esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Results of SBR Latex Mortar Strength Tests
### Tensile Strength of SBR Latex Mortar  
Figure 7 presents the tensile strength of mortar specimens mixed with SBR latex at various mixture ratios, measured at different ages (7 and 28 days). This comparison helps to explain how the incorporation of SBR latex affects the tensile strength development over time.  
It was found that the mortar mixed with 5% SBR latex exhibited higher initial tensile strength compared to mortars with other mixture ratios. However, at 28 days, the tensile strength of the 5% SBR latex mortar was lower compared to the mortars mixed with 7% and 9% SBR latex. The mortar mixed with 9% SBR latex was measured to have lower tensile strength than the mortar mixed with 7% SBR latex due to excessive flexibility.  
Figure 7b shows the tensile strength of the mortar specimens mixed with Tg \(-\)16 \({}^{\circ}\)C SBR latex to the mixture ratio according to ages of 7 and 28 days. Based on the mortar mixed with Tg \(-\)16 \({}^{\circ}\)C SBR latex, as seen with the result of compressive strength, it was shown that the higher the mixture ratio, the higher the tensile strength. The tensile strength data from Figure 7 suggest that a 7% mixture of SBR latex provides the best balance for enhancing the tensile strength in mortar. Higher ratios, such as 9%, may reduce the tensile strength due to increased flexibility. This information is crucial for optimizing mortar formulations to achieve the desired mechanical properties.  
To compare the tensile strength of Tg 4 \({}^{\circ}\)C SBR latex to Tg \(-\)16 \({}^{\circ}\)C SBR latex at a 5% mixture ratio, the results from the pre-test indicate that the tensile strength of Tg \(-\)16 \({}^{\circ}\)C SBR latex is lower by about 0.9 MPa at 7 days and by about 0.8 MPa at 28 days compared to Tg 4 \({}^{\circ}\)C SBR latex. This comparison is illustrated in Figure 8.  
The comparison highlights that Tg 4 \({}^{\circ}\)C SBR latex at a 5% mixture ratio provides better tensile strength, both initially and over time, compared to Tg \(-\)16 \({}^{\circ}\)C SBR latex. The difference in tensile strength, though not drastic, indicates that the higher glass transition temperature latex (Tg 4 \({}^{\circ}\)C) is more effective in enhancing the tensile properties of the mortar.  
Figure 7: Tensile strength of mortar specimen mixed with different mixture ratios of SBR latex with two glass transition temperatures.
## 4 Experiments
### Comparison with Baseline Techniques  
Four publicly available datasets BAVED, EMO-DB, SAVEE and, EMOVO are used to train the proposed transformer model for an effective SER system. The results demonstrated the effectiveness of the proposed transformer model in predicting emotions from audio signals. To achieve higher accuracy for SER models the proposed model was trained with highly effective features. To avoid overfitting the raw data was augmented with a data augmentation technique and then fed to the transformer model for training. Many techniques are applied for SER based on deep learning and machine learning algorithms taking high-level features to train them are not enough competent to predict higher accuracy, reduce computational time and lower model size [44]. As a way to address these problems a comparatively lighter transformer model was proposed that takes 273 acoustic features as input and can learn higher concentrations of input representations. The proposed model depicts higher accuracy for all datasets used to train the model. When the problem of accuracy was resolved then computational time was reduced by a lighter transformer model. To prove that the proposed transformer model provides robust results in solving the accuracy, computational cost, and model size problems the results were compared with the standard techniques used for speech emotion detection. The performance of the proposed model was compared to the baseline techniques using the same datasets for training and testing the model to evaluate its significance and strength. Table 6 comprehensively summarizes the comparative analysis of baseline techniques and the proposed model. The results in the table evidence the robustness and significance of the proposed SER model compared to baseline methods. However, the proposed model loses its performance a bit for some instances of individual emotions. For example, the proposed model obtained 98% accuracy for neutral emotion, but in [38] it is 100% in the EMO-DB SER model. On the other hand, the proposed model achieved the highest overall prediction accuracy compared to baseline methods with prediction accuracy. The proposed model is suitable for real-life applications because of its lightweight structure, low computational cost, and ability to predict accuracy for individual instances. To conclude, it can be stated that the proposed SER model is more reliable, generic, and accurate.
## 2 Experimental Section
### Apparatus  
The MA-SDE device consisted of an MAE system (Shanghai Xintuo Analytical Instruments Co.; Ltd, Shanghai, China) connected to a SDE unit (Shanghai Chaoyue Laboratory Instrument Works Co.; Shanghai, China). A schematic representation of the MA-SDE apparatus used in the current study is shown in Figure 1. The MAE apparatus was operated at an irradiation frequency of 2450 MHz with the output power in the range of 10-800 W and the effect mode of microwave was set as non-pulsed microwave heating. The dimensions of the interior cavity of the oven were 430 \(\times\) 512 \(\times\) 510 mm, making the oven large enough to accommodate a 500-mL flask.  
The MA-SDE device is made up of SDE unit and a MAE system. The microwave irradiation can disrupt plant cell walls of plant sample and the phytoconstituents are released into the solvent environment easily [26; 27]. Thus, microwave-assisted extraction process can improve extraction of polysaccharides and essential oils. The water in the sample flask and extraction organic solvents are heated to boil, respectively. Then, the water steam with volatile compounds and organic solvents are condensed in the condenser. The volatile compounds are extracted from water into organic solvents in the extraction cell. The organic solvents containing volatile compounds and water will return to each flask through each return arm. Finally, the essential oils are obtained from organic solvent and polysaccharides are obtained from water. In other words, polysaccharides and essential oils are simultaneous extracted from _T. chinensis_ var. _mairei_ by the MA-SDE device.  
Figure 1: Schematic representation of the MA-SDE device: (1) air-vent; (2) condenser; (3) vacuum tube; (4) water outlet; (5) extraction cell; (6) valve cock; (7) flask I; (8) organic solvents; (9) water bath; (10) power switch; (11) vacuum tube; (12) water inlet; (13) video display; (14) program display; (15) control panel; (16) power switch; (17) microwave generator; (18) flask II; (19) water; and (20) plant materials.
# Pgds-YOLOv8s: An Improved YOLOv8s Model for Object Detection in Fisheye Images
## 4 Results
### Ablation Experiment
#### 4.2.2 Performance Comparison of Improved Models on the VOC-360 Dataset  
Figure 11 shows the test results of some fisheye images in the VOC-360 test set. The advantages of the MPGD and APGD modules are demonstrated in Figure 11b. The MPGD module is good at extracting effective features in fisheye images, while the APGD module better solves the problems of missed and wrong detection. Introducing the MPGD and APGD modules into the YOLOv8s model significantly improves the detection accuracy of distorted and discontinuous objects in fisheye images. At the same time, the leakage detection is effectively improved, and the detection frame region is more accurate. In addition, as shown in Figure 11c, the YOLOv8s model, with the introduction of the C2 fs and SPPFSE modules, which extracts informative channel features and focuses on regions of interest, effectively improves the detection accuracy of objects in fisheye images. Finally, as shown in Figure 11d, the PGDS-YOLOv8s module introduces the proposed modules, which perform very well in the overall performance. Its detection frames are more accurate and detailed, resulting in excellent recognition of objects of different scales in fisheye images.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **Lr** & **Params (M)** & **GFLOPs** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** & **FPS** \\ \hline YOLOv8s & 0.01 & 11.14 & 28.7 & 73.0 & 52.6 & 59.4 & 35.3 & 286 \\ YOLOv8s & 0.005 & 11.14 & 28.7 & 75.2 & 58.0 & 63.8 (+4.4) & 40.2 (+4.9) & 286 \\ YOLOv8s + MPGD + APGD & 0.005 & 10.79 & 28.5 & 82.6 & 72.0 & 78.7 (+19.3) & 62.4 (+27.1) & 256 \\ YOLOv8s + C2 fs + SPPFSE & 0.005 & 11.14 & 28.7 & 80.8 & 65.5 & 72.8 (+13.4) & 51.4 (+16.1) & 286 \\ PGDS-YOLOv8s & 0.005 & 10.79 & 28.5 & 85.1 & 71.3 & 79.2 (+19.8) & 62.8 (+27.5) & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of improved model results on the VOC-360 dataset.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 3 Optimization Models
### Load Balancing and Link Failure Protection (LB&LFP)  
Problem LB&LFP is used to determine load-balanced routing and backup paths planning to protect any single link failure. The decision variables have the same meaning as those used in Problem LB&NFP. The additional input constants used in the problem are set S and \(L_{s}\). In most communication networks, if two nodes, \(x\), and \(y\) are neighbor nodes to each other, there are two directional links, (\(x\),\(y\)) and (\(y\),\(x\)), between them. When a link failure event disconnects the two neighbor nodes x and y, we assume both directional links (\(x\),\(y\)) and (\(y\),\(x\)) cannot transmit packets anymore. Therefore, for every link failure event, two-directional links are failed in the formulation. Our model can be modified for the network if each failure event only results in a single directional link failure. Since the techniques used in the formulation are the same as those used in Problem LB&NFP, we directly include the formulation below and skip detailed expression for each constraint.  
Input Constants:  
\(\underline{S}\): failure set;  
\(\underline{L_{s}}\): the two-directional links that become failed when event s happens.  
Problem (LB&LFP):  
\(\underline{min}\;u\)  
Subject to:  
\[\sum\limits_{l\in L^{st}}x_{dl}-\sum\limits_{l\in L^{in}}x_{dl} =r_{nd} \forall\;d\in N,\;n\in N\backslash\{d\} \tag{19}\] \[\sum\limits_{l\in L^{st}_{dl}}x_{dl} =0 \forall\;d\in N\] (20) \[\sum\limits_{l\in L^{st}_{dl}}x_{dl} =\sum\limits_{n\in N}r_{nd} \forall\;d\in N\] (21) \[\sum\limits_{d\in N}x_{dl} \leq\;u\;C_{l} \forall\;l\in L\] (22) \[x_{dl} \leq\;y_{dl}C_{l} \forall\;l\in L,s\in S,\;d\in N \tag{23}\]\[p_{t(l)d}^{s} -p_{h(l)d}^{s} \geq M(y_{dl}-1)+1 \forall\,l\in L,s\in S,\,d\in N \tag{24}\] \[p_{t(l)d}^{s} -p_{h(l)d}^{s} \geq M(z_{dl}^{s}-1)+1 \forall\,l\in L,s\in N,\,d\in N\] (25) \[p_{dd}^{s} =0 \forall\,d\in N,\,s\in S\] (26) \[\sum_{\begin{subarray}{c}l\in L^{\text{out}}\backslash L\\ x_{dl}\end{subarray}}(y_{dl}\ +z_{dl}^{s})\geq 1 \forall\,n\in N_{s},\,d\in N\backslash\{s\},\,s\in S\] (27) \[x_{dl} \geq 0 \forall\,d\in N,\,l\in L\] (28) \[y_{dl} \in\,\{0\,,\,1\} \forall\,d\in N,\,l\in L\] (29) \[z_{dl}^{s} \in\,\{0\,,\,1\} \forall\,l\in L^{out}_{n}\backslash(n,s),\,n\in N_{s}\,,s\in S\] (30) \[p_{nd}^{s} \geq 0 \forall\,n\in N,\,s\in S,\,d\in N \tag{31}\]
## 5 Experimental Results and Analysis
### Experimental Results and Discussion on Algorithms Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(4.32\times 10^{6}\) & \(1.67\times 10^{6}\) & \(2.51\times 10^{6}\) & \(2.62\times 10^{6}\) & \(7.17\times 10^{5}\) \\ MOPSO & \(6.00\times 10^{6}\) & \(3.27\times 10^{6}\) & \(4.46\times 10^{6}\) & \(4.37\times 10^{6}\) & \(8.59\times 10^{5}\) \\ MOAO & \(7.95\times 10^{6}\) & \(2.57\times 10^{6}\) & \(5.00\times 10^{6}\) & \(4.84\times 10^{6}\) & \(1.24\times 10^{6}\) \\ MOHBA & \(4.00\times 10^{6}\) & \(1.59\times 10^{6}\) & \(2.66\times 10^{6}\) & \(2.77\times 10^{6}\) & \(6.98\times 10^{5}\) \\ MOWOA & \(7.60\times 10^{6}\) & \(2.97\times 10^{6}\) & \(4.20\times 10^{6}\) & \(4.58\times 10^{6}\) & \(1.22\times 10^{6}\) \\ MOGWO & \(6.53\times 10^{6}\) & \(2.31\times 10^{6}\) & \(4.28\times 10^{6}\) & \(4.32\times 10^{6}\) & \(1.31\times 10^{6}\) \\ MOFECO & \(1.01\times 10^{7}\) & \(5.57\times 10^{6}\) & \(8.54\times 10^{6}\) & \(8.11\times 10^{6}\) & \(1.15\times 10^{6}\) \\ LSMOFECO & \(3.64\times 10^{6}\) & \(1.44\times 10^{6}\) & \(2.45\times 10^{6}\) & \(2.64\times 10^{6}\) & \(6.51\times 10^{5}\) \\ MOFECO-MS & \(4.91\times 10^{6}\) & \(2.31\times 10^{6}\) & \(3.27\times 10^{6}\) & \(3.37\times 10^{6}\) & \(7.97\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Statistical results of PD indicator of the eight comparison algorithms for _eil_51_\(n500\). \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(2.02\times 10^{7}\) & \(3.59\times 10^{6}\) & \(1.19\times 10^{7}\) & \(1.22\times 10^{7}\) & \(3.67\times 10^{6}\) \\ MOPSO & \(2.28\times 10^{7}\) & \(1.24\times 10^{7}\) & \(2.12\times 10^{7}\) & \(1.99\times 10^{7}\) & \(2.90\times 10^{6}\) \\ MOAO & \(3.89\times 10^{7}\) & \(1.57\times 10^{7}\) & \(2.19\times 10^{7}\) & \(2.28\times 10^{7}\) & \(5.62\times 10^{6}\) \\ MOHBA & \(2.95\times 10^{7}\) & \(1.50\times 10^{7}\) & \(2.02\times 10^{7}\) & \(2.10\times 10^{7}\) & \(4.46\times 10^{6}\) \\ MOWOA & \(2.84\times 10^{7}\) & \(1.05\times 10^{7}\) & \(2.29\times 10^{7}\) & \(2.16\times 10^{7}\) & \(4.80\times 10^{6}\) \\ MOGWO & \(2.80\times 10^{7}\) & \(1.30\times 10^{7}\) & \(1.83\times 10^{7}\) & \(1.95\times 10^{7}\) & \(4.64\times 10^{6}\) \\ MOFECO & \(2.95\times 10^{7}\) & \(1.66\times 10^{7}\) & \(2.09\times 10^{7}\) & \(2.22\times 10^{7}\) & \(4.00\times 10^{6}\) \\ LSMOFECO & \(1.53\times 10^{7}\) & \(4.00\times 10^{6}\) & \(9.63\times 10^{6}\) & \(1.00\times 10^{7}\) & \(2.80\times 10^{6}\) \\ MOFECO-MS & \(1.18\times 10^{7}\) & \(3.92\times 10^{6}\) & \(8.56\times 10^{6}\) & \(8.40\times 10^{6}\) & \(2.26\times 10^{6}\) \\ \hline \end{tabular}
\end{table} 
Table 26: Statistical results of HV indicator of the eight comparison algorithms for \(ch150\_n745\).
## 2 Results and Discussion
### TFME Procedure Optimization  
To yield the optimal conditions for TFME extraction, the most important factors affecting its effectiveness were determined, including the selection of adsorbent material and an appropriate mixture of desorption solvents, time of adsorption and desorption. Parameters with the highest efficiency and the best precision (i.e., the lowest RSD) were chosen at each step of the optimization process.
## 2 Materials and Methods
### Sampling and Analysis
#### 2.3.5. Gut Microbiome Analysis  
The samples of _C. gariepinus_ midgut content (\(n=9\) fish/group) were squeezed into Eppendorf tubes under sterile conditions and stored frozen at \(-80\)\({}^{\circ}\)C. The samples were analyzed by means of a metataxonomic approach in order to highlight any differences in microbiota composition. Total DNA was extracted from midgut samples of _C. gariepinus_ and used for 165 rRNA gene analysis (V3-V4 regions) with amplification performed by primers and procedures previously described [36]. Illumina metagenomic guidelines were followed for the PCR products and library pool. The Illumina MiSeq platform with V2 chemistry was used to obtain 250 bp paired-end reads. The raw files generated (_fastq_) were imported in QIIME 2, 2022.11 version software [37]. The _Cutapter_ function was used to remove primer sequences and DADA2 algorithms [38] to denoise the reads by using the q2-data2 plugin in QIIME 2. The taxonomy classification was performed by means of the QIIME2 feature-classifier against the _SILVA_ database, excluding the ASVs with less than five read counts in at least two samples to increase the confidence of the sequence reads.
# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 5 Discussion
Finally, the role of social media platforms in shaping public perceptions about EVs was also an important finding. [PERSON] et al. (2018) emphasise the impact of social media in influencing public perceptions and forming public opinion [71]. In this study, posts on the social media platform X rapidly spread and shaped social perceptions on the acceptance of EVs. Social media has become a platform that influences the social acceptance of EVs by organising users' perceptions according to social norms. This study makes valuable contributions to the literature analysing the social acceptance of EVs. The analysis of users' comments on social media reveals that various factors such as environmental concerns, technological barriers, social norms, and political views shape the acceptance of EVs. The findings suggest that the acceptance of EVs is based not only on individual preferences but also on social and cultural factors. The originality of the study lies in the use of social media data as an effective source to dynamically analyse social perceptions. In future studies, a more detailed examination of the views of users in different geographical regions on EVs will contribute more to the literature in this field. The higher proportion of neutral tweets in the Turkish dataset may reflect the relatively nascent stage of EV adoption in Turkey. According to reports by Turkey's Ministry of Energyand Natural Resources, public charging infrastructure is still developing, and government incentives are more limited compared to the EU or US. Moreover, national consumer surveys indicate a lack of firsthand experience with EVs, which may explain the prevalence of observational or neutral commentary rather than strong opinions. In contrast, English tweets--often from markets with higher EV penetration--show more polarised views due to direct user experience and policy engagement. The findings provide actionable insights for EV manufacturers and policymakers. For instance, improving public communication strategies and responding directly to social media concerns may help build trust. Marketers could emphasise economic and environmental benefits more strongly in regions where neutral sentiment is high. Policymakers can use social media signals to identify infrastructure gaps and prioritise investments in charging stations. One limitation of this study is the imbalance between the English and Turkish datasets. The relatively small number of Turkish tweets may not fully capture the diversity of public opinion in Turkey, potentially leading to higher variance and reduced generalisability. While oversampling techniques were considered, we prioritised using authentic, non-augmented data to maintain the natural distribution of user sentiments. Future research should aim to expand the Turkish dataset to ensure more robust cross-linguistic comparisons.
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 4 TFSS Algorithm
### System Model  
A traffic flow splitting model for multiple types of vehicle services is provided in Figure 3. The offloading model is carried by the in-vehicle gateway and is responsible for transmitting or receiving the communication data stream. According to the V2X communication scenario of the Internet of Vehicles, the vehicle communication network includes various types of service requirements, such as V2V, V2E, V2 MT, and V2R. For each type of in-vehicle service, the data stream is sequentially arranged into \(n\) queues on the transmitting end to wait for the offload processing. Nodes A, B, and C are equivalent to the data stream interfaces of each type of service accessing the uplink network. Therefore, if the in-vehicle gateway knows the available bandwidth of each uplink access network and the characteristics of the in-vehicle service that needs to be transmitted, it is easier to control the traffic ratio flowing into each communication link, thereby resulting in traffic offload in the in-vehicle heterogeneous network scenario. The data flow for each type of in-vehicle service conforms to the Poisson distribution (the parameter of the Poisson distribution is \(\lambda\)). Figure 2: V2X-based service architecture. Suppose the total service data arrival rate in the network is \(R\) (bps), the arriving data packet obeys the Poisson distribution with the parameter \(\lambda\), and the length of the data packet is \(L_{\text{p}}\), then \(\lambda=R/L_{\text{p}}\). The data rate relationship between the data traffic in the total in-vehicle network and the data traffic on each subnet can be expressed as: \(R=\sum\limits_{i=1}^{n}R_{i}\). Because \(\lambda_{\text{i}}=R_{i}/L_{\text{p}}\), the equation can also be written as \(\lambda=\sum\limits_{i=1}^{n}\lambda_{i}\). Suppose \(\mu_{\text{i}}\) is the \(i^{\text{th}}\) wireless communication technology (RAT), that is, the service rate of the \(i^{\text{th}}\) radio link, and that the number of available network resources of the \(i^{\text{th}}\) RAT is \(R_{\text{a}}^{i}\) (bps), then \(\mu_{\text{i}}=R_{a}^{i}/L_{\text{p}}\). Because this strategy uses delay and cost as joint optimization goals, we need to find the expression of the delay.
## 4 Experiments and Results
### Comparison with Other Methods
#### 4.2.2 In Terms of Runtime  
The main purpose of this algorithm is to reduce the parameters and reduce the computational complexity without affecting the image restoration quality as much as possible. As can be seen from the above figure, the method used in this article has significant image quality in terms of reconstruction quality, but it is sometimes lower than VDSR and DRCN when calculating the average PSNR and SSIM values of each data set. The computation time is lower than other algorithms.  
Table 2 lists the average running time of each algorithm on different data sets. As can be seen from this table, the running time of this algorithm is significantly lower than other algorithms, but, on some data sets, the running time of this algorithm is higher than SRCNN, mainly because SRCNN has fewer convolutional layers. The computational complexity is relatively low, and, for a deep convolutional neural network similar to this article, the running time will be two to four times lower.