esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, the spatiotemporal patterns of NPP and its correlation with climatic factors as well as vegetation phenology during 2001-2014 in China were investigated, and the main conclusions can be summarized as follows:  
1. Validation results showed that NPP derived from MODIS performed well in China's ecosystem compared with the other two models, with an increased \(R^{2}\) value and a decreased RMSE and MAE. However, this conclusion should be made with caution due to few observed sites.
2. During the entire study period, annual NPP showed an increasing trend at the national scale. However, the increasing trends in NPP were not linear, with a slower rate after 2004. At a regional level, annual NPP displayed an increasing trend, despite the occurrence of a turning point year in Northern China and the Tibetan Plateau. However, the NPP in Southern China decreased during the whole study period, which can mainly be explained by climate change as well as fierce anthropogenic activities.
3. Generally, climatic factors and LOS were positively correlated with NPP, and the direct effect of solar radiation on NPP was the largest compared with temperature and precipitation. Due to local climate conditions, the relationship between NPP and climatic factors were diverse at a regional scale.  
The following are available online at www.mdpi.com/1999-4907/8/10/361/s1, Table S1: Sites information of the field Net primary productivity (NPP) data used in this study; Figure S1: Vegetation types (DNF, deciduous needle-leaf forest; ENF, evergreen needle-leaf forest; EBF, evergreen broadleaf forest; DBF, deciduous broadleaf forest; and GM, grassland and meadow) as well as three sub-region divisions (**a**), and provinces distribution (**b**) in China; Figure S2: Distribution of meteorological stations across China, red points represent stations for temperature and precipitation, and black triangles represent stations for solar radiation; Figure S3: Spatial distribution of Turing Point (TP) years (**a**) and corresponding AAIC values (**b**) of the NPP in China for the period 2001-2014; Figure S4: Spatial distribution of the Hurst exponent in China from 2001 to 2014.  
This study is supported by the National Natural Science Foundation of China (Grant No. 41431178), the Natural Science Foundation of Guangdong Province, China (Grant No. 2016A030311016), the Innovation Project of Guangdong Province Water Resources Department (Grant No. 2015-02) and the Central Fund Supporting Nonprofit Scientific Institutes for Basic Research and Development (PM-zx021-201407-007).  
[PERSON] and [PERSON] outlined the research topic, assisted with manuscript writing, and coordinated the revision activities. [PERSON] and [PERSON] performed data collection, data analysis, the interpretation of results, manuscript writing, and coordinated the revision activities.  
The authors declare no conflict of interest.
# Biosynthesis of Silver Nanoparticles Using _Tabernaemontana ventricosa_ Extracts
## 3 Results and Discussion
### Fourier Transform Infrared (FTIR) Spectroscopy  
The FTIR spectroscopy measurements were undertaken to determine the biomolecules that are likely responsible for the reduction of Ag ions and the capping of the bio-reduced AgNPs produced by the leaf and stem extracts of _T. ventricosa_. The FTIR spectra displayed different stretches and bending of bonds at various peaks for all synthesized AgNPs using leaf methanol (LM), stem methanol (SM), fresh leaf (FL), fresh stem (FS), powdered leaf (PL), and powdered stem (PS) extracts due to the presence of different bioactive compounds in the above treatments (Table 4). However, the observed peak patterns were very similar \begin{table}
\begin{tabular}{c c c} \hline \hline
**Extracts** & **Nanoparticle** & **Zeta Potential (mV)** \\  & **Diameter (nm)** & **Zeta Potential (mV)** \\ \hline Leaf methanol & 80.9 \(\pm\) 8.8 & \(-\)3.5 \(\pm\) 5.1 \\ \hline Stem methanol & 70.2 \(\pm\) 6.1 & \(-\)30.3 \(\pm\) 0.1 \\ \hline Fresh leaf & 125.0 \(\pm\) 41.8 & 15.6 \(\pm\) 5.6 \\ \hline Fresh stem aqueous & 63.9 \(\pm\) 63.9 & 7.2 \(\pm\) 7.2 \\ \hline Powdered leaf & 120.5 \(\pm\) 36.2 & 3.5 \(\pm\) 0.1 \\ \hline Powdered stem & 147.4 \(\pm\) 7.4 & 5.9 \(\pm\) 0.0 \\ \hline Mean \(\pm\) standard error (\(n\) = 3). & & \\ \end{tabular}
\end{table} 
Table 3: Average nanoparticle diameter (nm) and zeta potential (mV) of the synthesized AgNPs using the leaf and stem extracts (methanolic, fresh, and powdered) of _T. ventricosa_. across all AgNPs produced, with a few significant differences in peaks observed for certain AgNPs (Figure 7).
## 4 Experiments
### Results on COVID-HSS Dataset
#### 4.3.1 Qualitative Results on COVID-HSS  
Figures 4-6 represent the infection segmentation results of the COVID-HSS dataset. The proposed transYLmtMT demonstrates clear superiority over various baseline methods. It is evident that the transYLmtMT algorithm provides a relatively fuzzy boundary, especially in subtle infection areas. The success of transYLmtMT can be attributed to the coarse-to-fine segmentation strategy employed in this paper, that is, the transYL network first roughly locates the lung infection area, then applies multiple edge attention modules to refine the contours. This mirrors the workflow of clinicians--broadly assessing for pathology first, then scrutinizing boundaries, resulting in excellent performance.  
Figure 4: Visual comparison of ordinary dataset results. [PERSON]. [25], [PERSON]. [26], [PERSON]. [50], [PERSON]. [19].  
Specifically, early coarse prediction primes the model to focus on infectious areas and provides context to guide localization. The fine-grained edge modules then adhere to subtle  
Figure 5: Visual comparison of serious dataset results. [PERSON]. [25], [PERSON]. [26], [PERSON]. [50], [PERSON]. [19].  
Figure 6: Visual comparison of critical dataset results. [PERSON]. [25], [PERSON]. [26], [PERSON]. [50], [PERSON]. [19].  
anatomical cues to precisely delineate tissue transitions. They capture nuanced topology critical for clinical assessment.  
In summary, transYL's hierarchical segmentation dynamically zooms in on areas of interest through learned attention. By coordinating the global and local views much like human experts, it extracts clinically valuable structure while ignoring irrelevant regions. The tailored strategy maximizes efficiency and accuracy for practical usage.  
Public datasets have limited data distribution, requiring algorithm validation on private datasets; clinical datasets present real-world challenges like artifacts, noise, and protocol variations absent in open datasets, necessitating robustness assessments; false and over-segmentation arise due to various factors, addressable via robust segmentation algorithms tailored to clinical data using techniques like deep learning, multi-modal integration, context-awareness, and post-processing alongside rigorous algorithm validation.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 3 Related Work
### Static Approach  
In 2018, [PERSON] et al. [12] proposed a malicious PowerShell detection method based on AST (Abstract Syntax Tree). They combined the traditional static analysis and deep learning technique, converted the PowerShell script into AST, and built embedding vectors for each AST node type based on the built PowerShell corpus. Thus, the type of malicious PowerShell family can be classified by learning the embedding of AST nodes. [PERSON] et al. [13] proved the effectiveness of character-level deep learning technology in malicious script detection. [PERSON] et al. [14] proposed a method for detecting malicious extension plugins of browsers based on machine learning. They extracted static features and dynamic features from the source code. The accuracy of their method on the testing dataset reached 95.18%, and the false positive rate was 3.66%. In 2019, [PERSON] et al. [15] constructed a malicious JavaScript detection model based on AST and CFG (Control Flow Graph), which analyzed the structure and behavior of code, extracted the syntax features of the code from AST by the tree-based CNN (Convolutional Neural Network), and extracted the semantic features of the code from CFG by the graph-based CNN. [PERSON] et al. [16] proposed a method for detecting PHP WebShell, which collected the syntax and semantic features of code, paid  
Figure 5: A malicious class file example.  
attention to (1) the communication between the WebShell and attacker, (2) the adaptation to the runtime environment, and (3) the use of sensitive operations; they then extracted (1) the use of global variables features, (2) code adaptation and automation features, and (3) data flow features. In 2021, after studying the existing malicious web script detection methods, [PERSON] et al. [17] found that most of the current research only focused on specific programming languages and lacked universality. Therefore, they proposed a detection method based on text features and AST node sequence features, building a model that can detect multi-language web scripts. In this model, TF-IDF is used to obtain the vector representation of AST, and the random forest model is trained by combining text features. In 2022, [PERSON] et al. [18] relied on SdA (Stacked Denoising Auto-encoder) to extract features from PowerShell scripts, eliminated the process of manually building features and trained the XGBoost model to realize detection.
## 3 Design of Fuzzy Logic System  
### Data Preparation  
A fuzzy logic control system has four inputs, namely temperature, charging current, battery SoC, and SC SoC, and two outputs, namely the battery transfer ratio and SC transfer ratio. The boundary ranges of these inputs were determined by the experimental studies, and a dataset of 1000 units was created. Table 4 shows the data entered into the \"Matlab(r) Fuzzy Logic\" system. The main objective is to prolong the battery life by maintaining battery health by directing energy from regenerative braking to the SC under high temperatures, high charging currents, and high battery SoC conditions.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 6 Conclusions  
This study presents a multi-objective path planning method tailored for typical urban traffic scenarios. The framework integrates A*search for global path computation with a Deep Q-Network (DQN) module for local refinement, enabling coordinated planning that balances efficiency, safety, and adaptability.  
To support context-aware optimization, a dynamic weight adjustment mechanism is introduced, allowing real-time prioritization among competing objectives such as travel time, congestion, risk, and avoidance zones. Multi-source data--including real-time traffic, historical usage patterns, and sensor-derived information from GPS and traffic cameras--are fused to enhance environmental awareness and decision quality. These trade-offs are particularly evident under varying travel scenarios. For example, A* performs well in predictable environments with static conditions, such as low-traffic or structured routes. In contrast, DQN excels in dynamic or uncertain settings, where obstacle avoidance and risk mitigation are critical. The hybrid approach adapts across these contexts, leveraging scenario-dependent objective weights.  
This balance reflects the classic exploration-exploitation trade-off in planning under uncertainty. A* emphasizes the deterministic exploitation of known paths, while DQN introduces learning-based exploration to handle unmodeled disruptions. Their integration enables both robust guidance and adaptive responsiveness.  
Experimental evaluations based on OpenStreetMap road network data demonstrate that the proposed method achieves superior performance across multiple real-world scenarios. Compared with Global A* and DQN-only approaches, the hybrid A*-DQN framework exhibits shorter paths than DQN, and lower risk than A*, validating the advantage of combining global search efficiency with local adaptability. Moreover, the results highlight the importance of dynamic weighting, which enables the planner to adapt its behavior to varying travel contexts, such as rush hour or night-time delivery.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
###### Abstract  
In this paper, we identify the main challenges and problems related with the management and orchestration of Virtualized Network Functions (VNFs) over aerial networks built with Small Unmanned Aerial Vehicles (SUAVs). Our analysis starts from a reference scenario, where several SUAVs are deployed over a delimited geographic area, and provide a mobile cloud environment that supports the deployment of functions and services using Network Functions Virtualization (NFV) technologies. After analyzing the main challenges to NFV orchestration in this reference scenario from a theoretical perspective, we undertake the study of one specific but relevant aspect following a practical perspective, i.e., the limitations of existing transport-layer solutions to support the dissemination of NFV management and orchestration information in the considered scenario. While in traditional cloud computing environments this traffic is delivered using TCP, our simulation results suggest that using this protocol over an aerial network of SUAVs presents certain limitations. Finally, based on the lessons learned from our practical analysis, the paper outlines different alternatives that could be followed to address these challenges.  
NFV; Management and Orchestration (MANO); SUAV; intermittent availability +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Effect of Heat Flux  
Figure 4 presents the alteration of the HTC of the evaporator with the applied heat flux to the evaporator. As can be seen, with increasing the applied heat flux to the evaporator region, the HTC of the heat pipe increases, which can be attributed to the intensification of the heat-transfer mechanism from convective heat transfer towards the nucleate boiling. Also, at higher heat fluxes, the rate of the evaporation increases as well. Notably, the slope of increase from a specific heat flux changes and is promoted. For example, for heat fluxes \(<\)25 kW/m\({}^{2}\), the HTC increases slightly with heat flux; however, for heat fluxes \(>\)25 kW/m\({}^{2}\) the HTC increases significantly, which can be attributed to the incipience of the nucleate boiling in the evaporator, which promotes the HTC of the system. As an example, at heat flux (HF) of 20 kW/m\({}^{2}\), the HTC for pentane was 1755 W/(m\({}^{2}\)K), though, at HF = 45 kW/m\({}^{2}\), the HTC was 2900 W/(m\({}^{2}\)K). Also, for the nanofluids, the HTC was larger than those of measured for pentane. For instance, for wt. % = 0.1, at HF = 90 kW/m\({}^{2}\), the HTC was 7900 W/m\({}^{2}\). K, whereas it was 8210 W/m\({}^{2}\). K and 9820 W/m\({}^{2}\). K for wt. % = 0.2 and 0.3, respectively.  
Figure 5 also shows the enhancement in the HTC of the evaporator. As can be seen, the enhancement in the HTC for region in which HF is \(<\)25 kW/m\({}^{2}\) is relatively small in comparison with those of recorded for the HF \(>\)25 kW/m\({}^{2}\). For example, for wt.% = 0.1, the HTC enhancement at HF of 15 kW/m\({}^{2}\), and 25 kW/m\({}^{2}\) was 1.08 and 1.12, respectively. However, at HF of 35 kW/m\({}^{2}\) and 80 kW/m\({}^{2}\), it was 1.24 and 1.13. As discussed, this can be attributed to the incipience of the nucleate boiling heat transfer, which further promotes the HTC of the system. Also, owing to the presence of the GNPs, the Brownian motion and the thermophoresis effect of the GNPs, improves the HTC of the system. Hence, nanofluid showed better HTC in comparison with pure pentane.  
Figure 4: Alteration of the HTC with heat flux for n-pentane and nanofluids.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 2 Technical Considerations for RIS
### Passive, Active, and Hybrid RIS
Only when \(N=2.5\times 10^{6}\) does the performance gain achieved by the passive RIS become comparable to the performance gain achieved by the active RIS. Although the active RIS has been studied in many applications, most of the existing work has focused on narrowband scenarios operating on a single carrier. However, in a wideband scenario, the amplitude and phase shift of the reflected signal depend on the frequency of the incident signal [130]. To address this issue, new device designs have been proposed to achieve wider bandwidths, as shown in Figure 33[131]. For the passive RIS, integrating multiple phase shifters operating at different frequencies in each unit cell can independently reconfigure the phase shift for incident waves of different bands to achieve broadband characteristics [132]. However, since the power ampl  
Figure 32: Passive vs. active RIS performance comparison. Adapted with permission from [71]. Copyright 2023 IEEE. (**a**) \(N\) ranges from 10 to 1000. (**b**) \(N\) ranges from \(10^{4}\) to \(3\times 1\). cannot be tuned independently for different frequencies, broadband characteristics can be achieved by integrating multiple amplification and phase shifters operating at different frequencies in each unit cell, as shown in Figure 34[133]. Recently, hybrid RIS structures have been introduced to overcome the limitations of passive RISs, especially in poor transmission scenarios such as low signal-to-noise ratio (SNR) and/or severe path loss [134; 135]. The core idea of hybrid RISs is to add a few active unit cells to the traditional passive RISs, which can simultaneously reflect and amplify the incident signal [136], as shown in Figure 35a. As a result, hybrid RISs can reduce the impact of redundant path loss and significantly improve the system performance in terms of spectral efficiency [137], secrecy rate [138], energy harvesting [139], and reliability [140], as shown in Figure 35b. Spectral efficiency and energy efficiency are key requirements for 6G networks, and the active RIS introduces a new feature called signal amplification, which helps to improve spectral efficiency at the cost of higher power consumption and hardware cost [141]. Therefore, hybrid RISs should flexibly determine the number of active and passive unit cells to maximize energy efficiency. Figure 34: Narrowband vs. wideband active RIS structure comparison. Adapted with permission from [133]. Copyright 2023 IEEE. Figure 33: Broadband active RIS structure. Adapted with permission from [131]. Copyright 2022 IEEE.
# Implementation Studies of Bacteriophage \(\lambda\) O
## 3 Discussion
### RK\({}^{-}\) Mutant Selection Considerations  
[PERSON] and [PERSON]'s laboratories collaborated in mapping [40] and sequencing [46] some of the nine \(r\) mutants selected by [PERSON] [20], who based his selection on the assumption that the \"replicator\" gene was different from initiator genes \(O\) or \(P\). They concluded that regions of the initiator gene, i.e., \(O\), overlap the replicator site, now termed _ori_\(\lambda\). We show herein that regions of _ori_\(\lambda\) are not essential for activity of the O initiator protein. We previously demonstrated [39] that \(\lambda\)_N_am7 am53 _cl_857 \(r95\), or \(r96\) prophages in _su\({}^{+}\)_ hosts (hence the prophages were phenotypically \(N^{+}\)) were defective in _ori_\(\lambda\) replication initiation, were 7- to 17-fold reduced in _pR-\(Q\)_ transcription, and did not yield any increase in phage titer after prophage induction. Five of the nine \(r\) mutants have now been sequenced and each has a small, in-frame deletion within \(O\). In contrast, among hundreds of RK\({}^{-}\) ilr mutants isolated from a defective \(N^{+}\) prophage (Figure 1), none were identified with in-frame deletions in \(O\). Nor, have other instances involving use of the PDS, or similar selections resulted in small in-frame deletions within O being reported [12; 16; 17; 58; 59]. The major theme of those reports was that perturbing the expression of _pR-\(O\)-\(P\)_ can influence replication initiation. The recent documentation on the lethal effect of \(P\) expression (see [25; 36] and included references) may help to explain the selection differences, i.e., constitutive \(P\) expression is lethal to a cell, even if there is no replication initiation from _ori_\(\lambda\). [PERSON]'s study required several other unstated assumptions: (i) replication initiation will occur from an induced \(N\) mutant prophage with reduced transcription of \(O\)-\(P\) (we note above that this was not observed for the \(r95\) and \(r96\) mutants); (ii) in induced \(N\)-defective prophage, sufficient rightward transcription occurs across (or near to) the replicator (_ori_\(\lambda\)) site to provide the requirement for transcriptional activation (as noted above, even when the \(r\)-mutant prophages were made \(su^{+}\) rightward transcription across \(pR\)-\(Q\) was significantly reduced); and (iii) the constitutive expression of the replication initiation proteins O and P or other de-repressed \(\lambda\) gene products will not be lethal to the host cell. Assumptions (i) and (ii) may still require additional study. A previous characterization of RK\({}^{-}\) ilr survivor mutations from \(N^{+}\) prophage, revealed that all were defective in \(P\) or had insertions in \(O\) that could limit downstream \(P\) expression [48], suggesting that assumption (iii) is unlikely.
# A Machine Learning View on Momentum and Reversal Trading
## 5 Discussion
### Analysis of the Net Asset Values of the Portfolios  
Figures 5-8 show the daily Net Asset Value (NAV) curves of portfolios built with the proposed machine learning models, while Figure 9 shows the whole portfolio performance comparison for the best strategies and models.  
We identified some interesting phenomena: (1) in the fluctuating market duration, both the best DT and MLP strategies performed poorly. Their NAVs were always below than that of either momentum or reversal strategy, or even both of them, for most of the time when the market trend was not clear. In contrast, the SVM was able to increase its NAV during this period. Furthermore, the LSTM achieved an excellent performance with an increasing NAV. (2) In the bullish market duration, the NAVs of all machine learning models went up dramatically. (3) In the bearish market duration, only the SVM and MLP models were stable and kept their returns. Unfortunately, the performances of LSTM and DT worsened quickly.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model/Strategy** & **Momentum** & **Reversal** & **DT** & **SVM** & **MLP** & **LSTM** \\ \hline Average Return & 21.73\% & 51.95\% & 55.73\% & **77.48\%** & 61.43\% & 45.89\% \\ Average Sharpe Ratio & 0.26 & 0.48 & 0.55 & **0.67** & 0.56 & 0.41 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Average measures for the models and strategies.  
These findings suggest we may adopt the LSTM in the fluctuating market, select any machine learning model in the bull market, and change to the SVM to avoid a great loss when a market crash is coming.