esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Evaluation
### Experimental Results
#### 4.3.3 Effects of Aspect, Sentiment, and Semantic Features on Rating Prediction  
This experiment explored the effects of aspect, sentiment, and semantic features on the rating prediction method (i.e., ADLRP method), and then compared the results of MAE and RMSE. Based on the experimental results in Section 4.3.2, the number of aspects for the ADLRP method was set as 15. The ADLRP method was the basis for comparing the following four features.  
1. ADLRP (Text): This method only considers aspect vectors for rating predictions.
2. ADLRP (Sem): This method integrates the aspect vectors (i.e., Section 3.2) and aspect semantic vectors (i.e., Section 3.5) for rating prediction.
3. ADLRP (Senti): This method integrates the aspect vectors (i.e., Section 3.2) and aspect sentiment vectors (Section 3.3.2) for rating prediction.
4. ADLRP: This method integrates aspect vectors, sentiment vectors, and semantic vectors, and uses a CNN for rating prediction.  
According to Figure 6, ADLRP (Text) only considers aspect features, and its rating prediction accuracy is the lowest. The prediction results of ADLRP (Sem) are better than those of ADLRP (Senti), which represents that aspect semantic features have a greater influence on the rating prediction method than aspect sentiment features do. The ADLRP method, which considers aspect, sentiment, and semantic features, helps to improve prediction accuracy.  
Figure 5: RMSE value under different aspect numbers.  
Figure 6: Comparison of semantic and sentiment features in consideration of aspect features.  
Figures 7 and 8 show the effects of using text, sentiment, semantic, and sentiment in combination with semantic features on the prediction results with and without considering the aspect features, respectively, for comparing the ADLRP-based method with the DLRP-based method. By setting the number of aspects of the ADLRP method as 1, this method is the deep learning rating prediction method (DLRP). The DLRP method does not consider aspect features, but rather employs sentiment and semantic features for rating prediction.  
Regardless of the features used, it was found that the MAE and RMSE in the ADLRP-based method are better than in the DLRP-based method. Therefore, analyzing the aspect features in reviews helps to improve prediction accuracy. In addition, both the ADLRP and DLRP methods produce the worst prediction results by using only textual features (i.e., ADLRP (Text) and DLRP (Text)), whereas integrating sentiment and semantic features can produce better prediction accuracy. Among the compared methods, the ADLRP method has the best prediction accuracy. Therefore, analyzing the user and product aspects, sentiment, and semantics in the review text can help to better understand user preferences and product evaluation, as well as improve prediction accuracy.
# The Application of Improved YOLO V3 in Multi-Scale Target Detection
## 3 Our Approach
### Improving YOLO V3 with More Scales  
The network of improved YOLO V3 is shown in Figure 5. We add scale 4 (red box) to improve the detection performance of the network.  
As shown in Figure 5, to improve the performance of YOLO V3, we proposed an enhanced object detection algorithm. YOLO V3 uses three scales to detect targets of different size and detects small targets with the feature map downsampled by 8\(\times\). To get more fine-grained features and location information from small targets, we explore to use the feature map downsampled by 4\(\times\) in the original network, because it contains more finer-grained information of small targets. To concatenate the feature map in the earlier layer to the feature map in the deeper layer, upsample the feature map  
\begin{table}
\begin{tabular}{c c c} \hline \(d\) & **Range** & **Anchor Boxes** \\ \hline
2 & \(21.8\leq\sqrt{h\times w}\leq 43.6\) & (21, 22), (32,24), (26, 40), (45, 40) \\
3 & \(43.6\leq\sqrt{h\times w}\leq 87.2\) & (63, 32), (62, 56), (73, 60),(106, 57) \\
4 & \(87.2\leq\sqrt{h\times w}\leq 174.4\) & (131, 118), (92, 116), (200, 130) \\
5 & \(174.4\leq\sqrt{h\times w}\) & (316, 181) \\ \hline \end{tabular}
\end{table}
Table 4: The allocation of clusters on KITTI dataset at each scale.  
Figure 5: The network of improved YOLO V3.  
downsampled by 8\(\times\) and concatenate it with the output of the second residual block in YOLO V3. The feature fusion target detection layer downsampled by 4\(\times\) is established to detect small targets. Three detection scales in the original YOLO V3 are extended to four detection scales. So the improved YOLO V3 has better performance to detect targets of different size.  
In front of the target detection layer of the YOLO V3 network, there are six convolutional layers. Inspired by DSSD [34], to avoid gradient fading and enhance the reuse of the features, we transformed the six convolutional layers into two residual units. This is shown in Figure 6.
## 1 Introduction  
The mechanical-empirical road pavement structure design systems consider the pavement structure as an elastic structure, applying the thin-layer theory or the elastic-layer theory [1].  
In any design procedure, knowledge of the loads on the pavement as well as the contact surface for loading is required. In the beginning, methods based on the Boussinesq equations supposed that the loading force is either concentrated or linearly distributed on the total wheel width. In the wake of pneumatic tyres, the main assumption was a circular contact area with an evenly distributed loading. This assumption has been adopted for a long time, providing simple calculations; moreover, a solution to the partial differential equations of elastic layers for this case only has been elaborated.  
Theoretical approaches have been later supplemented by experimental solutions of the problem; the contact area of rubber tyres was determined by moving wheels of different loads onto a painted film between paper sheets or by spraying sand around wheels. As a result of technological developments, solutions for the direct measurement of contact stresses have appeared, such as the \"Vehicle-Road Surface Pressure Transducer Array\" [2].  
According to current knowledge, stresses in the pavement structure are substantially determined by the value and distribution of contact stresses. Some distress types can be explained by these stresses, such as top-to-bottom cracking [3]. Despite experimental results, the majority of present-day design procedures still consider the contact area of truck twin tyres as two ellipses or even two rectangles, usually substituted by a circular surface of identical area. The distributed loading, i.e., the contact pressure, is supposed to be equal to the inflation pressure of the rubber tyre as a good approximation.  
The tyre-pavement interaction can be successfully analysed by the finite element method at the present level of computing technology, instead of expensive and time-consuming in situ or laboratory measurements. This topic is well documented in the former studies [4; 5; 6; 7].  
The results of computer simulations may indicate some observations that may help with choosing the parameters of simplified models to better represent the realistic circumstances and processes. The motivation behind these kinds of developments is that, sometimes, the reproduction of the results reported in scientific papers via the application of finite element models can be difficult because of the unavailability of certain software components; therefore, the comparison of different tyre models in the literature is not always possible. Consequently, a method that can simply characterise and numerically describe the contact behaviour of complex finite element rubber tyre models may provide a great advantage in the systematization of research results in this topic area.
Sensors_**2011**, _11_, 425-454; doi:10.3390/s110100425  
_Series of_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Self-Configuration and Self-Optimization Process in**  
**Heterogeneous Wireless Networks**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{2,\star}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{3}\)**  
\({}^{1}\) Computer Science Program, Distributed Mobile Computing--Network Security (DMC-NS) Group, Federal University of Santa Catarina--UFSC, Florianopolis, SC, Brazil  
\({}^{2}\) Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), School of Computer Science, Office 431, Universidad Complutense de Madrid (UCM), [PERSON] [PERSON] s/n, 28040 Madrid, Spain  
\({}^{3}\) System and Automation Department, Federal University of Santa Catarina--UFSC, Florianopolis, SC, Brazil  
E-Mails: [EMAIL_ADDRESS] (L.G.); [EMAIL_ADDRESS] (F.B.); [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (E.C.)  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-913-947-638; Fax: +34-913-947-547; Homepage: [[http://gass.ucm.es/en/](http://gass.ucm.es/en/)]([http://gass.ucm.es/en/](http://gass.ucm.es/en/)).  
_Received: 13 November 2010; in revised form: 20 December 2010 / Accepted: 22 December 2010 / Published: 31 December 2010_  
**Abstract:** Self-organization in Wireless Mesh Networks (WMN) is an emergent research area, which is becoming important due to the increasing number of nodes in a network. Consequently, the manual configuration of nodes is either impossible or highly costly. So it is desirable for the nodes to be able to configure themselves. In this paper, we propose an alternative architecture for self-organization of WMN based on Optimized Link State Routing Protocol (OLSR) and the ad hoc on demand distance vector (AODV) routing protocols as well as using the technology of software agents. We argue that the proposed self-optimization and self-configuration modules increase the throughput of network, reduces delay transmission and network load, decreases the traffic of HELLO messages according to network's scalability. By simulation analysis, we conclude that the self-optimization and self-configuration mechanisms can significantly improve the performance of OLSR and AODV protocols in comparison to the baseline protocols analyzed.  
keywords:WMN; self-organization; OLSR and AODV
## 4 Experiment
### Experiments on ICL Compound  
According to the statistics and analysis, the images in the 10 th, 12 th, 25 th, 27 th, 49 th, 56 th, 126 th, 132 nd, 168 th, 169 th and 215 th species of the ICL dataset are compound leaves, where a compound leaf splits several times in the middle to form two or more leaflets.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Accuracy** & **Year** \\ \hline MCC [23] & 73.17 & 2004 TCSVT \\ TAR [12] & 78.25 & 2007 PR \\ IDSC+DP [10] & 81.39 & 2007 TPAMI \\ MARCH [3] & 86.03 & 2015 IS \\ VGG16+FT [54] & 86.92 & 2015 ICLR \\ ResNet50+FT [55] & 94.58 & 2016 CVPR \\ KernelPool [36] & 97.86 & 2022 MTA \\ Ours & **98.67**(\(\pm\)0.16) & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy (%) comparison on ICL dataset with identical settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Methods** & **Accuracy** & **Year** \\ \hline  & SC+DP [9] & 82.04 & 2002 TPAMI \\  & IDSC+DP [10] & 71.60 & 2007 TPAMI \\ Hand & MDM [24] & 65.47 & 2012 TIP \\  & MTCD [13] & 85.12 & 2016 ECAI \\  & MFD [14] & 89.31 & 2019 SPIC \\  & MTD+LBP-HF [27] & 95.64 & 2021 PR \\ \hline  & AlexNet+relu5 [58] & 96.41 & 2012 NIPS \\  & VGG16+relu5\_2 [54] & 98.06 & 2015 ICLR \\  & Dual-Path CNN [19] & 94.58 & 2017 ICIP \\  & Deep Plant [18] & 92.16 & 2017 PR \\ DNN & HGO-CNN [20] & 94.02 & 2018 TIP \\  & MaskCOV [35] & 98.32 & 2021 PR \\  & KernelPool [36] & 99.37 & 2022 MTA \\  & MTD+relu5\_2 [38] & 99.09 & 2023 ESA \\  & Ours & **99.41**(\(\pm\)0.13) & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy (%) comparison on MEW2012 dataset with identical settings.  
Obviously, the compound leaf images bring more challenges to plant recognition. Therefore, [PERSON] et al. [59; 60] collected those leaf images to construct the ICL compound dataset; there are 11 classes and 654 leaves in total; example images are shown in Figure 9. In order to assess the effectiveness of our method on compound leaves, we conduct an experiment on the ICL compound dataset. The training ratio for each class is 70%; the remaining 30% is used for testing. As we can see from the comparison results in Table 7, our method also obtains the amazing accuracy of 100% for all the five repeated experiments, which again corroborates the effectiveness of our method in learning discriminative features for plant leaves.
## 2 Theoretical Background
### Scope of Explainability  
The scope of explainability defines the extent of an explanation produced by some explainable methods. Two recent literature studies on more than 200 scientific articles published on XAI deduced that the scope of explainability can be either _global_ or _local_[8; 9]. With a _global_ scope, the whole inferential technique of a model is made transparent or comprehensible to the user, for example, a decision tree. On the other hand, explanation with a _local_ scope refers to explicitly explaining a single instance of inference to the user, e.g., for decision trees, a single branch can be termed as a local explanation.
## 3 Results and Discussion
### From PDMS Planar Surfaces to Spiral-Shaped Microdevices
Panels (**c,d**): optimization of washes speed after miR adsorption. A total of 600 ng of miR-1246-TAMRA, diluted in human plasma added with lysis buffer, were incubated on microdevices A (Panel (**c**)) or B (Panel (**d**)); then a wash with water is performed at different fluxes. Relative volumes are reported (i.e., referred to each chip volume). affected. Further, in this case, the flow rate is crucial for removing all the unwanted material but preserving all the adsorbed microRNA. Moreover, the volume of water used in the washing step should not exceed the minimum value that is necessary to effectively substitute the channel solution with water, otherwise the equilibrium of microRNA adsorbed on surfaces could be shifted toward its release. Two flux rates were applied either to Microdevice A (Figure 5c) or to Microdevice B (Figure 5d) after the microRNA-capture step. Both fluxes gave similar results, removing the excess of microRNA solution up to a plateau. However, the flux of 1 \(\mu\)L/min for 10 min was selected as the most efficient in removing the unbound material, while better preserving the adsorbed microRNA. An injection time of 10 min, i.e., about one washing volume, was judged compatible with the overall duration of the assay, while guaranteeing not to remove the adsorbed microRNA. A scheme visualizing the whole process of microRNA insertion and washing is reported in Figure S3. In conclusion, moving from planar surfaces to microdevices, the silanization time was reduced to 5 min, the flow rate to insert microRNA was set to 10 \(\mu\)L/min, while the washing flow rate was reduced to 1 \(\mu\)L/min.
## 5 Conclusions  
A dynamic load-sharing model of a dual-input, dual-output, concentric gear split-torque system was established, which considered the translational and torsional vibration of gears. The correlation between the meshing stiffness and load was considered. Then, a time-varying meshing stiffness agent model was developed based on a feedforward neural network. According to the power flow characteristics of this system, two meshing states, pinion driving and face gear driving, were considered. The load-sharing characteristic analysis and optimization were completed in this study. The conclusions drawn from the study can be summarized as follows:  
(1) The results showed that reducing the support stiffness of Input Gear 1 decreases the load-sharing coefficients. Increasing the support stiffness of the tail gear increases the load-sharing capacity. Furthermore, the support stiffness of the idlers, Input Gear 2, and upper and lower face gears have different effects on \(\kappa_{in1}\), \(\kappa_{in2}\), and \(\kappa_{id}\) and need to be comprehensively considered when adjusting the load-sharing performance.  
(2) Based on the marine predator optimization algorithm, the load-sharing optimization model of the concentric gear split-torque transmission system was established. The optimization results showed that the load-sharing factor decreased from 1.375, 1.388, and 1.046 to 1.049, 1.052, and 1.000, respectively. Moreover, the optimization effect was verified by comparing the gear pair meshing force before and after optimization by the finite element method.  
(3) The analysis results showed that the load distribution of the gears can be more balanced by the support structure and the design of the support bearings. Therefore, the optimized support stiffness coefficients have practical importance for the design of each support bearing, as well as the support structure.  
Figure 23: Meshing force of each gear pair by finite element method: (**a**) Input Gear 1; (**b**) Input Gear 2; (**c**) idlers.  
**Author Contributions:** Conceptualization, F.G. and R.Z.; methodology, F.G.; investigation, Q.W.; resources, R.Z.; data curation, F.G.; writing--original draft preparation, F.G.; writing--review and editing, R.Z. and Q.W.; visualization, F.G.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the National Key R&D Program of China, grant number 2019 YFB2004700, and the National Natural Science Foundation of China, grant numbers 51775265 and 51775277.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Heat transfer enhancement in heat exchangers results in thermal efficiency and energy saving. In double-pipe heat exchangers (DPHEs), extended or augmented fins in the annulus of the two concentric pipes, i.e., at the outer surface of the inner pipe, are used to extend the surface of contact for enhancing heat transfer. In this article, an innovative diamond-shaped design of extended fins is proposed for DPHEs. This type of fin is considered for the first time in the design of DPHEs. The triangular-shaped and rectangular-shaped fin designs of DPHE, available in the literature, can be recovered as special cases of the proposed design. An \(h\)-adaptive finite element method is employed for the solution of the governing equations. The results are computed for various performance measures against the emerging parameters. The results dictate that the optimal configurations of the diamond-shaped fins in the DPHE for an enhanced heat transfer are recommended as follows: If around 4-6, 8-12, or 16-32 fins are to be placed in the DPHE, then the height of the fins should be 20%, 80%, or 100%, respectively, of the annulus width. If frictional loss of heat is also to be considered, then for fin-heights of 20-80% and 100% of the annulus width, the placement of 4 and 8 diamond-shaped fins, respectively, is recommended for an enhanced heat transfer. These recommendations are for the radii ratio (i.e., the ratio of the inner pipe radius to that of the outer pipe) of 0.25. The recommendations are be modified if the radii ratio is altered.  
double-pipe; heat exchanger; heat transfer; diamond-shaped fin; fined annulus +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Muscle Damage and Liver Function Markers  
One of the primary concerns regarding the RT protocol used in the present study was ensuring its safety for muscle health and liver function, particularly given the highly vulnerable and functionally limited population enrolled in this study. Although we observed increases in the activities of LDH, AST, and ALT, all the values remained within the normal range. These enzymatic changes likely reflect acute, exercise-induced responses rather than tissue injury or liver dysfunction. The transient elevations in the liver function markers suggest acute physiological adaptations to exercise rather than pathological processes, as further supported by the stable levels of CK activity, a more specific marker of muscle damage [39; 40]. Other studies in the literature have shown that exercise may induce transient elevations in liver transaminase levels [41; 42; 43]. Therefore, we evaluated the impact of low-volume RT intervention on liver function parameters, including AST and ALT enzyme activities, as well as the AST/ALT ratio (Figure 3). This ratio is an indicator of liver function, but it can also be affected by muscle damage, as AST is found in both liver and muscle tissues, while ALT is primarily liver-specific. Given that AST is expressed in skeletal muscle, the AST/ALT ratio serves as a useful tool for distinguishing between the liver and muscular sources of enzyme fluctuations [30; 31]. Notably, the AST/ALT ratio remained unchanged pre- and post-intervention and between the 1 SET and CON groups post-intervention, reinforcing the interpretation that the observed increases are physiologi  
Figure 6: Dot plots showing effects of intervention on serum malondialdehydedehyde (MDA) levels. Significance values are reported for within-group comparisons. Effect sizes (\(g\)) only apply to normally distributed variables.  
cal adaptations rather than indicators of tissue injury or liver dysfunction. Furthermore, the previous studies have shown that RT can induce transient increases in muscle enzymes, which reflect acute metabolic responses and muscle adaptation [44]. Further investigation into the underlying mechanisms is needed to better understand the clinical relevance of these enzyme fluctuations.
## 4 Results and Discussion
### Compilation and Comparison of Results
**(g)** Comparison of accuracy between our proposed BGM-GAN and related models, standard GAN, CNN, and DBN, when trained for 210 epochs across a varying number of features. To sum up, the novelty of this work lies in addressing the critical challenge of early detection of ransomware attacks, which is often compromised due to insufficient and \begin{table}
\begin{tabular}{c c} \hline \hline
**\# of Epochs** & **Total Training Time (Seconds)** \\ \hline
30 & 70 s \\ \hline
60 & 132 s \\ \hline
90 & 189 s \\ \hline
120 & 253 s \\ \hline
150 & 307 s \\ \hline
180 & 369 s \\ \hline
210 & 431 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training time for various number of epochs. Figure 4: (**a**) Comparison of the averaged recall (true positive rate) between our proposed BGM-GAN and related models, standard GAN, CNN, and DBN, w across a varying number of epochs. (**b**) Comparison of the averaged false positive rate (FPR) between our proposed BGM-GAN and related models, standard GAN, CNN, and DBN, when trained for 60 epochs across a varying number of epochs. quickly outdated attack patterns. Unlike previous studies that focused on selecting a subset of features that quickly become obsolete due to the dynamic nature of ransomware, this study explores a fundamentally different approach using GAN. The paper introduces an enhanced loss function for the GAN's generator by incorporating a gradual up-weighting coefficient, aimed at minimizing the distance between the real and artificially generated data distributions. This technique helps our GAN model to create synthetic ransomware samples that are close to the real attack patterns, overcoming the lack of real attack patterns during the pre-encryption phase. This enhanced loss function is incorporated into a GAN-based data augmentation module for generating more representative attack patterns. Notably this technique improves our measurements across the board. Our model contributes to a more robust and timely ransomware detection outcome, filling a gap in existing cybersecurity literature and best practice.