esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Performance Evaluation and Results
### Results and Discussion
#### 7.3.2 Complex Segment 2  
To provide more realistic scenarios, we obtained the prediction results of the DMLRF model for several complex segments located adjacent. This will allow drivers to prepare for their trip in advance by determining the CS that has the highest delay time and exit the highway before entering that CS. RMSE and MAE are close to zero for all the algorithms, but the \(R^{2}\) and adjusted \(R^{2}\) for Segments A and B are lower than 60%. However, the traffic patterns for these two segments are mostly normal. The accuracy of the DMLRF model improves when traffic flow patterns are abnormal and achieves good results under normal conditions. Figure 9 shows the comparison of DMLRF model performance in terms of predicting a week of traffic flow with actual SRI values of CS2. The actual SRI values increased when the predicted average speed values decreased. Table 3 compares the performance of the proposed model for Segment D of Complex Segment 2 with that of other baseline regression models. Overall, the proposed model outperformed the other models in all evaluation metrics and for all segments. In Segment D, the DMLRF model achieved the highest accuracy, which is 76%. LogR and KNN models performance were very low  
Figure 8: DMLRF predicted average speed compared to actual SRI values for Segments A, B, C, and D of complex segment 1 (tested on week with more abnormal traffic flow cases).  
Figure 7: DMLRF Predicted average speed compared to actual average speed for Segments A, B, C, and D of complex Segment 1 (tested on one week with more abnormal traffic flow cases).  
when compared to the DMLRF model for all segments. LR presented better performance than LogR and KNN in terms of all evaluation metrics for most of the segments.
## 4 Conclusions  
The recombination parameters of crystalline silicon solar cells are dependent on the excess carrier concentration. In order to improve the collection efficiency of photo-generated carriers, and to model  
Figure 10: Variation in fill factor (FF) with the \(J_{01}\)/\(J_{02}\) ratio for PERC and Al-BSF silicon solar cells under different injection levels.  
Figure 9: Effect of the \(J_{01}\)/\(J_{02}\) ratio on short-circuit current density (\(J_{\kappa}\)) for PERC and Al-BSF silicon solar cells under different injection levels.  
exactly the performance of solar cells in the field, it is very important to investigate the recombination parameters of solar cells under different injection levels. In this work, the recombination parameters of different regions were investigated under different injection levels for Al-BSF and PERC silicon solar cells. As expected, the recombination parameter \(J_{01}\) of the diffusion region and the recombination parameter \(J_{02}\) of the depletion region for PERC solar cells are smaller than those of Al-BSF solar cells at the same injection level. An interesting finding is that the recombination parameter \(J_{01}\) of Al-BSF solar cells increases quickly with the decreasing injection level compared with PERC silicon solar cells. It can be concluded that the output performance of Al-BSF solar cells decreases rapidly at low injection levels compared with PERC solar cells. Finally, the \(J_{01}\)/\(J_{02}\) ratio of the Al-BSF solar cell and the \(J_{01}\)/\(J_{02}\) ratio of the PERC solar cell were investigated under different injection levels. The effects of the \(J_{01}\)/\(J_{02}\) ratio on open-circuit voltage, short-circuit current density, and fill factor were also analyzed for Al-BSF and PERC solar cells under different injection levels. The results show that the recombination behavior of different cell regions will change with the injection level, and the output performance of Al-BSF and PERC solar cells is affected by the ratio of \(J_{01}\)/\(J_{02}\) under different injection levels. The conclusions obtained in this paper not only clarify the relationship between the recombination parameters and injection levels under non-standard test conditions, but also help to improve solar cell processes and accurately model daily energy production for crystalline silicon solar module energy rating purposes.  
Conceptualization, H.Y.; Methodology, H.Y.; Investigation, Q.B.; writing--original draft preparation, Q.B.; formal analysis, X.C.; software, X.C.; writing--review and editing, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFB1500700.  
The authors declare no conflicts of interest.
## 3 Methods for Assessing the Aesthetic Quality of Images  
The current research on image aesthetic quality assessment can be summarized into five tasks, namely aesthetic classification, aesthetic scoring, aesthetic distribution, aesthetic factors and aesthetic description. Aesthetic classification refers to the judgement of an image as \"good\" and \"bad\" or as \"high\" and \"low\" in aesthetic quality. The aesthetic score is a judgement of the aesthetic-quality score of an image, expressed as a continuous value. The aesthetic distribution is a histogram giving the distribution of aesthetic-quality scores for an image. The aesthetic factor is the evaluation of the image in terms of color scheme, composition, balance, depth of field and many other aspects. An aesthetic description is a description of an image using linguistic comments on aesthetics. The mainstream technology currently used for image aesthetic evaluation are deep neural networks, whose performance far exceeds that of traditional hand-designed aesthetic features.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l l l} \hline  & \multicolumn{2}{c}{**Compounds**} & \multicolumn{1}{c}{**RT**} & \multicolumn{1}{c}{**LRI**} & \multicolumn{1}{c}{**IUPAC name**} & \multicolumn{1}{c}{**Odour**} \\ \hline
1) & a-Guurjune * & 35.54 & 1550 & (1 aR,4R,4 aR,7b5-1),1,4,7-tetramethyl-1a,2,3,4,4a,5,6,7b-octahydro-1H-cyclopropq[e]azulene & balsamic, woody \\
2) & a-Cedrene * & 36.60 & 1568 & (3R-(3a,3 ab,7b,8 aa))-2,3,4,7,8,8a-hexahydro-3,6,8,8-tetramethyl-1H-3a,7-methanoazulene & woody, cedar sweet fresh \\
3) & a-Aromadendrene & 41.85 & 1658 & (1 aR,75,7b5-1),1,7-trimethyl-4-methylidene-2,3,4a,5,6,7,7a,7b-octahydro-1 aH- & woody \\ cyclopropq[e]azulene & & & & & & \\ \cline{3-5}
4) & (E)-\textgreater{}9-Famesene * & 42.36 & 1671 & (6E)-7,11-dimethyl-3-methylidene0 edoca-1,6,10-triene & woody, citrus, sweet \\
5) & D Germencrene & 42.67 & 1674 & (5I,2,6Z)-isopropyl-1-methyl-5-methylencyelocedoca-1,6-diene & weak fruity, apple like \\
6) & Valencene * & 43.12 & 1680 & (4a,-dimethyl)-3-propyl-1-2-z-yl,3,4,5,6,7-hexahydro-1H-naphthlene & citrus, green, woody \\  & \(\gamma\)-Gurjune & 43.30 & 1684 & (1R,4R,7R),1-4-dimethyl-7-propyl-1-z-2-yl,2,3,3,4,5,6,7-octahydroazulene & musty \\
8) & (E)-\textgreater{}9-Bisabolene & 44.03 & 1697 & (4R)-1-methyl-4-(6-methyltheta-1,5-dien-2-yl)cyclohexene & balsamic, woody, spicy \\
9) & \(\delta\)-Cadinene & 44.89 & 1711 & (1S,8A)-4,7-dimethyl-1-(propan-2-yl)-1,2,3,5,6,8a-hexahronophthalene & woody, weak, medicinal \\
10) & \(\beta\)-Selinene & 45.26 & 1718 & 3S,4 aR,8a-8)-8-methyl-5-methylidene-3-propyl-1-en-2-yl-1,2,3,4,4a,6,7,8- & herbal \\  & \multicolumn{2}{c}{octahyraphothane} & \multicolumn{1}{c}{octahyraphothane} & \multicolumn{1}{c}{octahyraphothane} & \multicolumn{1}{c}{} & \\
11) & a-Muroulene & 45.62 & 1721 & (1S,4 aS,8 aR)-1-isopropyl-4,7-dimethyl-1,2,4a,5,6,8a-hexahornophthalene & woody, floral, herbal \\
12) & a-Selinene & 45.68 & 1725 & 5,8a-dimerally-3-prop-1-en-2-yl-2,3,4,4a,7,8-hexahydro-1H-naphthlene & amber \\
13) & (Z)-\textgreater{}9-Bisabolene & 48.12 & 1768 & - methyl-4-(6-methyltheta-2,5-dien-2-yl)cyclohexene & balsamic, spicy \\
14) & (E)-Nerolidid * & 61.84 & 2024 & (6E)-3,7,11-trimethyldodeca-1,6,10-trien-3-ol & floral, green, citrus, woody \\
15) & Viridifflorolol & 68.49 & 2155 & (1 aR,4S,7R,7 aS)-1,1,4,7-tetramethyl-2,3,4a,5,6,7,7a,7b-octahydro-1 aH- & sweet, green \\  & \multicolumn{2}{c}{cyclopropq[e]azulene-4-ol} & \multicolumn{1}{c}{cyclopropq[e]azulene-4-ol} & \multicolumn{1}{c}{} & \\
16) & \(\gamma\)-Eudesmol & 69.10 & 2167 & 2-{(2R,4 aR)-4a,8-dimethyl-2,3,4,5,6,7-hexahydro-1H-naphthlene-2-yl}propan-2-ol & rose, apple, green, citrus \\  & \multicolumn{2}{c}{* standards available.} \\ \hline \end{tabular}
\end{table} 
Table 2: Retention Time (RT), Linear Retention Indexes (LRI) and odour for each sesquiterpene identified in red wine samples.
## 3 Analytical Tuning of Series PIDA Controller
### Illustrative Examples of PIDA Control
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(IAE_{1}\) & \(TV_{1}(y_{1})\) & \(TV_{1}(u_{1})\) & \(IAE_{2}\) & \(TV_{1}(y_{2})\) & \(TV_{1}(u_{2})\) \\ \hline MRDP & 23.2146 & 0.2691 & 0.0570 & 23.2778 & 0.3081 & 0.2865 \\ \hline APR & 27.7230 & 1 \(\times 10^{-20}\) & 0.0056 & 27.58388 & 1 \(\times 10^{-12}\) & 0.1924 \\ \hline PPM & 14.3636 & 1 \(\times 10^{-20}\) & 1 \(\times 10^{-20}\) & 14.3527 & 5 \(\times 10^{-13}\) & 2 \(\times 10^{-16}\) \\ \hline AW & 23.6241 & 0.2648 & 0.0551 & 23.6981 & 0.3084 & 0.2886 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance measures \(IAE\), \(TV_{1}(y)\) and \(TV_{1}(u)\) corresponding to the disturbance steps \(y_{1}=y(t_{1}),u_{1}=u(t_{1}),d_{11}=1,t_{1}\in[0,30)\) and \(y_{2}=y(t_{2}),u_{2}=u(t_{2}),d_{2}=0,t_{2}\in[30,60)\). Figure 7: IPDT process \(\dot{y}(t)=u(t-1)+d_{i}\) and constrained series PIDA controller with MRDP tuning (24), modified approximative tuning (33), PPM tuning (40) and constrained parallel PIDA controller modified by conditional integration according to [14]: disturbance step \(d_{i}=1\) at \(t=0\) and \(d_{i}=0\) at \(t=30\); \(T_{e}=0.8\); \(n=4;P_{n}(s)=(1+T_{f}s)^{4}\); \(T_{f}=T_{e}/4\); \(K_{sp}=K_{s}=1\); \(U_{max}=0.1\); \(U_{min}=-1.1\). number of monotonic segments. It is worth noting that excellent closed-loop results were obtained with the unmodified controller structure, which does not require anti-windup modifications used in parallel PIDA controllers. Moreover, the series controller structure directly provides the reconstructed disturbance signal.
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 4 In-Band Sensor Network Requirements
### Communications Channel  
The final critical factor in \(\overline{\text{DVO}}\) deployment is the implementation of a reliable, real-time communication channel between system components. Reliable communications are critical to overall system effectiveness, and to savings realized through the use of \(\overline{\text{DVO}}\). Intermittent communications certainly result in missed opportunities for power savings (inefficiency) and may also produce periodic supply voltage violations and brown outs in locations with highly variable load profiles. The deployment of a robust, grid-resident communication channel is the only way to assure reliable communications and synchronization between key \(\overline{\text{DVO}}\) components. In addition, a cost effective infrastructure deployment benefits greatly from sensors and communications that can support advanced applications, which complement and extend system capabilities beyond \(\overline{\text{DVO}}\). Savings from \(\overline{\text{DVO}}\) as a standalone application are significant. Adding other applications such as AMR/AMI, Fault Isolation, Grid Mapping, and EV management enhance the value in a complete smart grid deployment.
## 4 Experimental Results Analysis  
To evaluate the proposed algorithm's performance in optimizing the terminal trajectory of anti-ship missiles, a comparative analysis has been conducted involving the following seven optimization algorithms: PSO [15], GA [17], ACO [34], NSGA-III [30], MOEA/D [31], MOPSO [32], and the enhanced MOPSO-h. All algorithms follow the parameter configurations of the original references without any modifications, and the experimental setup has the same initial conditions. The optimization criteria encompass the following six key performance metrics: mean miss distance (MMD), extra time percentage (ETP), terminal landing speed (TLS), terminal landing angle (TLA), peak surge altitude (PSA), and computational time cost (TC). Specifically, PSA denotes the maximum altitude required for a missile's attitude adjustment during the terminal phase. To avoid interception, the smaller the PSA and the larger the TLA and TLS, the better. The calculation method for ETP is as follows:  
\[ETP=\left(\frac{V_{\max}t_{\text{end}}}{d_{0}}-1\right)\times 100\%. \tag{18}\]  
The particle populations of all algorithms are randomly initialized, and the average performance of each algorithm is statistically analyzed through 100 Monte Carlo simulations.
# Total Least-Squares Iterative Closest Point Algorithm Based on Lie Algebra
## 2 ICP Algorithm Based on Lie Algebra
### Iterative Closest Points  
We use ICP to compute a rigid transformation matrix between two frames. We measure points' coordinates from two frames, and their correspondences are known. Measurements are assumed without data, and we can obtain the equation:  
\[\overline{X}_{i}{}^{\prime}=R\overline{X}_{i}+t\overline{X}_{i}{}^{\prime}=R \overline{X}_{i}+t, \tag{1}\]  
where \(\overline{X}_{i}\) is a 3D point from the target model without noise, and \(\overline{X}_{i}{}^{\prime}\) is a 3D point of the source model without noise. \(\overline{X}_{i}=X_{i}+\Delta X_{i}\), where \(X_{i}\) is the measurement of the 3D point from the target model, and \(\Delta X_{i}\) is the measurement error in the target model. \(\overline{X}_{i}{}^{\prime}=X_{i}{}^{\prime}+\Delta X_{i}{}^{\prime}\), where \(X_{i}{}^{\prime}\) is the measurement of the 3D point from the source model, and \(\Delta X_{i}{}^{\prime}\) is the measurement error in the source model. The aim of ICP is to estimate the rotation matrix R and t using measurements \(\{X_{i}{}^{\prime}\}\) and \(\{X_{i}\}\). The least-squares (LS) method thinks that \(\Delta X_{i}{}^{\prime}=0\), so the optimization equation is:  
\[\underset{R,t}{min}\sum_{i=1}^{n}\Delta X_{i}{}^{T}\Delta X_{i}{}^{\prime}= \underset{R,t}{min}\sum_{i=1}^{n}\ (RX_{i}+t-X_{i}{}^{\prime})^{T}(RX_{i}+t-X_{i}{}^{\prime}), \tag{2}\]where \(n\) is the total number of measurements from the target model and source model. We can see from Equation (2) that the LS approach only minimizes residual error in the target model. TLS considers the error from both the target model and source model, and the optimization equation is:  
\[\underset{R,t}{min}\sum_{i=1}^{n}\Delta{X_{i}}^{\prime T}\Delta{X_{i}}^{\prime} +\Delta X_{i}^{T}\Delta X_{i}, \tag{3}\]  
We do not only minimize Equation (3); we must guarantee that \({X_{i}}^{\prime}+\Delta{X_{i}}^{\prime}=R(X_{i}+\Delta X_{i})+t\). We add a Lagrange multiplier to Equation (3) and the optimization equation becomes:  
\[\underset{R,t}{min}\sum_{i=1}^{n}\Delta{X_{i}}^{\prime T}\Delta{X_{i}}^{\prime }+\Delta X_{i}^{T}\Delta X_{i}+\sum_{i=1}^{n}2\lambda_{i}(R(X_{i}+\Delta X_{i })+t-{X_{i}}^{\prime}-\Delta{X_{i}}^{\prime}), \tag{4}\]  
where \(\lambda_{i}\) is a real number. Equation (4) is a constrained nonlinear optimization problem; we use the GHM model to solve this problem.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Evaluation of the Triggering Phase  
In this experiment, we can evaluate the triggering phase to validate if the attacker can trigger flow rules with large timeouts under different timeout strategies.  
For the adaptive timeout mechanism, we use the coarse-state-based method to trigger flow rules. We generate 100 attack flows and make them trigger flow rules with timeout values that are as large as possible. We record the returned timeout values (from the attacker hosts), which serve as the upper limit of the packet intervals in the maintaining phase, and compare them with the actual assigned timeout values (from the SDN controller). Ideally, the triggered flow rules should have timeout values close to the maximum value configured by the timeout assignment module. The results are shown in Figure 10.  
For TST, all attack flows can trigger flow rules with the idle timeout value of 3 s, the maximum timeout setting.The accuracy is 100%, so the results overlapped. For SmartTime and ITM, 98% of the flows can reach maximum timeouts, which is sufficient to launch the next attack phase. Considering that each flow requires up to four table-misses to trigger the maximum timeout, and only 2% of attack flows fail due to false probing responses, the results also demonstrate the high accuracy rate of the RTT-based measurement. These  
Figure 10: Timeout values of triggered flow rules acquired by the attacker and the actual values deployed by the SDN controller.  
Figure 9: The probed initial timeouts of flows in different types of timeout strategies.  
results demonstrate that our attack method can effectively trigger flow rules of specific states. However, if we recall the results presented in Section 3, the typical attack methods in previous studies only triggered flow rules with small timeout values.  
For the required minimal attack traffic rate, generating significantly larger timeout values proves beneficial in reducing the attack overhead. When flow rules are assigned with initial timeout values, each packet interval for the attack flows must not exceed the initial timeout threshold of 1 s. However, the attacker can induce the controller to deploy malicious flow rules with the maximum timeout value in the triggering phase, thus effectively reducing the minimum average attack rate (ranging from 1/3 for TST to 1/11 for ITM) compared to using the default timeout value in the aforementioned scenarios.
# Line-Detection Based on the Sum of Gradient Angle Differences
## 2 Related Work
However, the characterization requires computation of all responses to varying combinations of filter direction, smoothing factor, and degree of anisotropy, which makes it computationally expensive. A multi-step-based line-detection approach was proposed in [24]. This approach uses a combination of three filters--oriented elongated filters, multi-scale top-hat, and K-SVD--to detect cracks in paintings. This approach, too, is computationally expensive because it employs a set of elongated filters and multiple steps. A line-detection method that uses a creaseness measure calculated based on the sum of differences among the gradients of neighboring pixels was proposed in [25]. One of the limitations of this approach is that a simple sum of gradient differences may not be a plausible indicator of the existence of line pixels because each gradient vector loses its directionality when the direct sum of gradient differences is applied. The SGAD approach proposed herein overcomes this limitation by using the value derived by adding the absolute values of the differences of four pair of gradient vector directions, where the vector pair is made by each of a set of four predefined directions. Regarding the evaluation of line-detection methods, [PERSON] et al. [26] compared the performance of several line detectors for medical images based on receiver operating characteristic curves. In addition, [PERSON] et al. [25] evaluated several ridge- and valley-detection methods by using various types of images.
## 2 Materials and Methods
### Determination of Total Antioxidant Capacity  
The total antioxidant capacity was quantified using the FRAP (Ferric-Reducing Antioxidant Power) method, as described by [PERSON] et al. [39], with certain modifications. For each extract, 0.1 mL (extracts from fresh leaves) or 0.3 mL (extracts from dried tortilla powder) was combined with 4 mL of the FRAP solution. The FRAP solution was prepared by combining 2.5 mL of a 10 mM TPTZ solution (2,4,6-tripyridyl-1,3,5-triazine) in 40 mM HCl, 2.5 mL of a 20 mM FeCl\({}_{3}\)\(\times\) 6H\({}_{2}\)O solution and 25 mL of a 0.3 mM acetate buffer at pH 3.6 in a 100 mL flask. The flask was filled to a volume of 100 mL with distilled water, sealed and thoroughly mixed. The extracts were incubated with the FRAP solution in the dark for a period of 30 min. Subsequently, the absorbance was quantified at 593 nm using a UV-1800 spectrophotometer (Shimadzu, Tokyo, Japan). The experiments were conducted in six replicates. A standard curve was constructed using Trolox solutions with concentrations ranging from 40 to 4000 \(\upmu\)M. The total antioxidant capacity of extracts obtained from fresh leaves was expressed as micromodes of Trolox equivalents per gram of fresh weight (\(\umol\) TE/g FW). In the case of tortillas, the total antioxidant capacity was expressed as micromodes of Trolox equivalents per 100 g of dry matter (\(\umol\) TE/100 g DW).
## 4 Experiments
### Performance with Different Backbone Networks  
To further validate our UCDA method, we conducted extensive experiments to implement UCDA using different backbone networks, namely ResNet18, ResNet34, and ResNet50, giving 1% labeled target data on the PCam dataset. The results, as illustrated in Figure 5, affirm that all variants of UCDA with diverse backbones achieve satisfactory classification performance throughout model training.  
Figure 4: (**a,b**) are the respective feature visualizations of the UCDA model on PCam and LC25000 datasets with 1% labeled data.  
Figure 5: Variation in accuracy obtained using different backbone networks on the PCam dataset.  
In Figure 5, the performance of the UCDA model on different backbone networks is analyzed based on the provided medical images. The results indicate that as the complexity of the backbone network increases, the UCDA model achieves higher accuracy on both the PCam and LC25000 datasets. Specifically, when using the ResNet18 backbone network, the UCDA model achieves 81.4% accuracy on the PCam dataset and 82.3% accuracy on the LC25000 dataset. By utilizing the more complex ResNet34 backbone network, the model's accuracy improves to 83.3% on PCam and 86.5% on LC25000. Furthermore, employing the deeper ResNet50 backbone network yields even higher accuracy, with the model achieving 86.8% on PCam and 90.9% on LC25000.  
These findings suggest that selecting an appropriate backbone network is crucial for improving performance on medical image classification. Notably, the results highlight the advantage of deeper network structures in capturing image features and enhancing classification accuracy. While the choice of backbone network significantly influences the model's performance, it is important to consider other factors such as data preprocessing, loss function selection, and training strategies. These additional factors should be carefully optimized to further enhance the overall performance of the UCDA model.
## 2 Objectives and Scope  
Focused on the improvement lines identified in [10], this research work covers two main objectives. Firstly, the aim is to define improvement proposals for the physical layers of PRIME 1.3.6 [11], PRIME 1.4 [12], and G3-PLC [13; 14] technologies, trying to improve their response against disturbances present in the LV grid. Secondly, the goal is to measure and evaluate the performance enhancement of each proposed physical layer modifications following standardized test method and metrics according to European Telecommunications Standards Institute (ETSI) Technical Specification (TS) 103 909 v1.1.1 [16] and using a set of representative channel disturbances.  
The work will be organized as follows: Based on the PLC performance results obtained in [10], several physical layer improvement proposals for PRIME and G3-PLC are identified in Section 3. For each physical layer parameter under test, their original status, their room for improvement and the improvement proposal will be described. Once the improvements are defined, Section 4 introduces the methodology for the performance analysis of these physical layer modification proposals. Following this methodology, unitary results of each physical layer improvement are presented and discussed in Section 5. Section 6 extracts the main highlights of the PLC physical layer improvements under test. This study concludes in Section 7 exploring possibilities for future studies.