esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 2 Algorithm Description
### RFI Detection  
The RFI Detection subblock (Figure 4) uses the intermediate outputs generated by the Observable Generation block and creates blanking masks to allow mitigation of the input signals if they are contaminated by RFI. The main inputs of this subblock are the Equalized  
Figure 3: Observable Generation block diagram detailing the processing chain from 1 to bit inputs through windowing and FFT stages to truncated and equalized outputs. The different clock domains are highlighted in red and orange.  
STFT outputs obtained from the Observable Generation block, and the outputs correspond to metrics on the process and the blanking masks to be used when mitigating the input observables. The thresholds used in the detection of RFI signals can be tuned by changing the Threshold ports, both in time and frequency.  
Threshold Calculation  
The time and frequency thresholds are the detection thresholds for statistical and polarimetry metrics to determine that an RFI signal is present. The value of these thresholds may take two different specific values whether they are used for temporal or spectral moments. For the values \(K=1024\), \(M=4096\), and \(PFA=1\cdot 10^{-8}\), the time threshold is 0.3582, and the frequency threshold is 0.1791. These theoretical thresholds are obtained as:  
\[\alpha_{f} =\sqrt{\frac{4}{M}}\cdot\sqrt{2}\cdot\mathrm{erf}^{-1}(1-\mathrm{ PFA}) \tag{6}\] \[\alpha_{t} =\sqrt{\frac{4}{K}}\cdot\sqrt{2}\cdot\mathrm{erf}^{-1}(1-\mathrm{ PFA}), \tag{7}\]  
where \(\alpha_{f},\alpha_{t}\) correspond to the frequency and time thresholds, respectively, \(M,K\) correspond to the number of samples in the frequency and time domains, respectively, erf is the error function, and PFA is the Probability of False Alarm.  
A different threshold, the beta threshold (\(\beta\)) or maximum blanking threshold, is used to adjust the amount of positive detections in the masks that is allowable so as to not excise a significant part of the desired signal. More information on this procedure can be found in Section 2.1.6 of [17]. The RFI mitigation is based on the excision of the contaminated samples out of the set of all transformed samples. The RFI mitigation operates efficiently if few samples contain the largest fraction of the RFI power. However, this may not be the case when the RFI power is well-spread across the time-frequency space. In these cases, it may happen that almost all samples are discarded and, therefore, no signal remains at the output of the RFI mitigation algorithm. The value of \(\beta\) determines which type of mitigation approach is applied to the signal. A typical value for the beta threshold is 1.
## Appendix E. GDS Results and Implications  
Another factor that can lead to long-term disability in stroke survivors is post-stroke depression, which is reported in 5 to 64% [70; 71] of stroke survivors. Post-stroke depression is associated with increased mortality risk, impairments in motor function, and deterioration in the quality of life [72; 73]. After the three-month intervention, the experimental group showed a statistically significant decrease in the symptoms of depression, while the control group showed a non-statistically significant increase. Although all the study participants in both the experimental and control groups showed clinically insignificant depressive symptoms (GDS \(\leq\) 4) at baseline, _Neuro-World_ gameplay appears to have ameliorated depressive symptoms in the experimental group, which was statistically significant (BASE: \(3.79\pm 0.43\) vs. POST: \(3.00\pm 0.55\), \(t.ratio(27)=-5.01\), \(p<0.01\)). On the other hand, although statistically not significant, the control group showed a deterioration in their depressive symptoms between baseline and post-intervention (BASE: \(4.00\pm 0.00\) vs. POST: \(4.27\pm 0.46\), \(t.ratio(27)=1.27\), \(p=0.31\)). More importantly, four study participants in the control group became marginally depressed at the post-intervention visit (GDS \(\geq\) 5).  
However, despite the statistically significant improvement in the GDS score in the experimental group participants, the baseline GDS scores represented non-depressive symptoms (i.e., normal condition), and the observed change (\(-0.79\pm 0.70\)) was smaller than the minimal clinically important changes (i.e., 5.4 GDS point) [55]. Hence, although our results show that _Neuro-World_ has the potential to reduce or control depressive symptoms, future work should focus on using _Neuro-World_ in chronic stroke survivors exhibiting more depressive symptoms (GDS \(\geq\) 5) to more rigorously examine whether the use of _Neuro-World_ can lead to clinically significant changes in depressive symptoms.
## 3 Test Results
### Dynamic Test Results at Different Damage Levels
When Beam_GFRP_PostStr and Beam_CFRP_PostStr are subjected to the D\({}_{2}\) damage level, the second and third modal frequencies for both beams exhibit a similar decrease. However, the first modal frequency of Beam_CFRP_PostStr shows a more significant drop compared to Beam_GFRP_PostStr. This suggests that while both GFRP and CFRP help to maintain stability in the second and  
Figure 16: Modal frequency ratios in the vertical direction for the (**a**) first, (**b**) second, and (**c**) third modes of all beams. third modes, CFRP is less effective in preventing reductions in the first mode under higher damage levels. Figure 17 illustrates the modal frequency ratios for all beams in the horizontal direction across different damage levels. This figure highlights the effect of damage and strengthening on the dynamic behavior of each beam in the horizontal direction. Beam_GFRP_PreStr and Beam_GFRP_PostStr show stable frequency ratios across all modes, with the ratios remaining close to 100% after damage, indicating that GFRP is effective in maintaining the beams' horizontal stiffness values. Beam_CFRP_PreStr displays a more significant reduction in all modes compared to Beam_GFRP_PreStr, suggesting that CFRP is less effective than GFRP in the horizontal direction. Significant recovery in modal frequencies is observed in the first two modes of Beam_GFRP_PostStr and Beam_CFRP_PostStr after strengthening. The greater proportional recovery in Beam_CFRP_PostStr compared to Beam_GFRP_PostStr indicates that CFRP contributes more to the increase in stiffness than GFRP. The second mode remains the least affected across all beams, indicating that it is generally more resilient to damage.
## 3 Results and Discussion
### Spatiotemporal Variation of Urban Thermal Characteristics
#### 3.1.1 Spatial Distribution of Different Surface Temperature Levels
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Period** & **NLST Level** & **Low** & **Sub-Low** & **Medium** & **Sub-High** & **High** & **Total** \\ \hline \multirow{8}{*}{1987–1996} & Low & - & 11.6 & 10.0. & 1.2 & 0.6 & 23.4 \\  & Sub-low & 27.0 & - & 27.0 & 4.5 & 1.3 & 59.8 \\  & Medium & 26.6 & 29.2 & - & 67.7 & 18.0 & 141.5 \\  & Sub-high & 1.1 & 2.7 & 19.0 & - & 27.8 & 50.5 \\  & High & 0.9 & 0.8 & 6.1 & 27.0 & - & 34.7 \\  & Total & 55.6 & 44.3 & 61.9 & 100.4 & 47.7 & - \\ \hline \multirow{8}{*}{1996–2007} & Low & - & 9.9 & 37.7 & 10.4 & 2.3 & 60.3 \\  & Sub-low & 4.4 & - & 31.1 & 12.1 & 1.6 & 49.2 \\  & Medium & 3.4 & 10.0 & - & 39.6 & 6.1 & 59.1 \\  & Sub-high & 0.8 & 1.4 & 66.9 & - & 8.6 & _77.7_ \\  & High & 0.3 & 0.2 & 26.1 & 86.6 & - & 113.3 \\  & Total & 9.0 & 21.5 & 161.8 & 148.8 & 18.6 & - \\ \hline \multirow{8}{*}{2007–2016} & Low & - & 12.5 & 13.1 & 3.0 & 0.1 & 28.6 \\  & Sub-low & 5.0 & - & 12.6 & 2.8 & 0.1 & 20.4 \\  & Medium & 5.1 & 19.1 & - & 79.2 & 4.2 & 107.6 \\  & Sub-high & 2.8 & 1.6 & 56.9 & - & 24.4 & 85.7 \\  & High & 2.2 & 0.6 & 6.3 & 18.9 & - & 28.0 \\  & Total & 15.1 & 33.8 & 88.8 & 103.8 & 28.7 & - \\ \hline \multirow{8}{*}{1987–2016} & Low & - & 17.2 & 24.5 & 7.6 & 0.3 & 49.5 \\  & Sub-low & 7.6 & - & 35.9 & 16.3 & 1.8 & 61.6 \\ \cline{1-1}  & Medium & 5.8 & 12.4 & - & 89.4 & 18.9 & 126.4 \\ \cline{1-1}  & Sub-high & 1.3 & 1.5 & 36.6 & - & 8.3 & 47.7 \\ \cline{1-1}  & High & 2.1 & 0.8 & 33.8 & 73.5 & - & 110.1 \\ \cline{1-1}  & Total & 16.8 & 31.8 & 130.8 & 186.7 & 29.2 & - \\ \hline \end{tabular} Note: For each study period, the NLST categories in the columns represent the NLST levels at the start time point, and those in rows indicate the NLST levels at the end time point. The values are the transferred area from the NLST level of the start time point to that of the end time point. For example, the value “11.6” means that 11.6 km\({}^{2}\) of low temperature (level I) was transferred into sub-low temperature (level II) during 1987–1996.  
\end{table} 
Table 4: Transfer matrix among different NLST levels during 1987–2016 (km\({}^{2}\)).
# Fungal Pigments and Their Roles Associated with Human Health
## 5 Medical Relevance of Fungal Pigments
### Medical Roles of Other Pigments
#### 5.2.2 Anti-Biofilm Activity  
Melanin from the wood-ear edible fungus _Auricularia auricula_ has been demonstrated to display distinct anti-biofilm effects towards _Escherichia coli_ K-12, _Pseudomonas aeruginosa_ PAO1, and _P. fluorescens_ P-3, respectively, while no inhibitory activities were observed on bacterial growth [181]. [PERSON] et al. demonstrated that pigments from _A. auricula_ can repress the production of violacein, a quorum-sensing signal for biofilm formation in the reporter strain _Chromobacterium violaceum_ CV026 [182]. It is believed that the ability to form biofilm constitutes the predominant virulence factor for bacterial pathogens, as the biofilm formation is known to account for 80% of bacterial infections in humans [183]. Furthermore, biofilms represent a major cause of nosocomial infections, especially related to the emergence of multi-drug resistant strains, thereby contributing to refractory infectious diseases in humans. Thus, the quest of novel compounds that may effectively inhibit these biofilm-forming bacteria has been a hot topic in both pharmaceutical and clinical settings.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Diurnal Variation of Rainfall
#### 5.2.2 Remarks on the Diurnal Variation of Rainfall  
The differences in the diurnal cycle in time and amplitude could be attributed to the differences in heat capacity and thermal inertia between land and oceans, and could account for the differences in the dynamic and thermal processes between stratiform and convective precipitation, where the longwave radiative cooling of clouds may also have influence on the diurnal cycle [133].
## 3 Experimental Verification Using Benchmark Test Functions
### Time Complexity Analysis  
Time complexity usually depends on the initialization process, the number of iterations, and the solution update mechanism. In this section, we performed a time complexity analysis of the IRSA to determine the effect of improvisations on the computational time of the algorithm. For this purpose, we used standard benchmark test functions called CEC-2019. Table 4 delineates the specifications of these functions. The following equation is used to determine the complexity of the algorithm [53; 54].  
\[T=\frac{\widetilde{T}-T_{1}}{T_{\circ}} \tag{17}\]  
where \(T_{\circ}\) is the computational time of a specific mathematical algorithm. \(T_{\circ}\) was calculated to be 0.09 s; \(T_{1}\) is the computational time of a CEC-2019 test function for 15,000 iterations; \(\widetilde{T}\) is the mean computational time of the MH technique used to solve CEC-2019 test functions for 10 runs considering 15,000 iterations  
These computations were performed using a desktop computer Core i5 with 12 GB RAM considering a population size of 10. Tables 4 and 5 describe the result of the analysis. The tabular results clearly show that the improvisations greatly reduce the complexity of the RSA algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fun** & \(\mathbf{T}\)**IRSA** & \(\mathbf{T}\)**RSA** & \(\mathbf{T}\)**FDO** & \(\mathbf{T}\)**BMO** & \(\mathbf{T}\)**AOA** & \(\mathbf{T}\)**PSO** & \(\mathbf{T}\)**GWO** & \(\mathbf{T}\)**DGCO** & \(\mathbf{T}\)**FPA** & \(\mathbf{T}\)**DFA** \\ \hline
**CEC01** & 440.04 & 498.64 & 9569.9 & 512.80 & 468.96 & 472.38 & 472.49 & 472.884 & 702.73 & 901.60 \\ \hline
**CEC02** & 51.562 & 158.97 & 303.77 & 26.603 & 9.7899 & 10.097 & 11.746 & 14.144 & 420.60 & 858.17 \\ \hline
**CEC03** & 66.843 & 192.89 & 491.69 & 35.672 & 17.950 & 18.158 & 19.011 & 22.222 & 481.66 & 1018.4 \\ \hline
**CEC04** & 33.761 & 99.078 & 514.31 & 26.605 & 9.1060 & 10.075 & 11.035 & 12.389 & 266.56 & 627.08 \\ \hline
**CEC05** & 34.373 & 99.519 & 744.31 & 27.667 & 9.4832 & 10.545 & 11.180 & 12.966 & 267.42 & 491.911 \\ \hline
**CEC06** & 175.14 & 240.27 & 5176.0 & 189.23 & 165.30 & 169.64 & 168.52 & 170.18 & 423.23 & 641.59 \\ \hline
**CEC07** & 34.831 & 99.440 & 384.83 & 24.768 & 9.7759 & 10.604 & 11.566 & 12.993 & 266.66 & 483.13 \\ \hline
**CEC08** & 34.733 & 99.406 & 369.56 & 25.428 & 9.7797 & 10.683 & 11.522 & 12.848 & 267.13 & 481.31 \\ \hline
**CEC09** & 33.670 & 98.69 & 383.27 & 23.551 & 8.3110 & 9.1362 & 10.298 & 11.728 & 266.52 & 492.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(\widetilde{T}\left(sec\right)\) calculation for various metaheuristic algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fun** & \(\mathbf{T_{1}}\) & \(\widetilde{T}\)**IRSA** & \(\widetilde{T}\)**RSA** & \(\widetilde{T}\)**FDO** & \(\widetilde{T}\)**BMO** & \(\widetilde{T}\)**AOA** & \(\widetilde{T}\)**PSO** & \(\widetilde{T}\)**GWO** & \(\widetilde{T}\)**DGCO** & \(\widetilde{T}\)**FPA** & \(\widetilde{T}\)**DFA** \\ \hline
**CEC01** &
## 4 Proposed Hearing Aid System
### Composite of Deep Denoising Autoencoder (C-DDAEs)
#### 4.2.1 Training Phase (Offline)  
The training phase is presented in (Figure 5a). The composite-DDAEs (which we called C-DDAE) includes three multi-layers of the DDAE. Each DDAE works as an expert and is specialized for specific enhancement tasks, which is different to a single DDAE that has a fixed quantity of frames for general enhancement tasks. The output of each DDAE is the central pattern for the composite-DDAEs, which is either the original signal estimation or a referee that is used in the next DDAE to recover the speech--Noted here we used Wiener enhanced-and clean utterances features as the input and output of the composite-DDAEs, respectively, instead of noisy-clean speech. The composite-DDAEs works by adding one future and one past adjacent frame to the next DDAE, along with the outputs for each DDAE (Figure 7). Finally, the output of the central frame of third DDAE is considered as the target result. The C-DDAEs is trained offline and tested online, and the training set is divided into subsets, each of which corresponds to specific subtasks of the complete enhancement task, and the adjacent frame decides which DDAE should be used for each training case by making a stochastic decision about which single DDAE to use on each occasion. In this way, the training task learns the mapping function \(F_{DDAE_{i}}\) to produce an output vector \(y\in\mathbb{R}^{D}\), which is either the clean signal estimation or a referee that used for the next DDAE to recover the clean speech (desired signal) \(x_{t}\) at the \(t\)-\(th\) time index (\(t=0,\,1,\,\ldots,\,T-1\)), of zero means from the noise spectra.  
The DDAEs were set as follows:  
* \(DDAE1_{128\times 3}\) has three hidden layers and 128 units for each layer. One magnitude spectrum with 513-dimensionalis used as the input in the \(t\)-\(th\) time frame.
* \(DDAE2_{512\times 3}\) is with three hidden layers and 512 units for each layer. The three frames spectra used \(\left[\sum_{(t-1)^{\prime}}^{T}x_{t}^{T},x_{(t+1)}^{T}\right]^{T}\).  
Figure 7: The structure of the composite-DDAEs. The DDAE1 with one future and one past adjacent frame is fed as input to the DDAE2 with a windows length of three. The output for the DDAE2 with two future and two past adjacent frames is then input into the DDAE3 with a windows length of five.  
* \(DDAE3_{1024\times 3}\) has three hidden layers and 1024 units for each layer. The three frames used \(\left\|x_{(t-2)}^{T},x_{(t-1)}^{T},x_{t}^{T},x_{(t+1)}^{T},x_{(t+2)}^{T}\right\| ^{T}\)\(\left|x_{(t-2)}^{T},x_{(t-1)}^{T},x_{(t+2)}^{T}\right\|^{T}\).
# A Study of 5G Edge-Central Core Network Split Options
## 4 Network Management Split Models  
From the perspective of the network management, two different alternatives can be conceived: to have a part of the network management at the edge of the network, or to centralize the complete network management on the central entities. These options are illustrated in Figure 4 and presented underneath.  
In the centralized network management option, all the operations related to the management of the system are implemented only at the central location. To acquire the required information from the edge as well as from the central part of the network a minimal monitoring server enabling the appropriate storage and pre-processing of monitored data is installed. Furthermore, to transmit commands to the edge and to the central part, different element managers are installed for the different components. When having a centralized network management and orchestration, all the monitored information as well as all the commands will have to be exchanged across the backhaul, occupying a specific level of the backhaul capacity and inducing a certain level of delay on executing the procedures [8].  
Such a solution is highly attractive for edge nodes which are inter-connected with low delay and high capacity backhauls as the overhead of the management related communication is reduced while the system can respond to different events in a short time manner.  
A second alternative would be to place a local network management on the edge of the network. In this situation, different decisions may be taken locally based on the local monitored information and enforced directly to the local element managers. It is to be noted that such a solution uses a significantly higher level of local compute and storage resources as it must locally process the specific data and make decisions.  
Furthermore, an end-to-end orchestrator must be integrated into the system to coordinate the local network management decisions taken at the edge and at the central part of the network. It implies not only the introduction of a new network function able to coordinate the different elements, but also the development of new communication interfaces.
## 2 Developed Methodology
### Instance-Level Feature Importance Analysis for DSA-DL Model
#### 2.2.2 DSA-DL Model Construction  
A dataset for modeling, extracted from the generated DSA database, is pre-processed by normalization and feature dimension reduction, successively, followed by being divided into a training dataset and a testing dataset. A DL model is trained by the training dataset, and the performance test is carried out to ensure that the model is suitable for online applications.
## 7 Simulation Results and Discussions
### Dynamic Performance Analysis Using the Proposed Rotor-Side Controller  
In order to evaluate the dynamic performance of the proposed system, the real wind speed data measured at Rishiri Island, Hokkaido, Japan, shown in Figure 32, is used in the simulation. The power system model shown in Figure 11 is considered in this dynamic analysis. The capacities of the DFIG and the SCIG are 28 MW and 72 MW (The total capacity of the WF is 100 MW), respectively. Because this power ratings of the wind generators are stable case for the proposed system as presented in Section 7.1.2.  
Figure 33 shows the reactive power output of wind generators. The DFIG provides the necessary reactive power to the SCIG for voltage regulation. Thus, the connection point voltage at bus 12 is approximately constant, as shown in Figure 34. Figure 35 shows the active power outputs of the VSWT-DFIG and the FSWT-SCIG. The DC-link voltage of the DFIG is maintained constant, as shown in Figure 36. The variation of the DC-link voltage is very small, even though there are wide fluctuations in the wind speed. Figure 37 shows the responses of the blade pitch angle. The increase in the blade pitch angle will help to reduce the mechanical power extraction from the wind turbines. The total active and reactive power output of the wind generators at bus 12 is shown in Figure 38.  
Figure 31: Reactive power output of DFIG.  
Figure 32: Wind speed data.  
Figure 34: Voltage response at the connection point of wind generators.  
Figure 35: Active power output of wind generators.  
Figure 33: Reactive power output of wind generators.  
The active power output of conventional power plants (SGs) and the power system frequency response are shown in Figures 39 and 40. The conventional SGs adjust their active power output according to the fluctuating power injected from the WF. Moreover, the frequency variation lies within the permissible limit in Japan (\(\pm\)0.2 Hz).  
Figure 38: Total active and reactive power output at bus 12.  
Figure 36: DC-link voltage response of DFIG.  
Figure 37: Pitch angle of wind generators.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### PSS Graph Elicitation and Analytic Reasoning (GEAR) Method
The influence of knowledge gaps is context-sensitive, meaning their impact depends on the active PSS concept variation, as both variations and knowledge gaps alter the evaluation logic and thereby affect how influences propagate to the EVC. The reasoning analysis results provide an overview of the expected EVC achievement for the PSS concept and its variations, as well as insights into the significance of knowledge gaps. Using the graph model and the associated meta-information, decision makers can interpret these results and discuss targeted measures to improve the PSS concept and ensure reliable EVC evaluations. Step 4: Evaluation and discussion of potential measures  
The results of the reasoning analysis are provided for discussion to support decision-making regarding the further development of the PSS concept. This enables decision makers to gain a deeper understanding of the EVC and the underlying knowledge base regarding the PSS concept in which the evaluation is grounded. Analysis results presented for decision making should always be discussed, as external factors such as strategic contexts may exist that were not known or considered in the analysis [75]. The benefit of the analysis results concerning the expected EVC achievement levels is that they offer an indicator of the performance status of the PSS concept and help identify promising PSS concept variations, as well as knowledge gaps significant to evaluation of anticipated performance. The advantage of this approach is that the drivers behind the evaluation can be made transparent through the graph model, making it easier to quickly understand which causal relationships are particularly favorable or detrimental to the evaluation. By assessing the impact of extreme scenarios related to knowledge gaps, the effort required to close these gaps can be better justified. For knowledge gaps that significantly affect the reliability of the evaluation, greater effort to close these gaps is warranted. Gathering information to close these knowledge gaps is discussed as a discursive measure for risk minimization [75] and may include activities such as conducting studies, purchasing data, or developing prototypes [40]. As a result, further steps are defined to refine the PSS concept. Based on the information gathered through these activities, the GEAR method can be repeated, the graph model adjusted, and the next iteration of decision making supported.