esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 4 Bias Characteristics
Obviously, the \"O\(-\)B\" standard deviation for Channel 6 has no significant correlation with temperature or specific humidity variability at 350 hPa. For example, in the low water vapor variability area in the eastern tropical Pacific, the standard deviation of \"O\(-\)B\" is approximately 0.5 K, as in the surrounding area. The other channels also show similar results, and these results also fully demonstrate the suitability of background field for MWHS-2 data evaluation. Figure 10: Spatial distribution of: (**a**) “O\(-\)B” standard deviation (unit: K) for Channel 6 (WF peaks at 350 hPa); (**b**) variability of atmospheric temperature (unit: K); and (**c**) specific humidity (unit: kg/kg) at 350 hPa of ERA-5 from 24 September to 8 October 2021. The blank boxes in the Eastern Pacific are caused by quality control.
# Understanding Horizon 2020 Data: A Knowledge Graph-Based Approach
## 1 Introduction
Specifically, during the preparation of their future research proposals (for instance, proposals to be submitted in the recently launched calls of the Horizon Europe funding programme), organizations need to follow specific steps including the assessment of the innovative character of the idea and research to be funded, the identification of the most relevant call/programme within a funding framework, the check of diverse eligibility criteria, and the building of the appropriate consortium by making clear the role and capacity of each participant. For instance, when an ICT company specializing in AI-enhanced solutions, such as chatbots, etc., considers the preparation of a research proposal that aims to develop a beyond the state-of-the-art personal assistant for medical decision-making issues, it needs to obtain answers to queries such as:  
* _What are the topics of interest/expertise for each of these top-10 organizations that have coordinated (or participated in) the most health-related projects of H2020?_
* _For a given list of topics (e.g., Medical Novision Making, Natural Language Processing, Chatbot, Machine Learning, Medical Guidelines, Policy Making), which deliverables produced by these top-10 organizations were related to pilot applications?_
* _For the list of topics above, what types of deliverables have been produced by these top-10 organizations?_
* _Which people from these top-10 organizations have been working on this list of topics (by preparing the corresponding deliverables)? Have they also been working on additional ones?_  
Our analysis is performed on aggregated data concerning 32,090 funded projects, 34,295 organizations participated in them, and 87,067 public deliverables produced by these organizations. The source of these data is two CORDIS files, namely _cordis-h2020 projects.csv_ and _cordis-h2020 projectDeliverables.csv_. The first file contains the following information for each project: Record Control Number (RCN), the project ID (grant agreement number), acronym, status, start and end date, and its objective and total cost; additionally, it contains information about the following: funding scheme and programme, topic, EC contribution, call ID, coordinator, and participants (with their countries). The second file contains information about the deliverables, including a public URL to access the full content of each of them. The remainder of this paper is organized as follows. Section 2 introduces related work aiming to give the reader some basic information about the components of the proposed approach. Section 3 presents in detail the proposed pipeline towards meaningfully representing and analyzing the Horizon 2020 data; a set of representative queries is used to demonstrate the corresponding data visualization features. Finally, Section 4 presents preliminary evaluation results and sketches future work directions.
## 2 Related Work and System Model
### Related Work-Sco\(-\)AN: Perfect Channel Estimation
For the convenience of discussion, \(C_{\text{sec}}^{k}\) represents the change of secrecy capacity after adding the SCO\(-\)AN when compared to simply adding traditional AN. For the case of SCO\(-\)AN, to ensure the effectiveness of physical security, (13) must be guaranteed. \[C_{\text{sec}}^{k}=C_{\text{sec}}^{g}-C_{\text{sec}}^{a}>0, \tag{13}\]  
In (13), for the SISO communication system, \(C_{\text{sec}}^{a}\) is given by (6) and \(C_{\text{sec}}^{g}\) is given by (11). For the MIMO communication system, \(C_{\text{sec}}^{a}\) is given by (7) and \(C_{\text{sec}}^{g}\) is given by (12). In Figure 2, the dashed line represents the secrecy capacity of AN calculated by (7), and the solid line is the secrecy capacity of SCO\(-\)AN calculated by (12). The legitimate channel \(\mathbf{H}\) and the eavesdropper channel \(\mathbf{G}\) are Rayleigh fading channels. The signal \(\mathbf{x}_{k}\) is a complex covector. Figure 2 shows that SCO\(-\)AN provides more secrecy capacity than AN does. The noise in \(\mathbf{H}\) and \(\mathbf{G}\) are Gaussian white noise. The secrecy capacity increases with higher SNR.
## 4 Results and Discussion
### Performance Gain  
This section explains the performance gain and execution time of the proposed tool. Figure 15A shows the plot of the performance gain for all 14 sparse matrix features. The details of these sparse matrix features are listed in Table 3. Actual time is an idle case scenario and represents the least execution time associated with the target variable. The performance achieved is calculated against the best-case scenario, which is the actual time. The average represents the performance achieved when a random block size is selected  
Figure 14: RME Comparisons of (**A**) all, (**B**) basic, and (**C**) important sparse matrix features.  
for each matrix and is calculated as the average execution time for different block sizes. The maximum is the worst-case scenario with the maximum execution time. Among all predictive models, DT performed better than the other models and achieved nearly 73% of the maximum possible performance gain.  
Figure 15B shows the actual, maximum, average, and predicted time (AAQAL), which also includes cases in which our prediction is incorrect. For any incorrect prediction of block sizes, we used the execution time associated with the nearby block size. The Y-axis shows the execution time on the logarithmic scale, and the X-axis shows the sparse matrices in our dataset.  
Figure 16A shows a graph of the performance gain with basic features. The details of the basic features are listed in Table 3. The basic features are those with low computational complexity. With basic features, AAQAL achieved almost 92% of the actual value, while random selection of block sizes could achieve only 51.88%. The performance difference between the random selection of block sizes and AAQAL is almost 40%.  
Figure 16B shows the execution time comparison of the test set. The predicted time (AAQAL) was based on the basic feature set. The X-axis of the graph shows the execution time on a logarithmic scale, and the Y-axis shows different sparse matrices in our test set.  
Figure 17A shows the plot of the performance gain with the set of important features. Important features are based on the feature importance graph and include nnz-max, rows and columns, rows, columns, clustering, and bw-max. Our proposed method achieved a performance of up to 93.47%. The performance difference between our proposed tool and the random selection of block sizes is almost 31.27%. The comparison of the execution time of our proposed tool based on an important feature set is shown in Figure 17B.  
Figure 15: Performance gain (**A**) and execution time comparison (**B**) with all features (Test set).  
Figure 16: Performance gain with basic features (**A**) and execution time comparison (**B**) with basic features (test set).  
Figure 17: _Cont._
## 4 Numerical Computations
This value must be evaluated in the situation in which the human body is immersed in a uniform external magnetic field whose intensity is equal to that of previous analysis at the point at which the human skin is nearest to the coil. To compute \(\mathrm{J_{max}^{(u)}}\), another numerical analysis must be conducted. In order to obtain a quasi uniform magnetic field, the source may be constituted by a couple of horizontal circular coils, with a large radius \(\mathrm{R_{0}}\), centered at the z-axis and located at heights \((\mathrm{h_{0}}+\mathrm{R_{0}})/2\) and \((\mathrm{h_{0}}-\mathrm{R_{0}})/2\) from the xy plane, where \(\mathrm{h_{0}}\) is the eight of the human model. The two coils carry the same sinusoidal current of \(\mathrm{I}=1\) A. Of course, the use of the FEM-DBCI or FEM-SDBCI methods allows us to utilize the same mesh and hence the same global algebraic system of the previous analysis, except for the known term of the integral Equation (9). Once this new solution is obtained, one can compute the maximum value of the current density \(\mathrm{J_{max}^{(1)}}\) in the human model and the flux density \(\mathrm{B_{0}^{(1)}}\) in the same tetrahedron where the flux density \(\mathrm{B_{0}}\) was previously evaluated. Then, due to the linearity of the material of the human model, \(\mathrm{J_{max}^{(u)}}\) in (24) is evaluated as  
\[\mathrm{J_{max}^{(u)}}=\frac{\mathrm{B_{0}}}{\mathrm{B_{0}^{(1)}}}\mathrm{J_{ max}^{(1)}} \tag{25}\]  
In Figure 3, the behavior of the coupling factor K is given versus the distance d. As can be seen, the values obtained are in good agreement with those reported in [18]. Figure 3: Behavior of the coupling factor K versus the distance d.  
In Figure 4, the heating power density is drawn on the skin of the human model in front of the circular coil, for two different placements of the circular coil at distances of 10 cm and 20 cm.
## 3 Results
### Raman Results  
Raman spectroscopy allows us to determine the characteristics of the nano additives used in this research. It identifies defects in the carbon lattice by generating different intensity peaks using nondestructive methods. As illustrated in Figure 2, Raman spectroscopy illustrates the main peaks at different wavelengths of \(\sim\)1354 cm\({}^{-1}\) (D band), \(\sim\)1581 cm\({}^{-1}\) (G band), and \(\sim\)2719 cm\({}^{-1}\) (2D band). The sp\({}^{2}\) D carbon mode requires defects for activation which is illustrated by the D band intensity. In addition, the narrow G band demonstrates that optical E\({}_{2\text{g}}\) phonons are in the Brillouin zone due to stretching in sp\({}^{2}\) chains, bond, and rings. The D + D\({}^{\prime}\) peak at \(\sim\)2959 cm\({}^{-1}\) shows the exfoliation of graphite. The intensity ratio at the D and G bands is important to understand the transformation of sp\({}^{3}\) to sp\({}^{2}\) conversion of carbons; further, it is directly proportional to the defect rate. This graphene sample, Id/Ig is 0.153, is very low compared to restored graphene, proving that this graphene has very few defects in the carbon lattice [27; 28]. Along with that, lower D band intensity, with respect to G band, support less lattice defect in graphene layers [29].  
Figure 1: TEM images of (**A**) graphene, (**B**) titanium dioxide, and (**C**) graphene and titanium dioxide mixed together in powder form at 1 \(\upmu\)m scale and 25.00 kx magnifications.  
Titanium dioxide nano powder was investigated by the Raman technique as demonstrated in Figure 3. The powder generates a primary peak at ~144 cm\({}^{-1}\) and secondary intensity peaks at ~199 cm\({}^{-1}\), ~397 cm\({}^{-1}\), ~516 cm\({}^{-1}\), ~638 cm\({}^{-1}\), which is associated with different modes of energy such as E\({}_{\rm g}\) (strong), E\({}_{\rm g}\) (weak), B\({}_{\rm li}\), A\({}_{\rm 1g}\) + B\({}_{\rm li}\), E\({}_{\rm g}\) (intermediate), respectively [30]. These peaks represent the characteristics of the anatase phase of titanium dioxide nano particles.
## 3 ECG Biometrics using Pre-Configured CNN Models with Various Time-Frequency Representations
### ECG Biometrics Using Various CNN Models  
As the ECG signals are noise-sensitive, various studies have focused on the frequency domain to reduce the noise in the biometrics. Different methods used to transform signals into the time-frequency domain include the spectrogram, log spectrogram, mel spectrogram, MFCC, and scalogram. The time-frequency transformation of a 1D signal results in a 2D matrix, which can be applied to a 2D CNN. Time-frequency analysis can be used to calculate the frequency and phase of the signal. As the noise is mainly concentrated in a specific region of the frequency domain, the useful signal and noise can be independently extracted. The discriminative features might be narrow/restrictive due to similarities in the different ECG signals from each person. However, the hidden features can be visualized in the time-frequency domain by expressing the signals at various scales and frequencies. The ECG data that have been transformed from 1D to 2D using the time-frequency transform can be applied to the 2D CNN. The 2D CNN has shown good performance in image applications, which has led to new CNN models being proposed. The popular open source models include the VGGNet, ResNet, Xception, and DenseNet. Combinations of the time-frequency representations and pre-configured CNN models have not been investigated. In this paper, we evaluate ECG biometrics using pre-configured models of the CNN with various time-frequency representations. The PTB-ECG [39; 40] and CU (Chosun University)-ECG [18] databases were used in this study. PTB-ECG is a popular, open access ECG dataset, while CU-ECG was directly constructed for this study. Figure 8 shows the ECG biometrics using various time-frequency representations.  
We concentrated on the classification task. We used the accuracy as a performance measure of the ECG biometrics. The correct classification (CC) was divided by the total classification, i.e., the sum of the CC and WC (wrong classification) to obtain the accuracy [41] as shown in Equation (7).  
\[\text{Accuracy}=\ \frac{\text{CC}}{\text{CC}\ +\ \text{WC}} \tag{7}\]  
Figure 8: ECG biometrics using several time-frequency representations.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Genetic Approaches in Identification of mTOR Signaling Components
#### 4.2.2 dRheb Was Identified as a TSC2/TSC1 Target Upstream of mTOR1 in a siRNA Screen  
Given that a high mutation rate of GAP (GTPase activating protein) domain of TSC2 was observed in TSC patients and TSC2 shares a homologous GAP domain with Rap1-GAP, [PERSON] et al. reasoned that a GTPase would be a direct target for TSC2 in regulating mTOR1 activation [64]. Moreover, this GTPase would exert an opposite function from TSC2/TSC1 to maintain S6K-pT389 signals. By performing a siRNA-mediated screen in Drosophila S2 cells to search for loss of which GTPase(s) reduced S6K-pT389 [64], Rheb was identified as the GTPase negatively regulated by TSC2/TSC1. The GAP activity of TSC2/TSC1 towards Rheb was further confirmed by both in vitro and in cell assays [64].  
2.3 dRheb Was Identified in Regulating Cell Growth through Regulating mTOR Signaling in Drosophila Using Two Complementary Loss- and Gain-of-Function Genetic Screens  
A loss-of-function screen was performed using ethyl methanesulfonate (EMS) to induce mutations, and mosaic Drosophila animals with reduced head size were further analyzed with disruptions in _Rheb_ alleles through an _ey-Flp_ mosaic screen [65]. In addition, a gain-of-function screen was performed using insertions of EP elements to drive gene overexpression. In animals with increased eye sizes, an EP element in the _Rheb_ locus (EP 50.084) was identified [66]. Further genetic studies showed that _Pten_ or _Tsc1/Tsc2_ deficiency counteracted the effects of Rheb overexpression in promoting cell growth, and biochemical analyses revealed loss of _Rheb_ that resulted in reduced S6K kinase activity using larval extracts from WT- or _Rheb_-deficient Drosophila [66].
## 5 Performance Analysis and Optimization
### Optimization Performances on Lifetime
#### 4.2.1 Overview  
The maximum energy consumption under various environments is listed in Table 7. The average energy consumption of the networks adopting DDAR is much smaller than that of the networks adopting CDAR. The performance of optimization on lifetime is immune to the change of \(\mathcal{Q}\).  
As seen from Figure 12, the optimization performance disappears as \(\eta\) decreases. And the performance is not affected by the change of the difference of the service requirements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DDAR (J)** & **CDAR (J)** & **Ratio (\%)** \\ \hline \(\mathcal{Q}=3\) & 226.78 & 403.57 & 64.78 \\ \(\mathcal{Q}=5\) & 224.82 & 400.70 & 64.35 \\ \(\mathcal{Q}=7\) & 224.79 & 403.86 & 63.86 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average maximum energy consumption and the ratio in each environment.  
Figure 11: The distribution of delay when \(P_{\alpha}=0.5\), \(\lambda=0.6\), \(\eta=0.25\).  
20 of 29.
* The effect of \(P_{\alpha}\) and \(\lambda\)  
From Table 8 we can find that the maximum energy consumption decreases with the increase of \(P_{\alpha}\) in the networks with CDAR, and the performance is not affected by the change of \(P_{\alpha}\). Therefore, the optimization effect regarding lifetime decreases slightly as \(P_{\alpha}\) increases.  
Figure 13 illustrates that the maximum energy consumption in the networks with DDAR and CDAR and the difference of maximum energy consumption increases with the increase of \(\lambda\). The maximum energy consumption in the networks of these two schemes increases exponentially.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(P_{\mathbf{ff}}=\mathbf{0.1}\) & \(P_{\mathbf{ff}}=\mathbf{0.5}\) & \(P_{\mathbf{ff}}=\mathbf{0.9}\) \\ \hline DDAR (J) & & & \\ \hline \(Q=3\) & 229.41 & 224.38 & 229.54 \\ \(Q=5\) & 229.55 & 216.62 & 228.28 \\ \(Q=7\) & 229.09 & 218.42 & 226.87 \\ \hline CDAR (J) & & & \\ \hline \(Q=3\) & 423.44 & 396.69 & 390.58 \\ \(Q=5\) & 420.37 & 396.96 & 384.78 \\ \(Q=7\) & 422.43 & 398.75 & 390.40 \\ \hline Ratio (\%) & & & \\ \hline \(Q=3\) & 63.53 & 63.97 & 67.85 \\ \(Q=5\) & 63.30 & 62.18 & 67.63 \\ \(Q=7\) & 62.72 & 61.90 & 66.98 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average maximum energy consumption and the ratio vs. \(P_{\alpha}\).  
Figure 12: The ratio of lifetime vs. \(\eta\).  
Since the energy consumed in the network using CDAR increases faster than the energy consumed in DDAR networks, the ratio of the consumption decreases with the increase of \(\lambda\) (see from Figure 14). In the networks with a large value of \(\lambda\), the lifetime can be prolonged to twice. When the value of \(\lambda\) is small than 0.1, the negative optimization effect appears.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 3 Fouling and Concentration Polarization in Submerged HF Systems  
As noted earlier, submerged HFs can be operated in either a dead-end or cross-flow mode. In dead-end filtration, tangential shear is absent, while in cross-flow, there is shear on the membrane due to bubbling, vibration, or particle scouring. Figure 1 shows a schematic of dead-end and cross-flow filtration in a submerged system.  
The operation of submerged HF membrane systems under either dead-end or cross-flow (usually induced by bubbles) conditions involves very different dynamics. Ideally the process is at steady-state with a fixed flux and TMP for cross-flow, while the process is cyclic with repeatable and regular changes in the TMP for dead-end filtration. However, irrespective of the mode of operation, some degree of fouling inevitably occurs, although membrane fouling is relatively less extensive in the cross-flow mode due to the continuous tangential shear on the membrane.  
Accumulation of retained species on the membrane surface is unavoidable in membrane-based separation technologies for liquid feeds. In submerged HF membrane processes, depending on the membrane pore size, the retained species are particulates and macromolecules. The localized accumulation of particles or dissolved species on the membrane leads to concentration polarization  
Figure 1: A schematic of dead-end (**a**) and cross-flow; (**b**) submerged filtration.  
(CP), which is the primary reason for flux decline or TMP rise during the initial period of a membrane-based separation in low-pressure processes [3; 69; 70]. CP is considered to be reversible and can be controlled in a membrane module by means of optimizing the process hydrodynamics. Unfortunately, membrane fouling resulting from CP can lead to an irreversible loss of membrane permeability [69; 71]. Membrane fouling, the process by which foulants, namely colloidal (e.g., clays, flocs), biological (e.g., bacteria, fungi), organic (e.g., oils, polyelectrolytes, humic substances), and scaling (e.g., mineral precipitates in RO systems) foulants, deposit onto the membrane surface or in the membrane pores [72; 73], may take different forms, the main mechanisms of which are adsorption (physical and/or chemical), pore blocking, deposition of a cake layer, and gel formation [74; 75; 76; 77; 78; 79]. The extent of fouling, which stems from the nature of foulant-membrane interaction, is a complex function of the feed characteristics (e.g., foulant type, foulant concentration, and physicochemical properties of the foulants such as the functional groups, charge, size, and conformation [72; 80; 81; 82]), operating conditions (e.g., inadequate pretreatment, inadequate control of the hydrodynamics of the system, excessive flux, and low cross-flow velocity (in cross-flow systems) [72; 82; 83; 84]), and membrane properties (e.g., pore-size distribution, surface roughness, charge properties, and hydrophobicity [70; 85; 86; 87]).