esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Spatial Patterns of Vegetation Phenology  
Distinct biogeographic patterns in the timing of transition dates, especially POS and EOS, derived from SIF and EVI were observed (Figures 5 and 6), of which there was a considerable latitudinal shift in vegetation phenology (EVI) with a gradually delayed trend from north to south. This is in accordance with the in situ comparison that EVI displayed significantly late senescence at southern xeric sites as compared with GPP and SIF, particularly in the wet year of 2016-2017. In addition, the spatial phenomenon was in line with [PERSON] et al.'s (2013) findings [4], that the majority of EOS based on EVI in wet years (2005-2006, 2010-2011, and 2016-2017), especially in the southern NATT, was generally later than those in normal (2001-2002, 2014-2015) and drought years (2007-2008, 2018-2019) (Figure 5), probably as a consequence of the aforementioned \"hysteresis effect\". On the contrary, there was less difference in the spatial patterns of EOS based on SIF between the north and south NATT, as well as in normal/wet years (Figure 6), of which both showed apparently earlier EOS, presumably due to the rapidly decreasing solar radiation over the southern inland. However, as an example of moderately inconsistent phenological metrics compared with field measurement GPP (Table 1), the coarse spatial and temporal resolution of GOME-2 SIF, as well as high retrieval noises, impeded the full potential of capturing the seasonal and interannual variations in vegetation. Considering the drawbacks of existing greenness-based phenology products, as well as the highly heterogeneous composition of dryland ecosystems, our findings imply that new spaceborne SIF with improved spatiotemporal resolution, such as TROPOMI, has great capability for advancing our understanding of phenological characterization in Australia.  
Furthermore, SIF and EVI displayed a stronger temporal consistency over northern mesic biomes (forests and woodlands and Tussock grasslands) relative to southern xeric biomes (Hummock grasslands and shrublands) (Figure 8). Soil moisture can explain more than 60% of the seasonal and interannual variability in EVI over most biomes (except Hummock grasslands), suggesting it is the dominant factor controlling the dynamics of vegetation greenness across the NATT. By contrast, the temperature-related drivers could barely explain less than 5% of the variability (except forests and woodlands). Despite the fact that solar radiation has substantial impacts on the SIF signal, temperature- and moisture-related factors almost equally contributed to the process of light use efficiency for fluorescence (SIF\({}_{\text{yield}}\), except forests and woodlands). This could partially explain the temporal discrepancies between SIF and EVI across diverse biomes.
## 3 Discussion
One possible explanation is that only a single effector was examined in the experiments and an Agrobacterium-mediated transient expression assay was carried out in a nematode-free system meaning \"in the absence of other nematode effectors\". As a nematode effector expressed in _N. benthamiana_, MgPDI2 may be recognized by _N. benthamiana_'s immune receptors, and trigger cell death which is a characteristic of a hypersensitive response (HR). In spite of that, when MgPDI2 is secreted into host roots through the stylet after _M. graminicola_ natural parasitism, it may not be as strong to cause cell death in the rice root. Besides, MgPDI2 may not be recognized as a threat by the immune receptors of rice that trigger effector-triggered immunity (ETI) -type defense. Another explanation is that the nematode might have evolved an unknown mechanism which can interfere with the ETI-type defense to successfully parasitize rice [26]. In the nematode-host interactions, other effectors of _M. graminicola_ may suppress cell death induced by MgPDI2. For example, the _M. graminicola_ effector, MgGPP, was reported to suppress cell death [17]. Further work is required to determine the role of MgPDI2 in the natural parasitism of _M. graminicola_. Consequently, our study proposes that MgPDI2 contributes to _M. graminicola_ infection of rice based on our characterization of the gene expression, transcript localization, biochemical characterization, and gene silencing-mediated physiological assays. In addition, MgPDI2 might contribute to the activation of host defense responses due to its ability to induce cell death in _N. benthamiana_.
# The Antimicrobial Effectiveness and Cytotoxicity of the Antibiotic-Loaded Chitosan: ECM Scaffolds
## 2 Materials and Methods
### Direct Antimicrobial Assay  
A modification of the Kirby-Bauer antibiotic sensitivity test [30] was used to test the antimicrobial effectiveness of the different chitosan:ECM scaffold formulations when placed in direct contact with bacterial. Briefly, clinical isolates of _Staphylococcus Aureus_ (ATCC 29213) and _Escherichia Coli_ (ATCC 25922) were cultured and growth curves of absorbance vs colony forming units (CFU)/mL for both strains were established. For each bacterial strain, tryptic soy agar plates (100 mm) were prepared and 100 \(\mu\)L of bacterial suspension (\(1.5\times 10^{8}\) CFU/mL) was spread across the plate to form a lawn culture.  
Using a sterile biopsy punch, 6 mm discs of the chitosan:ECM scaffolds were prepared and placed onto the inoculated agar plates with sterile forceps. The discs were placed on the plate using a template to ensure 24 mm center to center spacing between each disc. As a comparison control, antibiotic sensitivity testing discs for rifampicin and minocycline (BD BBL Sensi-disc) were used. The plates were then cultured overnight at 37 \({}^{\circ}\)C and the zone of inhibition around the individual discs was measured using calipers. Each combination of scaffold was tested in triplicate and the average zone of inhibition calculated and expressed as a percentage of the control disc.
# Swarm of Drones in a Simulation Environment--Efficiency and Adaptation
## 2 The Drones and the Swarm of Drones  
Drones have gained an increasing share of the market as tools capable of performing a wide range of tasks in various fields. In the natural sciences, drones are used for remote environmental research. They collect data and information essential for analyzing and monitoring key aspects of the environment and infrastructure [24] (e.g., crop estimation, forestry remote sensing, disease observation, and power grid remote-sensing). The development of sensor technology for drones is growing dynamically and opens new possibilities for the control and applications of drone swarms. Advanced sensors and new software are gaining popularity and enable more effective and reliable management of drone swarms [25]. The authors in [26] proposed a mission definition system that enables efficient execution of autonomous missions. The application of deep reinforcement learning can also lead to the development of intelligent navigation methods that can guide drones to accidents, disasters, and anomaly locations using sensor data [27]. Simulation methods are also being developed for prototyping and rapid testing of control strategies that enable interactions between different units [28]. Industrial drones are also crucial for modern production processes. Current research focuses on identifying constraints related to the integration of UAVs with modern production processes [29].  
The technologies of drone swarms, facilitated by the implementation of modern sensors and the development of advanced control mechanisms, become increasingly valuable and prevalent in practical applications. Drones have a growing impact on various industrial sectors and scientific fields. Drones can contribute to the monitoring and preservation of the natural environment, investigate climate changes, analyze alterations in ecosystems, and collect data related to pollution. Swarm drones can be utilized for conducting rescue operations, locating victims in hard-to-reach areas, or monitoring public spaces to ensure safety and security.  
Drones play a vital role in the monitoring and inspection of large-scale construction projects and urban infrastructure. Drone swarms can efficiently scan extensive areas in a short period, providing precise data for the detection of damages or structural issues of power towers, lines, bridges, and railway lines [30]. By employing drones, farmers can monitor their crops across vast expanses, analyze soil conditions, track the health of plants and animals, and optimize resources such as water and fertilizers. Swarm drones can also assist in planting and harvesting crops.
# Light Propagation and Multi-Scale Enhanced DeepLabV3+ for Underwater Crack Detection
## 3 Experimental Setup  
### Dataset  
For the evaluation of the proposed underwater crack detection model, in this paper, a dataset containing 1037 underwater concrete cracks was used. These images were captured using underwater cameras mounted on remotely operated vehicles (ROVs): Chasing P200 Pro (as shown in Figure 6, Chasing-innovation Technology Co., Ltd., Shenzhen China). The dataset includes a diverse range of underwater environments, including natural underwater settings, man-made structures such as bridge piles and submerged pipelines, and varying lighting conditions. Additionally, the dataset contains annotations for the locations of cracks, which are used for training and evaluation purposes. A total of 80% (830 images) of the dataset is used to train the model, and the remaining 20% (207 images) is used to test the performance of the model. Annotation Methodology: pixel-level annotation--three civil engineering experts labeled cracks using the Labelbox(r) software (V3.6.0) with 0.5 px precision; quality control--double-blind annotation for 20% of images; and CRF refinement to resolve boundary disagreements. Statistical learning theory establishes that model generalization error \(\mathcal{E}_{g}\) scales inversely with dataset size \(n\). For segmentation tasks, the error bound follows  
\[\mathcal{E}_{g}\leq\mathcal{O}(\sqrt{\frac{VC\text{-}dim(\mathcal{H})+ln(1/ \delta)}{n}}) \tag{16}\]  
where VC-dim(\(\mathcal{H}\)) is the Vapnik-Chervonenkis dimension of the hypothesis space \(\mathcal{H}\), and \(\delta\) is the confidence parameter. Doubling \(n\) (from 450 to 1037) tightens this bound by \(\sim\)33, reducing overfitting and improving robustness to unseen underwater conditions (e.g., turbidity or lighting variations).  
Given the challenges associated with underwater imaging, including light scattering, color distortion, and murky water conditions, the dataset provides a comprehensive representation of real-world underwater inspection scenarios. It includes images with different crack sizes, orientations, and environmental complexities, ensuring that the model is trained and evaluated in varied conditions.
# Better Not to Use Vulnerability's Reference for Exploitability Prediction
## 3 NVD Reference Feature Classification  
### CVE Exploit Prediction and PoC  
As mentioned earlier, it is very useful to predict whether a vulnerability will be exploited. The most important information for prediction is the answer data indicating which CVE is exploited in the end. Since OSVDB containing such information has been shut down since 2016, only a few companies with their own exploit data can conduct CVE exploit prediction studies.  
However, a number of researchers have undertaken studies of the risk of CVE assuming that CVE in E-DB has been exploited, which is considered as a reasonable assumption in academia [4; 5; 6]. Recent risk prediction models that can obtain vulnerability information from NVD and answer labels from E-DB are shown in Figure 1. They then applied various machine algorithms to look for appropriate algorithms and parameters to obtain the best prediction result.  
The core function of Exploit-DB is to provide PoC codes that can exploit CVE. PoC is a simple code snippet that can explain how to exploit a vulnerability. For instance, a PoC code shown in Figure 2 is to exploit CVE-2016-10033, a vulnerability that PHPMailer can run an arbitrary code [24; 25]. Hackers can easily perform similar attacks if a PoC is available in the Exploit-DB.
## 4 Computational Results and Discussion
### Temperature Characteristics  
In this section, we analyzed the effect of Weissenberg number \(W_{e}\), power law index/fluid index \(n_{i}\), electroosmotic parameter \(m_{e}\), Helmholtz-Smoluchowski velocity \(U_{HS}\) and Brinkman number \(B_{r}\) on heat transfer characteristics. Figure 9a discloses the various values of \(W_{e}\), for temperature distribution \(\theta\). It examines that \(\theta\) enhances for more values of \(W_{e}\). Since \(W_{e}\) is directly proportional to velocity, so for higher values of velocity, temperature will be higher. Temperature variation against various values of \(n_{i}\) can be observed in Figure 9b. It portrays that \(\theta\) decreases as \(n_{i}\) increases. Figure 9c displays that temperature increases very quickly by increasing the values of electroosmotic parameter \(m_{e}\). Therefore, decrease in EDL produces rise in \(\theta\) remarkably. Figure 9d shows the effects of Helmholtz-Smoluchowski velocity \(U_{HS}\) on \(\theta\). It signifies that for more values of \(U_{HS}\), \(\theta\) declines. Figure 9e demonstrates the impact of \(B_{r}\) on \(\theta\) i.e., \(\theta\) increases for more values of \(B_{r}\). In other words, for higher values of \(B_{r}\), lower be the heat conduction due to viscous dissipation. Hence, temperature increases.  
Figure 10a-d represents the variation of Nusselt number \(N_{u}\) for various values of Weissenberg number \(W_{e}\), fluid index \(n_{i}\), electroosmotic parameter \(m_{e}\) and Brinkman number \(B_{r}\). Figure 10a highlights that by increasing the \(W_{e}\), magnitude of Nusselt number \(N_{u}\) increase. Figure 10b depicts that by increasing the \(n_{i}\), magnitude of \(N_{u}\) decreases. Figure 10c shows that by raising the values of \(m_{e}\), \(N_{u}\) increases. Also, Figure 10d illustrates that the magnitude of \(N_{u}\) increases as \(B_{r}\) increases.  
Figure 9: \(Cont\).  
Figure 9: _Cont._  
Figure 10: Cont.
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 8 Conclusions  
Recent years have witnessed explosive growth in using ML to solve networking issues. In particular, RL and DRL have been successfully applied in various networking areas. Specifically, this survey provides a comprehensive view of the applicability of RL and DRL techniques to perform resource management in 5G/6G network slicing. We reviewed representative research works and explored and discussed the feasibility and practicality of the proposed solutions in addressing admission control, resource allocation, resource scheduling, and resource orchestration challenges.  
Future networks will have to support diverse QoE and QoS performance requirements from emerging use cases and vertical applications in multitenant environments. Although RL/DRL-based network slicing resource management solutions have shown promising results in simulated or (some few) emulated scenarios, their scalability and practicability need to be evaluated with the envisioned volume of data, ultrahigh number of devices, and applications (especially with real-time constraints) in small, medium, and large scale networks. On the other hand, current RL/DRL-based approaches for network slicing resource management offer mainly centralized and offline learning. To meet resource management on 5G/6G and beyond networks that are distributed in nature and operate with highly-dynamic data, existing RL/DRL approaches should be enhanced or re-architected to realize E2E network slices. This survey discussed the above issues along with several other challenges and opportunities. Our findings motivate the need for more research to advance the state-of-the-art seeking to achieve the vision of zero-touch network resource management.  
Conceptualization, J.A.H.S., K.C. and O.M.C.R.; investigation, J.A.H.S., K.C. and O.M.C.R.; methodology, J.A.H.S., K.C. and O.M.C.R.; writing--original draft, J.A.H.S. and O.M.C.R.; writing--review and editing, J.A.H.S., K.C. and O.M.C.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Bicentennial Doctoral Scholarship for Excellence initiative sponsored by the Science, Technology and Innovation Fund of the Colombian Government's Royalties program number BB 2019 01 of the student [PERSON] and the Master's scholarship number 823 supported by the Ministry of Information and Communication Technologies, Colombia of the student [PERSON].  
Not applicable.  
The authors thank the University of Cauca and the Ministry of Information and Communication Technologies, Colombia, for supporting this investigation.  
**Conflicts of Interest:** The authors declare no conflict of interest. The financiers had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Discussion
### Communication Protocols Standardization Status  
A networking paradigm can only reach a wide community if it is based on (_de facto_ or _de jure_) standard protocols. LR-WPAN, WLAN and MNO UANs use open and standard protocols at all layers (note that the development of certified ZigBee products for commercial purposes requires payment of a fee). Remarkably, in IP-based LR-WPAN, WLAN and MNO UANs, the sensor nodes use IP, thus contributing to the Internet of Things (IoT). A key advantage of supporting IP is effortless Internet connectivity, and scalable application development, independent of the specific layers below IP. On the other hand, SOs are currently contributing to standardizing SO UAN protocols. Finally, communication protocols for DTN UANs have not yet been standardized, and constitute currently an open issue.
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 2 Materials and Methods
### Loam Slam  
The LOAM algorithm is developed to create maps with low odometry drift. In this method, a dense point cloud is created with low odometry drift and low computational complexity without the need for additional sensor measurements [1]. To reduce the computational complexity and optimize the performance, the LOAM algorithm is divided into two main parts. The first part generates high-frequency but low-accuracy odometry data to determine the speed and position of the LIDAR. The second part accurately maps the point clouds using low frequency and high accuracy odometry data.  
After registering point cloud data from LIDAR, the LIDAR odometry algorithm calculates the current speed, orientation, and position of the LIDAR. An additional correction is introduced to point clouds with calculated odometry data. The final point cloud is obtained by integrating two algorithms running at 10 Hz and 1 Hz. The most important factor affecting the spatial accuracy of the obtained point clouds is the accuracy of the odometry calculation. The odometry calculation used in the LOAM algorithm is examined under two main headings: feature extraction and feature matching [1].
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 3 Case Studies and Issues for Conventional CVR  
The concept of CVR began in the mid-1900's with the development of regulations, designs and application guidelines [16]. Utilities stopped this practice in the 1960s. In the 1970s the California PUC required CVR to conserve energy [16]. Research into the implementation and effectiveness of CVR is ongoing, and implementations are numerous. Most CVR research indicates that there is a 1:1 correspondence between reduction in supply voltage and savings in system power. In other words, for every 1% of voltage reduction a 1% savings in power is achieved. Most research also shows that for every 1% of voltage reduction 3% or more of reactive power reduction was also achieved. This is important because in most cases, reactive power is wasted as a system inefficiency, and requires additional equipment for power factor correction. Thus, limiting reactive power loss via CVR may also reduce capital costs. Most CVR pilot projects have reported a CVR factor of 1.0, although a variety of estimates are also described [24].
# Hybrid Strategy Improved Beetle Antennae Search Algorithm and Application
## 4 Simulation Experiment and Analysis
### Algorithm Data Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Problem** & **Statistical Results** & **GA** & **PSO** & **BAS** & **HSBAS** \\ \hline \multirow{3}{*}{\(f_{1}\)} & Ave & \(1.93\times 10^{-4}\) & \(1.11\times 10^{-16}\) & \(2.04\times 10^{3}\) & \(2.91\times 10^{-22}\) \\  & STD & \(2.17\times 10^{-4}\) & \(2.06\times 10^{-16}\) & \(1.97\times 10^{3}\) & \(1.13\times 10^{-22}\) \\  & Rank & 3 & 2 & 4 & 1 \\ \hline \multirow{3}{*}{\(f_{2}\)} & Ave & \(4.63\times 10^{-4}\) & \(1.05\times 10^{-12}\) & 18.66 & \(3.03\times 10^{-12}\) \\  & STD & \(7.08\times 10^{-4}\) & \(6.70\times 10^{-12}\) & 18.78 & \(7.55\times 10^{-13}\) \\  & Rank & 3 & 2 & 4 & 1 \\ \hline \multirow{3}{*}{\(f_{3}\)} & Ave & 1.35 & 1.38 & 13.29 & 0.45 \\  & STD & 1.49 & 0.85 & 114.31 & 0.57 \\  & Rank & 3 & 2 & 4 & 1 \\ \hline \multirow{3}{*}{\(f_{4}\)} & Ave & 253.60 & 1.43 & 159.20 & 9.44 \\  & STD & 585.90 & 1.00 & 320.37 & 4.37 \\  & Rank & 4 & 1 & 3 & 2 \\ \hline \multirow{3}{*}{\(f_{5}\)} & Ave & 6.63 & 0.0124 & 2.24e8 & 1.07 \\  & STD & 3.75 & 0.0268 & 1.57e8 & 0.49 \\  & Rank & 3 & 1 & 4 & 2 \\ \hline \multirow{3}{*}{\(f_{6}\)} & Ave & 19.79 & 1.11 & 19.85 & \(2.32\times 10^{-2}\) \\  & STD & 0.44 & 0.52 & 0.25 & \(3.71\times 10^{-2}\) \\  & Rank & 4 & 2 & 3 & 1 \\ \hline \multirow{3}{*}{\(f_{7}\)} & Ave & 17.12 & 0.11 & \(5.34\times 10^{8}\) & \(4.74\times 10^{-3}\) \\  & STD & 9.47 & 0.07 & \(2.52\times 10^{8}\) & \(6.15\times 10^{-3}\) \\  & Rank & 3 & 2 & 4 & 2 \\ \hline \multirow{3}{*}{\(f_{8}\)} & Ave & \(-0.97\) & \(-1.0316\) & \(-1.0013\) & \(-1.0316\) \\  & STD & \(9.31\times 10^{-2}\) & \(6.52\times 10^{-16}\) & \(0.12271\) & \(4.61\times 10^{-16}\) \\  & Rank & 4 & 2 & 3 & 1 \\ \hline \multirow{3}{*}{\(f_{9}\)} & Ave & 52.41 & 3 & 8.62e3 & 3 \\  & STD & 111.07 & \(1.54\times 10^{-15}\) & \(4.67\times 10^{4}\) & \(2.21\times 10^{-16}\) \\  & Rank & 3 & 2 & 4 & 1 \\ \hline \multirow{3}{*}{\(f_{10}\)} & Ave & \(-1.66\) & \(-3.27\) & \(-0.35\) & \(-3.35\) \\  & STD & 0.44 & \(5.99\times 10^{-2}\) & 0.68 & \(6.36\times 10^{-2}\) \\  & Rank & 3 & 2 & 4 & 1 \\ \hline \multirow{3}{*}{\(f_{11}\)} & Ave & \(-2.35\) & \(-9.8216\) & \(-4.73\) & \(-10.13\) \\  & STD & 0.75 & 2.19 & 3.59 & 0.59 \\  & Rank & 4 & 2 & 3 & 1 \\ \hline \multirow{3}{*}{\(f_{12}\)} & Ave & 3.36 & 1.82 & 3.64 & 1.27 \\  & Final Rank & 3 & 2 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of HSBAS and three other algorithms for 11 test functions. the BAS algorithm's local optimum and stability problems. In contrast, when dealing with optimization problems, the PSO and GA algorithms have strong global searching abilities with weak local searching abilities, which may lead to the premature maturity.