esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, the effects of the gap size, spacing between the adjacent sealing sheets, sealing number, bending angle of sealing sheets, and pressure difference on direct air leakage and orifice coefficient are experimentally and numerically studied. The numerical results using the Transition \(k\)-\(kl\)-\(\omega\) turbulence model are in good agreement with experimental data.  
The flow resistance decreases with the gap size, which leads to an increase in direct leakage. The sealing performance is enhanced by increasing the spacing of adjacent sealing sheets. The sealing sheets with positive bending angle have less air resistance in the flow path, which would lead to larger leakage. The increase in the number of sealing sheets gives rise to an increase in the generation probability of recirculation zone and vortex, which aggravates the mainstream energy loss.  
To obtain lower leakage rate, it is recommended for the tri-sectional air preheater that the rotor passes through flue gas section, secondary air section, and primary air section in sequence.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\alpha\) & \(A\) & \(B\) & \(C\) & \(D\) & \(R^{2}\) \\ \hline \(25^{\circ}\) & 0.17234 & 0.00238 & 0.0914 & \(-\)0.2396 & 0.924 \\ \(-25^{\circ}\) & 0.20501 & 0.0124 & 0.14665 & \(-\)0.2452 & 0.951 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power values for the correlation at different restrictions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Device Used** & **Device Range** & **Device Accuracy** & **Uncertainty** \\ \hline Gap size & Tape & 0–5 m & \(\pm\)0.001 m & 0.02\% \\ Static pressure & Pressure gauge & 0–99,990 Pa & \(\pm\)10 Pa & 0.01\% \\ Air temperature & Thermocouple & -50–150 \({}^{\circ}\)C & \(\pm\)0.1 \({}^{\circ}\)C & 0.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The main parts for the uncertainty analysis.  
As listed in Table 2, we can rank all parameters base on its absolute value. Thus, the pressure difference has a dominant position, the next are the number of sealing sheets and the spacing of adjacent sealing sheets, the last is the gap size. Here, we do not consider the importance of bending angle, which remains to be further studied.  
Conceptualization, H.Z. and H.P.; data curation, D.L. and Y.H.; formal analysis, D.L.; funding acquisition, L.W.; investigation, D.L. and Y.H.; methodology, D.L., L.W., Y.H., and Y.B.; project administration, L.W.; resources, H.Z. and H.P.; supervision, D.C.; writing--original draft, H.Z.; writing--review and editing, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central University, grant number XJJ2018071; China Postdoctoral Science Foundation, grant number 2019M653625; and National Natural Science Foundation of China, grant number 51806171.  
The authors would like to thank the State Key Laboratory of Multiphase Flow in Power Engineering for offering the centrifugal fan, and thank [PERSON] for his conscientious help and works.  
The authors declare no conflicts of interest.
# A Palm-Jacobaeus Loss Formula for Multi-Service Systems with Separated Resources
## 2 Model of the Group with Limited Availability  
Consider a system called LAG (limited-availability group) in the literature, cf. [36; 37]. The group is composed of \(k\) identical component resources, each with the capacity of \(f\) AUs (allocation units) (Allocation unit is a universal term describing the unit of resources required in a given system. An example of calculating the allocation unit in 5G systems with OFDM (orthogonal frequency division multiplexing)-based cells is presented in [42]), therefore defining the capacity of the system to be equal to \(V=kf\) AUs. The system services a call only when the call can be serviced by the available resources of one of the component resources. In traffic engineering, AU is defined as the greatest common divisor of the bitrates of all the call classes offered to the system [43; 44; 45; 46; 47].  
The assumption is that the LAG services a mixture \(M\) of independent Erlang traffic streams with the following intensities: \(A_{1},A_{2},\ldots,A_{M}\). A call of class \(i\) requires \(t_{i}\) AUs to set up a connection. A multi-dimensional Markov process in the LAG can be approximated by a one-dimensional Markov chain and can be described by the following equation [48]:  
\[n\left[P(n)\right]_{V}=\sum_{i=1}^{M}A_{i}t_{i}\xi_{i}(n-t_{i})\left[P(n-t_{i} )\right]_{V}, \tag{2}\]  
where \(\left[P(n)\right]_{V}\) is the probability that the LAG is in a state of \(n\) busy AUs in the system, whereas \(\xi_{i}(n)\) is the conditional transition probability for a traffic stream of class \(i\) between the adjacent (neighboring) states of the process. The blocking probability \(E_{i}\) for a stream of class \(i\) in the LAG model can be determined by the following equation:  
\[E_{i}=\sum_{n=0}^{V}\left[P(n)\right]_{V}\left[1-\xi_{i}(n)\right]. \tag{3}\]  
The conditional transition probability \(\xi_{i}(n)\) in the LAG model [48] can be approximated by the following dependency:  
\[\xi_{i}(n)=\frac{F(V-n,k,f,0)-F(V-n,k,t_{i}-1,0)}{F(V-n,k,f,0)}, \tag{4}\]  
where \(F(x,k,f,t)\) is the number of possible arrangements of \(x\) objects in \(k\) identical boxes, each of which is able to accommodate \(f\) objects. A further assumption is that there are at least \(t\) objects (from among all the \(x\) objects to be arranged) in each of the boxes:  
\[F(x,k,f,t)=\sum_{i=0}^{\left\lfloor\frac{x-kf}{f-1}\right\rfloor}(-1)^{i} \binom{k}{i}\binom{x-k(t-1)-1-i(f-t+1)}{k-1}. \tag{5}\]Note that Equation (4) applies to the arrangements of free (unoccupied) AUs in the LAG and can be interpreted as the ratio between the number of favorable arrangements, i.e., those in which at least one resource that has \(t_{i}\) free AUs can be found, and the number of all possible arrangements \(V-n\) of free AUs.
## 4 Experiments
### Performance Evaluation
#### 4.4.2 Comparison with Ground Truth  
In Figure 5, we visualize the comparison in 10 steps predicted by STGNN and the ground truth values of two representative metro stations for the Hangzhou dataset and one node for the simulation dataset. From the figures, we can observe that the passenger flow predicted by STGNN is close to the ground truth values.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Prediction Step = 30**} & \multicolumn{4}{c}{**Prediction Step = 60**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Inflow**} & \multicolumn{2}{c}{**Outflow**} & \multicolumn{2}{c}{**Inflow**} & \multicolumn{2}{c}{**Outflow**} \\  & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} \\ \hline LSTM & 34.41 & 22.38 & 43.47 & 26.94 & 37.20 & 24.25 & 46.42 & 29.39 \\ Reformer & 25.41 & 14.40 & 31.83 & 18.83 & 27.26 & 15.86 & 34.42 & 20.55 \\ Autoformer & 38.75 & 22.79 & 41.56 & 25.59 & 54.38 & 29.14 & 57.25 & 32.14 \\ TimesNet & 26.47 & 15.18 & 34.95 & 19.34 & 28.62 & 16.73 & 36.33 & 20.66 \\ \hline GCN & 69.20 & 40.50 & 76.46 & 43.34 & 73.83 & 44.84 & 80.11 & 46.76 \\ T-GCN & 32.96 & 20.08 & 40.79 & 25.99 & 35.33 & 23.45 & 44.32 & 28.34 \\ STG-NCDE & 38.95 & 20.45 & 51.39 & 27.40 & 41.81 & 22.03 & 54.53 & 30.10 \\ ASTGCN & 30.34 & 17.99 & 36.80 & 22.10 & 32.87 & 19.96 & 39.33 & 23.28 \\ ASTGNN & 23.97 & 13.92 & 30.43 & 18.32 & 25.59 & 14.95 & 32.92 & 19.96 \\ \hline \hline STGNN (ours) & 21.44 & 12.65 & 27.81 & 17.12 & 23.40 & 13.78 & 28.93 & 18.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average performance comparison over 30 and 60 steps for the Hangzhou dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Prediction Step = 10**} & \multicolumn{4}{c}{**Prediction Step = 20**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Inflow**} & \multicolumn{2}{c}{**Outflow**} & \multicolumn{2}{c}{**Inflow**} & \multicolumn{2}{c}{**Outflow**} \\  & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAE**} \\ \hline LSTM & 29.13 & 19.15 & 38.93 & 23.74 & 32.92 & 21.29 & 41.42 & 25.17 \\ Reformer & 22.47 & 13.27 & 30.83 & 18.01 & 24.43 & 14.01 & 31.29 & 18.47 \\ Auttoformer & 24.59 & 15.41 & 32.65 & 20.68 & 32.44 & 19.95 & 39.
###### Abstract  
Distributed and networked mobile sensor platforms using unmanned aerial and/or ground vehicles to survey areas of interest offer a safer and more efficient method for radiological contamination mapping; however, most applications rely on uniformly sweeping of the area in a raster-type motion without utilizing the information available in a dynamic sense. We have developed a fully autonomous optimal motion planning procedure for networks with two or more mobile sensors. The procedure utilizes well-established concepts of Gaussian processes in combination with control laws based on centroidal Voronoi tessellations to achieve optimal next-iteration sensor movements. A new method of informing optimal motion planning is proposed, whereby the absolute difference between the prior and current full-map prediction, referred to as the prediction-difference map, is used as the spatial density function within each Voronoi cell, providing immediate and iterative feedback for dynamic use of available information. The Gaussian process regression model used to estimate the contamination in unvisited locations also provides prediction uncertainties, and can be used as a quantitative metric to assess the confidence in the calculated contamination map; these estimates and prediction uncertainties are unavailable for standard uniform survey routines as they can only produce maps in the vicinity of observed locations. We present through simulation the achievable performance gains from using this new method by directly comparing to a uniform survey method. Results show that using the prediction-difference maps to inform motion planning procedures offers a faster rate of producing an accurate and convergent map relative to a uniform survey route.  
A Article  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
radiation mapping; mobile sensors; autonomous
# Study of Standing-Wave Thermoacoustic Electricity Generators for Low-Power Applications
###### Abstract  
This paper is concerned with the study of low-cost, low-power thermoacoustic electricity generators. Based on target electrical output power values of 50 and 100 W, three standing wave prototypes (of both one-stage and two-stage prototypes) integrating a commercial loudspeaker with different coupling arrangements are conceived. Each stage consists of a square-pore stack sandwiched between hot and ambient heat exchangers. The working gas is air at atmospheric pressure. The prototypes' efficiency in converting heat to electrical power is simulated by the specialized Design Environment for Low-Amplitude ThermoAcoustic Engines (DeltaEC) design tool based on the linear theory of thermoacoustics. At a given operation frequency, the optimal impedance matching between the loudspeaker and the engine is realized by adjusting both the engine parameters (stack location, stack length, heat exchangers length, loudspeaker location) and loudspeaker parameters (load resistance and box volume). Computations reveal that the one-stage engine and two-stage engine with loudspeakers coupled in side-branch mode are able to meet the target output power values with comparable thermal-to-electric efficiency (4.6%). The two-stage engine with the loudspeaker coupled in push-pull mode is unable to reach the desired power output and is characterized by low conversion efficiencies (2%) due to the poor loudspeaker-engine acoustic impedance matching.  
thermoacoustics; electricity generator; heat; acoustic power Article
## 3 New Multi-Key Variant of BGV
### Improved Multi-Key BGV
It follows that Hybrid 0 and Hybrid 1 are computationally indistinguishable from the perspective of any PPT adversary. Formally, we have:  
\[\left|\Pr\!\left[\mathrm{Exp}_{0}(\lambda)=1\right]-\Pr\!\left[\mathrm{Exp}_{ 1}(\lambda)=1\right]\right|\leq\mathrm{neg}(\lambda).\]In Hybrid 1, the adversary \(\mathcal{A}\) has no advantage beyond random guessing, and hence:  
\[\Pr[\mathrm{Exp}_{1}(\lambda)=1]=\frac{1}{2}.\]  
By combining the above, we conclude that the adversary's distinguishing advantage in the real IND-CPA experiment is negligible:  
\[\left|\Pr\left[\mathrm{Exp}_{0}(\lambda)=1\right]-\frac{1}{2}\right|\leq\mathrm{ neg}(\lambda).\]  
A similar analysis holds for extended ciphertexts. As a result, neither the secret keys nor the plaintexts are revealed by the public keys or ciphertexts, ensuring IND-CPA security for \(\mathbf{MK-BGV}\). **Correctness:** At level \(l\), a ciphertext \(\mathbf{c}=(c_{0},c_{1},\ldots,c_{n})\in R_{Q_{l}}^{n+1}\) is decrypted as \(\mu=(c_{0}+\sum_{i=1}^{n}c_{i}s_{i,l})\mathrm{mod}\ Q_{l}\mathrm{mod}\ p\), where \(s_{l,i}\) denotes the secret key of party \(i\) at level \(l\). As long as \(\left\|c_{0}+\sum_{i=1}^{n}c_{i}s_{i,l}\right\|_{\infty}<Q_{l}/2\), the reduction modulo \(Q_{l}\) and modulo \(p\) correctly recover the plaintext \(\mu\). Regarding the correctness of homomorphic evaluation, consider two ciphertexts \(\mathbf{c}=(c_{0},c_{1},\ldots,c_{n})\) and \(\mathbf{c}^{\prime}=\left(c_{0}^{\prime},c_{1}^{\prime},\ldots,c_{n}^{\prime}\right)\) at the same level \(l\). Their homomorphic sum is defined as \(\mathbf{c}^{\prime}=\mathbf{c}+\mathbf{c}^{\prime}\) component-wise modulo \(Q_{l}\). Decryption then yields \(\mu^{\prime\prime}=(c_{0}+c_{0}^{\prime})+\sum_{i=1}^{n}\left(c_{i}+c_{i}^{ \prime}\right)s_{i,l}=\mu+\mu^{\prime}\pmod{Q_{l}}\pmod{p}\). In homomorphic addition, the noise components of the two ciphertexts are added linearly. Similarly, their homomorphic multiplication is computed as \(\mathbf{c}_{mult}=\mathbf{c}\otimes\mathbf{c}^{\prime}\) and decryption of \(\mathbf{c}_{mult}\) yields \(\sum_{0\leq i\leq j\leq n}c_{i,j}\cdot s_{l,i}s_{i,j}=(c_{0}+\sum_{i=1}^{n}c_{i} s_{i,l})\left(c_{0}^{\prime}+\sum_{j=1}^{n}c_{j}^{\prime}s_{i,j}\right) \mathrm{mod}\ Q_{l}\mathrm{mod}\ p\), which simplifies to \(\mu\cdot\mu^{\prime}\pmod{p}\). In homomorphic multiplication, the noise components combine multiplicatively, and additional noise from cross-terms is introduced. However, with proper noise management techniques--such as gadget decomposition and modulus switching--the overall noise remains within the decryption threshold, ensuring that the correct product of the original plaintexts is recovered.
###### Abstract  
This paper presents a low-cost, low-sidelobe-level, differential-fed, substrate-integrated waveguide (SIW)-based slot array antenna with zero beam squint. The antenna consists of two identical six-way unequal power dividers (PDs) and a 6 \(\times\) 16 slot array and is realized on a single-layer substrate. The six-way unequal PD provides tapered amplitude and in-phase excitation for six SIWs, and each of them has 16 radiating slots. The 1 \(\times\) 16 linear slot array on each SIW is excited using a differential feed to avoid undesired beam squinting across its operating band. A two-way hybrid waveguide (WG)-to-SIW \(E\)-plane PD is developed to provide equal amplitude and out-of-phase excitation for two six-way unequal power dividers. Moreover, metallic decoupling walls are implemented between two adjacent linear slot arrays to reduce \(E\)-plane external mutual coupling. An antenna prototype is fabricated and experimentally verified. The fabricated antenna shows that the measured \(-10\) dB reflection bandwidth is 7.15% (9.57-10.28 GHz), with the achieved gain ranging from 20.30 to 21.92 dBi. A stable boresight beam is observed over the entire operating band. Furthermore, at the designed frequency of 10 GHz, peak SLLs of \(-29.1\) dB and \(-29.4\) dB are achieved in the \(E\)- and \(H\)-plane, respectively.  
differential feed; low side-lobe array; slot array; substrate integrated waveguide (SIW)
## 2 Method
### LEO Satellite Clock Determination in Near-Real-Time
Figure 3: The time reference of the CNES real-time GPS satellite clocks on DOY 227, 2018 before and after re-referencing to the satellite clock of G05. \[L_{i}^{s}=\rho_{i}^{s}+c(dt_{r}-dt_{s})+\lambda_{i}(N_{i}^{s}+b_{r,i}-b_{i}^{s} )-I_{i}^{s}+\varepsilon_{i,L} \tag{2}\]0 Precision of the real-time GPS satellite clocks from different analysis centers. With inconsistent and unstable time references for LEO satellite clocks determined during different sessions, jumps between sessions and degraded clock prediction results can occur. To solve these issues, the LEO satellite clock estimates need to be re-referenced to a stable time reference directly after the near-real-time clock determination in each session. In real-time, the time reference is proposed to be aligned to a selected satellite clock in the corresponding GNSS with good stability and completeness, avoiding at least jumps (red) in \[L_{i}^{s}=\rho_{i}^{s}+c(dt_{r}-dt_{s})+\lambda_{i}(N_{i}^{s}+b_{r,i}-b_{i}^{s} )-I_{i}^{s}+\varepsilon_{i,L} \tag{2}\]1 After aligning to a stable GPS satellite clock, as shown in the blue line in Figure 3, the standard deviation (STD) of the time reference is reduced from above 10 ns to 1.5 ns, with all the large jumps removed (blue). Figure 4 shows the Sentinel-3B satellite clocks determined using the CNES real-time clock products. It can be observed that when using the original real-time CNES clock products, the LEO satellite clocks (red) exhibit significant fluctuations. This is attributed to the unstable reference of real-time CNES clocks as shown in \[L_{i}^{s}=\rho_{i}^{s}+c(dt_{r}-dt_{s})+\lambda_{i}(N_{i}^{s}+b_{r,i}-b_{i}^{s} )-I_{i}^{s}+\varepsilon_{i,L} \tag{2}\]2 Aligning the CNES real-time clocks to a stable GPS satellite clock and then using it to determine the Sentinel-3B satellite clocks (blue), results in a substantial improvement in stability. The improvement in the stability of the time reference does not affect the clock estimation precision but will influence the LEO satellite clock prediction [18].
# Cortical Connectivity Response to Hyperventilation in Focal Epilepsy: A Stereo-EEG Study
## 1 Introduction
A better understanding of the relation between HV and seizure development could be offered by the analysis of intracranial EEG recording as allowed by Stereo-EEG (SEEG). This latter is an invasive surgical procedure that enables the recording of deep cortical signals, providing fundamental information for the accurate localization of the Epileptogenic Zone (EZ), i.e., the cortex area responsible for seizure generation [16]. To our knowledge, at the present time, there have been no research works investigating the HV mechanisms by exploiting the intracranial EEG signals. This study aimed to fill this gap and assess how the HV affects cortical brain structures in patients with focal epilepsy. Our fundamental hypothesis is that HV promotes an increase in cortical connectivity as occurs during the Non-REM sleep (N-REM) status, but such an effect is not directly associated with the outbreak of the seizure onset. To achieve this goal, we selected a cohort of focal epilepsy patients that underwent SEEG monitoring for pre-surgical evaluation. Then, we computed multiple quantitative metrics from the recorded signals, using the PTE algorithm to estimate cortical connectivity. To spotlight the HV effects, we compared the SEEG signals associated with HV against those associated with the awake status, N-REM, and ictal transition, i.e., the period during which the seizure starts to form.
## 2 System Overview and Power Channel Analysis
\begin{array}{c}I_{p}\\ I_{s}\end{array} \right]=\left[ \begin{array}{c}V_{p}\\ V_{s}\end{array} \right], \tag{3}\]  
where  
\[Z_{1}=j\omega_{p}(L_{1}+L_{T1}+L_{T3})+\frac{1}{j\omega_{p}C_{1}}+R_{1}, \tag{4}\]  
Figure 3: The schematic diagram of the SWIPT system equivalent circuit. Figure 2: The timing diagram of phase shift control signals. \[Z_{2}=j\omega_{p}(L_{2}+L_{T2}+L_{T4})+\frac{1}{j\omega_{p}C_{2}}+R_{2}. \tag{5}\]  
The power transfer frequency is necessary to work at the circuit resonance frequency. \(C_{1}\) and \(C_{2}\) compensate the inductance of the primary and secondary coils. The power resonant angular frequency \(\omega_{p}\) is obtained from the inductance and compensation capacitance values in the circuit. \[\omega_{p}=\frac{1}{\sqrt{(L_{1}+L_{T1}+L_{T3})C_{1}}}=\frac{1}{\sqrt{(L_{2}+ L_{T2}+L_{T4})C_{2}}} \tag{6}\]  
The active power \(P_{1}\) and \(P_{2}\) of the primary and secondary sides are  
\[P_{1}=\text{Re}\{V_{p}\cdot I_{p}^{*}\}=\frac{R_{2}V_{p}^{2}+\omega_{p}MV_{p}V_ {s}\sin\phi}{R_{1}R_{2}+\omega_{p}^{2}M^{2}} \tag{7}\]  
\[P_{2}=\text{Re}\{V_{s}\cdot I_{s}^{*}\}=\frac{-R_{1}V_{s}^{2}+\omega_{p}MV_{p} V_{s}\sin\phi}{R_{1}R_{2}+\omega_{p}^{2}M^{2}} \tag{8}\]  
According to the Equations (7) and (8), ignoring the resistances \(R_{p}\) and \(R_{s}\), the secondary side output power \(P_{out}\) is obtained. \[P_{out}\approx\frac{8}{\pi^{2}\omega_{p}M}V_{1}V_{2}\sin\alpha\sin\beta\sin\phi \tag{9}\]  
The range of \(\alpha\) and \(\beta\) is [0, \(\pi\)]. The range of \(\phi\) is [0, 2\(\pi\)]. Therefore, Equation (9) shows that the direction of the output power is determined by \(\phi\). The magnitude of the transmission power is affected by the phase shift angles \(\alpha\), \(\beta\), and \(\phi\). The bidirectional power transfer can be realized by changing the phase shift angle \(\phi\). Figure 4 shows the curve of output power with the phase shift angle \(\phi\). During a period cycle, in the interval [0, \(\pi\)], \(\text{sin}\phi\) > 0, the output power is transmitted from the primary side to the secondary side; in the interval [\(\pi\), 2\(\pi\)], \(\text{sin}\phi\) < 0, the output power is transferred from the secondary side to the primary side. However, in order to ensure the maximum output power, the value of \(\phi\) is chosen between \(-\pi/2\) and \(\pi/2\). In short, the working states of the proposed SWIPT system is decided by the values of \(\alpha\), \(\beta\), and \(\phi\).