esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Organisation of This Paper  
This paper is organised as follows: Section 2 provides the required background for the proposed ARounD scheme. Section 2.1 presents an overview of the IEEE 802.15.4/ZigBee cluster-tree topology and Section 2.2 presents the state-of-the-art on routing protocols for wireless sensor networks, highlighting applications for wide-scale wireless sensor networks, cluster-tree topologies and beacon frame scheduling. Section 3 presents the proposed ARounD communication scheme, highlighting its main algorithms and protocol mechanisms. Section 4 presents a simulation assessment of the proposed communication scheme. Finally, in Section 5, some conclusions and future considerations are presented.
## 2 Materials and Methods
### Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{3}{c}{**Main Effects**} & \multicolumn{3}{c}{**Interactions**} \\ \cline{3-8}  & & \(d\) & **C\({}_{\text{wall}}\)** & **C\({}_{\text{GR}}\)** & \(d\ast\) C\({}_{\text{wall}}\)** & \(d\ast\) C\({}_{\text{GR}}\)** & **C\({}_{\text{wall}}\ast\) C\({}_{\text{GR}}\)** \\ \hline \multirow{3}{*}{ES} & \(p\) & 0.05 & \textless{}10\({}^{-3}\) & \textless{}10\({}^{-3}\) & \textless{}10\({}^{-3}\) & & 0.001 \\  & \(\eta_{\text{P}}^{2}\) & 0.32 & 0.81 & 0.47 & 0.56 & - & 0.43 \\  & \(\omega\) & 0.86 & 1.00 & 0.98 & 1.00 & & 0.96 \\ \hline \multirow{3}{*}{GM} & \(p\) & 0.001 & 0.027 & 0.035 & 0.012 & & \textless{}10\({}^{-3}\) \\  & \(\eta_{\text{P}}^{2}\) & 0.43 & 0.21 & 0.19 & 0.27 & - & 0.45 \\  & \(\omega\) & 0.97 & 0.62 & 0.57 & 0.75 & & 0.98 \\ \hline \multirow{3}{*}{VM} & \(p\) & & 0.007 & \textless{}10\({}^{-3}\) & 0.003 & & \\  & \(\eta_{\text{P}}^{2}\) & - & 0.30 & 0.80 & 0.36 & - & - \\  & \(\omega\) & & 0.81 & 1.00 & 0.90 & & \\ \hline \multirow{3}{*}{VL} & \(p\) & & 0.002 & \textless{}10\({}^{-3}\) & 0.001 & & 0.008 \\  & \(\eta_{\text{P}}^{2}\) & - & 0.39 & 0.84 & 0.43 & - & 0.29 \\  & \(\omega\) & & 0.93 & 1.00 & 0.97 & & 0.79 \\ \hline \multirow{3}{*}{BF} & \(p\) & 0.008 & 0.004 & 0.03 & - & 0.003 & \\  & \(\eta_{\text{P}}^{2}\) & 0.29 & 0.33 & 0.21 & - & 0.35 & - \\  & \(\omega\) & 0.80 & 0.86 & 0.62 & & 0.89 & \\ \hline \multirow{3}{*}{ST} & \(p\) & \textless{}10\({}^{-3}\) & \textless{}10\({}^{-3}\) & \textless{}10\({}^{-3}\) & 0.02 & 0.001 & 0.001 \\  & \(\eta_{\text{P}}^{2}\) & 0.48 & 0.58 & 0.50 & 0.22 & 0.43 & 0.43 \\  & \(\omega\) & 0.99 & 1.00 & 0.99 & 0.65 & 0.97 & 0.96 \\ \hline \multirow{3}{*}{TA} & \(p\) & 0.007 & & \textless{}10\({}^{-3}\) & & 0.002 & \\  & \(\eta_{\text{P}}^{2}\) & 0.30 & - & 0.90 & - & 0.37 & - \\  & \(\omega\) & 0.81 & & 1.00 & & 0.91 & \\ \hline \multirow{3}{*}{GL} & \(p\) & \textless{}10\({}^{-3}\) & & \textless{}10\({}^{-3}\) & & \textless{}10\({}^{-3}\) & \\  & \(\eta_{\text{P}}^{2}\) & 0.61 & - & 0.63 & - & 0.61 & - \\ \cline{1-1}  & \(\omega\) & 1.00 & & 1.00 & & 1.00 & \\ \hline \end{tabular}
\end{table} 
Table 2: \(p\)-value, effect size (partial eta squared n\({}_{\text{P}}^{2}\)), and statistical power (observed power \(\omega\)) for main effects and interactions as deduced by the three-way ANOVA analysis. The statistical parameters are reported only for main effects and interactions with \(p<0.05\). No statistically significant effect was detected for the \(d\ast\) C\({}_{\text{wall}}\ast\) C\({}_{\text{GR}}\) interaction. Main effects: (1) change in ankle-wall distance \(d\) between \(d_{1}=0.5\cdot l_{\text{thigh}}\) and \(d_{2}=l_{\text{thigh}}\); (2) shift in C\({}_{\text{wall}}\) between the scapular and pelvic zone; (3) shift in C\({}_{\text{GR}}\) between rearfoot and forefoot. ES = erector spine, GM = gluteus maximus, VM = vastus medialis, VL = vastus lateralis, BF = biceps femoris, ST = semientdinosus, GL = gastrocnemius lateralis, TA = tibialis anterior.
## 3 Results and Discussions  
### UV-Vis Analysis  
UV-Vis spectroscopy is used to characterize the synthesized NDG. Figure 1 displays the UV-Vis spectra of GO and NDG. GO produces two distinct absorption peaks in the UV-Vis spectrum at wavelengths of about 230 nm and 300 nm corresponding to the \(\pi\)-\(\pi^{\star}\) transition of the C=C bond in the aromatic ring and n-\(\pi^{\star}\) for C=O bonds [18]. In contrast, NDG displays a shift in peak position from 300 to 283 nm. Peak shifting to a lower wavelength in the UV-Vis spectrum of NDG compared to GO indicates increased bond strengths than the precursor. This is supported by the process of removing most of the oxygenic groups and the restoration of C=C conjugation, leading to the strong bonding nature of electrons [19].
# Hardware-Accelerated Data Readout Platform Using Heterogeneous Computing for DNA Data Storage
## 6 Experimental Results and Analysis
### Resource Utilization and Power Consumption Analysis  
The resource utilization of each functional module in the hardware-accelerated kernel for DNA storage readout is summarized in Table 3. The LDPC decoder module was configured with 5-bit quantized inputs for log-likelihood ratios (LLRs), 7-bit storage for each check node, and a parallelism factor of 360. This module accounted for approximately 85% of the total logic resource, primarily due to the partially layered, parallel iterative decoding architecture. The copy count memory consumed 31.6% of the on-chip block RAM, as it stored sequencing copy counts for 12 oligo pools, each mapped to a distinct memory address range. In addition to the main processing modules, a considerable portion of hardware resources were also consumed by inter-module streaming interfaces and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\(P_{\text{sum}}\)** & **Coverage (\(\times\))** & **CPU (s)** & **FPGA (s)** & **Acceleration Ratio** \\ \hline \multirow{3}{*}{0.006} & 15 & 646.52 & 15.15 & 42.7 \\  & 20 & 504.85 & 19.00 & 26.7 \\  & 25 & 479.84 & 22.42 & 21.4 \\ \hline \multirow{3}{*}{0.010} & 15 & 1378.67 & 13.65 & 101.0 \\  & 20 & 912.99 & 16.80 & 54.3 \\ \cline{1-1}  & 25 & 855.12 & 19.95 & 42.86 \\ \hline \multirow{3}{*}{0.015} & 15 & 4627.51 & 12.40 & 373.1 \\  & 20 & 2020.79 & 14.82 & 136.4 \\ \cline{1-1}  & 25 & 1054.44 & 17.48 & 60.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of runtime consumption for CPU-based software and hardware implementations across various error rates and coverages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Acceleration Target** & **Read Length** & **Number of Reads** & **Time** & **Processing Speed (Reads/s)** \\ \hline DNA Assim [28] & Edit distance & 120 & 10,000,000 & \textasci{-}125 s & \textasci{-}80,000 \\ Our work & Complete DNA readout pipeline & 112 & 74,844,000 & 12.4 s & 6,035,806 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with existing DNA storage acceleration systems.  
external memory read/write controllers. These components are essential to support high-throughput data movement and ensure efficient pipelining across functional stages in the decoding workflow.  
Power estimates were obtained using the integrated Power Analysis and Optimization tool in Xilinx Vivado Design Suite 2022.1 at the post-implementation stage. At a clock frequency of 180 MHz, the total on-chip power consumption of the FPGA kernel was estimated to be 7.39 W, comprising 2.55 W of static power and 4.84 W of dynamic power. For the CPU, power consumption was measured by recording the average power during idle time and during software execution and calculating the difference between the two. Based on the time required to process the same 50 million reads, the energy consumption of the CPU and FPGA was estimated. As shown in Table 4, the comparison demonstrates that the proposed FPGA-based system achieves a significantly lower energy cost.
# An Experimental Tuning Approach of Fractional Order Controllers in the Frequency Domain
## 4 Controller Tuning
### Robustness Analysis  
Robustness to gain variations is indirectly imposed by the constraints for the phase in the interval comprising the resonant frequency. A straight line for the phase in an interval of frequencies ensure that, for small gain variations, the phase margin remains unchanged.  
The tuning of the fractional-order PID controller ensures robustness by specifying closed loop phase values in order to honor the iso-damping property. The closed loop phase constraints from Equation (18) strive to achieve a flat phase between \(\omega_{3}=87.96\ rad/s\) and \(\omega_{5}=251.3\ rad/s\), attaining robustness to small gain variations.  
In order to experimentally test the robustness of the obtained controllers from Equations (16) and (19), weights have been added to the free end of the beam such that the resonant frequency is moved  
Figure 10: Experimental disturbance rejection of the closed loop system with the fractional-order PID controller.  
from 14 Hz to 12.5 Hz and 11.7 Hz, respectively. Two Neodymium-Iron-Boron permanent magnetic disks with 10 mm diameter, 5 mm height, 30 g weight, and 1 kg strength were placed on each side of the free end of the beam. The magnets were attached at a 3 cm distance to the moving end, centered with respect to the upper and lower margins of the beam. The tuned fractional-order PD and PID controllers were tested for sinusoidal disturbances of the new resonant frequency.  
Figure 10(a),b presents the data obtained using the fractional controllers when the resonant frequency of the beam is 12.5 Hz. For the fractional-order PD controller, the settling time is 1.5 s, whereas the PID attenuates and the vibration in approximately 1 s. The oscillation amplitude is reduced by 75% with the fractional-order PD and by 80% with the fractional-order PID controller.  
Another test is performed when the resonant frequency of the beam is 11.7 Hz and the experimental data obtained with the two controllers is represented in Figure 10(c),d. The settling time offered by the fractional-order PD is 2.5 s and the amplitude is reduced by 60%. The fractional-order PID controller reduces the amplitude by 78% in a settling time less than 2 s.  
The fractional-order PID overall performance in terms of oscillation amplitude and settling time attenuation is better than the fractional-order PD performance, proving experimentally that the PID controller is more robust. The theoretical explanation is that in the case of the fractional-order PD controller, as there were three constraints imposed, only one constraint was imposed for the phase near the resonant frequency being impossible to impose a robustness condition. However, for the fractional-order PID, three equal phase margin constraints are imposed, leading to a closed loop flat phase around the resonant frequency making the controller robust.  
Figure 11: Experimental robustness validation when altering the resonant frequency of the beam.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 5 Formal Analysis of DTLS-SRTP Combined Protocol
### DTLS-SRTP Protocol Analysis  
DTLS (Datagram Transport Layer Security) and SRTP (Secure Real-time Transport Protocol) can be combined to provide end-to-end security, especially in real-time commun  
Figure 2: Combination protocol security proof flow chart.  
nication. The protocol is divided into a handshake phase and a key exchange phase, as illustrated in the sequence diagram in Figure 3.  
Here are the steps of the protocol interaction:  
1. The client and server establish a connection by initiating a handshake negotiation to establish a secure communication channel. During the handshake, the client sends a timestamp \(T_{s}\) and a random number \(R_{1}\) to the server.
2. The server sends information containing the digital certificate \(CA\) and the session key \(K\) to the client.
3. The client verifies the digital certificate \(CA\). Upon successful verification, it encrypts the random number \(R_{2}\) using the key \(K\) and sends it to the server. Data encrypted with the public key can only be decrypted by the server's private key. Once the DTLS handshake is successful, the communicating parties share session data.
4. The server verifies the client's digital certificate and decrypts the encrypted \(R_{2}\) using its private key. At this point, the handshake phase is completed, and the key exchange phase begins.
5. After the DTLS protocol connection is established, the SRTP protocol requires a set of encryption keys and authentication keys to ensure the confidentiality and integrity of media data. The shared key \(PK_{s}\) is calculated through an encryption algorithm with the following formula: \(PK_{s}=K_{s}\times C_{pk}=K_{c}\times S_{pk}\), where \(K_{s}\), \(C_{pk}\), \(K_{c}\), and \(S_{pk}\) are encryption parameters negotiated by the SRTP protocol.
6. Using the derived encryption key \(PK_{s}\), the client encrypts a message \(R_{3}\) and sends it to the server. The server also sends an encrypted message \(R_{4}\) to the client. Once  
Figure 3: DTLS-SRTP protocol interaction sequence diagram.  
both parties successfully decrypt the messages, the protocol's key exchange phase is complete, and secure transmission of communication data can begin.
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 1 Introduction  
### Motivation and Incitement  
Medium frequency transformer, MFTs, are fundamental to the development of newly advanced DC-DC converters [1, 2, 3, 4]. These converters are becoming key player in building Smart-Grid environments [5], in interfacing renewable energy to medium power grids [6, 7, 8, 9], in constructing the emerging solid-state transformer technology [5, 10], and in electric vehicles, as shown in Figure 1. Better performance MFTs are needed for improving such converters. Therefore, an important point is the selection of the core material in the pursuit of achieving high power density and excellent efficiency of the MFT [11, 12, 13, 14, 15].
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results  
This section first analyzes the theoretical multipath performance and thermal noise performance of the PAOC technique. Then, some simulation results are presented to compare the PAOC technique with existing multipath mitigation techniques based on the DBT loop.
## 3 Numerical Model of the Data Center
Although there are perceptible steps in the predictions of temperature and velocity fields, it is necessary to highlight that the selected meshes present a reasonable solution considering the results accuracy and the computational time required. Additionally, it must be noted that the numerical results simulate a real physical situation and their analysis and conclusions are in accordance with the computational resources required and computational time suitable for an engineering application. The equations described in Section 2 were solved by a numerical process where the control volume technique was used. This technique is used when the differential equations have no analytical resolution and the numerical model must allow the discretization of the equations. To achieve a possible resolution of the numerical method, the computational domain was first divided into 2,341,172 control volumes. As stated above, a high-density mesh delivers results with high accuracy. The second- order UpWind Scheme was applied to momentum, turbulent kinetic energy, turbulent dissipation rate, and energy to achieve more precise results. The accuracy of this method is reached on the faces of the control volumes by the expansion of the Taylor Series of the solution centered on the control volume compared to its centroid [42]. The PREssure STaggering Option (PRESTO!) interpolation method was used for pressure discretization. Its use is justified for controlling the sudden pressure variations between the centroids of the control volumes, considerable body forces, the presence of flows involving porous medium and other situations. It is based in the adoption of a staggered grid to calculate the pressure via a discrete continuity balance. In the staggered grid, the values of pressure in the center are known and these are the values at the interfaces in the normal grid. The PRESTO! scheme uses this technique, which resembles in characteristics the staggered-grid schemes used with structured meshes proposed by [PERSON] [42]. The Semi-Implicit Method for Pressure Linked Equations-Consistent (SIMPLEC) algorithm, published by [PERSON] and [PERSON] [43], was chosen in detriment to the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm for the pressure-velocity coupling [42], by allowing the convergence of the solution more quickly and ensuring the stability of the method.
## 3 Results and Discussions  
### Baseline Flow  
The initial numerical study was carried out to investigate the characteristics of baseline flow around the [PERSON] model without a control device. Figure 9 presents a time-averaged normalized streamwise velocity contour and streamlines in the near wake region in the symmetry plane (z = 0). The flow from the roof, underbody and side walls of the body are separated at the rear edges, forming a pair of counter-rotating recirculation zones. These two zones are quasi-symmetrical; however, the center of the lower zone is closer to the rear surface of the model than the top recirculation region. Such a phenomenon has also been observed in an experimental study on a similar geometry [14]. Further downstream, a saddle point S is identified at x = 0.38 m (x/H = 1.32) (Figure 9).  
Figure 8: Output signals of synthetic and unsteady jet devices.  
Figure 9: Time-averaged streamlines in the behind of the model, on symmetry plane of baseline flow.  
The normalized streamwise velocity profiles in the symmetry plane (z = 0) at different x/H are determined. They are plotted in Figure 10 in comparison to the numerical results of [PERSON] et al. (2013) at the same location. No significant difference in obtained velocity profiles is found between the two works. A backward flow is observed at all measured positions in the near-wake region, especially at x/H = 0.5 and x/H = 1, with a minimum negative backward velocity approximately equal to 0.4U\({}_{0}\). Moving towards the rear surface of the model (x/H = 0.1), the wall effect causes a reduction in the backward flow, and u\({}_{\text{min}}\) is around 0.1U\({}_{0}\) over the entire height of the back surface of the body (0.2 < y/H < 1).  
The flow visualization using the Q-criterion at a value of Q = 300,000 s\({}^{-2}\) also reveals that the flow field around the body is dominated by small-scale vortices ([PERSON], 2012). A laminar boundary layer is created at the front of the model, which subsequently separates and reattaches to form a separation bubble as the flow moves towards downstream, as illustrated in Figure 11. Consequently, a low pressure region is formed on the surface at the front portion of the model. Further downstream, the turbulent boundary layer develops, and the formation of small-scale vortices is highlighted. These vortices are separated at the rear edges of the model and enter the wake region. A turbulent shear layer is formed behind the body, in which the small-scale vortices interact and merge to develop large-scale vortical structures, as shown in Figure 10. In addition, the interaction of the wake region with the underbody flow is also a main reason for the complex flow structures behind the model.  
Figure 11: The normalized streamwise contour of baseline flow at iso-surface of Q = 300,000 s\({}^{-2}\).  
Figure 10: Vertical normalized streamwise velocity profiles, determined at different x/H positions in the symmetry plane.
## 2 Materials and Methods
### Novel FT-NIR Spectral Sensor Prototype  
FT-NIR spectral data collection was performed using a novel, handheld sensor based on an FT-NIR spectral sensor (Figure 1a). This sensor prototype comprises a NeoSpectra-Micro development kit (Si-Ware Systems, Cairo, Egypt), battery pack, cooling fan, sample rotation stage, gear motor, and USB port (Figure 1b,c). An Android-based tablet connects via Bluetooth to the FT-NIR sensor unit to control spectroscopic measurements and to transfer and analyze the data. The NeoSpectra-Micro development kit employs a palm-size FT-NIR spectral sensor (Figure 1c) that utilizes a single-chip Michelson interferometer with a monolithic opto-electro-mechanical structure coupled with a single uncooled InGaAs photodetector. The components are intrinsically aligned with lithography on the chip. Ground or intact cereal samples (~10 g) were placed in a glass Petri dish (Duroplan(r), DWK Life Sciences GmbH, Mainz, Germany) and placed on the rotating stage to ensure reproducible measurements of the heterogeneous samples via spectral averaging. Spectra were collected over a wavelength range of 1350-2560 nm with 16 nm resolution that was chosen based on full width at half maximum criterion. FT-NIR spectra were collected at room temperature for 20 s, co-adding individual spectral scans to improve the signal-to-noise ratio. Spectra were collected in triplicate for each sample. Collected spectra were directly transferred and stored in the Android tablet. The spectral data were displayed using SpectroMOST Software (Si-Ware Systems, Cairo, Egypt). Background spectra were also collected to eliminate environmental factors using a highly reflective (99%) diffuse reflectance standard (Spectralon(r), Labsphere, North Sutton, NH, USA).