esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Methodology
#### 3.2.2 Guided Random Mask  
Having identified the possible lesions indicated by the CAM, the next problem to be tackled is how to utilize it to regularize the training of DNNs. We aim to realize moderate regularization effectiveness, that is, to avoid entirely masking the lesions that may cause strong regularization or mask regions with a fixed size that introduce an extra hyper-parameter. Based on this consideration, we propose mask regions with a random size guided by CAM. Figure 3 illustrates the overall computation steps of the proposed GRM method. First, the inference of the input image is required in order to identify its category and locate the potential lesions, which is indicated by the procedures of 1, 2, 3. The next step is generating the bounding box of the CAM, which embodies the majority of regions with high values in CAM. The bounding box of CAM is computed from the binarized CAM, which is accomplished by using the 90 th percentile of the original CAM (i.e., a pixel larger than the 90 th percentile is 1, otherwise it 0). Then we randomly choose the central point in the bounding box and allocate the width and height with their maximum value the same as that in the bounding box. The bounding box of CAM and randomly generated mask are shown as the white and red boxes in Figure 3, respectively. Finally, the random mask is applied to the raw input by setting the area in the input to 0, later used to train the DNNs. Note that the GRM method is only used in the training phase, not including the test phase.  
The above computation processes are designed for the classification task, where its application to segmentation tasks is straightforward. The inference computation to compute CAM can be omitted for the segmentation task since the target is precisely described in the label. It only requires finding the bounding box of the target and generating a corresponding random mask within it. The introduced computational cost is negligible and can be implemented as the preprocessing procedure.  
The computational process of the proposed GRM is summarized in Algorithm 1.  
Figure 3: Computation procedures of the proposed GRM method for the classification task.  
```
Input :raw input image \(\mathbf{a}^{0}\) Output :random masked image for layer \(l\) from \(1\) to \(L-1\)do // forward computation \(\mathbf{a}^{l}=F(\mathbf{a}^{l-1};\mathbf{W}^{l-1})\) ; end for \(\mathbf{z}^{l}=\sum_{k=1}^{K}\mathbf{W}_{k}^{L-1}\cdot\sum_{w=1}^{W}\sum_{h=1}^{H}\mathbf{a} _{k,w,h}^{L-1}\) ; // find the category \(c=argmax(\mathbf{z}^{l})\) ; // compute and upsample CAM \(\mathbf{\tilde{M}}_{c}=\text{upsample}(\sum_{k=1}^{K}\mathbf{W}_{c,k}^{L-1}\cdot\mathbf{a} _{k}^{L-1})\) ; // find bounding box \((X,Y,W,H)=BBox(\mathbf{\tilde{M}}_{c})\) ; // uniform sampling center point \(x=\text{uniform}(X,X+W)\) ; \(y=\text{uniform}(Y,Y+H)\) ; // uniform sampling width and height \(w=\text{uniform}(1,W)\) ; \(h=\text{uniform}(1,H)\) ; // mask input \(\mathbf{a}^{0}\) \(\mathbf{x}-w:x+w,y-h:y+h]=0\)
```  
**Algorithm 1**Algorithm of the proposed GRM method for the classification task.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 6 Conclusions  
To solve the imaging satellite task planning problem in large areas, we proposed ECL-INS-LMOA. ECL-INS-LMOA adopts the idea of two-stage evolution. In the early stage of evolution, the proposed ECLUS and NSGA-II-SDE are alternately run to ensure fast convergence while balancing global optimization. In the later stage of evolution, only NSGA-II-SDE is run to ensure global optimization while balancing fast convergence. To enable ECL-INS-LMOA to quickly converge to the approximate optimal solution in the early stage of evolution, we proposed ECLUS based on accelerating the particle update, introducing flight time, and proposing BCSO. This paper conducts imaging task planning experiments on five large imaging regions with a global number of imaging bands greater than 100 and compares ECL-INS-MOEA with three comparative algorithms. The experimental results show that the proposed ECL-INS-MOEA can always achieve efficient and fast convergence in different regions and obtain reliable imaging schemes. For different regions, the regional coverage rate exceeds 98.8%, the satellite resource savings exceed 50%, and the coverage is uniform, with fewer redundant observations and gaps.  
With the increase in the number of satellites and the complexity and diversification of user needs, imaging satellite task planning will play an increasingly important role in both theoretical research and practical applications. We sincerely hope this paper can provide a reference for relevant research and stimulate more research interest.  
Conceptualization, Y.C., X.S. and G.Z.; methodology, Y.C. and X.S.; validation, Y.C., X.S. and Z.L.; formal analysis, X.S.; investigation, Y.C.; resources, Y.C.; data curation, X.S.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C.; visualization, Z.L.; supervision, X.S.; project administration, G.Z.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R & D Program of China (2022 YFB3902803) and the National Natural Science Foundation of China under grant 42171341.  
All data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors declare no conflict of interest.
# Multiple Reflections and the Near-Field Effects on a Metamaterial Quarter-Wave Plate
## 3 Near-Field Effects and Cascading Behaviors
To further show how multiple reflections impact the performance of the cascaded gratings as a HWP, a sweep of separation distances was performed as illustrated in Figure 1.1 For this simulation, the effective length of the QWP was not adhered to, and the distance between the two gratings was varied from 25 nm to 600 nm. The output of the simulation was measured at 150 nm from the back side of the second grating, ensuring the measured output was a uniform plane wave for each simulation. The input of the simulation was an 800 nm 45-degree polarization angle uniform plane wave for every simulation. Figure 8 shows the performance of the cascaded gratings in Figure 7 with respect to separation distance. A periodic pattern emerged on each of the figures, showing one of the characteristics of multiple reflections between devices. There are two transmittance peaks in Figure 8a, one at the 218 nm separation and one at the 489 nm separation. At these two peaks, the phase shift between the two orthogonal polarizations (i.e., \(-0.99\pi\)) is equal to twice the phase shift of a single QWP in Figure 1.2 This shows that a separation distance between the two gratings can be found, in which the effects of multiple reflections are mitigated and the expected phase shift can be observed. Throughout the sweep of separation distances, the DoLP remained above the desired FOM of 0.94. The output polarization was again unable to achieve the full 90-degree rotation (i.e., PCR = 1), but the desired PCR (i.e., \(>\)0.98) was achieved at specific separation distances, highlighting the need for an in-depth analysis  
Figure 1.3 The near-field output of the cascaded QWPs in Figure 1.4 The simulation follows the analysis process presented in Figure 3 with an 800 nm normally incident uniform plane wave. This diagram displays the (**a**) mean and (**b**) variance of the electric field magnitude with respect to the distance from the output of the second grating structure, showing that the X component dissipates within 100 nm similar to Figure 3, highlighting that the effective length of the QWP remains 100 nm. The (**c**) mean and the (**d**) variance of the radian phase components of the electric field polarizations. The output of the cascaded system is shown to be a uniform plane wave, as the Y and Z polarizations have a phase and magnitude variance that approaches zero. of metamaterial structures which presents the performance impact and considerations required for integrating into a larger system.
## 3 After Failure Strategy Approach
### Motion Planning along a Straight Path
#### 3.2.2 Real Time Approach  
Since the presented WEC formulation can only evaluate the force limits, and thus the acceleration ones, in only one point in time, it is necessary to analyse all the points composing the straight line path, which are not known beforehand.  
This work adopted a real-time approach to evaluate the acceleration limits since it is more flexible to changes in the SP position and robot configuration; moreover, the resulting limits are less strict, thus reducing the time needed for the recovery strategy. However, the real-time approach requires faster algorithms, thus a new strategy, based on the evaluation of the WEC presented in Section 3.1.2, has been implemented. Since the fast evaluation of the WEC does not consider the constraints on the actuator torque, the adopted procedure is the following:  
* Evaluation of the fast WEC and the acceleration limits along the direction \(d\);
* Given the acceleration limits, using (7) it is possible to evaluate the maximum and minimum value of the actuator torque corresponding to the acceleration limits;
* If the torque limits are not respected, the acceleration limits are reduced to fulfill the constraints. This step is iterative and is not completed until the torque constraints are respected.  
This procedure is applied for a variable number of points along \(d\): this number is a compromise between the desired accuracy and the required computation speed.  
Figure 3 shows an example of the maximum and minimum acceleration values along \(d\), in red and blue, respectively, with the x-axis representing the distance from the SP.  
Negative values of the distance, i.e., points which are placed beyond the SP from the starting position, have been considered, since it is possible that the end-effector speed is not null at the SP to respect the acceleration constraints, thus it is necessary to go beyond the point and reverse the motion direction. This is shown by observing the values of the minimum acceleration limit for distances greater than 40 m in Figure 3: indeed, the acceleration values are positive, i.e., the end-effector speed must increase to guarantee a positive tension on the wires. Therefore, it is possible that, as the distance between the end-effector and the SP increases, so does the possibility that the end-effector does not stop at the SP.  
This effect is considered in the motion-planning algorithm, to return the end-effector to the SP in the case that it goes beyond it.
## 3 Results and Discussion
### Effect of Different Factors on the Stability of the Hybrid Nanofluids
#### Influence of Thermal Cycling  
The absorbance was measured, as shown in Figure 12, with different numbers of thermal cycle of solidification and melting for the hybrid nanofluid sample at pH = 11, 0.1% nanoparticle concentration, 1% SDS, ultrasonic power 300 W and ultrasonic time of 120 min.  
The absorbance of hybrid nanofluids decreased slightly before the 6 th cycle, and stayed stable afterwards, with an overall decrease of 6.2% at the 10 th cycle as compared with the initial absorbance. This is mainly because some aggregation and deposition occurred as the balance between the van der Waals attraction and electrostatic repulsion was broken by crystallization and melting. Nevertheless, the decrease in stability was slight, owing to the screen function of dispersants surrounding the nanoparticles.
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 6 Performance Comparison
### Communication Cost  
The term \"communication cost\" refers to the extra bits added to the message. In this section, we compare our proposed scheme with relevant schemes offered by Eko  
Figure 4: Computation cost (in ms) [19; 28; 29; 30].  
[PERSON] et al. [30], [PERSON] and [PERSON] [19], [PERSON] and [PERSON] [28], and [PERSON] et al. [29], as depicted in Table 3, in terms of communicational overhead. For this purpose, we picked the values from [33] that are plaintext (\(1\,\mathrm{m}\,\mathrm{l}\)) and has a size of 1000 bits, while the size of the bilinear pairing (\(1\,\mathrm{G}\,\mathrm{l}\)) is 1024 bits, the size of the elliptic curve (\(1\,\mathrm{q}\,\mathrm{l}\)), is 160 bits, and the size of the hyperelliptic curve (\(1\,\mathrm{m}\,\mathrm{l}\)) is 80 bits, respectively. As demonstrated in Table 6 and Figure 5, our proposed technique provides more significant improvements over the schemes used in [19; 28; 29; 30]. Note that we have used the following formulas to compute the communication cost based on transmitted bits:  
* Communication cost formula for bilinear pairing-based schemes: \(1\,\mathrm{Message}\,\mathrm{l}\,+\,\mathrm{l}\,\mathrm{Total}\) number of Transmitted parameters \(1\,\ast\,1024\).
* Communication cost formula for elliptic curve schemes: \(1\,\mathrm{Message}\,\mathrm{l}\,+\,\mathrm{l}\,\mathrm{Total}\) number of Transmitted parameters \(1\,\ast\,160\).
* Communication cost formula for hyperelliptic curve-based schemes: \(1\,\mathrm{Message}\,\mathrm{l}\,+\,\mathrm{l}\,\mathrm{Total}\) number of Transmitted parameters \(1\,\ast\,80\).
## 2 Test and Result Analysis
### Test Results Analysis
#### 2.3.4. Rebar Strain  
Figure 12 shows the temporal evolution curves of the rebar strain at the effective measurement points of specimen G. After the blast, the rebar strain rapidly increased to a peak value, then it underwent oscillations and gradually decayed. As the measurement points approached the blast center, the peak strain significantly increased, with residual strain observed at points SR5 and SR7, which are closest to the blast center. Figure 13 shows the rebar strain time-history curves for specimen PCG. Similarly, significant residual strain was observed at measurement points SR6 and SR7, located near the blast center. A comparative analysis of the rebar strain at corresponding measurement points in specimens G and PCG reveals a high degree of similarity in both tensile and compressive strain peaks [30]. Figure 14 shows the time-history curves of rebar strain for specimen PCGR. When comparing the corresponding measurement points in PCG and PCGR, it was observed that the peak strain during the second explosion in PCGR was markedly higher than that of the first explosion. This increase is primarily attributed to the damage and defects formed within the box girder after the first explosion, which weakened the structure's blast resistance. Additionally, residual strain was also observed at measurement points SR1 and SR2, farther from the blast center. In summary, by comparing the time-history curves of rebar strain at different measurement points across specimens G, PCG, and PCGR, the conclusions are as follows: the peak rebar strain in the specimens is significantly influenced by the proximity to the blast center--the closer to the explosion, the higher the peak strain. Furthermore, the increased peak strain observed in specimen PCGR during the second explosion indicates the cumulative damage effect on the structure under repeated explosive loads.  
Figure 11. Test results of vertical shift of the baseplate.  
Figure 12. Measured rebar strain of specimen G.  
Figure 13. Measured rebar strain of specimen PCG.
# Fake News Detection on Social Networks: A Survey
## 3 Fake News Detection
### Fake News Detection Approaches
#### 3.3.3 Source-Based Methods  
The source-based methods evaluate the authenticity of news based on the credibility of the source. The source mainly includes the news authors and social media users. This method, an indirect way to detect fake news, may seem arbitrary but is very effective [17]. (1) News author-based  
Many studies have shown that considering news authors can improve the performance of fake news detection. For example, [PERSON] et al. [61] reported that adding features such as the number of authors and the historical connection between authors and fake news can improve fake news detection. They showed that fake news can be effectively detected by adopting a few features based on the source's credibility. [PERSON] et al. [62] indicated that the content-based and propagation-based methods are not suitable for the early detection of fake news. They pointed out that the credibility of publishers and users can be adopted to quickly locate fake news in a massive amount of news. They proposed a multi-head attention network that considers publishing and forwarding relations to optimize fake news detection. [PERSON] et al. [63] used the stacked BiGRU to extract dual emotional features derived from publisher emotions and social emotions. An adaptive genetic weight update-random forest (AGWu-RF) was proposed to improve the accuracy of fake news detection. (2) Social media user-based  
Fake news can also be detected by detecting malicious social media users. Social media users are the communicators of news on social media, and malicious users are more likely to spread fake news. The social robot, sybil account, fake profile, fake account, etc., are generally malicious users of social media [64]. [PERSON] et al. [65] proposed a deep neural network based on long short-term memory (LSTM) architecture. This network uses content and metadata to detect robots at the tweet level. [PERSON] et al. [66] pointed out that feature selection is the main process of fake account detection based on machine learning. A multi-objective hybrid feature selection method is used for feature selection. [PERSON] et al. [67] designed an end-to-end Sybil detection model based on the content. The self-normalizing CNN and bi-SN-LSTM are simultaneously adopted to improve the performance of fake news detection. [PERSON] et al. [68] pointed out that according to the fundamental theories in fake news detection, the credibility of authors and users varies across different themes. They proposed a network model named MVCAN, which jointly models the potential topic credibility of authors and users in fake news detection. [PERSON] et al. [69] extracted a set of explicit and implicit features from the text information, and a deep diffusion network model was proposed based on the connections among the news content, creators, and news subjects.
## 6 Conclusions  
This paper focuses on the full-coverage path planning problem for three-dimensional terrain farmland in hilly areas, with the main objective of solving energy consumption during the full-coverage operations of agricultural activities, such as sowing and harvesting.  
This paper proposes a 3D map modeling method for hilly terrain farmland. Addressing the path planning problem for autonomous mobile robots operating in farmland within hilly areas, this paper proposes a region decomposition method tailored for hilly terrain. This method was developed by analyzing the characteristics of hilly terrain and the energy consumption model of robots navigating non-flat surfaces. Furthermore, this paper outlines the design of a full-coverage path planning algorithm that optimizes energy consumption. Through simulation experiments, the feasibility of the proposed algorithm is verified. In the simulation experiments on five typical terrains, this paper's algorithm has obvious advantages in terms of total path length, total path elevation difference, and total energy consumption when compared with other algorithms, where the total energy consumption was reduced by 25.8%, 21.7%, 47.5%, 33.9%, and 30.8%, respectively, compared to the reciprocal coverage method, and by 19.6%, 10.3%, 5.2%, 24.2%, and 21.8%, respectively, compared to the improved A* algorithm.  
Conceptualization, H.J. and P.Y.; methodology, H.J.; software, H.J.; validation, H.J. and P.Y.; formal analysis, H.J.; investigation, H.J.; resources, H.J.; data curation, H.J.; writing--original draft preparation, H.J.; writing--review and editing, H.J. and P.Y.; visualization, H.J.; supervision, P.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## Materials and Methods
### Total RNA Isolation, cDNA Reverse Transcription, and RT-qPCR Analysis  
Total RNA from the flowers and leaves of 14-month-old _D. officinal_ \"Zhongke 5\" at the flowering stage were extracted using the Quick RNA Isolation Kit (Huayueyang, Beijing, China) according to the instruction manual. Genomic DNA contamination was eliminated with RNase-free DNase I (TaKaRa, Dalian, China). First-strand cDNA was synthesized by reverse transcription with the help of the PrimeScript(tm) RT Reagent Kit (Takara) according to the manufacturer's protocol. SYBR(r) Premix Ex TaqMan(TaKaRa) was applied for RT-qPCR analysis on a LightCycler 480 instrument (Roche Diagnostics, Mannheim, Germany) as described previously [41]. _D. officinalale_ elongation factor 1-\(\alpha\) (_DoEF-1\(\alpha\)_, GenBank accession no. JF825419) was selected as the internal reference gene [42]. At least three biological replicates were carried out, and relative mRNA expression data were quantified by the 2-\(\Delta\)ACT method [43]. The RT-qPCR primers of _TPS_ genes listed in Table S14 were acquired by the PrimerQuest tool ([[http://www.iddtna.com/Primerquest/Home/Index](http://www.iddtna.com/Primerquest/Home/Index)]([http://www.iddtna.com/Primerquest/Home/Index](http://www.iddtna.com/Primerquest/Home/Index))).
## 3 The Photosynthetic Machinery Used by Cyanobacteria
### AMGs with the Potential to Impact Electron Transport
Following infection, the host transcription levels of photosystem I assembly protein Ycf37, FNR, and other translation-related-genes were diminished, suggesting that these could affect the reducing power flow during the Calvin cycle carbon fixation [46]. A putative plastoquinol terminal oxidase (PTOX) is commonly found in cyanophage genomes [21]. Of the 153 sequenced cyanophages, PTOX is observed in 35 of these genomes, notably only pertaining to a subset of cyanophages known as myocyanophages. PTOX is thought to be a safety valve in these phages to protect photo electron transfer (PET) proteins from photodamage, especially under stressful conditions [38]. When the PQ pool is over reduced, often, as a result of high light or low iron conditions, electrons can be donated to oxygen through PTOX [72; 73]. This is a particularly important flow of electrons when considering the viral-induced inhibition of PSI and FNR [46]. This effect could explain why PTOX expression levels are seen to be upregulated in cyanophage-infected cells and exist in many cyanophages. High-light-inducible proteins (HLIPs) can also be found in many cyanophage genomes (90/153). In cyanophages, the function of HLIPs has been an enigma until recently, as very little information has been available on the role of HLIPs in the infection process. However, a recent knockout study of _hli_ genes in _Syntechocystis_ sp. PCC6803 showed that knockout mutants were sensitive to high irradiances [74; 75]. HLIPs, which contain one membrane-spanning helix and are located in the thylakoid [74], could associate with both PSII [76] and PSI [77]. The study demonstrated that HLIPs were constructive to PSI stabilization [77], tetrapyrrole biosynthesis regulation [78], the transient store of chlorophyll during PSII repair and assembly [79], and excess excitation energy dissipation [38; 75]. It is likely that cyanophage HLIPs have similar functions to cyanobacterial HLIPs. There are many genes involved in photosynthesis that are encoded within cyanophage genomes (Table 1). In addition to this, the expression levels of some host-encoded photosynthesis genes (such as COX) increased after infection. Transcription levels of ferredoxin NADP\({}^{+}\) reductase (FNR) and some photosystem I proteins were diminished, leading to the reduction of NADPH biosynthesis. More electrons were transferred to COX through the cyanophage PC, meaning more protons could accumulate for ATP synthesis, resulting in more energy for cyanophage DNA biosynthesis.