esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Pathfinder Barcelona Raman LIDAR  
For the CTAO-N purposes, the Institut de Fisica d'Altes Energies (IFAE, Barcelona, Spain), in collaboration with the Autonomous University of Barcelona (UAB, Barcelona, Spain), the Italian National Institute for Nuclear Physics (INFN, Padova, Italy) and the University of Nova Gorica (UNG, Nova Gorica, Slovenia) designed a RL [6; 16; 25], which we hereafter call the Barcelona Raman LIDAR (BRL).  
A pathfinder instrument for the BRL was assembled at the UAB Campus during the years 2014-2019. We label this instrument \"pathfinder Barcelona Raman LIDAR (pBRL)\". An exhaustive description of the technical details and design requirements of the pBRL was presented in a sister manuscript [16]. This work focuses on the performance of the instrument. The general idea is to apply the lessons learnt from the pBRL in order to upgrade it to its final version, the BRL, which will be delivered to the CTAO as the observatory RL for CTAO-N.  
The pBRL is shown schematically in Figure 1, and a picture of the instrument is shown in Figure 2. The pBRL was built by refurbishing a foldable commercial transport container containing a steerable telescope equipped with a 1.8 m diameter parabolic mirror of 1.8 m focal length, originally belonging to the Cherenkov Light Ultraviolet Experiment (CLUE) [26]. The mirror is a float-glass solid substrate with aluminum coating, which was recoated with a quartz protection in 2022 [16]. The pBRL hosts a pulsed 10 Hz Brilliant Nd:YAG laser from the company Quantel [27] emitting at 1064 nm with frequency doubling and tripling at wavelengths of 532 nm and 355 nm. The laser light is guided via a set of two mirrors to be made coaxial. This allows us to measure aerosol scattering and extinction through the two elastic and two vibro-rotational (VRR) anti-Stokes Raman backscattering on N\({}_{2}\)[28] at 387 nm and 607 nm. The latter was not used for the pBRL but is considered for the final BRL design. The focused light on the focal plane is transported via an 8 mm diameter, 3.2 m long Liquid Light Guide (LLG) of the Lumatec Series 300 [29], with a fused silica window and fluoropolymer tubing, to a custom-designed optical system for wavelength separation and light detection (the so-called \"polychromator\"). The polychromator is equipped with lens-doublets to focus the light beam and dichroic mirrors to progressively separate the four wavelengths of interest, and 10 nm wide interference filters to filter light at four photomultiplier tubes (PMT) units. The PMT signals are digitized in four commercial Licel Optical Transceiver Recorder (LOTR) units.
# Numerical and Parametric Analysis for Enhancing Performances of Water Photovoltaic/Thermal System
## 3 Results and Discussion
### Parametric Analysis
#### 3.2.2 Effect of Water Film Thickness
According to the negative dependence between electrical production and operating temperature of the PV cells, an effective cooling enhances electrical power. As shown in Figures 9 and 10, the PV/T system provide a better electrical power and efficiency of about 313 W and 18.54% at 13:00, compared with the conventional PV panel. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\mathbf{T_{PV}}\) (\({}^{\circ}\)C) & \(\mathbf{T_{out}}\)(\({}^{\circ}\)C) & \(\mathbf{E_{e}}\) (W) & \(\mathbf{E_{th}}\) (W) & \(\eta_{e}\) (\%) & \(\eta_{th}\) (\%) \\ \hline PV & 67.7 & - & 258.8 & - & 15.60 & - \\ \hline PV/T & 27.7 & 21.31 & 316.56 & 1334.5 & 19.07 & 80.3 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of PV and PV/T performances at \(E_{sun}\) = 1000 W/m\({}^{2}\), \(T_{a}\) = 35 \({}^{\circ}\)C, and \(T_{int}\) = 20 \({}^{\circ}\)C.  
Figure 7: Variation of solar irradiance and ambient temperature (28 July 2016).
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes
### Outage Probability and Transmitted Power
#### 3.1.1 Siso  
We first assume the SISO scheme, where the transmitter and receiver are equipped with only one antenna at each node. Then, the outage probability is [23]  
\[\mathcal{O}_{\text{SISO}}=\frac{\gamma\left(\frac{\zeta_{0}}{\overline{\zeta}},m \right)}{\Gamma\left(m\right)} \tag{9}\]  
where \(m\) is the Nakagami-\(m\) fading parameter, \(\gamma(\cdot,\cdot)\) is the lower incomplete gamma function ([24], Section 6.5.2) and \(\Gamma(\cdot)\) is the complete gamma function ([24], Section 6.1.1).  
With that in hand, we assume that the system operates with a given target outage probability \(\mathcal{O}^{\star}\), so that \(\mathcal{O}_{\text{sch}}=\mathcal{O}^{\star}\) is used to find the minimal required transmitted power \(P_{\text{out}}\) of each scheme, as in [25]. Then, in the case of the SISO scheme the transmitted power must be adjusted according to  
\[P_{\text{out}}^{\text{(SISO)}}=\frac{\xi_{0}\,N_{0}\,B\,F_{\text{rx}}}{\kappa\, \gamma^{-1}\big{(}\Gamma\left(m\right)\mathcal{O}^{\star},m\big{)}}, \tag{10}\]  
where \(\gamma^{-1}(\cdot,\cdot)\) is the inverse gamma function, and which depends on the communication distance, as well as on the employed receiver architecture. Furthermore, let us remark that \(P_{\text{out}}^{\text{(sch)}}\) represents the power used with the LNTA architectures, while there is an additional term due to the SAW-filters of the LNA architectures, as it will be detailed in Section 3.2.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 2 Background and Preliminary  
The rate-distortion theory is a branch of information theory that focuses on characterizing the fundamental limit of lossy data compression by the optimal trade-off between the minimum data rate required between the source and the destination (rate) and the maximum tolerable distortion allowed when reconstructing the source data by the destination user based on the received signal (distortion). More formally, for a given distortion level \(D\geq 0\), the fundamental tradeoff between the minimum achievable rate and maximum tolerable distortion is often represented by the rate-distortion function \(R(D)\), defined by  
\[R(D)=\inf_{P_{\hat{S}|\hat{S}}}I(S,\hat{S}) \tag{1}\] \[\text{s.t. }E[d(S,\hat{S})]\leq D,\]  
where \(d(S,\hat{S})\) is the distortion metric that is adopted by the source and destination users for measuring the difference between the original source signal and the signal recovered by the destination. In traditional Shannon rate-distortion theory, \(d\) is often measured by the symbol-level distortion such as Hamming distortion for discrete signal sources and mean squared error (MSE) distortion for continuous signal sources.  
Motivated by the fact that human users are more sensitive to the perceptual quality of the recovered signal, instead of symbol-level recovery, the rate-distortion-perception (RDP) theory has been developed by incorporating the constraint on the statistic divergence of the probability distributions, called perception level, between the source and the recovered signal [12; 13]. More formally, for a given maximum-tolerable distortion level \(D\) and a perception level \(P\), the rate-distortion-perception function \(R(D,P)\) is defined as  
\[R(D,P)=\inf_{P_{\hat{S}|\hat{S}}}I(S,\hat{S}) \tag{2}\] \[\text{s.t.}\ E[d(S,\hat{S})]\leq D,\] \[\theta(p_{\hat{S}},p_{\hat{S}})\leq P,\]  
where \(\theta(p_{\hat{S}},p_{\hat{S}})\) is the perception metric that measures the statistical divergence between the source signal distribution and the reconstructed signal distribution. Most existing works on RDP focus on a single perception metrics, such as the total variation (TV) distance [14; 15]. Recently, some researchers have investigated the RDP problem under other divergence metrics such as Wasserstein distance [16] and \(f\)-divergence [17; 18; 19; 20].  
In this paper, we investigate the rate-distortion theory of task-specific semantic communication. Our key innovation lies in introducing a generalized divergence metric that quantifies the statistical semantic distance between source and reconstructed signals, where the specific metric is adaptively selected based on communication task requirements. We also obtain the closed-from expressions of the semantic rate-distortion function under various task-specific divergence metrics. A comparison between the existing information-theoretic results of the RDP theory and our results under various types of source signals is shown in Table 1, where the columns of \"SI\" represent whether or not the corresponding result considers side information.
## 3 Experimental Verification of the SJB
### Protection and Diagnostic Tests  
Unintended open and short circuits can occur in the wiring harness connection between the SJB and the headlight. Thus, it is essential that the SJB is able to diagnose and protect against open- and short-circuit conditions. To evaluate this for the developed SJB, a protective operation and diagnostic test environment was constructed as shown in Figure 18. During testing, the open-circuit state was realized by opening a switch at the connection with the headlight, whereas the short-circuit state was configured via a switch connected to GND in parallel with the headlight.  
The results of the short circuit test of the H/LP High RH output are shown Figure 19, where it can be seen that if V\({}_{\text{ON}}\) was greater than 3.5 V when the short circuit appeared, the SJB recognized the short circuit and discontinued the supply of current.  
The results of the tests that were conducted to assess the overload and short circuit protection capability of the SJB are provided in Table 5. A test was deemed to have been passed when the output current of the IPS dropped to 0 in the event of an open or short circuit, yet the IPS continued to operate normally. In contrast, a test was deemed to have been failed when the output current of the IPS was non-zero or the IPS itself failed to operate correctly. As shown, the SJB passed all tests.  
Figure 17: Input and output duty cycles of the IPS for various input voltages: (**a**) IPS input and (**b**) IPS output.  
Figure 18: Protective operation and diagnosis test environment.
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 2 Low-Rank Representation and Local Binary Pattern  
### Latent Low-Rank Representation  
The low-rank representation (LRR) method aims at finding the lowest-rank representation among all the candidates. When the observed data matrix is used as the dictionary \(\mathbf{A}\), recovering the low-rank representation from the given observation \(X_{o}\) can be written as the following convex optimization problem [32; 33]:  
\[\begin{split}&\underset{Z,E}{\text{\emph{min}}}\left\|Z\right\|_{*}+ \lambda\left\|E\right\|_{1},\\ & s.t.X_{o}=AZ+E(X_{o}=X_{o}Z+E),\end{split} \tag{1}\]  
where \(\left\|\cdot\right\|_{*}\) denotes the nuclear norm of a matrix, i.e., the sum of the singular values of the matrix. \(\left\|\cdot\right\|_{1}\) is the L1-norm characterizing the sparse noise \(E\). \(\lambda>0\) is a regularization parameter for balancing the influences of the sparsity error term. [PERSON] et al. [33] applied LRR to multi-view spectral clustering by separately imposing a low-rank constraint on each view and achieved the multi-view agreement via an iterative fashion.  
While \(A=X_{o}\), this assumption may be invalid, and the data sampling is insufficient. So, LRR may not represent the subspaces effectively, and the recovery robustness may be weakened. LLRR can be regarded as an enhanced version of LRR, which constructs the dictionary **A** using both observed data \(X_{o}\) and unobserved hidden data \(X_{H}\); it is more accurate and robust to noise than LRR for subspace representation [34; 35]. To resolve the problem of insufficient sampling and to improve the robustness to noise corruption, LLRR is exploited to extract suitable image features during the PIH generation. An approximate recovery can be achieved by analyzing the properties of the hidden effects as follows:  
\[\begin{array}{l}\underset{Z,E}{min}\ \left\|Z\right\|_{*}+\lambda\left\|E \right\|_{1}\\ s.t.X_{o}=[X_{o},X_{H}]Z+E\.\end{array} \tag{2}\]  
The hidden effects recovery problem for corrupted data in Equation (2) can be solved by the following convex optimization problem:  
\[\begin{array}{l}min\ \left\|Z_{\mathrm{O}|H}\right\|_{*}+\left\|L_{H|O} \right\|_{*}+\lambda\left\|E\right\|_{1}\\ s.t.X_{o}=X_{o}Z_{O|H}+L_{H|O}X_{O}+E,\end{array} \tag{3}\]  
where \(Z_{O|H}\) and \(L_{H|O}\) correspond to the principal component and the salient component, respectively. For the sake of simplicity, we replace \(X_{o}\), \(Z_{O|H}\) and \(L_{H|O}\) with **X**, **Z** and **L**, respectively. Thus, the convex optimization problem in Equation (3) can be rewritten as:  
\[\begin{array}{l}min\ \left\|Z\right\|_{*}+\left\|L\right\|_{*}+\lambda \left\|E\right\|_{1}\\ s.t.X=XZ+LX+E,\end{array} \tag{4}\]  
where \(X\in\mathbb{R}^{d\times n}\), \(Z\in\mathbb{R}^{n\times n}\) and \(L\in\mathbb{R}^{d\times d}\). The parameters \(d\) and \(n\) are the feature vector size and the number of features, respectively. This problem can be solved via the Augmented Lagrange Multiplier (ALM) [36] method.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 5 Discussion and Open Research Challenges
### Uplink Design  
Most of the research in the VLC is focused on the downlink transmission to increase the data rate or mitigate interference issues. Less attention is given to uplink transmission from the user device. As mentioned earlier, a limiting factor in using the LED for the uplink from a mobile device is that it consumes not only significant energy but also causes visual disturbance to the user. Researchers propose RF and infrared. However, it would create heterogeneous networks due to different technologies at uplink and downlink. Coordination among multi-home clients, resource allocations and performance issues due to asymmetry heterogeneous networks are in the early phase of research.
## 3 Framework
### Chebyshev Pooling
#### 3.2.1 Chebyshev's Inequality  
As presented in our earlier work [34; 35], [PERSON]'s inequality was not suitable for this kind of analysis, because this inequality usually provides a poor upper bound, and the bound becomes increasingly weaker as the selected feature goes up, and it would cause  
Figure 3: Three-level stacking implementation of multilayer CARU.  
the result to exceed the unexpected value. We, thus, used a variant form of [PERSON]'s inequality, namely, the one-sided [PERSON]'s inequality [36] or [PERSON]'s inequality [37], written as:  
\[P(x-\mu\geq\lambda)\leq P_{max}\equiv\frac{\sigma^{2}}{\sigma^{2}+\lambda^{2}}, \tag{2}\]  
for any \(\lambda>0\), where \(x\) is a randomly selected feature in a tensor of samples with a finite mean \(\mu\) and variance \(\sigma^{2}\). This inequality provided us with a way to deal with extreme situations, in which case, the only knowledge we had about the samples or probability distribution was the mean and standard deviation. It meant that, in any probability distribution, almost all values were close to the mean, and the chance of a value outside that range did not exceed \(\frac{\sigma^{2}}{\sigma^{2}+\lambda^{2}}\). The useful property of Equation (2) is that it could provide a stronger bound \(P_{max}\in[0.0,1.0]\). Since the distribution could be arbitrary, this reason could be applied to various general situations to obtain results within the effective range. For more general applications, let \(t=\lambda+\mu\) and \(x\) be a sample obtained from a distribution with mean \(\mu\) and variance \(\sigma^{2}\), and, then, for \(x\geq t\), Equation (2) could be rewritten as:  
\[P(x\geq t)\leq P_{max}(t)\equiv\frac{\sigma^{2}}{\sigma^{2}+\left(t-\mu\right) ^{2}}. \tag{3}\]  
Suppose \(t\) was the current feature to be considered for pooling; then, Equation (3) would indicate that the probability of the sample was equal to or greater than \(t\) within its 2D kernel size (\(k^{2}\)), not exceeding \(P_{max}(t)\). Even for a tensor without normalization, since it was just a comparison of the values without considering other information, the bound could still provide a stable and better extraction of the number of interesting features within a given sample. Therefore, the Chebyshev pooling combined the function of maximum pooling and average pooling methods to calculate the probability distribution of the data to be extracted in the training process of CNN.
## 3 General Opportunities, Challenges, and Applications  
Recognizing the opportunities, understanding the challenges, and identifying new applications are critical in today's technology. We investigate the broad prospects of developing technologies, as well as their specialized applications in cellphones, organizational infrastructures, and IoT, while also addressing the challenges that come with these improvements.
# 3 GPP 5G V2X Error Correction Coding for Various Propagation Environments: A QoS Approach
## 5 Simulation Results
### Required SNR for Minimum Reliability  
Figure 4 shows the minimum required SNR to achieve the minimum reliability for each QoS scenario of Table 2 and for all system model simulation scenarios of Table 1. As expected, the BCJR and logBCJR algorithms have the best performance, which are almost identical. The maxlogBCJR algorithm needs a 0.2-0.3 dB higher SNR to reach the same performance as BCJR for each QoS scenario and for all system model simulation scenarios, whereas the SOVA algorithm requires 1.1-2.6 more dB to achieve the same reliability. The polar algorithm appears to behave slightly better than SOVA, but the difference in  
Figure 3: Mean percentage FER increase for 128-bit frames and for SOVA decoding algorithm. FER increase when (**a**) the vehicle speed changes from 50 to 100 km/h; (**b**) the channel state changes from LOS to NLOSv; (**c**) the channel state changes from LOS to NLOS for the urban traffic model only; and (**d**) the traffic model changes from highway to urban.  
performance is large compared to the BCJR-based algorithms. Finally, LDPC is the worst algorithm in terms of SNR since it has a difference of 1.9-2.7 dB.  
Furthermore, we observe that the transition from 90% to 95% reliability requires an increase in SNR of 0.4-0.8 dB, while the transition from 95% to 99% reliability requires an increase in SNR of 0.8-1.5 dB. This is because 90% and 95% reliability are achieved at relatively low SNR before error floor phenomena occur, while 99% reliability is in many cases achieved while the error floor effects that delay the convergence of the BCJR-based algorithms are present.  
Figure 4: Required SNR for minimum reliability per QoS scenario for simulation scenarios S1–S5 and 128-bit frame length. (**a**) QoS scenarios Q1 and Q5 (90% reliability); (**b**) QoS scenarios Q2 and Q4 (95% reliability); (**c**) QoS scenarios Q3 and Q6 (99% reliability).  
Polar coding has higher SNR requirements to achieve the same reliability metrics compared to the BCJR-based algorithms. This is clearly due to the slower convergence that polar coding has because the frame size is too small to exploit the polarization [37].  
Figure 4 also makes it clear not only the slow rate of convergence of SOVA but also that LDPC starts to become efficient in large SNRs. If we compare these two algorithms, we observe that SOVA at 90% and 95% reliability has a clearly superior behavior than LPDC, but at 99%, their performances are clearly comparable. Concluding, Figure 4 reveals the superiority of BCJR-based algorithms for 128-bit frames in terms of SNR that is required to achieve reliable communication in a 5G V2X channel.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 3 Inverse Halftoning Technology for Scanned Halftone Images
### Inverse Halftoning Based on Special Designed DCNNs for Scanned Halftone Images
Because sufficient paired images can be easily generated by using the halftone synthesizer, the sub-network used in the second stage is easily trained. The sub-network in the second stage is composed of U-Net structure, skip connection, and Residual-in-Residual Dense Blocks (RRDBS) 32]. The advantage of this method is that it does not need paired data in the first stage, whereas the second stage can be easily trained with synthetic data. However, it fails to input halftone prints with severely damaged areas.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 2 Principles
### Beam Pattern of PAAs  
PAAs using OBFNs consist of a 1D antenna array or a 2D antenna array and corresponding OBFNs, as shown in Figure 1a,b. For a 1D PAA with equal amplitude and equal spacing (_d_) between adjacent antennas, the beam pattern (array factor) can be expressed as follows: [40]  
\[F(\theta)=\sum_{n=1}^{N}e^{i(n-1)(kds\sin\theta-\Delta\phi)} \tag{1}\]  
where \(\Delta\phi\) is the progressive phase of the linear array antenna, \(k\) is the wave number of the RF signal exited from the antennas, and \(\theta\) is the beam angle. The beam angle (\(\theta\)) can be given as Equation (2) which is derived from \(F(\theta)\) obtaining the maximum value.  
\[\theta=\arcsin\left(\frac{\Delta\phi}{kd}\right) \tag{2}\]  
Owing to \(k=2\pi/\lambda_{\text{RF}}\), where \(\lambda_{\text{RF}}\) is the wavelength of the RF signal, Equation (2) can also be expressed as follows:  
\[\theta=\arcsin\left(\frac{\Delta\phi}{2\pi d}\lambda_{\text{RF}}\right) \tag{3}\]  
Meanwhile, for 2D M \(\times\) N PAA with equal amplitude and equal spacing (_d_) between adjacent antenna elements, the beam pattern can be expressed as follows: [40]  
\[F\left(\theta^{\prime},\ \varphi\ \right)=\sum_{m=1}^{M}\sum_{n=1}^{N}I_{mn}e^{i \left((m-1)(kds\sin\theta^{\prime}\text{cos}\varphi)+(n-1)(kds\sin\theta^{ \prime}\text{sin}\varphi)\right)} \tag{4}\]  
where \(\theta^{\prime}\) and \(\varphi\) are the azimuthal angle and polar angle in the spherical coordinate system, respectively, and \(I_{mn}\) is the excitation of antenna element at \(m\)-th row and \(n\)-th column.