating traffic and user demands. \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{Enhancing reliability in cluster-based sensor networks} & Analyzed fault tolerance in nodes; emphasized power management strategies for improved sensor availability and operational lifetime. & & Utilizes Markov models for optimizing resource allocation in OFDMA networks, prioritization throughput and latency for overall communication performance. \\ \hline \multirow{4}{*}{[23]} & \multirow{4}{*}{Enhancing energy efficiency in OFDMA networks} & Significant improvements in energy efficiency using RIS and resource allocation optimization via alternating optimization. & Does not address user demand and traffic pattern dynamics. & Implements advanced scheduling algorithms to maximize throughput and user fairness in WiMAX networks, ensuring optimal resource utilization. \\ \hline \multirow{4}{*}{[24]} & \multirow{4}{*}{Network performance simulation based on fixed assumptions} & Provided foundational insights into network performance with fixed model flexibility and real-world applicability. & & Implements a modified simulator with probabilistic ON/OFF periods, allowing realistic modeling of traffic patterns and accurate performance metric estimation. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative table of existing works.  
\(MCS_{k}\): The state of the radio channel, where \(0\leq k\leq K\), with \(K\) being the total number of Modulation and Coding Schemes (\(MCS\)). \(MCS_{0}\) indicates a failure state.
* \(m_{k}\): The number of bits transmitted per slot by a mobile station using \(MCS_{k}\).
# Pockels Effect at the Interface between Water and Ti Electrode
## 2 Materials and Methods
### Impedance Measurement  
To evaluate the Pockels coefficient of the interfacial layer involved, information on the electric field in the layer, as well as the refractive index change in the layer, is necessary. Therefore, the voltage distributed to each layer was evaluated by the AC impedance method. Measurements were made by the two-terminal method using an impedance/gain-phase analyzer (Model 1260, Solatron, Victorville, CA, USA) and a potentiostat/galvanostat (Model 1287, Solatron, Victorville, CA, USA).  
The working and counter electrode terminals were connected to the galvanostat and Ti electrode, respectively, and the Ti electrode and quartz cell were the same as those used in the electroreflectance measurement, and the inside of the cell was filled with NaCl solution (0.1 M) in the same manner. The electrodes were placed at opposite corners of the cell, as in the electroreflectance measurement. The experimental apparatus was controlled using the ZPlot 3.5g software. The software allows the user to set the amplitude of the applied voltage, the range of the measurement frequency, and the incremental width. In this measurement, the amplitude was set to 10 mV AC, and the frequency range to \(10^{-1}\sim 10^{6}\) Hz. Even though it is preferable to apply the same voltage of 2.0 V as in the reflectance change experiment, impedance measurement at higher voltages may induce disturbances in the measurement. For this reason, calculations were made based on impedance data obtained under lower voltage conditions.
## 1 Introduction  
With the continuous integration of information technology and traditional working modes, the application of Unmanned Aerial Vehicles (UAVs) in urban emergency management has attracted more and more attention. The UAV can be used as a mobile sensor platform to realize information interaction between air and ground. The UAV can monitor a scene in real time and transmit data information to emergency managers. This assists the emergency managers in accurately knowing the situation on the spot, saving lives and controlling the crisis in time. At present, most studies on air-to-ground channel models are focused on a single channel model without considering the height of the UAV. However, in a complex urban environment, the distribution density and location of complex scatterers such as buildings and trees vary with height. They make the signal experience different loss and attenuation when transmitted at different heights. Therefore, there are actually huge differences in channel characteristics at different heights. In this case, the single-channel model has some limitations, especially in emergency management, where communication reliability is highly required. Using only a single channel model will make the information unstable during transmission, which causes several problems such as interference and packet loss. Therefore, it is very necessary to establish an adaptive and more accurate channel model in an urban environment to ensure the reliability of information transmission in emergency situations. As a result, the impact and harm caused by emergencies on individuals and society will be reduced.  
Consequently, we propose to divide the UAV communication area into two parts: ultra-low altitude (0-100 m) and low altitude (100-1000 m). Firstly, the urban channel characteristics are analyzed. The analysis of urban channel characteristics is shown in Figure 1. In the ultra-low altitude area, reflection, refraction, and scattering caused by a variety of complex scatterers during signal transmission are considered by the SUUL channel model. The model focuses on shadow fading and multipath fading. In addition, the probability density function of related parameters and bit error rate are derived based on the improved LOO model. In the low-altitude area, the influence of shadow fading on the channel gradually decreases as the flying height of the UAV increases. The Doppler frequency shift spread caused by the flying speed of the UAV leads to severe channel fading. As a result, the SULA channel model proposed in this paper adopts the uniform circular array beamforming technology to design the Doppler frequency shift compensation algorithm. Finally, the UAV adjusts the channel model adaptively according to the height. The reliability of UAV communication is guaranteed and the effective monitoring range of UAVs has also been improved.
## 4 Analysis Results
### Fatigue Damage  
Using the stress transfer function calculated from the rigid and flexible body bases previously calculated, the fatigue damage at the 20 largest hot spot points among the fatigue damages calculated at the hot spot points is shown in Figure 13. When considering the linear springing effect at the point where the fatigue damage based on the rigid body was greatest, it could be observed that the fatigue damage was greater than 30%. This is where torsional moment occurred the most, close to 0.25 L. It can be estimated that the torsional moment increased with the hydroelastic effect and increased the fatigue damage due to the increase in the horizontal moment, which always interacts with the torsional moment.  
From Figure 16, when evaluating the fatigue strength evaluation of the hatch corner, which is the weak point of the fatigue strength of the container ship, it can be seen that the results using the 1D beam model and 3D full model were somewhat similar. These results are shown that the stress transfer function of the hot spot location could be properly calculated from the hull girder moment by applying the theory in Section 2.3.  
Furthermore, similar final fatigue damage can be obtained even if fluid-structure interaction analysis is performed using other structural models, such as a 1D beam or 3D global model. In other words, this means that the design effect is similar even if a completely different method is used. Moreover, because the structural members in the upper deck-top, such as the hatch corners, do not have internal and external loads, it is confirmed that the initial assumption that fatigue damage can be estimated with only hull girder loads is valid. However, it is considered that additional methodology is required to  
Figure 14: Stress transfer function at HC3.  
Figure 15: Stress transfer function at HC6.  
apply structural members such as the inner structural member in the ballast tank, where the internal and external pressures directly affect the fatigue damage.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 1 Introduction
Additionally, it is necessary to consider the visual content itself, i.e., a high quality WVSN should position the cameras in a more appropriate way, potentially avoiding occlusion by obstacles while trying to enlarge the covered areas. It is possible to notice that the definition of quality is subjective and it can vary considerably, which makes difficult the specification of quality assessment metrics [17]. An application can assess quality based on the amount of area covered, while other can considered the amount of area redundantly covered by at least \(k\)-sensors. This diversity of possibilities evidences the necessity of a sort of classification of quality metrics to foster the comparison among different WVSN implementations, supporting further developments in this area. In this article, we propose a categorization of quality assessment techniques in wireless visual sensor networks, surveying different research areas and comparing recent results. A novel classification is proposed, supporting better organization of the state-of-the-art. Moreover, qualitative comparisons about quality metrics are performed, indicating advantages and drawbacks. To the best of our knowledge, such surveying and comprehensive classification has not been proposed before. Therefore, we can summarize the contributions of this article as follows:  
* Reviewing of the literature covering different definitions and approaches related to visual monitoring quality;
* Definition of a novel classification methodology centered on visual monitoring quality, which allows fairer evaluation and comparisons of different research works;* Categorization and analyzing of metrics for different types of quality assessment of wireless visual sensor networks;
* Identification and discussion of open issues and promising research directions for the surveyed subjects. The remainder of the article is organized as follows. Section 2 presents the defined classification to evaluate visual quality monitoring by sensor networks. Quality assessment of visual coverage of targets, areas and barriers is surveyed in Section 3. In Section 4, recent works addressing quality with respect to the content of visual data are discussed, exploiting distance and angle of view. Section 5 addresses aspects related to the dependability of WVSN and its relation to quality evaluation. A broader discussion about the surveyed works, along with proper comparisons concerning the achieved results, is presented in Section 6. Section 7 presents some possible open research areas identified in this article, related to WVSN quality, indicating promising development trends. Finally, conclusions and references are presented.
## 3 Geophysical Data and Methodology
### Electrical Resistivity Tomography  
The ERT method allows the electrical resistivity properties of rocks beneath the surface to be calculated and is a well-established method in near-surface characterization studies [56; 57; 58; 59]. The subsurface resistivity is strongly influenced by a rock's properties, such as porosity, mineral composition, fluid content, and fault structure [60; 61]. In this study, the ERT method was only applied in Torrecilla and La Salud North.  
The ERT data was acquired with a 12-channel resistivimeter (ABEM Terrameter-RL-12) that involves 80 electrodes deployed at 10 m spacing. The electric spread consists of four lines with 21 electrode connections. For electronic continuity of the entire survey, the first and last electrodes are always overlapped between two lines (21-1 connections). This means that we worked with four cables registering at same time covering 800 m length in total. In order to reach the entire length of the profiles, we used the overlap technique shifting one electric line (21 electrodes) [62]. The apparent resistivities were obtained with the _gradient-plus_ electrode protocol, a hybrid electrode configuration that involves a combination of the symmetric Wenner-Schlumberger with the asymmetric dipole-dipole [63; 64]. This configuration provides a dense near-surface coverage and allows an investigation target of more than 120 m depth.  
During the field acquisition the data quality was controlled by measuring each pseudo-midpoint twice, and for differences of apparent resistivities > 1%, four measures were averaged. The processing workflow used is described in Table 1. To obtain the 2D geoelectric models, we use two commercial software packages, Porosys II (www:iris-instruments.com/, accessed on 22 March 2022) and Resix2 Dinv (www.geometrics.com, accessed on 22 March 2022). The first one was used to review the raw data, remove negative values, and define the track geometries. In a second stage, the Resix2 Dinv inversion software was run twice: first, with only the most spurious data removed; and second, with a new dataset including data with apparent resistivity differences lower than 25% (an example of this procedure for the La Torrecilla profile is shown in Figure 4). The resulting final models of Torrecilla and La Salud North profiles after 5 and 7 iterations, respectively, had absolute errors of 5.8 and 7.2, respectively.