esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Other more advanced ANN architectures such as convolutional, recurrent networks or physics-based neural networks [18; 32] were not explored in the study. The application of such architectures or the use of other machine learning regressors could improve accuracy and/or generalizability and could be included in future studies. Furthermore, in the study, the experimental validation was not conducted. The Authors refer directly to the homogenization method results, the effectiveness of which has already been confirmed in the literature, therefore, no validation is required. The main aim of the study is to replace the computations of homogenization according to laminate theory with an ANN approach and find its best structure. Therefore, to demonstrate the effectiveness of the proposed approach, the numerical examples provided were sufficient.
## 3 Method
### Visual Maintenance of Multi-Layer Reusable Library
#### 3.3.3 Code Generator Based on Google Blockly to Generate SC Code  
We design a component called the **Code Generator**, based on Google Blockly, to realize automatic code generation. It adopts the Blockly visualization method to construct the SC framework to be generated, including the basic information of the SC and the models based on BPMN to be associated with it, as shown in Figure 9. A BPMN model represents a multi-party interactive process, and an SC can be associated with one or more BPMN models. This component then adds the version of the compiler to the SC. The deployment environment determines the process and is transparency to the user.  
When building an SC framework using Google Blockly, we provide two frameworks: _Contract_ and _Interface_. _Contract_ can generate a complete SC, and _Interface_ can generate an interface, but it requires other contracts to inherit and implement and cannot be deployed directly. Developers need to select one of the two types and set the basic information, including the contract name, the contract description, the inheritance relationship with the external library contract, and the link to the BPMN models.
## 4 Efficient FFT Algorithms  
As discussed in the Sections 2.1 and 3.1, non-contiguous OFDM and non-contiguous MC-CDMA schemes are used in a DSA network to utilize non-contiguous blocks of subcarriers for the opportunistic transmission of secondary users. The CR transceivers deactivate some of the subcarriers in order to avoid interference with primary user's signal. Subcarriers are deactivated by assigning zero values to these subcarriers. Active subcarriers are assigned with data symbols. Thus, there are zero valued inputs for the IFFT and zero valued outputs for the FFT of the transceiver. When the number of zero valued inputs/outputs is more than the number of non-zero inputs/outputs, the conventional FFT/IFFT algorithms will not be efficient because of the unwanted operations on zero values. Therefore, the cognitive radio transceivers need some efficient FFT/IFFT algorithms that dynamically eliminate the multiplication and addition operations associated with deactivated subcarriers. Also, there are various efficient FFT algorithms that combine spreading and FFT operations. This section elaborates the efficient FFT algorithms proposed in the literature for cognitive radio systems.
## 2 Materials and Methods
### Evaluation of Classification Performance
#### 2.10.3 Comparison against In Situ Stage Data  
The RadWet-L-derived inundation extent was compared with in situ river stage data at five locations within the Amazon River Basin (Figure 6) as performed by [9]. River stage data was acquired from the Brazilian National Water and Sanitation Agency (ANA) online data portal HIDROWEB [59].  
These time series data provide a point reference of river heights between 2019 and 2023. To enable comparisons to be made with RadWet-L outputs, the upslope contributing area of each river stage gauge station (defined by the Global Runoff Data Centre [60]) were used to mask each RadWet-L classified output (Figure 6). The total inundated area could then be calculated to allow for a direct comparison between RadWet-L and river stage height. Pearson correlation was used to quantitatively compare RadWet-L derived inundation extent and river stage height at the date of the ALOS-2 PALSAR-2 image acquisitions.  
Figure 5: Location of G-REALM Satellite altimetry measurements on 6 lakes around the Central Amazon and associated drainage basins defined from HydroSheds Level 6 and 7 basin products [45]. (a) Lake Badajos, (b) Lake Mamia, (c) Lake Guaribas, (d) Lake Santo, (e) Lake Ariau, and (f) Lake Erepecu. Basemap ESRI Physical.  
Figure 6: Locations of river stage gauge stations and associated upslope contributing areas. (a) Estirao Do Repouso, (b) Seringal Moreira, (c) Estirao Da Santa Cruz, (d) Acanuai, and (e) Serrinha. Data provided from [59; 60].  
2.10.4. Comparison against Other Mapping Approaches  
Previous studies have sought to map the minimum and maximum extent of inundation within the Amazon River Basin using JERS-1 SAR [10; 11], ALOS-1 PALSAR-1 ScanSAR [12], and ALOS-2 PALSAR-2 ScanSAR [9] data at comparable spatial resolutions (50-100 m). A direct comparison of mapped minimum and maximum inundation extent was made for the period 2019 to 2023 for the whole Amazon Basin.  
Inundation maximum extent was defined where the instances of inundation throughout the time series between 2019 and 2023 were greater than the 5 th percentile (2 observations) of valid observations (maximum 43). Minimum inundation was defined where the count of inundation was greater than the 95 th percentile (41 observations) of valid pixels in the time series (usually 43).  
Despite the data-filling procedure, some areas of no data remain for certain observation cycles, usually visible as striping between orbit swaths. Consequently, in these areas, the maximum number of valid pixels through time was less than the 43 observations of the time series. Minimum inundation therefore was defined by the 95 th percentile of the number of valid observation cycles.  
Although studies mapping inundation within the Amazon Basin have covered different temporal periods (1996 [10; 11], 2006-2010 [12], 2014-2017 [9], and 2019-2022 for this study), performing a comparison in mapped extent is still relevant as the spatial patterns should be similar and this builds confidence in the mapping approach of this study.
## 5 Active File Mode Change Mechanism
To this end, the active file mode change mechanism may set a maximum allowable access delay threshold (_MADT_). To calculate the minimum size of a file to be changed to the local mode with the _MADT_ value, the client first measures the input throughput from the server. Input throughput is a measure of the speed at which the current client receives data from the server in megabytes per second (MBps). Multiplying the input throughput by the _MADT_ gives the minimum size of the file that requires the maximum allowable access time. Among the online mode files, the active file mode change mechanism excludes those of which original size is smaller than the minimum size from the local mode change target. This is because these files are downloadable from the server within the maximum allowable access time when the client accesses it. If the _MADT_ is less than or equal to 0, the active file mode change mechanism ignores the minimum size criterion of a file to be changed to the local mode, and all online mode files are eligible to be changed. The active file mode change mechanism sorts the online mode files larger than the minimum size in descending order based on the last access time, and the most recently accessed file is the first to be changed. The active file mode change mechanism does not change all selected online mode files to the local mode, but only changes to the local mode until the used storage ratio (_USR_) becomes greater than the threshold (_USRT_) by considering the available space of the local storage. Although the active file mode change mechanism changes all selected files to the  
Figure 6: Flow chart of online mode change. local mode, the _USR_ could be still smaller than the _USRT_. That is, there may be still enough local storage space. In this case, the mechanism additionally can change online mode files smaller than the minimum size to the local mode.
###### Abstract
Although the advantages of all constituent techniques are accumulated in a combined setup, individual disadvantages can be mitigated (if not avoided altogether). The major asset of the IRT technique is that it provides integral, direct access to thermodynamic aspects of plastic deformation and fracture on the macro-scale through the measurements of the local distribution of temperature on the surface--information that is not accessible otherwise. The RVI system clarifies the details of the occurrence of deformation and fracture mechanisms, which are reflected by the surface relief on the scale ranging from macro (a whole specimen) to micro (grain, sub-grain, individual slip lines, deformation twins, microcracks, etc. ), depending on the lens used. When powered by DIC, it quantifies the local strain and stress distribution in the region of interest (ROI). Recognition of unique benefits offered by a combination of these methods has prompted several researchers to implement them in a single experimental setup and explore new possibilities for monotonic and fatigue testing of different structural materials--aluminium alloys [50; 51; 52], titanium alloys [50], 316 austenitic stainless steel [53; 54], magnesium alloy AZ31 [55], various composites [6; 56], concrete [57], and rock [58]. It is known that, while the combination of IRT, AE, and RVI/DIC is a powerful approach, offering a quite unique and comprehensive piece of information regarding various aspects of the deformation and fracture of the testing material, this novel approach poses many challenges faced by laboratory practitioners worldwide. The burgeoning interest and the increasing number of publications exploiting the named techniques in various combinations motivated us to review the existing challenges and good practices to address them based on our first-hand experience. The main challenges to be addressed include: (i) high mechanical noise from the dynamic testing frames affecting AE measurements, (ii) electrical noise and interference between all the devices used in the setup influencing the measured AE signal, (iii) flexible synchronisation of all testing and measuring devices, (iv) movement of the specimen and the region of interest away from the camera's field of view, (v) combination of IRT and DIC images in the common data processing framework, (vi) finding the crack tip position by means of both IRT and DIC, (vii) correcting the thermal measurements for the lower than unity infrared emissivity, (viii) processing of noisy IRT information to calculate the heat dissipation from weak sources, etc. In what follows, we propose a versatile experimental setup addressing the named major and a few other minor (yet frequently faced and annoying) hardware and software challenges, thus enabling the seamless and synchronous operation of the IRT, AE, and RVI/DIC techniques, and report the preliminary results on the characterisation of plastic deformation and fatigue crack growth in the commercial Inconel-625 alloy.
## 3 Results and Discussion
### The Effect of the Fluorination Process on the Recovery of REMES by Vacuum Distillation
#### 3.3.1 The Volatility of Fluoride Impurities  
According to Figure 10a,b, NH\({}_{4}\)HF\({}_{2}\) also possesses the ability to fluorinate oxide impurities. Therefore, in order to detect the distillation temperature of fluoride impurities, the volatility of them should be studied.  
The reactions in Figure 10a,b were calculated by HSC 6.0 software. Additionally, the saturated vapor pressures of AlF\({}_{3}\), CaF\({}_{2}\), and MgF\({}_{2}\) were obtained from a book called _\"The Yaws Handbook of Yapor Pressure Antoine Coefficients\"_ and calculated by the Antoine equation [32]. Due to the lack of parameters, the interference of FeF\({}_{3}\) in the distillation of rare earth fluoride was examined in the distillation experiment.  
Figure 9: The Gibbs free energy changes of the reactions between the oxide impurities and NdF\({}_{3}\) at 0.1 Pa: (**a**) the changes in \(\Delta G\) for reactions of SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) with temperature; (**b**) the changes in \(\Delta G\) for reactions of CaO and MgO with temperature.  
SiF\({}_{4}\) is a gaseous substance, so it is separated during the fluorination process. As shown in Figures 6 and 10c, AIF\({}_{3}\) easily evaporates and combines with LiF. The saturated vapor pressures of CaF\({}_{2}\) and MgF\({}_{2}\) indicate that the distillation temperature of CaF\({}_{2}\) and MgF\({}_{2}\) might be higher than or around 1573 K, which might affect the purity of rare earth fluorides (see Figure 10d,e). Due to the low content of CaO and MgO and the evaporation of rare earth fluorides at 1473 K, the separation of CaF\({}_{2}\) and MgF\({}_{2}\) could be disregarded. Therefore, fluorinating REMES was deemed feasible.
## 2 Preliminaries and Problem Formulation  
Consider an uncertain nonlinear system as follows:  
\[\dot{x}(t)=f(x(t))+\Delta f(x(t))+[g(x(t))+\Delta g(x(t))]u(t),x(0)=x_{0} \tag{1}\]  
where \(x(t)\in\mathbb{R}^{n}\) is the system state, \(u(t)\in\mathbb{R}^{m}\) is the control input, \(f(x(t))\in\mathbb{R}^{n}\), \(g(x(t))\in\mathbb{R}^{n\times m}\) are known function, \(\Delta f(x(t))\in\mathbb{R}^{n}\), \(\Delta g(x(t))\in\mathbb{R}^{n\times m}\) are uncertain disturbing function.  
The control objective is to establish a robust control law \(u=u(x)\) in order that the closed-loop system is asymptotically stable for all allowed uncertain disturbances \(\Delta f(x(t))\) and \(\Delta g(x(t))\).  
As a general case, we first make the following assumptions to ensure that the state Equation (1) is well defined [1; 29].  
**Assumption 1**.: _In (1), \(f(x)+g(x)u\) is Lipschitz continuous with respect to \(x\) and \(u\) on the set \(\Omega\subseteq\mathbb{R}^{n}\) containing the origin._  
**Assumption 2**.: _For the free vibration system, \(f(0)=0,\Delta f(0)=0\), that is, \(x=0\) is the equilibrium point of the free vibration system._  
**Definition 1**.: _The system (1) is called to satisfy the system dynamics matched condition if there is a function matrix \(h(x)\in R^{m\times 1}\) such that_  
\[\Delta f(x)=g(x)h(x). \tag{2}\]  
**Definition 2**.: _System (1) is called to satisfy the input matched condition if there is a function \(m(x)\in R^{m\times m}\) such that_  
\[\Delta g(x)=g(x)m(x), \tag{3}\]  
_where \(m(x)\geq 0\)._  
**Definition 3**.: _If the system (1) satisfies the conditions (2) and (3) for any allowed disturbances \(\Delta f(x)\) and \(\Delta g(x)\), then the system (1) is called a matched uncertain system._  
**Definition 4**.: _If the system (1) does not satisfy the condition (2) or (3) for any allowed disturbances \(\Delta f(x)\) and \(\Delta g(x)\), then the system (1) is called a mismatched uncertain system._  
Next, we consider the robust control problem of nonlinear system (1) with matched and mismatched conditions, respectively.
## 6 Combining DP and HE in Federated Learning
### Discussion and Learned Lessons  
The combination of DP and HE in FL offers the potential to achieve a more comprehensive approach to privacy and security in federated learning. By leveraging the strengths of each technique, it becomes possible to mitigate their respective drawbacks and achieve enhanced privacy protection. HE can amplify the privacy offered by DP to protect the updates from all the parties, as in [PERSON] et al. [74]. While HE protects the intermediate updates from the server, DP also ensures the final model remains secure, preventing adversaries from performing model inversion attacks.  
This combination is interesting also in terms of model performance. In fact, augmenting DP with HE can allow adding less noise and, by the way, having more utility of the data. The authors of the aforementioned work refer to this approach as crypto-aided differential privacy, emphasizing its potential for balancing between privacy and utility.  
HE and DP can effectively mitigate various attacks from curious aggregators and from clients. By encrypting the data and applying differential privacy mechanisms, the privacy of the model updates and inference process can be safeguarded, preventing adversaries from extracting sensitive information. In addition, other attacks like collusion and poisoning attacks can be addressed using the combination of these techniques.  
However, it is essential to acknowledge that the combination of DP and HE in FL does come with certain trade-offs and complexities. As the number of participants in the learning process increases, managing these entities can become challenging. Furthermore, the computational overhead associated with HE introduces resource consumption, impacting communication and computation within the system.  
In summary, the integration of DP and HE in federated learning holds immense promise in enhancing privacy and security while striking a balance between utility and protection. However, it is crucial to carefully manage the system complexity and consider resource implications to fully harness the potential of this powerful privacy-preserving approach.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Year** & **Key Idea** & **Trade-offs and Shortcomings** \\ \hline
[70] & 2019 & Add less noise by amplifying privacy by homomorphic encryption & Trade-off between privacy, communication, and computation. \\ \hline
[71] & 2020 & Amplify privacy with the shuffle model and protect data against collision attacks using Encrypted oblivious shuffle. & Increased system complexity. \\ \hline
[73] & 2021 & Split the updates into two shares and send them to two non-colluding servers that add CDP and use additive secret sharing to mitigate poisoning attacks and conduct secure aggregation. & Increased system complexity. \\ \hline
[74] & 2022 & Protect updates from the server using homomorphic encryption and protect global updates from clients using DP & Computational overhead. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Privacy preserving FL combining DP and HE
## 3 Results
### End-to-End Communication System Emulation in Interfering Model
#### 3.5.3 Test 3--MODCOD Results  
As an example, we calculate the maximum INR for the MODCOD QPSK 3/5 (ID = 8) @ \(\eta_{tot}\) = 1.883. From (2), the received SNR at the demodulator input is 15.4 dB, and the minimum specified is:  
\[\left(\frac{E_{b}}{N_{0}}\right)_{required}=1.5\ \mathrm{dB},\therefore M=13.9\ \mathrm{dB}.\]  
For \(M\) = 3 dB, we have:  
\[\frac{E_{b}}{N_{0}+I_{0}}=4.47\ \mathrm{dB}\Longrightarrow\frac{C}{I}=5.4\ \mathrm{dB}. \tag{10}\]Like,  
\[\Delta=1+INR\leq M\Longrightarrow 1+INR\leq 10^{\frac{139}{10}}\therefore INR\leq 13.7 \text{ dB}. \tag{11}\]  
For downlink practical checking, the earth station antenna gain, \(G_{ant}(\text{RX})=55\) dB (from budget parameters):  
\[C=EIRP-losses+G_{ant_{RX}}=14.5-182.14+55\therefore C=-112.64\text{ dBm}.\]  
Like,  
\[\left(\frac{C}{N_{0}}\right)_{dontval}=95.5\text{ dBmHz},\therefore N_{0}=C-95.5=-208.1\text{ dBm}/\text{Hz},\]  
\[\Longrightarrow\frac{I_{0}}{N_{0}}=13.7,\therefore I_{0}=-194.4\frac{\text{dBm}} {\text{Hz}}.\]  
Given:  
\[I=I_{0}+10\text{log}_{10}(R_{b})-10\text{log}_{10}\eta_{tot}=-194.4+77-0.75=-1 18.15\text{ dBm},\]  
\[\therefore\frac{C}{I}=5.5\text{ dB}\;. \tag{12}\]  
Results (10) and (12) demonstrate the consistency. For the remaining MODCODs, the calculation sequence follows the same steps.
## 2 Materials and Methods
### Statistical Analyses
#### 2.6.2 Pearson Correlation Coefficient  
To determine whether there is a correlation between the climatic parameters (temperature, relative humidity, rainfall, wind strength and cloud abundance) and the phytochemicals from sea fennel EOs, phenolics, and carotenoids over a 12-month period, an analysis of Pearson's correlation coefficient was performed. The correlation was considered significant at the level of \(p\leq 0.05\). The data on climatic parameters were taken from the Croatian Meteorological and Hydrological Service weather station (coordinates: 43.516\({}^{\circ}\) N, 16.4167\({}^{\circ}\) E).
## 2 Experimental Setup  
In this study, FSW was performed on high-strength steel sheets with a tensile strength range of 590-1500 MPa. Table 1 lists the chemical compositions of the applied materials. All the sheets were 1.0 mm thick, except for the 1.2 mm-thick (TRIP: transformation induced plasticity) 1180 steel and hot press forming (HPF) 1500 steel. HPF1500 steel sheets have a strength of 1535 MPa and a hardness of approximately 500 Hv after heat treatment, which involves heating the sheets in a furnace at a temperature of 950 \({}^{\circ}\)C for 6 min and then cooling them down in a quenching die through which cooling water at a temperature of 25 \({}^{\circ}\)C is circulated [21].  
Figure 1a shows a schematic of the FSW process, where a position control welding machine (WX-FSW-03, WINZEN, Korea) is used. The tool was made of WC-Co12% with a concave-shaped shoulder and a conical frustum-shaped probe (Figure 1b). The tool  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Base Materials** & **C** & **Si** & **Mn** & **Cr** & **S** & **P** & **Al** & **Nb** & **Ti** \\ \hline DP590 (1.0 mm) & 0.07 & 0.36 & 1.81 & - & - & - & - & - & - \\ \hline DP780 (1.0 mm) & 0.06 & 0.97 & 2.25 & - & - & - & - & - & - \\ \hline DP980 (1.0 mm) & 0.17 & 1.34 & 2.00 & - & 0.001 & 0.016 & - & - & - \\ \hline TRIP1180 (1.2 mm) & 0.16 & 1.70 & 2.31 & 0.52 & 0.003 & 0.018 & & & \\ \hline MS1300 (1.0 mm) & 0.20 & 0.20 & 1.50 & - & 0.002 & 0.01 & 0.040 & 0.015 & 0.025 \\ \hline HPF1500 (1.2 mm) & 0.24 & 0.30 & 1.21 & 0.20 & 0.003 & 0.014 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of base materials (wt%).  
was tilted by 2\({}^{\circ}\) in the welding direction, which is called the push or forehand angle in welding. Table 2 lists the detailed FSW conditions. When welding steel with a position control welding system, the tool cannot reach the specified position because of the heavy loads and resultant deflections of the system; therefore, an additional compensation was applied to the command value of the plunging depth to achieve full penetration for each specimen [7,8].  
After FSW, the specimens were cut to a width of 25 mm, and the top and bottom surfaces were machined flat in accordance with the ISO 4136 standard. The tensile test was conducted at a head speed of 3 mm/min using a universal testing machine (AG-300 kNX plus, Shimadzu, Kyoto, Japan). The local deformation behavior of the specimen was recorded during the tensile test using the DIC technique (ARAMIS 3D, GOM GmbH, Germany). During the strain measurement, a trigger signal was sent to the camera every second. The transverse cross-section of weldment was polished and etched with 3% Nital solution (3 mL HNO\({}_{3}\) and 100 mL ethanol), and the microstructure was observed using optical microscopy (OM) and scanning electron microscopy (SEM). The Vickers hardness was measured along the central line in the transverse section with an interval of 0.2 mm under a dwell time of 10 s and a load of 1.96 N using a microVickers hardness tester (FM-7E, Future Tech, Kawasaki, Japan).