esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Discussion  
To investigate the mechanical characteristics and failure mode of the asphalt concrete substructure induced by the cyclic train load and ambient temperature changes, in situ forced vibration and temperature-induced deformation monitoring tests were performed based on a high-speed railway ballastless track project in north China. The main conclusions can be summarized as follows:  
* The bottom of the asphalt concrete layer is in the tensile state and the surface of the roadbed is in the compressive state under the action of cyclic train load. The surface of the asphalt concrete near the expansion joint is subjected to tensile stress under the action of the negative temperature gradient, which may cause potential cracks;
* The influence of environmental temperature changes on the mechanical characteristics of the asphalt concrete is greater than that of the cyclic train load. The tensile strain level, the earth pressure, and the diurnal variation range of the asphalt concrete near the expansion joint are also much larger than those near the middle position of the base plate. Therefore, more attention should be paid to the influence of temperature changes in future studies;
* Isolation layers, set under the base plate near the expansion joint, can significantly reduce the mechanical responses near the joint, which has a positive effect on resisting the passive tensile failure induced by the base plate. The geotextile is an effective material for the isolation layer and can be applied to similar railway projects in the future. However, the setting range of this isolation layer needs further studies;
* Besides, in the design and application of the asphalt concrete for the ballastless track substructure, the low-temperature cracking resistance and fatigue resistance should be paid more attention to, and asphalt materials with a good low-temperature and fatigue performance are more suitable, such as crumb rubber-modified asphalt binders.  
The authors confirm contribution to the paper as follows: Methodology: Q.F., X.C., D.C.; data collection: Q.F., L.L.; formal analysis: Q.F.; writing--original draft: Q.F.; writing--review and editing: X.C.; Supervision: X.C., D.C.; funding acquisition: X.C., D.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 51778136, 41972299) and the Special Scientific Foundation of China Academy of Railway Sciences Co. Ltd. (grant number [2019G003]).  
Figure 9: Crack near the expansion joint of the base plate without the geotextile.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Data Availability:** All the test data of this study have been included in the article.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 7 Conclusions  
Augmented reality and 5G networking are paving the way to a whole new set of possibilities in the progressive digitization of modern industries. Remote assistive maintenance based on egocentric environment-aware devices represents a viable solution to improve workers' safety in dangerous locations and the communication efficiency between on-site operators and remote assistants.  
In this paper, we presented a fully-developed assistive maintenance system based on a wearable AR device equipped with environment-understanding and thermal sensors. A set of algorithms was specifically designed to detect potentially dangerous objects and heat-sources, while the holographic viewer enables the visualization of such information directly in the operator's field of view. Finally, deploying the analysis software on an edge computing platform connected to the device via a 5G link, allows for all the required processing to be performed in real-time.  
Conceptualization, M.M. and S.M.; Formal analysis, S.V. and M.M.; Funding acquisition, M.M. and S.T.; Investigation, S.V. and M.M.; Methodology, M.M. and S.M.; Resources, M.M.; Software, S.V.; Supervision, M.M.; Writing--original draft, S.V., M.M. and S.M.; Writing--review and editing, S.V., M.M., S.M. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The 5G Programme is founded by the Italian Ministry of Economic Development (MISE). The activity was accomplished in collaboration with Vodafone Italia S.p.A.  
The authors declare no conflict of interest.
## 3 Models  
### Multi-Fiber Beam Modeling  
To decrease the number of degrees of freedom, 3D Timoshenko multi-fiber beam elements were used [12]. The main advantage of such a description lies in the fact that the behavior is based on a 1D nonlinear model for both the concrete and rebar. Compared to a classical 3D FEM, this technique significantly reduces the size of the problem; on the other hand, however, the kinematic constraints must be assured between two successive elements so that displacement remains continuous and sections remain plane during loading.  
_Concrete_: The useful part (5.1 m) was modeled using 51 elements 0.10 m in length. The section was divided into 5 \(\times\) 8 fibers 0.10 m to a side. The beam was completed on both sides by a part with a variable cross-section up to the strut support. The heads of the beam are not represented. However, their presence creates a mechanical effect on the discretized part (Figure 1b), which results in a moment applied on both sides of the beam (partial embedment), as evaluated from knowledge of the geometry of these heads and the position of beam supports that alter dead load effects during the experiment.  
_Reinforcements_: Only the longitudinal reinforcements are represented by bar elements, placed in accordance with the reinforcement mapping of the beam (Figure 1a).  
_Struts_: These elements are depicted with bar elements with respect to the characteristics of the actual struts (0.32-m diameter tube with a total stiffness of 3.25 GN/m). Struts and rebar are connected to the same embedment as the concrete beam.
## 3 Research Methodology
### Personalized Client Models
a neural network model can achieve good results and improve the generalization of the model input by simultaneously learning multiple related tasks [25]. This study integrates the principles of federated learning with multitask learning in a non-IID dataset. As the data distribution of each client cannot represent the global data distribution, each client is regarded as a different task with relatedness in multitask learning. The effectiveness of multitask learning led to the discovery of a global feature extraction neural network. Consequently, each client possesses a feature extraction function for obtaining effective features from the input data. The client neural network model comprises a Convolutional Neural Network (CNN) and a Fully Connected Neural Network (FCNN), as shown in Figure 1.  
The CNN kernel function considers the relationships between a single pixel and a hidden layer neuron and between the neighboring pixels. This renders CNNs efficient and resilient in extracting image features. In this study, the CNN of each client model serves as a feature extraction method to obtain a global feature function. However, in a FCNN, all the dimensions of the input data are connected to the hidden layer neurons, and the distance between the input features is not considered. This property effectively considers all the features and maps them to the prediction output space to achieve the function of a classifier. The FCNN part of the client model serves as the category prediction layer and is trained separately based on the data distribution of each client to achieve personalized properties.
## 3 Related Work and Literature Review
### Emerging Trends in Forensic Watermarking
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Study** & **Domain** & **Key Contributions** & **Limitations** \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [16]} & \multirow{2}{*}{Mobile Internet images} & User/device identification; tamper detection & Limited to mobile images; no integration with forensic toolkits \\  & & & Comparison of fragile methods; advantages of active forensics & Neglects investigative context; no consideration of evidence handling \\  & & & Rate-distortion preservation; video-only approach; encoding-time & incompatible with forensic disk images \\  & & & Invisible text watermarking; ML-based manipulation detection & Narrow focus on text; lacks broader forensic compatibility \\  & & & Encryption with watermarking; third-party cloud security & Audio-specific; overlooks legal concerns including right to be forgotten \\ \hline \multicolumn{3}{p{142.3 pt}}{} & \multicolumn{1}{p{142.3 pt}}{**Proposed Approach (Addressing Previous Limitations )**} \\ \hline \multirow{3}{*}{This work} & \multirow{3}{*}{Forensic disk images and JPEG files} & Disk image watermarking without structure alteration; recipient-specific watermarks for traceability; full compatibility with standard forensic tools & **Application Scope:** Works with both standalone JPEGs and disk images; compatible with multiple disk image formats; extensive testing across diverse datasets \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent forensic watermarking techniques. Legal considerations, especially regarding privacy rights and compliance with regulations such as GDPR's right to be forgotten provision, lack thorough examination. This study introduces a technique that directly addresses these limitations by embedding secure watermarks into JPEG images within forensic disk images. Our method preserves the structural integrity of disk images, enables stand-alone JPEG extraction, and maintains seamless compatibility with existing forensic tools. It incorporates chain-of-custody considerations, which is an aspect frequently overlooked in prior research. This work represents the first application of forensic watermarking at the disk image level to improve traceability and reliability throughout the evidence distribution process during investigations. The following sections outline the method for embedding and extracting secure watermarks from JPEG files without compromising the disk image structure or forensic reliability.
## Appendix A Frequency-Dependent Constitutive Equation for the Dissipative Structure
### Asymptotic Formula for the Radiated Pressure Far Field  
At point \(\mathbf{x}\) in the external domain \(\Omega_{E}\), the radiated pressure \(p(\mathbf{x},\omega)\) is given (see Equation (A24)) by \(p(\mathbf{x},\omega)=\mathbf{Z}_{\text{rad}}(\mathbf{x},\omega)\,v\). For an observation point \(\mathbf{x}\) that is far from the structure, the Green function is rapidly oscillating that induces numerical difficulties for performing the integration over \(\Gamma_{E}\). To circumvent this difficulty, asymptotic formulas can be used. Introducing the following parameterization of vector \(\mathbf{x}\),  
\[\mathbf{x}=R\,\mathbf{e}\quad\text{with}\quad R=\|\mathbf{x}\|\,,\] (A58)  
the asymptotic formulas are written as  
\[\lim_{R\to+\infty}\mathbf{R}_{\text{S}}(R\,\mathbf{e}\,;\omega/c_{\text{E}})= \mathbf{R}_{\text{S}}^{\infty}(R,\mathbf{e}\,;\omega/c_{\text{E}})\,,\] (A59)  
\[\lim_{R\to+\infty}\mathbf{R}_{\text{D}}(R\,\mathbf{e}\,;\omega/c_{\text{E}})= \mathbf{R}_{\text{D}}^{\infty}(R,\mathbf{e}\,;\omega/c_{\text{E}})\,,\] (A60)  
in which the operators \(\mathbf{R}_{\text{S}}^{\infty}(R,\mathbf{e}\,;\omega/c_{\text{E}})\) and \(\mathbf{R}_{\text{D}}^{\infty}(R,\mathbf{e}\,;\omega/c_{\text{E}})\) are defined by  
\[\mathbf{R}_{\text{S}}^{\infty}(R,\mathbf{e}\,;\omega/c_{\text{E}})\,v=\tfrac{ 1}{R}e^{-i\omega R/c_{\text{E}}}\int_{\Gamma_{E}}\!\!\!N_{\mathbf{e}}(\mathbf{y })\,v(\mathbf{y})\,ds_{\mathbf{y}}\,,\] (A61)\[\mathbf{R}_{\mathrm{D}}^{\infty}(R,\mathbf{e}\,;\omega/c_{\mathrm{E}})\,\psi_{ \Gamma_{\mathrm{E}}}=\frac{i\omega}{c_{\mathrm{E}}}\,\frac{1}{R}e^{-i\omega R/c _{\mathrm{E}}}\int_{\Gamma_{\mathrm{E}}}\mathbf{e}\cdot\mathbf{n}_{\mathbf{y}} \,N_{\mathbf{e}}(\mathbf{y})\psi_{\Gamma_{\mathrm{E}}}\left(\mathbf{y}\right) \,ds_{\mathbf{y}}\,, \tag{100}\]  
with  
\[N_{\mathbf{e}}(\mathbf{y})=-\frac{1}{4\pi}\,\exp(i\,\mathbf{e}\cdot\mathbf{y} \,\omega/c_{\mathrm{E}})\,. \tag{101}\]  
From Equation (101), the asymptotic formula for the radiation impedance operator is then written as  
\[\lim_{R\to+\infty}\mathbf{Z}_{\mathrm{rad}}(R\mathbf{e},\omega)=-i\omega\rho_ {\mathrm{E}}\{\mathbf{R}_{\mathrm{S}}^{\infty}(R,\mathbf{e}\,;\omega/c_{ \mathrm{E}})-\mathbf{R}_{\mathrm{D}}^{\infty}(R,\mathbf{e}\,;\omega/c_{ \mathrm{E}})\,\mathbf{B}_{\Gamma_{\mathrm{E}}}(\omega\,c_{\mathrm{E}})\}\,.\]
# Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics
## 2 Model of Wind Turbine Systems
### Electromagnetic Dynamics of the DFIG System  
The electromagnetic dynamics of the DFIG system is described in this section. The mechanical dynamics of the DFIG system will be described in the next section since the shaft of the DFIG system is coupled to the drive-train. The DFIG system is nonlinear and mutually coupled between the stator phases and rotor phases. As described in assumption 1, the Park transformation is applied to decouple the stator phases and rotor phases. In the d-q frame, the dynamic equation of the DFIG system with constant coefficients is [19; 20]:  
\[\left[\begin{array}{c}u_{\rm ds}\\ u_{\rm qs}\\ u_{\rm dr}\\ u_{\rm qr}\\ \end{array}\right]=\left[\begin{array}{cccc}R_{\rm s}&0&0&0\\ 0&R_{\rm s}&0&0\\ 0&0&R_{\rm r}&0\\ 0&0&0&R_{\rm r}\\ \end{array}\right]+\frac{\rm d}{\rm d}\left[\begin{array}{c}\psi_{\rm ds}\\ \psi_{\rm qs}\\ i_{\rm dr}\\ i_{\rm qr}\\ \end{array}\right]+\left[\begin{array}{cccc}0&-\omega_{\rm s}&0&0\\ \omega_{\rm s}&0&0\\ 0&0&0\\ 0&0&\omega_{\rm s}-\omega_{\rm g}&0\\ \end{array}\right]\left[\begin{array}{c}\psi_{\rm ds}\\ \psi_{\rm qs}\\ \psi_{\rm dr}\\ \psi_{\rm qr}\\ \end{array}\right] \tag{1}\]  
where \(u\), \(i\), \(\psi\), \(R\), and \(\omega\) represent voltage, current, flux, resistance, rotational speed, respectively; subscripts r, s, d, q represent rotor-side, stator-side, d-axis, and q-axis, respectively. Note that \(\omega_{\rm s}\) is synchronous speed, and \(\omega_{\rm g}\) is the mechanical speed of the generator rotor. d/dt represents the differential operator.  
In the d-q frame, the flux equation of the DIFG system is [19; 20]:  
\[\left[\begin{array}{c}\psi_{\rm ds}\\ \psi_{\rm qs}\\ \psi_{\rm dr}\\ \psi_{\rm qr}\\ \end{array}\right]=\left[\begin{array}{cccc}L_{\rm s}&0&L_{\rm m}&0\\ 0&L_{\rm s}&0&L_{\rm m}\\ L_{\rm m}&0&L_{\rm r}&0\\ 0&L_{\rm m}&0&L_{\rm r}\\ \end{array}\right]\left[\begin{array}{c}i_{\rm ds}\\ i_{\rm qs}\\ i_{\rm dr}\\ i_{\rm qr}\\ \end{array}\right] \tag{2}\]  
where \(L_{\rm s}\), \(L_{\rm r}\), and \(L_{\rm m}\) represent the self-inductance of the equivalent stator winding, the self-inductance of the equivalent rotor winding, and mutual inductance between the equivalent stator winding and the equivalent rotor winding, respectively.  
The electromagnetic torque of the DIFG system is [19; 20]:  
\[T_{\rm e}=n_{\rm p}L_{\rm m}\big{(}i_{\rm qs}i_{\rm dr}-i_{\rm ds}i_{\rm qr} \big{)}=n_{\rm p}\big{(}i_{\rm qs}\psi_{\rm ds}-i_{\rm ds}\psi_{\rm qs}\big{)} \tag{3}\]  
where \(n_{\rm p}\) represents the number of pole pairs.  
Under assumption 2, the stator flux and voltage in d-q frame are:  
where:  
\[b=\frac{1}{L_{\rm m}^{2}-L_{\rm s}L_{\rm r}} \tag{7}\]  
Under assumptions 1-3, substituting Equations (4) and (5) into Equation (3) produces:  
\[T_{\rm e}=n_{\rm p}\frac{L_{\rm m}}{L_{\rm s}}i_{\rm dr}\psi_{\rm s} \tag{8}\]
## 4 Multilevel Inverters (MLIs)
### Classification of Multilevel Inverters
#### 4.2.3 Cascaded H-Bridge  
[PERSON] and Bannister [129] patented the first converter topology in the mid-1970s, which was capable of producing multilevel voltages through the source of specific _DC_ voltage. A cascaded _H_-bridge converter was suggested by [130] for the elimination of the drawbacks of _FLC_ and _NPC_ topologies, such as the additional clamping diodes and capacitors. The cascaded _H_-bridge _MLI_ requires fewer parts per switching stage when compared to diode-clamped and flying capacitor inverters. The group of switches and  
Figure 32: Flying capacitor multilevel inverter [23].  
condensers in a cascade \(H\)-bridge _MLC_ is termed as \(H\)-bridge, comprising isolated _DC_ voltage sources [120; 131]. This topology utilizes more than one _DC_ source in the \(H\)-bridge inverter. Each converter achieves output at various levels, as shown in Figure 33[114], due to the connection of different power conversion cells. The \(H\)-bridge is made up of a pair of switches and condensers. For each \(H\)-bridge, a separate input _DC_ voltage is achieved as it generates a sinusoidal voltage output. Series of attached \(H\)-bridge cells are used in the inverter, with each having three different levels of _DC_ voltage, which are zero, negative _DC_ voltage, and positive _DC_ voltage. Each \(H\)-bridge cell has its average voltage output as the sum of all the produced voltages.  
If the number of cells is m, the number of output voltage levels will be (\(2^{m+1}\)). Figure 33 depicts the structure of a five-level \(H\)-bridge inverter [113]. The study by [PERSON] and [PERSON] (1997) focused on the peculiarities of the _NPC_ and _FLC_ topologies; the study was later patented in 1997. Cascaded \(H\)-bridge _MLCs_ (_CHBMLCs_) have since attracted great interest in several applications due to their attractive characteristics, such as:  
* Ease of packing and storing.
* Producing voltage in common mode, reducing stress.
* Small distortions in the input current.
* Functioning at both simple switching frequencies.
* The _THD_ of the output waveform being very small without any filter circuit. However, the inverter has drawbacks, such as:
* Each module needs different _DC_ sources or capacitors.
* The large quantity of capacitors requires a more complex controller.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### General LPWAN Architecture
At the NS, different services can be provided, including security services (authentication) through the AAA server, performance evaluation through the Perf server, and management processes through the management server. In terms of developing a generic architecture, the IETF is at the forefront towards the realization of a seamless interoperability platform for both new and existing LPWAN technologies. Thus, one major task of the IETF WG is to identify common functionalities in the LPWAN-GW and to standardize new and existing protocols required for seamless interoperability [25]. Such a generic architecture should ensure that LPWAN characteristics such as long battery lifetime, low data rates, low deployment/device costs, and scalability, are maintained across heterogeneous LPWAN technologies. Thus, the goal of a generic LPWAN architecture is to converge diverse radio technologies towards a central commonality for easy migration between different technologies. This easy migration will ultimately benefit end-users, which should be the case for the success of any new technology. Figure 1: A simple LPWAN architecture.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Integration with Electronic Health Records  
#### 5.8.1 Challenges  
When integrating an AI system with an electronic health record (EHR) system Figure 6, significant compatibility challenges can arise due to differences in data formats, structures, and communication protocols. For instance, an AI system might use JSON for data exchange, while the EHR system uses HL7 FHIR, leading to discrepancies in data interpretation. A concrete example is the integration of IBM Watson for Oncology with EHR systems like Epic; data transformation tools had to be developed to convert the oncology-specific data from Watson into a format that Epic could process accurately. Additionally, variations in system architectures can exacerbate these issues. In practice, this means that an AI system developed in a cloud-based environment might face integration hurdleswith an EHR system hosted on-premises. To overcome these challenges, organizations often need to invest in custom middleware solutions and extensive testing. For example, the integration efforts at Mayo Clinic involved creating a dedicated interface engine to bridge the data format differences and ensure seamless data flow between the AI system and the EHR system. Addressing these compatibility issues requires not only technical solutions but also a deep understanding of both the AI and EHR systems' requirements and constraints [146].
# A Ku-Band Bi-Directional Transmit and Receive IC in 0.13 \(\upmu\)m CMOS Technology
## 2 Design of Ku-Band Bi-Directional Transmit and Receive IC
### Switches Bi-Directional Amplifier  
The bi-directional amplifier in the transmit and receive IC for the phased array antenna is generally implemented with two SPDT switches, an LNA, and a PA. Two SPDT switches for the bi-directional operation result in an additional loss and a larger chip size. Figure 7 shows the schematic of the proposed bi-directional amplifier. The switchless bi-directional amplifier is composed of a paired two-stage distributed cascode amplifier. Since the transmit and receive switching operation is provided by switching the cascode transistors in the proposed amplifier, the small size can be achieved by removing the two transmit and receive SPDT switches. In the receive operation from P\({}_{1}\) to P\({}_{2}\), the transistors of M1/M5 and M2/M6 are in ON states and the transistors of M3/M7 and M4/M8 are in OFF states. Therefore, it works equivalently as a distributed amplifier. The transmit operation is vice versa. Generally, the insertion losses in the control circuits have a negative slope versus the frequencies, and so the amplifier is designed to have a positive gain slope. Flat overall gain performance can be achieved in the Ku-band transmit and receive IC. The symmetric layout was performed for identical operational characteristics in the transmit and receive operation modes.