esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### SCAs  
SCAs are a non-invasive technique aimed at exploiting the physical characteristics leaked by cryptographic implementations, specifically targeting the hardware behavior of microcontrollers in IoT devices. Since encryption algorithms are ultimately executed on microcontroller chips, attackers can analyze physical signals, such as power consumption or electromagnetic emissions, to infer the encryption keys. Common SCA techniques consist of Simple Power Analysis (SPA) [18], Differential Power Analysis (DPA), and Correlation Power Analysis (CPA) [19]. In this study, CPA is employed as the primary attack method because it effectively analyzes the correlation between the power consumption of the encryption device and the encryption key.  
During the AES encryption process, attackers typically focus on the output of the S-Box in the first round as the Point of Interest (PoI), since at this stage, the signal has only passed through the AddRoundKey() and SubBytes() operations. Attackers record the power consumption or electromagnetic emissions during the encryption process, converting these signals into traces, which are binary representations of the recorded data. These traces are then subjected to statistical analysis.  
CPA works by correlating hypothetical key guesses with the actual recorded traces, using correlation analysis to infer the correct key. Since there is a relationship between the electromagnetic emissions and power consumption, CPA groups the traces based on power consumption patterns. The power model used for grouping is the Hamming Weight (HW) [20], which categorizes each trace based on the number of '1s' in the binary representation. For AES-128, HW values range from 0 to 8, creating a total of nine groups, as shown in Figure 2[21]. By this method, the intermediate value \(x\) (the output of the first round S-Box) is obtained for a set of random plaintexts and hypothetical keys. The  
Figure 1: AES encryption process flow.  
traces \(y\), recorded during the encryption process, are then used in correlation coefficient calculations to predict the correct key for the Device under Test (DUT) based on the results of the correlation analysis, as shown in Equation (1).  
\[r_{j,k,t}=\frac{\sum_{i=1}^{n}\left(x_{j,k,i}-\overline{x}_{j,k}\right)\left(y_{ t,i}-\overline{y}_{t}\right)}{\sqrt{\sum_{i=1}^{n}\left(x_{j,k,i}-\overline{x}_{j,k} \right)^{2}\sum_{i=1}^{n}\left(y_{t,i}-\overline{y}_{t}\right)^{2}}} \tag{1}\]
# Nanosponges for Water Treatment: Progress and Challenges
## 1 Introduction  
One of the most critical aspects in human life is access to safe drinking water. Consequently, water treatment is currently vital, particularly because of population growth and the burden of environmental pollution by domestic waste, sewage, chemical materials, industrial waste, and organic/inorganic pesticides, among others. These pollutants cause toxic effects or generate concerns regarding the color, odor, or taste of water. Microbial contaminations are also of serious trepidation, as has been addressed in the field of nanotechnology and nanomaterials sciences [1]. Improvements on water quality using conventional techniques, such as coagulation/settling treatment methods, membrane-based systems, absorption-based systems, and direct filtration, have been widely deployed, but they encompass several disadvantages/limitations of low efficiency, selectivity, and specificity (Table 1) [2, 3]. Thus, there is a vital demand for planning efficient strategies or designing smart and low-cost (nano)systems with high sensitivity/specificity for the efficient removal of pollutants from wastewater and for improving the quality of drinking water [4]. Nanosponges with cavities and mesh-like/colloidal structures comprising solid nanomaterials are suitable for encapsulating various substances/compounds, such as proteins/peptides, drugs, genetic materials, antineoplastic agents, and volatile oils, among others [19]. There are various methods for synthesizing \(\beta\)-cyclodextrinrin-based nanosponges, including emulsion solvent evaporation, hyper cross-linked cyclodextrin, ultrasound-assisted production, and interfacial phenomenon techniques [20]. Several categories of cyclodextrin-based nanosponges have been introduced, such as plain (urethane-, carbonate-, ester-, and ether-based linkages connecting cyclodextrin to the cross linker), modified (fluo
## 5 Back-Calculation of Piecewise Stiffness  
### System Identification Method  
To accurately calculate the stiffness during the destructive process, the system identification method was utilized [22]. Based on this method, a beam piecewise stiffness back-calculation method, which relies on deflection values, was developed.  
The basic procedure is shown in Figure 14. Specifically, it can be divided into the following steps:  
1. Apply the load to the actual beam, and measure the deflections of the beam with known load values.
2. Establish a mechanical model of the beam, for which the finite element model was adopted. Input the piecewise seed moduli and the load in step (1), and the calculated deflections can be obtained at the same position as the measured deflections.
3. According to the parameter adjustment algorithm, the input moduli of the model will be gradually adjusted, aimed at matching the calculated deflection to the mea  
Figure 13: The reduction coefficient of the stiffness at the mid-span position using different methods.  
sured deflection. The adjusted moduli will be accepted until the errors between the calculated deflections and measured deflections reach an acceptable range.  
The moduli adjustment algorithm is a sensitivity analysis method, expressed as follows:  
\[[F]\{\Delta E\}=\{e\} \tag{3}\]  
\[[F]=\begin{bmatrix}\frac{\partial W_{1}}{\partial E_{1}}&\frac{\partial W_{1}}{ \partial E_{2}}&\dots&\frac{\partial W_{1}}{\partial E_{n}}\\ \frac{\partial W_{2}}{\partial E_{1}}&\frac{\partial W_{2}}{\partial E_{2}}& \dots&\frac{\partial W_{2}}{\partial E_{n}}\\ \dots&\dots&\dots&\dots\\ \frac{\partial W_{n}}{\partial E_{1}}&\frac{\partial W_{n}}{\partial E_{2}}& \dots&\frac{\partial W_{n}}{\partial E_{n}}\\ \end{bmatrix} \tag{4}\]  
where \(W\) is the calculated deflection, \(E\) is the modulus of each section, \(m\) is the number of deflection observation points (it is 11 in this case), and \(n\) is the number of segments in the beam (it is 1 in Case 1, it is 2 in Case 2, and it is 3 in Case 3).
# Design, Manufacturing, and Flight Testing of an Experimental Flying Wing UAV
## 2 Aircraft Conceptual Design
### Turn Radius  
The minimum turn radius and the maximum turning rate are critical performance characteristics for a fighter aircraft. For the design of a commercial transport aircraft or experimental UAV, very high turning performance is not required. The required turning radius is 15 m at a speed of 10 m/s and cruising altitude of 100 m. The minimum turning radius is formulated. According to the required turn radius at a specific speed and cruising altitude, the power loading at sea level can be calculated as the following.  
With the velocity, \(V\), and load factor, \(n\), at minimum turn radius [5]  
\[(V_{\infty})_{R_{\text{min}}}=\sqrt{\frac{4K(W/S)}{\rho_{\infty}(T/W)}},\ n_{R_{ \text{min}}}=\sqrt{2-\frac{4 KC_{D_{0}}}{\left(T/W\right)^{2}}} \tag{5}\]  
The minimum turn radius can be written as [5]  
\[\begin{array}{ll}R_{\text{min}}&=\frac{(V_{\infty})^{2}_{R_{\text{min}}}}{g \sqrt{\eta_{R_{\text{min}}}^{2}-1}}=\frac{4K(W/S)}{\rho_{\infty}(T/W)}\frac{1} {g\sqrt{2-\frac{4 KC_{D_{0}}}{\left(T/W\right)^{2}}-1}}\\ &=\frac{4K(W/S)}{g\rho_{\infty}(T/W)}\frac{1}{\sqrt{1-4 KC_{D_{0}}/\left(T/W \right)^{2}}}\end{array} \tag{6}\]  
The thrust can be replaced by the power  
\[T_{SL}=\frac{\eta_{pr}P_{SL}}{V_{R_{\text{min}}}} \tag{7}\]  
Therefore, Equation (6) becomes  
\[R_{\text{min}}=\frac{4K(W/S)}{g\rho_{\infty}\Big{(}\frac{\eta_{pr}P_{SL}}{V_{ R_{\text{min}}}}/W\Big{)}}\frac{1}{\sqrt{1-4 KC_{D0}/\Big{(}\frac{\eta_{pr}P_{SL}}{ W_{\text{min}}}\Big{)}^{2}}}\]  
The power loading at the sea level is then  
\[(\frac{W}{P_{SL}})_{turn}=\frac{(\eta_{pr}\sigma)/V_{turn}}{\Big{(}\Big{(} \frac{V_{turn}^{4}}{\left(R_{S}\right)^{2}}+1\Big{)}\frac{K(W/S)}{0.5\rho V_{ turn}^{2}}+0.5\rho V_{turn}^{2}\frac{C_{D_{0}}}{W/S}\Big{)}} \tag{8}\]  
where \(\eta_{pr}\) is the propeller efficiency, \(\sigma\) is the ratio of the cruising altitude density to the sea level density, \(V_{turn}\) is the specific speed at the turning radius, and \(R\) is the turning radius.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Methods
#### 2.2.3 Heading Estimation
 \tag{16}\]  
and exploiting common trigonometric formulas yields  
\[\left\|\mathbf{a}_{NE}(t)\right\|^{2}=A\cos(2\omega_{k}t)+B\sin(2\omega_{k}t) +C\,. \tag{17}\]In order to find the time \(t^{*}\) corresponding to the maximal value of the planar acceleration, it is necessary to set to zero the derivative with respect to time  
\[\frac{\mathrm{d}\
orm{\mathbf{a}_{NE}(t)}^{2}}{\mathrm{d}t}=-2\omega_{k}A\sin(2 \omega_{k}t)+2\omega_{k}B\cos(2\omega_{k}t)=0, \tag{18}\]  
which yields  
\[\tan(2\omega_{k}t^{*})=B/A\,. \tag{19}\]  
By substituting the actual values of \(\omega_{k}\), \(A\), \(B\) it is possible to write \(t^{*}\) as a function of the four integrals  
\[t^{*}_{h}=\frac{T_{k}}{4\pi}\mathrm{atan}\Big{(}\frac{\alpha_{N}\beta_{N}+ \alpha_{E}\beta_{E}}{\beta_{N}^{*}+\beta_{E}^{*}-\alpha_{E}^{*}}\Big{)}+h\frac {T_{k}}{4}\,,\quad h=0,1,2,3 \tag{20}\]  
where the correct solution is the first one providing a negative second order derivative  
\[\frac{\mathrm{d}^{2}\
orm{\mathbf{a}_{NE}(t)}^{2}}{\mathrm{d}t^{2}}\Bigg{|}_{ t=t^{*}}=-4\omega_{k}^{2}[A\cos(2\omega_{k}t^{*})+B\sin(2\omega_{k}t^{*})]<0\,. \tag{21}\]  
Finally, considering the point \(V\big{(}a_{x}(t^{*}),a_{y}(t^{*})\big{)}\), the heading angle in the NED frame results in  
\[\theta=\mathrm{atan}2(a_{N}(t^{*}),a_{E}(t^{*}))\,. \tag{22}\]  
By averaging two consecutive values of the heading angle, it is possible to obtain a smoothed signal for which the occurring spikes or errors given by the pelvis rotation during walking are reduced.
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 1 Introduction
First, the EOMF spatially cancels the interference with an adaptive array antenna. Then, the OMF suppresses the residual interference signal. It was shown that the EOMF improved the error convergence of the adaptive algorithm and compared the effectiveness of the EOMF with the conventional interference cancellation method by a computer simulation [18]. In addition, the optimum parameters through mathematical modeling and theoretical analysis of the EOMF were derived [19]. In fact, the EOMF cannot detect signals from multiple users due to its structure. That is, it does not support multiple user detection. Even in the environment of a wireless ad hoc network, there may be situations in which the spreading sequences of multiple users are known. Hence, multiuser detection may be necessary to improve communication quality and network capacity. When a conventional EOMF is prepared for the number of desired users and multiuser detection is performed, the computational complexity (multiplication, addition/subtraction, total sum of products) increases in proportion to the number of desired users and, at the same time, the circuit scale increases. When assuming IoT applications with limited hardware scale and battery capacity, such as a mobile robot that collects various data from sensors in the factory and a monitoring system using a wireless ad hoc network formed by UAVs, the computational complexity and circuit scale should be as small as possible. The purpose of this research is to design a novel EOMF structure that supports multiuser detection and that can suppress an increase in the computational complexity and the circuit scale. This research contributes to the construction of highly reliable and energy-efficient wireless ad hoc networks for IoT applications for which there are many users. The remainder of this paper is organized as follows. In Section 2, the principle of the EOMF is summarized. In Section 3, the proposed structure of the EOMF is described, and the computational complexity is compared with that of a conventional EOMF. Performance evaluations are provided in Section 4. Conclusions and suggestions for future research are presented in Section 5.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 5 Technological Infrastructure
### Cloud Computing  
Cloud computing is a transformative technology that allows individuals and organizations to access and store data, applications, and computing power over the internet rather than relying on local servers or personal devices [129]. Cloud technology offers several key advantages, including scalability, flexibility, cost-efficiency, and accessibility [130]. Cloud services are typically categorized into three main types: infrastructure as a service (IaaS) [131], which provides virtualized computing resources over the internet; platform-as-a-service (PaaS) [132], which offers hardware and software tools for application development; and software as a service (SaaS) [133], which delivers software applications over the internet on a subscription basis [134]. Major cloud service providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer robust solutions that cater to a wide range of needs, from startups requiring minimal resources to enterprises needing extensive infrastructure and advanced services.  
Cloud computing has revolutionized various industries by enabling more efficient and innovative business models. For instance, in the healthcare sector, cloud computing facilitates the secure storage and sharing of patient data, supports telehealth services, and enhances collaborative research through data analytics [135]. In finance, it allows for real-time transaction processing and advanced fraud detection. Additionally, cloud computing supports the growing field of remote work by providing seamless access to applications and data from any location, fostering collaboration and productivity [136]. Despite its many benefits, cloud computing also poses challenges such as data security, privacy concerns, and dependency on internet connectivity. However, ongoing advancements in cloud security protocols and hybrid cloud solutions, which combine private and public cloud resources, are addressing these issues and enhancing the reliability and security of cloud services.
## 4 Results and Discussion
### Results of Hydrodynamic Coefficient Identification
\begin{table}
\begin{tabular}{c c c c} \hline
**Coefficients** & **CFD \(\times\) 10\({}^{-4}\)** & **Bayesian \(\times\) 10\({}^{-4}\)** & **Errors Compared to CFD Method \%** \\ \hline \(Z_{w}\) & \(-131.85\) & \(-144.05\) & 9.25 \\ \(M_{w}\) & \(95.34\) & \(98.99\) & \(3.82\) \\ \(Z_{q}\) & \(-71.32\) & \(-79.43\) & \(11.37\) \\ \(M_{q}\) & \(-35.59\) & \(-34.35\) & \(-3.49\) \\ \(Zw\) & \(-110.19\) & \(-115.55\) & \(4.87\) \\ \(M_{\dot{w}}\) & \(-5.02\) & \(-2.85\) & \(-43.26\) \\ \(Z\dot{q}\) & \(-7.68\) & \(-8.39\) & \(9.30\) \\ \(M_{\dot{q}}\) & \(-7.88\) & \(-7.39\) & \(-6.21\) \\ \(Z_{\dot{\delta}s}\) & - & \(-49.21\) & - \\ \(M_{\dot{\delta}s}\) & - & \(-36.66\) & - \\ \(Y_{v}\) & \(261.14\) & \(280.76\) & \(75.51\) \\ \(N_{v}\) & \(-142.96\) & \(-145.05\) & \(1.46\) \\ \(K_{v}\) & - & \(-6.46\) & - \\ \(Y_{r}\) & \(57.35\) & \(57.05\) & \(-0.53\) \\ \(N_{r}\) & \(-50.20\) & \(-46.31\) & \(-7.75\) \\ \(Y\dot{v}\) & \(-139.94\) & \(-141.68\) & \(1.24\) \\ \(N\dot{v}\) & \(2.57\) & \(2.92\) & \(13.76\) \\ \(Y\dot{r}\) & \(4.32\) & \(4.47\) & \(3.47\) \\ \(N\dot{r}\) & \(-7.96\) & \(-7.31\) & \(-8.11\) \\ \(Y_{\dot{\delta}r}\) & - & \(71.43\) & - \\ \(N_{\delta r}\) & - & \(-26.83\) & - \\ \(K_{\delta r}\) & - & \(-0.10\) & - \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of the hydrodynamic coefficients obtained from CFD simulation restraint model tests and Bayesian ridge regression identification. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Coefficients** & **Bayesian \(\times\) 10\({}^{-4}\)** & **Coefficients** & **Bayesian \(\times\) 10\({}^{-4}\)** & **Coefficients** & **Bayesian \(\times\) 10\({}^{-4}\)** \\ \hline \(X\dot{u}\) & \(-8.31\) & \(Z\dot{q}\) & \(-8.39\) & \(M_{\dot{q}}\) & \(-17.2\) \\ \(X_{uu}\) & \(-11.0\) & \(Z_{\varpi r}\) & \(-453.8\) & \(M_{q}\) & \(-34.35\) \\ \(X_{\varpi}\) & \(171.6\) & \(Z_{q}\) & \(-71.47\) & \(M_{w}\) & \(98.99\) \\ \(Y\dot{\varpi}\) & \(-141.68\) & \(Z_{\varpi}\) & \(-144.05\) & \(M_{\varpi r}\) & \(-5.2\) \\ \(Y\dot{p}\) & \(-5.11\) & \(K\dot{v}\) & \(-0.52\) & \(M_{\delta s}\) & \(-36.66\) \\ \(Y\dot{r}\) & \(4.47\) & \(K\dot{p}\) & \(-0.03\) & \(N\dot{v}\) & \(2.92\) \\ \(Y_{r}\) & \(57.05\) & \(K_{p}\) & \(-4.8\) & \(N\dot{r}\) & \(-7.31\) \\ \(Y_{p}\) & \(-29.7\) & \(K_{r}\) & \(2.6\) & \(N\dot{p}\) & \(-0.086\) \\ \(Y_{\dot{\varpi}|r|}\) & \(4.5\) & \(K_{\varpi r}\) & \(11.3\) & \(N_{r}\) & \(-46.31\) \\ \(Y_{\dot{\varpi}}\) & \(280.76\) & \(K_{v}\) & \(-6.46\) & \(N_{v}\) & \(-145.05\) \\ \(Y_{\dot{\varpi}|r|}\) & \(-1160.1\) & \(K_{\delta r}\) & \(-0.1\) & \(N_{\dot{\varpi}|r}\) & \(-20.8\) \\ \(Y_{\dot{\varpi}}\) & \(71.43\) & \(Z_{\rm 1|r|}\) & \(-20.0\) & \(N_{\delta r}\) & \(-26.83\) \\ \(Y_{\dot{\varpi}}\) & \(-3025.7\) & \(Z_{\delta s}\) & \(-49.21\) & & \\ \(Z\ddot{w}\) & \(-115.55\) & \(M\ddot{w}\) & \(-2.85\) & & \\ \hline \end{tabular}
\end{table} 
Table 5: Identification results based on the Bayesian ridge regression model.
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 4 Results  
### Snr  
This section presents the measured SNR for each distance and BW, where it was averaged over the number of subcarriers, shown in Table 5, and the 180 combinations (6 \(\times\) 6 \(\times\) 5, since a 6 \(\times\) 6 URA and five antennas were used for transmission and reception, respectively). Figure 6 shows the average SNR used to obtain the throughput. It shows the SNR obtained at 38 GHz and 65 GHz in each of the studied bandwidths.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**MCS** & **Modulation** & **Cotification (Coding Rate \(\times\) 1024)** \\ \hline
4 & 4 QAM & 602 \\ \hline
10 & 16 QAM & 658 \\ \hline
19 & 64 QAM & 873 \\ \hline
27 & 256 QAM & 948 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modulation and coding schemes (MCS).  
Figure 5: Block diagram of the physical layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**BW (MHz)** & **RBs** & **Number of Subcarriers (N) (RBs \(\times\) 12)** \\ \hline
100 & 66 & 792 \\ \hline
200 & 132 & 1584 \\ \hline
400 & 264 & 3168 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bandwidths, RBs, and number of subcarriers.  
The SNR was normalized by choosing an average value of \(-8\) dB in the worst case (65 GHz, using a bandwidth of 400 MHz, for a distance d = 3.8 m). This SNR threshold value was chosen to be able to observe variations in the throughput, since at higher SNRs, no errors occurred for the 38 GHz band. The calculation method applied was described in [31], with an equivalent isotropic radiated power (EIRP) of \(-9.3\) dBm, and a noise power for the 100, 200, and 400 MHz bandwidths, equal to \(-89\), \(-86\), and \(-83\) dBm, respectively.  
The analysis showed that the SNR was higher at the 38 GHz band, and that the ratio of SNR\({}_{38\text{GHz}}\)/SNR\({}_{65\text{GHz}}\) reached 20 dB in the best case. This value takes into account both the frequency difference and the antenna patterns. It can be observed that the SNR difference between the 100 MHz and 200 MHz bandwidths was 3 dB, while between 100 MHz and 400 MHz, it was 6 dB because of the applied noise power difference in each case. Even though SNR had been averaging, fadings were observed due to the geometry of the scenario, for example, between 2 and 2.5 m. This was due to the constructive and destructive effect of contributions from objects in the scenario.
## 3 Results and Discussion
### Fracture Analysis  
The fracture surface of different samples was investigated by SEM. As shown in Figure 8, the fracture surface of the E51 sample was relatively smooth and homogeneous, and the shearing deformation was low. The phenomena showed a brittle fracture under no uninterrupted crack propagation path which will lead to poor resistance to crack  
Figure 7: TGA curves of different samples. (**A**) E51 and E51 + E-POSS(10%), (**B**) E51 and E51 + GF(16%), (**C**) E51 and E51 + E-POSS(10%) + GF(16%).  
propagation. The fracture surface image of the E51 + E-POSS (10%) sample is shown in Figure 8B. It was found that the fracture surface morphology contained some wrinkles and a few toughening whorls. It can be inferred that when the E51 + E-POSS (10%) sample is destroyed, more fracture energy is absorbed which will hinder the further destruction of E51 + E-OSS (10%). The fracture surface image of the E51 + GF (16%) sample is presented in Figure 8C. It can be clearly seen from the fracture surface image that the glass fiber at the fracture interface was partially pulled out of the resin matrix and partially fractured. It can be speculated that the strong interaction between the epoxy resin matrix and GF is the reason for the above phenomenon [37,38]. Figure 8D presents the fracture surface image of the E51 + E-POSS (10%) + GF (16%) sample, and shows that the glass fiber at the fracture interface was partially pulled out of the resin matrix, and the circular boundary of the generated voids was not perfect, indicating that the adhesion between the GF interface and resin matrix is good. The mechanical bonding between the epoxy resin matrix and GF was being further reinforced because the reactive epoxy group of E-POSS can directly participate in the curing reaction with PEPA. Therefore, the E51 + E-POSS (10%) + GF (16%) sample exhibited better mechanical and thermal properties.