esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Part of the reason is that it is difficult to observe the cells in real time, or the time of occurring during the stretching experiment is unexpected. As shown in Figure 1b, the cell culture medium may subtly influence the bottom surface of the elastic membrane because of its gravity. When stretched, the target on the membrane moves horizontally out of the field of view of the microscope. Therefore, previous studies have attempted to design a cyclic stretch system that compensates the movement in the horizontal direction [16; 38; 39; 40]. [PERSON] et al. developed a system for cell cyclic stretch, and time-lapse images of cells were acquired every 3 min after the motor was held at the stretched position for \(\sim\)30 s [39]. Similarly, [PERSON] et al. designed a stretch system using PDMS membrane and a vacuum chamber. Upon the application of vacuum, the elastic membrane stretched and moved closer to the bottom glass slide and living cell images were obtained after relaxing the cells to their original state [38]. However, from the previous studies, we know that the bottom surface has a vertical shift, which always results in out-of-focus images. Furthermore, the vertical shift could not be deleted even when researchers tried to fabricate and use a micro-scale chamber [41; 42]. Therefore, in order to record a real-time movie of cells exposed to cyclic stretch during each cycle of cyclic stretching, it is necessary to consider the displacement trajectory of the stretch chamber in the vertical direction at each time point to control the motorized stage in real time. To solve this problem, we measure the displacement characteristics of a stretch chamber, and newly propose a methodology to establish a cell cyclic stretch system. By using the technique that can track the target, such a stretch system can perform on-line cell observation during each cycle of cyclic stretching. Through the basic experiments, the performance of the developed system was confirmed. By using our method, the position of the target cell is maintained under the microscope, and the deformed cells are easily monitored even in a dynamic state in which a high magnification lens is used. A part of this work has been reported as a proceeding of a conference [43].
## 4 Experiments
### Benchmark Comparisons on Public Datasets
#### 4.6.1 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{\begin{tabular}{c} **Params** \\ **(K)** \\ \end{tabular} } & \multirow{2}{*}{**FLOPs (G)**} & \multirow{2}{*}{\begin{tabular}{c} **Set5** \\ **PSNR (dB)/** \\ **SSIM** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Set14** \\ **PSNR (dB)/** \\ **SSIM** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **BSD100** \\ **PSNR (dB)/** \\ **SSIM** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Urban100** \\ **PSNR (dB)/** \\ **SSIM** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Manga109** \\ **PSNR (dB)/** \\ **SSIM** \\ \end{tabular} } \\ \hline SRCNN [3] & 57 & 52.7 & 30.48/0.8628 & 27.49/0.7503 & 26.90/0.7101 & 24.52/0.7221 & 27.66/0.8505 \\ FSRCNN [4] & 12 & 4.6 & 30.71/0.8657 & 27.59/0.7535 & 26.98/0.7150 & 24.62/0.7280 & 27.90/0.8517 \\ VDSR [5] & 665 & 612.6 & 31.35/0.8838 & 28.01/0.7674 & 27.29/0.7251 & 25.18/0.7524 & 28.83/0.8809 \\ EDSR-baseline [6] & 1518 & 114.0 & 32.09/0.8938 & 28.58/0.7813 & 27.57/0.7357 & 26.04/0.7849 & 30.35/0.9067 \\ CARN [7] & 1592 & 90.9 & 32.13/0.8937 & 28.60/0.7806 & 27.58/0.7349 & 26.07/0.7837 & -/- \\ IMDN [8] & 715 & 40.9 & 32.21/0.8948 & 28.58/0.7811 & 27.56/0.7353 & 26.04/0.7838 & 30.45/0.9075 \\ RFDN [9] & 550 & 23.9 & 32.24/0.8952 & 28.61/0.7819 & 27.57/0.7360 & 26.11/0.7858 & 30.58/0.9089 \\ LatticeNet [31] & 777 & 43.6 & 32.18/0.8943 & 28.61/0.7812 & 27.57/0.7355 & 26.14/0.7844 & 30.54/0.9075 \\ LAPAR-A [48] & 659 & 94.0 & 32.15/0.8944 & 28.61/0.7818 & 27.61/0.7366 & 26.14/0.7871 & 30.42/0.9074 \\ EMASRN [49] & 546 & - & 32.17/0.8948 & 28.57/0.7809 & 27.55/0.7351 & 26.01/0.7938 & 30.41/0.9076 \\ SMSR [50] & 1006 & 57.2 & 32.12/0.8932 & 28.55/0.7808 & 27.55/0.7351 & 26.11/0.7868 & -/- \\ DeFiAN [51] & 1065 & 12.8 & 32.16/0.8942 & 28.63/0.7810 & 27.58/0.7363 & 26.10/0.7862 & 30.59/0.9084 \\ ShuffleMixer [52] & 411 & 28.0 & 32.21/0.8953 & 28.66/0.7827 & 27.61/0.7366 & 26.08/0.7835 & 30.65/0.9093 \\ SAFMN [32] & 240 & 14.0 & 32.18/0.8949 & 28.60/0.7813 & 27.58/0.7359 & 25.97/0.7809 & 30.43/0.9063 \\ DIPNet [53] & 543 & 72.97 & 32.20/0.8950 & 28.58/0.7811 & 27.59/0.7364 & 26.16/0.7879 & 30.53/0.9087 \\ DVMSR [33] & 424 & 19.67 & 32.19/0.8955 & 28.61/0.7823 & 27.58/0.7379 & 26.03/0.7838 & 30.48/0.9084 \\ GASSL-B [54] & 694 & 39.9 & 32.17/0.8950 & 28.66/0.7835 & 27.62/0.7377 & 26.16/0.7888 & 30.70/0.9100 \\ DRFAN (ours) & 1057 & 65.1 & 32.30/0.8960 & 28.65/0.7831 & 27.63/0.7381 & 26.19/0.7882 & 30.67/0.9100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparisons on benchmark datasets under a \(\times\)4 scale factor, where the best and second-best results are highlighted in red and blue, respectively. FLOPs is calculated with a 1280 \(\times\)720 GT image.
## 5 Axial Piston Pump Simulation Test
### Comparative Analysis of Experimental Data
#### 5.2.4 Performance Comparison of Different Anomaly Detection Methods  
Both the OCSVM and isolation forest algorithms are frequently employed for anomaly detection. The OCSVM algorithm maps data onto a high-dimensional space and aims to find the optimal hyperplane that maximizes the distance between the training samples and the origin as much as possible. By contrast, the isolation forest algorithm uses a randomized data segmentation approach, which is known for its high computational efficiency. When anomaly detection is performed, the advantages of different algorithms are comprehensively considered, and an appropriate algorithm is selected to obtain more accurate and reliable anomaly detection results. The two anomaly detection methods were compared, and the results are listed in Table 4.  
The findings indicate that anomaly detection using the isolation forest algorithm yields an average increase of 3.23% in precision, 1.83% in recall, and 2.59% in the F1 score compared with the OCSVM algorithm. The isolation forest algorithm, built upon decision tree principles, exhibits notable strengths when dealing with large-scale datasets and shows enhanced robustness against noise and diverse anomaly types. Therefore, the use of the isolation forest algorithm for anomaly detection offers superior detection performance compared with the OCSVM algorithm.
# Silicon Integrated Nanophotonic Devices for On-Chip Multi-Mode Interconnets
## 2 Silicon Multi-Dimensional (De)Multiplexers
### Densely Packed Waveguide Array
For the bent waveguides, the phase-matching condition can be written as [161]:  
\[\mathrm{n_{eff,0}R_{0}=n_{eff,1}R_{1}}, \tag{4}\]  
where \(\mathrm{n_{eff,0}}\) and \(\mathrm{n_{eff,1}}\) are the effective indices, \(R_{0}\) and \(R_{1}\) is the bending radii. From the equation, it can be seen that the adjacent cores can be phase-mismatched even with the same core widths (\(\mathrm{n_{eff,0}}=\mathrm{n_{eff,1}}\)) as long as the bending radii are different (\(\mathrm{R_{0}\
eq R_{1}}\)), which leads to the densely packed bent-waveguide array (DPBWA), as shown in Figure 10c. In [159], a 16-channel DPBWA-based MDM was demonstrated with low losses (IL \(<1\) dB), low crosstalk (XT \(<-20\) dB) and a broad bandwidth (BW \(>80\) nm). All the waveguides in the proposed DPBWA have the same core widths, which makes the DPBWA quite easy to fabricate. In [160], a similar super-lattice structure was also demonstrated with low losses (IL \(<0.5\) dB) and low crosstalk (XT \(<-19.5\)). The working performances for the reported silicon DPWAs are summarized in Table 4. One can see that the major advantage of the DPWA/DPMWA/DPBWA-based MDM is the large capacity (up to 16 channels) since the channel number can be easily scaled up by involving more waveguides. The losses and crosstalk are also quite weak due to the convenient mode (de)multiplexing. We believe that the novel DPWA can offer great opportunities for high-density on-chip optical transmissions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Size (\(\upmu\)m)**} & \multirow{2}{*}{**Capacity**} & \multicolumn{2}{c}{**IL (dB)**} & \multicolumn{2}{c}{**XT (dB)**} & \multicolumn{2}{c}{**BW (nm)**} \\ \cline{6-10}  & & & & & **Sim. ** & **Exp. ** & **Sim. ** & **Exp. ** & **Sim. ** & **Exp. ** \\ \hline
[154] & 2015 & 15 & 3 & 0.05 & / & \(-20\) & / & 100 & / \\
[155] & 2015 & \(<\)100 & 11 & / & / & \(<\)\(-20\) & \(<\)\(-20\) & 70 & 70 \\
[157] & 2015 & 60 & 5 & 0.05 & 0.6 & \(-20\) & \(-15\) & 80 & 80 \\
[159] & 2016 & \(\approx\)30 & 16 & 0.1 & 1 & \(-30
## 3 Results and Discussion  
### Performance Analysis of Models  
To compare the precision of the model, various statistical indexes, such as absolute average relative deviation percent (AARD%), mean square error (MSE), the regression coefficient (R\({}^{2}\)), and root mean square error (RMSE), have been utilized. These indexes are introduced as follows:  
\[AARD\%=\frac{1}{N}\sum_{i=1}^{N}\big{(}\Big{|}\frac{Y_{i.real}-Y_{i.pred}}{Y_{i. real}}\Big{|}\big{)}\times 100 \tag{3}\]  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\big{(}Y_{i.real}-Y_{i.pred}\big{)}^{2} \tag{4}\]  
\[R^{2}=\frac{\sum_{i=1}^{N}\big{(}Y_{i.real}-\overline{Y}_{real}\big{)}^{2}- \sum_{i=1}^{N}\big{(}Y_{i.real}-Y_{i.pred}\big{)}^{2}}{\sum_{i=1}^{N}\big{(}Y_{ i.real}-\overline{Y}_{real}\big{)}^{2}} \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\big{(}Y_{i.real}-Y_{i.pred}\big{)}^{2}} \tag{6}\]  
where \(Y_{i.real}\), \(\overline{Y}_{act}\), and \(Y_{i.pred}\) represent the real values, the mean of real values, and values of predicted pressure drop, respectively. Moreover, \(N\) indicates the dataset's number.
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 4 Discussion
### The Adverse Effects of Low LAI on the Correlation between SDR and Temperature  
The analysis of the data from 15 trees revealed that the LAI was significantly negatively correlated with CST (r = \(-\)0.9121) in individual trees, and the LAI had a major impact on CST. Under a low LAI, the CST of a tree crown was affected by the radiation from the soil below the canopy; this led to mixed of crown/soil radiation that was not representative of the real temperature of the canopy. After the abnormal data was removed from three trees with low LAIs (<1), the relationship between the SDR and CST increased from 0.0907 to 0.6936. Hence, 30 trees with different SDRs were selected from the two plots to analyze the correlation between the extracted canopy temperature and the actual SDR. The great coefficient of determination (R\({}^{2}\) = 0.7890) indicated that TIR was able to monitor the effect of bark beetles, as was also the case at the leaf scale [29]. Moreover, it performed better using TIR data than the spectral index approach, including normalized differential vegetation index (NDVI) and leaf pigment activity indices to estimate SDR (R\({}^{2}\) = 0.67) [22].  
The negative correlation between the LAI and temperature was also expressed at the plot scale (r = \(-\)0.5902). At this scale, the relatively weak correlation was influenced by shrubs within the plot. The lower temperature of the shrubs contributed to the overall temperature of the plot; therefore, if a plot included more shrubs, the average temperature  
Figure 9: The correlation analysis of plot SDR and \(T_{\rm p}\) with a leaf area index \(>\) 1.  
decreased. As it is difficult to measure the LAI of shrubs, LiDAR is required for correction, although the results may not be entirely accurate. As such, the effects of non-target objects on monitoring results still exist on a large scale. It is necessary to eliminate this influence using other additional data sources such as LiDAR and hyperspectral images. The combination of structure and spectral information generated from LiDAR and hyperspectral data can take advantage of different sources of information to retrieve the parameters, such as LAI and vegetation indices, for monitoring forest health for improving the accuracy of SDR detection [47,48].
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 2 Methods and Mathematical Models  
This section begins with the feasibility and benefits of this method for signal acquisition. Then, the mathematical model is established for the minimum shared PRN codes, which is similar to the optimum graph coloring problem. Finally, we describe the calculation process of LEO satellite PRN code assignment method.
# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 3 Results and Discussion
### Comparison with CNOSSSO-EU Model  
The obtained result from the GRU model has been further compared to the EU standard traffic noise model, the CNOSSSO model. The CNOSSSO model is expressed in octave bands in the frequency range from 125 to 4 KHz, for a reference speed of 70 km/h and a road surface of stone mastic asphalt and dense asphalt concrete. The vehicles are classified into four categories: light motor vehicles, medium heavy vehicles, heavy vehicles and powered two-wheelers [49]. Each vehicle is considered as a point source. The rolling noise sound power level and propulsion noise sound power level were calculated for each vehicle in the selected roundabout in Blansko over a time window of 15 s, same as the development of GRU model. The propagation part was calculated with CNOSSSO propagation formula, considering only the attenuation caused by geometric divergence. The standard parameters of the CNOSSSO model for all member states of the EU were used for the source emission and propagation calculations [50]. Compared to the ground truth  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{R^{2}}\) & **Adjusted \(\mathbf{R^{2}}\)** & **RMSE** & **MAE** & **MAPE** \\ \hline
1 st iteration & 0.6953 & 0.6888 & 2.373 & 1.857 & 0.024 \\
2 nd iteration & 0.6629 & 0.6557 & 2.648 & 2.111 & 0.027 \\
3 rd iteration & 0.5359 & 0.526 & 2.993 & 2.406 & 0.030 \\
4 th iteration & 0.7766 & 0.7718 & 2.09 & 1.673 & 0.021 \\
5 th iteration & 0.7319 & 0.7262 & 2.136 & 1.848 & 0.024 \\ \hline Average & 0.6805 & 0.6737 & 2.448 & 1.979 & 0.0252 \\ Standard dev. & 0.0816 & 0.0834 & 0.3371 & 0.2551 & 0.0031 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The evaluations on GRU model, Architecture 2, on testing data.  
Figure 20: Final model performance and residual distribution (data from Mic 8): (**a**) comparison between predicted SPL values and actual SPL values based on testing data; (**b**) the distribution of residuals based on testing data.  
values, the RMSE, MAE and MAPE of the CNOSSOS predicted values were calculated. The obtained result is shown below in Table 8:  
As shown in Table 8, given the 95% confidence interval, the CNOSSOS model achieves an RMSE of 7.143 \(\pm\) 0.93 dB and a MAE of 5.703 \(\pm\) 0.98 dB, for traffic noise prediction with Blansko dataset. It turns out that the RMSE achieved by the GRU model is around 4.7 dB smaller than CNOSSOS model. The proposed GRU model outperforms CNOSSOS model for traffic noise short-term prediction.
## 4 Conclusions  
There is a lack of knowledge in understanding the effect of storage on different seed behaviors post-combined treatments. This study was an attempt to investigate the combined effects of non-thermal plasma (NTP) and hydropriming on the germination performance and seedling characteristics of a specific variety of cucumber and broccoli seeds after short-term storage for the first time. For cucumber seeds, a combined 15-min NTP and hydropriming treatment significantly enhanced germination rates and seedling vitality indexes. Positive outcomes observed in cucumber seeds may be attributed to the stimulatory effects of reactive oxygen and nitrogen species generated by NTP, which can act as signaling molecules to enhance stress tolerance and promote early growth. In contrast, broccoli seeds showed no significant improvement. These findings highlight the importance of tailoring seed treatments to specific plant species or varieties, as their physiological responses to NTP and hydropriming vary considerably after storage. Additionally, the portable and cost-effective nature of the NTP device used in this study presents practical potential for agricultural applications, particularly for pre-sowing seed treatments.  
Conceptualization, V.S., P.D., and B.S.; methodology, P.D.; software, B.S.; validation, P.D. and B.S.; formal analysis, P.D.; investigation, P.D., L.T., J.K., and X.C.; resources, P.D.; data curation, P.D.; writing--original draft preparation, P.D. and V.S.; writing--review and editing, V.S., J.K., L.T., and B.S.; visualization, P.D.; supervision, V.S. and B
###### Abstract
To address these gaps, we propose a unified framework, Evolution of Heuristics with Memetic Reflection (EoH-MR), that seamlessly integrates LLM-guided operator generation, adaptive local refinement via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an explicit evolution-path memory for data-driven operator selection, and a periodic reflection mechanism to analyze population diversity, performance gaps, and stagnation indicators. In this framework, the LLM contributes domain knowledge and complex reasoning to generate and modify candidate heuristics, CMA-ES refines numerical parameters within promising solutions, the memory module records and generalizes successful evolutionary transitions to guide future search, and the reflection layer applies meta-cognitive insights to dynamically rebalance exploration and exploitation. This synergistic combination enables our method to navigate complex heuristic design spaces more effectively than prior hyper-heuristics, neural metaheuristics, or LLM-assisted techniques. We empirically validate our approach on a suite of benchmark combinatorial optimization problems, demonstrating that it consistently achieves higher solution quality, faster convergence, and greater interpretability than state-of-the-art hyper-heuristics, neural metaheuristics, and LLM-assisted techniques. The main contributions of this work are:  
* A novel suite of LLM-guided evolutionary operators that combine interpretable algorithmic reasoning with adaptive recombination and mutation strategies. * An adaptive memetic framework that integrates CMA-ES-based local search with an explicit evolution-path memory, enabling data-driven operator selection and fine-grained parameter tuning. * A meta-cognitive reflection mechanism that continuously monitors diversity, performance gaps, and stagnation to dynamically adjust exploration-exploitation trade-offs and usage frequencies of different operators. The rest of this paper is organized as follows. Section 2 reviews related work in automatic heuristic design, LLM-assisted methods, and memetic algorithms. Section 3 formalizes the studied problem. Section 4 presents the EoH-MR framework in detail, including operator definitions, CMA-ES local search, evolution-path memory, and the reflection mechanism. Section 5 introduces the tasks considered as well as the evaluation metrics. Section 6 reports experimental results and ablation studies, and Section 7 concludes with a discussion of limitations and directions for future research.
# An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks
## 3 Preventing Social Engineering Attacks Using Security Principle
### Framework Design Description
#### 4.3.2 Multiaccess Control Management  
Each blockchain interacting entity's access management and privileges are achieved through the identity verification and validation process and via the access control list enforced by following detailed policies.  
Identity Verification and Membership Validation of Different Actors  
Once the recipient sends a request for further data or task completion, it is first handled by other persons specified according to the organization's policy. They will use their digital identities in the form of cryptographic digital certificates according to X.509 standards generated by the organization's certification authority to endorse or reject the recipient's request. Only permitted persons could join the endorsement process according to the request type and content.  
The implementation of this scenario is achieved through the use of private blockchain technology. It consists, as shown in Figure 3, of generating digital identities that contain the digital signature generated through the public and private keys of the person verified and issued by the certification authority. Any suspicious or invalid identities are stored in a revocation list to accelerate the verification process and detect fraudulent identities. According to the organization's policy, these valid digital certificates are stored on a membership service provider (MSP). The MSP's role is to verify if the person who has the specific digital certificate is allowed to participate in the transaction validation. The organization determines and sets the eligibility of each endorser (person) through policies. The MSP also has a role in turning newly defined entities into recognized entities in the blockchainnetwork. In this way, the MSP enables any newly defined organization entity to participate in the private blockchain network.  
Once all involved parties agree on the transaction and endorse it, a new block is created and added to the main organization's blockchain ledger. Otherwise, the transaction is rejected. Thus, access management is achieved by generating digital entities via the certificate authorities (CA) and validating the person's eligibility to join the transaction verification process through the MSP.  
Access Control List Enforcement through Policies  
Access to the blockchain is determined by the policies defined during the establishment of the blockchain network. The policies identify how to add a new entity or remove it. These policies also describe the characteristics of entities and their privileges to access the blockchain. The organization could change the policies according to their needs even after blockchain creation since we adopted a permissioned blockchain technology. Details and samples of access control list policies are in the implementation and results section.
## 4 Solution Algorithms
### An Adaptive Large Neighborhood Search Algorithm with Efficient Initial Solution
#### 4.6.4 Weight Adjustment  
The selection of operators in each iteration is based on their weights then a roulette-wheel procedure is applied to select the operator to generate the neighborhood. Let \(D=\{d_{i}|i=1,\ldots,m\}\) be the set of m destroy operators and let \(R=\{r_{j}|j=1,\ldots,n\}\) be the set of \(n\) repair operators. The initially equal weights of the heuristics are denoted by \(w(d_{i})\) and \(w(r_{j})\), so that the probabilities to select the operator are \(p(d_{i})=\frac{w(d_{j})}{\sum_{i=1}^{m}w(d_{i})}\) and \(p\big{(}r_{j}\big{)}=\frac{w(r_{j})}{\sum_{j=1}^{n}w(r_{j})}\).  
For the weight adjustment, we adopt the strategy in [57]. Where \(w(h)\) denotes the weight of a heuristic operator \(h\), \(u(h)\) denotes the number of times the heuristic h has been used, and \(s(h)\) denotes the success of \(h\). The success \(s(h)\) of \(h\) is initialized with zero and is increased by \(\delta_{i}\) if the resulting solution is of the corresponding quality: \(\delta_{1}\)- the new solution is the best one found thus far; \(\delta_{2}\)- the new solution improves the current solution. We need to ensure the inequality \(\delta_{1}>\delta_{2}\) and use a reaction factor \(0\leq\rho\leq 1\) controls the influence of the recent success of a heuristic on its weight. Finally, we calculate the weights in each iteration by \(w(h)=(1-\rho)w(h)+\rho\frac{s(h)}{u(h)}\) when \(u(h)>0\).