esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### CO\({}_{2}\) Emissions for the Whole English Housing Stock
With the penetration level increased to 75%, the emissions reduction increases to 81%, _i.e._, a further 10% fall compared with only 5% for the high emissions scenario. This picture of an additional 10% improvement with an increase in penetration level is broadly repeated with lower efficiency heat pumps, _i.e._, 68%-78% and 69%-79% for heat pumps at 200% and 250% respectively. It is interesting to note that improvements in heat pump efficiency has very little overall effect in reducing emissions with there being only an additional 3% reduction with the heat pump efficiency moving from 200% to 450%. Looking at the different scenarios and how they are illustrated in Figures 7 and 8, it can be seen that the largest impact on the reduction of CO\({}_{2}\) emissions across the English housing stock comes from  
Figure 8: Emissions at different electricity grid emissions factors, heat pump efficiencies and penetration levels. Figure 7: Emissions reduction at different electricity grid emissions factors, heat pump efficiencies and penetration levels. reducing the emissions intensity of the electricity grid. Even with no heat pumps installed, a CO\({}_{2}\) emission reduction of 47% is achievable. This emissions reduction value can be improved upon if there is a significant penetration of heat pumps, with an 81% reduction possible at a 75% level of penetration. Moreover, improved heat pump performance has little overall effect on this reduction. This demonstrates that the majority of future emissions reduction arises from the reduction in CO\({}_{2}\) emissions intensity from the electricity grid rather than any improvement in heat pump efficiency. However, it should be noted that given the aforementioned uncertainty in projecting the UK electricity grid emission factor out to 2030, the \"best case\" emission scenario should be considered within this context. Parametric analysis shows that, given a pragmatic scenario of a 2030 emission reduction of 50% below current levels, then CO\({}_{2}\) emissions would be greater than the \"best case\" by a factor of 5.
# Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images
## 3 Experimental Results and Analysis
### Experiment 1
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & **Scale** & **Bicubic** & **L1** & **L2** & **SSIM** & **MS-SSIM** & **L1+MS-SSIM** \\ \hline \multirow{2}{*}{neck} & \(\times\)2 & 0.6928 & 0.9876 & **0.9900** & **0.9900** & 0.9898 & 0.9897 \\  & \(\times\)4 & 0.5442 & 0.9308 & 0.9349 & 0.9367 & 0.9358 & **0.9392** \\ \hline \multirow{2}{*}{breast} & \(\times\)2 & 0.8848 & 0.9492 & 0.9763 & 0.9802 & 0.9797 & **0.9816** \\  & \(\times\)4 & 0.4830 & 0.8979 & 0.9031 & 0.9070 & 0.9075 & **0.9082** \\ \hline \multirow{2}{*}{knee} & \(\times\)2 & 0.9185 & 0.9760 & 0.9633 & 0.9641 & 0.9637 & **0.9774** \\  & \(\times\)4 & 0.5649 & **0.8987** & 0.8844 & 0.8881 & 0.8867 & 0.8873 \\ \hline \multirow{2}{*}{head} & \(\times\)2 & 0.6025 & 0.9605 & 0.9592 & 0.9597 & 0.9593 & **0.9609** \\  & \(\times\)4 & 0.5623 & 0.9128 & 0.9064 & 0.9064 & 0.9011 & **0.9609** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SSIM values of different loss functions (double cubic downsampling). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & **Scale** & **Bicubic** & **L1** & **L2** & **SSIM** & **MS-SSIM** & **L1+MS-SSIM** \\ \hline \multirow{2}{*}{neck} & \(\times\)2 & 15.80 & 24.16 & 24.32 & 23.98 & 24.24 & **24.36** \\  & \(\times\)4 & 9.29 & 18.54 & 19.76 & 18.85 & 19.26 & **19.77** \\ \hline \multirow{2}{*}{breast} & \(\times\)2 & 13.52 & 31.75 & 31.82 & 31.78 & 31.96 & **31.98** \\  & \(\times\)4 & 7.68 & 25.62 & 25.94 & 25.15 & **25.98** & **25.98** \\ \hline \multirow{2}{*}{knee} & \(\times\)2 & 11.83 & 31.49 & 31.52 & 30.81 & 31.09 & **31.73** \\  & \(\times\)4 & 5.97 & 26.09 & 26.31 & 24.86 & 25.48 & **26.49** \\ \hline \multirow{2}{*}{head} & \(\times\)2 & 6.29 & 27.89 & **29.87** & 26.98 & 27.97 & 28.76 \\  & \(\times\)4 & 2.95 & 20.75 & 20.43 & 20.97 & 18.53 & **20.98** \\ \hline \end{tabular} Note: The numbers in bold are the best results.  
\end{table} 
Table 3: PSNR values of different loss functions (K-space truncation downsampling).
## 3 Results
While the tool reduced the DPIA generation time to 5 h for a team of three, manual generation involved six individuals, each dedicating 18 h. To assess user-friendliness quantitatively, we obtained a System Usability Scale (SUS) [28] including the experiences of five members of our institute in different positions who already utilized the DPIA click&go tool for generating DPIAs. The obtained scores ranged from 72.5 to 82.5 (other scores: 75, 77.5, 77.5). It is worth highlighting that a DPIA drafted by a multi-professional team consisting of research, IT and legal experts, quality assurance staff, and process managers among others is considered the gold standard, as it incorporates all different perspectives that are crucial to obtain a complete risk and TOM evaluation as each profession is familiar with its specific risk scenarios. Thus, legal expertise is of significant advantage in interpreting and deciphering legal requirements. Within the non-legal expert group, the tool notably facilitated the recognition of risk sources primarily associated with management, patient consent, and data subject rights. These specific risks often fall outside the scope of the conventional data flow diagram, are thus prone to oversight, and are usually included by the legal experts within the DPIA team. Additionally, the tool proved instrumental in identifying relevant TOMs, especially those aligned with existing institutional guidelines or SOPs that frequently lie beyond the traditional purview of research considerations. Even if the suggested guidelines were not directly applicable to the project at hand, they served as valuable prompts, necessitating the formulation of similar or analogous measures tailored to the new project's requirements.
## 3 IWOA Simulation Experiments and Result Analysis
### IWOA Result Analysis
#### 3.2.2 Algorithm Comparison  
After 50 independent experiments, the best values, average values, and standard deviations obtained from each algorithm were as shown in Table 4. The best and average values reflect the algorithm's optimization capability, while the standard deviation indicates its stability. For unimodal test functions \(F_{1}\) to \(F_{4}\), IWOA's optimization accuracy and stability were significantly higher than those of the compared algorithms. For multimodal test functions \(F_{5}\) to \(F_{8}\), the optimization performance of IWOA was comparable to that of SSA and MPA, with all three able to find the optimal value before reaching the maximum number of iterations. To further validate IWOA's superior optimization performance, the average values of each algorithm from Table 4 were ranked, as shown in Table 5. The results indicate that IWOA ranked first across all algorithms, demonstrating the best overall optimization capability.  
Figure 5: Convergence curve graph under different inertia weights. (**a**) Total number of iterations: 10. (**b**) Total number of iterations: 15. (**c**) Total number of iterations: 20.
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 1 Introduction
The algorithm schedules every node across 3-hop neighbor for the purpose to avoid the interference, hence to improve the delay, throughput and energy efficiency. Similarly, in some TDMA scheduling the knowledge of 2-hop neighborhoods is assumed to avoid the interference. In this paper, we propose a new intra-cluster scheduling scheme to schedule the flows from source to destination node in the TDMA time slots using three scheduling algorithms, namely the IntraSend, InterComm and IntraRecv algorithms. The IntraSend scheduling algorithm schedules the data in the IntraSend time slots from the source node to the source cluster head. The InterComm scheduling algorithm schedules the data in the InterComm time slots from the source cluster head to the destination cluster head. The IntraRecv scheduling algorithm schedules the data in the IntraRecv time slots from the destination cluster head to the destination node. Similarly, for the outgoing cluster flows in IntraSend scheduling, the proposed scheme utilizes both the IntraSend and IntraRecv time slots in the IntraSend scheduling. Furthermore, if the cluster has incoming flows the proposed IntraRecv scheduling can utilize both the IntraSend and IntraRecv time slots. Thus, the proposed scheme is scheduling more flows per time frame and reduces the schedule length and as a result increases the acceptance rate of flows.
## 3 Multi-Objective Optimization Algorithms
### Energy Efficiency Feature  
The energy efficiency (\(\eta\)) feature is very important in estimating the lifetime of any type of network, especially for the networks that operate using batteries such as WSNs. This feature should be maximized to increase the QoS of the network. The energy efficiency feature is affected by two factors namely, packet payload length and packet error rate. This model is written as Equation (17) [53]:  
\[\eta=\frac{E_{c}\cdot payload}{E_{c}\cdot(payload+h_{(L_{MH}+L_{MAC})})+E_{s}} \cdot(1-PER), \tag{17}\]  
where,  
* \(E_{c}\) is the energy consumption through the communication;
* \(E_{s}\) is the energy consumption in start-up mode;
* _payload_ is the size of data in the packet in byte;
* \(h(L_{MHR}+L_{MAC})\) is the packet header length, which is the summation of both \(L_{PHY}\) and \(L_{MHR}\). \(L_{PHY}\) is the size of physical header in byte while \(L_{MHR}\) is the size of MAC header in byte;
* _PER_ is the Packet Error Rate.
# Durability Assessment of Eco-Friendly Bricks Containing
## 5 Discussion of Results from the Tests  
### Compressive Strength (CS) and Flexural Strength (FS)  
Table 3 shows the CS and FS results obtained for the samples that contained varying contents of TRW, while Figures 4 and 5 show the plots for the CS and FS obtained, respectively.  
It can be observed from Figures 4 and 5 that, with an increase in TRW content, there was a reduction in both CS and FS. Furthermore, it can be observed that with increasing hydration duration from 7 to 28 days, there was an increase in both CS and FS. Additionally, the bricks produced with up to 10% TRW replacement passed the FS requirements for concrete bricks, which range between 1.4 and 3.4 MPa [69]. As required by standards, the bricks that contained 10 and 5% TRW met the non-load- and load-bearing requirements for masonry units, respectively, [49]. It can be concluded that the bricks produced with up to 10% TRW passed the CS and FS requirements for units that can be used in small buildings.  
A theoretical model that can be used to correlate the CS and FS of the green bricks was developed. The exponential, linear, power, and logarithmic models were produced, and the model that gave correlation coefficients (R) of almost 1 was selected as the most acceptable relationship. Hence, the power model was selected as the most acceptable relationship, which is consistent with the work carried out by [PERSON] and [PERSON] [14], as shown in Table 4. Figure 6 shows the relationship between the CS and the FS of the bricks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mathematical Relationship** & **Mathematical Model** & **Correlation Coefficient (R\({}^{2}\))** \\ \hline Exponential & \(\sigma=0.5244\mathrm{e}^{0.3073\mathrm{c}}\) & 0.8237 \\ Linear & \(\sigma=0.3326\mathrm{f}_{\mathrm{c}}+0.5022\) & 0.9509 \\ Power & \(\sigma=0.9519\mathrm{f}_{\mathrm{c}}\)\({}^{0.4729}\) & 0.9807 \\ Logarithmic & \(\sigma=0.4157\mathrm{ln}(\mathrm{f}_{\mathrm{c}})+1.1719\) & 0.8531 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mathematical models for different relationships.  
Figure 5: Effect of TRW on the 7 th-, 14 th-, and 28 th-day strength (FS).  
Figure 6: Relationship between the CS and FS using the power model.  
From the above models, the model that can be used to predict the FS of the TRW bricks in the absence of a test is the power model, which is expressed in Equation (4) below:  
\[\text{f}_{\text{cf}}=0.9519\text{f}_{\text{c}}^{0.4729} \tag{5}\]  
where  
\(\text{f}_{\text{cf}}=\text{Flexural strength}\);  
\(\text{f}_{\text{c}}=\text{Compressive strength}\).
## 4 Optimization
### Sensibility Analysis  
This subsection shows a way to analyze the Gradient vector values and the constraints.  
The optimization problem can be defined as:  
\[\begin{array}{c}\min_{\mathbf{X}}f(\mathbf{X},\alpha)\\ s.t.\\ \mathbf{1}<=\mathbf{X}<=\mathbf{h}\end{array} \tag{49}\]  
And the constraints can be redefined as:  
\[\begin{array}{c}\min_{\mathbf{X}}f(\mathbf{X},\alpha)\\ s.t.\\ \mathbf{X}-\mathbf{1}>=0\\ \mathbf{h}-\mathbf{X}>=0\end{array} \tag{50}\]  
In this case, the Lagrangian is given by:  
\[L(\mathbf{X},\delta,\pi)=f(\mathbf{X})-\delta^{T}(\mathbf{1})-\pi^{T}( \mathbf{h}-\mathbf{X}) \tag{51}\]  
and the KKT :  
\[\
abla\mathbf{f}(\mathbf{X})-\delta+\pi=0 \tag{52}\]  
with \(\delta,\pi>=0\), defining \(\lambda=\delta-\pi\)  
\[\
abla\mathbf{f}(\mathbf{X})-\lambda=0 \tag{53}\]  
\[\
abla\mathbf{f}(\mathbf{X})=\lambda \tag{54}\]  
Then  
\[\delta_{i}=\begin{cases}0&if\quad\lambda_{i}=0\\ \lambda_{i}&if\quad\lambda_{i}>0\\ 0&if\quad\lambda_{i}<0\end{cases} \tag{55}\]  
\[\pi_{i}=\begin{cases}0&if\quad\lambda_{i}=0\\ 0&if\quad\lambda_{i}>0\\ -\lambda_{i}&if\quad\lambda_{i}<0\end{cases} \tag{56}\]  
In other words, if the \(i-th\) entry of the Gradient has a value equal to 0, the constraint is not affected. If the value is greater than 0, the constraint of the lower border is affected. Finally, if the value is smaller than 0, the constraint of the higher border is affected.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 3 Results
### Mathematical Analysis of PP Interval Series
#### 3.2.1 Time-Domain Analysis  
Table 5 presents the results that were obtained in the time domain (statistical and geometric parameters) of the Pulse Rate Variability analysis. The study covered two groups of individuals (82 people in total): Healthy subjects (40 people) and diagnosed with the disease arrhythmia individuals (42 people).  
The values that were obtained for the investigated Time-Domain parameters indicate that the parameters SDNN (\(p\) value = 0.0088), SDANN (\(p\) value = 0.0385), RMSSD (\(p\) value = 0.0718), and pNN50 (\(p\) value = 0.0363) are statistically significant (\(p\) value \(<\) 0.05) and they can be used as factors in determining the degree of the disease under study (and perhaps an additional aid in the diagnosis and treatment of the disease). The parameters MeanNN, MeanHR, and SDIndex have no statistical significance (NS), because the values of \(p\) value are for these parameters \(>\) 0.05.
# Asymmetry Propagation in a Pipe Flow Downstream of a 90\({}^{\circ}\) Sharp Sarp Elbow Bend
## 2 Numerical Setup
### Validation  
Our simulations were validated against the DNS results of turbulent flow in straight pipes at \(Re=5310\)[45] and \(Re=11,700\)[46]. Figure 4 shows a comparison between the mean streamwise velocity profiles of our LES results in the recycling inlet section and the DNS results for two \(Re\). LES results of turbulent flow at \(Re=5600\) were obtained using the middle mesh, whereas the results at \(Re=10,000\) were obtained using the fine mesh. Comparison showed a very good agreement with the DNS results for the lower \(Re\) and small discrepancies between the LES and DNS at the higher \(Re\) in the buffer layer. Similarly, Figure 5 shows mean streamwise velocity fluctuations for the two \(Re\) at the same location in the inlet recycling section. Similarly, a very good agreement between LES and DNS was observed at \(Re=5600\), and slightly larger discrepancies were observed at \(Re=10,000\), particularly in the buffer layer. Nevertheless, these discrepancies are reasonable for the applied LES method and are attributed to several factors, which are part of our numerical approach. Namely, the observed overprediction in the peak of the streamwise velocity fluctuation is related to the inability of the model to redistribute enough energy from the streamwise velocity fluctuation component to the spanwise and wall-normal components. This has been studied by [PERSON] et al. [47], who showed that such behaviour is expected when one applies so-called \"industrial LES\": i.e., (a) a finite volume method using a collocated mesh with second-order accurate spatial and temporal discretisation schemes, (b) an implicit top-hat filter, where the filter length is equal to the local computational cell size, and (c) eddy-viscosity-type LES models. This was the case for all our simulations. However, the small discrepancies were noticeable only in higher-order statistics, thus, the achieved accuracy of the applied numerical model was more than sufficient for our investigation of the asymmetry propagation downstream of the pipe bend.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reynolds Number (\(Re\))** & **1400** & **5600** & **10,000** \\ \hline Mean (bulk) velocity \(U_{b}\) [m/s] & 0.04469 & 0.1792 & 0.32 \\ Time-averaging duration [s] & 800 & 400 & 55 \\ Convective time units \((D/U_{b})\) & 1880 & 3750 & 950 \\ Number of time steps & \(10\times 10^{6}\) & \(26\times 10^{6}\) & \(15\times 10^{6}\) \\ \hline \end{tabular}
\end{table}
Table 2: Duration of time-averaging for all three computations.
# Dynamic Analysis of Tethered Satellites with a Payload Moving Along a Flexible Tether
###### Abstract  
This study investigates the dynamic behavior of a three-body tethered satellite system with a flexible tether, focusing on a scenario in which a payload is transported along the tether connecting two satellites. Traditional models use rigid tethers, limiting the analysis of complex dynamics such as tether deformation, slack, and rebound. To address these limitations, we employed the absolute nodal coordinate formulation (ANCF) to model the flexibility of the tether. We derived nonlinear equations of motion using [PERSON]'s equation and solved them using the Newmark time integration method to obtain the dynamic responses of the satellite system. Our findings revealed that the Coriolis effect caused significant deviations in the payload trajectory as its mass and speed increased, leading to greater tether deformation and slack, and potential system destabilization. Additionally, axial force fluctuations in the tether varied notably as the payload moved, transitioning between tensile and compressive states. This study provides a more accurate representation of three-body tethered satellite systems by incorporating tether flexibility, offering valuable insights into the dynamic behavior and stability of the system.  
tethered satellite system; flexible tether; satellite attitude; absolute nodal coordinate formulation; Coriolis effect +
Footnote †: journal: Applied sciences
## 4 Diagnostics of Transition Zones in the Experimental Section by Geodetic Methods  
Since 2017, six geodetic measurements of transition zones, at the ends of the fixed tracks, have been carried out in the locality \"Trencin--A new railway bridge\" in Figure 6, [16]. The measurements of TZ took place in the section towards \"Bratislava\" on the right side of the river Vah in 122.219-122.239 km (both track axes) and in the section towards \"Zilina\" on the left side in 122.748-122.768 km in track axis no. 1 and 122.763-122.783 km in track axis no. 2. Height measurements were performed on both track axes (no. 1 and no. 2) and both their rails, with detailed points being signaled in the longitudinal direction of the track at a distance of about 5 m from each other, which represents every eighth fastening of the rail to the sleeper. Measurements were performed with a DNA03 digital leveling device with a unit standard deviation \(m_{0}=0.3\) mm, which represents the accuracy of determining the elevation of a 1 km long leveling. To maintain the accuracy and unambiguity of the results, when comparing the individual measuring stages, the calculated height changes of the track were determined separately for the right and left rails (with cant \(D\) of the track curve).  
Figure 5: Railway tunnel Trencianske Bohuslavice \(V=200\) km/h [13]: (**a**) Situation of TZ; (**b**) Photo: TZ in front of the tunnel and bridge with a fixed track [Hodas]; (**c**) Designed cross-section with TZ in [15].  
At each stage, the geodetic measurements are related to the reference height network, which consists of permanently stabilized marks, stabilized either at the base of the masts or in the concrete curb of the railway bridge. The heights of the reference points were determined by precise leveling.  
Evaluation of height changes of transition zones. The most critical parts of the construction of a fixed track for the height changes of the track, affected by the traffic load, are the transition zones, i. j. between a fixed track and the classic construction of a railway superstructure with a ballast bed. In the transition zone in the part towards \"Bratislava\" in KM-position in Figure 6a, four points \(P_{i}\) in track axes no. 1 and no. 2 are measured, the final height changes of which are shown by the graphs of time dependence in Figure 7a,b.  
The transition zone in the part of the track towards \"Zilina\" are in Figure 6, and the height changes are shown by the time dependence graphs in Figure 8. Their relative position of TZ is shifted in the longitudinal direction in the photo in Figure 6b.  
Figure 6: Railway double-track bridge in Trencin \(V=140\) km\(/\)h: (**a**) Scheme of TZ; (**b**) Photo: Transition zones—ballasted track to bridge with a fixed track [13; 16].  
Figure 7: Time dependence of height changes of TZ, towards “Bratislava”: (**a**) Track axis no. 1; (**b**) Track axis no. 2.