esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Analysis
The values were close to those obtained for the total phenolics, suggesting the presence of proanthocyanins as the major phenolic species present. The PAC values from the three vnifications (Red > Thermo > White) showed the enhanced PAC extraction by Red and Thermo methods, indicating pomace (including skin and seeds) may be a good source of PAC. Skin and seeds were demonstrated to contain a high amount of PAC in previous studies [7]. It has been reported that there are high amounts of cell wall-bound PACs in cranberry that are resistant to common extraction, meaning that the PAC contents can be underrated [35]. The increased amount of PACs in the wines may originate from these cell-wall bound PACs, which were better extracted during heat-treatment (Thermo method) and from pomace during fermentation. Among the three cultivars, Pilgrim wine contains the lowest amount of polyphenols and AOC, indicating [PERSON] and Bergman cultivars as the more suitable cranberry varieties for winemaking in the interest of achieving higher polyphenol content in the final product. [PERSON], et al. [36] studied the bioactivity and sensory quality of Brazilian blueberry wine, and found a positive effect of phenolic compounds on wine flavour, as the samples with higher phenolic compounds were correlated with higher preference in sensory. If there is an optimized phenolic content range for good sensory evaluation is still unclear, which needs to be investigated in our further study. \begin{table} \begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **TPC A** & **Total ANC B** & **Total PAC C** & \multicolumn{2}{c}{**Total AOC D**} & **pH** & **Brix (\({}^{\circ}\))** \\ \hline  & & & & & **FRAP** & **ABTS** \\ \hline _[PERSON]_ & 882.0 \(\pm\) 2.5 b  & 283.3 \(\pm\) 21.3 a  & 445.8 \(\pm\) 22.2 b  & 977.0 \(\pm\) 563.0 a  & 9506.9 \(\pm\) 21.3 a  & 2.85 \(\pm\) 0.04 a  \\ _[PERSON]_ & 660.1 \(\pm\) 12.5 c  & 168.1 \(\pm\) 9.8 c  & 397.7 \(\pm\) 21.1 c  & 740.0 \(\pm\) 264.6 b  & 8429.5 \(\pm\) 307.4 b  & 2.98 \(\pm\) 0.15 a  & 5.20 \(\pm\) 0.20 a  \\ _[PERSON]_ & 393.87 \(\pm\) 10.3 a  & 210.27 \(\pm\) 5.5 b  & 583.9 \(\pm\) 12.8 a  & 990.8 \(\pm\) 333.9 a  & 9557.9 \(\pm\) 24.1 a  & 2.99 \(\pm\) 0.18 a  & 5.43 \(\pm\) 0.32 a  \\ \hline \hline \end{tabular}   
* A TPC expressed as mgallic acid equivalent (GAE) per L
* A NC expressed as mg cm-Clus equivalent per L
* BNC expressed as mg cm-Clus equivalent per L
* C PAC expressed as mg cm-Clus equivalent per L
* D Total AOC expressed as mg cm-Clus equivalent per L
Figure 1: Total phenolic (TPC) (**A**), anthocyanin (ANC) (**B**), proanthocyanin (PAC) (**C**) contents and antioxidant capacities (ACC) (**D**) FRAP (ferric reducing ability of plasma method), (**E**) ABTS (free radical-scavenging activity method) of the wine samples determined by spectrophotometric assays. Values identified by the same letters are not significantly different at the 0.05 level (One-way ANOVA-Tukey).
# A Versatile Resilience Packet Ring Protocol Model for Homogeneous Networks
## 4 Simulation Results
### Packet Loss  
Packet loss in a network is a very critical problem. The VRPR-based network model is designed to minimize packet loss. It provides complete redundancy in wired and wireless media. To test our proposed model, we have run the simulation eight times and sent different packets to the network to check the packet loss. During the test, we observe no packet loss, which proves that our proposed model is efficient. Figure 9(b) shows the obtained results. In these experiments, we considered only the LAN. The blue color shows packets sent by the central server. The red color represents packets received by the central server. We varied the number of packets sent from 17 to 50. In all cases, the number of received packets is the same as the number of sent packets.
# Software Integration of Power System Measurement Devices with AI Capabilities
## 4 Laboratory and Field Tests
### Laboratory Testbed and Results
Thanks to the high quality of both devices, very similar measurements were obtained. In the upper portion of Figure 8, the frequency measurements from both devices are shown to overlap. It can be seen how there is a delay between the signal measured by the analyzer and that measured by the \(\upmu\)PMU. This effect arises because the \(\upmu\)PMU transmits measured values every 10 milliseconds, with timestamps applied at the source. In contrast, the PQM PQube3 sent a single data point every second, with timestamps recorded by the TSDB. The latency for PQM measurements is much higher than for \(\upmu\)PMU ones. Approximately, the added latency is one second. In Figure 8, the whisker and violin diagrams are displayed side by side. The superiority of the \(\upmu\)PMU in detecting small variations is particularly evident in the violin diagram. On the other side, beyond the differences in terms of synchronism, the way of measuring voltages and currents between both equipments also differs. Both models, being of the same brand and equivalent in range and quality, have the same hardware, same CT and PT, same A/D converter, and so on. Consequently, the measurements collected are the same, but their processing varies. Figure 8: Frequency measurements of a PQM PQube3 vs. \(\upmu\)PMU. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Grid**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Median**} & \multicolumn{2}{c}{**Standard Deviation**} \\  & **PQM** & \(\upmu\)PMU & **PQ3** & \(\upmu\)PMU & **PQM** & \(\upmu\)PMU \\ \hline
200 \(\Omega\) & 49.994275 & 49.994592 & 49.992897 & 49.993469 & 0.0112464 & 0.01139 \\ \hline
100 \(\Omega\) & 50.001653 & 50.002085 & 50.000908 & 50.001339 & 0.011112 & 0.011363 \\ \hline
500 \(\Omega\) & 50.020218 & 50.020781 & 50.020374 & 50.021168 & 0.011356 & 0.011303 \\ \hline \end{tabular}
\end{table} 
Table 3: Frequency comparison. The results of comparing the collected currents and voltages are consistent with those observed in the frequency measurements. The data collected by the PQM PQube3 are smoother and exhibit less noise than the data collected with the higher sampling rate of the \(\upmu\)PMU. However, both datasets show the same central tendency, with similar means and medians (Table 4). Once again, the \(\upmu\)PMU plots reveal small variations that are not visible in the PQM plots (Figure 9 from the same test as Figure 8). The time lag between the PQM and \(\upmu\)PMU measurements still exists. An important point to note is that the \(\upmu\)PMU, despite being designed to measure phasors, provides RMS measurements as its output. This practice is common and helps align its measurements with those from other electrical monitoring devices.
## 5 Future Directions
### Quantified Trajectories and Comparative Baselines  
Performance ranges reported across the literature are highly application-specific; to aid interpretation, all percentage improvements are expressed relative to the comparator baselines defined in the source studies (see Section 2.4 for normalization and aggregation assumptions). These directional ranges are descriptive, not meta-analytic.  
Quantum-assisted scheduling and control integrated with high-reliability wireless testbeds indicate up to ~30% end-to-end control-loop latency reduction versus conventional industrial Ethernet/Wi-Fi cells tested under lab-scale URLLC profiles [133; 135]. Model-based hybrid stacks (DT + advanced control) report ~15-20% energy savings relative to single-loop or fixed-gain MPC implementations in multi-axis arms [100; 112; 134]. Human-centric collaboration and circular-design guidance emerging from the Industry 5.0 agenda suggest ~10-15% production-waste reduction compared with the throughput-only optimization that is characteristic of early Industry 4.0 deployments [113; 136; 138].  
Latency is only one lever. Field trials and DT/HRC simulation studies show ~10-20% throughput gains relative to static workstation layouts when reconfiguration policies are driven by live DT data over high-bandwidth networks [112; 133; 137]. Additive and hybrid manufacturing workflows tightly coupled to DT analytics have reported ~20-30% material-waste savings versus subtractive or batch-fixture baselines while increasing cell adaptability by ~15% through rapid 4D (time-aware) reconfiguration [112; 139]. Distributed learning and quantum-inspired optimization are projected to reduce model training or path-planning compute times by ~10-25% relative to purely classical centralized training loops [134; 139; 140]. Although these ranges vary by domain, they provide actionable targets for R&D roadmapping and technology selection.
## 3 Experiments
### Data Collection and Preprocessing  
In this study, the Machine Learning for Signal Processing (MLSP) 2014 Schizophrenia classification challenge data were used. The data can be download from [[https://www.kaggle.com/c/mlsp-2014-mri](https://www.kaggle.com/c/mlsp-2014-mri)]([https://www.kaggle.com/c/mlsp-2014-mri](https://www.kaggle.com/c/mlsp-2014-mri)). They were collected on a 3T MRI scanner at the Mind Research Network and funded by the Centers of Biomedical Research Excellence. Image preprocessing was performed using statistical parametric mapping software (SPM, [[http://www.fil.ion.ucl.ac.uk/spm](http://www.fil.ion.ucl.ac.uk/spm)]([http://www.fil.ion.ucl.ac.uk/spm](http://www.fil.ion.ucl.ac.uk/spm))). Further feature extraction was done by the GIFT Toolbox ([[http://mialab.mrn.org/software/gift/](http://mialab.mrn.org/software/gift/)]([http://mialab.mrn.org/software/gift/](http://mialab.mrn.org/software/gift/))), yielding different imaging modalities, i.e., SBM and FNC features for structural MRI and resting state functional MRI, correspondingly.  
The data consisted of 40 patients with SZ and 46 HCs. A diagnosis of SZ was made by using the Structured Clinical Interview for DSM-IV (SCID; Diagnostic and Statistical Manual of Mental Disorders, DSM) [46]. Each sample had 410 features (32 for SBM and 378 for FNC). SBM features were weights of brain regions, and they indicated the concentration of grey matter in different regions of the subject's brain [47]. FNC features were the pair-wise correlation values between the time-courses of 28 brain regions and can be seen as a functional modality feature describing the subjects' overall level of synchronicity between brain areas [48]. These 28 brain regions were selected according to the anatomical automatic labeling (AAL) template, and they are shown in Figure 10, while the connections between the brain regions corresponding to these FNC features are shown in Figure 11.
## 3 Results and Discussion
### Bioactive Compounds and Antioxidant Potential of the Buritirana Fruit Fractions  
#### 3.5.1 Phenolic Compounds Profile  
The phenolic compound profile of the buritirana fruit fractions was determined using UHPLC-ESI-MS. This was the first time that the profile of phenolic compounds of the buritirana fruit fractions was evaluated, and they are shown in Table 3. Investigating phenolic compounds is paramount since they are non-nutritive ingredients synthesized by the secondary metabolism of plants and play an important role in human health, in addition to having several bioactive properties [47; 48; 4]. Nine compounds were identified and quantified: quercetin, rutin, epicatechin/catechin, and ferulic, sinapic, p-comamic, chlorogenic, protecatechuic and quinic acids. However, quercetin and sinapic acids were below the limit of quantification (<LOQ). The quinic and protocatechuic acids and epicatechin/catechin were quantified in all fractions. They showed the highest contents among the acids; their content ranged from 0.03 \(\pm\) 0.07 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\) for SE (_p_ < 0.05) to 6.77 \(\pm\) 1.43 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\) in the WS fraction. The PU fraction showed the highest content of rutin (1.14 \(\pm\) 0.01 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)) and ferulic acid (0.05 \(\pm\) 0.00 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)) (_p_ < 0.05). _p_-Coumaric acid content was similar (_p_ > 0.05) between fractions WS and PE (0.01 \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\)) and was not determined in the PU and SE fractions. Phenolic compounds were also investigated in the oil of the buritirana fruit fractions (_Mauritiella armata_), and the authors reported that the fractions WS, PU and PE also presented phenolic compounds similar to those found in this study [12]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{4}{c}{**Fractions**} \\ \cline{2-5}  & **WS** & **PU** & **PE** & **SE** \\ \hline TPC 1  & 8.51 \(\pm\) 0.14 b & 10.60 \(\pm\) 0.08 a & 4.70 \(\pm\) 0.10 c & 1.54 \(\pm\) 0.03 d \\ TPC 2  & 0.53 \(\pm\) 5.97 b & 0.75 \(\pm\) 0.38 a & 0.29 \(\pm\) 0.43 c & 0.02 \(\pm\) 0.07 d \\ DPPH 3  & 140.75 \(\pm\) 0.30 b & 234.25 \(\pm\) 4.42 a & 136.95 \(\pm\) 0.26 b & 24.35 \(\pm\) 0.50 c \\ TEAC 3  & 743.02 \(\pm\) 9.94 b & 448.40 \(\pm\) 9.83 c & 781.09 \(\pm\) 432 a & 38.44 \(\pm\) 1.70 d \\ ORAC\({}_{\mathrm{HF}}\) 3  & 1.82 \(\pm\) 0.01 b & 2.12 \(\pm\) 0.13 a & 1.55 \(\pm\) 0.17 b & 0.50 \(\pm\) 0.03 c \\ \hline \hline \end{tabular}  
* WS: whole without seed; PU: pulp; PE: peel; SE: seed; 1: result expressed as mg GAE g\({}^{-1}\) fdw (freeze-dried sample); 2: result expressed as mg CE g\({}^{-1}\) fdw; 3: result expressed as \(\mathrm{\SIUnitSymbolMicro mL^{-1}}\) fdw; Averages with different letters in the line indicate statistical differences according to Turkey (_p_ < 0.05).  
\end{table} 
Table 4: Content of total flavonoids, content of total phenolic compounds and antioxidant capacity of the buritirana fruit fractions.
## 4 Solution Approach and Research Framework
### Algorithm and Evaluation Measure  
We applied a Deep Learning technique called Convolutional Neural Networks (CNN). We have run the code on Convolutional Neural Networks with and without regularization techniques. The model is shown in Figure 3.  
1. For CNN without regularization, we used four convolutional layers, four pooling layers. Data augmentation is used to avoid overfitting. Two activation functions, ReLU and Sigmoid, are used. ReLU is used in the inner layers of CNN and Sigmoid is used in the output layer of CNN. Adaptive momentum estimation (Adam) is used for optimization and Max-pooling is used in the pooling layer.
2. For CNN with regularization, we used five CNN layers and five pooling layers. ReLU and Sigmoid activation functions are used. Adam Optimizer, Max-pooling, and L2 regularization with different weight functions are used for the evaluation. We calculate the accuracy every time we run the code and the best accuracy on the test set is taken into the account.
## 1 Introduction
We employ and compare the efficiency of the RDVs under two different popular frameworks in order to realize the WI-SV system. The first one consists of a binary support vector machine (SVM), while the second utilizes a decision stump learning algorithm equipped with a Decision Stump Committee (DSC) structure under the Gentle Ada Boost framework initially proposed by [28] and among others employed also for WI-SV purposes in [37]. The experimental setup consists of blind disjoint learning \(\mathbb{L}\) and testing \(\mathbb{T}\) sets in both intra-lingual and cross-lingual test sets. 4. We follow two distinct methodologies for the purpose of fusing the resultant local \(v\) or global common pole \(dv\). For this purpose, related coordinates between equimass spatial segments \(S\) between a pair of signature images \(\mathbf{Img}_{\mathbf{X}^{\prime}}\mathbf{Img}_{\mathbf{Y}}\) are selected; thus, a pair of covariance matrices, \(\mathbf{X}_{S},Y_{S}\), is evaluated for any two visual segments. Then, the local \(\Psi_{\mathbf{X},\mathbf{Y}}\) or common pole \(\Psi_{\mathbf{X},\mathbf{Y}}^{\mathbf{I}}\) RDVs of a sequence of image segments can be fused under two modes: (a) a serial one, in which the resulting scores from each segment are combined in order to create a score as a function of the segments and (b) a parallel one in which vectored forms \(v\) or \(dv\) are appended in order to form an extended vector with larger dimensionality, accompanied by one score. The remainder of this paper is organized as follows: Section 2 provides a summary of the literature regarding WI-SV and introduces the key elements of the proposed idea. Section 3 outlines the SPD manifold accompanied by the theoretical elements and mathematical tools of the proposed methodology. Section 4 provides details regarding the experimental protocol, and Section 5 displays the corresponding experimental results. Finally, conclusions are drawn in Section 6. Our source code will become available at [[https://github.com/ezois/RDV](https://github.com/ezois/RDV)]([https://github.com/ezois/RDV](https://github.com/ezois/RDV)) (accessed on 26 May 2025) for reproducibility purposes.
## 5 Results
### Analysis of Adaptive MMAC  
#### 5.3.1 Normalized Throughput  
Figure 9 describes the results of the adaptive MMAC protocol that dynamically changes to behave similarly to the E-SA-MMAC and BiMMAC protocols depending on  
Figure 8: Channel access delay comparison of the two MMAC protocols, E-SA-MMAC, and Max-E-SA-MMAC, versus others. (**a**) Three channels with IEEE 802.11, (**b**) three channels, (**c**) four channels, and (**d**) twelve channels.  
the wireless ad hoc channels availability. Figure 9a shows the adaptive MMAC protocol switching to the E-SA-MMAC mode when there are only three available data channels. The protocol provides the best throughput figures as it enables the control channel to be used for data transmission when all the other channels are busy. Figure 9b,c shows the adaptive MMAC switching to the BiMMAC mode and disabling data transmission in the control channel as that would block negotiations for the two mandatory frame time backoff. By opting for the BiMMAC mode, only control packets are transmitted in the control channel, negotiating available data transmission channels.
# Design Patterns and Electric Vehicle Charging Software
## 3 Design Patterns and EV Charging Software
### Cloud Design Patterns
#### 3.2.1 Publisher/Subscriber Pattern  
The publisher/subscriber design pattern impacts the resilience of a system because it allows better communication over the internet in cloud-based and distributed systems, between their components when events occur.  
The message must be sent asynchronously, and senders must be decoupled from the consumers. Additionally, a global message queue with a filtering capability must be added. A message is just a data pack, while an event represents a message that informs other constituents about changes or actions that have occurred.  
Publisher/subscriber messaging has the following benefits:  
* Publishers are loosely coupled to subscribers and can remain oblivious to their existence. Publishers and a subscriber can even be temporarily decoupled.
* Provides scalability and improves responsiveness of the sender.
* Improves reliability.
* It ensures a cleaner integration between systems that employ different platforms, programming languages and communication protocols.  
The publish-subscriber model is supported by a series of messaging products and services, among which we recall ServiceBus and EventGrid in Azure.  
Figure 10 exemplifies the publish/subscribe diagram [43]. Multiple subscribers receive messages from the publisher using a message broker as a message queue.  
Applicability:  
For the EV charging station platform, the mobile app user can subscribe, when creating an itinerary, to multiple charging station situated along the way. The user will see in the mobile application or web application if any charging station changed its state and became unavailable with the option to choose another available station close by.
## 4 SMA Polymer Derivatives
The absence of carboxylic acid  
Figure 2: Family of SMA copolymers used to solubilize memteins. The chemical structures of copolymers developed for solubilizing membrane:protein complexes from biological membranes into native nanodiscs include: SMA [20], SMA-EA [34], SMA-ED, SMAd-A [36], SMA-QA [37], SMI [38], SMA-SH [43], zSMA [39], DIBMA [40], Arama [42], and PMA [41]. The non-polar sidechains are colored green. groups provides compatibility with a broad range of solution conditions such as low pH and high polyvalent cation concentrations. An alternative copolymer with diisobutylene and maleic acid residues termed DIBMA lacks the styrene groups. It enables very mild fragmentation of membranes and is well suited to placing labile protein complexes and large memteins in nanodiscs with diameters of 12 to 29 nm [40]. An advantage of DIBMA over SMA polymers is their lack of a styrene-based signal in ultraviolet (UV) and circular dichroism spectra. Similarly, a series of 25 polymethacrylate (PMA) copolymers, based on a distinct backbone with varying ratios of hydrophobic and cationic chains, were synthesized to replace the ultraviolet light-absorbing styrene group [41]. Nanodiscs with 17 nm diameter and 5.5 nm thickness could be formed from DMPC vesicles with members of this series, and their fragmentation of bacterial membranes yielded \(\sim\)100 nm diameter particles based on electron microscopy analysis. The effect of varying the hydrophobic group was further explored with a set of alkyl polyacrylic acid (APAA) copolymers [42]. Both the copolymer concentration and inclusion of butyl, pentyl, or hexyl sidechains influence the diameters of resulting nanoparticles, which range from 7 to 17 nm. All could solubilize DMPC vesicles at pH values over 6.5, with slight differences in divalent cation sensitivities and gel to liquid crystalline phase transition temperatures. A high yield of total membrane proteins comparable to SMA(3:1) could be obtained with the longest, hexyl chain variant. This augments the structure-activity relationship information available for these amphipathic copolymers, paving the way for further optimization of polymers for solubilizing membranes with specific protein targets or proteome-level analysis. The functionalization of SMALPs has been demonstrated by addition of thiol groups to the MA sidechains. In particular, the SMA-SH polymer contains an amino-mercapto-ethane group to which fluorescent dyes or molecular tags can be conjugated [43]. This allows affinity purification and fluorescence detection of the nanodiscs once biotin tags or Alexa Fluor 488 groups are attached here. This offers methods to readily purify and observe nanodiscs containing endogenous proteins.
# Study of Solid Particle Erosion on Helicopter Rotor Blades Surfaces
###### Abstract  
In this study, titanium alloy (Ti-4 Al-1.5 Mn), magnesium alloy (Mg-Li9-A3-Zn3), or aluminum alloy (Al7075-T6) were used to construct the shell model of helicopter rotor blade to study the solid particle erosion of helicopter rotor blades. The erosion resistance of the three materials at different angles of attack (6\({}^{\circ}\), 3\({}^{\circ}\), or 0\({}^{\circ}\)) and particle collision speeds (70, 150, or 220 m/s) was examined using the finite volume method, the discrete phase method, and erosion models. In addition, the leading edge of the helicopter blades was coated with two types of bionic anti-erosion coating layers (V- and VC-type), in an attempt to improve erosion resistance at the angles of attack and particle collision speeds given above. The results showed that Ti-4 Al-1.5 Mn had the best erosion resistance at high speed, followed by Al7075-T6 and Mg-Li9-A3-Zn3. The angle of attack appeared to affect only the surface area of the blade erosion, while the erosion rate was not affected. Finally, the results of this article showed that the V-type bionic coating had better erosion resistance than the VC-type coating at the same impact speeds. The angle of attack did not have a significant effect on the erosion rate of the bionic coating.  
numerical study; solid particle erosion; materials; bionic coating +
Footnote †: journal: Applied Sciences