esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work and Our Motivations
### Identity-Based Broadcast Encryption  
Ref. [30] provided the broadcast encryption (BE) scheme. From then on, many BE schemes have been proposed [31; 32; 33]. Broadcast encryption is widely used to multicast communication, copyright management, et al. For example, to solve the redundancy problem in information transmission for the vehicular ad hoc network, [PERSON] et al. [34] used broadcast encryption as the secure data sharing scheme from vehicle to infrastructure communication mode.  
[PERSON]. [35] constructed the first identity-based BE (IBBE) under the random oracle model (ROM). Since then, scholars have conducted in-depth research on IBBE from the aspects of efficiency and special performance, obtaining many achievements. [PERSON] et al. [36] designed an IBBE scheme and proved its security in the standard model (STDM). In their proposed scheme, the length of ciphertext and public key are fixed. [PERSON] et al. [37] gave an IBBE in STDM and proved its security with dual-system technology. Their scheme has a fixed private key and ciphertext length. The anonymous IBBE constructed by [PERSON] et al. [38] has a ciphertext that is not fixed in length and is positively related to the number of recipients.  
The anonymous IBBE given by [PERSON] et al. [39] has a fixed ciphertext length, but the key is too long. The scheme is provably safe under STDM through dual-system technology. [PERSON] et al. [40] gave an anonymous certificate-based broadcast encryption. [PERSON] et al. [41] gave an IBBE from inner products with fixed private key length, which supported infinite private key query in ROM. [PERSON] et al. [42] proposed an efficient IBBE with keyword search in cloud computing. It provides data retrieval and resists internal attack. [PERSON] et al. [43] presented a weak black box IBBE scheme in ROM, which has fixed private key size and public traceability for ciphertext. The tracking was performed through employing a public key of some suspicious user instead of its private key. [PERSON] et al. [44] gave an efficient  
Figure 1: The relationship between the three leakage models.  
identity based anonymous broadcast encryption for cloud storage services, which has a fixed size for its public parameters, private key and ciphertext.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 2 Background
### HLF Architecture
#### 2.2.7 Chaincode  
Chaincode is HLF's equivalent of smart contracts and defines the business logic of transactions. It runs in a secure execution environment, typically inside Docker containers. Unlike Ethereum's smart contracts, which are limited to Solidity, HLF allows chaincode to be written in Go, Java, and JavaScript [33], making it accessible to a broader range of developers. Chaincode execution follows an endorsement policy, specifying which peers must approve a transaction before it is committed to the ledger.
## 5 Experimental Results
### The PLC Performance in the Presence of EMI  
Figure 9 show the Frame Error Rate (FER) percentage with the modulation index values in the case of using the utilized modulation signals in our setup, respectively. In case of using the periodical modulating signals sine wave, the FER performance confirms the results shown in Figures 7 and 8, as both show high values of FER in the case of a low modulation index (\(\beta<315\)), as the sampling frequency of the modulating signal is higher than the sub-carrier frequency of the G3-PLC (\(f_{sub}=1500\)). Then, the FER decreases when  
Figure 8: The channel capacity loss vs. the modulating index at constant \(\alpha=15\%\).  
Figure 7: The calculated channel capacity vs. the modulating index at constant \(\alpha=15\%\).  
the modulating signal sampling frequency becomes less than the sub-carrier frequency of the G3-PLC, at which the modulation index \(\beta\) becomes greater than 315.  
In the case of a non-periodical signal like the RandomPAM one, the FER increases with the modulation index value until the sampling frequency of the modulating signal becomes near the sub-carrier frequency of the G3-PLC. Then, when the modulating signal sampling frequency decreases, the FER decreases as the frequency of the modulating signal becomes far away from the sub-carrier frequency of the G3-PLC.
## 3 Results and Analyses
### Distribution and Characteristics of NPP
Moreover, NPP\({}_{\text{mean}}\), as the average value of the ecological productivity quality, was used to reflect the overall productivity level of plants within a specified zone. The NPP\({}_{\text{max}}\) and NPP\({}_{\text{mean}}\) values from 2015 to 2019 are shown in Table 2.  
From Table 2, the NPP\({}_{\text{max}}\) showed an overall decreasing trend with seasonal change in each year. During the earthquake, the NPP reached the lowest value of 199.9 gC/m\({}^{2}\) and only showed an increase of 2.8 gC/m\({}^{2}\) during the month after the earthquake, which was attributed to the aftershocks. The NPP\({}_{\text{mean}}\) value was around 110.1 gC/m\({}^{2}\) in July, 98.4 gC/m\({}^{2}\) in August and 71.9 gC/m\({}^{2}\) in September. Its variation was similar to that of NPP\({}_{\text{max}}\). The NPP\({}_{\text{mean}}\) value, less affected by earthquake-induced geological disasters, also quantitatively represented the damage and restoration of vegetation before and after the earthquake and can reveal the change law of vegetation NPP. Compared with the NPP values in 2015 and 2016, those in the same month in 2018 and 2019 were higher, and the integral decline decreased year by year. It is indicated that the vegetation was reconstructed by natural restoration (restoration of soil fertility and species diversity) and artificial intervention (flood control, afforestation and reducing human engineering activities) [37], which could restore the productivity of forest-grass-shrub ecosystems and promote ecological restoration. Furthermore, the NPP\({}_{\text{max}}\) value reached the highest level (261.9 gC/m\({}^{2}\)) in August 2019, suggesting that the ecosystems were rebuilt. The NPP values show an overall downward trend and then an upward tendency.
## 5 Future Direction: AI-Based State Consistency Architecture
### Challenges of AI-Based SDN
#### 5.1.1 New ML Techniques  
As computer learning advances, machine learning becomes a versatile tool. Techniques like learning graphs help detect network topology and adapt to dynamic network changes, especially in deterministic scenarios. ML, using AI techniques like Convolution Neural Networks (CNNs), Q-learning, and other different deep learning techniques, can model and understand graph theory effectively. However, in non-deterministic networks, ML struggles to ensure networking and identify faults.  
When the training set is appropriately represented, ML adaptation operates as intended.  
Unfortunately, even with AI installation, we still require a deep understanding of the correlation between the ML model's correctness, the network's properties, and the size of the training set. In this case, we are addressing the requirement for ML to evaluate a set of SDN setups, load balancing, and traffic using new tools [49].
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
###### 1 Introduction
In the previous research [27], the entire robot system was introduced, including path planning algorithms. The focus of this article is a new motion planning method suitable for task scenarios. In this research, a multi-DOF robot motion planning method for fixed scenes, dynamic targets and dynamic obstacles is proposed based RRT*. The growth-withering method is proposed to expand the tree offline, which can fill the high-dimensional space with fewer nodes and realize the optimal wiring of the tree. Aiming at the constraints of the planning problem, the wiring is optimized by dynamic sampling and pruning to make the planning results meet the task requirements and reduce the randomness. For live-line working scenarios, this paper introduces the method of path planning for dynamic targets and dynamic obstacles by using an offline tree. Simulations and experiments show that this method can realize automatic path planning of manipulators in live-line working scenarios and meet the requirements of live-line maintenance. In this paper, the robot system and live-line maintenance are introduced in Section 2. In Section 3, we introduce the method for building rapid exploring random tree offline. Section 4 describes the method of path planning using offline random trees. Section 5 introduces the simulation and experiment. Finally, this article is summarized in Section 6.
## 4 Results and Discussion
### Supply-Side Analysis
#### 4.2.2 Zambia's Forecasted Electricity Deficit  
Zambia's failure to increase its investment in the diversification of the electricity generation mix will result in a consistent deficit, depicted in Figure 13. Clean energy sources are supposed to be integrated to ensure universal access to clean and affordable energy [33]. Expanding rural and urban electricity access is one of the country's sustainable development goals. On the renewable energy side, Zambia is yet to reach its full potential in solar energy generation. To further assess the solar energy potential and distribution in the country, ArcGIS software has been utilized to show that Zambia has a technical solar energy potential of approximately 20,422 TWh/year and receives 2109 KWh/m\({}^{2}\) of solar energy per year with 4403.12 h of sunshine [18]. Another renewable source considered is wind energy. In Zambia, wind energy is a good source when water levels in the country are low. Recent studies have been conducted to verify the stability and reliance of wind energy as a potential source in the country. In 2015, a renewable energy wind mapping framework for Zambia was initiated by the World Bank and the Ministry of Energy. The potential for wind energy in Zambia has also been assessed based on wind speed data and showed sites with potential for wind energy. Potential sites for wind energy close to the grid were also found [34].  
Figure 12: Electricity generation and emissions under the baseline scenario.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Grid Monitoring and Control in Operation
#### 3.1.1 Real-Time Monitoring and Digital Platform Monitoring  
Modern distribution systems enhance operational efficiency through effective real-time grid monitoring by utilizing digital technologies. One method uses low-voltage measurements for medium-voltage waveform analysis to improve monitoring accuracy and enhance fault detection and grid stability in the presence of DERs [176].  
Further innovations include the development of AC/DC switching technology and digital platforms that integrate intelligent power equipment into the power IoT ecosystem, enhancing features like real-time temperature detection [87]. The IoT's versatility extends to systems aimed at preventing food spoilage through real-time anomaly detection [88] and web-based tools for accessible power quality monitoring [177].  
Edge computing is also employed to boost grid operation efficiency and reliability [145], whereas continuous power quality monitoring in ADNs emphasizes quick fault detection [178].  
These advancements underscore the importance of real-time monitoring in improving smart grids' reliability and security, addressing challenges like data complexity and cybersecurity and fostering a more efficient energy landscape.  
Advanced Digital Monitoring in Power Distribution: SCADA and IDMS Platforms  
Advanced digital monitoring technologies like SCADA (supervisory control and data acquisition) and integrated distribution management systems (IDMSs) are crucial for the evolution of power distribution systems, driving improvements in grid efficiency and reliability. SCADA systems facilitate real-time data collection, remote control, and network communications, which are essential for the operation of modern grids.  
The SCADA-based approach reported in [179] enhances real-time voltage stability monitoring, streamlining grids for better renewable energy integration. Studies on the health of SCADA infrastructure in power transmission [146, 180] utilize SNMP for proactive management and early fault detection.  
IDMSs, which integrate outage management and smart meter data, modernize grid management systems. A case study involving Unareti, the DSO for Milano and Brescia,  
Figure 5: Framework of the grid operation subsections and key points.  
showcases the retrofitting of substation monitoring with middleware to improve sensor integration, highlighting challenges such as data exchange latency.  
These advancements emphasize the vital impact of SCADA and IDMSs on modern grid management, addressing operational efficiencies and the challenges of integrating new technologies and marking significant steps towards a resilient and sustainable power infrastructure.
# Evaluation of Algal Control Measures in Eutrophic Reservoir Based on Aquatic Ecosystem Models
## 3 Results and Discussions
### Reservoir Water Level Scheme Analysis
(2) The distribution of nutrients, nitrogen, and phosphorus displays minor variations, with the total nitrogen concentration being positively correlated with algal biomass and the total phosphorus exhibiting the opposite trend. The simulation results clearly indicate that the total phosphorus in the dock bay area is significantly higher than in other regions, while the total nitrogen concentration is lower. This disparity arises because algae absorb more inorganic nitrogen, and the amount of phosphorus released from the sediment exceeds the amount that is deposited, whereas the opposite trend applies to total phosphorus. Therefore, in reservoir management, when considering flood control and storage, as well as ensuring flood discharge safety and water resource protection, it is crucial to establish an appropriate ecological water level based on the relationship between algae and the local reservoir water level [56]. Figure 7: Comparison of carbon content of blue-green algae (**a**,**e**,**i**), chlorophyll concentration (**b**,**f**,**j**), total nitrogen concentration (**c**,**g**,**k**), and total phosphorus concentration (**d**,**h**,**l**) in scenarios with a water level of 75 m (**a**–**d**), 80 m (**e**–**h**), and 85 m (**i**–**l**).
## 3 Structural Reliability under Non-Stationary Loads  
As recalled above, a structure shall be designed and executed in such a way that it will sustain all actions and influences likely to occur during its intended life with an appropriate degree of reliability and in an economical way.  
In the Eurocodes, the adoption of characteristic values of actions, in conjunction with the use of the partial factor method in limit state verifications, should lead to the achievement of a reference reliability level, corresponding to a reliability index \(\beta\approx 3.8\) for a 50-year reference period [3] for a structure with \"normal\" consequences of failure.  
In order to define the probability of failure \(P_{f}\) and then the reliability index \(\beta\), it is assumed that structural behavior is described by a set of random variables describing actions, mechanical properties, geometrical data, and model uncertainties. A limit state function is then defined as the safety margin \(M=R-E\), where \(R\) is the structural resistance and \(E\) the load effect.  
Considering that climate change, even if it could potentially act on resistances, mainly affects the actions on the structure [23], this paper focuses on the influence of non-stationary loads on long-term structural reliability, presenting a methodology to evaluate the climate change impact on atmospheric actions.  
In previous studies [23; 24], time-dependent reliability in the presence of non-stationary loads has been studied considering how the probability of failure varies as a function of the mean value of the load intensity and the load occurrence rate. As illustrated in the following, the present study aims to widen the current horizon of this research field, evaluating time-dependent reliability as a function of changes, not only of the mean values of the environmental loads but also of their variances.
## 3 System Model  
This paper proposes a novel GSTBC-EFOOSM architecture consisting of N\({}_{\rm t}\) lasers and N\({}_{\rm r}\) photodetectors operating over M distributed channels across T time slots. Figure 2 shows the system model of GSTBC-EFOOSM, where the information bits of length B were divided into K blocks to map K EFOGSM symbols, which were then called into the OSTBC structure for encoding with a coding ratio R = K/T. This innovative approach enables communication over long distances with minimal distortion, making it an ideal choice for a wide range of applications. Explicitly, for the kth block, the information bit of length \(B_{k}=\left\lfloor\log_{2}\sum\limits_{N_{u}=2}^{Nt}\binom{N_{t}}{N_{u}} \right\rfloor+M_{\rm k}\) was further divided into two parts for the combination of the selected activation laser and the \(M_{\rm k}\)-PAM modulation mapping EFOGSM symbol \(\mathbf{x}_{k}\), respectively. Where \(M_{\rm k}=\log_{2}P_{M_{\rm k}}+\log_{2}S_{M_{\rm k}/2}\), \(P_{M_{\rm k}}\) and \(S_{M_{\rm k}/2}\) stand for primary and secondary signal constellations, respectively.  
After using the EFOGSM mapping, the K EFOGSM symbols were then called into the OSTBC structure for encoding. Consequently, the data transmission rate \(R^{\prime}_{p}\) of the proposed GSTBC-EFOOSM scheme can be expressed as follows:  
\[R^{\prime}_{p}=\frac{\sum\limits_{k=1}^{K}\left\lfloor\log_{2}\sum\limits_{N _{u}=2}^{Nt}\binom{N_{t}}{N_{u}}\right\rfloor+M_{\rm k}}{T} \tag{2}\]  
Due to the availability of numerous OSTBC codes with varying coding ratios, the proposed GSTBC-EFOGSM architecture can offer flexible transmission rates and diversity orders that are tailored to the specific GSTBC configuration, and the number of lasers employed. In the subsequent section, we introduce the proposed GSTBC-EFOGSM scheme via an alternative GSTBC scheme, thereby highlighting the versatility and adaptability of the proposed approach.
# LAAVOS: A DeAOT-Based Approach for Medaka Larval Ventricular Video Segmentation
## 4 Experimental Results and Analysis
### Comparative Experiments  
#### 4.2.1 Quantitative Results  
This experiment evaluates the segmentation performance of the proposed LAAVOS model against several widely used semi-automatic video object segmentation methods, including U-Net, TBD [21], STAVOS, and DeAOT. Table 1 presents a comprehensive comparison in terms of segmentation accuracy (mIoU, J-Mean, F-Mean, J&F-Mean) and inference speed (FPS).  
In our experimental setup, all evaluations were conducted on the self-constructed medaka ventricle video dataset at 480p resolution, with individual video frame sizes matched to those in the DAVIS2017 validation set. It is important to note that J-Mean is equivalent to mIoU in our binary-class segmentation task and can be used interchangeably for performance comparison.  
The LAAVOS model achieves a J-Mean of 92.89%, significantly surpassing U-Net's mIoU of 79.41%, highlighting its capacity for fine-grained segmentation in challenging video conditions. Compared to other state-of-the-art matching-based models, LAAVOS demonstrates consistently superior accuracy. Specifically, it improves upon TBD by 13.01%, 8.72%, and 10.86% in J-Mean, F-Mean, and J&F-Mean, respectively. When compared with STAVOS, which integrates spatiotemporal attention but suffers from higher latency, LAAVOS still achieves notable gains of 10.54%, 8.19%, and 9.36% across the same metrics. Furthermore, relative to the more lightweight and efficient DeAOT model, LAAVOS yields improvements of 4.83%, 3.71%, and 4.26%, while maintaining competitive inference speed (25 FPS vs. 27 FPS).  
These results confirm that LAAVOS achieves a strong trade-off between segmentation accuracy and computational efficiency, owing to the integration of its attention-enhanced encoder (LA-ResNet) and progressive decoding architecture (AFPViS). Its high accuracy and real-time speed make it well suited for medaka cardiac analysis scenarios requiring both precision and throughput.
## 3 Differences from Existing Work
### Suitable MPR Selection Techniques  
Another critical difference between the existing research on OLSRv2 and this research is the ability to utilize four (4) factors--the battery level of the nodes (BL), mobility speed (MB), the degree of nodes (ND), and connection to the base station (CBS)--to choose relays in the best possible way, lengthen the network's lifespan, and use less energy. The proposed MPR selection mechanism combines these four parameters into one metric to reduce energy dissipation and control routing overhead due to transmit each parameter individually.
# Exploring the Impact of AI-Based Cyber Security Financial Sector Management
## 4 Experiments and Evaluation
The findings verified the efficacy of the proposed approach in delivering financial sector cyber security. Cyber Security for Financial Sector Management (CS-FSM) has been evaluated to determine how effective it is in boosting financial sector cyber security. According to the findings, CS-FSM is superior to more conventional approaches to cyber security on all fronts, including data privacy (18.3%), scalability (17.2%), risk reduction (13.2%), data protection (16.2%), and attack avoidance (11.2%). This suggests that the proposed approach is useful in countering cyber threats in the banking sector. Artificial intelligence (AI) algorithms such as the Enhanced Encryption Standard (EES) and the K-Nearest Neighbor (KNN) algorithm are used in CS-FSM to provide a holistic approach to cyber security. While the EES method is used to encrypt and decode data in the banking sector, the KNN algorithm is employed to identify and prevent malware threats. The suggested method can classify cyberattack problems and provide solutions through the use of the KNN algorithm, which learns from the training data and makes predictions.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO2 Geological Storage in China
#### 5.1.5 Mineralogical Carbon Storage in China  
Mineralogical carbon storage, also known as carbon mineralization or mineral carbonation, is a process, in which injected CO\({}_{2}\) reacts with geological rocks or soil containing high concentrations of divalent cations to form stable carbonate minerals [169; 183]. It results in carbon permanently stored with a negligible risk of returning to the atmosphere while generating carbonate products [184; 185] that can be beneficially utilized. Therefore, it is considered to be a reliable technology with excellent potential for CO\({}_{2}\) storage [185].  
Currently, mineralogical carbon storage is still in the stage of laboratory-based and field-based experiments and has not yet been implemented on a large scale throughout the world [169]. The requirement for a significant quantity of fresh water for CO\({}_{2}\) dissolution to accelerate the carbonation reaction is one of the primary reasons preventing the large-scale implementation of this technology. Because of this, the research focus of this technology in China is mainly on increasing carbonation reaction speed. The results show that car -bonation reactions can be accelerated by optimizing reaction conditions [186; 187; 188], such as pressure, temperature and solid-liquid ratio, and employing pretreatments [189; 190], such as heat treatment and mechanical activation. China has abundant mineral resources of wollastonite [183] and serpentine [191], which are favorable raw materials for carbonation reactions. Therefore, mineralogical carbon storage theoretically has a huge potential for CO\({}_{2}\) storage in China.