esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### IEEE-39 Bus System
#### 5.1.1 Comparison of Economy and HVDC Consumption  
The operating cost of three cases and average HVDC transmission power are shown in Table 1 and Figure 3. The concrete calculation results are shown in supplementary.  
In case 0, HVDC operation cost is much lower than conventional units so that all two HVDCs are operated in rated capacity (1000 MW). In case 1, in order to ensure frequency deviation less than 0.2 Hz after one HVDC blocked, generating units start up while the HVDCs' average power decreased to 989.2 MW. Although the total cost of case 1 is larger than case 1, the safety of the power system is already improved. In case 2, due to security constraints after the secondary frequency finished, two HVDCs' average power continue to reduce to 967 MW and 974.9 MW, respectively. The safety of the power system would be further enhanced and the operational cost increase to $1,279,549, which is the highest of the three cases. From the perspective of computational efficiency, case 2 has the most calculation iterations, with 12 times and about 48 s.
## 5 Conclusions  
In order to solve the morbidity problem of traditional algorithms in grounding grid impedance imaging, this paper proposes a grounding grid impedance imaging method based on the improved CGAN. The input measured boundary voltage value is imaged through the improved CGAN algorithm. The algorithm generator is mainly composed of four modules: the preprocessing module, encoder module, decoder module and attention module. The discriminator is a PatchGAN structure. The grounding grid impedance image is reconstructed through adversarial training and optimization of the objective function. Through simulation experiments of different corrosion positions and different corrosion degrees of the grounding grid, it is proven that this method significantly improves the precision and accuracy of the grounding grid corrosion diagnosis image and obtains the grounding grid image with complete boundaries and clear corrosion conditions.  
The accuracy of this method in detecting double-branch corrosion is lower than that of single-branch corrosion. The reason is that there are more types of double-branch corrosion, and it is difficult to extract voltage characteristics. The error in detecting slight corrosion is larger than that in moderate corrosion. Considering that the cause of the problem is that the voltage characteristics in complex and slight cases do not change significantly, error in judging the degree of corrosion occurs.  
Future work will integrate feature detail extraction and expand the dataset to improve the model's weak recognition performance when imaging slight corrosion and multiple corroded branches, optimize the imaging resolution of the CGAN imaging network and more accurately display the corrosion status of the grounding grid. In summary, the method proposed in this paper provides a new idea for grounding grid impedance imaging, which is expected to be applied in grounding grid corrosion diagnosis and inspection.  
Conceptualization, K.Z.; methodology, K.Z.; software, D.L.; validation, D.L.; data curation, Z.X.; investigation, Z.F.; writing--original draft preparation, D.L.; writing--review and editing, K.Z.; visualization, X.B. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Imaging accuracy after different noises.  
**Funding:** The research was funded by the Science and Technology Project of the State Grid Sichuan Electric Power Company: Research on online monitoring technology and system development of grounding grid operation status (52199723001M).  
**Data Availability Statement:** The data supporting the reported results are not publicly available due to privacy or ethical restrictions. Please contact the corresponding author for more information.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Operational Aspects and Judement of the Plant Managers
#### 3.2.2 Driving Forces of MBR Installation  
Figure 5 shows the driving forces that led to the installation of the MBR systems: the main ones are the lack of space and the possibility of increasing the treatment capacity, both for industrial and urban WWTPs. Furthermore, the higher quality of the effluent (in terms of low pollution), related both to operating authorizations and to the more stringent discharge limits, is a reason repeatedly mentioned by the managers of all MBR plants analyzed in the survey.  
As reported by [PERSON] and [PERSON] [31], the MBR are used with the aim to reuse the wastewater treated. The high robustness of MBR technology provides a fairly constant effluent with a large reduction of the pollutants, with a highly reliable operation.  
The high effluent quality highlighted by several plant managers lead to an increase in the installation of MBRs in many regions suffering an acute lack of water (even in the south of Europe) to solve the hydric deficit. The treated wastewater can be reused in tasks where drinking water is not required, such as irrigation [32], cleaning or cooling water on industrial site, or domestic purposes (e.g., toilet flushing) [33]. Furthermore, the higher ability of MBR (with respect to CAS) to remove endocrine disrupting compounds (EDCs) [34] and trace pollutants [35] is documented.  
In comparison with the CAS process, MBR has about a 50% higher annualized operating cost, mainly due to the need for replacing the membranes and the higher associated energy requirement. Therefore, MBR cannot compete with the CAS process unless a very high quality effluent is required, and for wider application of MBRs, the lower energy demand of the process is required. In fact, the MBR market is driven by the increased scarcity of water, ever stricter rules for water discharge and reuse quality legislation, decreasing investment cost, and the wide acceptance of the technology [36].  
As reported in the introduction, the MBR market is growing significantly; this growth is led by the Asia-Pacific market. This is mainly due to the increasing demand for wastewater treatment from the Chinese market [36]. Concerning the Chinese situation, it is worth mentioning that [PERSON] et al. [37] highlighted that the primary driving forces in the last several decades were the water resource stress and the public policy related to resources and the environment. The future driving forces for MBR applications include declining capital investment and decreasing operating cost.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with IA Transmission  
The achievable rate is influenced mainly by the strong interference and is quite insensitive to the weak interference. Therefore, eliminating very weak interference hardly improves the achievable rate, but requires additional signal dimensions. In addition, eliminating very weak interference may even degrade the performance because the power of the desired signal cannot be collected in the signal space used to cancel interference. IA is a well-known signal processing approach that attempts to simultaneously align the interference on a lower dimensional subspace at each receiver, so that the desired signals can be transmitted on the interference-free dimensions. Intuitively, we can consider IA within every cluster, which has very strong intra-cluster interference. Herein, we improve our virtual cell merging algorithm with the three strongest interference strengths in order to adapt the IA algorithm.
## 5 System Model
### Distributed System Model  
To introduce our SDN model, we start by presenting the model of a typical distributed system as a point of departure. A typical distributed system is composed of different separated entities that communicate with each other by exchanging messages. It is assumed that no global time reference exists and the message transmission delay is arbitrary and unpredictable. Furthermore, for simplicity in this work, it is assumed that no message is lost.  
At a high level of abstraction, a distributed system can be described based on the following sets: \(P\), \(M\), and \(E\), which correspond, respectively, to the set of processes, the set of messages, and the set of events.  
* Processes: programs, instances of programs, or threads running simultaneously and communicating with other programs, instances of programs or threads. Each process belongs to the set of processes \(P\). A process \(p\in P\) can only communicate with other processes by message passing over an asynchronous communication network.
* Messages: Abstractions of any type of messages, which can contain either arbitrarily simple or complex data structures. Each message in the system belongs to the set of messages \(M\). A message \(m\in M\) is sent considering an asynchronous and reliable network.  
* Events: An event \(e\) is an action or occurrence performed by a process \(p\in P\). Each event \(e\) in the system belongs to the set \(E\). There are two types of events under consideration: internal events and external events. An internal event is an action that occurs at a process in a local manner. An external event is an action that occurs in a process, but it is seen by other processes and affects the global system state. The external events are the send and delivery events. A send event identifies the emission event of a message \(m\in M\) executed by a process, whereas a delivery event identifies the execution performed or the consumption of an ingoing message \(m\) by a recipient process.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 4 Taxonomy of Vulnerability Detection Approaches and Findings Analysis
### AI-Based Approach
#### 4.4.2 Findings Analysis  
An existing link between the two first methods lies in their shared use of deep learning techniques to improve vulnerability detection and automation processes. While VulDeePecker focuses on analyzing code gadgets through a BLSTM network, achieving a 93% accuracy rate, the method based on NER uses the same network's feature outputs for sequence labeling and feeds them into a CRF to automate CPE extraction, reconstructing CPEs in 67.44% of CVEs. The recommender system for tracking vulnerabilities integrated machine learning, NLP, and fuzzy matching to significantly narrow down the search for affected products in the NVD, achieving notable success of 40% for software and 48% for hardware vulnerabilities. Similarly, the method based on looking-back-enabled machine learning techniques employed the random forest classifier to detect and mitigate DoS/DDoS attacks in IoT systems with an impressive accuracy of 99.81%. These approaches leverage different machine learning models to enhance the efficiency and accuracy of the vulnerability assessment process. VERNIER and ASSBert highlighted the importance of data accuracy in improving vulnerability detection. VERNIER identified, measured, and mitigated inconsistencies in software names from multiple major vulnerability databases using NER models. By integrating a correcting tool, such as a reward-punishment matrix, it uncovered and alerted incorrect software names to improve the vulnerability detection process, achieving a 99.5% accuracy and an F1 score of 95.1%. ASSBert incorporated the BERT model with active learning and semi-supervised learning, demonstrating a performance rate between 79% and 89%, depending on the dataset used during the learning phase. Another noteworthy method, VulEval, employed a multi-model approach combining static code analysis, supervised machine learning, and large language models (LLMs). Experimental results recorded a precision of 69.78% with the PILOT method, although further exploration of repository-level vulnerability detection remains necessary. The last method incorporated a hybrid approach using double machine learning models (GMB and LR) to identify ransomware threats in (IIoT). The promising results indicated a detection accuracy of 92%. The use of multiple models, along with other feature sets and datasets, has been critical in enhancing learning phases and improving the accuracy of AI models in the vulnerability detection process.
## 4 Results and Discussion
### The Threshold Current  
To further characterize the performance of the ECDL, the variation in the output power with the current is measured at a wavelength of 1550 nm, as shown in Figure 5. As can be seen from the picture, the threshold current of the ECDL is 68 mA. Due to the ultra-low reflectivity of the rear facet (\(R_{1}\), 0.01%), the ECDL has a relatively low threshold current. The slope efficiency is about 0.19 W/A. Additionally, an output power of 25.6 mW is obtained with an injection current of 200 mA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Wavelength (nm) & 1525 & 1535 & 1545 & 1555 & 1565 \\ \hline The SMSR with filter (dB) & 55 & 56 & 56 & 57 & 55 \\ \hline The SMSR without filter (dB) & 54 & 52 & 55 & 54 & 55 \\ \hline \end{tabular}
\end{table}
Table 1: The SMSRs of the ECDLs with and without interference filter.  
Figure 4: The optical spectra without an interference filter are measured with an injection current of 200 mA. The peaks correspond to different grating positions over the tuning range.
## 4 Results
### Results of Analyses of Effects of Two Factors
#### 4.2.1 Analysis of the Pair Camera Type and Rotation  
The first pair was camera type and camera rotation. To make the calculations easier and more precise, the same camera rotation groups were used that were previously described in Section 4.1.3. Every level of camera type was paired with every level of camera rotation group. The number of groups involved is equal to 4; the first is named \"Orthographic, NO_R\", and it contains 611 tests. The others are \"Orthographic, INC_R\", with 680 tests; \"Perspective, NO_R\", with 602 tests; and \"Perspective, INC_R\", with 816 tests. For the numerical average rates and dispersions belonging to the groups, see Table 15; and for the results of the logistic regression analysis, see Table 10.  
According to the _p_-values, every camera type is significantly better than \"Orthographic, NO_R\". There is no significant difference between the \"Orthographic, INC_R\" and \"Perspective, NO_R\". This difference was calculated by means of a _t_-test and has a _p_-value of 0.7126. The group \"Perspective, INC_R\" has the best results, and the improvement as compared to \"Perspective, NO_R\" is significant (_p_-value = 0.0033).  
Moreover, the authors investigated an additive model on the basis of the variables camera type and camera rotation by allowing their interaction. The result can be seen in Table 11.  
The estimated coefficients indicate that both camera type and camera rotation have influence, and _p_-value = 0.0459 means that there is an interaction, as well. The negative sign of \(-0.08995\) was a surprise to the authors, but this is due to the speed of improvement. The measures of the improvement cannot be summed, the result is a little bit lower than that caused additively. Table 12 shows the results of their interactions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type and Rotation** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.52784 & 0.02309 & 22.864 & \(<\)2 \(\times\) 10\({}^{-16}\) \\ Orthographic, INC\_R & 0.18323 & 0.03199 & 5.728 & \(1.02\times 10^{-8}\) \\ Perspective, NO\_R & 0.19943 & 0.03334 & 5.982 & \(2.21\times 10^{-9}\) \\ Perspective, INC\_R & 0.29271 & 0.03102 & 9.437 & \(<\)2 \(\times\) 10\({}^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Logistic regression analysis results by pairing the camera type and rotation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device Used** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.69002 & 0.01249 & 55.231 & \(<\)2 \(\times\) 10\({}^{-16}\) \\ Gear VR & 0.07595 & 0.02805 & 2.708 & 0.00677 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Logistic regression results of the device used.  
According to \(p\)-value = 0.0459, the authors concluded that the model that takes the interactions into account provides a better probability.
## 4 HydroSAR Product Portfolio and Product Algorithms  
Based on the information in Section 2, developing a SAR-based weather hazard monitoring system capable of providing low-latency flood impact information on a regional scale and during all weather conditions is a necessity [30] for the HKH [4].  
Therefore, in collaboration with the end user partners of ICIMOD, we designed the HydroSAR service to provide near real-time access to a comprehensive set of SAR-derived information products to allow assessment of the surface water extent, water depth, and flood impacts on agriculture. Our coordinated set of hazard information products, along with their data dependencies, is shown in Figure 3. The product portfolio combines visual aids to be used by analysts with quantitative products on flood status and flood impact.  
All HydroSAR products were generated in cloud-optimized GeoTIFF (CoG) format so that data could be efficiently accessed and distributed in the cloud (Section 6). They  
Figure 2: NASA SERVIR iterative co-development workflow used in HydroSAR service development to ensure that HydroSAR is sustainable and meets the end user’s needs. Each co-development cycle of this workflow follows a sequence of end user needs assessment, service design, and service delivery. At the end of each cycle, a service evaluation and impact assessment step is performed from which a subsequent co-development cycle is initiated until service convergence is achieved.  
were presented to analysts and other end-users through a dedicated web portal discussed in Section 5.  
The purpose and implementation of each individual product is described in the following subsections. For the purpose of brevity, we will focus on the intermediary layers (HAND), visual aids (RTC30 and RTC30-Color), and quantitative flood water products (HYDRO30 and WD30) in this paper, for which automatic processing workflows are implemented. While HydroSAR also includes an agriculture extent product (AG30), this product is not yet included in the HydroSAR web portal and will not be discussed here. Readers interested in the concepts behind AG30 may refer to [31] for more information.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 8 Conclusions  
In this work, we have proposed strategies using a new mother-daughter architecture for rovers and landers to explore extreme and rugged environments off-world, such as cave and lava tube extensions on the surface of the Moon and Mars. Exploring these harsh, rugged environments will likely provide the best evidence yet of the early formation of the planetary bodies and moons, together with vital clues of how these bodies evolved to their present state. Moreover, exploring these sites could ascertain the range of conditions that can support life and identify planetary processes responsible for generating and sustaining habitable worlds. Unfortunately, current landers and rovers cannot access these sites as they are large, contain many sophisticated instruments, and require hopping or flying to these locations. Instead, we propose using SphereX robot drones to hop into these rugged environments without risking a high-value rover or lander. The SphereX robots would operate through a mix of autonomy and human interaction and perform science tasks complementary to the large rover or lander, such as getting closer to a site of interest and obtaining high-resolution images, 3D maps, spectroscopy, and samples.  
These small robots would also perform complementary operational tasks. This includes overhead surveying of the pit entrance, entering the pit, and determining the extent of the cave/lava tube. During an exploration mission, it may be strategic to place multiple robots along the cave/lava tube to maintain a direct line-of-sight connection link, which can be used for communication and wireless power transfer. The mission would continue even if one or more of the SphereX drones were lost or destroyed. The SphereX drone utilizes the rover or lander services such as the power to recharge and a long-distance communication link to Earth. As we have shown, this mother-daughter architecture compartmentalizes risk to the SphereX robots while benefiting from the rapid infusion of new technology and the ability to pursue high-risk, high reward science without compromising the overall mission. This proposed architecture opens whole new avenues for exploring off-world environments in the solar system and can even lay the foundation for a future high-risk and more sophisticated exploration mission.
# Landsat Surface Product Validation Instrumentation:
## 7 Summary/Conclusions/Path Forward
### Summary
An accuracy of 0.002 reflectance units, with respect to the known ground truth, was demonstrated with arrays of at least three instruments at a site over the course of a single growing season.