esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The non-destructive evaluation (NDE) of materials using diverse physical methods is a cornerstone of quality assurance in modern industry, ensuring the safety and reliability of products both during production and in service. Material structures often contain surface or internal defects or undesirable variations in shape and microstructure, which can significantly impact a product's service life or even compromise the integrity of entire systems. While it is unrealistic to eliminate material defects from a physical standpoint, standards and regulations define permissible limits for such imperfections. The unique advantage of NDE lies in its ability to inspect materials without altering their structure or functionality, making it indispensable for maintaining quality and performance [1, 2, 3].  
Electromagnetic non-destructive evaluation (eNDE) is an advanced subset of NDE that leverages the interaction of electromagnetic fields (EMFs) with material structuresto detect defects, measure material properties, and evaluate structural integrity. This approach allows for the identification of micro-cracks, corrosion, microstructural changes, and coating variations without compromising the mechanical properties or functionality of the inspected components. The dynamic development of eNDE is driven by its ability to bridge physics, materials engineering, and computational technologies, offering robust and highly efficient solutions for critical applications in safety-critical industries such as aerospace, power generation, and medical systems [4; 5; 6; 7].  
This paper presents a novel approach to evaluating the hardness of austenitic stainless steel AISI 304 (American Iron and Steel Institute) subjected to cold rolling and various heat treatments. Unlike traditional hardness evaluation methods, this research harnesses the capabilities of anisotropic magneto-resistance-based magnetic field sensors to perform the non-contact mapping of the intrinsic magnetic fields of cold-rolled austenitic steels. These magnetic field profiles serve as the basis for hardness classification, achieved through the implementation of convolutional neural networks (CNNs). The integration of advanced neural network models, such as a modified GoogleNet architecture, enables the highly efficient, accurate, and automated classification of material hardness. Furthermore, this work reveals a novel correlation between heat treatment temperature and magnetic field strength, providing new insights into the sensitivity of magnetic properties to thermal processes. This innovative combination of non-contact magnetic field sensing and machine learning offers a transformative approach to material characterization, paving the way for more efficient and reliable industrial applications.
## 2 Materials and Methods
### 2.2. _Shrinkage Measurement_  
Prismatic specimens with dimensions of 40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\) equipped with stainless steel markers embedded into the ends of specimens were used for shrinkage measurement. The measurement set contained three test specimens. The measurement started immediately after demolding of the specimens at the age of 24 h and continued at regular intervals until the time when the length changes and mass losses were stabilized. The changes in length were measured using the measurement gauge equipped with the digital probe with a resolution of 0.001 mm. Simultaneously, the mass of each specimen was recorded using the scale with a resolution of 0.1 g. The specimens were stored in an air-conditioned laboratory with a temperature of (21 \(\pm\) 2) \({}^{\circ}\)C and RH of (55 \(\pm\) 10)% during the whole time of measurement.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology
### Measured Path Loss  
The measured path loss values can be estimated by knowing the transmitted power, the gain of the transmitting and receiving antennas and the received power at the Rx according to Equation (1) [37].  
\[PL(d)[dB]=P_{t}-P_{r}(d)+G_{t}+G_{r} \tag{1}\]  
where \(P_{t}\) is the transmitted power in dBm, \(P_{r}\) is the received power in dBm and \(G_{t}\) and \(G_{r}\) are the gain of the transmitting and receiving antennas in dBi. The power, \(P_{r}\), will be the one measured with the different measuring instruments used in this work.
# Modeling and Analysis of RFI Impacts on Imaging between Geosynchronous SAR and Low Earth Orbit SAR
## Appendix A  
Let us suppose that \(S_{1}(f)\) and \(S_{2}(f)\) are the frequency domain expression of two chirp signals with time domain amplitude of 1, different pulse width (\(T_{p1}\) and \(T_{p2}\)) and FM rate (\(k_{1}\) and \(k_{2}\)), so they can be written as  
\[S_{1}(f)=1/\sqrt{k_{1}}\text{rect}\big{(}f/k_{1}T_{p1}\big{)}\exp\Big{\{}j\pi f^ {2}/k_{1}\Big{\}} \tag{1}\]  
\[S_{2}(f)=1/\sqrt{k_{2}}\text{rect}\big{(}f/k_{2}T_{p2}\big{)}\exp\Big{\{}j\pi f ^{2}/k_{2}\Big{\}} \tag{2}\]  
Considering \(S_{1}(f)\) as the frequency domain matched filter, matched filtering operates and the result in frequency domain can be written as  
\[S(f)=\frac{\text{rect}\big{(}f/\min\big{(}k_{1}T_{p1},k_{2}T_{p2}\big{)}\big{)} }{\sqrt{k_{1}k_{2}}}\exp\bigg{\{}j\pi\bigg{(}\frac{1}{k_{2}}-\frac{1}{k_{1}} \bigg{)}f^{2}\bigg{\}} \tag{3}\]  
When \(k_{2}\) approaches \(k_{1}\), (3) will degenerate into a simple rectangle function and it can be written as  
\[S(f)=1/k_{2}\text{rect}\big{(}f/k_{2}T_{p2}\big{)} \tag{4}\]  
Then, the amplitude gain for matched filter can be written as  
\[G_{matched}=T_{p2}f_{s} \tag{5}\]  
where \(f_{s}\) is the sampling rate [39].  
Generally, the FM rate of GEO SAR and LEO SAR are quite different. Therefore, when \(k_{1}\
eq k_{2}\), (3) can be written as  
\[S(f)=1/\sqrt{k_{1}k_{2}}\text{rect}\big{(}f/\big{(}k_{r}T_{p}\big{)}\big{)} \exp\Big{\{}j\pi f^{2}/k_{r}\Big{\}} \tag{6}\]  
It's apparent that \(S(f)\) can be seen as the frequency domain expression of a chirp signal \(s(t)\) with FM rate of \(k_{r}\) and pulse width of \(T_{p}\). Then, \(k_{r}\) and \(T_{p}\) can be written as  
\[k_{r}=k_{1}k_{2}/|k_{2}-k_{1}| \tag{7}\]  
\[T_{p}=\min\big{(}k_{1}T_{p1},k_{2}T_{p2}\big{)}/k_{r} \tag{8}\]  
According to the principle of stationary phase (POSP), \(s(t)\) can be written as  
\[s(t)=1/\sqrt{|k_{2}-k_{1}|}\text{rect}\big{(}t/T_{p}\big{)}\exp\Big{\{}-j\pi k _{r}t^{2}\Big{\}} \tag{9}\]
## 5 RT POD Processing and Analysis
### Correlation Analysis of PPP-B2b Reception Ratio and Accuracy Improvement Ratio  
The correlation between \(R_{r}^{N}\) given by Equation (23) and \(\delta\) given by Equation (24) is next analyzed, where \(R_{r}^{N}\) is the ratio of the number of navigation satellites receiving PPP-B2b corrections to the number of navigation satellites observed at a certain epoch. The superscript \(N\) indicates the navigation system. \(N\) = G indicates the GPS whereas \(N\) = C indicates the BDS. The subscript \(r\) represents reception. \(NUM_{B2b}\) is the number of PPP-B2b corrections that the TJU-01 satellite receives at a certain epoch for a certain navigation system whereas \(NUM_{all}\) is the number of the navigation satellites observed by the TJU-01 satellite at the same epoch. \(\delta\) is the percentage improvement, due to PPP-B2b, in the accuracy of the RT POD of the TJU-01 satellite relative to the broadcast ephemeris. \(S_{Broad}\) and \(S_{B2b}\) are the RT POD errors for the TJU-01 satellite when using the broadcast ephemeris and PPP-B2b for navigation system N in the three orthogonal directions at a certain epoch. Minor gross errors in \(\delta\) are removed, and \(\delta\) time series are then fitted and smoothed. The time series of \(R_{r}^{N}\) and \(\delta\) over the course of one day are shown in Figure 15. It is seen that a larger \(\delta\) corresponds to a greater improvement in the PPP-B2b orbit determination accuracy. We have  
\[\delta=\frac{S_{broad}-S_{B2b}}{S_{B2b}} \tag{23}\]  
\[R_{r}^{N}=\frac{NUM_{B2b}}{NUM_{all}} \tag{24}\]  
Figure 15: Correlation analysis of the PPP-B2b reception ratio and accuracy improvement ratio.  
Figure 15 shows that \(R_{r}^{N}\) and \(\delta\) are positively correlated. As the proportion of PPP-B2b corrections received increases, the improvement in the accuracy of the RT POD of the TJU-01 satellite using PPP-B2b increases. However, the correlation between the GPS PPP-B2b reception ratio and accuracy improvement ratio is slightly different from that for the BDS-3. This is mainly due to the different missing observation epochs between the GPS and BDS-3 in the TJU-01 satellite observations and the interpolation of \(R_{r}^{N}\) and \(\delta\) in the different missing, thus causing correlation differences. In addition, for the same reception ratio, different numbers of GPS and BDS-3 satellites are observed by the TJU-01 satellite. As the numbers of GPS and BDS-3 satellites that can receive PPP-B2b are different, there are differences in the improvement in the observation accuracy. Therefore, there are differences between the GPS and BDS-3 in the correlation between the PPP-B2b reception ratio and accuracy improvement ratio.
# An Analysis of Sound Event Detection under Acoustic Degradation Using Multi-Resolution Systems
## 3 Results and Discussion
### Results over DESED Public Evaluation Set  
The results of the seven systems over the original DESED Public evaluation set are presented in Figure 2. The figure represents the \(F_{1}\) scores of each system in groups of bars, one group for each event category and an additional one for the macro average, which represents the global performance.  
In terms of macro \(F_{1}\), the _3 res_ and _5 res_ systems both outperform every single-resolution system. However, this improvement is not applicable to every target class. Whereas most event categories obtain their best performance when using a multiresolution system, other classes reach their maximum \(F_{1}\) score with a single-resolution system; this is the case with Alarm/bell/ringing (\(T_{+}\)), Dishes (\(F_{+}\)), Dog (\(T_{+}\)), and Speech (\(F_{+}\)).
## 3 Graph to Metric Space Transformation  
In this section, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein.  
As discussed in Section 2.3, the nodes of the graph do not lie on a metric space, e.g., edges do not reflect the Euclidean distance between the nodes. The standard Euclidean distance and spherical distance defined over the adjacency or Laplacian matrices above failed to capture similarity information among the nodes of a complex network. On the other hand, the algorithms developed based on the shortest path or Jaccard similarity are computationally inefficient and have less success in terms of standard evaluation criteria (like conductance and modularity).  
In this work, we have tried to develop the notion of similarity among the nodes using some new matrices derived from the adjacency matrix and the degree matrix of the graph. Let \(A\) be the adjacency matrix and \(D\) the degree matrix of the graph \(G=(V,E)\). The Laplacian \(L=D-A\). We have defined two diagonal matrices of the same size \(D(\lambda)\) and \(D(\lambda_{x})\), where \(\lambda\) is a parameter determined from the given graph and can be optimized from the optimization criteria of the problem under consideration. In \(D(\lambda)\), a fixed optimally-determined value is used in the diagonal entries of the matrix \(D\), and in \(D(\lambda_{x})\), a variable value, also optimally determined, is used in the diagonal entries of the matrix \(D\). The similarities are defined on matrices \(L_{1}\) and \(L_{2}\), where \(L_{1}=D(\lambda)+A\) and \(L_{2}=D(\lambda_{x})+A\), respectively, are the spherical similarity among the rows and determined by applying a concave function \(\phi\) over the standard notions of similarities, like the Pearson coefficient (\(\sigma_{PC}\)), the Spacerman coefficient (\(\sigma_{SC}\)) or the cosine similarity (\(\sigma_{CS}\)). \(\phi(\sigma)()\) must be chosen using the chord condition to obtain a metric.
# An Efficient Advantage Distillation Scheme for Bidirectional Secret-Key Agreement
## 3 Bidirectional Secret-Key Agreement
### Proposed Advantage Distillation scheme
#### 3.1.3 SKA from [PERSON] to [PERSON]
\begin{split}\Pr(z^{1}_{ae_{k}}\
eq m^{1}_{ab_{k}})& =\Pr(\epsilon^{0}_{b_{k}}=1)\Pr(\epsilon^{0}_{b_{k}}=0)\Pr( \epsilon^{1}_{a_{k}}=0)+\Pr(\epsilon^{0}_{b_{k}}=0)\Pr(\epsilon^{0}_{b_{k}}=1 )\Pr(\epsilon^{1}_{a_{k}}=0)\\ &\quad+\Pr(\epsilon^{0}_{b_{k}}=0)\Pr(\epsilon^{0}_{b_{k}}=0) \Pr(e^{1}_{a_{k}}=1)+\Pr(\epsilon^{0}_{b_{k}}=1)\Pr(\epsilon^{0}_{b_{k}}=1) \Pr(\epsilon^{1}_{a_{k}}=1)\\ &=\alpha(1-\beta)(1-\beta)+(1-\alpha)\beta(1-\beta)+(1-\alpha)(1- \beta)+\alpha\beta\beta\\ &=\alpha(1-\beta)^{2}+2(1-\alpha)(1-\beta)\beta+\alpha\beta^{2}. \end{split}  \tag{25}\]  
Let \(C_{m}\) and \(C_{w}\), \(p_{m}=2\alpha(1-\alpha)\), and \(p_{w}=\alpha(1-\beta)^{2}+2(1-\alpha)(1-\beta)\beta+\alpha\beta^{2}\) denote the capacity of the main channel and eavesdropper's channel, bit error probability of main channel and eavesdropper's channel, respectively. Assuming \(\alpha=\beta\), the BER of the main channel and the eavesdropper's channel are summarized in Table 2. We can also receive the secret-key capacity \(C_{s}\),  
\[C_{s}=C_{m}-C_{w}=(1-H_{b}(p_{m}))-(1-H_{b}(p_{w})), \tag{26}\]  
where \(H_{b}(p)\) is the binary entropy function. Assuming \(\alpha=\beta\), \(p(m^{1}_{ab_{k}}=1)=p(m^{1}_{ab_{k}}=0)=\frac{1}{2}\). Then, we can obtain  
\[ \begin{split} C_{s}&=(1-p_{m}\log_{2}\frac{1}{p_{ m}}+(1-p_{m})\log_{2}\frac{1}{1-p_{m}})-(1-p_{w}\log_{2}\frac{1}{p_{w}}+(1-p_{w}) \log_{2}\frac{1}{1-p_{w}})\\ &=(2\alpha-2\alpha^{2})\log_{2}(2\alpha-2\alpha^{2})+\frac{1}{2} \log_{2}(1-2\alpha+2\alpha^{2})\\ &-(3\alpha-6\alpha^{2}+4\alpha^{3})\log_{2}(3\alpha-6\alpha^{2}+4 \alpha^{3})-\frac{1}{2}\log_{2}(1-3\alpha+6\alpha^{2}-4\alpha^{3}).\end{split}  \tag{27}\]  
For the varying \(\alpha\) and \(\beta\), the corresponding \(C_{m}\), \(C_{w}\) and \(C_{s}\) are summarized in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\alpha=\beta\) & **0.01** & **0.02** & **0.03** & **0.04** & **0.05** \\ \hline \(C_{m}\) & 0.87 & 0.79 & 0.72 & 0.66 & 0.61 \\ \(C_{w}\) & 0.83 & 0.72 & 0.63 & 0.56 & 0.50 \\ \(C_{s}\) & 0.04 & 0.07 & 0.09 & 0.10 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Channel capacity and secret-key capacity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha=\beta\) & **0.01** & **0.02** & **0.03** & **0.04** & **0.05** \\ \hline \(C_{m}\) & 0.87 & 0.79 & 0.72 & 0.66 & 0.61 \\ \(C_{w}\) & 0.83 & 0.72 & 0.63 & 0.56 & 0.50 \\ \(C_{s}\) & 0.04 & 0.07 & 0.09 & 0.10 &
## 3 Results and Discussion
### Effect of Initial pH
\[4\mathrm{Ca}^{2+}+2\mathrm{Al(OH)}_{4}{}^{-}+2\mathrm{Cl}^{-}+4\mathrm{OH}^{-}+ \mathrm{n}\mathrm{H}_{2}\mathrm{O}\leftrightarrows\mathrm{Ca}_{4}\mathrm{Al}_{ 2}\mathrm{(OH)}_{12}\mathrm{(Cl)}_{2}\mathrm{\cdot nH}_{2}\mathrm{O} \tag{6}\]  
\[\mathrm{Ca}_{4}\mathrm{Al}_{2}\mathrm{(OH)}_{12}\mathrm{(NO}_{3})_{2}\mathrm{ \cdot nH}_{2}\mathrm{O}\leftrightarrows\mathrm{4 Ca}^{2+}+2\mathrm{Al(OH)}_{4}{ }^{-}+2\mathrm{NO}_{3}{}^{-}+4\mathrm{OH}^{-}+\mathrm{nH}_{2}\mathrm{O} \tag{7}\]  
\[4\mathrm{Ca}^{2+}+2\mathrm{Al(OH)}_{4}{}^{-}+2\mathrm{OH}^{-}+4\mathrm{OH}^{-}+ \mathrm{nH}_{2}\mathrm{O}\leftrightarrows\mathrm{Ca}_{4}\mathrm{Al}_{2}\mathrm{ (OH)}_{12}\mathrm{(OH)}_{2}\mathrm{\cdot nH}_{2}\mathrm{O} \tag{8}\]  
Therefore, the adsorption of some inorganic anions on hydrocalumite-like LDHs was a dissolution-precipitation process. [PERSON] et al. [38] found that the removal mechanism of phosphate by hydrocalumite involved the free calcium and aluminum ions released due to dissolution and then precipitated with phosphate to form hydroxyapatite. However, the exchange reaction of interlay anions was the main adsorption mechanism of chromate on hydrocalumite. In this work, the phase variation of the products was not pH-dependent and no calcium hydroxide phase observed in the XRD patterns (Figure 7) indicating that the removal of chloride ion was the result of anion exchange reaction between NO\({}_{3}\)- and Cl\({}^{-}\) rather than the precipitation of chloride ion with calcium ion released by the dissolution of CAL. Moreover, the enhancement of chloride uptake with increasing pH value may be related to the inhibition of CAL dissolution at a higher pH [38]. Figure 6: Chloride uptake of CAL-60 in NaCl solution with different pH (temperature = 25 \({}^{\circ}\)C; CAL/NaCl solution = 10 g/L; initial chloride ion concentration was set as 80 mmol/L).
# Smart Mobility and Aspects of Vehicle-to-Infrastructure: A Data Viewpoint
## 7 Conclusions  
This paper provides a data viewpoint on Smart Mobility, specifically V2I communication. The beginning of this paper is focused on data production of vehicles sensors, which are implemented in currently used vehicles. Described database solutions are not sufficient to handle the mentioned estimations of data production in real time. In the following part of this paper Smart Mobility concepts and their current goals in a near future are discussed. Then the focus is shifted to V2I communication, its possible communication technologies and the categorization of data that will be sent within V2I communication. Then, HD maps are considered as one of the key technologies for autonomous vehicles to receive information about road infrastructure and current traffic situations as they provide the precise location of the vehicle.  
In the previous section were discussed the main concerns about the data aspects of V2I communication. Covering and satisfying all the needs of reliable, secure and fast communication between V2I components is a key role in achieving the goals of the Smart Mobility concept. Reaching the milestones defined in the Smart Mobility concept is a complex matter. Individual aspects of the data viewpoint of V2I communication present several challenges for the automotive industry as well as the professional community.  
Conceptualization, M.M. and K.P.; methodology, M.M. and K.P.; validation, B.B. and K.Z.; formal analysis, M.M. and K.P.; investigation, B.B.; resources, K.Z.; writing--original draft preparation, M.M., B.B. and K.P.; writing--review and editing, M.M., B.B. and K.P.; visualization, K.P.; project administration, B.B; funding acquisition, K.Z. All authors have read and agreed to the published version of the manuscript.  
This publication was realized with support of Operational Program Integrated Infrastructure 2014-2020 of the project: Innovative Solutions for Propulsion, Power and Safety Components of Transport Vehicles, code ITMS 313011V334, co-financed by the European Regional Development Fund.
###### Abstract  
Due to the barely resonant earned system used in the transmission line, it is more challenging to identify faults at a 66 kV voltage level because of insufficient fault identification techniques. In this paper, a 66 kV transmission line fault identification method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) is proposed to address the difficulties in extracting and detecting characteristic parameters. The maximum matrix of the dbN wavelet was used to determine its decomposition scale and construct the fault characteristic matrix based on the energy values of frequency bands. The decomposition scale of the dbN wavelet was determined by the modulus maximum matrix to ensure the integrity of fault information. The fault feature matrix was then constructed based on the energy values of frequency bands and the fault feature was accurately extracted. In this research, aiming at the problems such as slow convergence speed and a tendency to fall into local minima, the WNN algorithm is enhanced with the IPSO algorithm. This significantly increased the convergence speed of the identification model and its ability to discover the global optimal solution. The simulation results demonstrate that this method can effectively and accurately identify the fault type with high identification accuracy, quick identification, and robust adaptability. Under challenging working conditions, it is capable of accurately identifying the fault type of 66 kV transmission lines.  
66 kV transmission line; resonant grounding system; wavelet neural network; characteristic matrix
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Load Disaggregation Algorithm (LDA)  
Load disaggregation and appliance recognition are some of the key aspects in Non-Intrusive Load Monitoring (NILM). The main purpose of the NILM approach is to better understand the power usage in the home. It also have the potential to help the different households to more optimal power savings. This is done by informing the residents about the power consumption of individual appliances. It is shown that informing a household about its usages pattern can lead to significant savings [41]. This makes the basis for load disaggregation, where appliance specific load is deduced based on the main meter load. This enables the user to know what uses the energy and when.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
## 2 Methodology
### Objective Function  
In our proposed prediction model, at time step \(t\) there are observed values and predicted values, denoted as \(O^{r}_{t}\) and \(P^{r}_{t}\), respectively. The observed values are considered as the true values. The optimization goal is to make the predicted values as close as possible to the true values by back propagation. The loss function can be given as:  
\[\mathbf{RMSE}=\sqrt{\frac{\sum_{i}^{N}(O^{r}_{i}-P^{r}_{i})^{2}}{N}}, \tag{12}\]  
where \(i\) and \(N\) are the subscript of the time step and the prediction duration, respectively. The smaller the value of RMSE is, the better our model. Dropout [42] is used to avoid over-fitting during training.