esa-sceva/satcom-chunk-collection huggingface dataset:
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 8 Performance Evaluation of the Proposed FL-IDS
### Federated Learning Frameworks  
To develop the federated learning solution, two frameworks were considered: Flower [71] and TensorFlow Federated (TFF) [72]. While Flower is a viable option for testing and developing FL solutions, TFF provides a more comprehensive representation of the FL setting. This is due to its extensive features and capabilities, which make it a more suitable choice for complex FL scenarios. Furthermore, TFF provides ready-to-use, secure aggregators, which are indispensable for the implementation of robust solutions and the enhancement of the security and reliability of federated learning systems.  
TFF is distinguished by its approach to training monitoring, which is exclusively allowed on the server side. This setup mirrors a more stringent yet realistic scenario for FL, where client-side data privacy is paramount.  
TFF provides a simple method for integrating differential privacy, ensuring that individual data points remain confidential during the training process. Additionally, the framework supports secure aggregators in various formats, which can be easily incorporated into the FT process. These secure aggregators play a crucial role in maintaining the privacy and security of the data by aggregating updates from multiple clients without exposing individual data points.  
Given these features, the experiments were conducted using TFF. The reported metrics were extracted from the server, ensuring a rigorous and realistic evaluation of the FL process. In fact, following the FT, the server performs a centralized validation step over its validation data.
## 4 Results and Discussion
### Experimental Determination of the Capillary Action
#### 4.1.3 Copper Powder Sintering  
In this case, tests were performed for different sintering samples with copper powder as the porous medium and methanol as the working fluid. As the objective is to determine the effect of pore size on the porous media capillary action curve [43], four samples of sintered copper powder, each with different pore sizes, were built. The complete characterization of the four samples is shown in Table 5, and the results of liquid height are plotted in Figure 8a.  
These results confirmed that the lower the porous size, the higher the liquid height, so a larger pumping capacity-capillary action can be achieved, evident from the \(\alpha\) values provided in Table 5.  
The maximum liquid height for the 30\(\upmu\)m pore size sample was 101.7 mm, 102% higher than the liquid height for the 130 \(\upmu\)m pore size sample, 73% higher than for the 95 \(\upmu\)m sample, and 9.5% higher than for the 60 \(\upmu\)m sample. Flow velocity in the 30 \(\upmu\)m pore size sample was 11% faster than in the 60 \(\upmu\)m pore size sample, while it was 200% higher than in the 130 \(\upmu\)m pore size sample. In this case, the contribution to the ascension velocity of the second exponential was negligible, as the time constant (\(t_{2}\)) was very small. In all cases, the variation ratio was identical, as \(t_{1}\) was the same in all of them (Figure 8b).  
Pumping capacity varied considerably, with a 77% pore size reduction resulting in a 200% capillary capacity increase. All samples also met the required advancing speed (\(\alpha>0.031\) mm/s).  
Figure 7: Capillary pumping curve for stainless-steel mesh for the three different pitches; (**a**) liquid height and (**b**) advance speed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**D (\(\upmu\)m)** & \(\varepsilon\) & **A (mm/s)** & \(\mathbf{A_{1}}\) & \(\mathbf{t_{1}}\) & \(\mathbf{A_{2}}\) & \(\mathbf{t_{2}}\) \\ \hline
30 & 0.573 & 0.6 & 106.1836 & 149.382 & 15.533 & 0.00626 \\ \hline
60 & 0.573 & 0.54 & 96.93418 & 149.39 & 14.18 & 0.00626 \\ \hline
95 & 0.573 & 0.27 & 61.30656 & 149.39 & 8.96829 & 0.00626 \\ \hline
130 & 0.573 & 0.2 & 52.43766 & 149.39 & 7.67 & 0.00626 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of sintered powder copper samples and capillary action (methanol).  
Figure 8: Pumping or capillary action curve for sintered copper powder samples of different pore size; (**a**) liquid height and (**b**) advance speed.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 4 Results and Discussion
### Single Channel Spectral Efficiency
For the best relative improvement scenario for NN2 (\(f_{Dm}\) = 75 Hz, SNR = 12 dB), considering a frequency range of 40 MHz, the NN2 contribution amounts to \((0.68\,-\,0.36)\,\mathrm{b/s/Hz}\,\times\,40\) MHz = 12.8 Mb/s.
## 3 Results and Discussion
### Temperature Field
#### 3.1.2 Temperature Field of Waveguide Rods with Different Sizes  
Next, this article will further investigate the effect of the specific dimensions of the waveguide rod made of SS316L on its cooling effect. Carry out parameter scanning on five sets of width and thickness values, using the controlled variable method. The results of the parametric scans are shown in Figure 6. Figure 6a shows the temperature profiles of the waveguide rod at different width values, and Figure 6b shows the temperature profiles of the waveguide rod at different thickness values. The results show that the width of the waveguide rod is not the main factor affecting its cooling effect, while the thickness significantly affects its temperature buffering effect. Specifically, when the thickness of the waveguide rod reduces, the temperature drops faster. The laboratory used a waveguide rod with a width of 20 mm and a thickness of 1 mm, after considering the difficulty of factory processing.  
Figure 5: Temperature curves for SS316L, aluminum, and copper waveguide rods.  
Figure 6: Temperature buffer curves for waveguide rods of different sizes. (**a**) Temperature buffer curves for waveguide rods of different widths (16 mm, 18 mm, 20 mm, 22 mm, and 24 mm); (**b**) Temperature buffer curves for waveguide rods of different thickness (0.5 mm, 1 mm, 3 mm, 5 mm, and 7 mm).  
In order to verify the simulation results, a test was conducted using an electric heating device, as shown in Figure 7. The thickness of the specimen was 50 mm, and the waveguide rod was 300 mm \(\times\) 20 mm \(\times\) 1 mm. The specimen was heated to be in a stable state of 500 deg C, and then an infrared temperature gun was applied to measure the temperature along the waveguide rod.  
Temperature measurements were taken at 30 mm intervals on the waveguide rod using an infrared temperature gun. Figure 8 shows the comparison between the experimental results and the simulation results. Both the experimental and simulation results show the same trend of temperature decrease, and the temperature at the top of the waveguide rod is lower than 34 deg C. The above comparison shows the reliability of the thermal simulation.
## 2 Materials and Methodology
### Calculation of Fractal Dimensions Based on the Transformation of Pore Radius of NMR  
According to basic principles of nuclear magnetic detection [15], the surface relaxation can be expressed as:  
\[\frac{1}{\mathrm{T}_{2}}=\ \rho_{2}(\frac{\mathrm{S}}{\mathrm{V}})_{\mathrm{ pore}} \tag{2}\]  
Internal pores of the rock are often simplified as spherical pores in NMR testing, and Formula (1) can be expressed as:  
\[\frac{1}{\mathrm{T}_{2}}=\rho_{2}\frac{\mathrm{F_{s}}}{\mathrm{r_{c}}} \tag{3}\]  
Figure 1: Intuitive presentation of experimental steps. In Equations (2) and (3), \(\left(\frac{\mathrm{S}}{\mathrm{V}}\right)_{\mathrm{pore}}\) is the ratio of pore surface area to volume, Fs is the pore shape factor (for spherical pore, Fs = 3), rc is the pore radius. \(\rho_{2}\) represents the surface relaxation strength of T2, which mainly depends on the mineral composition of the rock and properties of the pore surface. Based on literature [38] test results of rock surface relaxation strength with high silicate mineral content are selected, and the value of \(\rho_{2}\) is 0.0045, C is Fs \(\times\rho_{2}\), namely, C = 0.0135. The value of C is also consistent with the value of C (0.01-0.015 um/ms) of most sandstones in China [15]. Therefore, Formula (3) can be expressed as:  
\[\mathrm{r_{c}=CT_{2}=0.0135T_{2}} \tag{4}\]  
According to Formula (4), the pore radius of rock is a one-to-one correspondence with the value of T2, and T2 distribution of measured pores represents the pore size distribution of rock. Many scholars have discussed and analyzed the classification of pore structure in rock and put forward various methods. Referring to research results of the classification of sandstone pore structure [9; 39], this study divides internal pores of the material into three types: micropore (pore radius \(<\) 0.1 um), mesopore (0.1 um \(<\) pore radius \(<\) 1 um), macroporous (pore radius \(>\) 1 um), the specific division is shown in Figure 2.  
Furthermore, T2 spectral area is an important parameter reflecting the number of pores with different radius, it can quantitatively describe the distribution of pores in the total pores of different structural types within the rock. The ratio  
Research results of fractal theory [40; 41; 42; 43; 44] show that the internal pore structure of porous materials such as rock and concrete has obvious fractal characteristics, and the ratio of pore cumulative volume (pore radius \(<\) r) to pore volume (S\({}_{\rm v}\)) and T\({}_{2}\) accords with the following expressions:  
\[\ln(\rm{S_{v}})=(3-D)[\ln(\rm{T_{2c}})-\ln(\rm{T_{max}})] \tag{6}\]  
Formula (6) shows that if the pore size distribution of the material conforms to the fractal geometric characteristics, there is a linear correlation between \(\ln\) (S\({}_{\rm v}\)) and \(\ln\) (T\({}_{\rm max}\)). Accordingly, the fractal dimension of pore size distribution can be calculated by linear regression analysis, and results are shown in Table 5.
# Orthogonal Subblock Division Multiple Access for OFDM-IM-Based Multi-User VLC Systems
## 1 Introduction
Moreover, differing from classical OFDM which can enable OFDMA for MU-VLC by allocating different users with different subcarriers, subcarrier allocation is not suitable for OFDM-IM as both OFDM-SM-IM and OFDM-DM-IM are performed within a complete subblock. To the best of our knowledge, the efficient application of OFDM-IM techniques in practical bandlimited MU-VLC systems has not yet been considered in the literature. In this paper, we propose an orthogonal subblock division multiple access (OSDMA) scheme for OFDM-IM-based MU-VLC systems, where both OFDM-SM-IM and OFDM-DM-IM are considered. Moreover, DFT spreading and subcarrier interleaving are employed to address the LED nonlinearity and the low-pass frequency response, respectively. Simulations and experiments have been conducted to verify the feasibility and superiority of the proposed scheme.
## 3 Experiment and Result Analysis  
### Phase Analysis  
The XRD patterns of the samples formed by SLM under different laser powers are shown in Figure 3. Figure 3a shows the scanning results over a large angle range. It can be seen from the figure that the Inconel 718 alloy formed by SLM has a face-centered cubic austenite phase comprising Ni, Cr, and Fe, and a precipitation strengthening \(\gamma^{\prime}\) phase consisting of Ni\({}_{3}\) (Al, Ti). The diffraction peak intensity of the \(\gamma^{\prime}\) phase is proportional to its volume fraction, and any peak broadening is primarily the result of an increased dislocation density [24]. Since the \(\gamma^{\prime}\) phase and the matrix \(\gamma\) coincided within a large angle range, further XRD testing was conducted on \(\gamma\) within a small angle range of 20 = 40-46\({}^{\circ}\), and the results are shown in Figure 3b. The values of the main diffraction angle, diffraction intensity, and half-peak width are shown in Table 3. It can be observed that the peak intensity gradually weakened as the laser power gradually increased from 285 W to 335 W. Such feature indicates that with the increase in laser power, auxiliary strengthening phases such as \(\gamma^{\prime}\) precipitated in the matrix. From Figure 3b and Table 3, it can be seen that as the laser power increased from 310 W to 360 W, the diffraction angle at the diffraction peak gradually decreased, and the diffraction peak gradually shifted to the left. In accordance with the Bragg diffraction equation, the spacing between crystal planes increased. No other phases were detected via XRD.  
In the Bragg diffraction equation Formula (1), d is the crystal plane spacing; \(\theta\) is the diffraction angle; n is the diffraction order; and \(\lambda\) is the wavelength of the X-rays. The decrease in 20 indicates an increase in the spacing between crystal planes during angular reduction. This suggests that during the SLM forming process, lattice distortion occurred due to the thermal stress generated by the rapid laser heating and cooling. Thermal accumulation in the molten pool is equivalent to preheating the produced parts and conducting an annealing heat treatment, which can reduce the residual stress and change the crystal lattice parameters [25].  
\[2 dsin\theta=n\lambda \tag{1}\]  
In accordance with Table 3, as the laser power increased from 285 W to 360 W, the half peak width first increased and then decreased. In accordance with [PERSON]'s Formula (2), the value of Dc could be obtained as the laser power increased from 285 W to 360 W, as shown in Table 3. Dc is the average thickness of the grain perpendicular to the grain planedirection; \(\lambda\) is the wavelength of the X-rays; B is the half peak width of the X-ray diffraction peak; and \(\theta\) is the X-ray diffraction angle. The grain size decreased with the increase in half-peak width. It means that under the experimental conditions, the grain size underwent a fluctuation process, first decreasing and then increasing.  
\[D_{c}=\frac{0.9\lambda}{Bcos\theta} \tag{2}\]
# An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs
## 2 Problem Statement and Our Contribution  
Multi-UAV enabled WSNs prove to be of considerable advantage, but, at the same time, require careful selection of the metrics. A random choice of way-points or a scheme that restricts UAV to a fixed particular topology leads to poor coverage and node starvation. Clustering techniques are helpful to prevent long-range broadcasting, collisions, and multi-hopping, but a large number of clusters in a geographical region prevents every node from getting an equal opportunity as well as increasing the waiting time. Strategically important locations can range from dense to scarce based on the number of active nodes. Developing attraction metrics from node density and message relay timings can help in topographic UAV movements and help prevent loss of important information.  
The proposed system model incorporates an easy and efficient UAV movement technique that helps increase the coverage as well as provides reliable data dissemination. An efficient scheme for clustering and head selection that provides every node with an opportunity to transmit as well prevent the overhead communication between the cluster head and the node whenever UAV is in the range is also presented. The stand-alone regions/not in range of UAV, are also provided with a mechanism to transmit whenever they have data to send. Based on existing issues and the above-mentioned shortcomings, the proposed framework provides the following contributions:  
1. A simple, easy to implement mobility model that divides the overall topology into densely and scarcely populated regions. This division is on the basis of average transmission densities of the regions. These densely and scarcely populated regions, in turn, serve as the basis for UAV way-point selection.
2. An implicit self-clustering scheme for data accumulation whenever UAV is not in range. Cluster head swapping mechanism, which provides every node with a direct UAV link in order to achieve increased transfer rates. The implicit clustering also serves as the basis for UAV way-point selection.
3. A modified version of [PERSON]'s Single Source Shortest Path algorithm where edge weight is calculated not on the basis of region density but on the average transmission density (attraction factor), providing preference to the strategically important locations.
4. An easy mechanism that facilitates data transfer for the nodes that don't fall along the mobility path of the UAV. These scarce nodes can send data transmissions either towards the base station or the nearest cluster head according to an easy comparison.  
The major advantage of the proposed approach is in its simplicity as it doesn't require any special configurations. It employs the existing hardware, software and available specifications in a more effective and efficient manner.
## 5 Experiment
### Experimental Results Analysis  
#### 5.2.1 Analysis of Computational Complexity  
According to Algorithm 1, the main computational load is singular value decomposition operation in updating matrix \(S\) and \(L\). As the size of matrix \(D\) is \(d\times K\), the computational complexity is reduced from \(\left(dK^{2}\right)\) to \(\left(drK\right)\) by the low-rank constraint, where \(r\) denotes the rank of matrix. In our experiments, \(d=25\), \(K=100\), so the computational complexity is low.
# Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation
## 2 Depth Image super-resolution Algorithm  
Super-resolution of depth images requires image enlargement by interpolation of images. The main theoretical basis of such methods is that there is a purely sampling relationship between low-resolution images and high-resolution images. In the up-sampling process, only the pixel values of the low-resolution image need to be fitted to obtain a continuous image, and then sampling can be performed to obtain the target-resolution image. This method benefits from conceptual simplicity and low computational difficulty and is widely used for image super-resolution in industry. However, due to theoretical limitations, the super-resolution effect is not satisfactory when the images contain noise or blur, i.e., when the relationship between the high- and low-resolution images is not purely a sampling relationship.
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### Overall Architecture and Measurement Setup
#### 3.4.1 Setup 1: Using FRER for Redundant Transmissions on the Same 5G Mid-Band System  
In production use cases, the use of a 5G system on an industrial shop floor, especially operating in mid-band (sub 6 GHz) frequencies, is becoming common. In Setup 1, as shown in Figure 5, we investigate the use of FRER when packets are replicated on multiple UEs belonging to the same network. As shown in Figure 4, data are transmitted via a TSN network integrated with the 5G system towards the receiver end. Please note that in our evaluation, we consider the sender and receiver endpoints on a PC with emulated data transmissions from the Milling Machine use case as described in Section 2. The data arrive from the sender to Switch 1 and is replicated and sent via Ports 1 and 2, sent via two different 5G-end devices to the 5G mid-band system using one core, and finally to Switch 2, which forwards only the first copy of the packet and discards any replicated copies of the packet. The timestamps of the data are measured after the replication and before the elimination to evaluate the effects of an increase in reliability for the 5G transmissions. The wireless propagation causes occasional link outages, which are rectified by the hybrid automatic repeat request (HARQ) retransmission scheme and via radio link control (RLC) retransmission mechanism in the 5G protocol stack [25]. The error control mechanisms mentioned above induce extra delays. The likelihood that errors are induced at the same time on multiple independent links using FRER is significantly reduced; thereby, an increase in reliability of the 5G transmissions is observed. Please note that the setup described above shows only the uplink transmissions. The same setup has also been used for downlink transmissions.  
Figure 5: Setup 1: FRER setup for redundant transmissions in the same 5G mid-band system.
# Reconstruction of Isolated Moving Objects by Motion-Induced Phase Shift Based on PSP
## 2 The Principle of the Traditional PSP  
Assuming that N-step PSP is employed, the captured fringe patterns of reference plane and object can be described as [18]:  
\[S_{n}(x,y)=a+b\cos(\phi(x,y)+2\pi(n-1)/N) \tag{1}\]  
\[I_{n}(x,y)=a+b\cos(\phi(x,y)+\varphi(x,y)+2\pi(n-1)/N) \tag{2}\]  
where \(n=1\), \(2\), \(3\), \(\cdots\), \(N\); \(S_{n}(x,y)\) and \(I_{n}(x,y)\) are the intensity values for the \(n\)th fringe pattern on the reference plane and object, respectively; \(a\) is the ambient light and is the fringe pattern intensity amplitude; \(\phi(x,y)\) is the phase distribution of the reference plane; \(\varphi(x,y)\) is the phase difference introduced by the object shape.  
The parameters \(a\), \(b\), \(\phi(x,y)\) and \(\varphi(x,y)\) are unknown; \(S_{n}(x,y)\) and \(I_{n}(x,y)\) are known. Therefore, when the number of the fringe pattern is more than three, the phase value of the reference plane and object can be obtained by:  
\[\phi(x,y)=\arctan\frac{-\sum\limits_{n=1}^{N}S_{n}(x,y)\sin 2\pi(n-1)/N}{\sum \limits_{n=1}^{N}S_{n}(x,y)\cos 2\pi(n-1)/N} \tag{3}\]  
\[\phi(x,y)+\varphi(x,y)=\arctan\frac{-\sum\limits_{n=1}^{N}I_{n}(x,y)\sin 2\pi(n -1)/N}{\sum\limits_{n=1}^{N}I_{n}(x,y)\cos 2\pi(n-1)/N} \tag{4}\]  
In Equations (3) and (4), the phase value is wrapped into \(-\pi\) to \(\pi\) because of the operation \(\arctan(\cdot)\). The wrapped phase value has ambiguity and phase jump in different fringe periods. Phase unwrapping [19; 20] is applied to obtain the unwrapped phase map with a monotonous value. Finally, the object is reconstructed based on the unwrapped phase information and the calibration parameters of the system.  
From Equations (1) and (2) we can see that the traditional PSP introduces the phase shift by projecting multiple phase-shifting fringe patterns. A constant phase shift is set among the fringe patterns. In Equations (3) and (4), the intensity values for the same point are used directly for the phase value calculation. The phase shift is also exactly set in advance. Therefore, when the object with motion is reconstructed, position mismatch and unknown phase shift will be introduced.
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 3 Results and Discussions
### Method Validation  
Method validation was processed in the optimal conditions in order to investigate the applicability of the developed method. Linearity with the matrix-matched calibration curve for 2-methyl-3-heptanone was investigated and proved very well in the range of 0.33 to 33.33 \(\upmu\)g/g with the linear regression correlation coefficient higher than 0.999, which is good for the quantitative analysis. LOD and LOQ were 0.01 and 0.03 \(\upmu\)g/g, respectively. The precision was evaluated through spiking 2-methyl-3-heptanone (5 \(\upmu\)L 50 \(\upmu\)g/mL) in mutton. The RSD of six replications was less than 15%, indicating that the developed method was capable to be applied in the routine analysis.