esa-sceva/satcom-chunk-collection huggingface dataset:
## Evaluation
### Discussion  
The above experimental results have demonstrated the potential of the SOOA in compromising the robustness of challenge-based CIDNs, that is, slowing the detection speed of malicious nodes. As this is an initial study in this field, there are some improvements that can be made in our future work.  
* _The impact of partner nodes' selection_. In this work, we assume that each partner has the same impact on the decision in the target node. Thus, there is no need to consider how to select a partner node for the SOOA. However, if we consider that the target node may give different weights to its partner nodes, then there is a need to identify which type of partner node can be attacked. This is an interesting topic for our future work.  
Figure 7: Trust value of malicious node under different scenarios in a real environment.  
* _A variety of combinations._ In this work, we only evaluated two combinations, namely, T3U3 and T4U2, under two scenarios. The obtained results have demonstrated the influence of the SOOA on the robustness of the challenge mechanisms. In future work, it will be an interesting topic to investigate the trend of trust values in other combinations, for example, T4U3 and T5U5.  
To defend against this type of attack, there is a need to consider deploying additional security tools to enhance the performance of challenge mechanisms.  
* _Emphasizing the impact of malicious actions._ In CIDNs, if a node wishes to evaluate a node's trustworthiness, it has to collect information from other trusted nodes. Our attack allows malicious nodes to behave maliciously, without a timely detection. One potential solution is to punish more for malicious actions, if detected by a node, for example, by building a trust computation by means of IS, which can give greater weight to expert nodes.
* _Employing additional measurements._ In challenge-based CIDNs, the trustworthiness of a node is mainly determined by challenges, but the challenges have to be sent over a period of time, rendering the network vulnerable to advanced attacks. To enhance the robustness of CIDNs, additional measures should be considered to evaluate the trustworthiness of a node, for example, packet-level trust [11].  
Overall, our work validates that existing challenge mechanisms would suffer from advanced insider attacks in practice, as a result of the adopted assumptions. Additional security mechanisms can be considered to enhance the robustness of challenge-based CIDNs.
# Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset
## 6 The Proposed HTTP-GET Log CCE Dataset Methodology
### Gathering and Storing Logs  
This stage involves collecting and storing all the logs from the sources that changed during the HTTP-GET request generation. These sources include the access log, the VM activities log, and the VM resources log. These logs have large volumes (vast) and require a proper solution to handle them. Figure 4 illustrates the method of the proposed dataset.  
The Hadoop ecosystem is a common solution for big data [62]. It offers a high-availability distributed solution for big data challenges. Several platforms use Hadoop to spread their data. These platforms are Microsoft Azure HD Insights, Apache Hadoop, IBM BigInsights, Hortonworks, and Cloudera tools [63]. Hadoop has the main components, such as a software system for distributed storage called HDFS, a data-collecting tool called Flume, and a MapReduce programming model to process big data.  
This solution can manage a large volume and complexity of raw data logs from different sources. It can take these data logs and put them into HDFS by Flume. Then, it can process them through MapReduce (the data processor).  
* **Gathering the log using Flume** In this stage, we used Flume [64; 65] to collect and store all the logs from the sources that changed during the HTTP-GET requests generation. These sources are the access log, the VM activities log, and the VM resources log. These logs have large volumes (vast) and require a proper solution to handle them. Using Flume can improve the detection and prediction of DDoS attacks [63]. Furthermore, Flume can gather and aggregate large and complex amounts of log data from multiple sources without changing or affecting the original logs during the transfer from sources to the storage system.
* **Logs repository** The HDFS supports fast repository storing and the splitting of vast and complex data to guarantee the availability of logs data on the Hadoop nodes, which helps with parallel processing as the system can equip it and allow MapReduce [63; 64] to be feasible for the analysis; additionally, it can filter out the basic features and convert the raw data logs into a clean dataset that is easily readable without modifying the content.
## 1 Introduction
Landsat remote sensing data have 30 m spatial resolution and can provide sufficiently detailed spatial information to capture the footprints--thus, they are useful for model parametrization [24]. Landsat data, with high spatial resolution of 30 m and a spectral sensitivity consistent with plant function, can provide valuable information used to estimate vegetation GPP [25]. For example, Landsat-derived vegetation indices have been used to accurately simulate GPP for croplands and wetlands [25; 26]. This is because the patterns and spatiotemporal variability of GPP are attributed to many processes, including land-use changes occurring at fine spatial scales such as agricultural intensification and deforestation [19]. Therefore, Landsat data are well suited to assessing the feedbacks between these finer-scale processes and GPP variability [24]. Previous studies have reported that high resolution GPP products were superior to medium resolution GPP products for assessing details at finer scales in land management activities such as crop agriculture, forestry, and grazing [19]. The 16-day revisit cycle and cloud contamination provide limitations on using Landsat data alone. However, in this work, we tested whether using fine scale data in combination with flux tower footprints and coarse resolution MODIS data could give a more accurate assessment of vegetation GPP. To our knowledge, no study has optimized the parameters of LUE models at both 500 m and 30 m spatial resolution and then compared the model performances using each optimized parameter at 500 m and 30 m spatial resolution, respectively. The main aims of our study were to: (1) calculate the Landsat-footprint NDVI using a flux footprint prediction model and the MODIS-footprint NDVI using a fixed footprint of 3 km \(\times\) 3 km or 1 km \(\times\) 1 km; (2) optimize the parameters and compare the performancesof the optimized model using the Landsat-footprint NDVI and MODIS-footprint NDVI, respectively; and (3) examine the degree of improvement in the simulated GPP using the high resolution Landsat NDVI as compared to the coarser MODIS NDVI.
# The Aerodynamic Analysis of a Rotating Wind Turbine by Viscous-Coupled 3D Panel Method
## 2 Numerical Formulation  
### Potential Flow  
Potential flow theory assumes an inviscid, incompressible (\(\
abla\cdot\overset{\rightharpoonup}{u}=0\)), irrotational (\(\
abla\times\overset{\rightharpoonup}{u}=0\)) fluid flow \(\overset{\rightharpoonup}{u}\), which may, due to irrotationality, be expressed as the gradient of a scalar, i.e., \(\overset{\rightharpoonup}{u}=\
abla\phi\). This scalar, called velocity potential, is substituted into the continuity equation \(\
abla\cdot\overset{\rightharpoonup}{u}=0\) to give us the Laplace equation,  
\[\
abla^{2}\phi=0 \tag{1}\]  
This is the governing equation for potential flow, and two of its solutions are:  
\[\phi=-\frac{\sigma}{4\pi\sigma} \tag{2}\]  
\[\text{and }\phi=\frac{\mu}{4\pi}\frac{\partial}{\partial n}\left(\frac{1}{r}\right) \tag{3}\]  
which are the potentials for a source \(\sigma\) and a doublet \(\mu\). Panel methods are able to simulate the flow fields around arbitrary bodies by exploiting the velocity fields induced by distributions (panels) of such solutions. The perturbation potential at each panel's centroid is obtained by summing the influences of all body and wake panels:  
\[\sum_{k=1}^{N_{p}}\frac{1}{4\pi}\int_{\text{body}}\mu\overset{\rightharpoonup} {n}\cdot\
abla\left(\frac{1}{r}\right)dS+\sum_{l=1}^{N_{\text{br}}}\frac{1}{4 \pi}\int_{\text{wake}}\mu\overset{\rightharpoonup}{n}\cdot\
abla\left(\frac{ 1}{r}\right)dS-\sum_{k=1}^{N_{p}}\frac{1}{4\pi}\int_{\text{body}}\left(\frac{ \sigma}{r}\right)dS=0 \tag{4}\]  
Differentiating doublet strengths with respect to local coordinates gives the induced velocity field on the body surface, Equation (5), which is then used to obtain the pressure field, Equation (6):  
\[\overset{\rightharpoonup}{u}_{\text{local}i}=\frac{\partial\mu_{i}}{\partial x _{\text{local}i}} \tag{5}\]  
\[C_{p,i}=1-\frac{\overset{\rightharpoonup}{u}_{i}^{2}}{U_{\text{rel}}^{2}} \tag{6}\]  
where \(U_{\text{rel}}\) is the undisturbed relative flow velocity.  
Figure 1: Wind turbine rotor with trailing helical wake, showing quadrilateral panels.
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 4 Simulation Results
### DAILC Under Sensor Saturation and Variable Loads  
In this case, we applied the DAILC method to the test AC microgrids considering both sensor saturation and variable load conditions. In microgrids, the unpredictability of load variations is primarily driven by the uncertainty of industrial production activities, fluctuations in commercial demand, changes in residential electricity usage habits, and variability in seasonal and weather conditions. Therefore, microgrids must possess excellent adaptability and rapid response capabilities to ensure the provision of stable and reliable power supply under various load demand scenarios. As shown in Figure 5, the saturation threshold of the sensor was set to 315 V, and load3 on the DG2 bus was added and removed at 0.8 s and 1.6 s, respectively. Observations from Figure 5 indicate that during the transient process of the microgrid, the peak output voltage was limited to the saturation threshold of  
\begin{table}
\begin{tabular}{l l l} \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(\beta_{1}\) & Step size & 0.3 \\ \(\delta_{1}\) & PPDs penalty factor & 0.5 \\ \(\beta_{2}\) & Weight factor & 0.8 \\ \(\delta_{2}\) & Input penalty factor & 0.5 \\ \(\varepsilon\) & Threshold value & \(1\times 10^{-4}\) \\ \(T\) & Number of iterations & 30 \\ \hline \end{tabular}
\end{table}
Table 2: Control parameters.  
Figure 4: Comparative analysis of microgrid output voltage with and without considering sensor saturation. (**a**) Output voltage without considering sensor saturation. (**b**) Output voltage with sensor saturation considered. (**c**) Output voltage error.  
315 V. With the application of the DAILC method, even under changing load conditions, the output voltage quickly recovered to the preset value of 311 V, and the final error converged to zero. These results validate the effectiveness and practicality of the DAILC method in handling load variations.
# Assessing the Efficacy of Seawater Batteries Using NASICON Solid Electrolyte
## 5 Conclusions  
This study aims to study the production and identification of ceramic membranes that contain Na\({}_{3.06}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) and Na\({}_{3.18}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\), and to evaluate their stability in seawater batteries that are solid electrolytes. The specific surface area of 7.17 m\({}^{2}\) g\({}^{-1}\) for Na\({}_{3.06}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) powder was discovered by the surface analysis to be greater than the Na\({}_{3.18}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) powder's 6.61 m\({}^{2}\) g\({}^{-1}\). The measurement revealed that the NASICON samples had ionic conductivities of 8.5 \(\times\) 10\({}^{-5}\) and 6.19 \(\times\) 10\({}^{-4}\) S cm\({}^{-1}\). Charge and discharge batteries with different current values (50-100 uA) using Pt/C as a catalyst and seawater as an analyzer were the tasks of testing. A NASICON membrane and Pt/C catalysts with 0.00033 g of platinum content in an electrochemical cell were employed to evaluate reproducibility for 2 h, and the total operating time was approximately 100 h. Charge and discharge voltage efficiency was 71% (50/100 uA) and 83.5% (100 uA) after 100 h of operation. The battery's power was 0.153 mW and 0.2177 mW when it was charged with a current of 100 uA and discharged with 50 uA, respectively. Downloading and loading were 0.273 mW and 0.327 mW, respectively, at a current of 100 uA on both download and load. The energy obtained for a current of 0.1 mA was 32.2 mWh per charge and 22.98 mWh per discharge at a current of 50 uA, while for a current of 100 uA, it was  
Figure 14: Time evolution of the energy accumulated by the NASICON membrane battery at currents of 0.05 and 0.1 mA.  
Figure 15: Time evolution of the NASICON membrane battery capacity at currents of 0.05 and 0.1 mA.  
45.98 mWh per charge loading and 37.82 mWh per unloading. The applied current causes the accumulated energy to increase, as evidenced by the analysis of energy curves. Using a current of 50/100 uA (discharge/charging) was obtained a capacity of 100 uA per charge and 0.19 mAh per discharge while a current of 100 uA was obtained capacity of 0.033 mAh per charge and 0.04 mAh per discharge.  
Conceptualization, M.I. and M.B.; methodology, M.I. and M.B.; software, G.E.U.; validation, M.I., A.M. and A.O.; formal analysis, M.B. and S.E.B.; investigation, M.I., M.B., A.O., S.E.B. and G.E.U.; writing--original draft preparation, M.I., M.B. and A.O.; supervision, A.M.; project administration, M.I. All authors have read and agreed to the published version of the manuscript.  
The Ministry of Research, Innovation, and Digitization of Romania provided financial support for this work through the projects PN 23 15 01 03 and PN 23 15 01 04, Contract No. 20N/2023, project no. 345/2021, 4C-ICSI project, SMIS 125119; project no. 308/2020, HyRo 2.0 project, SMIS 127318.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
No conflicts of interest have been declared by the authors.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 3 Wireless Communication System Applied in Underwater Devices
### Development and Applications of Multimodal Underwater Communication
These advancements are complemented by innovations in localization technologies, such as GPS-denied acoustic positioning and vision-based navigation, which improve the coordination and operation of underwater platforms. The growing development of the IoUT further underscores the demand for scalable, flexible, and energy-efficient communication systems [87; 88]. IoUT networks must support large numbers of heterogeneous devices, including AUVs, fixed sensors, and surface platforms, requiring flexible communication architectures capable of integrating acoustic, optical, and RF links [89]. Additionally, energy harvesting techniques, such as TENGs and microbial fuel cells, are being explored to extend the operational life of underwater nodes in remote deployments. In summary, overcoming the limitations of single-mode communication requires not only the development of multimodal communication systems but also the integration of intelligent adaptive algorithms and cross-medium communication strategies. These innovations will be pivotal in enabling reliable, high-speed, and energy-efficient underwater networks for future marine exploration and environmental monitoring applications.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products
### Pathogens in Meat
#### 3.2.5 Bacillus Cereus  
The group of Bacillus cereus, otherwise called B. cereus sensu lato, incorporates eight firmly related species: _B. anthracis, B. cereus sensu stricto, B. cytotoxicus, B. myocytes, B. pseudomycoides, B. thuringiensis, B. toyonensis_, and _B. weihenstephanensis_[31]. Research reported that in China, among the 603 meat and meat products, B. cereus was distinguished in 159 (26.37%) samples, including 22.58% (28/124) from hamburger, 26.73% (27/101) from chicken, 41.38% (24/58) from ducks, 22.73% (5/22) from sheep, and 25.17% (75/298) from pork, while its levels in processed meat products (41.94%) were observed as much more significant (_p_ = 0.003) than raw samples (24.58%) [32].
## 3 Methodology  
### Data Acquisition  
Through the ship-shore communication system, data from the alarm monitoring system (AMS) was transferred to the shore end. After imputation and outlier filtering, data was stored in an elastic cloud server. The data selected in this article was read directly from the server.  
To adapt to different countries, ports, and navigation areas, a ship's mechanical system usually needs multiple modes to meet the requirements of navigation and environmental safety, such as low emission requirements of ports and different navigation modes for sea conditions. If the working modes of the system are different, the monitoring parameters change greatly. The effect of changing operational conditions has far greater significance than the influence of degradation uncertainty on system life. Therefore, when predicting the health condition of a system, the working mode of the system needs to be identified first.  
If the state parameters are recorded with pattern information, pattern recognition is not needed. Otherwise, a pattern recognition algorithm is needed to identify the working mode \(o_{t}\) according to the measured data. The pattern recognition algorithms include k-means clustering [21], fuzzy c-means algorithm [22], and Bayes classifiers [23]. The measurement data for each time in this study were classified into the known \(M\) working modes \(o=\{o_{1},o_{2},\ldots,o_{M}\}\). The data that could not be classified into the known working mode could be discarded temporarily. When enough similar data are accumulated, a new working mode could be established.
# Refined Prior Guided Category-Level 6D Pose Estimation and Its Application on Robotic Grasping
## 4 Results
### Comparison with State-of-the-Art Methods  
Tables 1 and 2 present the comparison of the mean Average Precision (mAP) of the method proposed in this paper with the previous state-of-the-art methods [7; 8; 28; 29; 30; 31]. On the CAMERA25 dataset, our experimental results for 3D50, 3D75, 5\({}^{\circ}\)2 cm, 5\({}^{\circ}\)5 cm, 10\({}^{\circ}\)2 cm, and 10\({}^{\circ}\)5 cm are 93.8%, 89.2%, 70.2%, 72.6%, 86.7%, and 90.4%, respectively. Notably, our method achieves the highest mAP in the 3D75, 10\({}^{\circ}\)2 cm, and 10\({}^{\circ}\)5 cm metrics, exceeding the previous best mAP by 0.2%, 4.0%, and 0.9%, respectively. On the REAL275 dataset, our experimental results for 3D50, 3D75, 5\({}^{\circ}\)2 cm, 5\({}^{\circ}\)5 cm, 10\({}^{\circ}\)2 cm, and 10\({}^{\circ}\)5 cm are 82.1%, 66.1%, 38.7%, 40.3%, 65.1%, and 79.7%, respectively. Our method achieves the highest mAP in the 3D50, 10\({}^{\circ}\)2 cm, and 10\({}^{\circ}\)5 cm metrics on this dataset as well, surpassing the previous best mAP by 2.0%, 1.6%, and 0.5%, respectively.  
Figure 8 illustrates a comparison between our method and the results obtained by SGPA in two scenarios. The red boxes represent the model's predicted results, while the green boxes represent the ground truth bounding boxes. The top two scenarios show the predictions made by SGPA, while the bottom two scenarios display the predictions made by our method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **3D50** & **3D75** & **5\({}^{\circ}\)2 cm** & **5\({}^{\circ}\)5 cm** & **10\({}^{\circ}\)2 cm** & **10\({}^{\circ}\)5 cm** \\ \hline NOCS [7] & 83.9 & 69.5 & 32.3 & 40.9 & 48.2 & 64.6 \\ SAR-Net [31] & 86.8 & 79.0 & 66.7 & 70.9 & 75.3 & 80.3 \\ SPD [8] & 93.2 & 83.1 & 54.3 & 59.0 & 73.3 & 81.5 \\ CR-Net [30] & **93.8** & 88.0 & 72.0 & 76.4 & 81.0 & 87.7 \\ RPB-Pose [29] & 93.1 & 89.0 & **73.5** & **79.6** & 82.1 & 89.5 \\ SGPA [28] & 93.2 & 88.1 & 70.7 & 74.5 & 82.7 & 88.4 \\ Ours & 93.4 & **89.2** & 70.2 & 72.6 & **86.7** & **90.4** \\ \hline \end{tabular}
\end{table}
Table 1: Comparison with state-of-the-art methods on CAMERA25 dataset. The bolded parameters indicate the optimal values for each metric.  
The experimental results on both datasets indicate that our method achieves superior results in the 10\({}^{\circ}\)2 cm and 10\({}^{\circ}\)5 cm metrics. Additionally, the visual validation results further demonstrate that our method outperforms SGPA in predicting rotation and scale, thereby proving the effectiveness of our approach.
## 4 Conclusions  
In this study, _S. cerevisiae_ NCUF309.2 was fermented with three different ester-producing yeasts (C. _glabrata_ NCUF308.1, _P. anomalous_ NCUF306.1, and _W. anomalous_ NCUF307.1) under different inoculation ratios to simulate the co-fermentation of blueberry wine. The growth of the yeasts, fermentation characteristics, and the accumulation of ester compounds during the fermentation were studied. The results show that, compared with the S sample, the presence of _P. anomalous_ NCUF306.1 and _C. glabrata_ NCUF308.1 could affect the growth of _S. cerevisiae_ NCUF309.2 and decrease the population of _S. cerevisiae_ NCUF309.2, leading to a decrease in the content of ethanol. However, the growth of _S. cerevisiae_ NCUF309.2 was not affected in the sample of WS5, and the presence of _W. anomalous_ NCUF307.1 had little effect on ethanol content. The results also showed that, compared with the S sample, the co-fermentation of _S. cerevisiae_ NCUF309.2 with ester-producing yeast could produce wine with distinct flavor characteristics (Figure 6), especially in the samples of CS1, PS5, and WS5. Additionally, the contents of ester compounds in these samples were significantly increased. Among them, the CS1 sample had the highest contents of odor active compounds, including phenethyl acetate, isoamyl acetate, ethyl octanoate, and ethyl hexanoate, which could directly endow fruit wine with more floral and fruity flavor, compared with other samples. In conclusion, the co-fermentation of C. _glabrata_ NCUF308.1 and _S. cerevisiae_ NCUF309.2 at the ratio of 1:1 was a feasible method for improving the aroma diversity of blueberry wine and meet the requirements of consumers.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11223655/s1](https://www.mdpi.com/article/10.3390/foods11223655/s1)]([https://www.mdpi.com/article/10.3390/foods11223655/s1](https://www.mdpi.com/article/10.3390/foods11223655/s1)), Table S1: The population of _S. cerevisiae_ NCUF309.2 during the simulated fermentation of blueberry wine.  
Conceptualization, G.F., Y.W. and W.C.; methodology, B.L.; software, W.C.; validation, Y.C., H.F. and M.D.; formal analysis, W.C.; investigation, B.L.; resources, G.F. and Y.W.; data curation, B.L.; writing--original draft preparation, W.C. and B.L.; writing--review and editing, W.C., Y.W. and G.F.; visualization, W.C.; supervision, B.L.; project administration, N.L. and M.L.; funding acquisition, Y.W. and G.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 31960474).  
Not applicable.  
The datasets generated for this study are available on request to the corresponding author.  
This project was supported by the National Natural Science Foundation of China (No. 31960474).  
The authors declare no conflict of interest.