esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
's [30] use of Quantum Blockchain, which enhances security through quantum secure communication, preventing unauthorized access to ballots. Focusing on integrity, the study in [31] has assessed the use of blockchain technology in implementing decentralized electronic voting systems that highlight blockchain's intrinsic security features, such as cryptography, decentralization, and consensus, which ensure data integrity. The study in [31] proposes an alternative electronic voting system based on blockchain, aiming to overcome the challenges and limitations of existing systems. It evaluates well-known blockchain frameworks to develop this blockchain-based e-voting system. Furthermore, a case study is analyzed to explore the practical applications and potential of distributed ledger technology in enhancing electronic voting systems. As noted in [25], the scalability of these systems remains an open challenge, particularly in national elections where voter turnout can exceed millions. Solutions like sharding and layer-2 protocols are still under research to make these systems scalable for real-world applications. The auditability of voting systems has also been a focal point, with [PERSON] et al. [17] proposing an architecture that automatically generates vote counts. Additionally, [PERSON] et al. [32] introduced a cryptographic model using ZCash that operates a dual blockchain structure to independently record voter actions and tally votes, enhancing the transparency and verifiability of the electoral process. In terms of system integrity, [PERSON] et al. [33] and [PERSON] et al. [27] have implemented Ethereum-based systems that rely on trusted miners within the consensus process to mitigate biases, highlighting the role of blockchain in fostering fair electoral environments. Privacy concerns have been addressed by incorporating advanced cryptographic techniques such as unlinkable signatures and two-phase verification processes, as discussed by [PERSON] et al. [30]. These methods keep voter information encrypted and private during the voting process. However, several studies have pointed out that techniques like zero-knowledge proofs and homomorphic encryption are still costly in terms of computing power, making them difficult to use in large-scale systems [25]. Because of these challenges, researchers continue to work on finding a balance between privacy, auditability, and scalability in blockchain-based voting systems. Finally, the scalability of these systems has been tested through different implementations. [PERSON] et al. [34] and [PERSON] et al. [27] highlight the need for strong frameworks that can manage large elections without losing speed or security. Future research should focus on improving consensus algorithms and looking into layer-2 solutions to increase the efficiency of blockchain-based voting systems, especially in countries with large populations.
# Theoretical Analysis on the Effectiveness of Pipe Roofs in Shallow Tunnels
## 1 Introduction  
Pipe roof pre-support technology is a commonly used auxiliary method in the construction of shallow underground tunnels. The purpose of this technology is to control stratum deformation and face stability in the process of construction and to ensure construction safety. Therefore, as the key technology for shallow underground tunnel construction, this technology is of important guiding significance to study the action mechanisms of a pipe roof and to reasonably evaluate its supporting effects.  
A large number of scholars [1; 2; 3; 4; 5] have researched pipe shed pre-support technology through experimental research, numerical simulations, and theoretical analyses. In terms of the action mechanisms of pipe roofs, taking a single steel pipe of a pipe roof as the research object, some scholars [6; 7; 8; 9] considered the restraint effects of initial support and rock masses in front of the tunnel face on the steel pipe of the pipe roof and analyzed the stress and deformation characteristics of the steel pipe of the pipe roof under the action of the surrounding rock pressure. On the basis of this experimental research, [PERSON] [10] put forward a scaffolding principle for pipe roofs and theoretically analyzed this principle's spatial action mechanisms. A large number of scholars have carried out research on the effects of pipe shed pre-support. Based on the elastic foundation beam theory combined with practical engineering cases, [PERSON] et al. [11] analyzed the influence of relevant parameters on the supporting effects of a pipe roof. [PERSON] et al. [12] studied the pre-support effects of a pipe roof using a centrifuge model test. Using a model test, [PERSON] [13] studied the influence of design parameters on the supporting effects of a pipe roof. [PERSON] et al. [14]published a comparative study on the pre-strengthening effects of a pipe roof and double-row conduit using a similar model test. Additionally, some scholars [15; 16; 17] established three-dimensional numerical models to study the pre-support effects of pipe roofs in detail.  
As mentioned above, there have been many achievements in research on the mechanisms and effects of pipe-roof support. However, most of these research results have been summarized, and their theoretical depth did not reach the level needed to guide the design and construction of pipe roofs. Notably, to date, there is no quantitative theoretical description for the pre-support effects of pipe roofs. Therefore, it is impossible to construct a general design theory for a pipe-roof pre-supporting structure based on the classification of the surrounding rock. In this paper, we take a pipe roof in a shallow soft surrounding rock tunnel as the research object, work from the mechanism of pipe shed support, and construct a theoretical analysis model of pipe-roof pre-support effects based on the index of the formation loss rate and surface settlement. The purpose of this paper is to provide a theoretical basis for the design, optimization, and application of pipe roofs in shallowly buried weak surrounding-rock tunnels.
## 3 Results
### Temporal Trend of NPP and NEP
#### 3.3.2 Time Series Change Trend Distribution  
The Mann-Kendall significance test was chosen to analyze the slope change trend of NEP and NPP, and significance level \(\alpha\) = 0.05 (\(\mid\)z\(\mid\)\(\geq\) 1.96) was selected to test the significance. There were obvious differences between NPP (Figure 6a) and NEP (Figure 6b), and the area of increase in NPP was more than that of NEP. The area that showed an increased trend in NPP (63.7%) was greater than that in NEP (53.4%). The area where NPP exhibited a significant increase accounted for approximately 13.1% of the total grassland area, and the area where NEP showed a significant increase accounted for about 8.6% of the total grassland area. The areas with significant increases of NPP and NEP were mainly concentrated in the south of the Afghanistan's Hindu Kush Mountain and the northeast of the Qinghai-Tibet Plateau. The region where NEP showed a downward trend  
Figure 4: The spatial pattern of (**a**) averaged grassland net ecosystem productivity (NEP) during 2001–2018 and (**b**) NEP difference between 2018 and 2001.  
Figure 5: Simulated trends in NPP, heterotrophic respiration (Rh), and NEP in HKH grassland ecosystems from 2001 to 2018.  
accounted for approximately 46.7% of the total grassland area and was mainly mapped to the southwest of the Qinghai-Tibet Plateau.
## 5 MARL-Based Scheduling Solution
### Reward Modeling  
The reward function is designed to guide the agent toward minimizing the residence time at intermediate TSN switches. The reward function is expressed in Equation (16):  
\[r_{n}=10\delta-(pos_{act}-pos_{opt})\ mod\ prd, \tag{16}\]where:  
* \(pos_{act}\) is the position selected by the agent.
* \(pos_{opt}\) is the optimal position that the agent should have chosen (i.e., the next available position starting from the current one).
* \(\delta\) is a binary value set to 1 when \(pos_{act}\) and \(pos_{opt}\) have the same value; otherwise, it is 0.  
The agent is rewarded with 10 points when making optimal decisions (when \(pos_{act}\) matches \(pos_{opt}\)) and penalized in the rest of the cases. The penalty increases with the distance between the selected position and the optimal position. The timing evolution is also considered, making the worst policy making the selection of the immediately previous position to the current one, as this leads to maximum queuing delays. Since the episode length is only one time step, there is no previous reward to consider in the calculation.  
Several training rounds have been performed with more gradual penalties provided by exponential decay functions as formulated in Equation (17):  
\[r_{n}=10\delta-e^{((pos_{act}-pos_{opt})/a)\ mod\ prd}, \tag{17}\]  
where \(a\) is a scaling factor that regulates the slope of the exponential function. Results show that the model is unable to converge into an optimal solution for values of the parameter \(a\) lower than 4. In contrast, for values higher than 3, the model converges into solutions whose optimality is comparable to the levels provided by the models based on linear decay functions.
## 3 Unified Calculation of Four Statistical Moments of Epistemic Uncertainties
### Moments Calculation for Evidence Theory Variable  
The epistemic uncertainty, represented using evidence theory, can be regarded as a summation of multiple uncertain single intervals whose weight ratios are their corresponding BPAs. The \(l\)th-order  
Figure 2: Four moments of the triangular fuzzy variables (**a**) The first-order moment (mean); (**b**) Second-order moment (variance); (**c**) Third-order moment (skewness); (**d**) Fourth-order moment (kurtosis).  
origin mean \(E\big{(}x^{l}\big{)},l=1,2,3,4\) of epistemic uncertainty \(x\) is a function of BPAs and the mean of every subinterval.  
If the epistemic uncertainty \(x\) is represented using two subintervals \(x_{1}=\big{[}\underline{x}_{1},\overline{x}_{1}\big{]}\) and \(x_{2}=\big{[}\underline{x}_{2},\overline{x}_{2}\big{]}\) with corresponding BPAs \(w_{1}\) and \(w_{2}\), respectively, \(E\big{(}x^{l}\big{)}\) is calculated using Equations (14)-(17):  
\[E(x)=w_{1}E(x_{1})+w_{2}E(x_{2}), \tag{14}\]  
\[E\big{(}x^{2}\big{)}=w_{1}^{2}E\big{(}x_{1}^{2}\big{)}+2w_{1}w_{2}E(x_{1})E(x_{ 2})+w_{2}^{2}E\big{(}x_{2}^{2}\big{)}, \tag{15}\]  
\[E\big{(}x^{3}\big{)}=w_{1}^{3}E\big{(}x_{1}^{3}\big{)}+3w_{1}^{2}w_{2}E\big{(} x_{1}^{2}\big{)}E(x_{2})+3w_{1}w_{2}^{2}E(x_{1})E\big{(}x_{2}^{2}\big{)}+w_{2}^{3}E \big{(}x_{2}^{3}\big{)}, \tag{16}\]  
\[\begin{array}{ll}E\big{(}x^{4}\big{)}&=w_{1}^{4}E\big{(}x_{1}^{4}\big{)}+4w_ {1}^{3}w_{2}E\big{(}x_{1}^{3}\big{)}E(x_{2})+6w_{1}^{2}w_{2}^{2}E\big{(}x_{1}^ {2}\big{)}E\big{(}x_{2}^{2}\big{)}\\ &+4w_{1}w_{2}^{3}E(x_{1})E\big{(}x_{2}^{3}\big{)}+w_{2}^{4}E\big{(}x_{2}^{4} \big{)}\end{array} \tag{17}\]  
where \(w_{1}+w_{2}=1\), \(E\big{(}x_{1}^{l}\big{)}\) and \(E\big{(}x_{2}^{l}\big{)}\) are the \(l\)-order means of subintervals \(x_{1}\) and \(x_{2}\), respectively. It is impossible to acquire an accurate PDF in every subinterval. \(E\big{(}x_{1}^{l}\big{)}\) and \(E\big{(}x_{2}^{l}\big{)}\) are calculated to be intervals. Therefore, the four statistical moments \(M_{1}\sim M_{4}\) are also intervals, and the lower and upper bounds of \(M_{1}\sim M_{4}\) are calculated using the optimization method presented in Equation (12). An evidence theory variable is shown as an example, which is composed of three subintervals \([10,13]\), \([13,17]\), and \([17,20]\), with BPAs of \(0.2\), \(0.7\), and \(0.1\), respectively. The histograms of \(M_{1}\sim M_{4}\) are shown in Figure 3. Using the optimization method presented in Equation (12), the uncertain intervals of \(M_{1}\sim M_{4}\) are shown to be \([13.87,15.48]\), \([3.51,6.66]\), \([-10.01,9.46]\), and \([34.25,103.51]\), respectively.  
Figure 3: Four moments of the evidence theory variable (**a**) First-order moment (mean); (**b**) second-order moment (variance); (**c**) third-order moment (skewness); (**d**) fourth-order moment (kurtosis).
# Dynamic Multi-Attribute Decision-Making Method for Risk-Based Ship Design
## 2 A Dynamic Multi-Attribute System for RCO Ranking  
### FSA and the ALARP Principle  
The ALARP principle sets the standard for risks, meaning that the risk level should be minimized as much as possible, within technically and economically feasible limits. As shown in Figure 1, the normal ALARP-based standard establishes an upper limit on the maximum tolerable risk, which should not be exceeded, regardless of the cost of keeping the risk below that limit. It is also possible to set a lower limit, below which the level of risk below that curve is determined to be negligible, and below which no mandatory risk reduction measures are required. The level of risk between these two limits could then be required to be kept as low as reasonably practicable, based on cost-benefit considerations and the ALARP principle.  
FSA is a structured and systematic methodology for conducting risk analysis and cost-benefit assessments and can be used as a tool to help assess ship security in five steps. (1) step 1 of the FSA involves identifying the potential causes and consequences of scenarios relevant to all incidents. (2) Through the risk assessment based on the ALARP criterion in step 2 of the FSA, risk areas needing control can be identified. (3) In step 3 of the FSA, potential RCOs are identified by using structured review techniques. There is a wide variety of RCOs, ranging from technical measures included in the design of a ship to specific changes in the ship's watch plan. A published report describes the typical and recommended RCOs [35]. (4) Next, step 4 of the FSA compares the benefits and costs associated with implementing each identified RCO. In this step, certain CEA indices can be used to rank the RCOs from a cost-benefit perspective (e.g., to screen for products that are not cost-effective or impractical). The CEA index can be the cost of averting a fatality, namely, the gross cost of averting a fatality (GCAF) or the net cost of averting a fatality (NCAF). Similarly, [PERSON] et al. [36] proposed the CATS as a way to determine the CEA index for accidental oil spills. (5) Step 5 of the FSA is to provide recommendations for decision-making, including cost-benefit information on hazards, associated risks, and alternative RCOs.
# Structure and Photoelectrical Properties of Natural Photoactive Dyes for Solar Cells
## 1 Introduction
[41] reported a theoretical study of the betalain pigments using density functional theory (DFT) and time-dependent density functional theory (TD-DFT), and the results indicated that the lowest unoccupied molecular orbital (LUMO) had a close spatial proximity to bound TiO\({}_{2}\), assuming that betalain binding occured through the two carboxylic groups on the dihydropyridine ring. [PERSON] et al. [42] analyzed the spectral features of lawsome (2-hydroxy-1,4-naphthoquinone), an active component of the natural dye henna in ethanol by using experimental and computational methods; the study showed that the 1,4-naphthoquinone classes of compounds can provide the basis for the design of novel compounds for DSSCs to enhance its efficiency. Charge transfer enhancement in DSSCs was reported by changing the electron donor of the famous porphyrin-based sensitizer YD2-o-C8 [43]. In this work, six natural dyes, named as D1, D2, D3, D4, D5 and D6, were successfully extracted from the leaves of Euodia meliaefolia ([PERSON]) [PERSON], _Matteuccia struthiopteris_ ([PERSON] [PERSON], _Corylus heterophylla_ Fisch, _Filipendula intermedia, Pteridium aquilinum_ var. latiusculum and Populus L (see Figure 1), respectively. The optical and electrical properties of the dyes applied in DSSCs were measured via UV-Vis absorption spectra and cyclic voltammetry. To compare with the experimental results, the chlorophyll was selected as the reference molecule, and the ground and excited state properties of chlorophyll were calculated via DFT and TD-DFT methods, respectively. The obtained results indicate the DSSC sensitized by the dye D1 exhibits the highest PCE of 1.08% among the investigated six natural dyes, with an open circuit voltage (\(V_{OC}\)) of 0.58 V, a short-circuit current density (\(J_{SC}\)) of 2.64 mA cm\({}^{-2}\) and a fill factor (_FF_) of 0.70.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 3 Star Background Simulation
### Star Brightness Calculation  
We calculated the stellar dynamic range using the specifications of the Space Infrared Imaging Telescope (SPIRIT III) sensor on the MSX satellite. This instrument underwent sufficient corrections before the measurement to ensure the accuracy of the results. Equation (6) shows the exact calculation, where the dynamic range of the star is determined by its own irradiance and the sensor noise equivalent irradiance (NEI) and saturated equivalent irradiance (SEI) together.  
\[D_{s1}=\min\left(D_{sensor},E_{0}\times 10^{\frac{m_{0}-m_{1}}{2512}}/NEI \right), \tag{6}\]  
where \(D_{s1}\) and \(m_{1}\) are the dynamic range and magnitude of the star, \(D_{sensor}\) is the dynamic range of the sensor, and \(E_{0}\) and \(E_{1}\) are the irradiance of the 0-magnitude star and target star, respectively. For instance, in the case of the C band in the MSX catalog, stars with magnitudes less than 4 are not relevant and can be ignored during storage, which simplifies subsequent star filtering. The relevant information is presented in Table 2.  
The diffraction effect causes the star's infrared radiation to be imaged as a point with a certain area as it passes through the optical system. To improve the accuracy of the simulation's background, we considered the primary diffraction spot in the background simulation and assumed that the energy of the star obeys a Gaussian distribution in Equation (7) within the spot [37].  
\[PSF(x,y)=\frac{1}{2\pi\sigma^{2}}\text{exp}\left(-\frac{\left(x-x_{i}\right)^{ 2}+\left(y-y_{i}\right)^{2}}{2\sigma^{2}}\right), \tag{7}\]  
where \((x_{i},y_{i})\) are the coordinates of the target on the sensor, and \(\sigma\) is the standard deviation which indicates the width of the point diffusion.  
The radius of the Airy spot is given by Equation (8). Combining the pixel pitch of the sensor, we can calculate the size of the Airy spot and the gray values of different pixels based on the dynamic range of the star.  
\[R=1.22\lambda L/D, \tag{8}\]  
where \(\lambda\) is the wavelength, \(L\) is the focal length, and \(D\) is the diameter of the optical pupil.
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 1 Introduction
Other important issues are the network size, node density, and scalability owing to variations in topology. Over time, several factors originate that modify the network topology. Good MACs should accommodate these modifications. Considering these requirements, the IEEE 802.15.4 standard is receiving attention, which is of continuous interest. The sensor node of a wireless sensor network (WSN) interlocks with a server which is connected to the wired Internet backbone network through a gateway. At this time, the traffic type of the WSN is divided into downstream from the gateway to the sensor node and upstream from the sensor node to the gateway. For downstream traffic, data loss can be prevented even during congestion if the gateway has a sufficient buffer. Typically, a gateway is a device connected to the wired Internet backbone network, located in an area with a stable power supply and a buffer of sufficient capacity. However, because sensor nodes are battery-based low-power devices, these are implemented to minimize the operation of the radio interface and buffer capacity. Therefore, in the case of upstream routers, if the nodes do not transmit data rapidly, data loss may occur at the nodes functioning as intermediate routers in the event of traffic congestion. In WSNs, it is common to utilize a low-power radio interface. Additionally, the actual traffic generated in WSNs is typically minimal, resulting in the network operating at a low duty cycle. This means that the network remains in a sleep state, where the radio frequency (RF) is turned off most of the time and periodically wakes up to communicate and reduce power consumption. The IEEE 802.15.4 standard is a technology that operates in the ISM band, similar to the IEEE 802.11 standard. However, it faces a challenge in ensuring stable data transmission, due to interference from various pieces of equipment sharing the same band. Moreover, when WSNs are applied in smart factories, congestion points can occur as traffic is aggregated towards the gateway due to the traffic characteristics. To mitigate this, traffic processing measures are required to reduce transmission delays, especially when multiple hops are involved. Considering this, a plan is required to increase the transmission performance of the network and prevent data loss by operating as a multi-channel while satisfying the requirements of a WSN operating at a low power. Therefore, this study proposes a multichannel MAC protocol to improve network performance in a smart grid environment. The proposed technique can reduce the routing paths by a new address allocation scheme and transmit data by preventing interference by a multi-channel management scheme.
###### Abstract  
The device anomaly detection in an industrial control system (ICS) is essential for identifying devices with abnormal operating states or unauthorized access, aiming to protect the ICS from unauthorized access, malware, operational errors, and hardware failures. This paper addresses the issues of numerous manufacturers, complex models, and incomplete information by proposing a fingerprint extraction method based on ICS protocol communication models, applied to an anomaly detection model fine-tuned using the Llama3 model. By considering both hardware and software characteristics of ICS devices, the paper designs a fingerprint vector that can be extracted in both active and passive network communication environments. Experimental data include real ICS network traffic from an oilfield station and extensive ICS device traffic data obtained through network scanning tools. The results demonstrate that the proposed method outperforms existing methods in terms of accuracy and applicability, especially in differentiating devices from various manufacturers and models, significantly enhancing anomaly detection performance. The innovation lies in using large language models for feature extraction and the anomaly detection of device fingerprints, eliminating dependency on specific ICS scenarios and protocols while substantially improving detection accuracy and applicability.  
industrial control systems; anomaly detection; ICS device fingerprint; Llama3 model; cybersecurity