esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
The effectiveness of the proposed method is shown in this section. The parameters used in the computer simulations are shown in Table 2. We create an urban environment with a number of buildings, as shown in Figure 4. There is an intended node marked by a diamond. There are three eavesdroppers deployed on the roof of some buildings, marked by the black stars. The destination set \(\mathcal{D}\) is marked by a blue disk. Our UAV starts from \(p(0)\). Applying the proposed scheme, the UAV path is generated in less than one second. We show the UAV path in Figure 5. We plot the results of the residual energy, the flight time and the time window in which the communication is secure in Figure 6. The UAV completes the flight to reach the destination set \(\mathcal{D}\) in 102 seconds, and the residual energy is 100 units. In 34% of this period, the UAV can securely communicate with the intended node.  
For comparison, we replace the path selection metric by the minimization of the flight time, and apply it to the above case. The obtained UAV path by this compared method is shown in Figure 7. The corresponding results are shown in Figure 6. Following this path, the UAV arrives at the destination set in 92 seconds, see Figure 5(b). The residual energy is 110 units. This path outperforms the above one in these two terms. However, as seen from Figure 5(a), this path enables the secure communication in only 6% of the period, which is much less than the proposed method.  
The proposed method is not restricted to any environment. We create another urban environment and then apply the proposed method and the compared method to construct the UAV path. The UAV path obtained by the proposed method is shown in Figure 8 with both 2D and 3D views. The time window in which the UAV can securely transmit and the residual energy are shown in Figure 9. Similar to the results in Figure 6, the proposed method obtains the UAV path that has a similar flight time and similar residual energy, but a much larger time window for secure communication than the compared method.  
Figure 6: Simulation results of when the UAV can securely transmit data (**a**) and the residual energy (**b**) in case 1.  
Figure 7: 3D (**a**) and 2D (**b**) views of the UAV trajectory by the compared method.
## 3 System Architecture: Edge-FLGuard
### Communication and Coordination Model  
The system's communication stack prioritizes efficiency and resilience, structured as follows:  
* Upstream Flow: IoT devices send preprocessed data or events to their nearest edge node via MQTT or CoAP protocols.
* Lateral Flow: Edge nodes may share alerts with neighboring gateways for consensus in case of high-severity incidents.
* FL Model Exchange: Model updates are transmitted over encrypted gRPC or MQTT channels using TLS 1.3. Updates are compressed using quantization (e.g., 8-bit float) and may be sparsified to reduce payload size.  
The system is robust to delays and packet loss, with fallback procedures to cache updates locally if communication fails.
# High-Quality Text-to-Image Generation Using High-Detail Feature-Preserving Network
## 4 Experiments
### Ablation Study  
1. Effectiveness of FESFE Module and CA Mechanism: The proposed HDFpNet model comprises the iT2 IG module, which is based on DM-GAN [14]; the FESFE module; and the CA mechanism. An ablation experiment was conducted to evaluate the effectiveness of the CE block and CS block, the FFE block in the FESFE module, and the CA mechanism. The results are presented in Table 3. The feature extraction capability of the proposed model was significantly improved by increasing the number of channels, while including the CA mechanism optimized the overall network structure. The results demonstrate that the combined use of these blocks enabled the outstanding performance of the proposed model for text-to-image generation, thereby verifying the effectiveness of the FESFE module and CA mechanism. Notably, the +CE +FFE +CA +CS sample in the fourth row of Table 3 was created by extracting features through the FFE block before they were processed by the CA mechanism and then by the CS block. This sample was created to verify the effectiveness of the CA mechanism in the HDFpNet model. The results confirm that initiating the CA mechanism while the FFE block was still extracting features reduced the model's performance. In contrast, initiating the CA mechanism after feature extraction by the FFE block had been completed yielded optimal model performance.
2. Study of \(\lambda_{h}\): The hyperparameter \(\lambda_{h}\) was employed to improve the performance of the HDFpNet model. An experiment was conducted using two \(\lambda_{h}\) values: 0.1 and 1. The larger \(\lambda_{h}\) value (i.e., \(\lambda_{h}\) = 1) was found to have a negative impact on the model performance (Table 4). As a result, \(\lambda_{h}\) = 0.1 was used for subsequent analyses. Evaluations based on the FID and R-precision indicators also confirmed that this value yielded the optimal results.
3. Study of F\({}_{\text{HDFpNet}}\): An experiment was conducted to determine the optimal number of \(F_{\text{HDFpNet}}\) iterations required to generate the best feature map. Between one and five iterations were performed. It was found that lower iteration numbers (i.e., \(\leq\)3) resulted in higher performance, with the optimal performance being achieved at three  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\lambda_{h}\) & FID \(\downarrow\) & R-Precision \(\uparrow\) \\ \hline \(\lambda_{h}=0.1\) & 13.89 & 84.33\% \\ \(\lambda_{h}=1.0\) & 15.42 & 82.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation of different \(\lambda_{h}\) on CUB-Bird dataset in terms of FID and R-precision.  
Figure 6: Different styles of images generated by HDFpNet from the same text description.  
iterations (Table 5). In contrast, iteration numbers \(>\) 3 reduced the model performance. As a result, the iteration number was set to three to obtain optimal results in the subsequent analyses. This configuration was verified to yield outstanding image generation and quality results.
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 1 Introduction
### Common Vulnerabilities and Exposures (CVE)  
The CVEs (Common Vulnerabilities and Exposures), maintained by the same organization (MITRE CVE repository: [[https://cve.mitre.org/index.html](https://cve.mitre.org/index.html)]([https://cve.mitre.org/index.html](https://cve.mitre.org/index.html)), last accessed on 24 October 2022, in process of migration to [[https://www.cve.org/](https://www.cve.org/)]([https://www.cve.org/](https://www.cve.org/)), last accessed on 20 August 2022), are related to specific instances of a vulnerability in different products or systems, being made public with various levels of technical details.  
There are currently (20 August 2022) 182,763 vulnerabilities and exposures listed as CVEs, their number growing from year to year. According to MITRE metrics (MITRE CVE metrics: [[https://www.cve.org/About/Metrics#PublishedCVERecords](https://www.cve.org/About/Metrics#PublishedCVERecords)]([https://www.cve.org/About/Metrics#PublishedCVERecords](https://www.cve.org/About/Metrics#PublishedCVERecords)), last accessed on 20 August 2022), 18,375 CVEs have been discovered in 2020, 20,161 CVE in 2021, and in the first half of 2022 a number of 12,380 have been already discovered and published. These vulnerabilities and exposures of various products and systems have different levels of severity, calculated as a Base Score with values from 0 (None/Low Severity) to 10 (Critical Severity), provided per each CVE by the National Vulnerability Database (US National Institute of Standards and Technologies (NIST) National Vulnerability Database: [[https://nvd.nist.gov/](https://nvd.nist.gov/)]([https://nvd.nist.gov/](https://nvd.nist.gov/)), last accessed on 20 August 2022) (NVD). The score depends on the level of exploitability and the impact once the vulnerability is successfully used by attackers.  
The vulnerabilities and exposures identified for specific products and published as CVEs are real-time implementations of CWE technical weaknesses. One CVE may be mapped to multiple CWEs and the opposite, one CWE may be the cause of multiple products vulnerabilities (CVEs). For example, one of the most well-known weaknesses, CWE 89 (Improper Neutralization of Special Elements used in an SQL Command/'SQL Injection') corresponds to 1263 CVEs (to be read as vulnerable software products) in the NVD database, with an average severity of 8.66 (High Severity), which makes it the third security flaw of year 2022 in Top 25 CWE maintained by MITRE.
# Best Practices for Applying and Interpreting the Total Operating Characteristic
## 2 Materials and Methods
We use the maps at 2000 and 2010 to compute four distance maps using the distance module in the GIS software TerrSet version 19.0.8. The first distance is to the crop change pixels during the time interval 2000-2010; thus, the crop change pixels display a distance of zero and other pixels display a positive distance. The second distance is to the crop presence pixels at 2010. The third distance is to the crop change patches during 2000-2010, where a change patch is 1000 or more connected change pixels. The fourth distance is to the crop presence patches at 2010, where a presence patch is 1000 or more connected presence pixels. Figure 4 expresses the distances as Log base 10 of meters + 1 to facilitate visual interpretation. A distance of zero meters appears as zero in the legend. A distance of approximately 1000 m appears as approximately 3 in the legend because Log base 10 of 1000 equals 3. The Log transformation maintains the ranking of the distances; thus, TOC curves are insensitive to the Log transformation. Figure 4a shows the first distance map while Figure 4b shows the fourth distance map, where the darker shades show farther distances for the region within the black polygon. The change pixels are white in Figure 4a, while the presence patches are white in Figure 4b. In the east, Figure 4a shows more smaller distances than Figure 4b because the east has many isolated crop pixels that do not form patches. In the west, Figure 4a shows positive distances from the change pixels, where Figure 4b shows zero distance from the presence patches. This indicates how Figure 4a ignores the stable presence during 2000-2010; thus, Figure 4a shows positive distances in some pixels that have crop presence at 2010, which are not candidates to gain crop during the validation interval. The TOC Curve Generator is free software that reads GIS and other types of files to generate TOC curves [28]. The TOC Curve Generator plots in one TOC parallelogram a TOC curve for each of the methods to compute the distance. A mask restricts the spatial extent to the 74.6 million pixels that display the absence of crop at 2010 because these are the only pixels where crop can gain during the validation interval. The 7.75 million pixels that gained crop during 2010-2020 constitute presence in the binary variable for  
Figure 3: Map of overlay of absence or presence of crop at 2000, 2010, and 2020. The sequence of three letters in the legend indicates the trajectory through time. For example, AAP means absence at 2000, absence at 2010, and presence at 2020.  
the TOC curves. The procedure uses all the pixels in the masked extent, meaning without sampling. Smaller distances appear earlier in the ranking for each of the four distance variables. Thresholds from zero to five appear in increments of 0.2 on a Log base 10 scale. We examined the sensitivity of the results to various increments.
## 5 Results and Discussion
### Discussion
A detailed breakdown of these anomalies and their origins not only validates the model's effectiveness but also provides actionable insights for maintenance teams. Early detection of such issues is vital for taking quick actions to prevent costly downtimes and maintain operational efficiency. Blade misalignment (Figure 7a): Detected in the turbine's compressor blades, misalignment can cause uneven loading of the blades, leading to excessive vibration and potential mechanical failure. This directly affects the efficiency and longevity of the turbine. To avoid the proposed problem, routine visual and instrumental inspections should be performed to check blade alignment and wear. Moreover, the use of dynamic balancing techniques during maintenance cycles to ensure uniform weight and alignment of turbine blades can prevent the aforementioned problem. Air intake obstruction (Figure 7b): This anomaly was traced to the air inlet section of the turbine. Debris or other obstructions can disrupt airflow, causing instability in combustion and increasing operational risks. To avoid the proposed problem based on Figure 7b, implementing the following solutions can help prevent anomalies: (1) Advanced Filtration System: upgrade existing air filtration systems to prevent debris from the intake tracts, (2) Regular Cleaning Schedules: establish strict cleaning schedules for the air inlet area to remove any accumulated debris, and (3) Real-time Monitoring: deploy sensors at strategic points in the air inlet system to monitor for any signs of obstruction or reduced airflow efficiency.
###### Abstract  
Twin-I girder bridge systems composite with precast concrete deck have advantages including construction simplification and improved concrete strength compared with traditional multi-I girder bridge systems with cast-in-place concrete deck. But the cracking is still a big issue at interior support for continuous span bridges using twin-I girders. To reduce cracks occurrence in the hogging regions subject to negative moments and to guarantee the durability of bridges, the most essential way is to reduce the tensile stress of concrete deck within the hogging regions. In this paper, the prestressed tendons are arranged to prestress the precast concrete deck before it is connected with the steel girders. In this way, the initial compressive stress induced by the prestressed tendons in the concrete deck within the hogging region is much higher than that in regular concrete deck without prestressed tendons. A finite element analysis is developed to study the long-term behaviour of prestressed concrete deck for a twin-I girder bridge. The results show that the prestressed tendons induce large compressive stresses in the concrete deck but the compressive stresses are reduced due to concrete creep. The final compressive stresses in the concrete deck are about half of the initial compressive stresses. Additionally, parametric study is conducted to find the effect to the long-term behaviour of concrete deck including girder depth, deck size, prestressing stress and additional imposed load. The results show that the prestressing compressive stress in precast concrete deck is transferred to steel girders due to concrete creep. The prestressed forces transfer between the concrete deck and steel girder cause the loss of compressive stresses in precast concrete deck. The prestressed tendons can introduce some compressive stress in the concrete deck to overcome the tensile stress induced by the live load but the force transfer due to concrete creep needs be considered. The concrete creep makes the compressive stress loss and the force redistribution in the hogging regions, which should be considered in the design the twin-I girder bridge composite with prestressed precast concrete deck.  
concrete creep; prestressing stress; compressive stress; FE analysis; force transfer Article
# Anaerobic Digestion of Duckweed Used to Remediate Water Contaminated with Zinc and Ammonium
## 4 Discussion
### Anaerobic Digestion of Duckweed Used for Zn Phytoremediation
#### 4.2.2 Effect of Zn on Acidification Step  
Zn is also essential for hydrolysis and the acidogenesis process of AD [67]. The solid and liquid fractions with different background levels of Zn indeed produced different amounts of VFAs (Figures 7 and 8). Higher VFA concentrations were related to the fast hydrolysis and acidification of the duckweed substrate, which slowed down the start ofmethanogenesis in all solid fractions (Figure 8). Indeed, the degradation of the substrate and production of VFAs changed the pH of the AD processes (Figure 6c), especially for duckweed cultured with Zn (pH of 5.13-5.80), resulting in the inhibition of methanogenic activity, methane production, and acido-/acetogenic bacterial activity. Moreover, [PERSON] and [PERSON] [68] found that low and high concentrations of Zn inhibited the production of acetic acid in AD of dairy wastewater, while the present study showed that Zn enhanced the production of AA in the solid fractions of duckweed cultured with NH\({}_{4}\)+-N (T5 and T6; Figure 8e,f). In contrast, in the liquid fractions of duckweed cultured with solely Zn, the presence of Zn did not significantly affect VFA production (T2 and T3; Figure 7b,c).
## 3 Results and Discussion
### Machine Learning Classification
#### 3.2.4 Model Evaluation  
Model performance, encompassing both the baseline and LBP-based SVM classifiers, is assessed using real-time EEG data. This evaluation, detailed in Table 1, includes metrics computed post-training and testing phases, ensuring the models' effectiveness in classifying brain states from live EEG readings. The evaluation criteria focus on accuracy, sensitivity, and specificity, providing a comprehensive understanding of each model's capabilities.  
Figure 7: The baseline model and the Local Binary Pattern Histogram (LBPH) approach are exemplified using a sample brain topology measurement.
## 6 Experiments and Results
### Heating Performance Test of the Conductive Center Pole at Room Temperature  
A conductive center pole and a conventional wire were used to power the carbon nano-material heater and heat the thermal oil, respectively, to compare its electrothermal conversion efficiency. The experimental platform is shown in Figure 23.  
Figure 21: Physical prototype of the conductive center pole.  
Figure 22: Conductive center pole resistance test.  
Figure 23: Comparative heating performance experiment.  
Using a 12 V DC power supply to supply power to the load, 100 mL of thermal oil was heated with a heating target temperature of 90 \({}^{\circ}\)C, and kept at a constant temperature for 10 min after reaching the target temperature. The results of the experiment are shown in Figure 24. The thermal oil was heated to 90 \({}^{\circ}\)C through the conductive center pole and the conventional wire, which took 8 min and 8 min and 35 s, respectively.
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 5 Summary and Conclusions  
In this study, we evaluated different single- and two-level flux filtering strategies with regards to decoupling and its impact on above canopy derived CO\({}_{2}\) fluxes. We hypothesized that conventional single-level EC flux filtering strategies like the u*filtering might not fully capture the forest CO\({}_{2}\) exchange at the studied ecosystem. Concurrent below- and above-canopy EC measurements for the year 2019 showed the different carbon exchange dynamics of the respective layers well. While the whole canopy was a net carbon sink during spring and summer, the below canopy layer was a net source over the whole year. Though we observed regular and frequent decoupling of the below and above canopy air masses, we could not observe an effect of decoupling on the annual carbon exchange. We conclude that the exceptional flat terrain around our study site inhibits the removal of CO\({}_{2}\)-rich air from the measurement volume, consequently, a potentially biasing impact of decoupling on above canopy derived CO\({}_{2}\) exchange on longer time scales.  
Our study underlines the need of explicit decoupling investigations at each forest ecosystem, as decoupling is strongly site-specific, depending on canopy properties, vegetation structure, site meteorology and tower surrounding topography. Synthesis decoupling analyses of a multitude of forest sites around the globe can shed more light on the characteristics of decoupling in dependency of canopy properties, site meteorology and tower surrounding topography.  
Conceptualization: N.K. and G.J.; Methodology, N.K., G.J. and L.S.; Software, N.K. and G.J.; Validation, N.K., G.J. and L.S.; Formal Analysis: N.K. and G.J.; Investigation, N.K.; Resources, N.K. and G.J.; Data Curation, N.K. and G.J.; Writing--Original Draft Preparation, N.K.; Writing--Review and Editing, G.J., L.S. and M.P.; Visualization, N.K., Supervision, M.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education, Youth and Sports of the Czech Republic (CR) within the CzeCOS program, grant number LM2018123 and project SustES Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions CZ.02.1.01/0.0/0.0/16_019/0000797.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** This work was supported by the Ministry of Education, Youth and Sports of CR within the CzeCOS program, grant number LM2018123. N.K and L.S. acknowledge the support by the Ministry of Education, Youth and Sports of the Czech Republic for SustES--Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16019/0000797).  
**Conflicts of Interest:** The authors declare no conflict of interest.