esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**TEST**} & **P** & **Recovery** & **Recovery** \\  & & **[bar]** & **(Purity = 95\%)** & **(Purity = 70\%)** & **Selectivity** \\  & & & **[\%]** & **[\%]** & **[\%]** \\ \hline HCA\_200\_0 & CO2/CH4 & 2 & \(31.98\pm 0.13\) & \(60.12\pm 0.35\) & \(1.88\pm 0.02\) \\  & & 5 & \(36.09\pm 0.87\) & \(68.11\pm 0.10\) & \(2.10\pm 0.02\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the results of the adsorption tests (mixture CO2/CH4). Figure 5: CO2 sorbent capacity vs. PCO2. zeolites, activated carbon, and fly ash shows that, at the same operating conditions, the HCA exhibits much better results in terms of CO\({}_{2}\) capture capacity. Figure 6: Comparison of CO\({}_{2}\) sorbent capacities with literature data obtained with batch equilibrium method (BPL: commercial activated carbon [48]; C35N400: ammonia-treated activated carbon [49]; G-900: activated graphite fibers [50]; MFB-600: N-doped activated carbon [51]; RN-800: ammonia-treated activated carbon [52]; RFL-500: N-doped porous carbon [53]; DO-88-M: activated carbon from petroleum pitch [54]; AS-2-600: sawdust-based porous carbon [40]). Figure 7: Comparison of CO\({}_{2}\) sorbent capacities with literature data obtained with the dynamic experimental method (pressure swing adsorption (PSA)) (A1, A2, A3, A4, B1, B2: zeolites from fly ash; SG: commercial silica gel; AC: commercial activated carbon; 13X: commercial zeolite [22]). Figure 7 shows that the HCA_200_0 has the best CO\({}_{2}\) sorbent capacity: 6.569 mmol/g, threefold concerning the best performance of traditional sorbents [22]. Another significant result appears in Figure 7. Hydrochars prepared after 120 min of HTC reaction halve their performance, even though remaining well above commercial zeolites and similar materials. This suggests investing in further research aimed to ascertain if a reaction time exists, which maximizes the sorption capacity. This more-in-depth investigation is of the utmost importance for industrial-scale process optimization. All of the results here reported highlight the concrete possibility of exploiting the residual biomass as an adsorption medium for biogas upgrading and encourages continuing research in this way.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 3 Proposed Approach
### Local Global Feature Combination Block  
The LGFCB is designed for extracting local details and global features. To better take advantage of a CNN and transformer, we explore an elaborate structure, as shown in Figure 2. Given the input feature x, we adopt two parallel paths to obtain local and global features simultaneously. Among them, the multi-scale residual block (MRB) is a CNN structure for extracting local features, and the transformer block is for extracting global features. To make further use of the extracted features, we design cross-path feature fusion to introduce global or local features during the feature-extraction process of each path. Thus, the two paths are not independent anymore. When we obtain global features, we introduce MRB\({}_{1}^{\text{out}}\) which can attract local attention. Additionally, while obtaining local features, we fuse the transformer block output before MRB\({}_{3}\) and integrate more global features. Finally, we concentrate the MRB\({}_{3}^{\text{out}}\) and transformer block output together, then apply a \(1\times 1\) convolutional layer to reduce the channel number.
## 2 Results  
### SCN5A Mutation Analysis on TD and IDL Segments in BrS Patients at High Risk of SCD  
The 36 high-risk BrS patients were categorized based on the presence of mutations in TDs or IDL regions (Table 1; Figure 1A). Among them, 9 patients had mutations in the IDL regions and 27 in the TDs. Mutations occurring in the IDL domains were categorized as '_benign_' by in silico tools at a significantly higher rate compared to mutations in TDs (44% vs. 15%) (Table 1). At a clinical level, 22% of patients with IDL mutations had documented appropriate implantable cardioverter defibrillator (ICD) therapies for ventricular arrhythmias, 10% had experienced an aborted cardiac arrest, 77% reported syncope, 33% exhibited a spontaneous type 1 pattern, and 44% had a family history of sudden death. In BrS patients with mutations in the TDs, 48% had documented appropriate ICD therapy for ventricular arrhythmias, 18% had experienced aborted cardiac arrest, 40% reported syncope, 25% exhibited a spontaneous type 1 pattern, and 22% had a family history of sudden death. However, patients with IDL mutations had significant atrial fibrillation compared to those carrying TDs. Table 1 also includes the filtered QRS duration (f-QRSd), root mean square voltage of the terminal 40 ms of the filtered QRS complex (RMS40), duration of low-amplitude signal < 40 uV (LAS40) values, and mapping of substrate size and potential duration (PD) at baseline and after ajmaline administration (Table 1 and Supplementary Table S1). Supplementary Tables S2 and S3 delineate the clinical phenotypes of BrS patients further categorized in the IDLs and TDs based on predictions of being 'benign' and 'damaging'. Notability, there were no significant variations observed in clinical features when mutations were categorized based on their predicted pathogenicity, both in TD and in IDL.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 5 Simulation Details and Parameter Selection  
To evaluate the performance of the proposed framework and the efficiency of federated learning to make accurate predictions, the proposed approaches were applied to an ECG anomaly detection application. Poisson analysis for the ALOHA-based network was used to evaluate the performance of the networking layers. Federated learning was implemented in Python, which ran on top of the networking layer.  
All the codes for evaluation were executed on a machine configured with Intel(R) Core(TM) i5-8250U CPU clocked at 1.60 GHz, no GPU, and a single 8 GB DDR4 RAM. The ECG-5000 dataset was used for training and testing purposes [29]. The original ECG-5000 dataset is a 20 h long ECG downloaded from Physionet. The dataset was then pre-processed to extract each heartbeat and make them equal in length. The dataset contains 5000 labeled samples of ECG data. Each sample consists of 140 data points and a label. The value of the label is either 0 or 1, representing abnormal or normal heartbeats, respectively. A dense neural-network-based autoencoder was trained on the dataset and was used to make the predictions.
## 4 Semi-Active Magnetorheological Damper Device for Chatter Mitigation  
### Experimental Setup of MR Damper Assembly  
The experimental setup is implemented as shown in the optical photograph (Figure 8a). This system uses the same components described in Section 3.2, but now, it also includes the semi-active MR damper device. The MR damper device is a pool-like container or chamber fabricated in acrylic material; a non-magnetic material avoids reducing the magnetic flux density generated by an electromagnet. The acrylic chamber contains the magnetorheological fluid with an internal diameter of 200 mm and 19 mm height. In order to avoid spills, an acrylic plate is placed covering the area not in contact with the beam. The MR damper assembly is supported with 3D-printed columns so that an electromagnet could be placed between the worktable and the MR damper. An HP/Agilent power supply, model 6032A was connected to a electromagnet, model VEM200 (Electroimanes NAFSA, Gernika, Spain), which accepts a maximum input current of 3.4 A at 25 V. Once the electromagnet is energized independently of the machining, it generates a magnetic flux density that changes the yield stress of the MR fluid. The chamber was fully filled with an MRF-122 EG MR fluid (LORD Corporation, Cary, NC, USA).  
The cross-section A - A presented in Figure 8b shows a better view of the experimental setup and allows us to observe that the beam is sunk about 1 mm in the MR fluid with a contact area of about 50 cm\({}^{2}\). It is important to sink the beam to guarantee that it is always in contact with the MR fluid during machining, because the amplitude of the vibrations is expected to be in the range of \(\pm\) 1 mm. Once the workpiece is attached to the beam, the experimental setup is ready for FRF measurements and experimental milling cuttings.  
Figure 7: Continuous wavelet transforms (**a**,**b**,**c**), sampled acceleration signals (**d**,**e**,**f**), Poincaré diagrams (**g**,**h**,**i**), and power spectral density (**j**,**k**,**l**) for unstable cut A (**a**,**d**,**g**,**j**), stable cut B (**b**,**e**,**h**,**k**), and quasi-periodic unstable cut C (**c**,**f**,**i**,**l**).
## 4 Mobility and Environmental Data Integration: System Overview
### System Architecture
#### 4.2.3 Data Aggregator  
Spatial join returns a unified view of mobility, pollution, and meteorological data, enriched with neighborhood data. This view is then fed to a data aggregator, where the newly joined dataset can be queried through user defined queries. Aggregations can be performed on the pollution and meteorological data with predicates on the mobility data and vice versa, allowing a numerous range of possible cross-domain insights to be extracted. The output of this stage is a new dataset of contextualized mobility data that can be interactively analyzed, and for which results can efficiently be exported to other services, workflow pipelines or databases. An example query can ask to \"sort all city regions by highest number of mobility where PM concentrations exceed a threshold value\". This, for example, can reveal the regions with high vehicle-caused PM concentrations, i.e., those that have elevated PM concentrations caused by the excess mobility of vehicles within the city for an extended timeframe.
# Uniform Pooling for Graph Networks
## 3 The Proposed Method
### Model Architecture  
To make fair comparisons and reduce the variables, we applied the same network architecture as the previous studies [22; 23; 33], and compared our UGPool method to the baseline on the same network architecture.  
Graph convolution layer. Graph convolution is the basis of learning graph embedding. While there are many types of graph convolution models, we applied the most widely used model proposed by [PERSON] et al. [7], and its network layer function is as follows:  
\[H^{(l+1)}=\sigma(\tilde{D}^{-\frac{1}{2}}\tilde{A}\tilde{D}^{-\frac{1}{2}}H^{ (l)}W^{(l)}) \tag{14}\]  
where \(H^{(l)}\in R^{N\times D^{(l)}}\) is the node features of the \(l^{th}\) layer, \(W^{(l)}\in R^{D^{(l)}\times D^{(l+1)}}\) is the trainable weight matrix and \(\tilde{A}\) is the adjacency matrix with added self-connections. \(\tilde{D}\) is a diagonal matrix to normalize the adjacency matrix with \(\tilde{D}_{ii}=\Sigma_{i}\tilde{A}_{ij}\). We adopt the widely Rectified Linear Unit (ReLU) as an activation function \(\sigma(\cdot)\).  
Readout layer. The readout layer can embed the node features into the embedding of the whole graph, and the readout layer should be able to handle graphs with different scales and have permutation invariance. We used a readout method that calculates the sum and maximum of all node features as the representation of the graph level [22].  
\[s=\frac{1}{N}\sum_{i=1}^{N}x_{i}\|\max_{i=1}^{N}x_{i} \tag{15}\]  
where \(N\) is the nodes number, \(x_{i}\) is the feature of \(i^{th}\) node, and \(\|\) denotes concatenation operation.  
Global and hierarchical pooling. In the present study, we compared the global pooling and hierarchical pooling architectures as done in [22; 23]. The hierarchical pooling architecture includes three graph convolution layers, each followed by a pooling layer (Figure 1). In contrast, the global pooling architecture consists of three graph convolution layers, followed by a pooling layer after the last graph convolution layer. The output of each pooling layer passes through a readout layer, and the outputs of all readout layers are summed as the final output of the whole GCN. Finally, there are three fully connected layers as prediction layers.
## 2 Theoretical Foundations  
### Frequency Domain Elastic VTI Equation  
The 2D frequency-domain anisotropic elastic Equation [10] is given as:  
\[\begin{split}\rho\omega^{2}u_{x}+C_{11}\frac{\partial^{2}u_{x}} {\partial x^{2}}+C_{44}\frac{\partial^{2}u_{x}}{\partial x^{2}}+(C_{13}+C_{44} )\frac{\partial^{2}u_{x}}{\partial x\partial z}+f(\omega)=0,\\ \rho\omega^{2}u_{z}+C_{44}\frac{\partial^{2}u_{z}}{\partial x^{2} }+C_{33}\frac{\partial^{2}u_{z}}{\partial z^{2}}+(C_{13}+C_{44})\frac{\partial ^{2}u_{x}}{\partial x\partial z}+g(\omega)=0,\end{split} \tag{1}\]  
where \(\rho\) is the density, \(f(\omega)\) and \(g(\omega)\) are Fourier components of horizontal and vertical body forces, respectively; \(u_{x}\) and \(u_{z}\) are the horizontal and vertical displacements, respectively; and \(\omega\) is the angular frequency. The elastic stiffness is written as:  
\[\begin{split} C_{11}&=(1+2\varepsilon)\rho v_{pr}^{ 2},\\ C_{33}&=\rho v_{pr}^{2},\\ C_{13}&=\sqrt{2\delta C_{33}(C_{33}-C_{44})+(C_{33}- C_{44})^{2}}-C_{44},\\ C_{44}&=C_{55}=\rho v_{sr}^{2},\end{split} \tag{2}\]  
where \(v_{pr}\) and \(v_{sr}\) are the P-wave and the SV-wave velocity, respectively; \(\varepsilon\) and \(\delta\) are [PERSON]'s anisotropic parameters [29].  
The frequency-domain finite-difference method can easily introduce attenuation [30]. The constant-Q model is adopted here, and the complex velocity is given as:  
\[\begin{split}\frac{1}{v_{p}(\omega)}&=\frac{1}{v_{ pr}}\left(1-\frac{1}{\pi\Omega_{p}}\ln\left|\frac{\omega}{\omega_{r}}\right| \right)\cdot\left(1-\frac{\mathrm{i}}{2\Omega_{p}}\right),\\ \frac{1}{v_{s}(\omega)}&=\frac{1}{v_{sr}}\left(1- \frac{1}{\pi\Omega_{s}}\ln\left|\frac{\omega}{\omega_{r}}\right|\right)\cdot \left(1-\frac{\mathrm{i}}{2\Omega_{s}}\right),\end{split} \tag{3}\]  
where \(v_{p}\), \(v_{s}\) is the complex p-wave and SV-wave velocity, respectively; and \(Q_{p}\), \(Q_{s}\) is the quality factor of the P-wave and SV-wave; \(\omega_{r}\) is the dominant frequency of the source wavelet; \(i\) represents the imaginary unit. Substituting Equation (3) into Equation (2) yields the viscoelastic anisotropic wave equation.
## 4 Conclusions  
In the present study, the antimicrobial properties of N-TiO\({}_{2}\) nanoparticles under visible-light irradiation on _Salmonella_ Typhimurium biofilms were investigated. To achieve that, N-TiO\({}_{2}\) particles were synthesized via the sol-gel method. The particles were fully characterized by FTIR, DLS, BET, SEM/EDS, and TEM, before assessing their antibacterial behavior. It was revealed that the particles formed were nanoscale (5-15 nm), and the expected Ti-O bonds and elemental N were present. When in water dispersion, the particles tended to form agglomerates and had a zeta potential of \(-\)20 mV, making the suspensions moderately stable. They had a high surface area, making them more photoactive, and they were porous, with a 0.3 cm\({}^{3}\) g\({}^{-1}\) pore volume and 13 nm average pore diameter.  
The antimicrobial photocatalytic testing was conducted under visible-light irradiation, on _Salmonella_ Typhimurium biofilms that were formed on stainless steel coupons. After the biofilm development, the coupons were placed on a horizontal, batch, rectangular reactor equipped with a vis-LED irradiation source, in the presence of N-TiO\({}_{2}\) NPs. After 1, 2, and 3 h of irradiation, the biofilm population was assessed. An inhibition of proliferation under visible-light irradiation was noticed when the N-TiO\({}_{2}\) was present, compared to when the light source was missing. It should be noted that the TiO\({}_{2}\) NPs tend to attract bacteria on their surface, so in some cases the biofilm population was increased compared to the control coupons. This was due to the morphology of the NPs, which created \"trapping\" sites working as shelters, along with their higher surface area available for the bacteria. However, the antibacterial activity of the N-TiO\({}_{2}\) NPs was noticeable, as only 6% of _Salmonella_ biofilm cells remained viable after the first hour of visible-light irradiation. Therefore, N-TiO\({}_{2}\) can be considered to be a promising multipurpose material with biomedical and environmental photoinduced properties, able to reduce biofilm formation under visible-light irradiation.  
Conceptualization, M.-E.K., N.C. and E.A.P.; methodology, M.-E.K., N.C. and E.A.P.; software, M.-E.K.; validation, N.C., E.A.P. and G.-J.N.; formal analysis, M.-E.K. and N.C.; investigation, M.-E.K., N.C. and E.A.P.; resources, E.A.P. and G.-J.N.; data curation, M.-E.K.; writing--original draft preparation, M.-E.K.; writing--review and editing, N.C. and E.A.P.; visualization, E.A.P. and G.-J.N.; supervision, E.A.P. and G.-J.N.; project administration, E.A.P. and G.-J.N.; funding acquisition, E.A.P. and G.-J.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors would like to acknowledge [PERSON] and [PERSON] (Institute of Nanoscience and Nanotechnology, National Center for Scientific Research \"Demokritos\", Agia Paraskevi, Greece) for the TEM images and analysis.  
The authors declare no conflict of interest.
## 3 Experimental Section
### Immunostimulatory Test  
Immunostimulating activity was evaluated as the ability to induce release of interleukin-6 (IL-6) from human peripheral blood mononuclear cells (PBMCs). Human PBMCs were isolated from buffy coats of healthy donors using Ficoll gradient (Amersham Biosciences, Buckinghamshire, UK) and cultured inRPMI 1640 (GIBCO) supplemented with 100 U/mL penicillin, 100 ug/mL streptomycin and 2 mM glutamine(all from GIBCO), and 10% heat-inactivated Fetal calf serum (Euroclone, Milan, Italy). Buffy coats from healthy donors were obtained from the Blood Transfusion Section, Empoli Hospital. Informed consent was obtained before all blood donations. The study protocol was approved by the Empoli Hospital ethical committee and conforms to the ethical guidelines of the 1975 Declaration of Helsinki. PBMCs were seeded in 96-well round bottom plates (1 \(\times\) 10\({}^{5}\) cells/well) and stimulated over night with different concentrations of microalgal extracts. Supernatants were collected and IL-6 concentration was measured by standard ELISA. For each samples, the maximum efficacy of activation was expressed as the percentage of maximum activation elicited by the benchmark compound PAM\({}_{3}\)CSK\({}_{4}\).
# Effect of Blood on Synovial Joint Tissues: Potential Role of Ferroptosis
## 3 Interplay of Erythrocytes, Hemoglobin/Iron, and Synovium
### Role of Iron  
Ferrokinetic studies with radiolabeled RBCs suggest microbleeding as a contributory factor where iron is a major catalyst in the production of hydroxyl radicals, which can induce considerable tissue damage, degrade synovial fluid hyaluron, and attack the articular surface [5]. Erythrophagocytosis resulting in lysis of RBCs by synovial cells occurs in culture, during which synovial cells absorb hemoglobin from the culture medium within 4 h of blood introduction and convert the iron to ferritin [47; 48]. Ferric iron may either be reduced to the ferrous form and promote the formation of toxic-free radical species,  
Figure 2: Inflammatory pathway highlighting the effect of red blood cell lysis and iron exposure on oxidative stress and cytokine release.  
or stimulate collagenase and prostaglandin release from synovial macrophages [49; 50]. Synovial cells, and in particular the Type A or macrophage-lining cells, have been reported to have a strong affinity for iron [51]. Blood-injured cartilage has also been reported to be less capable to withstand loading in vivo [53; 55].
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 4 Simulation Setup
Figure 4: Simulated PON downstream link configuration with ML-assisted optical processor for 64 users. tion [35] to identify parameter configurations that minimize the BER, leading to enhanced performance. The method starts with a broad exploration to gather performance data and progressively focuses on promising areas, ensuring efficient and effective optimization. Comprehensive control of the AOP's behavior is taken by the decider. Dynamic switching between tasks (equalization or chromatic dispersion compensation) is performed based on current network conditions, and optimal values for sub-task components are selected. This dynamic selection ensures that a performance boost is tailored to each specific scenario. The optimizer sweeps various fiber lengths, accounting for different chromatic dispersion levels and optical power variations due to attenuation and dispersion, ensuring the parameters work universally across different fiber lengths while maintaining FEC limit performance. This dynamic selection, along with training across a range of fiber lengths, ensures the AOP's adaptability to environmental changes such as temperature fluctuations and mechanical strains, which typically affect signal quality in real-world scenarios. Table 2 depicts the task and sub-task parameters that the ML decider algorithm chooses for each of the two simulated PON scenarios.The decider's flexibility in managing the AOP is a key factor in boosting performance and robustness, allowing it to surpass even the most complex digital FFE and DFE systems.
## 5 Experiment
### Performance Comparison with Setup 3  
Figure 3 shows the experimental results when selecting the GCN and GAT models as target models with Setup 3. The experiments are validated when conducted on both the Cora and PubMed datasets. The results indicate that fake nodes with additional fake edges generally enhance the attack capability, particularly for the G-NIA method. When attacking the GCN model utilizing the Cora dataset, the G-NIA method with \(\Delta_{e}=3\) achieved the optimal outcome. In contrast, an increase in \(\Delta_{e}\) has a minimal impact on the attack performance of the proposed GCIA method. Nevertheless, the GCIA method consistently secured the best attack performance in most scenarios. Notably, when attacking the GAT model in the PubMed dataset, the GCIA-1 method performed worse as the number of fake edges increased. This may be attributed to the GCIA-1 method's reliance solely on unsupervised data information, thus rendering it incapable of selecting threatening fake edges when faced with the attention mechanism in the GAT model.