esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Protocols  
We conducted ice melting tests in controlled laboratory conditions at the International Laboratory of Anti-icing Materials (AMIL). The tests were carried out in a climate chamber measuring 9.1 m in height, 5.5 m in length, and 3.5 m in width. The chamber is designed to maintain a controlled air temperature ranging from 0 \({}^{\circ}\)C to \(-\)35 \({}^{\circ}\)C with a precision of 0.5 \({}^{\circ}\)C. The temperature inside the chamber for our tests was set at \(-\)10 \({}^{\circ}\)C. For all tests, we used commercially available solid-state sodium formate (NaFo) as the RDP. NaFo is commonly used for winter maintenance at airports, including the Montreal airports, because of its environmental and anti-corrosion properties and its cost-effectiveness. This RDP is a white granular solid with particles aggregated into non-cohesive flakes measuring 1-3 mm in diameter and whose freezing point is \(-\)16 \({}^{\circ}\)C for a mass percentage of 24.70% \(w/w\)[32].  
Sodium formate is effective at preventing re-icing down to \(-16\)\({}^{\circ}\)C. When NaFo is dissolved in water, it reduces the freezing point of the solution. As a result, the solution remains liquid at lower temperatures than pure water would [32]. It prevents ice reformation by maintaining a liquid state even in cold conditions. It also rapidly penetrates solid snow and ice layers, detaching them from asphalt or concrete surfaces.  
Our protocol involved running three distinct tests: (i) ice melting assessments conducted according to standardized procedures using thermometric observations; (ii) ice melting evaluations conducted using the existing airport RDP application rate, supplemented with thermometric observations; and (iii) a detailed examination of ice melting phenomena, incorporating both thermometric and Raman spectroscopic observations.
# Mod2 VQLS: A Variational Quantum Algorithm for Solving Systems of Linear Equations Modulo 2
## 6 Related Classical and Quantum Methods for Solving Linear Systems
### Quantum Linear Solvers
#### 6.1.1 Specific Differences from HHL  
The Mod2 VQLS encoding furnishes several advantages that address the HHL caveats delineated by [25]. For starters, in HHL the load vector \(b^{\prime}\) must be loaded onto the quantum processor and this task in itself may require an exponential number of steps (with respect to the number of qubits in the circuit) ([25], Caveat 1). By contrast, in our setting the load vector \(b\) is merely an \(m\)-bit string so it corresponds to an \(m\)-qubit computational basis state. As such, it can be efficiently loaded on the quantum computer using at most \(m\) NOT or Pauli-\(X\) gates, although it turns out it is not even necessary to load \(b\) onto the quantum processor in our current implementation.  
Moreover, HHL assumes that the quantum computer can apply the unitary operator \(e^{-iA^{\prime}t}\) efficiently for various values of \(t\) ([25], Caveat 2). In our setting, the closest analog is applying the matrix-vector product operator defined below, and doing so requires precisely \(N\) two-qubit gates, where \(N\) is the number of non-zero entries in \(A\). The complexity here indicates that Mod2 VQLS directly benefits from the sparsity of \(A\), which is useful in applied settings where one typically deals with large, (very) sparse matrices. For instance, the matrices encountered in recent large factoring calculations have millions of rows and columns, but only a few hundred non-zero entries per row.  
Finally, extracting the solution vector \(x^{\prime}\) upon executing the HHL algorithm requires an exponential number of circuit measurements ([25], Caveat 4). Again, the exponential here is with respect to the number of qubits in the HHL circuit. By contrast, in our modulo 2 setting the solution vector \(x\) is an \(n\)-bit string, which corresponds to an \(n\)-qubit computational basis state, so it can be read off from the optimized quantum ansatz using a fixed number of shots. An added benefit of our method is that the optimized quantum ansatz obtained upon running Mod2 VQLS is a superposition over computational basis states corresponding to every possible solution to \(Ax=b\), so we can effectively sample the solution set by measuring the optimized quantum state. This is quite useful in the factoring application, where we typically need to find more than one element in the kernel of the coefficient matrix in order to build a factor.
The results showed that AES+Pre achieved a good balance between transmission efficiency and implementation complexity, and is especially suitable for high-frequency and large-scale data transmission application scenarios. 4. Enhanced the security and efficiency of multi-client equipment information transmission. By introducing the preprocessing mechanism, TLS and AES encryption algorithm, the speed of encrypted transmission was greatly improved while protecting data confidentiality and integrity, which meets the dual requirements of data security and communication efficiency in the port environment.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 1 Introduction
The BG-MC model is employed to jointly exploit both the sparse structure of the active UE and the slow change feature in consecutive time slots of the AS. Indeed, the performance of the UAV-BS detector can be improved by updating the AS while considering the temporal correlation. Hence, both the forward messages from the previous slot and the backward messages from the next slot are considered in the MP algorithm at each iteration. 2. Making use of the HMP rule, the GAMP-BG-MC algorithm is proposed to detect the UAV-BS received signal of multiple superimposed UEs. The proposed GAMP-BG-MC algorithm not only calculates the more accurate posterior probability density function (PDF) of the transmitted signal but also adaptively learns the parameters of the prior model during the estimation procedure. Simulation results show that the GAMP-BG-MC algorithm has a better signal-to-noise ratio (SNR) performance as compared to the GAMP-SBL [13; 14] and the GAMP-PCSBL [14] algorithms, respectively, while keeping the complexity. The remaining parts of this paper are organized as follows. Section 2 introduces the system model of the UAV-based uplink GF-NOMA network. Section 3 proposes the GAMP-BG-MC algorithm as the adaptive UAV-BS detector. Then, the numerical results of the proposed algorithm are given in Section 4. Finally, the conclusions of this paper are presented in Section 5. _Notation_: Boldface lowercase and uppercase letters denote vectors and matrices; \((\cdot)^{\mathsf{T}}\) and \((\cdot)^{*}\) denote transposition and conjugation, respectively. The expectation of a function \(f(x)\) w.r.t. a PDF \(g(x)\) is expressed as \(\left<f(x)\right>_{g(x)}\); \(\mathcal{CN}(x;\mu,v)\) denotes \(x\) following a complex Gaussian distribution with mean \(\mu\) and variance \(v\). \(\delta(\cdot)\) is the Dirac delta function.
## 2 Characterization of Marine and Continental Shale Reservoirs
### Rock Mechanical Properties of Marine and Continental Shale Reservoirs  
Shale develops laminations and natural cracks, which are typical of various anisotropic rocks. In the process of shale fracturing, the initiation and expansion of cracks are closely related to their own mechanical properties; so, it is necessary to determine the rock's mechanical parameters. In this paper, the experimental samples were selected from the same  
Figure 3: XRD mapping of marine/continental shales.  
Figure 2: Location map of coring areas. (**A**) Regional location map of Sichuan Basin, China; (**B**) marine shale sampling sites; (**C**) continental shale sampling sites.  
regional outcrops as the samples used in the hydraulic fracturing experiments. Five sets of identical samples were selected for each mechanical parameter. The specimen was cored in the direction of the bedding surface. The parameters of rock mechanical strength, elastic modulus, and Poisson's ratio were obtained through indoor rock mechanical experiments. The specific experimental results are summarized in Table 1.  
From the above table, it can be seen that the uniaxial compressive strength of the marine shale is in the range of 57.99-80.60 MPa, while the uniaxial strength of the continental shale is in the range of 30.51-46.23 MPa, which is lower than that of the marine shale by about 20.28-62.15%. After fracturing the specimens, the rupture form of marine shale after damage is relatively singular, which is reflected as a high-angle shear damage surface running through the specimen from top to bottom, showing good britteness; the continental shale is dominated by localized fragmentation and slip and shear damage, with the intensive formation of numerous fine microcracks crushing next to the main crack, and the specimen fragmentation characteristics are obvious. Moreover, during the test, the marine shale had a crisp, short rupture sound when it reached the peak strength and was destroyed, while the continental shale showed a low muffled sound when it was destroyed, reflecting the difference in the brittleness of the rock. The tensile strength test results show that the tensile strength of marine and continental shales varies greatly, with the tensile strength of marine shale ranging from 15.92 to 18.63 MPa and that of continental shale ranging from 4.58 to 6.54 MPa. This is due to the anisotropy of shale mechanical properties; in order to ensure that the influencing factors are the same, all the shale specimens were subjected to the load in accordance with the direction of the laminae, and the Brazilian splitting experiments obtained indirect results. The tensile strength is essentially the tensile strength of the shale laminae. The fracture toughness results show that the marine shale is twice as strong as the continental shale.
###### Abstract  
Research on helicopter dispatching has received considerable attention, particularly in relation to post-disaster rescue operations. The survival chances of individuals trapped in emergency situations decrease as time passes, making timely helicopter dispatch crucial for successful rescue missions. Therefore, this study investigates a collaborative helicopter rescue routing problem with time window and limited survival time constraints, solving it using an improved iterative greedy (IIG) algorithm. In the proposed algorithm, a heuristic initialization strategy is designed to generate an efficient and feasible initial solution. Then, a feasible-first destruction-construction strategy is applied to enhance the algorithm's exploration ability. Next, a problem-specific local search strategy is developed to improve the algorithm's local search effectiveness. In addition, the simulated annealing (SA) method is integrated as an acceptance criterion to avoid the algorithm from getting trapped in local optima. Finally, to evaluate the efficacy of the proposed IIG, 56 instances were generated based on Solomon instances and used for simulation tests. A comparative analysis was conducted against six efficient algorithms from the existing studies. The experimental results demonstrate that the proposed algorithm performs well in solving the post-disaster rescue helicopter routing problem.  
Post-disaster rescue; collaborative helicopter rescue routing problem; limited survival time; time windows; improved iterated greedy algorithm +
Footnote †: journal: _Algorithms_
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Modulation Techniques
#### 5.2.5. Quadrature Amplitude Modulation in BackCom  
QAM is a technique that combines two amplitude-modulated signals to double the effective bandwidth. Figure 23 illustrates the block diagram of QAM in BackCom. The notations \(Z_{1}\), \(Z_{2}\), \(Z_{3}\),.., \(Z_{M}\) show the \(M\) number of load impedance to backscatter signals, and \(S_{1},S_{2},S_{3},...,S_{N},S_{N+1}\) represent the \(M+1\) number of switches. The QAM modulation has the advantage of considering no on-chip inductors [98]. Tags in QAM can send higher than one data bit in each symbol period, permitting tag makers to use low power on-chip oscillators. QAM maintains data throughput equal to ASK or PSK by using a frequency equal to the symbol rate. [PERSON] et al. suggested a dual band 4-QAM Amb-BackCom circuit for modulation that utilizes a dual-band Wilkinson power divider in addition to a modulator made up of two transistors [116]. This circuit consumes 27 nW of power for a 500 kbps data rate.  
**Discussion:** Table 7 gives a summary of modulation techniques used in BackCom. The key conclusions are as follows:  
1. The AM technique, based on OOK, improves the communication range in BiS [50].
2. Differential modulation can be used in Amb-BackCom systems for signal detection [90].
3. QAM is used to double the effective bandwidth [95].
4. Frequency modulation utilizes FSK and GFSK to extend the communication range by changing the frequency to the Bluetooth spectrum [97].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Modulation** & **Techniques** & **Objective(s)** \\ \hline
[50] & \(\bullet\) Amplitude Modulation & \(\bullet\) ON–OFF Keying (ASK). & \(\bullet\) Increase the range of BiS. \\
[66] & \(\bullet\) Frequency Modulation & \(\bullet\) Frequency shift keying. & \(\bullet\) Extend the range of coherent bistatic devices. \\
[90] & \(\bullet\) Amplitude Modulation & \(\bullet\) Differential modulation. & \(\bullet\) Solve signal detection problems for Amb-BackCom. \\
[97] & \(\bullet\) Frequency Shifting & \(\bullet\) Gaussian frequency shift keying. & \(\bullet\) Create frequency components within a Bluetooth low energy Channel. \\
[95] & \(\bullet\) Amplitude Modulation & \(\bullet\) Quadrature amplitude modulation. & \(\bullet\) Double the effective bandwidth. \\
[96] & \(\bullet\) Frequency Shifting & \(\bullet\) Bringing signals to FM range signals. & \(\bullet\) Decode BackCom transmission with FM receivers. \\ \hline \end{tabular}
\end{table}
Table 7: Summary of modulation techniques for BackCom.  
Figure 23: Quadrature amplitude modulation (QAM) in BackCom.  
Figure 22: Differential encoder for a passive tag.
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 4 Experiments Settings and Results
### Main Results
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Target Model** & **Attack** & **ASR** & **CD** & **HD** & **L2** & **AT** \\ \hline \multirow{9}{*}{**PointNet++**} & PGD & 34.48\% & 0.00512 & 0.02053 & 2.56925 & 0.98669 \\  & I-FGSM & 8.91\% & 0.00009 & 0.00836 & 0.20468 & 1.08552 \\  & SIADV & 13.33\% & 0.00180 & 0.06930 & 1.85480 & 2.35535 \\  & Saliency Map & 14.10\% & 0.00493 & 0.11499 & None & 1.27082 \\  & Add & 9.93\% & 0.00186 & 0.00903 & 1.46697 & 3.34064 \\  & AOF & 65.19\% & 0.00708 & 0.02498 & 3.13824 & 10.16391 \\  & IA3-attack & 17.10\% & 0.00076 & 0.01834 & 0.70428 & 1.75391 \\  & **G&G Attack** & **69.25\%** & 0.00153 & 0.00389 & 1.48500 & 14.72075 \\ \hline \multirow{9}{*}{**DGCNN**} & PGD & 44.65\% & 0.00512 & 0.02053 & 2.56925 & 0.14294 \\  & I-FGSM & 26.74\% & 0.00009 & 0.00836 & 0.20467 & 1.11005 \\  & SIADV & 16.86\% & 0.00180 & 0.06930 & 1.85480 & 1.54077 \\  & Saliency Map & 55.19\% & 0.00495 & 0.11492 & None & 1.05804 \\  & Add & 32.01\% & 0.00186 & 0.00902 & 1.46481 & 3.85096 \\  & AOF & 87.76\% & 0.00702 & 0.02507 & 3.12184 & 36.2557 \\  & IA3-attack & 35.94\% & 0.00076 & 0.01834 & 0.70428 & 0.82829 \\  & **G&G Attack** & **94.77\%** & 0.00139 & 0.00359 & 1.45356 & 13.18447 \\ \hline \multirow{9}{*}{**CurveNet**} & PGD & 44.65\% & 0.00512 & 0.02053 & 2.56925 & 1.04294 \\  & I-FGSM & 18.44\% & 0.00009 & 0.00836 & 0.20466 & 0.80368 \\  & SIADV & 14.10\% & 0.00180 & 0.06930 & 1.85480 & 1.91420 \\  & Saliency Map & 31.85\% & 0.00492 & 0.11534 & None & 1.43144 \\  & Add & 18.68\% & 0.00186 & 0.00902 & 1.46567 & 3.43966 \\  & AOF & 74.64\% & 0.00708 & 0.02513 & 3.13730 & 13.58630 \\  & L3A-attack & 22.45\% & 0.00076 & 0.01834 & 0.70428 & 1.91566 \\  & **G&G Attack** & **87.84\%** & 0.00150 & 0.00376 & 1.50523 & 9.00795 \\ \hline \multirow{9}{*}{**PointCNN**} & PGD & 36.51\% & 0.00512 & 0.02054 & 2.56925 & 1.61203 \\  & I-FGSM & 30.67\% & 0.00009 & 0.00835 & 0.20464 & 1.57526 \\  & SIADV & 20.75\% & 0.00180 & 0.06930 & 1.85480 & 1.83620 \\  & Saliency Map & 49.43\% & 0.00492 & 0.11484 & None & 3.30967 \\  & Add & 27.76\% & 0.00186 & 0.00903 & 1.46394 & 4.54837 \\  & AOF & 53.40\% & 0.00706 & 0.02498 & 3.13396 & 13.46032 \\  & L3A-attack & 29.86\% & 0.00076 & 0.01834 & 0.70428 & 1.80675 \\  & **G&G Attack** & **90.52\%** & 0.00137 & 0.00345 & 1.43998 & 12.94643 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attack success rate (%) of black-box transfer attacks for each method with sor.
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 1 Dynamic Modeling of Integrated Bearing Systems
### Dynamical Differential Equations of Balls  
Figure 2 illustrates the force exerted on the ball during the integrated bearing's operation. In the figure, subscripts (\(i\)) and (\(e\)) denote the inner and outer raceways. The subscript (\(j\)) denotes the (\(j\))-th ball. The subscripts (\(b\)) and (\(c\)) denote the ball and the cage, respectively. The angles (\(\alpha_{ij}\)) and (\(\alpha_{ej}\)) denote the contact angles between the inner and outer raceways and the ball. The forces (\(Q_{ij}\)) and (\(Q_{ej}\)) denote the contact forces between the inner and outer raceways and the ball. The forces (\(T_{\eta ij}\)), (\(T_{\eta ej}\)), (\(T_{\zeta ij}\)), and (\(T_{\zeta ej}\)) denote the drag forces acting between the inner and outer raceways and the ball. The forces (\(F_{\eta j}\)) and (\(F_{\tau j}\)) represent the inertial force components of the ball. The forces (\(P_{R\eta j}\)) and (\(P_{R\xi j}\)) indicate the rolling friction at the contact points between the cage and the ball, whereas (\(P_{S\eta j}\)) and (\(P_{S\xi j}\)) represent the sliding friction at these contact points. The forces (\(F_{H\eta ij}\)), (\(F_{H\eta ej}\)), (\(F_{H\xi j}\)), and (\(F_{H\xi ej}\)) denote the horizontal components of fluid dynamic pressures exerted on the ball. Meanwhile, (\(F_{R\eta ij}\)), (\(F_{R\eta ej}\)), (\(F_{R\xi ij}\)), and (\(F_{R\xi ej}\)) denote the fluid dynamic pressure friction forces in the contact zone between the raceways and the ball. The force (\(m_{b}g\)) represents the weight of the ball. The forces (\(Q_{c\eta j}\)) and (\(Q_{cj\chi z}\)) are the projections of the contact forces between the cage and the ball within the ball's coordinate system. The angle (\(\varphi_{j}\)) represents the position angle of the ball, while (\(\gamma_{jy}\)) denotes the angle between forces (\(Q_{cijy}\)) and (\(y_{bj}\)).The angle (\(\gamma_{jz}\)) represents the angle between (\(Q_{cj\chi z}\)) and (\(z_{bj}\)), while (\(R_{jz}\)) denotes the distance from the contact point of the cage pocket to the ball along the direction of (\(z_{bj}\)). The quantities (\(J_{x}\)), (\(J_{y}\)), and (\(J_{z}\)) represent the moments of inertia of the ball about the three axes in its coordinate system. The quantities (\(G_{\eta j}\)) and (\(G_{zj}\)) represent the inertial moments of the ball in the (\(y_{bj}\)) and (\(z_{bj}\)) directions, respectively. The force (\(F_{Dj}\)) represents the resistance exerted by the oil-gas mixture on the ball. The quantities (\(\omega_{xj}\)), (\(\omega_{yj}\)), and (\(\omega_{zj}\)) represent the angular velocity components of the ball in its coordinate system, while (\(\dot{\omega}_{xj}\)), (\(\omega_{yj}\)), and (\(\dot{\omega}_{zj}\)) denote the angular acceleration components of the ball in the same coordinate system. Establish a unified set of differential equations for the dynamics of bearing balls. Figure 2: Force diagram of the ball. \[
## 4 Results and Discussion
### Measurements under Mechanical Excitation  
Working conditions 4 and 5 use mechanical force excitation from outside the pipeline wall in a position directly under that of the sound source in the previous working conditions, as shown in Figure 12. Corresponding to working conditions 2 and 3, the single frequencies of excitation applied in working conditions 4 and 5 are 4.2 and 5.2 kHz, respectively. The measurement results from the hydrophones and vibration sensors are shown in Figures 13-16. In contrast to the response of the acoustic signal, the excitation of the exc  
From the vibration measurement results in Figure 14, it can be seen that a vibration signal at the main frequency can still be measured on the wall at 5.3 m, but it is attenuated compared with the measurement from the sensor at 1.3 m, and the hydrophones in the pipeline are unable to detect any signal at 5.3 m. It can be deduced that the signal at 4.2 kHz is propagated mainly through the pipe wall in the form of vibrations. The second and third peaks in Figure 14 result from frequency doubling. The exciter generates frequency doubling when transmitting a single-frequency signal, which is a consequence of its own physical structure and has no effect on the results of this experiment.  
Figure 14: Mechanical excitation at a single frequency of 4.2 kHz. Measurement results from vibration sensors at different distances from the excitation point: (**a**) 1.3 m; (**b**) 5.3 m.  
Figure 15: Mechanical excitation at a single frequency of 5.2 kHz. Measurement results from hydrophones at different distances from the excitation point: (**a**) 1.1 m; (**b**) 1.1 m (local); (**c**) 5.1 m; (**d**) 5.1 m (local).  
For excitation at 5.2 kHz, which is above the cutoff frequency, Figure 15a,b shows the measurement results from hydrophones 1.1 m from the excitation point, and Figure 15c,d those from hydrophones 5.1 m from the excitation point, with Figure 15b,d being partial displays of the frequency band near the main frequency shown in Figure 15a,c, respectively. Figure 16 shows the measurement results from the wall vibration sensors 1.3 and 5.3 m from the excitation point.  
The sound power at the main frequency suffers almost no attenuation in the axial direction from 1.1 to 5.1 m, and conforms to the Bessel function dependence in the radial direction. It can be deduced that the signal at 5.2 kHz is propagated mainly in the form of sound through the fluid in the pipeline.
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 6 Efficiency  
Let us compare the two approaches (unicast and multicast) to distribute the group key to all the members in terms of delay, energy consumption, storage requirements, and packet fragmentation. The storage requirements and packet fragmentation are analyzed using both simulations and theory. The used topology is shown in Figure 1. In this topology, the NMM is in the cloud. The publisher is a child of the BR. Each member of the multicast group (M) is located at a certain number of hops from the BR (varying in different tests), with a particular number of intermediate modes (I) in between. Other configuration parameters can be found in Table 1.