esa-sceva/satcom-chunk-collection huggingface dataset:
# Physicochemical Characterization and Antioxidant Activity of Jara Honey Produced in Western Georgia
## 3 Results and Discussion
### Antioxidant Activity by the DPPH Method  
Honey is a well-known abundant source of both enzymatic (glucose oxidase and catalase) and non-enzymatic (L-ascorbic acid, flavonoids, and phenolic acids) antioxidants, which have been shown to have health-promoting anti-oxidative properties [10]. Consuming honey is a successful strategy for boosting total plasma antioxidants and reducing capacity in people [20; 81]. The determination of antioxidant activity in food products by the free radical--DPPH method is a highly adapted method. The method is simple and completed in a short time [82]. 2,2-Diphenyl-1-picrylhydrazyl is a stable compound at room temperature and rapidly recovers in solution in the presence of antioxidants. The violet color of the radical disappears or turns yellow, so the absorption index also decreases at 517 nm [83]. The antioxidant activity of the analyzed sample is calculated by the difference in absorbance values. In particular, the lower the difference index, the higher the antioxidant activity of the analyzed sample. The smaller mass sample achieves 50% inhibition of the DPPH radical [84]. This method is also actively used to determine the antioxidant activity of honey because honey is rich in secondary metabolites--phenolic compounds [3; 56].  
Among Jara's honey samples, those with a total phenol content of 801.61 to 1105.56 mg/kg had a relatively high antioxidant activity, so less of the honey mass was required to inhibit 50% of the radical, namely 75.1 to 90, 68 mg, while 622.34-752.95 mg/kg of the total phenols were inhibited by 94.75-128.08 mg of honey. In the presented samples, the relatively high activity (75.1 mg, 50% inhibition of honey 0.1 mm DPPH) of one particular sample stood out, the honey grown in the Keda municipality--JH-2, which contained a large amount of total phenols (1105.56 mg/kg), total phenolic acids (682.4 mg/kg), and total flavonoids (321.4 mg/kg) (Table 6).  
A decrease in the amount of phenolic compounds (total phenols, total phenolic acids, and total flavonoids) in honey also leads to a decrease in antioxidant activity. The amount of phenolic compounds in Jara honey sample 7 was the lowest (total phenols 622.34 mg/kg, total phenolic acids 388.7 mg/kg, and total flavonoids 151.0 mg/kg), and more honey was required for 50% inhibition of 0.1 mm DPPH (128.08 mg) (Table 6).  
A directly proportional relationship was established between the quantitative content of phenolic compounds, color, and the antioxidant activity of honey (Table 2).
## 3 Results
### Aerobic Capacity and Second Vertilatory Threshold
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & & **Effect:** & **Effect:** & & & \\  & & & & **Group** & **Time** & & & \\
**Variable** & **Group** & **Pre** & **Post** & **F** & **F** & **F** & **Post-Hoc** & **ES** \\  & & & & **p** & **p** & **n\(\mathbf{p^{2}}\)** & & \\ \hline \multirow{2}{*}{\(\mathrm{P}_{\mathrm{max}}\)} & CON & 248 \(\pm\) 27.84 & 242.5 \(\pm\) 28.44 & 11.22 & 12.71 & 9.01 & 0.78 & 0.20 \\  & ST & 235 \(\pm\) 33.86 & 254 \(\pm\) 38.75 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & 0.52 \\  & AIT & 286.87 \(\pm\) 33.69 & 299.53 \(\pm\) 35.84 & 0.35 & 0.23 & 0.3 & 0.05 & 0.36 \\ \hline \multirow{2}{*}{\(\mathrm{rel}\_\mathrm{P}_{\mathrm{max}}\)} & CON & 3.38 \(\pm\) 3.04 & 3.3 \(\pm\) 3.17 & 13.41 & 7.73 & 7.46 & 0.66 & 0.03 \\  & ST & 2.84 \(\pm\) 4.06 & 3.04 \(\pm\) 4.63 & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 & 0.01 & 0.05 \\  & AIT & 3.72 \(\pm\) 3.93 & 3.88 \(\pm\) 4.25 & 0.39 & 0.15 & 0.26 & 0.09 & 0.04 \\ \hline \multirow{2}{*}{\(\mathrm{HR}_{\mathrm{max}}\)} & CON & 186.33 \(\pm\) 10.43 & 184.33 \(\pm\) 10.22 & 0.29 & 1.08 & 1.44 & 0.76 & 0.19 \\  & ST & 184.6 \(\pm\) 9.47 & 186 \(\pm\) 9.77 & 0.99 & 0.97 & 0.25 & 0.93 & 0.15 \\  & AIT & 184.87 \(\pm\) 10.88 & 185.53 \(\pm\) 14.2 & 0.01 & 0.01 & 0.06 & 0.99 & 0.05 \\ \hline \multirow{2}{*}{\(\mathrm{VO}_{\mathrm{2 max}}\)} & CON & 3.16 \(\pm\) 0.41 & 3.14 \(\pm\) 0.41 & 6.69 & 19.51 & 6.63 & 0.99 & 0.05 \\  & ST & 3.06 \(\pm\) 0.55 & 3.34 \(\pm\) 0.56 & 0.002 & \textless{}0.001 & 0.003 & \textless{}0.001 & 0.50 \\  & AIT & 3.58 \(\pm\) 0.36 & 3.78 \(\pm\) 0.37 & 0.24 & 0.32 & 0.24 & 0.02 & 0.55 \\ \hline \multirow{2}{*}{\(\mathrm{VO}_{\mathrm{2 max}}\)} & CON & 43.87 \(\pm\) 7.19 & 43.33 \(\pm\) 6.75 & 7.42 & 11.58 & 5.23 & 0.98 & 0.08 \\  & ST & 37.4 \(\pm\) 8.02 & 40.07 \(\pm\) 7.21 & 0.002 & 0.001 & \textless{}0.001 & 0.02 & 0.35 \\  & AIT & 46.93 \(\pm\) 5.91 & 49.47 \(\pm\) 6.09 & 0.26 & 0.22 & 0.19 & 0.03 & 0.42 \\ \hline \multirow{2}{*}{RER} & CON & 1.18 \(\pm\) 0.09 & 1.14 \(\pm\) 0.09 & 4.88 & 11.98 & 0.04 & 0.32 & 0.44 \\  & ST & 1.19 \(\pm\) 0.08 & 1.16 \(\pm\) 0.09 & 0.01 & 0.001 & 0.96 & 0.49 & 0.38 \\ \cline{1-1}  & AIT & 1.12 \(\pm\) 0.05 & 1.08 \(\pm\) 0.07 & 0.19 & 0.22 & 0.001 & 0.28 & 0.80 \\ \hline \end{tabular}
\end{table} 
Table 4: Effects of strength training on parameters noted at maximal intensity (data are presented as mean \(\pm\) SD). Figure 3: Effect of strength training on maximal power output (\(\mathrm{P}_{\mathrm{max}}\)) (*: \(p<0.005\); NS: non-significantly; CON: control group; ST: strength training; AIT: aerobic interval training; vertical bars indicate a 0.95 confidence interval).
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Discussion  
Different classification frameworks were presented using supervised and un-supervised learning-based approaches in the past. Experiments prove that convolution neural networks are effective for shot classification. We evaluated the performance of different convolution networks, and it has been observed that the proposed AlexNet convolution network performed better in classifying different shots of the sports videos. The use of response normalization rectified linear unit layer and the drop out layer on the training data makes the training much faster. In fact, once validation loss is observed to be zero, the network stops training and is ready for classification. In comparison with different classifiers like KNN++, KNN, SVM, ELM, and standard CNN, we found that the proposed system can train and validate the data by itself. It has also been brought into consideration that enhanced KNN and KNN classifiers perform significantly better than SVM and ELM classifier. The major reason is due to the integral weight factor and the distance parameters of these classifiers.  
Moreover, we also observed during experimentation of the proposed method that the computers embedded with high performance Graphics Processing Unit (GPU) can further increase the speed and accuracy of the proposed framework. For fast training of dataset, AlexNet uses a Graphical Processing Unit (GPU) if its integrated on a system. It requires a parallel computing toolbox with CUDA enabled GPU, otherwise it uses Central Processing Unit (CPU) of a system. Our system is not integrated with GPU, therefore the proposed framework used CPU for training of sports videos.  
It has also been observed that the proposed network stops training once it is confirmed that no validation loss is taking place, i.e., the dataset has been trained to a maximum limit. This is the advantage of our proposed network over standard CNN. Moreover, if Weight Learn Rate Factor (WLRF) and Bias Learn Rate Factor (BLRF) values of fully connected layers are increased, the leaning rate of training rises significantly. In addition, we observed during the experimentation that decreases the size of dataset to 25% increases the learning rate of the data.
## 3 Results and Discussion  
### Ftir  
The FTIR spectra of the partially bio-based prepolymers, without and with lignin incorporation, are depicted in Figure 2. As expected, due to the relatively low incorporation of lignin (4% wt.), the same band patterns were found in the FTIR spectra of both bio-based resins. The carbonyl stretching mode of the ester groups can be associated with the intense bands in the region of 1715 cm\({}^{-1}\)[44], whereas the band in the region of 1250 cm\({}^{-1}\) can be attributed to the C-O-C and C-O stretching of those same groups [45]. The (-C=C-) double bond from fumaric acid is apparent in the band located at 1645 cm\({}^{-1}\)[46]. Regarding the transmittance peaks in the regions of 1600 cm\({}^{-1}\) and 1475 cm\({}^{-1}\), these should highlight the presence of the aromatic rings in the polymer structure. The bands in the region of 2900 cm\({}^{-1}\) can be attributed to the stretching vibration mode of methylene (-CH2) [47]. The band corresponding located in the region of 3500 cm\({}^{-1}\) can be attributed to the vibration of the hydroxyl group (-OH) [48].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Resin**} & \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**Post-Curing**} & \multirow{2}{*}{**Runs**} \\  & & **Temperature (\({}^{\circ}\)C)** & & \\ \hline \multirow{3}{*}{BVE} & BVE-1 & & & 1 \\  & BVE-2 & & & 1 \\  & BVEL-1 & 100 & 4 & 2 \\ \cline{1-1}  & BVEL-2 & & & 2 \\ \cline{1-1}  & BVEL-3 & 110 & 24 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of DMA experiments.
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
Intuitively, different from existing propagation-based recommendation methods, CKGAT can distinguish the importance of different multi-hop ripple sets of either users or items and take full advantage of the topological proximity structures of entities in the multi-hop ripple sets to refine user representations and item representations, thus capturing more accurate potential user interests in items. Specifically, based on the heterogeneous propagation strategy, CKGAT employs the knowledge-aware GAT to extract the topological proximity structures of entities in the multi-hop ripple sets and then learn high-order entity representations, thereby generating refined ripple set embeddings. Furthermore, CKGAT adopts the attention aggregator to perform weighted aggregation on the ripple set embeddings, user/item initial entity set embeddings, as well as the original representations of items, so as to generate accurate user embeddings and item embeddings for top-N recommendations. We used four real-world datasets, including Last.FM, Book-Crossing, MovieLens 20M, and Dianping-Food, to empirically evaluate the performance of CKGAT. Experimental results show that CKGAT overall outperforms three baseline methods and six state-of-the-art propagation-based recommendation methods in terms of recommendation accuracy, and outperforms four representative propagation-based recommendation methods in terms of recommendation diversity. The contributions of this work are summarized as follows:We propose a novel method called collaborative knowledge-aware graph attention network (CKGAT) for top-N recommendation. This method can learn refined ripple set embeddings, thereby generating accurate user embeddings and item embeddings, so as to accurately capture users' potential interests in items. To the best of our knowledge, it is the first method that uses the knowledge-aware graph attention network to learn the refined ripple set embeddings;
2. We use the attention aggregator to generate accurate user embeddings and item embeddings for top-N recommendation;
3. Extensive experiments on four real-world datasets demonstrate the superiority of our CKGAT in comparison with state-of-the-art methods. The remainder of this paper is organized as follows. In Section 2, we briefly summarize the related work focusing on the knowledge graph-based recommendation methods. After the problem formulation in Section 3, we describe in detail our proposed CKGAT method in Section 4. Section 5 presents the experiments. Finally, we conclude our work in Section 6.
## 4 Case Study  
### Generating Typical Scenarios  
The historical data used in this case study are all from wind farms, photovoltaic power stations, and load centers in the same region, ensuring the time series of new energy output and load demand while retaining the correlation between new energy output and load demand. To characterize the uncertainty in renewable energy output and load demand, the typical day curves used in the model are obtained by clustering by season, with each typical day curve representing the predicted renewable energy output and load levels for that season. The wind power output, photovoltaic power output, and load demand curves used in this case study are shown in Figure 3.  
Figure 3: Typical scenarios of wind power, photovoltaic output, and load demand in the IEEE RTS-24 system.
## 3 Results
### Influence of GFCSs and Nano-CaCO\({}_{3}\) Mixture on the Bearing Capacity of Clay Soil  
It was previously found that the optimum ratio for the utilization of nano-CaCO\({}_{3}\) was 0.75% and that of GFCSs was 2.0% at which both materials separately led to significant enhancements in the clay soil media. By using the two materials together, nano-CaCO\({}_{3}\) at the optimum ratio and GFCSs at 0.0%, 0.5%, 1.0%, 1.5% and 2.0% ratios, bearing capacity improvement ratios of up to 1.68, 2.21, 2.43, 2.58 and 2.59 were reached in comparison with the unimproved clay state. Eventually, the optimum ratio for GFCSs was specified to be 1.5% when used with the optimum ratio of 0.75% nano-CaCO\({}_{3}\) together. Thus, the usage of the two materials together enabled a reduction in the optimum utilization ratio of GFCSs (from 2.0% to 1.5%) along with providing significantly higher degrees of bearing capacity improvements (2.58 times) compared with the cases of using the two materials separately. Figures 11 and 12 are presented below to illustrate the obtained results. Figure 11 presents the load-displacement curves corresponding to varying proportions of GFCSs and nano-CaCO\({}_{3}\) additive. Figure 12a illustrates the bearing capacity values obtained from the peak points of the curves in Figure 11. Figure 12b, on the other hand, provides a comparative analysis of these bearing capacities in a non-dimensionalized form to highlight the relative effects of additive content.  
Figure 11: qu-s/D relationship for GFCSs and nano-CaCO\({}_{3}\) mixtures.  
Figure 10: For various ratios of GFCSs: (**a**) bearing capacity values, (**b**) bearing capacity ratios (BCRs).  
In the sequence of the occurring chemical reaction, adherence between the particles of nano-CaCO\({}_{3}\) and the clay soil increases and nano-CaCO\({}_{3}\) serves as a bonding agent. Thus, nano-CaCO\({}_{3}\) provides bonding between the GFCSs additive and clay soil and leads to the occurrence of a more rigid structure. Concordantly, in such hybrid mixtures, it is advised to utilize bonding agents along with the used additive materials to improve the structure even further.
## 4 Analysis of Experimental Results
### Simulation Experiment
#### 4.1.3 Statistical Significance Testing  
In this work, the Wilcoxon statistical test and the Friedman test are performed on the best results of the 50 experiments, and further follow-up verification was conducted using the Holm method to more accurately evaluate the optimization performance of each method on the test function. 1. Wilcoxon test for statistics. If IGEO obtains the best results, it is then evaluated against the eight comparison algorithms using the rank-sum test, which is carried out at the 5% significance level. The best results from 50 separate tests make up the data vectors for comparison.
# Design of Particle Dampers for Laser Powder Bed Fusion
## 2 Review
### Applications and Research Activities  
[PERSON] et al. analysed forced vibrations of particle-damped beams manufactured of Inconel 718 [12; 13; 19; 20; 34; 35] and AISI 316L stainless steel [14; 35]. The influence of the cavity position and number for a clamped beam are investigated. The beams are excited to vibrate by means of a shaker and the vibration response of the beam is measured with a laser vibrometer and that of the shaker with an acceleration sensor. Due to the beam clamping on the shaker, no significant increase in the component damping for the particle-filled beams could be demonstrated, such that the focus of the evaluation was placed on the second and third bending modes. It was shown that the component damping could be increased by up to a factor of six, even with a low cavity volume of only 4% in relation to the beam volume [12; 34]. These results were then used to build a model based on a regression analysis [19; 20; 35].  
[PERSON] et al. investigated the effect of particle damping using laser beam melted walls of 316L by pulse hammer excitation [10]. Here, the damping was characterized as  
Figure 1: Laser powder bed fusion of a particle damped gear, reprinted with permission from ref. [7]. Copyright Elsevier.  
Figure 2: Schematic diagram of an additive manufactured particle damper, according to [7].  
a function of cavity height for the first three modes in a frequency range up to 3000 Hz. It was found that the effect of particle damping increases with increasing cavity height as well as at higher modes. For the particle-damped sample with the largest cavity, a maximum damping ratio of particle-damped to fully-fused of 225 was realized at the third mode [10]. Furthermore, a change in the natural modes of vibration has been shown for the particle-damped components.  
[PERSON] and [PERSON] applied the performance approach of [PERSON] [36] to evaluate the effect of particle damping independent of a main vibrating structure [17]. This made it possible to map only the dynamics of the particles and derive maps of the loss factor for the 316L material. The maps are generated for different primary volumes and void volumes. In addition, the shape of the cavity is varied (cube cuboid, cylinder and sphere). Characteristic damping bands in the investigated frequency range up to 5000 Hz could be created [17]. In further work, [PERSON] and [PERSON] were able to demonstrate that the sound radiation of a laser beam melted anchor plate for an electromagnetic spring-applied brake could be reduced by 7.86 db(c) [16].  
In the field of machining, the use of laser beam melted cutting tool holders made of 1.2709 has already been investigated [18; 37]. The dynamic behaviour of the cutting process could be improved by shifting the natural frequencies and increased damping.
## 3 Results and Discussion  
### Chemical Composition and Characteristics of Silages  
Silage bags were visually monitored once weekly, and no vacuum failure was detected during the whole storage period. No effluent or signs of molding were detected in any bag upon opening. The content of all of the bags had a sweet-vinegar odor and normal greenish color typical for well-fermented forage silages [8], apart from the UM silages, which had a strong, bad odor and were dark-brown colored. As shown in Table 1, the amount of fresh matter in the bags was greater (\(p<0.05\)) in the silages treated with urea and molasses (UM and FAUM) compared with the CON and FA silages, which was attributed to the addition of molasses. The recovery of DM in the silages ranged from 87.7% for CON to 96.8% for FA silage. These values are consistent with the low production of gas observed in all bags and the lack of effluent in any silage. The DM content of all silages was similar to the expected value (30-32%), although it was greater for the silages treated with formic acid (FA and FAUM) than for the CON silage. In agreement with previous reports [21; 22; 23], the silages treated with urea had greater (\(p<0.05\)) pH values than the CON and FA silages, which was due to the alkalizing effect of the NH\({}_{3}\)-N produced by the enzymatic decomposition of the urea. The urease activity in the forage silages has been reported to be optimum at pH close to 8 and to be inhibited by pH values below 5.0 [24]. The high pH values in the UM and FAUM silages possibly contributed to the high urea decomposition, as indicated by the high NH\({}_{3}\)-N concentrations observed for these silages. In fact, the NH\({}_{3}\)-N concentrations were low for CON silage and undetectable for FA silage, whereas both UM and FAUM had greater (\(p<0.05\)) concentrations (Table 1). The NH\({}_{3}\)-N concentration is considered to be an indicator of proteolytic activity during silage fermentation, but in our study, the high NH\({}_{3}\)-N concentrations in UM and FAUM silages were probably due to the decomposition of the urea added. The lower concentrations of isobutyric, valeric, and isovaleric acids observed for UM and FAUM compared with the CON silages would suggest a lower proteolytic activity in the silages treated with urea, as these VFA are produced in the degradation of branched-chain amino acids [25]. The NH\({}_{3}\)-N content for CON and FA silages accounted for 1.21 and 0.0% of the total N, respectively, which was lower than the maximal recommended value of 12% for
## 2 Requirements of 5G-Based V2X Use Cases
### The Security Model  
In regular applications, we mostly handle the security questions by encrypting the message exchanges. This ensures that the content of the messages cannot be eavesdropped on. The currently available V2X message standards, like those of the European Telecommunications Standards Institute (ETSI), have established standardization for the Cooperative Awareness Basic Service and Decentralized Environmental Notification Basic Service. These standards aim to facilitate vehicular safety and enhance traffic efficiency by providing continuous status updates on nearby vehicles and asynchronous event notifications [17; 25; 26]. The specifications for these standards encompass the packet formats for both the Cooperative Awareness Message (CAM) and the Decentralized Environmental Notification Message (DENM) [26]. The CAM and DENM typically operate only with a signature; no encryption is involved in the message profiles. It is because those messages are only broadcasted in the close physical neighborhood of the sender entity, and all the subsequent entities need to process that data. However, the authenticity of the message has to be protected, which is handled by signatures. A public key infrastructure architecture was also defined in order to enhance interoperability. It is also worth mentioning that the traceability of the vehicles also has to be considered. This is handled by so-called pseudonymity protection techniques, which ensure that all unique identifiers of the vehicles are exchanged regularly. Nevertheless, these statements are only valid for standardized message services, which typically enhance the awareness of the traffic participants. Other use cases, especially centralized use cases, like digital twins or remote driving, are not discussed here. In those cases, the security model is different. This paper does not introduce and discuss security standards in a comprehensive manner. It is beyond the scope of this paper, as several other works focus on the security aspects of cyber-physical systems [14; 22].