esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Grapes Physicochemical Composition Evaluation  
Berry weight (an indicator of berry development or dehydration), pH and tittatable acidity (to estimate wine acidity and indirectly taste and microbial stability) [46; 47], and sugar content (to estimate wine alcohol content) [47], were used to evaluate the oenological potential of the six grape varieties under study, considering the three harvests (2010-2012) and the set of vineyard parcels (Table 2). Considering all the assayed samples, berry weight ranged from 1.0 to 2.1 g, tending to be higher in red varieties (1.6-2.1 g), except for BA-VA1 grapes from 2010 harvest, than in the white ones (1.0-1.7 g), also except for BI-SM1 (2012) and SB-SM2 (2011 and 2012) grapes. Although within a tiny range (ca. 1.5-1.6 g) compared to the one determined in this work (1.3-2.1 g) (Table 2), similar values were already reported for Baga variety [25]. The pH values were very similar among the studied _V. vinifera_ varieties, harvests, and vineyard parcel conditions (2.7-3.3) (Table 2). However, for Sauvignon Blanc (3.0-3.2) (Table 2), these were like the ones reported for this white variety (3.06-3.14) collected in a similar period (2011-2013) in New Zealand [48]. For all assayed samples, the tittatable acidity ranged from 4.1 to 7.9 g tartaric acid L\({}^{-1}\), and the sugar content ranged from 163.8 to 254.2 g L\({}^{-1}\) (Table 2). These parameters were very variable among the six varieties, harvests, and parcel conditions. Globally, Sauvignon Blanc and Castelao grape varieties (5.2-7.6 and 5.2-7.5 g tartaric acid L\({}^{-1}\), respectively) exhibited the higher acidity values, while Touriga Nacional exhibited the lower ones (4.2-5.8 g tartaric acid L\({}^{-1}\)). Additionally, Sauvignon Blanc and Touriga Nacional had higher sugar content (201.7-247.6 and 184.7-254.2 g L\({}^{-1}\), respectively), while the lower one was determined for Baga (163.8-214.8 g L\({}^{-1}\)) (Table 2). The lower sugar content of Baga grapes from Bairrada Appellation was already reported [25]. The phenolic content and antiradical activity were also considered indicators of the wines' color and astingency and the bioactive potential [49] of the grape varieties under study. The phenolic content ranged from 197.5 to 1341.3 mg GAE L\({}^{-1}\), being higher in red varieties (494.1-1341.3 mg GAE L\({}^{-1}\)) than in the white ones (197.5-467.6 mg GAE L\({}^{-1}\)). Phenolic compounds are well known for their antiradical properties [49]; thus, as expected, higher activity was determined in red varieties (51.9% to 89.6 % DPPH\({}_{\rm rem}\)) when compared to the white ones (62.7% to 89.9 % DPPH\({}_{\rm rem}\)). Moreover, Touriga Nacional, which is characterized by high phenolic content (501.6-1341.3 mg GAE L\({}^{-1}\)), was already reported as a source of phenolics, such as anthocyanins and proanthocyanathins, which contribute to the color, astringency, and bitterness of the resulting wines [50; 51].
## 3 Results
### Principal Component and Correlation Analyses  
The principal component analysis (PCA) showed the distribution of different treatments in maize crop as presented in the PCA-biplot (Figure 4). Significant results were obtained from the biplot of PCA performed for two factors (cumulative variance 83.9%), the first factor represents 56.3% variation, while 27.6% of the difference is explained by the second factor. Hence, great variation was found among all the treatments in maize plants. Here, visual impact (Figure 4) highlights the relationship and variation observed among all studied parameters of maize plant. Moreover, significant positive and negative correlations were observed between various studied parameters (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Biochar Level (\%)** & **RWC** & **Chlorophyll a** & **Chlorophyll b** & **Chlorophyll** & **SOD** & **CAT** \\
**(\%)** & **(\%)** & **mg g\({}^{-1}\) Fresh** & **mg g\({}^{-1}\) Fresh** & **a + b** & **u \(\mathbf{g^{-1}}\) fw** & **U g\({}^{-1}\) fw h\({}^{-1}\)** \\ \hline Control & 61 \(\pm\) 2.08d & 1.40 \(\pm\) 0.11c & 0.82 \(\pm\) 0.02e & 2.20 \(\pm\) 0.13c & 11.93 \(\pm\) 0.73a & 15.20 \(\pm\) 1.17 ab \\
0.5 & 63 \(\pm\) 1.86 cd & 1.55 \(\pm\) 0.07c & 0.87 \(\pm\) 0.03 de & 2.40 \(\pm\) 0.09c & 11.80 \(\pm\) 0.98a & 14.68 \(\pm\) 0.86 ab \\
1.0 & 67 \(\pm\) 2.31 cd & 1.98 \(\pm\) 0.08b & 0.97 \(\pm\) 0.04 cd & 2.95 \(\pm\) 0.13b & 11.68 \(\pm\) 0.88a & 13.68 \(\pm\) 0.88b \\
1.5 & 68 \(\pm\) 3.53 bd & 2.43 \(\pm\) 0.11a & 0.99 \(\pm\) 0.05 bc & 3.41 \(\pm\) 0.16a & 11.31 \(\pm\) 0.65a & 14.33 \(\pm\) 0.34 ab \\
2.0 & 71 \(\pm\) 2.96 bc & 2.31 \(\pm\) 0.06a & 1.17 \(\pm\) 0.04a & 3.47 \(\pm\) 0.10a & 11.66 \(\pm\) 0.32a & 15.68 \(\pm\) 0.88 ab \\
2.5 & 76 \(\pm\) 1.53 ab & 2.30 \(\pm\) 0.13a & 1.10 \(\pm\) 0.05 ab & 3.39 \(\pm\) 0.18a & 12.34 \(\pm\) 0.33a & 16.01 \(\pm\) 0.58 ab \\
3.0 & 84 \(\pm\) 2.91a & 2.20 \(\pm\) 0.08 ab & 0.92 \(\pm\) 0.04 ce & 3.11 \(\pm\) 0.07 ab & 12.68 \(\pm\) 0.88a & 16.68 \(\pm\) 1.33a \\ \hline \end{tabular}  
* Data is the average of three repeats. Columns sharing similar letters do not differ significantly (at \(p\leq 0.05\)).  
\end{table}
Table 3: Biochar effect on relative water content (RWC), chlorophyll (a and b), and enzyme (catalase (CAT), super oxide dismutase (SOD)) activity of maize seedlings.
## 2 Results  
### VOCs Synthesized by O. vulgare Endophytes Possess Antibacterial Activity  
_O. vulgare_ ssp. _vulgare_ endophytic strains were tested for their VOC production potential through the cross-streaking method, using Petri dishes with a septum physically separating the culture plate into two compartments, as described in Materials and Methods. Ten Bcc strains (either isolated from the environment or from cystic fibrosis patients) belonging to four species, _B. cepacia_, _B. cenocpacia_, _B. multivorans_, and _B. ambifaria_, were chosen as targets for the inhibition test. The eight tester strains were chosen from a larger panel of bacterial endophytes since they showed a high antagonistic activity against Bcc and/or other human pathogens (Coagulase-Negative Staphylococcus, _Staphylococcus aureus_, and _Klebsiella pneumoniae_), when tested on Petri dishes without the separating septum [27]. The data obtained are shown in Figure 1, whose analysis revealed that all tester strains exhibited a complete or strong growth inhibiting capacity against target strains of human origin, except for OVF10. Finally, target strains LMG13010 and LMG19230, belonging to _B. multivorans_ and _B. cenocpacia_ species, respectively, showed the highest resistance profile, being inhibited by OVF22, OVS8, and OVS26 only.  
Figure 1: Antibacterial activity of _O. vulgare_ ssp. _vulgare_ associated endophytes against Bcc strains.  
The experiments were repeated twice. The inhibition values reflect three different inhibition levels, that is, complete (red), strong (orange), weak (salmon), and absence of inhibition (white). ND (not detected) refers to results that were not obtained. The origin of the target strains is referred to as CF (cystic fibrosis patients) and ENV (environmental).
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Supply-and-Demand Balancing
#### 2.1.4 Modeling and Analysis  
An increased reliance on VRE is likely to create more bottlenecks, which is presently the case within transmission and distribution networks. In the absence of solutions, this would curtail VRE use and cause failure of supply-and-demand balancing. Various technologies of power flow control mitigate the immediate need for reinforcement of transmission and distribution networks. These include dynamic line rating (DLR), flexible AC/DC transmission systems, and high-temperature low-sag (HTLS) conductors [30, 31, 32]. This implies that network modeling and power flow analysis should be improved to estimate and minimize potential curtailments of VRE use across networks with different voltages [33]. Unit commitment and dispatch models and grid expansion models must also be improved to fully exploit the new power flow control technologies and analyze their individual and synergistic effects. Moreover, future grid expansion modeling and analysis tools should be continually expanded to include the existing and forthcoming gas and hydrogen infrastructures and the up-to-date power-to-X conversion technologies [34].  
Traditionally, power grid models were often simplified to facilitate the analysis of large-scale networks with many components while still ensuring high analytical accuracy. However, a very detailed model of a grid with 100% renewable electricity is required when analyzing the uncertainty and variability of supply-and-demand balancing and, consequently, the stability of grid operation, as discussed in Section 2.2. Moving from hour-resolution modeling to minute- or second-resolution modeling will greatly improve understanding of the real-time power balancing and dynamic responses of future grids with large numbers of VRE inverters. For example, unit commitment and dispatch models are widely used to assess the hourly or 5-15 min operations of generators. When updated with stochastic decision-making schemes of higher resolutions due to better VRE forecasting, the models will better assess the operational flexibility of generators and allocate operating and regulation reserves over a rolling planning horizon. The commitment and dispatch models can be further improved by having them reflect real-time power balancing issues, such as inertial responses, and frequency and voltage controls, under normal and abnormal grid conditions.
## 6 FWA Transport Network Solutions
In [47], we present a comprehensive overview of several potential fronthaul interfaces. The CPRI aids in the effective distribution of digital baseband signals over the fronthaul network. Nevertheless, for seamless connection among the network elements, some stringent requirements regarding latency, jitter, and bandwidth are imposed on the fronthaul network. As will be explained in Section 8, the huge bandwidths required for the CPRI employment may hinder the CPRI-based fronthaul links implementation in the 5G and beyond networks [47; 361; 365]. Note that 5G-based networks will comparatively impose additional requirements on the transport network as massive MIMO and broader spectrum will be employed [47; 366]. Depending on the deployment scenarios and the use cases, the requirements may be related to the connectivity, bandwidth, latency, reliability, and SDN capability openness [366]. As aforementioned, PON-based schemes like WDM-PON and Ultra-Dense WDM-PON (UDWDM-PON) offer feasible solutions for attending to the capacity requirement. Furthermore, it is remarkable that the PON architectures are well-suited for the 5G network applications and can efficiently support both wired and wireless broadband service delivery. Exploiting the PON architecture, each DU can effectively communicate with the CU with a dedicated wavelength. In addition, in the upstream (US) direction, the wavelengths can be aggregated and multiplexed further onto a single shared fiber infrastructure at the remote node and de-multiplexed at the CO [47; 361]. Furthermore, as stated earlier, optical and wireless networks convergence is an attractive solution for exploiting the inherent huge-bandwidth and high reliability of an optical system, as well as the wireless connectivity, ubiquity, mobility, flexibility, and cost-saving advantages of the wireless/cellular net works [367]. This can aid in achieving the energy efficiency and capacity that are predicted for the 5G networks. Moreover, another attractive and feasible broadband access solution is optical wireless communication (OWC). For instance, OWC systems such as Visible-light communications (VLC) and FSO are attractive broadband access technologies. Being an optical-based solution, OWC can support networks in which high density, high capacity, and low latency are required at a relatively lower cost. Consequently, OWC is an alternative and/or complementary solution to the wireless RF solutions [162; 164; 165; 366; 367; 368; 369]. Primarily, the prime transport solution selection is based on the site structure and availability of transport infrastructure. In this section, we discuss a number of potential wired and wireless transport solutions that are capable of supporting the 5G FWA deployment scenarios expressed in Section 3.2, as well as different FSOns presented in Section 8.2.
## 2 System Concept
While a higher data carrier frequency (e.g., 27.12 MHz) could be chosen to theoretically increase the communication bandwidth, it also correlates with a higher sampling rate, implying a higher power consumption, and with a reduced frequency separation of energy and data carrier, increasing interference for the same steepness of the filtering stages. As far as the system dimensions are considered, miniaturized neural probes are accompanied by coils [24] and circuit boards [25; 26; 27] measuring up to several centimeters, which is still feasible as they are embedded in upper tissue layers. Consequently, implant coils and prototype circuits for concept evaluation shall be realized with a maximum edge length of 10 mm, i.e., they shall cover a maximum circuit board area of 100 mm\({}^{2}\) each. The appropriate design mechanism of the individual circuit blocks of Figure 2 are detailed in the following, along with the mathematical foundations leading to a suitable dimensioning of all components.
## 3 State-of-the-Art MIMO-SDR Systems
### MIMO Systems  
The work conducted in this general area could be broadly classified into two groups: the platforms and testbeds developed for educational purposes and the innovation introduced in the hardware/architecture implementation.
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
## 3 Numerical Simulation of Blasting Vibration Control
### Model Overview
#### 3.3.2 Comparison of Vibration Velocity  
The vibration velocity of five measurement points at the arch bottom, arch foot, arch waist, spandrel, and arch top of the existing tunnel are shown in Figure 7.  
The peak vibration velocity of each measuring point of the existing deterioration tunnel and the rate of change is shown in Figure 8.  
As can be seen from Figures 7 and 8, compared with full-section ordinary detonator blasting. Mechanical cutting combined with the detonator blasting method can effectively reduce the vibration velocity of each measuring point in the existing tunnel. Under the two schemes, the position of the peak vibration velocity of the tunnel does not change, and both are in the vault. The change rate of peak vibration velocity clearly shows that the biggest change of vibration velocity is point D, that is, the spandrel position on the side of the explosion. Measuring point D is the closest to the source of the explosion, which more intuitively demonstrates the vibration reduction effect of mechanical cutting combined with an ordinary detonator.
# Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1
## 2 Materials and Methods
### Molecular Dynamics Simulations and Analysis
#### 2.7.5 MM-GBSA Binding Energies  
For each system, the molecular mechanics/generalized Born surface area (MM-GBSA) binding energy was calculated based on the last 200 ns simulation results. The OPLS3 force field, VSGB 2.0 solvation model, and the default Prime parameter were used for the MM-GBSA calculations, which consisted of three calculation steps: receptor alone minimization, ligand alone minimization, and receptor-ligand complex minimization. The total binding free energy was finally calculated as \(\Delta\)G\({}_{\text{(bind)}}\) = E\({}_{\text{complex(minimized)}}\) - (E\({}_{\text{ligand(minimized)}}\) + E\({}_{\text{receptor(minimized)}}\)). For each of the minimization steps, three energy components were analyzed, including E\({}_{\text{electrostatics}}\)(H\({}_{\text{bond}}\) + E\({}_{\text{coulomb}}\) + E\({}_{\text{GB\_ solvation}}\)), E\({}_{\text{vdW}}\) (E\({}_{\text{vdW}}\) + E\({}_{\text{Tr-\pi stacking}}\) + E\({}_{\text{self-contact}}\)), and E\({}_{\text{lipophilic}}\).
## 3 Results
### Data Normalization and DEPs Identification  
Then, the influence of icarini on cytokines in EVs secreted by BMECs were investigated with an injury model. Data normalization and cross-comparability were evaluated using the principal component analysis (PCA) to confirm biological variability between different EVs samples (Figure 3A). In total, 62 DEPs were identified between the EVs isolated from the Model group (BMECs treated with glucocorticoids) and the control group, including 11 upregulated and 51 downregulated DEPs listed in Table S2 (Supplementary Materials). Meanwhile, 29 DEPs were detected between EVs isolated from the Icarini-treated group  
Figure 2: Icarini treatment and analysis of package in EVs. (**A**) Proliferation rate of BMECs increased with the concentration of icarini; effects of icarini in the \(5\times 10^{-5}\) M and \(1\times 10^{-4}\) M groups were most significant; (**B**) protein expression bands of VEGF, TGF-\(\beta\)1, CD9, and CD81 in the EVs isolated from BMECs in the icarini group and the control group; (**C**) morphology of isolated EVs from BMECs in the icarini group and the control group, as determined by transmission electron microscopy; (**D**) the expression level of hsa-miR-1469, hsa-miR-133a-5p, and hsa-miR-10b-5p in EVs isolated from the icarini group and the control group measured by using qPCR. HYD indicated hydrocortisone (0.3 mg/mL). Data analysis was performed using \(t\)-test analysis and ANOVA, error bars indicate standard deviation, and **\({}^{**}\)** means \(p<0.01\), \({}^{*}\) means \(p<0.05\).  
and the Model groups, including 25 upregulated and 4 downregulated DEPs (Figure 3B). In addition, a heatmap were generated using the R ggplot2 package (Figure 3C). The clustering pattern showed that protein expression of EVs isolated from the Icarini-treated and Model groups was substantially different from each other. Elisa assay confirmed the difference in the expression level of five DEPs, including EMMPRIN, Galectin-3, ICAM-1, MIF and MMP-3 (\(p<0.01\)) (Figure 4).  
Figure 4: Relative expression level of five proteins identified by Elisa assay. Data analysis was performed using \(t\)-test analysis and ANOVA, error bars indicate standard deviation, and ** means \(p<0.01\), \({}^{*}\) means \(p<0.05\).  
Figure 3: **(A)** Principal component analysis of different samples; **(B)** a volcano plot of identified protein in EVs isolated from BMECs in the Icarrin-treated group and the Model group. The blue dots represent DEPs ( 1 FC1 > 1.2 and adj. \(p\). value < 0.05), the red dots represent proteins without significant difference. There were 25 differentially upregulated genes and 4 differentially downregulated genes; and **(C)** a heatmap of 29 differentially expressed proteins between the Icarini-treated group and the Model group. Hierarchical clustering analysis of z-scored FPKM was performed for each DEPs and the color scale represents FPKM normalized log10 transformed counts. Horizontal bars represent genes and the vertical column represents samples. Red color represents differentially upregulated genes, while blue color indicates differentially downregulated genes.
## 4 Machine Learning Pipeline for Passive Inspection of TLS-Encrypted Traffic
### Machine Learning Algorithms and Model Selection  
Various machine learning algorithms are available owing to extensive studies in this field. Once the feature representation is completed, various the algorithms are readily applied in TLS-encrypted traffic. For example, [PERSON] and [PERSON] [96] provide a detailed comparison for malware detection among several well-known machine learning models: linear regression, logistic regression, support vector machine (SVM), decision tree, random forest, and multilayer perceptron (MLP), given a set of extensive dataset engineered by security experts. Furthermore, the researchers also considered the possibility of noisy labels. According to their work, random forest is the most robust machine learning classifier for malware detection.  
When several machine learning algorithms are required to be evaluated, along with a comparison for model selection, we are required to employ performance metrics. Accuracy, precision, recall, and F1-score are the typically used metrics in encrypted NTA. One interesting metric especially proposed for SOCs is accuracy at a 0.00% false discovery rate (FDR), appeared in [17; 83; 96]. Note that FDR is defined as the expectation of a false positive/(false positive + true positive) [111] and performance evaluation with controlling FDR has been widely used in statistics and genomics. In contrast, while an exception [112] can be found in traffic classification literature, controlling FDR was rarely conducted in malware detection literature. As we can observe in its definition, FDR highly depends on false positives. Clearly, an incident response team in a SOC may not conduct machine learning-based methods if too many false positives occur, and a recent research [113] conduct an online survey to understand SOC analysts' perspective on this issue in depth. Thus, we can conduct feature selections for each machine learning algorithm to control the FDR. In this context, ref. [17; 83] successfully justified the necessity for combining TLS metadata, SPLT, and BD feature sets to achieve better accuracy at a 0.00 % FDR, where the accuracy at a 0.00% FDR refers to the accuracy in the controlled trial.  
In contrast, an increasing trend can be observed in the research efforts to conduct NTA without security experts with the assistance of deep learning. [PERSON] and [PERSON] [24] introduced a set of deep learning-based methods for traffic classification. Similarly, [PERSON] et al. [21] provide an excellent systematic framework for comparison of deep learning architectures for mobile encrypted traffic classification.  
Meanwhile, with the advance of AutoML systems, model selection can be automated. In nPrintML [110], AutoGluon-Tabular [114] is used, which trains, optimizes, and tests over 50 machine learning models, such as tree-based methods, deep neural network models, and neighbor-based classification models.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### The 5G Project Use Cases
#### 3.2.6 Augmented and Virtual Reality for Cultural Heritage  
Another aim is to evaluate the enhancement of cultural heritage, thanks to new experiences provided by immersive 360-degree visits of churches, museums, historic buildings, and archaeological sites, with digital content displayed through virtual reality (VR) and augmented reality (AR) viewers. Smart Glass and live streaming online or social networks can be used to enhance the guided visits of cultural heritage sites. It will be possible to enjoy content by means of VR and AR inside the Pecci Museum and the Tissue Museum in Prato, and inside museums (MuNDA museum), historic buildings, churches, and archaeological sites in L'Aquila.