esa-sceva/satcom-chunk-collection huggingface dataset:
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Carthamus tinctorius L
#### 3.1.3 Antibacterial Effect  
Antimicrobial substances extracted from various plants can be a source of initial concepts for novel drug compounds that can improve human health. The literature data indicate the potential antibacterial properties of safflower extracts [24]. Ethanol extract of _Carthamus tinctorius_ L. with a concentration of 1.0 mg/mL effectively inhibited the growth of _E. coli_ isolated from laying hens [25]. An inhibitory effect against pathogenic bacteria _S. aureus_ and _S. Typhi_ was observed in the case of safflower petal methanolic extracts with a concentration of 240 mg/mL. The authors also noted that the inhibitory effect differed significantly between various safflower cultivars which were used to prepare the extracts [19]. _C. tinctorius_ ethanolic leaf extracts exhibited varying degrees of antimicrobial effects against harmful pathogens such as _S. aureus_, _P. aeruginosa_, and _E.coli_. The concentration of 100 mg/mL was distinctly more effective than the lower concentrations of ethanolic leaf extracts (respectively, 20, 40, 60, and 80 mg /mL) [26].
## 3 Results and Discussion
### Results of Ultimate Analysis and Proximate Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Biomass Samples**} & \multicolumn{6}{c}{**Ultimate Analysis (wt.\%)**} & \multicolumn{6}{c}{**Proximate Analysis (wt.\%)**} \\ \cline{2-10}  & **C** & **O** & **H** & **N** & **S** & **VM** & **FC** & **Ash** & **M** \\ \hline Ph & 46.03 & 42.43 & 6.01 & 0.178 & 0.167 & 66.64 & 12.09 & 21.27 & 6.01 \\ Po & 46.43 & 45.29 & 6.10 & 0.140 & 0.122 & 72.89 & 14.80 & 12.31 & 9.42 \\ Pi & 47.90 & 45.29 & 6.33 & 0.045 & 0.048 & 82.89 & 14.45 & 2.66 & 9.14 \\ P1 & 46.23 & 44.10 & 6.12 & 0.038 & 0.052 & 72.29 & 11.22 & 16.49 & 8.10 \\ P2 & 46.76 & 45.17 & 6.20 & 0.110 & 0.105 & 79.82 & 15.35 & 4.83 & 9.31 \\ P3 & 47.44 & 45.73 & 6.34 & 0.028 & 0.031 & 83.99 & 13.07 & 2.94 & 8.02 \\
**Average** & **46.80** & **44.67** & **6.18** & **0.090** & **0.088** & **76.42** & **13.50** & **10.08** & **8.33** \\
**SD 1** & **0.73** & **1.22** & **0.13** & **0.062** & **0.052** & **6.87** & **1.64** & **7.80** & **1.29** \\ \hline \hline \end{tabular}  
* SD: standard deviation.  
\end{table} 
Table 1: _Cont._  
Figure 2: [PERSON] diagram of biomass samples with the atom ratios of H/C and O/C from the ultimate analysis results.
# Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications
## 3 Performance Parameters
### Linearity  
Under certain conditions, the percentage of maximum deviations between the calibration curve and the fitting line with full-scale output is called the linearity. Linearity is an important performance parameter because the linearity of sensors makes the signals accurate and is very beneficial in the follow-up processing of the output signal. Nonlinearity causes a large deviation between the output signal and the measurement parameter, which results in a complicated calibration process and signal distortion. Carbon black nanoparticle/PDMS composite-based strain sensors typically exhibit linearity and little deviation upon both stretching and releasing with low sensitivity (GFs \(\approx\) 5.5) (Figure 2a). EGaIn/PDMS-embedded structure based strain sensors typically exhibit linearity and stretchability during thousands of cycles of stretching/releasing but still with low sensitivity (GFs \(\approx\) 3.2) (Figure 2c) [67]. Graphene-on-polymer strain sensors and nanoscale crack sensors show excellent sensitivity (GFs \(>\) 103) but nonlinearity and low stretchability (\(\epsilon<\) 10%) (Figure 2b,d) [73; 78]. The main reason for nonlinearity in the resistance response curve is the non-uniform deformation of the sensing element layer on PDMS substrates. A strain sensor responds to the applied strain with nonlinearity when it undergoes heterogeneous deformation. Highly sensitive strain sensors typically respond to the applied strain with nonlinearity and low stretchability because they are always non-uniform deformation. Therefore, developing strain sensors with high linearity, stretchability, and sensitivity is still a grand challenge.  
Figure 2: **(a)** Resistance change ratios obtained from three identical conductive PDMS strain sensors with a maximum strain of 10% after stabilization. Reprinted with permission from [75], Copyright Elsevier, 2014; **(b)** The graphene-on-polymer strain sensors show high gauge factors under electrical resistance of graphene woven fabrics, which increases exponentially with tensile strain. Reprinted with permission from [73], Copyright Springer Nature, 2012); **(c)** Strain sensing capability of stretchable EGaIn/PDMS composite strain sensor with cycles of stretching/releasing. Reprinted with permission from with [67], Copyright AIP Publishing, 2016; **(d)** Resistance of sensors based on nanoscale crack junctions at low loading-unloading rate compared with the theoretical fit. Reprinted with permission from [78], Copyright Springer Nature, 2014.
# Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification
## 5 Conclusions and Future  
We designed a novel aspect-based sentiment analysis model (ATGCN). Through research, we pointed out the deficiencies of the existing methods. Firstly, the current methods do not well analyze the role of words and long-distance dependence in sentences. Secondly, there is insufficient interaction and fusion of aspect and context information. In order to solve the first problem, we used graph convolutional networks to obtain syntactic information and long-distance dependencies within sentences. In order to effectively realize the interaction between aspect terms and context, we used multi-headed attention to achieve the first interaction to obtain interactive information, then bidirectional attention is used to further obtain effective interactive information. Finally, we conducted experiments on three open standard datasets. The experimental results showed that ATGCN is effective for aspect-based sentiment classification.  
Although our proposed approach has great potential in the field of aspect-based sentiment classification, it still has shortcomings. We ignored the importance of commonsense knowledge for the aspect-based sentiment classification. In future work, we will use graph convolutional networks to apply commonsense knowledge to our tasks. In fact, this is a challenging research hotspot, and we figure that commonsense knowledge will play a key role in the field of sentiment classification in the near future.  
Conceptualization, J.L. and P.L.; Funding acquisition, P.L.; Methodology, J.L.; Project administration, Z.Z.; Resources, P.L. and Z.Z. Writing--original draft, J.L.; Writing--review and editing, X.L., G.X.; Visualization, J.L.; Investigation, J.L.; Validation, J.L. All authors have read and agreed to the published version of the manuscript.  
Our work was supported by the Science Foundation of the Ministry of Education of China (no. 14Y)[S60042), the National Social Science Fund (19 BYY076), and the Shandong Provincial Social science Planning Project (no. 19 BJCJ51/18 CXWJ01/18 BYJ04/16 CFXJ18).  
Figure 5: The case predicted by IAN and ATGCN.
## 3 Proposed Model and Experiment Setup  
Emergency responders require real-time imagery and information in cases of disasters that threatens lives. These events involve large number of workers carrying out rescue missions for injured or missing people at rugged or isolated areas, which require more effort, cost, and risk. Figure 2 shows a proposed model architecture consisting of two main segments, a space segment, and a ground segment, to serve the purpose of carrying out search-and-rescue operations to detect casualties or lost people within rugged natural and/or manmade disaster zones using a drone integrated with 5G telecommunication payloads and a thermal camera. The space segment contains a drone with communication payloads such as a thermal camera, a GPS sensor, and telecommunication payloads with 5G MIMO antennae. The ground segment consists of fixed or mobile users, as well as a ground control center (GCC) that acts as the focal point between two segments and hosts gateways to external networks.  
In the literature, well-known search techniques that allow UAVs to divide a target area for search-and-rescue operations include strips, lines, or random techniques [34; 36]. In our study, we used the strips technique, as Figure 3 shows, since it is the most suitable approach to achieving our research goal. To illustrate, when a drone conducts a search mission it makes sense to divide a target area into strips so that they can go over the divided areas one-by-one, performing only one turn at the end, which in turn  
Figure 2: Proposed structure of a drone for use in search-and-rescue operations.  
first is coverage of each raw target area; the second is energy savings in comparison to other search techniques.
## 4 Out-of-Plane Flexural Behavior of SC Composite Beams with Different Shear Connection Ratios  
The influence of the shear connection ratio on the out-of-plane flexural behavior of 3 m long SC composite beams was investigated. In this paper, six SC composite beam finite element models with 0.5, 0.7, 1.0, 1.3, 1.7 and 2.0 shear connection ratio were developed. The low shear connection ratio includes 0.5 and 0.7, the middle shear connection ratio includes 1.0 and 1.3, and the high shear connection ratio includes 1.7 and 2.0. The space of tie-bars in six SC composite beams was constant, 210 mm, and the shear capacities of tie-bars in each SC composite beam were calculated. According to the research on the shear behavior of tie-bars under cyclic loading carried out by the authors, the mean shear stiffness could be assumed as 40 kN/mm [12]. The force displacement of tie-bars was assumed to be linear. The parameters of tie-bars in six SC composite beams are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\gamma\) & \(\mathbf{V_{u}}\) **(kN)** & \(\mathbf{s_{u}}\) **(mm)** \\ \hline
0.5 & 25.2 & 0.63 \\
0.7 & 35.4 & 0.88 \\
1.0 & 50.1 & 1.25 \\
1.3 & 65.2 & 1.63 \\
1.7 & 84.9 & 2.13 \\
2.0 & 100.3 & 2.51 \\ \hline \hline \end{tabular} Note: \(V_{u}\) is the shear capacity of tie-bars, \(\mathbf{s_{u}}\) is the slip corresponding to \(V_{u}\).  
\end{table}
Table 2: Parameters of tie-bars in SC composite beam under monotonic loadings.  
Figure 8: Distribution of interfacial shear slip of SC composite beams in the condition that concrete was uncracked. (**a**) Theoretical model; (**b**) simulation results.  
The sensitivity analysis of the shear span versus depth ratio, number, and stiffness of tie-bars on the interfacial shear distribution of SC composite beams is investigated in Section 4.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Client Scheduling  
During the phase of client scheduling, the computing capability and the quality of the local data are considered simultaneously. In this work, data quality is measured using the entropy weight method. Specifically, assume that each client \(i\) has \(K_{i}\) attributes. We randomly select \(M\) samples from the local dataset \(\mathcal{D}_{i}\) of each client \(i\) and calculate  
\[Q_{i}=\frac{1}{M}\sum_{k=1}^{K_{i}}\sum_{m=1}^{M}w_{k}^{(i)}y_{km}^{(i)} \tag{13}\]  
where \(w_{k}^{(i)}=\frac{1-e_{k}^{(i)}}{\sum_{k=1}^{K_{i}}1-e_{k}^{(i)}}\) is the entropy weight of the \(k\)-th data attribute of client \(i\), \(y_{km}^{i}\) is the value after data attribute standardization, and the information entropy of each data attribute [16] can be expressed as follows:  
\[e_{k}^{(i)}=-\frac{1}{\ln m}\sum_{n=1}^{m}\frac{y_{kl}^{(i)}}{\sum_{n=1}^{m}y_ {kl}^{(i)}}\ln\left(\frac{y_{kl}^{(i)}}{\sum_{n=1}^{m}y_{kl}^{(i)}}\right). \tag{14}\]  
The client scheduling metric is based on its computing capability \(P_{i}\) and the indicator of local data quality \(Q_{i}\). It is assumed that the \(P_{i}\) and \(Q_{i}\) of each client are stable during the training process and the clients with a higher value of \(\phi_{i}=\gamma P_{i}+(1-\gamma)Q_{i}\) have priority to be selected to participate in the FL training. The steps of selecting \(x_{j}\) clients from the range of edge server \(j\) are as follows:  
Step 1: The value of \(|N_{selected}|\) updates. If \(|N_{selected}|>k\), select \(x_{j}^{best}\) clients from \(N_{selected}\) within the coverage of edge server \(j\) and with the highest value of \(\phi\).  
Step 2: If \(x_{j}^{best}<x_{j}\), \(x_{j}-x_{j}^{best}\) clients are randomly selected from \(I-N_{selected}\) that are within the range of the edge server \(j\). The value of \(\phi\) of these clients is calculated, and the clients are then added to \(N_{selected}\).  
Step 3: Finally, all clients in \(N_{selected}\) participate in the next round of training, and the cloud server distributes the latest model parameters to these clients.  
The time complexity of this algorithm for calculating the entropy weight of each client is related to the product of the number of data samples chosen and also the number of clients selected, which is \(\mathcal{O}(M|N_{selected}|)\).
# Discussion on the Design of Sprayed Eco-Protection for Near-Slope Roads Along Multi-Level Slopes
## 6 Cross-Sectional Design of Near-Slope Roads
### Dimensional Design of Turnaround Areas  
The size of a turnaround area must comprehensively account for vehicle dimensions and required turning width. Specifically, the longitudinal direction of the turnaround area defines its length, while the transverse direction determines its width. To ensure smooth turning and parking of vehicles, the length of the turnaround area should exceed the maximum design vehicle length by 10-15%. This additional length provides sufficient space to avoid jamming or collisions during maneuvers. For width calculations, Equations (2) and (3) are applied, incorporating factors such as vehicle wheelbase, track width, and turning radius to ensure the width meets turning requirements (The formulas are derived from AASHTO). This design approach enhances both the operational efficiency and safety of turnaround areas.  
\[R=L/2(1-cos\sigma) \tag{2}\]  
\[D=w+2R(1-cos\sigma) \tag{3}\]  
where \(R\) is turning radius (m); \(\sigma\) is maximum steering angle (\({}^{\circ}\)); \(w\) is maximum vehicle width (m); \(L\) is turnaround area length (m); \(D\) is turnaround area width (m).
# Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models
## 3 Proposed Method
### Feature Extraction  
Wavelets are capable of analyzing signals in various time and frequency domains from time series, biological signals, audio signals, and images and are capable of multi-resolution analysis, allowing the extraction of fine features. In this study, wavelet scattering and continuous wavelet transforms were used based on wavelet analysis technology to extract features.
## 2 AM Categories and Common Metal AM Processes
### Directed Energy Deposition (DED)
In addition to the above, the BPT includes various techniques such as Laser Metal Deposition (LMD), Laser Solid Forming (LSF), Direct Laser Metal Deposition (DLMD), Direct Laser Deposition (DLD), Direct Light Fabrication (DLF), Laser Deposition Welding (LDW), Powder Fusion Welding (PFW), Directed Light Fabrication (DLF), Electron Beam Direct Manufacturing (EBDM), and Direct Metal Tooling (DMT) depending on the application or specifications of the method [55; 56; 57; 58]. Among these technologies, LMD has received attention from researchers and industries in the last few years [59]. In this process, a metallic powder, which is carried by an inert gas, is melted using a high-power laser beam. Within this process, the material is sprayed through a nozzle that has a coaxial laser beam passing through it [60]. This process has been used for manufacturing high precision near net shape products and repairing high complex shapes [61]. This is due to the fact that the resulting heat affected zone of this process is small, which results in low distortion. LC is another process of the DED process is in which a laser heat source is applied to deposit a thin layer of preplaced materials on a substrate, which is moved using a controlled system [62]. According to [PERSON] et al. [63], LC uses the following feeding methods: a one-step process that includes paste feeding, powered injection and wire feeding, and a two-step process, which is called replaced powder. During the one-step process, the cladding materials are deposited into the substrate and a shallow molten pool is created concurrently; whereas during the two-step process, the cladding material, which is in the form of powder, is deposited on the substrate's surface and then the Lister beam is irradiated [64]. The LC technology has the capability to be attached to a 5-axis robotic arm [65]. This gives the opportunity to add metal coats onto curved surfaces, and to repair damaged parts, particularly for the aerospace applications (see Section 2.2). Another thermal-based DED process technology is hybrid system (HS), wherein a DED process is combined with subtractive processes such as milling in a single machine to provide tighter tolerances and multi-tasking capabilities [66].
## 4 Discussion
### The Relationship Between Perceived Source Elevation and the Vertical Localisation Threshold  
The results showed that the difference in perceived elevation between the main layer only and phantom image conditions differed for each source. For example, the speech source that was presented as a vertically oriented phantom image with 0 dB ICLD was perceived as being significantly higher than that for main layer only presentation for both of the ICTDs tested. On the other hand, judgments for the oboe source were generally consistent, irrelevant of how the source was presented to subjects. The remaining sources were more inconsistently affected. The increases in median perceived elevation for the conga, guitar and quartet sources were significant when the ICTD was 1 ms. Despite this, for the 0 ms ICTD the difference was not significant, even though the median perceived elevation notably increased in each case. These results would seemingly indicate that vertical interchannel crosstalk has a source dependent effect with respect to the perceived migration of the main channel signal from the position of the main layer.  
With respect to the oboe source, an explanation of the results obtained is thus offered. The literature suggests that narrowband stimuli incident from the median plane are localised on the basis of frequency, with increases in frequency corresponding to increases in perceived elevation (i.e., pitch-height effect) [11; 12; 13]. The spectrum for the oboe source was notably narrow, with its predominant energy focused around 1 to 2 kHz, as can be seen from Figure 2. According both to the literature and to the results presented in the present study, band-limited stimuli in this frequency range are localised at a similar vertical position, regardless of which loudspeaker layer presented the source, for both vertical stereophonic [13] and vertical quadraphonic [24] loudspeaker arrangements. Therefore, it might be that the pitch-height effect determined localisation judgments for the oboe, with no relation to the difference in energy in the 7-9 kHz region between the phantom image and main layer only conditions. It might be further suggested that the suppression of vertical interchannel crosstalk is predominantly needed for broadband sources, with less relevance to the sources that are both narrowband and absent in high frequency energy.
## 4 Results and Discussion
### Binder Testing Results and Discussion
#### 4.1.2 Glover-Rowe Parameter  
Figure 6 shows the G-R parameter for different binders. Two boundary curves that indicate the crack onset (G-R = 180 kPa) and significant cracking (G-R = 600 kPa) are added to the Black space diagram. The Black space diagram combines the complex modulus and phase angle to assess both the stiffness and relaxation capability of asphalt materials. With the increase of G* and decrease of phase angle, the asphalt mixtures on field are expected to be more prone to cracking [35]. Therefore, as the G-R values move further up and to the left, the cracking potential of asphalt binders increases. It can be seen in Figure 6 that all the G-R values are lower than the crack onset threshold. However, the cracking potential is expected to be higher for the 4%EMAA-modified binder, as the corresponding G-R parameter becomes closer to the thresholds. The rankings of cracking resistance can be in the following order: EMAA/SBR compound-modified asphalt mixtures > SBR-modified asphalt mixtures > virgin asphalt mixtures > EMAA-modified asphalt mixtures.  
Figure 5: Complex modulus master curves of different binders.