esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 4 Implementation and Performance Evaluation
### Compression Results  
The method mentioned in Section 3.3.2. is applied to denoise the physiological signal based on Wavelet Transform (WT). In our work, the soft threshold function [36; 37] is used for all the physiological signals. The criteria for evaluating the denoising performance is the signal-to-noise ratio (SNR), which yields  
\[\text{SNR}=10\log_{10}\left(\frac{P_{signal}}{P_{noise}}\right)= 10\log_{10}\left(\frac{\frac{1}{N}\sum_{i=1}^{N}x^{2}(i)}{\frac{1}{N} \sum_{i=1}^{N}\left(x(i)-\hat{x}(i)\right)^{2}}\right) \tag{3}\]  
Figure 17: A scenario of experiments.  
Figure 18: The conceptual communication cycles and transmission slots from Figure 17.  
where \(x(i)\) is the noisy signal, \(\hat{x}(i)\) is the denoised signal, and \(N\) is the length of the signal. Observe that the average noise levels can be reduced effectively with the noise reduction process, as shown in Table 3.  
From Section 3.3.3, DWT is applied for physiological signal compression. The criterion of fidelity is adopted to evaluate the compression methods parameters, compression ratio (CR), and percent root-mean-square difference (PRD).  
\[\text{CR}=\frac{N_{org}}{N_{comp}} \tag{4}\]  
\[\text{PRD}=100\cdot\sqrt{\frac{\sum_{n=1}^{N}\left[x_{org}(n)-x_{r}(n)\right] ^{2}}{\sum_{n=1}^{N}\left[x_{org}(n)\right]^{2}}} \tag{5}\]  
where \(N_{org}\) is the number of bits in the original signal, \(N_{comp}\) is the number of bits in the compressed signal, \(x_{org}\) is the original signal, and \(x_{r}\) is the reconstructed signal after compression. Note that the PRD is used to measure the difference between the original signal and the reconstructed signal. The compression result of physiological signals is shown in Table 4.
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### Operations of the Receiver  
The operations of the receiver are performed in several steps as follows:  
1. The navigation software on the PC determines which GPS satellites are visible and estimates the Doppler for each satellite. This information is then sent to the FPGA through the UART interface. This allows a reduction of the frequency search space for the acquisition thus a reduction of the acquisition time.
2. The acquisition searches the satellites in view within a frequency search space around the coarse Doppler given. Once a satellite is acquired, there is a transition phase before the tracking to determine the position of the bit edge. Following this, tracking is started and the Time Of Week (TOW) is decoded from the received navigation data.
3. The measurements (pseudoranges, pseudorange rates, satellite PRN, estimated C/No and TOW) are sent to the computer by the FPGA at a rate of 0.1 Hz and a PVT solution is computed.
## 4 Experimental Validation and Results Analysis
### Experimental Results and Analysis
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Power Level** & \multicolumn{2}{c}{\(\mathbf{P_{in}}\)**: \(-25\) dBm} & \multicolumn{2}{c}{\(\mathbf{P_{in}}\)**: \(-24\) dBm} \\ \hline \multirow{2}{*}{**Scheme**} & **ACPR (dBc)** & \multirow{2}{*}{**EVM**} & **ACPR (dBc)** & \multirow{2}{*}{**EVM**} \\  & **Lower \& Upper** & & **Lower \& Upper** \\ \hline Original signal (APA input) & \(-45.48/-45.93\) & – & \(-45.48/-45.93\) & – \\ \hline Original signal (APA output) & \(-34.77/-32.81\) & \(11.26\) & \(-33.36/-31.58\) & \(12.18\)\% \\ \hline w/Ideal DPD (GMP) & \(-36.50/-36.56\) & \(4.11\) & \(-36.42/-36.36\) & \(4.46\)\% \\ \hline w/JSG-DPD (GMP STS1000) & \(-36.43/-36.20\) & \(\mathbf{4.33\%}\) & \(-36.55/-36.23\) & \(\mathbf{4.67\%}\) \\ \hline w/JSG-DPD (GMP STS100) & \(-36.12/-35.44\) & \(4.73\) & \(-35.71/-35.63\) & \(4.84\)\% \\ \hline w/JSG-DPD (GMP STS10) & \(-35.10/-34.34\) & \(4.95\) & \(-34.46/-35.06\) & \(5.84\)\% \\ \hline w/JSG-DPD (GMP w/o STS) & \(-30.68/-32.14\) & \(10.68\) & \(-30.14/-32.99\) & \(10.51\)\% \\ \hline w/Ideal DPD (MP) & \(-35.00/-34.95\) & \(4.83\) & \(-36.96/-35.19\) & \(4.65\)\% \\ \hline w/JSG-DPD (MP STS1000) & \(-34.80/-34.38\) & \(\mathbf{4.56\%}\) & \(-36.85/-35.23\) & \(\mathbf{4.88\%}\) \\ \hline w/JSG-DPD (MP STS100) & \(-34.63/-33.35\) & \(5.44\) & \(-36.22/-35.64\) & \(5.13\)\% \\ \hline w/JSG-DPD (MP STS10) & \(-35.27/-33.08\) & \(5.80\) & \(-35.63/-35.83\) & \(6.22\)\% \\ \hline w/JSG-DPD (MP w/o STS) & \(-35.85/-35.24\) & \(10.31\) & \(-34.52/-35.27\) & \(11.44\)\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Measured ACPR and EVM results with different methods for different input power levels from VSG. and the difference is about 0.2-0.8%. Moreover, the MP model is still more robust than the GMP model for ACPR improvement, which might depend on its low number of parameters and insensitivity to low SNR responses. In summary, the problem of low SNR due to AWGN channels can be effectively solved after 1000 or 100 times of superimposed training sequences. For the 10 to 15 min of LEO satellite transit, the pilot sequence length of 40,000 times 1000 or 100 is acceptable, accounting for less than 1 min. More critically, it is not necessary to re-adjust the DPD coefficients for each transit. However, only when the communication quality degrades significantly and the BER of the ground station crosses the preset threshold. It is at this point that the DPD coefficients are re-adjusted based on the operational status of the space-borne APA using the proposed JSG-DPD technique to restore the normal transmission state, allowing the linearity and power efficiency of the space-borne APA to be enhanced without taking any additional space-borne resources. Although the STS method used in this paper to improve SNR appears simple, it validates the feasibility of the proposed JSG-DPD technique. There is still a lot of room for further exploration and performance improvement, such as adopting deep learning approaches.
## 4 Exact Algorithm
### Eccentricity Bounds
```
1:Input: Graph \(G(V,E)\)
2:Output: Vector \(\varepsilon\), containing \(\varepsilon(v)\) for all \(v\in V\)
3:\(W\gets V\)
4:for\(w\in W\)do
5:\(\varepsilon[w]\gets 0\)\(\varepsilon_{L}[w]\leftarrow-\infty\)\(\varepsilon_{U}[w]\leftarrow+\infty\)
6:endfor
7:while\(W\
eq\emptyset\)do
8:\(v\leftarrow\textsc{SelectFrom}(W)\)
9:\(\varepsilon[v]\leftarrow\textsc{Eccentricity}(v)\)
10:for\(w\in W\)do
11:\(\varepsilon_{L}[w]\leftarrow\max(\varepsilon_{L}[w],\max(\varepsilon[v]-d(v,w),d(v,w)))\)
12:\(\varepsilon_{U}[w]\leftarrow\min(\varepsilon_{U}[w],\varepsilon[v]+d(v,w))\)
13:if\((\varepsilon_{L}[w]=\varepsilon_{U}[w])\)then
14:\(\varepsilon[w]\leftarrow\varepsilon_{L}[w]\)
15:\(W\gets W-\{w\}\)
16:endif
17:endfor
18:endwhile
19:return\(\varepsilon\)
```  
**Algorithm 2**BoundingEccentricities  
As an example of the usefulness of the bounds suggested in this section, consider the problem of determining the eccentricities of the nodes of the toy graph from Figure 2. If we compute the eccentricity of node G, which is 3, then we can derive bounds \(2|4\) for the nodes at distance \(1\) (B, C, F, J and K), \(2|5\) for the nodes at distance \(2\) (A, H and I) and \(3|6\) for the nodes at distance \(3\) (D, E and L), as depicted by the left graph in Figure 3. If we then compute the eccentricity of node E, which is \(6\), we derive bounds \(5|7\) for node I, \(4|8\) for node F, \(3|9\) for nodes A, B and G, \(4|10\) for nodes C, J and K, \(5|11\) for node K, and \(6|12\) for nodes D and L. If we combine these bounds for each of the nodes, then we find that lower and upper bounds for a large number of nodes have become equal: \(4|4\) for C, F, J and K, \(5|5\) for H and  
Figure 3: Eccentricity bounds of the toy graph in Figure 2 after subsequently computing the eccentricity of node G (left) and node E (right). I, and \(6|6\) for D and L, as shown by the right graph in Figure 3. Finally, computing the eccentricity of nodes A and B results in a total of \(4\) real eccentricity calculations to compute the complete eccentricity distribution, which is a speedup of \(3\) compared to the naive algorithm, which would simply compute the eccentricity for all \(12\) nodes in the graph. To give a first idea of the performance of the algorithm on large graphs, Figure 4 shows the number of iterations (vertical axis) that are needed to compute the eccentricity of all nodes with given eccentricity value (horizontal axis) for a number of large graphs (for a description of the datasets, see Section 6.1). We can clearly see that especially for the extreme values of the eccentricity distribution, very few iterations are needed to compute all of these eccentricity values, whereas many more iterations are needed to derive the values in between the extreme values.
## 4 Analysis
### Impact of the Angle of Attack on the Vertical Plane
According to the above, it can be concluded that, under the flow speed \(v=1\) m/s, when the angle of the vertical plane of attack \(\beta\) exceeds \(25^{\circ}\), the effects of \(F_{y}\), \(M_{x}\), and \(M_{z}\) cannot be ignored. The influence of the yawing moment (\(M_{z}\)) is particularly prominent, with its magnitude close to or exceeding the mean value of the pitching moment (\(M_{y}\)). Furthermore, it can still be observed that the overall trend of the \(M_{z}\) magnitude is generally consistent with the \(M_{x}\) magnitude. Figure 22 demonstrates the distribution of vorticity at different sections of the NEU model for moments with attack angles (\(\beta\)) equal to \(20^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(80^{\circ}\). In the case of \(\beta=20^{\circ}\), the vorticity distribution on both sides of the hull is relatively symmetrical. In addition, the horseshoe vortex movement process can be seen near the enclosure. As \(\beta\) increases to \(30^{\circ}\) and \(60^{\circ}\), the asymmetry in the vorticity distribution between the left and right sides of the hull becomes more pronounced. Finally, at \(\beta=80^{\circ}\), the vorticity distribution in different sections of the hull appears to be noticeably more irregular.
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 1 Introduction
However, such performances are also of concern among users of an RV reducer. In addition, the most important point that is often neglected is the load impact on the tooth profile modification. Regarding the multi-tooth contact model introduced in the references herein, the accuracy of the results will be significantly affected because it is based on a single contact stiffness model. In this paper, each tooth contact area is replaced by multiple differential contact units, and a multi-tooth contact model and a TE model of an RV reducer are first established. The proposed models are then verified using the finite element method and experimental measurements. On this basis, the influence of load on the different modification methods is studied in detail. The evaluation index considered includes the transmission accuracy, mechanical performance, and transmission efficiency. Finally, considering the load influence, the proper applications of different modification methods are discussed.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
###### Abstract  
The orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplexing (OFDM) in a high-mobility environment with high time-frequency dispersive channels. Since radio frequency (RF) impairments have a direct impact on waveform behavior, this paper investigates the experimental implementation of RF-impairments that affect OTFS waveform performance and compares them to the OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, and then an experimental framework is established for investigating the impact of RF-impairments, including non-linearity, carrier frequency offset (CFO), I/Q imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-defined radio (SDR) at carrier frequencies of 2.4 GHz and 5 GHz based on the Keysight EXG X-Series devices. The comparison of the performances of OFDM and OTFS in the presence of RF-impairments reveals that OTFS significantly outperforms OFDM.  
O OTFS; OFDM; RF-impairments; I/Q imbalances; DC-offset; phase noise; carrier frequency offset +
Footnote †: journal: _Sensors_  
the authors propose an OTFS-based matched filter where it is shown that OTFS provides better tracking speed and radar distance range compared to the OFDM waveform. The authors of [11] demonstrated that the OTFS has a simply sparse structure for every possible type of prefix/suffix. In addition to this, they introduce a different structure for the OTFS, which they call the reduced-full CP (RFCP), while in [10] the authors examined OTFS performance in vehicle applications for mono-static radar. In [12; 13], NOMA is integrated with OTFS to provide spectral efficiency and serve multiple users with different mobility characteristics (i.e., stationary and high mobility) in heterogeneous networks. In [14; 15], the authors investigated the performance of OTFS in mm-Wave communications. It is shown that OTFS provides better robustness against high Doppler shift and phase noise that exists in mm-Wave communications. Furthermore, in [16] the author proposed multiple-mode OTFS-index modulation to enhance OTFS system BER while providing high spectral efficiency. Furthermore, the OTFS waveform is appropriate for high mobility IoT applications and wireless sensor communication networks (WSNs) due to the ability to obtain a full diversity of the channel caused by spreading the information in time and frequency block [17; 18]. The 5G low-density parity-check code (LDPC) and 3 GPP turbo code were utilized as channel coded-OTFS by the authors of [19]. It was shown that for high-mobility channels, 3 GPP turbo code provides the best performance  
However, the effect of the waveform's physical design should be considered to have a clear understanding of the system's performance. As a result, the impact of RF-impairments has to be defined in accordance with the results of the experimental evaluation. The research that is associated with RF-impairments is discussed in the next part.
## 2 Materials and Methods
### Recycled Concrete Aggregates (HR and CR)
#### 2.2.2 CSF as an Additive for Soil Stabilization  
A well-known use of filler materials is for the improvement of poor soils, and CSF was also tested as an additive for soil stabilization. A sample of a low-quality reference soil with a 5% California Bearing Ratio (CBR) value was taken from a metro station excavation site in Thessaloniki, Greece. Two addition rates of CSF in soil were examined: 10 and 20 wt %. The maximum dry density and optimum moisture content according to the modified Proctor test and the CBR coefficient of soils were measured and compared.
## 4 Cavitation Effects
### Mass Flow Rate and Capacity Loss in the Hydrodynamic Torque Converter  
The dimensionless cavitation number \(\sigma\) was employed to evaluate the cavitation risk. A smaller cavitation number indicates greater cavitation risk. The cavitation number is defined by  
\[\sigma=\frac{p_{ref}-p_{v}}{0.5\rho{v_{ref}}^{2}}, \tag{18}\]  
where \(p_{ref}\), \(p_{v}\), and \(v_{ref}\) are the reference pressure (charge pressure), vapor pressure, and reference velocity, respectively. Mass flow rate represents the cyclic mass flowing through the torque converter per unit time, and specifically refers to the mass flow of the flow section at the inlet of the stator. For a hydrodynamic torque converter, the cavitation  
Figure 11: Stator blade load distributions with cavitation and non-cavitation at different \(SR\)s.  
number is calculated by substituting \(v_{ref}\) with the average circumferential velocity (\(U\)) in the torus cross-section of the torque converter. This is defined using  
\[U=\frac{\dot{m}}{\text{A}\rho}, \tag{19}\]  
where \(\dot{m}\) is the circulating mass flow rate of the hydrodynamic torque converter. The mass flow rate depends on the cascade parameters and operating conditions. Therefore, the cavitation number comprehensively expresses the operating conditions, charge pressure, and cascade shape of the hydrodynamic torque converter. For a given torus design, A is constant. In this study, A was set to 2.56 \(\times\) 10\({}^{-2}\) m\({}^{2}\).  
Figure 12 shows the relationship between the cavitation number and dimensionless capacity constant and vapor volume. The vapor volume decreased as the cavitation number increased. As the capacity constant approached unity, the influence of cavitation on the hydrodynamic performance and internal flow field decreased. When the cavitation number \(\sigma\geq\) 10.5 (i.e., \(SR\geq\) 0.3), cavitation had an almost negligible effect.  
Figure 13 compares the mass flow rate and capacity constant with and without cavitation. Cavitation decreased the mass flow rate by nearly 50 kg/s under the stall operating condition, which reduced \(TR_{0}\) and \(CC_{0}\). The capacity constant and mass flow rate showed the same tendencies with and without cavitation at high \(SR\)s, which indicates that cavitation had disappeared.  
Figure 12: Non-dimensional capacity constant and vapor volume under various dimensionless cavitation numbers (\(\sigma\)).
## 5 Concluding Remarks  
Pool fires on ships and offshore installations pose a serious threat because of the spreading behaviour of the spilled flammable liquid under motion. Spillages in large compartments are more likely to induce large-scale pool fires owing to the larger spilling extent.  
This study experimentally investigated the spreading behaviour of spilled fuel on inclined surfaces due to ship motion. JP-5 was discharged onto a steel plate under different inclination angles and spill rates, and the spilled pool shape was measured. Empirical formulae for predicting the length, breadth, and area of the spilled pool were developed based on the experimental results. To validate the proposed formulae, the experimental conditions were substituted into the formulae, and the predictions were compared with the measured data. Furthermore, a modified formula predicting the HRR of pool fires in inclined conditions was proposed. Finally, the effects of substrate inclination on the fire consequences were investigated by conducting fire analyses. The main findings are as follows:  
Figure 14: Heat flux measurements: (**a**) M1, (**b**) M2.  
1. The experiment revealed that the length and area of the spilled pool were proportional to the inclination angle and spill rate, whereas the pool breadth was proportional to the spill rate and inversely proportional to the inclination angle.
2. The empirical formulae for the pool dimensions agreed well with the experimental data and exhibited higher accuracy compared with an existing model, as confirmed by the corresponding mean and COV. Thus, the proposed formulae are more applicable to fuel spillages on inclined surfaces compared with the existing models.
3. The fire analyses of pool fires on surfaces without and with inclination revealed that the substrate inclination significantly influenced fire consequences. In particular, compared with the scenario without substrate inclination, the fire on an inclined substrate exhibited 36% higher HRR and thermal loads acted further from the spillage location.  
In future studies, the structural response to pool fires on inclined surfaces will be analysed using the empirical formulae. Furthermore, the effects of the material properties of fuel, such as fuel viscosity, density, and surface tension, on the spill shape will be examined for different fuel types and substrate conditions. Additionally, further experiments for additional substrate inclinations and fuel discharge rates will improve the applicability of the proposed formulae.  
D.B.: Conceptualisation, methodology, software, writing--original draft; H.L.: Methodology, writing; S.L.: data curation; J.K.S.: Conceptualisation, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Defense Rapid Acquisition Technology Research Institute (DRATRI) of Korea project code 912849501.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
## 5 Test Results and Discussion  
By analyzing the data collected by sensors installed in various parts of the model, the following main experimental results can be obtained:  
1. The three seismic waves achieved the expected delay and amplification effect. Although the delay and amplification effect achieved by each seismic wave were different due to the different spectral characteristics, the differences in values were small. It indicates that the steel frame with viscous dampers have a significant delay and amplification effect on seismic waves;
2. There was no significant change in the acceleration amplification coefficient of the model before and after the seismic wave delay and amplification. It indicates that the delay and amplification of seismic waves has essentially no effect on the acceleration response of the structure;
3. The lateral displacement of the model in the X direction changed significantly before and after the delay and amplification of the seismic wave, while there was no significant change in the lateral displacement of the model in the Y direction. It shows that the delay of seismic waves has a great influence on the displacement response of the structure, while the amplification of seismic waves has little effect.
## 3 Security Analysis of ULRAS Protocol
### Security Analysis of Aghili and Mala Improvement to ULRAS  
There are several important points to note about Aghili and Mala's [25] improvement to ULRAS:  
* The use of a rotation operation several times is like using one rotation i.e., \(M_{2}=Rot(Rot(K\oplus R_{t}\oplus T_{R},ID\oplus R_{t}),K_{X}+R_{t})\) in the Aghili and Mala improvement equals with \(M_{2}=Rot(K\oplus R_{t}\oplus T_{R},i)\) where \(i\) is a value between 0 to \(L\). The same point applies to \(M_{1}\) message.
* Based on this fact given \(M=Rot(X,Y)\)\(mod\)\(L\) and \(X\), if we rotate right \(M\) for \(i=0,\ldots,L\) and comparing the result with \(X\), one can determine \(Y\), the adversary with eavesdropping two sessions of protocol messages without completion of protocol sessions which leads to not updating secret values, can conduct secret disclosure attack which reveals \(ID\) and \(K\). Precisely, given \(M_{2}=Rot(K\oplus R_{t}\oplus T_{R},i)\) and \(M^{\prime}_{2}=Rot(K\oplus R^{\prime}_{t}\oplus T^{\prime}_{R},j)\), \(R_{t}\), \(R^{\prime}_{t}\), \(T_{R}\), \(T^{\prime}_{R}\), \(M_{1}\) and \(M^{\prime}_{1}\), the adversary for \(i,j=0,\ldots,L\) verifies whether \(RoR(M_{2},i)\oplus R_{T}\oplus T_{R}\stackrel{{?}}{{=}}Rot(M^{ \prime}_{2},j)\oplus R^{\prime}_{T}\oplus T^{\prime}_{R}\) to retrieve \(K\) as \(RoR(M_{2},i)\oplus R_{T}\oplus T_{R}\). Similarly, for \(i,j=0,\ldots,L\) the adversary verifies whether \(RoR(M_{1},i)\oplus R_{T}\oplus T_{R}\stackrel{{?}}{{==}}RotR(M^{ \prime}_{1},j)\oplus R^{\prime}_{T}\oplus T^{\prime}_{R}\) to retrieve \(ID\oplus K\) as \(RoR(M_{1},i)\oplus R_{T}\oplus T_{R}\). Given that \(K\) has already been acquired, the adversary can get \(ID\) and can verify the correctness of the obtained values by using other protocol's messages.
* Since all the secret values of the protocol are revealed, it is easy to do a variety of attacks including impersonation attacks, traceability attacks, de-synchronization attacks, etc.