esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 2 MIMO-QSM Transmission System  
The system model of the MIMO-QSM transmission scheme is presented in Figure 1. We considered a transmitter with \(n_{T}\) transmit antennas and a receiver with \(n_{R}\) receiving antennas. Thus, the end-to-end configuration can be considered as a \(n_{R}\times n_{T}\) MIMO transmission system. We assumed a rich scattering, Rayleigh wireless channel with flat and slow fading, where the channel between transmitter antenna \(j\) and receiver antenna \(i\) can be modeled as a complex Gaussian gain \(h_{ij}\sim\mathcal{C}(0,1)\) of zero mean and variance 0.5 per dimension. This gain remains constant for several symbol intervals, after which it changes to a new independent channel realization. The system considered here can transmit \(m_{QSM}=\log_{2}(M)+2\log_{2}(n_{T})\) bits in each time slot, where \(M\) is the size of the \(M\)-ary quadrature amplitude modulation (QAM) constellation \(\mathcal{S}=\{s_{1},s_{2},\cdots,s_{M}\}\) and \(n_{T}\) is the size of the QSM transmission vector. Thus, the general MIMO-QSM communication system can be mathematically modeled as:  
\[\begin{bmatrix}y_{1}\\ \vdots\\ y_{n_{R}}\end{bmatrix}=\sqrt{\gamma}\begin{bmatrix}h_{1,1}&\cdots&h_{1,n_{T}}\\ \vdots&\ddots&\vdots\\ h_{n_{R},1}&\cdots&h_{n_{R},n_{T}}\end{bmatrix}\begin{bmatrix}x_{1}\\ \vdots\\ x_{n_{T}}\end{bmatrix}+\begin{bmatrix}n_{1}\\ \vdots\\ n_{n_{R}}\end{bmatrix}, \tag{1}\]  
which can be expressed equivalently as:  
\[\mathbf{y}=\sqrt{\gamma}\mathbf{H}\mathbf{x}+\mathbf{n}, \tag{2}\]  
where \(\mathbf{x}\in\mathbb{C}^{n_{T}\times 1}\) is the overall transmission vector and \(\mathbf{y}\in\mathbb{C}^{n_{R}\times 1}\) is the received vector. \(\mathbf{H}\in\mathbb{C}^{n_{R}\times n_{T}}\) is the channel matrix; \(\gamma\) is the signal-to-noise ratio (SNR) at each antenna; and \(\mathbf{n}\in\mathbb{C}^{n_{R}\times 1}\) represents the AWGN vector. The generated noise samples are independent and identically distributed (i.i.d.) with \(\mathcal{CN}(0,\sigma^{2})\).  
Further assumptions were that all the antennas transmit information symbols from the same \(M\)-QAM constellation, the receiver has perfect channel state information (CSI), and the receiver is perfectly synchronized to the transmitter.
## 3 Results
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CS70 (\(n\) = 61)**} & \multicolumn{2}{c}{**CS90 (\(n\) = 56)**} & \multicolumn{4}{c}{**CS70 vs. CS90**} \\ \cline{2-9}
**Polymorphism** & \(n\) & \% & \(n\) & \% & \(p^{\text{ \tt a}}\) & \begin{tabular}{c} **Compared** \\ **Genotypes or** \\ **Allees** \\ \end{tabular} & \(\mathbf{OR}\) (95\% CI) & \(p^{\text{ \tt b}}\) \\ \hline \multicolumn{9}{c}{Th1111I genotype} & \multicolumn{9}{c}{0.351} \\ \hline CC & 32 & 52.46\% & 22 & 39.29\% & CC + CT vs. TT & \begin{tabular}{c} 1.095 \\ (0.194–6.198) \\ \end{tabular} & 1 \\ \hline CT & 25 & 40.98\% & 30 & 53.57\% & CC vs. CT + TT & \begin{tabular}{c} 1.698 \\ (0.768–3.8) \\ \end{tabular} & 0.194 \\ \hline TT & 4 & 6.56\% & 4 & 7.14\% & CC vs. TT & \begin{tabular}{c} 1.446 \\ (0.241–8.66) \\ \end{tabular} & 1 \\ \hline \multicolumn{9}{c}{Tth111I allele} & \multicolumn{9}{c}{} \\ \hline C & 89 & 72.95\% & 74 & 66.07\% & C vs. T & \begin{tabular}{c} 1.383 \\ (0.762–2.52) \\ \end{tabular} & 0.259 \\ \hline T & 33 & 27.05\% & 38 & 33.93\% & & & \\ \hline \multicolumn{9}{c}{N363S genotype} \\ \hline AA & 57 & 93.44\% & 53 & 94.64\% & \begin{tabular}{c} AA vs. AG + \\ GG \\ \end{tabular} & 0.808 & 1 \\ \hline AG & 3 & 4.92\% & 3 & 5.36\% & & & \\ \hline GG & 1 & 1.64\% & & & & \\ \hline \multicolumn{9}{c}{N363S allele} \\ \hline A & 117 & 95.90\% & 109 & 97.32\% & A vs. G & \begin{tabular}{c} 0.645 \\ (0.098–3.406) \\ \end{tabular} & 1 \\ \hline G & 5 & 4.10\% & 3 & 2.68\% & & & \\ \hline ER22/ER23 EK & & & & & & \\ \hline AG & & 1 & 1.79\% & & & \\ \hline GG & 61 & 100.00\% & 55 & 98.21\% & & \\ \hline ER22/ER23 EK allele & & & & & & \\ \hline A & & & 1 & 0.89\% & & & \\ \hline G & 122 & 100.00\% & 111 & 99.11\% & & & \\ \hline Tth111I/N363S/ & & & & & & \\ ER22/23 EK & & & & & - & \\ haplotype & & & & & & \\ \hline CAG & 84 & 68.85\% & 72 & 64.29\% & CAG vs. other & \begin{tabular}{c} 1.227 \\ (0.687–2.195) \\ \end{tabular} & 0.490 \\ \hline TAG & 33 & 27.05\% & 36 & 32.14\% & TAG vs. other & \begin{tabular}{c} 0.784 \\ (0.428–1.429) \\ \end{tabular} & 0.473 \\ \hline CGG & 5 & 4.10\% & 2 & 1.79\% & CGG vs. other & \
\begin{tabular}{c} 2.342 \\ (0.374–25.078) \\ \end{tabular} & 0.449 \\ \hline TAA & & & 1 & 0.89\% & & \\ \hline TGG & & & 1 & 0.89\% & & \\ \hline \hline \end{tabular} 
\begin{tabular}{c} CS70\(-\)patients with 70–89\% stenosis; CS90—patients with 90% or more stenosis. \({}^{\text{ \tt a}}\)\(p\)-values were calculated with the \(\chi^{2}\) test. \({}^{\text{ \tt b}}\)\(p\)-values were calculated with [PERSON]’s exact test. Moreover, we tried to reduce the influence of sex hormones. Therefore, we conducted separate analyses to compare men with male patients without CA stenosis (Table 5) as
## 2 Materials and Methods
### Applied Materials  
In microchannels, water or nanofluids are usually used as the coolant. [PERSON] and co-workers [27] analyzed water and three nanofluids as coolant in wavy channels. They found that the use of nanofluids in curved microchannels is not appropriate. Therefore, water is chosen as the coolant in the present analysis. Aluminum was chosen as the material in which the microchannels are made because it is a material with high thermal conductivity and corrosion resistance. The water and aluminum materials coefficients are given in Table 2.  
In Table 2, references are made to the DIPPR equations, which are defined in the DIPPR-801 project, released in the early 1980s by the Design Institute for Physical Properties (DIPPR, New York, NY, USA) to describe the temperature dependence of various important physical properties of substances [34]. They are the industry standard in many applications where substance data are needed for optimizing and designing chemical processes as well as in process synthesis, simulations, and development. The DIPPR-801 database contains parameter sets for about 2000 substances. The equations have proven to be highly reliable and accurate in predicting the temperature dependence of various thermophysical properties.  
According to DIPPR, the water viscosity (14) and density (15) can be calculated using the following correlations:  
* viscosity. \[\eta=\exp\biggl{(}A+B\frac{1}{T}+C\cdot\ln(T)+D\cdot T^{E}\biggr{)}\] (14)
* density. \[\rho=A\cdot B^{-(1+(1-\frac{T}{E})^{D})}\cdot M_{mol}\] (15)  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Property** & **Water** & **Aluminum** \\ \hline Density, kg/m\({}^{3}\) & DIPPR 105 (Equation (15)) & 2719 \\ Viscosity, kg/m-s & DIPPR 101 (Equation (14)) & \\ Specific heat, J/kg-K & 4182 & 871 \\ Thermal conductivity, W/m-K & 0.6 & 202.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The properties of the components used in the calculations.  
In the above, the corresponding constants were taken for water from the DIPPR database. \(M_{mol}\) is molar mass and is used to recalculate the molar to mass density. Table 3 presents values for both correlations.
## 5 Key Enabling Technologies of Healthcare 4.0  
To meet the demands of Healthcare 4.0, novel technologies should be integrated via a reliable network structure. Figure 2 presents the main key enabling technologies of Healthcare 4.0 (20).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Challenges** & \multicolumn{2}{c}{**Issues with Healthcare 4.0**} & \multicolumn{2}{c}{**Key Solutions**} \\ \hline Energy & • & Healthcare 4.0 systems should support a ten-year battery life of medical sensors. Thus, communication networks and protocols used for such systems should meet this demand. & • & Energy-efficient network algorithms Edge computing Quantum computing \\ \hline Bandwidth & • & With the growing use of telemedicine and other remote healthcare services, communication networks must be able to facilitate high-quality video and audio streaming with sufficient bandwidth. & • & Edge computing \\ • & • & Healthcare 4.0 faces significant difficulty in allocating resources optimally for vital medical traffic. & • & 5G communications \\ \hline Network availability and reliability & • & Achieving ultra-high availability and reliability is a challenge that needs to be resolved by introducing novel technologies. & • & 5G communications \\ \hline End-to-end & • & A part of the Healthcare 4.0 services are ultra-low latency services that require an end-to-end latency of 5-1 msec for the communicated data. & • & D2D \\ \hline Security & • & The required security level of Healthcare 4.0 applications cannot be achieved by the existing traditional schemes. & • & Trust management Blockchain Quantum encryption \\ \hline Scalability & • & The designed networks and protocols for Healthcare 4.0 applications should support high scalability since medical devices are growing rapidly. & • & 5G communications D2D Edge computing \\ \hline Massive data & • & The massive amount of network traffic due to numerous devices deployed in the network should be managed using robust methods to avoid network failure. & • & Big data D2D \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of Healthcare 4.0 challenges.  
Figure 2: Key enabling technologies of Healthcare 4.0.  
Wearables and IoT devices have gained significance in the Healthcare 4.0 ecosystem. IoT devices can capture real-time patient data, monitor medical equipment, and track inventory, enhancing patient outcomes and decreasing costs [29]. It provides efficient technology due to the high flexibility supported by IoT networks. Moreover, the availability and plurality of medical sensors facilitate the introduction of IoT in Healthcare 4.0 systems. A detailed analysis of medical IoT devices that can be used to assist Healthcare 4.0 was introduced in Section 3.2.
## 4 Implementation and Discussion of Results
### Comparative Analysis  
Table 4 and Figure 20 show that the LSTM model performs better than any of the other models in predicting the confirmed and death cases, with lower RMSE and MAPE values and greater \(R^{2}\) values. In addition, CNN is better at predicting recovered cases than LSTM and ARIMA based on the \(R^{2}\) value. The evaluation measure values show that LSTM is superior to the other models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Predicted variable** & **RMSE** & **nRMSE** & **MAE** & **nMAE** & **MAPE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{4}{*}{ARIMA} & Confirmed & 578.1 & 13.7 & 258.6 & 6.1 & 21.4 & 0.15 \\  & Deaths & 3.6 & 7.0 & 2.25 & 4.33 & 16.27 & 0.88 \\  & Recovered & 473.48 & 10.05 & 248.62 & 5.28 & 34.98 & 0.39 \\  & Confirmed & 196.58 & 4.64 & 104.78 & 2.47 & 11.65 & 0.96 \\ \multirow{2}{*}{LSTM} & Deaths & 3.29 & 6.33 & 2.34 & 4.51 & 16.9 & 0.94 \\  & Recovered & 641.3 & 13.6 & 312.8 & 6.64 & 32.7 & 0.31 \\  & Confirmed & 200.62 & 4.74 & 97.58 & 2.31 & 10.03 & 0.95 \\ \multirow{2}{*}{CNN} & Deaths & 4.41 & 8.48 & 2.75 & 5.28 & 17.62 & 0.90 \\  & Recovered & 426.29 & 9.05 & 213.26 & 4.53 & 38.86 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation of the three predictive algorithms.  
Figure 19: Prediction over a 60-day forecast for the death cases using the CNN model.  
Figure 18: Prediction over a 60-day forecast for the recovered cases using the CNN model.
## 4 Processing of Experimental Data
### Relationship between Photovoltaic Efficiency and Solar Irradiance  
This study explores the close relationship between photovoltaic efficiency and solar radiation, displaying the instantaneous photovoltaic efficiency curves over operating time in Figures 12-14.  
In the overcast condition (30 December), as shown in Figure 12, the photovoltaic efficiency gradually increases with the increase in solar radiation intensity, especially after 11:30. Despite the weakening of solar radiation intensity, both the PVT and PV panels exhibit a relatively stable trend in photovoltaic efficiency, with average instantaneous efficiencies reaching 21.03% and 19.32%, respectively, until a sharp decrease in solar radiation occurs around 16:30. Throughout the experiment, the average photovoltaic efficiencies of the PVT and PV panels are 19.69% and 18.03%, respectively, representing an increase of 1.34% and 1.29% compared to the baseline condition.  
Figure 12: Variation Curve of Solar Radiation Intensity and Photovoltaic Efficiency of PVT/PV Panels under Overcast Conditions.  
Figure 13: Variation Curve of Solar Radiation Intensity and Photovoltaic Efficiency of PVT/PV Panels under Cloudy Conditions.  
In the cloudy condition (3 January), as depicted in Figure 13, the photovoltaic efficiency increases with the enhancement of solar radiation intensity until 11:00. However, from 11:00 to 13:30, the emergence of clouds causes fluctuations in solar radiation intensity, thereby affecting the photovoltaic efficiency of both the PVT and PV panels. The average instantaneous photovoltaic efficiencies reach 23.49% and 22.47%, respectively. Overall, the average photovoltaic efficiencies of the PVT and PV panels are 21.12% and 19.49%, respectively, representing an increase of 2.37% and 2.98% compared to the baseline condition.  
In the sunny condition (4 January), the photovoltaic efficiency increases with the increase in solar radiation intensity and stabilizes after reaching its peak, then decreases as solar radiation decreases. Both the PVT and PV panels exhibit similar arch-shaped trends in photovoltaic efficiency with solar radiation intensity. The average photovoltaic efficiencies are 22.37% and 20.47%, respectively, representing an increase of 1.9% for the PVT panel compared to the PV panel.  
By calculating with theoretical Formula (5), the photovoltaic efficiency of the PVT panel is 19.84%. Compared to the experimental results, its relative increases in partly cloudy, sunny, and partly sunny conditions are 1.19%, 3.65%, and 2.53%, respectively. This comparison further validates the potential of the system in improving solar energy conversion efficiency.
## 6 Results and Comparison
### Performance Comparison
#### 6.3.4 Virtex 7 Comparison  
In this comparison, we have observed that the proposed two-stage pipelined architecture outperforms both the non-pipelined and pipelined designs presented in [15] on various metrics. The proposed non-pipelined architecture uses 37.5% fewer hardware resources and operates at 1.06 times the clock frequency of the non-pipelined design, resulting in a 1.337 times higher throughput/area ratio. Similarly, when compared to the pipelined design in [15], the proposed architecture uses 25.4% fewer hardware resources while operating at a clock frequency that is 1.74 times higher, resulting in an impressive 1.21 times higher throughput/area ratio. These results demonstrate that our proposed architecture offersa highly efficient solution for cryptographic computations in low-resource environments, while also providing a suitable trade-off between hardware resources, clock frequency, and throughput/area ratio.  
The proposed architecture and the work in [34] are not directly comparable because they use different implementation fields. However, it is still possible to compare the two architectures by considering the clock frequency, hardware resources, and throughput/area ratio. For d = 59, the Virtex-7 implementation of the proposed non-pipelined architecture achieves a clock frequency that is 1.44 times higher and utilizes 52.9% fewer resources. In addition, the proposed non-pipelined architecture achieves a higher throughput/area ratio. The proposed pipelined architecture further improves the speedup and resource reduction. For d = 59, the Virtex-7 implementation of the proposed pipelined architecture achieves a clock frequency that is 2.34 times higher and utilizes 39.43% fewer resources. In addition, the proposed pipelined architecture achieves a higher throughput/area ratio than the work in [34].  
In general, it has been observed that using a pipeline with a hybrid algorithm can result in superior performance while requiring fewer hardware resources. The suggested design capitalizes on these advantages to achieve impressive throughput/area ratios.
###### Abstract  
In this study, the effects of graphene and design differences on bow-tie microstrip antenna performance and bandwidth improvement were investigated both with simulation and experiments. In addition, the conductivity of graphene can be dynamically tuned by changing its chemical potential. The numerical calculations of the proposed antennas at 2-10 GHz were carried out using the finite integration technique in the CST Microwave Studio program. Thus, three bow-tie microstrip antennas with different antenna parameters were designed. Unlike traditional production techniques, due to its cost-effectiveness and easy production, antennas were produced using 3D printing, and then measurements were conducted. A very good match was observed between the simulation and the measurement results. The performance of each antenna was analyzed, and then, the effects of antenna sizes and different chemical potentials on antenna performance were investigated and discussed. The results show that the bow-tie antenna with a slot, which is one of the new advantages of this study, provides a good match and that it has an ultra-bandwidth of 18 GHz in the frequency range of 2 to 20 GHz for ultra-wideband applications. The obtained return loss of \(-10\) dB throughout the applied frequency shows that the designed antennas are useful. In addition, the proposed antennas have an average gain of 9 dBi. This study will be a guide for microstrip antennas based on the desired applications by changing the size of the slots and chemical potential in the conductive parts in the design.  
antenna performance; bow-tie antenna; bandwidth; graphene; chemical potential; 3D printer +
Footnote †: journal:  
1
## 2 Proposed Prognostic Framework  
### Degradation Dataset  
Mechanical systems are subjected to repeated cyclic loading under real-time scenarios which leads to failure over time. When the structure is subjected to cyclic loading, stress gets concentrated at the location of impact, resulting in a crack. Even though the crack propagates at a slow rate, it can eventually lead to failure of the structure. Therefore, it is essential to monitor the structure for crack propagation and ensure proper maintenance strategies are adopted to improve the lifetime of the mechanical structure. Many researchers are focusing on understanding the fatigue mechanisms of composite materials used in mechanical structures to achieve higher reliability and accurate damage prognosis.  
[PERSON] et al. [22] assessed the delamination growth in GFRP materials using an optical transmission system (OTS). The OTS setup used by the authors comprised a laser source and a photodetector. The GFRP samples were illuminated using the laser source, and the photodetector recorded the power transmitted through the samples. In the presence of deformities in the sample such as delamination (air gap), the transmitted radiation gets altered. The images obtained from the OTS setup were analyzed comparing a healthy sample and an impacted sample. The authors further adopted advanced signal processing methods to obtain the damage growth trend of the impacted samples shown in Figure 1. Here, three eight layered S2 GFRP laminates were subjected to repeated cycles of low-velocity impacts. Of the three laminates, one was subject to 15 impacts and scanned at every interval using the OTS technique. All the fifteen scans were processed using image segmentation, and the delamination severity was categorized as 1, 2, and 3+.  
The extracted trend of damage growth area with respect to the cumulative impact energy of sample S1 (defined as the summation of energy transferred from all the impact cycles; note, however, that the incremental change in impact energy for every additional impact cycle is not constant, which is why the artificial unit of \"time\" here is \"impact energy\" and not number of cycles) is shown in Figure 1. It is evident from Figure 1 that the crack nucleation, growth, and delamination process have two distinct regions and the knee point (change point) falls at 20 J for all the three cases. The authors considered the initial distribution of jump energy based on the experimental data. However, the jump energy can vary for other specimens or if the sequence of low-velocity impacts is altered. In order  
Figure 1: The damage growth area of sample S1 against the cumulative low-velocity impact energies.  
to overcome this drawback, we intend to perform an online prognostic study on this data and deduce the jump energy online and also estimate the remaining useful life.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 5 Discussion and Conclusions
**Abbreviations**  
Polar Code International Code for Ships Operating in Polar Waters  
IMO International Maritime Organization  
IACS International Association of Classification Societies  
PC Polar Class  
SOLAS International Convention for the Safety of Life at Sea  
MARPOL International Convention for the Prevention of Pollution from Ships  
FR Functional Requirements  
RMRS Russian Maritime Register of Shipping  
DOF Degree of Freedom  
MV Motor Vessel  
IB Icebreaker  
RQ Research Question  
**Notations**  
\(M_{real}\) Total reduced mass of the ship-ice system: \([t]\)  
\(F\) Total ice load, \([MN]\)  
\(A\) Nominal contact area, \(\left[m^{2}\right]\)  
\(\zeta_{n}\) Indentation depth, \([m]\)  
\(t\) Time, \([sec]\)  
\(p\left(\zeta_{n},\zeta_{n}\right)\) Contact pressure, \([MPa]\)  
\(a\) Exponent of the configuration of the ice edge at the point of impact  
\(G\) Coefficient of the geometric parameters of the ship and ice  
\(\sigma_{\alpha}\) Ice compressive strength, \([MPa]\)  
\(C\) Indentation coefficient  
\(T_{1}\) Kinetic energy of the ship, reduced toward the line of impact, \([J]\)  
\(T_{2}\) Kinetic energy of the ice, reduced toward the line of impact, \([J]\)  
\(U\) Ice crushing related work, \([J]\)  
\(V\) Potential bending strain energy of a semi-infinite ice plate, \([J]\)  
\(M_{real}ship,M_{rad}ice\) Reduced masses for the ship and ice floe, \([t]\)  
\(\
u_{1},\
u_{2}\) Reduced speeds for the ship and ice floe, \([m/sec]\)  
\(P_{v}\) Vertical component of contact force, \([N]\)  
\(\gamma_{i}\) Specific weight of ice, \(\left[N/m^{3}\right]\)  
\(D\) Flexural stiffness of ice plate, \([Pa\)-\(m^{3}]\)  
\(E\) Elastic modulus of ice, \([Pa]\)  
\(h_{i}\) Ice thickness, \([m]\)  
\(\mu\) Poisson ratio for ice  
\(\sigma_{\sigma}\) Ice crushing strength, \([MPa]\)  
\(\zeta_{max}\) Maximum indentation depth, \([m]\)  
\(R\) Radius of an ice floe, \([m]\)  
\(\Phi\) Ice edge opening angle, \([deg]\)  
\(\zeta_{x}\) Projected horizontal indentation depth, \([m]\)  
\(\zeta_{y}\) Projected vertical indentation depth, \([m]\)  
\(\alpha\) Waterline angle, \([deg]\)  
\(\beta\) Normal frame angle, \([deg]\)  
\(\gamma\) Stem angle, \([deg]\)  
r Contact radius, \([m]\)  
\(p\) Average pressure, \([MPa]\)  
\(P_{0}\) Average pressure on \(1\ m^{2}\), \([MPa]\)
## 2 Materials and Methods
### Data Description
#### 2.1.1 SCADA Dataset  
The SCADA dataset contains more than 300 variables as the main systems of the turbine are all monitored (pitch, main shaft bearing, gearbox, generator, etc.). Sampling frequency is 10 min and quantities such as the arithmetic mean, minimum, maximum and standard deviation are computed with the data acquired for this period. The format of the SCADA dataset, as well as the name of the variables and position of the sensors, may vary according to the manufacturer of the turbine. An example of the dataset used in this research is provided in Table 1.