esa-sceva/satcom-chunk-collection huggingface dataset:
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
## 2 Compressed Sensing of Binary Signals
### MCMC Algorithm for Recovery
As a remark, we mention that in practice, MCMC methods are often implemented using _annealing_, which in our case amounts to basically running Glauber dynamics with a noise variance \(\sigma^{2}\) which is larger than the true noise. This can help steer the system away from local maxima of \(\mathbb{Q}\), by \"smoothing\" it out. We would like to emphasize that condition (6) is very pessimistic, and in practice Glauber dynamics appears to mix rapidly at substantially lower noise levels than predicted there. For example, in the setup we consider below, \(M=2^{14}\), \(n=2^{11}\), \(\
u=16\), \(s=128\), so that the bound \(\sigma_{0}^{2}=\
u(s-1)/4=508\), translates into energy per transmitted bit as \(E_{b}/N_{0}=\frac{\
u}{2\sigma^{2}-\frac{1}{82}(M)}=\frac{1}{7(s-1)}\approx 0.001\), which is roughly \(-29.5\) dB. This \(E_{b}/N_{0}\) is very far from sufficient for reliable recovery of \(\widehat{\mathbf{x}}\) even when \(k\) is small (see experiments in Section 3.1). In this regime, while indeed Lemma 2 holds in the sense that Glauber dynamics mixes fast, the error rate of the optimal estimator is too high to be of use. Thus, Lemma 2 should **not** be thought of as an accurate predictor for the performance of Glauber dynamics for binary compressed sensing. Instead, it should be thought of as a \"sanity check\"--evidence that Glauber dynamics is a reasonable thing to do, at least in _some_ regime of the problem. On the same note, we observed, that when \(k\) is large, Glauber dynamics tends sometimes to get stuck at \"bad\" local maxima, even when the noise is moderate. To mitigate this, one can initialize \(\mathbf{x}^{(0)}\)_reasonably close_ to the true signal \(\mathbf{x}\), using an off-the-shelf compressed sensing solver like NNLS--and then use Glauber dynamics as a _refinement step_. Applyingthis additional step of Glauber dynamics may improve the performance _substantially_ (see the numerical results in Section 3.1). Of course, the result of Lemma 2 does not predict in any way this behavior; rather, it is completely agnostic to the starting location. Additionally, the bound on the mixing time there does not depend at all on \(k\), which, as just mentioned, is crucial for the behavior of Glauber dynamics in practical regimes. A more sophisticated analysis of Glauber dynamics for compressed sensing of binary signals, which takes into account the points above, is an interesting problem, and, to the best of our judgement, highly nontrivial.
## 5 Experiment Verification for the Flow Rate of the Piezoelectric-Hydraulic Pump  
In order to validate the results from the computational simulation, an actual piezoelectric-hydraulic pump for the UAV brake system is fabricated and represented in Figure 11. The piezoelectric-hydraulic pump components were designed and made for the easiest removal and replacement. Most of these components were manufactured by precision machining. However, the check valve was made using wire-cut electric discharge machining, since it has a complex shape and thin plate.  
Figure 10: The flow rate simulation result at several driving frequencies.  
Figure 9: The displacement of check valves at several driving frequencies. (**a**) Driving frequency: 10 Hz. (**b**) Driving frequency: 120 Hz. (**c**) Driving frequency: 150 Hz.  
In addition, to fabricate a piezoelectric-hydraulic pump, some important equipment is required to conduct the experiment in order to reinforce the results from the simulation, such as a DAQ program, a pressure sensor, a solenoid valve, a high-voltage amplifier, and an electronic scale, as shown in Figure 1. The high-voltage amplifier is used to amplify the created signal and then applies that signal to the piezoelectric actuator at a desired operating frequency. The DAQ program \"dSPACE\" allows the collection and creation of the digital and analog signal data.  
The solenoid valve was linked to the exhaust port of the piezoelectric-hydraulic pump and made a major contribution to the exhaust performance. When the solenoid valve was closed, a closed-loop hydraulic circuit, such as that in a brake system, was formed. In addition, the flow rate in the no-load state was evaluated when the solenoid valve was opened. A pressure sensor was used to measure the pressure of the load side. As for the fluid, Mil-PRF-83282, commonly used in actual aircraft hydraulic brake systems, was used in this research. The flow rate experiment was conducted at several driving frequency values from 0 to 150 Hz. The flow rate performance was collected and is shown in Figure 12.  
A good match between the estimation method and the CFD simulation, which is shown in Figure 12, is guaranteed. Therefore, the estimation by using a simulation is effective for a coarse approximation of the check valve motion and flow rate performance of the piezoelectric hydraulic pump.  
Figure 11: Components of the designed piezoelectric-hydraulic pump used in the experiment.  
Figure 12: The flow rate experiment results at several driving frequencies.
## 2 Materials and Methods
### Automated DEM-Based Surface Water Mapping  
As an available tool for rapid surface water mapping, the DEM-based methods can provide the areal extent of water surface during or after the flood event, which can be validated by satellite images, and the rough floodwater depth that both are essential information for the first responders and mitigation assessment. Here, we used a new cloud-based GIS tool using the DEM information, the Floodwater Depth Estimation Tool (FwDET) [40; 63; 64], for estimating floodwater extent and depth based solely on a DEM and a flood inundation map (if available). FwDET allows for fast floodwater depth estimation given by the flood extent, and here, we used the study area as the flood extent. The DEM data include ALOS, GMTED, HydroSHEDS, HydroSHEDS conditioned, MERIT, and SRTM. More details of the steps to generate the FwDET water depth and inundation map can be found in [40; 64].
## 3 Results and Discussion
### Correlation Analysis of Flavor Compounds and Microorganisms
In previous studies, _Saccharomyces_ and _Lactobacillus_ have been reported to be two of the main contributors to the formation of flavor compounds in Baijiu [49]. _Lactobacillus_ is also active in transcribing genes involving the biosynthesis of flavor compounds and their precursors by Metatranscriptomic analysis [50]. In addition, _Clostridium_ had been confirmed to have the ability to produce organic acids and short-chain fatty acids [46]. The results also confirmed the conclusion, that is, _Lactobacillus_, _Saccharomyces_, and _Clostidium_ had strong positive correlations with the production of the main ethyl esters of Baijiu. During the fermentation process, these microorganisms could use small molecular substances to produce the precursors of ethyl esters such as ethanol, organic acids such as acetic acid, butyric acid, and hexanoic acid (Figure S5), so that the substrates are sufficient to promote the esterification reaction in the middle and late stages of fermentation [51]. In addition, some unreported microbial genera in our study, such as _Chaenothecopsis_, _Cloicobacterium_, and _Anaerosporobacter_ showed obviously positive correlations with ethyl esters. These results indicated that the dominant microorganisms in fermentation made a significant contribution to the production of flavor compounds. The RDA indicated strong correlations between the core functional microorganisms and the main flavor ethyl esters, which were more prominent in sample 3 and sample 4, could promote the formation of the flavor compounds and ensure the stability of Baijiu. Our studies confirmed that combining the core functional microorganisms was a stable way to Baijiu fermentation and promoted ester synthesis to ensure the product's quality. Figure 9: Association of microorganisms with key flavor compounds analyzed by RDA. (**a**) Bacteria, (**b**) Fungi.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Obtaining Pure Oxides and Metals
#### Hydrogen Reduction  
Adding an excess of NaOH to a solution of gallium or indium allows the precipitation of their hydroxides, from which their oxides are then obtained via simple roasting or calcination [31; 96]. In\({}_{2}\)O\({}_{3}\) and Ga\({}_{2}\)O\({}_{3}\) can then be reduced with hydrogen (in the case of gallium oxide, the process is carried out at 900 \({}^{\circ}\)C), allowing for the obtainment of raw metals [96].
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 3 Performance Analysis for Downlink WPCN
### Harvested Energy  
When the received _SIR_ of UE is less than \(\theta\), it will harvest energy from BSs in the whole plane. The average harvest energy can be calculated as  
\[\begin{array}{rcl}\mathbb{P}_{EH\_t,k}&=&\sum_{j=1}^{K}\mathbb{E}\left[\sum_{y \in\Phi_{j}}P_{t,j}h_{1,y}L_{1,y}^{-1}\right]\\ &\stackrel{{(h)}}{{=}}&\sum_{j=1}^{K}2\pi\lambda_{j}\int_{0}^{ \infty}\left[1-L_{h_{1,y}}\left(P_{t,j}^{-\alpha}y^{-\alpha}\right)\right]ydy \\ &=&\sum_{j=1}^{K}2\pi\lambda_{j}\int_{0}^{\infty}\left[1-\frac{1}{1+P_{t,j}y^{- \alpha}}\right]ydy\\ &=&\sum_{j=1}^{K}\frac{2\pi^{2}\lambda_{j}P_{t,j}^{-\alpha}}{\pi\sin(2\pi/ \alpha)},\end{array} \tag{15}\]  
where (h) is from the [PERSON] theorem for PPP.  
It should be noted that the average harvested energy is not impacted by user association probability and receive threshold, but the density of users and the transmit power of BSs. This is because the received power of nodes is affected by the distance from the BSs and the transmit power of BS. However, the bias of BS cannot affect the actual receive power of nodes.
# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 1 Introduction
### Paper Contributions and Structure
Section 2 contains the description of the system model, with Section 2.1 describing the satellite network and Section 2.2 describing the channel modeling and simulation. Section 3 presents the proposed channel state prediction and modulation and coding (MODCOD) selection strategy, with Section 3.1 describing the algorithms used for SNR prediction and Section 3.2 describing spectral efficiency evaluation protocol and MODCOD selection for both single channel evaluation and the scenario where two satellite channels are considered. Section 4 presents the obtained results and discussion, with Section 4.1 describing the SNR prediction results. Section 4.2 is the obtained spectral efficiency improvement for single channel evaluation, Section 4.3 is the evaluation of two satellite channels, and Section 4.4 is the discussion. Finally, the conclusion is given in Section 5, alongside the directions for future work.
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 5 Conclusions  
Based on the QoS requirements of different application streams in SG neighbor regional networks, this paper presents a QoS-guaranteed and congestion-controlled SDN routing method. First, the congestion levels of link loads in the network are analyzed. According to the analysis results, congestion mapping can find the congestion link in a continuous period, and the congestion link is processed by building a virtual non-congested topology to replace the congested link. Secondly, an optimal frame is introduced into the system for the purpose of maximizing the utility of the whole network considering the new virtual topology. We propose a dispatching algorithm for the nodes in the network guiding packets to select the appropriate route, according to the various needs of SG packets. Numerical results illustrate that the QCORS method proposed in this paper is better than many existing methods in terms of end-to-end delay and packet losing rate. Therefore, the method of this paper is feasible and efficient.  
Methodology, validation and analysis: Y.S. and H.C.; writing of original draft and writing--review and editing, Y.S., P.J., X.D. and H.C.; project administration and funding acquisition: X.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China Project (62172441, 62172449, 61772553), the local science and technology developing fundation guided by central goverment (Free exploration project 2021 Szvup166), the Opening Project of State Key Laboratory of Nickel and Cobalt Resources Comprehensive Utilization (GZSYS-KY-2020-033), and the Fundamental Research Funds for the Central Universities of Central South University (2021 zzts0201).  
Not applicable.  
The authors would like to thank the Editor-in-Chief, the Associate Editor, and the reviewers for their insightful comments and suggestions.  
The authors declare no conflict of interest.
## 5 Energy-Efficient Resource Matching Algorithm for D2D Relay Communications  
As the mixed integer programming (8) is an NP-hard problem that cannot be solved in polynomial time, we investigate an approach based on matching theory to provide a sub-optimal solution. However, the MINLP problem with a four-dimensional variable \(\mathcal{X}\) leads to a four-dimensional matching, which is highly complicated. The problem defined in (8) is a mixed integer nonlinear program because it involves integer optimization variables for relay selection and spectrum allocation and continuous optimization variables for power allocation. By observing (1) and (2), we notice that in the first hop, both signal power and interference power received by the RS are related to the result of relay selection, while in the second hop, only signal power received by the RX is related to the result of relay selection. Thereupon, we start from the optimization of RB selection in the second hop link, which is independent of relay selection. First of all, each two-hop D2D link is matched with an RB for its second hop D2D transmissions, which can minimize its received co-channel interference, and then, it is matched with an RS and another RB for its first hop D2D transmission, which can maximize the EE of two-hop D2D links. Thus, the original four-dimensional matching can be approximatively decomposed into a two-dimensional matching between D2D TRs and RBs for the second hop and a three-dimensional matching among D2D TRs, RSs and RBs for the first hop. Note that we ignore the effect of dimensionality reduction on CUEs for simplification. For each matching problem, firstly, we introduce how to establish the preference list and then propose a pricing-based iterative matching algorithm.
## 3 Laboratory Experiment and Measurement
### Dynamic Fuzzy-PID Controller Based on the LabVIEW Platform
#### 3.2.3 Hardware Design of the Control Facility  
The control facility was an actuator which receives the control parameter and performs the actual electrical operation of the CO\({}_{2}\) compensation in the greenhouse. The main components of the control facility included the wireless module, electric control part, electronic driving part, and solenoid valve, whose practical photo is shown in Figure 11a. A micro-controller processes and converts the received control parameters through the wireless module to a specific control signal for the 8-bit addressable latch (type: SN74 LS259 BN, Texas Instruments, Dallas, TX, USA), which enhanced the controllability and scalability. The electric signal indirectly controlled the electronic driving part through an optical coupler (type: TLP521, TOSHIBA, Tokyo, Japan) to isolate interference. In the electrical part, a logic control circuit composed of 8 Darlington (type: ULN2803, UTC, Shenzhen, China) supplied the required large driving current for the solenoid valve. A 99.99% CO\({}_{2}\) gas in a 20 MPa cylinder was used as gas source for the compensation of CO\({}_{2}\) concentration in the greenhouse. The outlet of the cylinder was connected to a pressure relief valve. The gas from the cylinder was delivered by a plastic catheter with diffusion holes spaced at a fixed distance, as shown in Figure 11b. Between the control facility and gas source, a pressure relief valve was used to reduce the pressure to less than 1 MPa. On cost principles, using the solenoid ZS-DN-5 (JVL, Shang Hai, China), whose power supply and control signal were transmitted by a coordinator node directly, less than 1-s lag was used for CO\({}_{2}\) compensation.  
Figure 11: (**a**) The photo of the control facility; (**b**) The practical installment of control facilities in the greenhouse.
## 2 Results and Discussion
### Hydrogen Reactivates the Reaction
The Q\({}_{\text{NBO}}\) values of the Zr atom also diminish from 1.358 e in the reactant [Zr]-DVB-PP\({}_{1}\) to 1.112 e in the intermediate IM2. Meanwhile, the WBI values of the Zr-H\({}_{1}\) (from 0.056 to 0.880) and the C\({}_{1}\)-H\({}_{2}\) (from 0.001 to 0.892) bond gradually become larger from the transition state TS1 to the intermediate IM2, indicating the formation of the Zr-H\({}_{1}\) and the C\({}_{1}\)-H\({}_{2}\) bond. The Q\({}_{\text{NBO}}\) value of the H\({}_{2}\) atom also gradually increases from 0.000 e in the reactant to 0.261 e in the intermediate IM2. The WBI value (0.877) of the Zr-H\({}_{1}\) bond in [Zr]-H is slightly smaller than that of IM2 (0.880) by 0.003 A, due to the interaction between the central metal Zr and the two side-C\({}_{5}\) ring ligands becoming stronger. The hydrogen introduction is an exothermic reaction (exothermic, 7.0 kcal/mol), and the reaction barrier is very low (14.2 kcal/mol), indicating that the reaction is thermodynamically feasible. The mechanism for the activation reaction between zirconocene-BVPE and H\({}_{2}\) is similar to that for DVB. The respective energy values are given in the brackets in Figure 2 and represent the relative energy along the reaction coordinate for the BVPE reaction process; the main transition states (TSs) and configurations of the intermediates (IMs) are listed in Figure 4. The entire reaction passes through two TSs and two intermediates and finally reaches the product BVPE-PP\({}_{1}\) and [Zr]-H catalyst components. The reaction potential barrier is 12.5 kcal/mol, which is slightly lower than that of DVB, and the reaction is exothermic, 7.7 kcal/mol. It was also easy to perform the experimental reaction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**WBI**} & \multicolumn{2}{c}{**Q\({}_{\text{NBO}}\) (e)**} \\ \cline{2-7}  & **B (Zr-C\({}_{1}\))** & **B (H\({}_{1}\)-H\({}_{2}\))** & **B (Zr-H\({}_{1}\))** & **B (C\({}_{1}\)-H\({}_{2}\))** & **Zr** & **H\({}_{2}\)** \\ \hline
[Zr]-DVB[BVPE]-PP\({}_{1}\) + H\({}_{2}\) & 0.609 & 1.0 & & & 1.358 & 0.000 \\
[0.619] & [1.0] & & & [1.362] & [0.0] \\ TS1 & 0.563 & 0.923 & 0.056 & 0.001 & 1.375 & 0.011 \\  & [0.571] & [0.942] & [0.040] & [0.001] & [1.403] & [0.005] \\ IM1 & 0.590 & 0.713 & 0.236 & 0.049 & 0.877 & 0.100 \\  & [0.596] & [0.712] & [0.236] & [0.050] & [0.879] & [0.100] \\ TS2 & 0.504 & 0.496 & 0.416 & 0.239 & 0.815 & 0.132 \\  & [0.509] & [0.496] & [0.417] & [0.239] & [0.815] & [0.132] \\ IM2 & 0.003 & 0.000 & 0.880 & 0.892 & 1.112 & 0.261 \\  & [0.003] & [0.000] & [0.882] & [0.895] & [1.089] & [0.259] \\ DVB[BVPE]-PP\({}_{1}\) + [Zr]-H & & & 0.877 & 0.902 & 1.311 & 0.245 \\  & & & [0.877] & [0.912] & [1.311] & [0.240] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Wiberg bond indices (WBI) and natural charges (Q\({}_{\text{NBO}}\)) for some key bonds and atoms of the reaction pathway involving hydrogen; the values of BVPE are given in brackets.
###### Abstract  
We consider the numerical solution of the discrete multi-marginal optimal transport (MOT) by means of the Sinkhorn algorithm. In general, the Sinkhorn algorithm suffers from the curse of dimensionality with respect to the number of marginals. If the MOT cost function decouples according to a tree or circle, its complexity is linear in the number of marginal measures. In this case, we speed up the convolution with the radial kernel required in the Sinkhorn algorithm via non-uniform fast Fourier methods. Each step of the proposed accelerated Sinkhorn algorithm with a tree-structured cost function has a complexity of \(\mathcal{O}(KN)\) instead of the classical \(\mathcal{O}(KN^{2})\) for straightforward matrix-vector operations, where \(K\) is the number of marginals and each marginal measure is supported on, at most, \(N\) points. In the case of a circle-structured cost function, the complexity improves from \(\mathcal{O}(KN^{3})\) to \(\mathcal{O}(KN^{2})\). This is confirmed through numerical experiments.  
multi-marginal optimal transport; Sinkhorn algorithm; fast Fourier transform; image processing; optimal transport; Wasserstein barycenter 202215311. 202221531.  
descent [23], the Gauss-Seidel method [22], or proximal splitting [24] have been used to iteratively determine a minimizing sequence of the MOT problem.  
However, the problem suffers from the curse of dimensionality as the complexity grows exponentially with the number \(K\) of marginal measures. One way to circumvent this lies in incorporating additional sparsity assumptions into the model. Polynomial-time algorithms to solve certain sparse MOT problems have been studied in [13; 25; 26]. We will assume that the cost function decouples according to a graph, where the nodes correspond to the marginals and the cost function is the sum of functions that depend only on two variables which correspond to two marginals connected by an edge of the graph. For example, the circle-structured Euclidean cost function reads as  
\[c(x^{1},\ldots,x^{K})=\left\|x^{1}-x^{2}\right\|_{2}^{2}+\cdots+\left\|x^{K-1} -x^{K}\right\|_{2}^{2}+\left\|x^{K}-x^{1}\right\|_{2}^{2},\quad x^{1},\ldots,x^ {K}\in\mathbb{R}^{d}.\]  
MOT problems with graph-structured cost functions with a constant tree-width can be solved with the Sinkhorn algorithm in polynomial time [27]. In [25], polynomial-time algorithms were presented for the MOT problem and its regularized counterpart for the cases of a graph structure and a set-optimization structure, as well as a low-rank and sparsely structured cost function. Another sparsity assumption lies on the transport plan to be thinly spread, which is, e.g., the case for the \(L^{2}\)-regularized problem [21; 22].
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design  
The growing need for broadband RF switches stems from the ever-increasing demand for bandwidth and data rates in microwave and millimeter-wave transceiver systems [36; 37]. These RF switches are pivotal components in a variety of wireless applications (including space-based systems) for controlling RF signal flow at both the input and output ports [17; 38; 39; 40; 41]. In wireless communication systems, RF switches must exhibit low insertion loss, high isolation, and a broad operating bandwidth extending up to millimeter-wave frequencies [18; 42; 43].  
For millimeter-wave applications, FET-based switches are preferred over PIN diodes due to their ease of integration with other transceiver components, rapid switching speed, and low power dissipation [38; 42]. However, the challenges with FET switches include reduced isolation at high frequencies, lower breakdown voltage, and limited power handling capabilities. These drawbacks are often attributed to the gate-drain parasitic capacitance, denoted as \(C_{gd}\). In this design, a 0.15 \(\upmu\)m pHEMT process from WIN Semiconductors Corporation is selected to realise a broadband Single-Pole Double-Throw (SPDT) switch suitable for satellite and 5G communication applications. Each branch of this well-matched SPDT switch is composed of four stacked FET units in shunt configuration, along with two series FETs to achieve higher voltage handling [44; 45; 46]. The presence of series FETs allows the SPDT switch to operate down to DC by isolating the off-state capacitance effectively.