esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### An Overview of Federated Learning
#### 2.2.2 Components of a Federated Learning Framework  
In today's data-driven world, the conventional centralized approach to ML--in which data from multiple sources is pooled on a single server for training--is encountering obstacles, particularly regarding privacy and efficiency. This technique collects data from various sources and then stores it on a server. FL has emerged as a potential solution, enabling decentralized training while ensuring that data is kept on its original device, thereby reducing the overhead associated with data transfer [24]. This section discusses the fundamental elements that comprise an FL framework.  
* Client devices: These are edge devices, including smartphones, tablets, IoT devices, and even personal computers; they can store and process data locally and oversee local model training.
* Central server: This entity serves as the primary aggregation point in the FL structure. The central server is responsible for communicating with client devices, collecting model updates, and disseminating the global model back to the clients [21].
* Local models: Each client device is equipped with its own version of the ML model, which is trained using the local data available on that device.
* Global model: This model aggregates all the local models stored on the client devices and is hosted on the central server.
* Communication protocol: The primary objective of the communication protocol is to establish reliable and effective communication between the client devices and the central server while ensuring the security of the data sent. It is responsible for overseeing the transmission of updates to the model and the distribution of the global model.
* Aggregation algorithm: The algorithm is implemented on the central server, integrating the model updates received from all client devices to enhance the global model.
* Privacy mechanisms: During model aggregation and communication, additional layers of data security can be added by integrating various techniques, such as differential privacy and Secure Multiparty Computation (SMPC).  
By gaining a comprehensive understanding of the fundamental components of FL, one can develop a deeper appreciation for the complexities and possibilities that FL offersin addressing contemporary challenges within the field of data science. The significance of such decentralized techniques is expected to continue expanding as the digital ecosystem evolves, making FL a cornerstone in the future of ML.
## 3 GAs for Multi-Objective Routing Optimization
### GA Parameters and Tuning  
GAs rely on various parameters that significantly influence their performance and convergence to optimal or near-optimal solutions. The proper tuning of these parameters is essential to strike a balance between exploring and exploiting the solution space [6]. The commonly tuned parameters include population size, crossover rate, mutation rate, and termination criteria. The population size determines the number of individuals (routes) in each GA generation. A larger population size allows for the greater exploration of the solution space but increases computational complexity. On the other hand, a smaller population size may lead to premature convergence and suboptimal solutions. The choice of population size should consider the problem complexity and available computational resources. The crossover rate represents the probability of performing a crossover operation between two parent routes to generate offspring [31]. A higher crossover rate promotes the exploration and diversification of the solution space. However, an excessively high crossover rate may result in the loss of good solutions, while a lower rate may slow down convergence. The selection of the crossover rate should consider the problem characteristics and the desired trade-off between exploration and exploitation. The mutation rate determines the probability of introducing random changes in the genes (locations) of an individual route [29]. Mutation helps maintain diversity and allows for the exploration of new regions in the solution space. A higher mutation rate encourages exploration but may slow down convergence, while a lower rate risks premature convergence to local optima. The choice of the mutation rate should balance the exploration-exploitation trade-off, considering the problem's complexity. The termination criteria define the conditions for stopping the GA's execution. Common termination criteria include reaching a maximum number of generations, achieving a satisfactory fitness level, or reaching a predefined computational time limit. The choice of termination criteria should consider the problem's characteristics, available computational resources, and optimization goals.  
Proper parameter tuning is essential to achieve the best performance of the GA for multi-objective message routing in EnFVs. It requires a thorough experimentation and analysis to strike the right balance between exploration and exploitation, considering the problem's complexity, available computational resources, and optimization objectives.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 3 Parametric Analysis
### Independently Controllable Notch AWS Band (1.8-2.2 GHz)  
Figure 6 illustrates the optimization of an \"E\"-shaped stub accountable for mitigating AWS (1.8-2.2 GHz) interference. It is evident from Figure 6 that E2 was the controlling parameter of the notch for the AWS band of the proposed antenna.  
\[L_{stub}=\frac{c}{2f_{n notch}\cdot\sqrt{\varepsilon_{eff}}} \tag{2}\]  
\[\varepsilon_{eff}=\frac{\varepsilon_{r}+1}{2} \tag{3}\]  
For the optimized dimension of E2 = 10 mm, the first frequency band notch was centered at 2 GHz, the second frequency band notch was centered at 6 GHz, and the third frequency notch was centered at 10 GHz. When the parameter E2 was modified from 8 mm to 9 mm, the first notch band shifted in the regime of the higher frequency range, whereas the second and third notch bands remained unaltered. The optimized equation of the \"E\"-shaped stub affecting the AWS wireless communication system is given by Equation (2), where (E1 = 34 mm, E2 = 10 mm, E3 = 1.2 mm) is the stub length, \(\varepsilon_{r}\) is relative permittivity, \(\varepsilon_{eff}\) is effective dielectric constant of the substrate calculated using Equation (3), \"\(c\)\" corresponds to the speed of light, and \(f_{n notch}\) is the notch center frequency. The theoretically calculated \"E\"-shaped stub length for 2 GHz is 59.29 mm, while the optimum value for the \"E\"-shaped stub is 55.2 mm. The detailed parametric variations is illustrated in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**AWS** & \multicolumn{2}{c}{**C Band**} & \multicolumn{2}{c}{**X Band**} \\ \hline
**E\({}_{2}\) (mm)** & **Bandwidth (GHz)** & **E\({}_{\text{MIN}}\) (mm)** & **Bandwidth (GHz)** & **C\({}_{2}\) (mm)** & **Bandwidth (GHz)** \\ \hline
8 & 2.1–2.5 & 4 & 4.11–7.14 & 2.2 & 10.2 to 10.69 \\
9 & 2–2.4 & 4.2 & 4–7.13 & 2.3 & 10.06 to 10.57 \\
10 & 1.8–2.2 & 4.4 & 3.79–7.05 & 2.4 & 9.86 to 10.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parametric variation of notched bands.  
Figure 6: Parametric variation of independently controllable AWS band notch by varying E2.
# Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach
## 1 Introduction  
Emergency medical services are critical to the survival of patients facing life-threatening situations. Ambulances are used as the primary means of transport to healthcare facilities, and studies have shown a strong correlation between transport times and patient survival outcomes [1]. This highlights the importance of fast and efficient ambulance operations.  
The concept of the \"golden time\" is critical in emergency medical services. Defined as the time from dispatch to first patient contact, meeting the golden hour is critical to patient survival. Different studies have defined the golden hour in different ways. While there is no clear definition of the golden time across countries, the general consensus is that it is important to transport patients quickly. However, in urban centers with heavy traffic and a high population density, arriving quickly is often a challenge. There may be a \"zero zone\" where a patient cannot be reached within the designated golden time.  
Traditionally, ambulances are deployed from designated locations, such as fire stations, which facilitates vehicle maintenance, but can limit operational efficiency. Given the dynamics of urban environments, such as changes in traffic patterns and fluctuations in population density, a static approach to ambulance deployment can be inefficient.  
This study aimed to define and optimize a multi-period ambulance relocation problem with temporal and spatial variations in demand. We propose a mathematical optimization model based on integer programming. We considered not only fire stations, but also other administrative organizations as potential deployment sites. The contribution of this study was to dynamically relocate ambulances based on real-time traffic data and historical emergency response data. By doing so, we aimed to maximize the supply of emergency medical services in areas where emergencies are likely to occur.  
The remainder of this paper is organized as follows: Section 2 presents the related work. Section 3 describes the proposed model. In the last section, we present a solution analysis to validate the effectiveness of our approach.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs
### Real Application Example of a Tube under Tension and Torsion
#### 3.2.4 Principal Directions  
Usually, only the maximum or minimum principal strains are provided. The associated principal strain directions are not recognizable. For the example of the simple tensile test of the externally twisted specimens from Figures 13 (right) and 15, this is shown in Figure 16 (left).  
There, a facet size of 9 px (projected onto the \((Y/Z)\)-plane) is chosen. It can be seen that the orientations have seemingly no preferred direction. If the facet size is increased to 19 px, Figure 16 (right), the orientations slightly rotate in load direction.  
An analogous behavior can also be observed in the commercial program ARAMIS. However, there is no precise indication of how these directions are calculated. Furthermore, by using the RBF approach, the evaluation of all geometric information can be completed at arbitrary points--which is presented in Figure 16.  
For future investigations, it is still an open question what information is obtained in the case of strongly inhomogeneous deformations and how it is to be interpreted.
## 3 Results and Discussion
### Discussion  
This study addresses several key technical challenges in accurately predicting the anisotropic mechanical behavior of SFRP composites. One of the primary challenges lies in capturing the complex relationship between fiber orientation strongly influenced by the injection molding process and the resulting mechanical properties. To overcome this, we employed a multi-scale modeling approach that integrates fiber orientation tensor data from injection molding simulations into representative volume element RVE-based FEA analysis. This integration allows for the derivation of orthotropic material properties that reflect the actual microstructural characteristics of the molded part.  
A notable innovation in this study is the direct mapping of fiber orientation data to RVE models, enabling the prediction of direction-dependent stiffness values. As demonstrated in Table 8, the elastic modulus in the primary flow direction (\(E_{1}\)) increases significantly with higher fiber alignment, confirming the strong anisotropic behavior of the composite. This highlights the importance of incorporating process-informed microstructural data into structural simulations.  
Another technical advancement is the validation of simulation results through both static and dynamic experimental tests. The tensile and compressive test results show strong agreement with simulation predictions, particularly in terms of fracture location and strength values. To quantitatively assess this agreement, we compared the maximum tensile stress values from both experimental and simulation data across three fiber orientations (0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\)). The calculated mean absolute error (MAE) was approximately 6.67 MPa, and the root mean square error (RMSE) was approximately 7.07 MPa, indicating a high level of accuracy in the static mechanical predictions.  
Furthermore, the use of frequency response function (FRF) analysis and hammering tests provides additional confirmation of the model's ability to capture dynamic stiffness characteristics, which are highly sensitive to anisotropic stiffness distributions. The estimated MAE and RMSE for the frequency response in the X, Y, and Z directions were approximately 1.0 dB/g, further validating the model's predictive capability in dynamic conditions.  
Compared to conventional modeling approaches that assume isotropic or simplified anisotropic behavior, our method offers a more accurate and scalable framework for predicting the performance of SFRP components under real-world loading conditions. This is  
Figure 15: Hammering test results and simulation results by X, Y, Z direction.  
particularly valuable in automotive applications, where material behavior must be precisely understood to ensure safety and performance.
# Extraction of Flaw Signals from the Mixed 1-D Signals by Denoising Autoencoder
## 2 Methods and Materials
### Data Acquisition  
The A-scan data from the flaws were collected with an ultrasonic testing system shown in the figure below. Figure 3 presents the equipment used for the UT inspection of the pipe weldment specimen. The UT equipment used in this study digitalized the ultrasonic data so that it could be displayed on a laptop where a developed UT program was installed.  
Figure 1: Ultrasonic A-scan signal with the flaw indication and the background: (**a**) initial signal and wedge noise; (**b**) flaw signal; (**c**) weldment interface signal and geometrical signal.  
Figure 2: Specimens used in the acquisition of data.  
In addition to the ultrasonic testing system, a total of 18 types of ultrasonic testing probes or transducers were used to acquire the ultrasonic A-scan data. The ultrasonic transducers were made by three different companies, Krautkramer, GE, and Olympus. Each type of transducer included three angles of incidence, \(45^{\circ}\), \(60^{\circ}\), and \(70^{\circ}\). Table 1 shows the probes used for the acquisition of the ultrasonic A-scan data.
## 3 Analysis of the Lane Control Strategy Based on Truck Platooning Simulation
### Analysis of the Lane Control Strategy
#### 3.2.3 Comparative Analysis by Vehicle and Lane  
To further analyze the impact of optimal speed control on different vehicle types and lanes, the 4F32T strategy with truck platooning in separation phase in the weaving section was selected with traffic volumes of 5000, 6000, and 7000 veh/h. In the context of this experiment, ATP and HDT were both set to for 10%. Figure 7 presents a comparison in terms of delay difference and conflict rate difference among different vehicle types with and without optimal speed control. ALL, CAR, HDT, and ATP denote all vehicle models, human-driven cars, human-driven trucks, and self-driving truck platoons, respectively, and 5000, 6000, and 7000 veh/h denote the simulation input traffic, respectively. The blue column on the left shows the delay difference for each model when the simulated input traffic is 5000 veh/h, the purple column shows the delay difference for each model when the simulated input traffic volume is 6000 veh/h, and the red column shows the delay difference for each model when the simulated input traffic volume is 7000 veh/h. Further, the dark green column on the right shows the conflict rate difference for each model when the simulated input traffic volume is 5000 veh/h, the green column shows the conflict rate difference for each model when the simulated input traffic volume is 6000 veh/h, and the yellow column shows the conflict rate difference for each model when the simulated input traffic volume is 7000 veh/h.  
The delay and conflict rate data in the weaving section can be obtained for different vehicle types by setting a travel time detector. Overall, truck platooning control exhibits improved delay and conflict rates for all vehicle models. Furthermore, these rates are proportional to traffic volume. Because travel time minimization is one of the objectives of this study, the optimal speed control strategy for truck platoons optimizes traffic through the interleaving zone for each vehicle model. The greatest extent of improvement was seen in van platooning, followed by minibus platooning.  
Figure 8 shows a comparison, in terms of volume difference and velocity difference, of different lanes with and without optimal speed control under the same strategy as in Figure 7. In the context of this figure, the lanes are sequentially labeled from the inside to the outside.  
Figure 7: Comparison of different vehicle types under the 4F32T strategy.  
Overall, there is an improvement in the number and speed of vehicles passing through the cross-section in the presence of truck platooning control, as the control method maximizes traffic volume through the bottleneck area while minimizing total vehicle travel time. In the same lane under the section, traffic volume decreases, whereas speed remains approximately the same. In the same flow, subsequent lanes exhibit a decrease in traffic and increase in speed.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 3 The Proposed Approach  
In the section, we expound in detail the identification method of FEC coding type based on RNN and we will apply and verify the reliability of this method in the next section.
## 2 Materials and Methods
### Isotope Measurements
#### 2.2.1.2 \(\delta^{18}\)O and \(\delta^{2}\)H Determinations from Honey Water  
The water extraction from honey samples without fractionation was performed through cryogenic distillation under vacuum, and the method was presented in detail in our previous work [16]. Briefly, the water extraction, without any isotopic fractionation, was simultaneously performed on 12 samples using the above-mentioned method. For the quality control of the water extraction process, each sample set contained at least one sample in duplicate. If two specimens from the same sample did not have a comparable isotopic value (within the measurement uncertainty), the entire sample set was subjected to a new water extraction step.  
1. Determination of \(\delta^{18}\)O and \(\delta^{2}\)H Values from Honey Water  
For \(\delta^{18}\)O and \(\delta^{2}\)H isotope values measurements, a liquid-water isotope analyzer (DLT--100, Los Gatos Research, San Jose, CA, USA) was used [5]. The results were expressed delta scale (\(\delta\)%), versus the international Vienna Standard Mean Ocean Water (V-SMOW) standard. For quality control, five international working standards (WS) were measured: WS 1 (\(\delta^{18}\)O = \(-\)19.57% and \(\delta^{2}\)H = \(-\)154.1), WS 2 (\(\delta^{18}\)O = \(-\)15.55% and \(\delta^{2}\)H = \(-\)117.0), WS 3 (\(\delta^{18}\)O = \(-\)11.54% and \(\delta^{2}\)H = \(-\)79.0%), WS 4 (\(\delta^{18}\)O = \(-\)7.14% and \(\delta^{2}\)H = \(-\)43.6%) and WS 5 (\(\delta^{18}\)O = \(-\)2.96 \(\pm\) % and \(\delta^{2}\)H = \(-\)9.8%). The measurement uncertainty was 0.2 % for \({}^{18}\)O/\({}^{16}\)O and 1 % for D/H.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 3 GIMAD Prototype Description
### Signal Processing Module (SPM)  
Three main operational blocks run under the SPM, which are named after the three main functionalities that they pursue: Detection, Characterization and Localization. Figure 5 summarizes the SPM modules in a block diagram.  
The selected algorithms running behind them are described next.  
Figure 4: GIMAD CC example.  
Figure 5: SPM modules.
## 3 Mathematical Approach  
The analysis of heterogeneous media involves two well-distinguished scales: the macroscopic scale (\(\mathbf{x}\)), with characteristic length (\(\mathbf{L}\)), in which the size of the heterogeneities is very small; and the microscopic scale (\(\mathbf{y}\)), with characteristic length (\(\ell\)), which is the scale of the heterogeneities. To sustain this statement, we must assume the length separation scale condition, i.e., \(\mathbf{L}\gg\ell\). In order to deal with the macroscopic scale as a continuum in a macroscopic material point, the microscopic scale is homogenised in terms of its effective behaviour over a Representative Volume Element (RVE). An RVE must statistically represent the underlying microstructure of the specimen, in such a way that every material point of the macroscopic region is featured by such RVE. Another definition of the RVE may be established as it being the smallest microstructural volume that sufficiently and accurately represents the overall macroscopic properties of interest. The minimum required RVE size also depends on the type of material behaviour, macroscopic loading path and difference of properties between heterogeneities.  
In the following, the variable \(\mathbf{x}\) stands in the homogeneous macroscopic body, whereas \(\mathbf{y}\) describes the microscopic scale of the RVE. In the microscopic scale, the domain is composed by the (solid or bulk) bone (B) and pore (P) bodies, so that RVE \(\equiv\mathbf{\Omega}_{B}^{\varphi}\cup\ \mathbf{\Omega}_{P}^{\varphi}\). The linear elastic equations are posed in the RVE domain to obtain the averaged, apparent or homogenised mechanical properties and the microscopic distribution of the stress and strain fields. Then, the primary variables are asymptotically expanded as:  
\[\mathbf{u}^{\varphi}=\mathbf{u}^{0}(\mathbf{x})+\mathbf{\varphi}\mathbf{u}^{*}( \mathbf{x},\mathbf{y}) \tag{1}\]  
where \(\mathbf{u}^{\varphi}\) is the total displacement field, \(\mathbf{u}^{0}\) is the associated field variable averaged at the macroscopic scale and \(\mathbf{u}^{*}\) is the perturbation (oscillating) functions due to the heterogeneity of the microscopic scale. In Equation (1), \(\mathbf{\varphi}=\ell/\mathbf{L}\) refers to the ratio between the microscopic and macroscopic levels.  
For averaging the macroscopic fields, the following operator is defined over a microscopic volume generally denoted by \(\mathbf{\Theta}\):  
\[\langle\blacksquare\rangle=\frac{1}{|\mathbf{\Theta}|}\int_{\mathbf{\Theta}}\blacksquare d\mathbf{\Theta} \tag{2}\]  
Figure 3: (**a**) Non-contact two-dimensional (2D) deformation system used to obtain the strain maps in the bone samples; (**b**) strains measured in the region of interest of the sample.  
This expression enables us to obtain macroscopic quantities and, eventually, the macroscopic response (behaviour) induced by the underlying microstructure. This approach is called homogenisation. The inverse procedure, localisation, allows to determine the microscopic values from the macroscopic ones. Both are outlined below.