esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Experiments and Discussion
#### 5.4.5 Embedded Deployed Results  
By utilizing our lightweight network model and inference pipeline, the Jetson TX1 intelligent car can effectively capture images from its camera, conduct real-time recognition of meter readings, and demonstrate robust anti-interference capabilities. After executing the inference process, the resulting output of the intelligent car is presented in Figure 19.  
Moreover, we conducted real-world testing on a smart car to compare the performance of DAMP-YOLO and YOLOv8 under varying lighting conditions. The results, presented in Figure 20, demonstrate that YOLOv8n fails to detect objects in scenarios with high or low brightness, whereas DAMP-YOLO maintains consistent and accurate detection capabilities. This further validates the robustness and reliability of DAMP-YOLO in real-world applications.
## 5 Simulation Results
### SNR Analysis
When the SOA placement is performed according to the optimal \(h\), the SNR is improved by 4.26 dB, 8.91 dB, 13.69 dB and 18.65 dB when the network size is \(8\times 8\), \(16\times 16\), \(24\times 24\) and \(32\times 32\), respectively. Therefore, the degradation of the worst-case SNR in the large-scale HONoC can be mitigated through the placement of SOAs. Figure 14 shows the worst-case SNR reduction of the non-SOA region surrounded by the SOA-placed links, in accordance with the SOA gain. As previously analysed in Section 4.2.2, there is a path in which the SNR decreases due to the several amplified crosstalk noises, compared to the equivalent optical signal power if the SOAs are not placed on all links (\(h\
eq 0\)). When \(h\) becomes smaller, the SNR of the longest path traveling along the boundary decreases more as the SOA gain increases. This result denotes that the shorter path length is more affected by the amplified crosstalk noise. However, as marked X in Figure 14, the appropriate SOA gain for the worst-case SNR on the whole network differs depending on \(h\). The SNR losses at the mark X are 2.06 dB, 3.96 dB and 2.26 dB when \(h=2\), \(h=4\) and \(h=8\), respectively. The SNR reduction is the  
Figure 14: SNR reduction in the region surrounded by the SOA-placed links. X indicates the appropriate SOA gain for the worst-case SNR. Figure 13: SNR improvement in an average basis for the 1 st, 2 nd and 3 rd longest paths of the SOA-enabled HONoC according to different \(h\) and network sizes. smallest when \(h=2\); however, there are more paths in which such SNR degradation occurs in the network because the SOA spacing is narrower than in other cases. In other words, it means that SNR degradation may occur when SOA spacing is narrow but it does not occur in the same path when SOA spacing is wide. Therefore, \(h=8\) can be the best case considering both the magnitude of SNR reduction and the probability that these SNR-reduced paths will occur.
###### Abstract  
Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.  
5G; genetic algorithm; indoor environment; machine learning; network planning; path loss; modeling +
Footnote †: journal: Medical Imaging
## 3 What Is SDN?
### Working Principle
#### 3.1.1 OpenFlow  
In 2008, the OpenFlow protocol was created by researchers from several universities, including Stanford, MIT, and Princeton. The goal was to allow researchers to try out new network protocols on their campus networks. Later, SDN and OpenFlow gained prevalent academic and industry adoption. Several commercial network switch vendors now integrate the OpenFlow API into their products. Major tech players like Microsoft, Facebook, Verizon, Google, Yahoo, and Deutsche Telekom have come together in funding the Open Networking Foundation (ONF) to promote SDN through open standards development [65].  
Figure 4: OpenFlow to P4-based evolution, where (**a**) shows traditional networking, (**b**) represents the first generation of SDN where the data plane and control plane are separated and the control plane is programmable, and (**c**) shows the second generation of SDN with P4.  
In 2012, Google started using SDN to connect its data centers worldwide because it offered significant flexibility for managing traffic between data centers.  
Figure 5 shows the specification of OpenFlow; from 2009, many specifications belonged to OpenFlow. From 2009, it was the very first one to be v1.0 using a single flow table and IPv4. If we look forward, v1.1 has a group table, v1.2 is the IPv6, v1.3 is a meter table (apart from other things), v1.4 is the synchronized table, and, finally, v1.5 is the egress table [63].
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 3 Baseband Modulator Architecture
### Limitations and Scope  
Achieving flexibility in hardware designs for digital signal processing has a cost in terms of power consumption or circuit area [37]. The presented FPGA-based design is not suitable for user terminals in wireless communications, where low-power and low-cost, high-volume production constraints make Application-Specific Integrated Circuits (ASICs) a better solution. Regarding base stations, the combination of General Purpose Processors (GPPs) and Graphics Processing Units (GPUs) yields high flexibility, but requires a high power consumption. Moreover, the performance loss related to GPP-GPU data transfers may be prohibitive in real-time applications [38]. In turn, FPGA's parallelism and programmability are very convenient for flexible and upgradeable base stations with better processing throughputs than digital signal processors and better power efficiency than GPP- and GPU-based solutions [39].  
The high versatility of the proposed architecture is advantageous in different 5G scenarios. For instance, this baseband infrastructure could be used in a multi-service/waveform base station where primary transmissions are 4G OFDM-based (implemented on RP1), while opportunistic secondary transmissions can be based on OFDM, FBMC, or UFMC (implemented on RP2 or RP3). The three independent baseband modulators could also work as part of a multidimensional PHY for CA-based primary communications, where each RP would implement waveform generation for each component carrier to aggregate. Another application scenario to exploit this versatility is centralized baseband processing in C-RANs: the RPs could be dynamically reconfigured to support different radio access technologies and modes operated by RRHs. The multidimensional nature of our baseband infrastructure is a considerable advantage compared to the monolithic approaches from [16; 17; 19].  
Although we have defined only two modes of operation for three waveforms, the functionality can be easily expanded. When implementing the system using the Xilinx Vivado EDA tool, a static-only design checkpoint with locked placement and routing was saved. To upgrade the system with new functionalities, it is only necessary to load new RP configuration designs into the static design check point and generate partial bitstreams for those configurations. The main limitation of the creation of new RP configurations is the RP size and available resources. This design reusability makes the system adaptable and reduces the upgrade design time.
## 3 Results and Discussion
### Negative Refraction and Wave Trapping  
A first illustrative example is shown in Figure 5 using several panels to demonstrate and explain negative refraction and wave trapping phenomena. Isofrequency contour (IFC) diagrams are often also referred to as slowness contour diagrams in the literature. However, in this article, the contours are represented in terms of dimensionless wave-number components rather than slowness components (whose dimensions are s/m). In  
Figure 4: (**a**) Meshed domain in the vicinity of a regular Swiss cross-shaped hole. (**b**) Mesh in the vicinity of rough cross-shaped holes defined by the roughness parameters \(\delta=5\) μm and \(\lambda_{0}=5\) μm.  
Figures 5a,b, the IFCs of both the interior and exterior media are shown. The black solid lines in Figure 5a represent the IFC of the microstructured medium, whereas the blue dashed lines represent the IFC of plain-strain aluminium (see Equation (2)).  
The IFC of the Swiss crosslike hole system is repeated periodically outside of the first Brillouin zone (see the blue square in Figure 5a). In addition to the IFC, which gives information about the phase velocity of the interior and exterior media, we also show the group velocity in the microstructured medium (see the grey arrows), estimated by  
Figure 5: Scattering of a shear-dominated Gaussian beam at centre frequency \(f=2.985\) MHz and an angle of incidence \(\alpha=35^{\circ}\). Panels (**a**,**b**) show the IFC diagram (and its magnification); the red asterisk represents the intersection point of the Gaussian beam propagation direction and the isofrequency contours. Panels (**c**–**f**) show the magnitude of the displacement field at different times in the FE simulation, with the total time being \(T=120\)\(\mu\)s.  
evaluating the numerical gradient (see Equation (6)), associated with the Bloch-Floquet dispersion surfaces.  
Figure 5b is an enlargement of Figure 5a, by which the group velocity distribution can be observed more clearly. It is shown that close to the coincidence point, i.e., at \(\alpha=35^{\circ}\) and marked with a red asterisk, the group velocity is negative and points in the opposite \(x\)-direction, with respect to the group velocity of the shear-dominated incident wave. This situation results in negative refraction at the first interface (i.e., the top row of Swiss-cross holes), which can be seen in the transient FE instantaneous displacement in Figure 5c,d.  
Figure 5e shows a time-frame at which the beam emerges from the slab, with approximately the same angle as the angle of incidence but shifted. At approximately three quarters of the transient simulation duration, Figure 5f shows transient localisation of waves, with an accumulation of the displacement field on the left side of the slab. This is consistent with the general group velocity landscape in Figure 5b, where the favoured direction of the group velocity, in the vicinity of the coincident point, is towards the left.
## 3 Results and Discussion  
### Effective Atomic Number  
Effective atomic number \(\left(Z_{eff}\right)\), the key in seeking accurate estimation of absorbed dose, recognizes that the different photon interaction processes provide different weights to the atomic numbers, different in the different energy regions in which the particular processes dominate. With multi-element materials clearly not being represented by a single atomic number, \(Z_{eff}\) is conventionally considered to be a \begin{table}
\begin{tabular}{c c c} \hline
**Brand Name** & **Country of Origin** & **Average Mass (g)** \\ \hline Proton & Malaysia & 0.050 \(\pm\) 0.002 \\ Toyota & Japan & 0.040 \(\pm\) 0.003 \\ Honda & Japan & 0.040 \(\pm\) 0.002 \\ \hline \end{tabular}
\end{table} 
Table 1: Details of windshield glass samples used in this study. suitable parameter in describing multi-element materials within particular photon energy regions [16]. The well-established Mayneord equation [17] is typically used to calculate \(Z_{eff}\) as follows:  
\[Z_{eff}=\left(a_{1}Z_{1}^{m}+a_{2}Z_{2}^{m}+\cdots+a_{n}Z_{n}^{m}\right)^{\frac {1}{n}} \tag{1}\]  
with \(a_{1}\), \(a_{2}\), \(a_{3}\)... \(a_{n}\) the fractional weight contributions of each element in the material, obtained from the ratio of number of electrons per gram of each element to the total number of electrons within the mixture, \(a_{i}=\frac{n_{e}}{n_{e}t}\), while \(m=2.94\), a value typically adopted for photon practical purposes [18], reflective of the dominance of the photoelectric effect in multiple scattering events, the latter being expected in bulk media, humans included. Based on the weight fraction values obtained in use of Energy Dispersive X-ray (EDX) spectroscopy and listed in Table 2, the number of electrons per gram of material can be calculated using the following Equation (2) [19];  
\[Ne=\frac{N_{A}Z}{A_{W}}\ \mathrm{W_{i}} \tag{2}\]  
with \(N_{A}\) the Avogadro number (\(6.02\times 10^{23}\)), \(A_{w}\) atomic weight, \(\mathrm{W_{i}}\) fractional weight and \(Z\) atomic number. Table 2 shows the greater constituency of the three windshield samples, with as an example oxygen contributing 50.9% of the mass of Honda windshields (the \(Z_{eff}\) of this medium \(=12.2\)), 50.6% of the mass of Toyota windshields (the \(Z_{eff}\) of this medium \(=12.0\)) and 50.3% of the mass of Proton windshields (the \(Z_{eff}\) of this medium \(=11.8\)). Figure 1: EDX output from a Honda windshield sample (**a**) and associated SEM image (**b**).
## 6 Simulation Study
### Summary  
Figure 12 presents a histogram of FSF variation of the characteristic harmonic component at the frequency of \(2f_{r}\) for a healthy motor and motors with three different pure DE levels, under different rated loads. It can be seen that the value of FSF increases under lower load and higher level of DE. The induction motor with 10% of pure DE under no load has the highest value of FSF. Figure 12 indicates that at fixed load, the increase of the level of DE leads to the increase in the value of FSF. Besides that, it is obvious that the increasing of rated load results in the decreasing of FSF at a fixed level of DE.  
It is noteworthy that the inherent eccentricity in the induction motor produces an initial value of FSF. Therefore, it is necessary to conduct the ex-work checking for motor and evaluate the initial value of FSF as the reference standard.  
The comparison results between the simple use of WPD and the proposed method are shown in Figure 13. The extraction signal is located around the frequency of \(2f_{r}\). The FFT spectrum confirms the effectiveness of proposed method. By comparing the simple use of WPD, two advantages are shown according to Figure 13. First of all, the overlap problem has been solved shown in the red circles. Although the noisy components around 25.39 Hz and 44.18 Hz are not entirely eliminated, these two  
Figure 11: Variations of FSF for the induction motor at different loads: (**a**) healthy, (**b**) 2% DE, (**c**) 6% DE, (**d**) 10% DE.  
Figure 12: FSF variation of harmonic components versus different loads at the healthy motor and motors with different DE levels.  
parts have been suppressed. Secondly, within the passband, the noisy signal close to the characteristic harmonic is also suppressed, shown in the black circle. The amplitude around 47.9 Hz is decreasing from 5.96 VA to 4.56 VA. In this paper, the apparent power signal around \(2f_{r}\) can be considered as a main index for purely DE fault in the induction motor under different rated loads, even with low levels of DE.
# Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
## 1 Introduction  
Many systems of interest can be naturally characterised by complex networks; examples include social networks [1; 2; 3], biological networks [4] and technological networks. Trends, opinions and ideologies spread on a social network, in which people are nodes and edges represent relationships. Networks are mathematically represented by graphs. Of crucial importance to the understanding of the properties of a network or graph is its spectrum, which is defined as the eigenvalues of its adjacency or Laplacian matrix [5; 6]. The spectrum of a graph can be considered as a natural set of graph invariants and has numerous uses including estimating the graph connectivity, e.g., the mixing time on random graphs [7], detecting the presence of specific structures in the graph [8; 9; 10], graph isomorphism test [11] and measuring graph similarity [12], and graph classification [13]. Practically, it has been extensively studied in the fields of chemistry, physics, computer science and mathematics [14]. One of the main limitations in utilising graph spectra to solve problems such as measuring graph similarity and estimating the number of clusters (these are just two example applications of the general method we propose for learning graph spectra in this paper) is the inability to automatically and consistently learn an everywhere-positive and non-singular approximation to the spectral density. Full eigen-decomposition (which is prohibitive for large graphs) or the Lanczos algorithm both give a Dirac sum that must be smoothed to be everywhere positive. The choice of smoothing kernel \(k_{\sigma}(x,x^{\prime})\) and kernel bandwidth choice \(\sigma\), or number of histogram bins, which are usually chosen in an ad-hoc manner, can significantly affect the resulting output.  
In this paper, we propose a maximum entropy (MaxEnt) approach based on the novel maximum entropy algorithm [15] to learn the spectrum of massive graphs. The main contributions of the paper are as follows:We prove that kernel smoothing, commonly used in methods to visualise and compare graph spectral densities, biases moment information;
* We propose a computationally efficient and information-theoretically optimal smooth spectral density approximation based on the method of maximum entropy, which fully respects the moment information. It further admits analytic forms for symmetric and non-symmetric Kullback-Leibler (KL) divergences and Shannon entropy;
* We utilise our information-theoretic spectral density approximation on two example applications. We investigate graph similarity and to learn the number of clusters in a graph, outperforming iterative smoothed spectral approaches on both synthetic and real-world data sets.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 3 Multi-Network Prior Reconstruction Method
### Single-Pixel Imaging Based on EMNP
\[\text{SSIM}=\frac{\big{(}2\mu_{x}\mu_{y}+c_{1}\big{)}(2\sigma_{xy}+c_{2})}{(\mu_ {x}^{2}+\mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{7}\]  
In Equation (7), \(\mu_{x}\) is the mean value of x, \(\mu_{y}\) is the mean value of y, \(\sigma_{x}\) is the variance of x, \(\sigma_{y}\) is the variance of y, \(\sigma_{xy}\) is the covariance of x and y.
## 3 4-SSB System Design
### The Concept of Application of 4-SSB into OFDM M-QAM 4-SSB Uncoded System Using the Shadow Equalizer
#### 4.3.2 The Proposed 4-SSB M-QAM MF-SIC-SAC Scheme Design  
The proposed system consists of three algorithmic categories. We first introduce the general SIC scheme. Then we explain the branch of MF-SIC. The MF structure requires additional computational complexity. However, shadow concentration is designed to decrease the complexity of the system. The process is made by design of reliable and non-reliable symbol [13].  
In general, the conventional SIC algorithm is used in the MMSE filter as \(N_{R}\,\times\,1\) filter vectors corresponding to all the antennas. The function of MMSE is to estimate the symbol by making the candidate symbol as null or successful cancellation for perfect optimized detection. The stream of the antenna is denotes as \(\hat{d}_{i,SSB,n}[i]=\left[\hat{d}_{i,SSB,1}\hat{d}_{i,SSB,2}\ldots\hat{d}_{i,SSB,N_{R}}\right]^{T}\) where the receiver \(\hat{d}\) is an estimated symbol of 4-SSB. The SIC algorithm of the estimated symbol is expressed as follows:  
\[\hat{d}_{i,SSB,n}[i]=Q\left(w_{n}^{H}\,d_{i,SSB,n}[i]\right), \tag{28}\]  
Figure 3: The proposed 4-SSB multiple-input multiple-output (MIMO) system.  
where the \(Q(.)\) function denotes the hard slice function of the receiver symbol which is summarized in the following conditional equation:  
\[\begin{array}{l}\ddot{d}_{i,SSB,n}[i]=d_{i,SSB,n}[i],n=1,\\ \ddot{d}_{i,SSB,n}[i]=\ d_{i,SSB,n}[i]-\sum\limits_{k=1}^{n-1}(H)_{k}\ddot{d}_{k }[i],\hskip 28.452756 ptn\leq\ 2,\\ w_{n}=\ \left(\overline{H}_{n}\overline{H}_{n}^{H}+\ \sigma^{2}I\right)^{-1}(H)_{n} \end{array} \tag{29}\]  
where \(\overline{H}_{n}^{H}\) denotes the represented matrix with columns \(n,\ n+1,\dots\ N_{T}\) of \(H.\ \ddot{d}_{n}\) is the receiving symbol of the 4-SSB post-demodulation process. This symbol is obtained after the constellation of the previously detected \((n-1)\) symbols.