esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 6 Conclusions  
In this paper, we present the experimental results on wireless channels of the wireless avionics intra-communications (WAIC) in the real aircraft environment (KT-1 Woongbi). To transmit and receive wireless signals at the WAIC bands (4.2-4.4 GHz), we made monopole antennas. We adopted IEEE 802.11 physical-layer specification as a candidate techniques for high data-rate applications of the WAIC. We performed tests under several measurement points in the real aircraft and obtained channel measurement results. Based on the results, we analyzed power delay profile and the path loss characteristics of the WAIC. Furthermore, we simulated the BER performance using Rayleigh channel coefficients and our measurement data when we considered maximum ratio combining (MRC) and space-time block code (STBC) schemes in uplink and downlink transmissions, respectively. Our results emphasize the crucial importance of studying and applying wireless technologies in LOS dominant environment rather than simply assuming Rayleigh fading channel. In addition, the installation of relay nodes is highly required to guarantee the reliable wireless communication within the aircraft. An additional real-field experiment design using multiple relay nodes to wirelessly cover a big-sized aircraft remains as future work for in-depth WAIC feasibility test. The experimental results in this paper can be applied to design another aircraft.  
**Author Contributions:** Formal analysis, I.B.; Investigation, I.B., J.-M.W., T.-W.B. and B.C.J.; Methodology, H.N.; Project administration, P.P. and B.C.J.; Resources, W.C. and C.-Y.K.; Software, T.K.; Supervision, B.C.J.; Validation, W.C., J.-M.W., C.-Y.K., T.-W.B. and P.P.; Writing--original draft, I.B.; and Writing--review and editing, B.C.J.  
**Acknowledgments:** This research was supported by Defense Acquisition Program Administration and Agency for Defense Development under Implementation Technology on High Reliability Wireless Networks for an Aircraft (UD150027 JD).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Optimization Design Algorithm
### Convergence Condition  
The process of judging the convergence of the algorithm is as follows:  
1. Firstly, a large iteration period is preset as the stopping condition of the algorithm. Then, the objective function values corresponding to the Pareto solutions obtained by each iteration are stored. These objective function values are arranged from small to large according to the gas consumption (or according to the bearing capacity) to form a \(2\times n\) matrix \(F_{t}\); \(n\) is the number of Pareto solutions, and \(t\) is the number of iterations;
2. Determine whether the number of Pareto solutions of the \(t+1\) generation is the same as that of the previous generation. If it is different, the Pareto solutions are still changing, so let the algorithm continue to iterate. If not, go to the next step to judge its convergence;
3. Equation (16) can express the convergence judgment formula. Firstly, the Frobenius norm of the difference between the \(t+1\) generation objective function matrix \(F_{t+1}\) and the previous generation objective function matrix \(F_{t}\) is calculated and then divided by the Frobenius norm of \(F_{t}\). When the result is less than the given small amount \(\varepsilon\), it is considered that the objective function matrix is almost unchanged, so the algorithm is considered to have converged: \[\frac{\left\|F_{t+1}-F_{t}\right\|_{F}}{\left\|F_{t}\right\|_{F}}\leq\varepsilon\] (16)  
The algorithm's parameters, such as population number and iteration period, are determined through the trial and error method. We fixed the population number to 30, and the archive's capacity is 50. We compared the convergence of Latin hypercube initialization and random initialization and ran each algorithm ten times, respectively. The convergence situation is as follows (Table 3):  
Latin hypercube sampling can obtain a more diverse initial population distribution with less population size, and the convergence is very stable in 10 runs. The population initialization distribution using random sampling may be evenly filled in the target space  
Figure 10: Distribution of (**a**) Latin hypercube and (**b**) random initialization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initialization** & **Average** & **Minimum** & **Maximum** \\
**Method** & **Convergence Period** & **Convergence Period** & **Convergence Period** \\ \hline Latin hypercube & 41.5 & 40 & 43 \\ Random & 47.2 & 39 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of convergence period.  
and too concentrated. Therefore, in 10 runs, the convergence period of random initialization fluctuates greatly. In the trial operation process, we obtained the maximum convergence period of 55, so we set the iteration termination condition to a maximum of 60 iterations to ensure the reliability of the algorithm's convergence in the real operation.
## 3 Simulation Results
### Optimized Design Variables for Sector Pole Head  
The conceptual idea is to design a novel pole head for the MR brake to improve its effective working surface area. Lord MRF-140 CG fluid's high-yield stress gradient led to its selection for this design. Since a thinner layer produces a higher magnetic field, the MR fluid layer thickness was fixed at 0.5 mm. The brake's axial width measured 28 mm, the rotor's radius fixed at 38 mm, and the magnetic core radius fixed at 8 mm. The SS-400 steel was used for the core blocks, rotor shaft, rotor disc, and stator caps because of its strength. The Nelder and Mead optimization strategy [22] is reasonably easy to implement to obtain the optimal design of MR brakes. It is utilized to choose the most efficient value from a range of choices. One of the most time-efficient methods is to use the Heuristic  
Figure 10: Flux flow through adjacent poles.  
Figure 9: Two-dimensional view of Flux flowlines in a MRB.  
methodology to obtain an approximate solution to a problem. In this context, the optimum value (maximum or lowest) of the aim is obtained by searching in a space.  
The design variables of pole head gap and coil current were shown to be important in enhancing the magnetic field strength and efficiency of the MRB. With 0.1 Amp increments, the input current is adjusted between 1.6 Amps and 2.0 Amps. Another factor that affects torque is the pole head gap, which was shown to be between 8 and 10 mm. Figure 11 illustrates how, up to several iterations, the brake torque value increased appropriately with the design factors. Thus, for the suggested design, values at iteration 63 were deemed to represent the best feasible pole head gap of 8.67 mm, with an ideal coil current of 1.8 Amp.
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 2 Methods
### CNN Architectures  
We developed three CNNs in this work. After converting the sampled IMU data to a vector and normalizing it, the vector was fed to convolutional layers and max-pooling layers. The activation function applied after convolutional layers were ReLU. After extracting the features by convolutional and pooling layers, the vector was flattened and fed to fully connected layers. The network configurations are shown in Figure 2. We used TensorFlow to program, train, and test the CNNs.  
Figure 1: Time\(-\)divided IMU outputs.
# On the Competitiveness of Oblivious Routing:
## 4 New Performance Metrics for Static Routing  
The competitive ratio is a _worst-case_ metric. Underlying this metric is the intuition that a low competitive ratio guarantees good routing, because we can expect the worst-case congestion to appear for only a few pathologic traffic matrices. To determine whether this intuition is plausible, we need to be able to answer questions like \"What is the chance that we experience congestion somewhere in the network?\", or \"How grave a congestion can we expect?\". To do this, we need to develop new, fundamentally _statistical_ performance measures. Consider the below definitions.  
**Definition 1**.: _Given a static routing function \(\mathcal{S}\), the probability of congestion for \(\mathcal{S}\) is the probability that at least one network link becomes overloaded by \(\mathcal{S}\):_  
\[\eta(\mathcal{S})=\Pr(\kappa_{\mathcal{S}}(\theta)>1)\;. \tag{2}\]  
According to this definition, the PoC takes its values from the interval \([0,1]\). The lower the value of the PoC, the smaller the chance that at a random time instance that we find the network in a congested state.  
**Definition 2**.: _Given a static routing function \(\mathcal{S}\), the expected value of congestion is the mean value of the maximum link utilization produced by \(\mathcal{S}\):_  
\[\mu(\mathcal{S})=\mathbb{E}[\kappa_{\mathcal{S}}(\theta)]\;. \tag{3}\]  
The EVoC quantifies the extent of congestion one can expect at a random time instance. As such, if \(\mu(\mathcal{S})\leq 1\) then the network is in a safe operational domain, and \(\mu(\mathcal{S})>1\) forecasts severe congestion.
## 3 Current Decoupling Control Based on ITHESO for Torque Ripple Reduction
### Overall Decoupled Current Control Based ITHESO  
The illustrated diagram of the all-encompassing control strategy that incorporates decoupled current control via ITHESO is depicted in Figure 5. This setup incorporates three current loops and a singular speed loop. To enhance torque efficiency and curtail copper losses, the paper employs the MTPA control methodology. In this arrangement, the opposing \(U_{0}\) voltage is modulated through the \(i_{0}\) current produced by the dual inverter. After determining the current disturbances using the proposed ITHESO observer, the proposed decoupled current control initiates the process of rejecting disturbances while reducing the torque ripple. The main purpose of this method is to combine the position-dependent periodic disturbance with current disturbances for effective torque ripple reduction. The results produced from the ITHESO observers are fed back to be compared with the dq0-axis current references through a proportional term, thereby facilitating the concurrent regulation of the current control loops.
## 3 Exact and Approximate Solutions
\tag{52}\]  
where  
\[\
u=\eta(\
u_{\mu}+1,\
u_{\varepsilon}) \tag{53}\]  
with \(\text{Re}\
u>0\), \(\text{Re}\
u^{\prime}>0\), and  
\[\
u_{\mu}=\frac{1}{\pi}\cos^{-1}\frac{\mu_{\gamma}-1}{2(\mu_{\gamma}+1)},\; \
u_{\varepsilon}=\frac{1}{\pi}\cos^{-1}\frac{1-\varepsilon_{r}}{2(1+ \varepsilon_{r})}, \tag{54}\]  
\[\eta(a,b)=\left\{ \begin{array}{ll}a&\text{for $\text{Re}a\leq\text{Re}b$,}\\ b&\text{for $\text{Re}a\geq\text{Re}b$.}\end{array} \right. \tag{55}\]  
Applying the fundamental theorem for the asymptotic behaviors of the Fourier integrals [25], we can show that \(\Psi_{(+)}(\pm b,\alpha)\) and \(J_{-}(\pm b,\alpha)\) asymptotically behave like  
\[\Psi^{\prime}{}_{(+)}(\pm b,\alpha)=O(\alpha^{-1/2}),\text{ for $\tau>-k_{2}$, for $L_{5}>0$,} \tag{56}\] \[=O(\alpha^{-\
u}),\text{ for $\tau>-k_{2}$, for $L_{5}=0$,}\]  
\[J_{-}(\pm b,\alpha)=O\Big{(}\alpha^{-3/2}\Big{)},\text{ for $\tau<k_{2}\cos \theta_{0}$, for $L_{5}>0$,} \tag{57}\] \[=O\big{(}\alpha^{-1-\
u}\big{)},\text{ for $\tau<k_{2}\cos \theta_{0}$, for $L_{5}=0$.}\]  
as \(\alpha\to\infty\). Thus, applying (56) and (57) to (40) and (42), respectively, we can obtain  
\[U_{(+)}(\alpha),V_{(+)}(\alpha)=O(\alpha^{-1/2}),\text{ for $\tau>-k_{2}$, for $L_{5}>0$,} \tag{58}\] \[=O(\alpha^{-\
u}),\text{ for $\tau>-k_{2}$, for $L_{5}=0$,}\]  
\[J_{-}^{s,d}(\pm b,\alpha)=O(\alpha^{-3/2}),\text{ for $\tau<k_{2}\cos \theta_{0}$, for $L_{5}>0$,} \tag{59}\] \[=O(\alpha^{-1-\
u}),\text{ for $\tau<k_{2}\cos\theta_{0}$, for $L_{5}=0$.}\]  
It is shown that the left- and right-hand sides of (50) are regular in the lower \((\tau<k_{2}\cos\theta_{0})\) and upper \((\tau>-k_{2})\) half-planes, respectively, and both sides have a common strip of regularity \((-k_{2}<\tau<k_{2}\cos\theta_{0})\). By using the analytic continuation argument demonstrates, it can be shown that both sides of equation (50) must be equivalent to an entire function. Further, this function is determined to be identically zero by utilizing (58), (59), and [PERSON]'s theorem. Thus, it can be concluded that  
\[ \begin{array}{ll}-\frac{U_{(+)}(\alpha)}{M_{+}(\alpha)}-\frac{-2 ik\sin \theta_{0}\cos(kb\sin\theta_{0})}{M_{+}(k\cos\theta_{0})(a-k\cos\theta_{0})}\\ +\sum\limits_{n=1,\text{odd}}^{\infty}\frac{M_{+}(\tau_{n})e^{-\gamma n+5}C_{ \infty}^{*}(-i\gamma_{n})}{2i\gamma_{n}(a+i\gamma_{n})}=0.\end{array}  \tag{60}\]  
The same procedure can be employed to solve a different Wiener-Hopf Equation (39). Omitting the particulars, we arrive at  
\[ \begin{array}{ll}-\frac{V_{(+)}(\alpha)}{N_{+}(\alpha)}+\frac{2k\sin \theta_{0}\sin(kb\sin\theta_{0})}{N_{+}(k\cos\theta_{0})(a-k\cos\theta_{0})}\\ -\sum\limits_{n=0,\text{even}}^{\infty}\
u_{n}\frac{(i\gamma_{n})e^{-\gamma n +5}C_{\infty}^{*}(-i\gamma_{n})}{2i\gamma_{n}(a+i\gamma_{n})}=0.\end{array}  \tag{61}\]Equations (60) and (61) involve the unknown coefficients \(C^{+}_{5n}(-i\gamma_{n})\). Appendix A contains an analysis of the correlation between the unknown functions and the unknown Fourier coefficients.
# Convex-Hull Algorithms: Implementation, Testing, and Experimentation
## 5 Testing
### Uncovering Algorithm Vulnerability  
When using our test framework to test [PERSON] code [4], we realized that not only the program, but the underlying algorithm was incorrect. In the following narration, we will verify this claim.  
Let us recall the operating principle of [PERSON] version of torch[2]. There are four basic steps: (1) Find the poles by scanning the input sequence twice. (2) Compute the boundary points in each of the four quadrants. This gives us up to four staircases. (3) Concatenate the staircases to form an approximation of the convex hull. (4) Remove all concave points from this approximation using the standard backtracking procedure [33]. Be aware that the algorithm tries to compute the vertices of the convex hull in counterclockwise order.  
The backtracking procedure is known to work for a star-shaped polygon, but one has to be careful if there are duplicates or if there are several points with the same polar angle when sorted around the centre of the polygon--as explained in [5]. It also works for a monotone polygonal chain, provided that the poles are treated separately--as explained in [3]. But the procedure may fail for a self-intersecting polygon--as reported in [9].  
**Fact 4**.: _The backtracking procedure--as it is used by the original version of torch to eliminate all concave vertices from a simple polygon--may fail if some of the vertices overlap._  
Proof.: As a concrete example, consider an input sequence that contains four points from the parabola \(y=x^{2}\): \((3,9)\), \((1,1)\), \((2,4)\), \((0,0)\). All these points are maximal and, since they are monotone in the \(y\)-direction, none of them will be eliminated during the staircase formation. There are two staircases:one going up from the origin via the other points to the top and another going down the same way from the top to the origin. Thus, the approximation contains seven points: \((0,0)\), \((1,1)\), \((2,4)\), \((3,9)\), \((2,4)\), \((1,1)\), \((0,0)\). When this polygon is processed using the standard backtracking procedure, the (incorrect) output has two points: \((0,0)\) and \((1,1)\).  
The problem is that the orientation predicate cannot distinguish the cases whether the turning angle, when traversing from a point to another point via a third point, is equal to \(\pi\) or \(2\pi\). Hence, when the test is applied to the points \((2,4)\), \((3,9)\), and \((2,4)\), the middle point will be eliminated--which is a wrong decision. The problem also occurs when there are several points on a vertical line. If the points with the same \(x\)-coordinate are not sorted according to their \(y\)-coordinates, it can happen that the topmost point will be wrongly eliminated.  
Since we process the staircases one by one, they are monotone in the \(x\)-direction, and we handle the poles separately (collinear points are only a problem at the poles), the correctness of our approach follows directly from the work of [PERSON] [3].
# SIV: Raise the Correlation of Second-Order Correlation Power Analysis to 1.00
## 4 Application to OpenSSL AES
### Finding SIV Based on Operational Characteristics  
Listing 2 is MixColumns implementation of the OpenSSL version 1.1.1c, which is the latest version. In this listing, t is the AES state, which is the 32-bit array of length 4; r0, r1, and r2 are 32-bit variables for xtime operations and matrix multiplication.  
Figure 3: Distributions of power consumption that occurs when storing \(S_{1}\) and \(S_{2}\).  
```
1 for(i=0;i<4;i++)
2{
3r0=t[i];
4//BytewiseMSBextraction
5r1=r0&0x8080800;
6//xtime
7r2=((r0&0x7f7f7f)<<1)^((r1-(r1>>7))&0x1b1b1b1b);
8//matrix multiplication
9t[i]=r2
10((r2^r0)<<24)^((r2^r0)>>8)^(r0<<16)^(r0>>16)^(r0<<8)^(r0>>24);
11}
```  
Listing 2: The implementation of MixColumns in OpenSSL version 1.1.1c aes_x86 core.c.  
In this listing, line 7 is the implementation of xtime. Line 7 can be divided into two parts: performing bytewise 1-bit left shift operation (multiplication by \(x\)) as the left-side of the XOR operator ((r0&0x7f7f7f)<<1) and executing bytewise reduction as the right-side of the XOR operator ((r1-(r1\(\rightarrow\)7))&0x1b1b1b1b).  
Note that the bytewise MSB of the input is not only extracted and stored to r1 at line 5 but also calculated by itself at line 7. Table 4 shows the binary representation of the SIVs, wherein X[31:24] denotes the first byte of x. In particularly, because the MSB of each byte of the input determines 7 bits of the subtraction result \(S_{5}\), it can reduce the effect of noise to the maximum, similar to Property 2. Therefore, the subtraction result can be utilized as the SIV with the best property. Thus, we define the Property 3 as follows.  
**Property 3**.: _Let the binary representation of input \(d\) be \(\left(d_{31}d_{30}\cdots d_{0}\right)_{2}\), then the MSB of each byte is \(d_{31}\), \(d_{23}\), \(d_{15}\), \(d_{7}\). Assume, without loss of generality, that the analyst considers the first byte of the intermediate value. Thus, the power consumption of \(S_{i}\)\((i\in\{3,4,5,6\})\) is given as follows:_  
\[P_{S_{i}}=e\times(n\times d_{31})+\left(n\times\sum_{i=0}^{2}d_{8i+7}+P_{noise }\right),\]  
_where \(n\) is the Hamming weight of \(S_{i}\). The remaining 24 bits behave similar to noise. Note that if \(n\) is large, the effect of \(P_{noise}\) can be relatively reduced._
## 7 Comparison with Other Models
Indeed, one should recall that the volume change \(\Delta V_{v}\) upon vacancy formation is \(\Delta V_{v}/\Omega=r_{v}+1\), where \(r_{v}\) is the corresponding relaxation volume and \(\Omega\) is the volume per atom [56]. Taking into account the volume change \(\Delta V_{i}\) occurring upon interstitial formation (see Section 5.2 above) and accepting, e.g., for Al, \(r_{i}=1.9\) and \(r_{v}=-0.38\)[24], one can easily arrive to the ratio of the relative volume changes produced by interstitials and vacancies, \((\Delta V/V)_{i}/(\Delta V/V)_{v}=(r_{i}-1)/(r_{v}+1)=1.45\). This means that the volume changes for interstitials and vacancies have the same sign and are quite comparable in the magnitude [100]. Thus, a decrease of the defect concentration within both the free volume model and the IT should lead to the densification of the glass by about the same amount. Moreover, since the free volume in both models is proportional to the number of the defects, one can naturally expect a correlation of material properties with the amount of the free volume. However, there are major differences between these approaches. In the former one, it is the free volume that constitutes the principal source for the property changes. The IT considers the features of the interstitial-type defect (large shear susceptibility, high formation entropy, and specific strain fields) to be of major importance while defect-related volume changes are of secondary relevance. It is also very important that vacancy-like free volume appears to have the spherical symmetry, which is why it _should not_ interact with the external shear stress. Conversely, an interstitial defect is strongly asymmetric (Figure 1b) and constitutes an elastic dipole displaying strong sensitivity to the external shear load. Moreover, if a metallic glass is formed by melting of a loosely packed crystalline structure (related to the generation of interstitial-type defects), the creation of the free volume is not necessary at all. In this case, the density of glass can be even bigger than that of the maternal crystal (see Section 5.2 above) that cannot be understood within the free volume approach.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 4 QEMLR Routing Protocol  
As depicted in Figure 4, the protocol is segmented into routing establishment, routing maintenance, and routing decision. The routing establishment is responsible for establishing the network topology by periodically transmitting Hello packets (Protocol Data Units, or PDUs). Additionally, it is responsible for creating and maintaining the routing and neighbor tables for each node. The routing maintenance is primarily utilized to predict the link failure time, which is used to delete failed routes from the routing table, which is shown by cross marks in Figure 4. Finally, the routing decision is used to query the routing table for each outbound packet and determine the optimal transmission route, denoted by red arrows in Figure 4.  
The establishment and maintenance of routing are integral processes throughout the entire protocol operation. They provide and sustain multiple real-time optional routes for each node. When transmitting data packets, the routing decision selects the most stable one-hop link for transmission based on the node's routing table.  
This protocol is a proactive one based on Q-learning. The route updating mechanism is suitable for ad-hoc wireless networks in dynamic network topologies or unreliable  
Figure 4: Routing Process.  
communication environments. The port for transmitting data packets differs from the port for transmitting Hello packets. To simulate the packet-sending process, this paper utilizes the Discard port (RFC863) and UDP port 9. Figure 5 depicts the flowchart of the entire protocol.