esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
Our results also showed that the expression levels of the transcripts encoding the enzyme PME and PG in the alternative D-galacturonate pathway significantly increased in Issai, which might contribute to the higher AsA content in Issai, indicating that the alternative D-galacturonate pathway is also important in hardy kiwifruit AsA biosynthesis. APX, AO, GR, DHAR, and MDHAR are key enzymes in the oxidation and recycling of AsA and play a very important role in regulating the content of ASA and DHA. [PERSON] and [PERSON] [27] conducted an APX study and found that APX activity decreased as the concentration of AsA decreased. AO maintains the redox ratio of the AsA pool mainly by regulating the AsA redox state in vitro of plant cell protoplasts. In the present study,our results found that APX and AO were higher in Issai fruits with high concentrations of AsA, suggesting that APX and AO are closely related to AsA content. The expression and enzyme activities of MDHAR were both low in the two cultivars. At the same time, the enzyme activity in the high AsA content cultivar was lower than that in the low AsA content cultivar, indicating that MDHAR played a minor role in the metabolic pathway of AsA in hardy kiwifruit. This is consistent with the findings of [PERSON] [28]. DHAR is an important enzyme for the regeneration of AsA, which can contribute to the accumulation of AsA in 'white' kiwifruit [29]. [PERSON] [30] overexpressed the wheat DHAR in tobacco, resulting in an increase in the AsA/DHA ratio and a fourfold increase in AsA content. Our results found that although the difference in DHAR gene is not significant, the enzyme activity differs by more than 4 times. What's more, although the oxidation and recycling of AsA in Issai is more active than that in Ana because of the higher expression of oxidation and recycling transcripts in Issai, the expression of all oxidation and recycling transcripts is relatively lower than the biosynthetic transcripts, indicating that the AsA accumulation is mainly from biosynthesis other than from recycling. By contrast, the expression of MIOX family genes in another alternative AsA biosynthesis pathway is lower in Issai than that in Ana, indicating that MIOX might not have obvious effects on AsA accumulation in hardy kiwifruit, while it might including other functions such as construction of cell wall and the biosynthesis of sugar alcohol as previously suggested [31].
## 2 CubeSat Antennas, Challenges, and Requirements  
Various research efforts have explored different frequencies, techniques, materials, and dimensions to address the challenges and limitations in designing high-performance antennas for CubeSats. CubeSat antennas must meet specific requirements to be suitable for space communications. CubeSats are limited by size and weight, typically within 10 \(\times\) 10 \(\times\) 10 cm\({}^{3}\) units (1U) or multiples. Antennas need to be compact and lightweight to adhere to these limits. Deployable structures are often used for more extensive or higher-gain antennas. They must be securely deposited, compacted during the process, and efficiently positioned once in orbit. CubeSats typically operate within designated frequency bands, such as VHF, UHF, S-band, or X-band. The antenna must be adjusted to operate within the target frequency band and comply with ITU and other international standards for satellite communications. Depending on the mission, the antenna may require omnidirectional coverage--especially for low Earth orbit (LEO) missions--to maintain connectivity regardless of the satellite's orientation. Alternatively, directional antennas with high-gain patterns may be used to focus energy for enhanced communication reliability [4].  
Other critical design considerations can be addressed as follows:  
_Polarization_: circular polarization (CP) is often preferred due to its resistance to orientation mismatches, although linear polarization may also be employed with precise alignment;  
_High efficiency_: the antenna must maximize limited power resources onboard;  
_Sufficient bandwidth_: it should support the necessary communication protocols, including telemetry, commands, and data downlinks;  
_Low-power operation_: since CubeSats have constrained power budgets, the antenna must perform efficiently with limited power;  
_Environmental resilience_: antennas must withstand extreme temperature variations and radiation in space, ensuring long-term operational reliability;  
_Structural durability_: The design should be able to endure launch vibrations and shocks. Deployable antennas, in particular, must have reliable mechanisms for flawless operation in microgravity and vacuum conditions;  
_Multifunctionality_: antennas may need to support multiple tasks, such as data transmission, telemetry, and intersatellite communication;  
_Minimal interference_: designs should avoid disrupting other onboard systems;  
_Cost-effectiveness_: manufacturing must be affordable, as CubeSat missions often operate within strict budgets.  
Considering these requirements and the techniques to improve them, the following research on antenna design was performed and proposed for CubeSat and satellite communications.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 5 Conclusions  
1. The present study established a mathematical optimization model based on LS used in thin walls of turbine blades, which was simplified as an LS channel using an integral optimization design method. Two selected optimization problems (Op-I and Op-II) were proposed to investigate optimum structures of LS, and the optimal results calculated by NSGA-II algorithm were proved to be accurate and reasonable.
2. For Op-I ( \(Nu\) and \(\overline{p}\) were the objectives), the overall sensitivity of \(D\) and \(\omega\) for \(Nu\) was higher than \(\overline{p}\). The single sensitivity of \(D\) for \(\overline{p}\) was higher than Nu; the single sensitivity of \(\omega\) for \(Nu\) was higher than \(\overline{p}\). Compared to the initial LS, \(Nu\) increased by 24.1% and \(\overline{p}\) decreased by 31%, respectively. The optimal structural parameters were 2.3 mm \(\leq D\leq\) 2.9 mm, and \(52^{\circ}\leq\omega\leq 66^{\circ}\).
3. For Op-II (\(Nu\), \(freq1\), and \(\overline{p}\) were the objectives), the overall sensitivity of \(D\) and \(\omega\) for the objectives was ranked as \(freq1\), \(Nu\), and \(\overline{p}\). The single sensitivity of \(D\) for the objectives was ranked as \(freq1\), \(\overline{p}\), and \(Nu\); the single sensitivity of \(\omega\) for the objectives was ranked as \(Nu\), \(\overline{p}\) and \(freq1\). Compared to the initial LS, \(Nu\) increased by 28.8%,  
Figure 13: Streamline of the target surface, \(Nu\) distribution of the target surface, and \(Nu\) comparison: (**a**) Initial model; (**b**) model of Op-I; (**c**) model of Op-II; (**d**) initial model; (**e**) model of Op-I; (**f**) model of Op-II; and (**g**) comparison of \(Nu\) in different models.  
while _freq_1 and \(\overline{\rho}\) were almost unchanged. The optimum structural parameters were 2.0 mm \(\leq D\leq\) 3.5 mm, and \(56^{\circ}\leq\omega\leq 70^{\circ}\).
4. The fitting function of Pareto fronts of Op-I and Op-II was obtained, which may provide guidance for structural parameters design for LS channels used in turbine blades.  
Conceptualization, L.X. ([PERSON]) and Q.S.; methodology, Q.S. and L.X. ([PERSON]); software, Q.S. and L.X. ([PERSON]); validation, L.X. ([PERSON]), Q.S. and Q.R.; formal analysis, L.X. ([PERSON]); investigation, Q.S.; resources, L.X. ([PERSON]); data curation, Q.R.; writing--original draft preparation, Q.S.; writing--review and editing, Q.S.; visualization, L.X. ([PERSON]); supervision, J.G., L.X. ([PERSON]) and Y.L.; project administration, L.X. ([PERSON]); funding acquisition, L.X. ([PERSON]). 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 YFB1106400, the National Natural Science Foundation of China, grant number 51876157; Natural Science Foundation of Shaanxi Province in China, grant number 2019 JM-096.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Results and Analysis
### Dataset Description
#### 3.2.2 WiderPerson Dataset  
The WiderPerson dataset is a publicly available dataset designed for outdoor pedestrian detection. It is characterized by high target density and severe occlusion, especially in crowded scenes with substantial target overlaps. These characteristics closely resemble industrial environments, which often involve stacking, occlusions, and densely arranged objects. Therefore, this dataset was selected in this study for training and evaluating the object detection module. Considering that some images lack annotation information, the original dataset was filtered and preprocessed, resulting in a total of 13,381 fully annotated image samples. Among them, 7999 images were used for training, 1000 for validation, and 4385 for testing. This dataset provides strong support for evaluating detection algorithms under complex scenarios.
# An Improved Vicsek Model of Swarms Based on a New Neighbor Strategy Considering View and Distance
## 3 The Framework of the Proposed VM
### The Improved DPVM  
Previous research has proved that adding a dynamic feature or non-homogeneous feature to the VM can improve the performance of VM [24; 28]. Additionally, the model of dynamic type can adapt to conditions with noise better, as mentioned before. Therefore, this paper proposes DPVM to improve SPVM further, overcoming SPVM's weakness in noisy conditions. The DPVM is assumed to share the same rules with SPVM, except that each particle has a different view angle \(\omega\) and a selection proportion \(p\), which change along with the simulation time and the local level of consensus. The dynamic features of parameters \(\omega\) and \(p\), as well as the features that each particle has different \(\omega\) and \(p\), could help the model perform better in noisy conditions.  
Figure 2: Illustration of particles’ neighbor strategy of the improved VM proposed.  
The following order parameter \(V_{local}(k)\) measures the degree of movement consensus of the neighbors of particle \(i\). The information \(V_{local}(k)\) provides will determine the view angle and selection proportion of certain particle \(i\) at the next time step \(k+1\):  
\[V_{local}(k)=\frac{1}{n_{i}(k)}\biggl{|}\sum_{j=1}^{n_{i}(k)}e^{i\theta_{j}(k)} \biggr{|},j\in\Gamma_{i}(k).\quad 0\leq V_{local}(k)\leq 1 \tag{4}\]  
In Equation (4), only particles belonging to the neighbor set of particle \(i\) are measured when calculating \(V_{local}(k)\) of particle \(i\), so \(V_{local}(k)\) represents the degree of consensus of the neighbors of particle \(i\) at time step \(k\). The definition of neighbor degenerates to that of the original VM when calculating \(V_{local}(k)\), which means all particles falling into the disc with radium \(R\) centered at particle \(i\) are regarded as neighbors. In other words, view angle \(\omega\) is not taken into consideration when calculating \(V_{local}(k)\). When particles move in a highly synchronized manner, all particles move in almost the same direction, and thus, the value of index \(V_{local}(k)\) will be close to 1.  
In DPVM, the view angle \(\omega\) and selection proportion \(p\) of particle \(i\) are not constants. Instead, they are functions of \(V_{local}(k)\). Therefore, in DPVM, each particle will constantly adjust its way of choosing neighbors according to its nearby consensus level. There is no limitation to the design of the functions, but by applying different functions, the model will demonstrate different characteristics and performance. DPVM will degenerate to SPVM if the functions applied are constant functions.
## 5 Experimental Results
### Results
Similar to the malware detection scenario, the training time for the chosen classifiers in multi-class settings also decreases on selected features compared to the original ones. In this scenario, the best-performing RF classifier took 14.855 s and 1.251 s, respectively, on the full and selected features without affecting the performance. We also summarize the performance of the chosen classifiers as ROC curves in Figure 7(a) (on full features) and Figure 7(b) (on selected features). We illustrate the ROC curves for malware detection problems because, in this scenario, they make more sense. ROC curves show that the RF classifier was accurate and consistent in both settings: on the full dataset Figure 7(a) and the selected subset of features Figure 7(b).
## 5 ANOVA and Parametric Influence on Performances
### Parametric Influence on the Performance Measures  
Figure 8 depicts the surface plots of the MRR and NP responses for electrochemical machining of Monel 400 alloys. In each surface plot, one parameter was kept constant, and other parameters were varied with defined levels. The surface plots show that the machining parameters V, FR, and EC influenced the material removal and the discharge of nickel elements in the sludge significantly. It can be observed that the change in electrolyte concentration from 150 g/L to 190 g/L gradually increased the sludge removal. A higher concentration helped increase the speed of the electrochemical reactions and resulted in more removal ([PERSON], 2010; [PERSON] and [PERSON], 2015). A lower concentration led to a lower ion content, which reduced the rate of the electrochemical reaction ([PERSON], [PERSON], [PERSON], and [PERSON], 2003). A flow rate above 1.5 L/min removed the sludge from the gap between the tool and the workpiece better and exposed the specimen's new surface for different electrochemical reactions ([PERSON] and [PERSON], 2014). High flow rates increased the nickel ions in the electrolyte discharge and the MRR ([PERSON] and [PERSON], 1999).  
Figure 9 represents the main effect plots of the MRR and NP responses using MiniTab17 software. It was noticed that the electrolyte flow rate and electrolyte concentration were the influencing factors for the MRR and NP, respectively. Variations in the voltage also produced significant results in the responses.  
Figure 10 shows the interaction plots of the ECM parameters for different process attributes. As shown, the interaction terms such as EC*V and V*FR had a lean influence on the responses. The MRR and NP models were insignificant when the \"Prob > F\" values were more significant than 0.05. The term EC*FR influenced the NP at a high flow rate of 3 L/min.  
Figure 8: Surface plot of responses for various combinations of parameters.  
Figure 9: Main effects plots of MRR and NP.
## 1 Introduction
### Contributions and Structure of the Paper  
This paper is an extension of studies in [20; 23]. It extends the analyses of [20; 23] by adopting unequal signal and noise powers combined with received signals correlation at the antenna arrays of SUs in centralized CSS under FS sensing channels according to the IEEE 802.22 standard for WRAN [22] for different numbers of SUs and antennas per SU. Analyses of CFCPSC with HD in [23] substitutes the analyses of CFCPSC with partial data fusion in [20]. Since this paper does not adopt CC traffic analysis and CFCPSC with total or partial data fusion, it has identical detection performances under error-free CC, which is the case adopted in this study. This paper also proposes a different approach in terms of DN compared to the one in [20]. Specifically, noise powers in [20] are independently and identically distributed at the front-end of each multiantenna-SU assuming equal noise powers, meaning that the noise powers at the antennas of a given SU are identical, and, therefore, calibrated. In this paper, however, we assume unequal noise power at each antenna of each SU, i.e., we assume that the antennas of given SU may be uncalibrated. The results show a different picture compared to the results in [20] and consistency compared to [23]. Now, detection performances of GLRT are much more affected than in [20] and drastically degrade under DN powers independently if received signals are correlated or not. CFCPSC, on the other hand, has performance improvements while the performances of GLRT degrades, also independently if received signals are correlated or not. The weighting schemes of [20] for WSF and WEF could not bring any performance improvements and, therefore, should not be used in the adopted scenarios. Results are consistent with those in [23], and as in [23], the existence of correlation among received signals can be detrimental or beneficial to the detection performances of spectrum sensing depending on the test statistic under analysis. GLRT performs better than CFCPSC only in a few cases, and CFCPSC is a better choice in the majority of the performance analyses under the adopted spectrum sensing scenarios.  
In summary, the contributions of this paper are  
1. The performance analyses of [20] combined with correlated received signals at the antenna array of SUs;
2. The performance analyses of [23] combined with unequal signal and noise powers;
3. The combined performance analyses of [20; 23] adopting a new DN model in which the noise power levels at each antenna of each SU may vary, which is called dynamical noise, and also are independent and, possibly, non-identically distributed random variables.  
The structure of the rest of the paper is organized as follows. Section 2 presents a background on SS as a binary hypothesis testing the problem and the system model, including the DN model, the GLRT, and CFCPSC algorithms in centralized CSS with SF and DF, and the GLRT weighting schemes for SF and EF. Section 3 presents numerical results and discussions, and Section 4 concludes the paper and gives some directions for further related research.
## 6 Discussion, Limitations, and Future Work
### Limitations of the Current Study  
Despite its potential, we acknowledge several limitations that frame the scope of our current findings and guide future work.  
* Limited Validation Scope: We agree with the reviewer that the validation scope is a limitation of the current study. A single case study, while providing deep insights, does not permit broad generalizability. The conclusions drawn are specific to the system under test, though the methodological value of the framework is generalizable.
* Parameter Uncertainty: Accurate calibration of parameterized coefficients \((\lambda,\alpha,\beta,\gamma,\eta)\) is crucial but complex due to diverse system architectures and threat profiles, risking distorted assessments if they are mis-tuned. This is a key challenge for practical implementation.
* Entropy Oversimplification: The current scalar definition of environmental threat entropy (\(\sigma\)) may oversimplify multi-vector or multi-stage attacks, potentially under-representing true resilience demands in complex scenarios.  
* Implementation Burden: Calculating \(RC(t)\) in real time for large, intricate infrastructures can be computationally intensive, posing scalability issues for some organizations.
## 3 Results and Discussion
### Editing the YOLOv2 Output Data into a Convenient Form
#### 3.4.3 Morphological Image Processing  
Morphological image processing provides tools for representing, describing, and analyzing shapes in images. The primary morphological operations are erosion, dilatation, opening, and closing, where a structuring element is applied. The structuring element is usually represented as a small matrix, the shape and size of which impact the results of applying a certain morphological operator to an image [36]. First, the orthomosaic was denoised by removing the small pixel groups that were not part of the sealed joints. Then, dilatation was applied with a structuring element of a square shape of a size of 5 to repair breaks and add pixels within the joints, followed by a closing operation with a structuring element of a square shape of 50 to fill the remaining gaps. Morphological image processing was concluded by extracting and labeling connected regions, and their properties were measured. The sealed joints create the edges with a rectangular shape around the concrete slabs. Thus, the measurement was focused on measuring properties that shape to obtain the slab coordinates; however, not only rectangular shapes connected to the concrete slabs were measured, but other objects were also included. These objects covered small or large patches around which a joint was created during the repair process. Knowing the dimensions of the concrete slabs and the GSD value, the rectangular objects that did not fulfill the prescribed slab size with a set tolerance of 1 m in length and 0.5 m in width were removed.  
The rectangular boxes obtained in this way are only related to the concrete slabs with the coordinates [x, y, width, and height], where (x, y) specifies the left upper corner of the slab and (width, height) are its size in the pixel coordinates system. The final output of image segmentation and morphological processing is outlined in Figure 10 for one concrete  
Figure 9: (**a**) The detail of the joint filled with asphalt grout between two concrete slabs and (**b**) the corresponding histogram.  
slab, where the black and white color was reversed to correspond with the black color of asphalt grout, as in reality. These methods are particularly effective for determining the coordinates of constituent slabs when their dimensions differ, mainly due to construction reasons during runway construction.