esa-sceva/satcom-chunk-collection huggingface dataset:
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 3 Hash-Chain
### Node Function and Role Classification of Hash-Chain System  
In the hash-chain system, nodes have different functions according to the degree of resources scheduled. As the complexity of the system continues to increase, the tasks and permissions of the nodes also have a more evident division of labor. The system functions possessed by hash-chain nodes include bookkeeping function, data storage, query function, transaction submission function, routing function, and member management function. In a traditional blockchain system, the bookkeeping rights of nodes are confirmed through a consensus mechanism, and the common consensus algorithms have proof of work (PoW) [29], Proof of Stake (PoS) [30], PBFT, etc.  
Each node in the network is divided into different roles according to the differences in function. The permissions obtained by different characters are also different. In the entire network, the characters responsible for maintaining the operation of the system are mainly the following types:  
(1) Full node: The full node keeps a complete and up-to-date copy of the account book, has data storage and query function, a routing function, and an accounting function. It can independently verify all transactions without the help of other node data support. Due to the large amount of ledger data in the entire network, full node requires abundant storage resources and computing resources. Therefore, in the SAGIN, a node with substantial computing power and sufficient storage resources in the ground network can be used as a full node.  
(2) Simple payment verification (SPV) node: The SPV node is also called a \"lightweight node\". Unlike the full node, the SPV node does not have a complete copy of the ledger and only stores block header data, which significantly reduces the overhead of storing resources. During transaction verification, through the \"SPV\" method, the complete transaction information required from all nodes is synchronized to minimize the time and load of verifying transactions. In the SAGIN, nodes such as drones can be used as SPV nodes due to limited battery and storage resources.  
(3) Accounting node: The accounting node is a node that writes blocks in the hash chain through a consensus algorithm, and has accounting function, data storage function, and routing function. The accounting node considers using a simplified consensus algorithm to calculate the hash value of the block in a trusted environment, packaging, and maintaining the block.  
(4) Sorting node: The sorting node is to verify and sort the transaction data. By receiving the wireless signal feature identity of the cross-regional vehicle node, the reliability of the vehicle node is confirmed, and the transactions are sorted at the same time to ensure that the hash-chain node records the deal with the same logical order.
The limits of the equations presented are discussed in Section 3.\" should be replaced by \"Following the same approach to calculate the roll angle \(\gamma\) by using the zero field property does not lead to any result, since the analytic equation obtained is linearly dependent. The state of the art now is that the roll angle cannot be calculated analytically. On the one hand, it can be calculated numerically using Equation (5), which leads to a very complicated system of equations since it now additionally contains the sensor location, which is unknown. On the other hand, the roll angle can be measured by additional sensors, e.g., by an inertial measurement unit (IMU), which is present in almost all UAVs. In this case, the yaw and pitch angles calculated by Equations (9) and (10) can be used as correction information, since the IMU provides all three angles. For the following considerations in connection with the UAV position calculation, all three angles are assumed to be known, either through calculation or measurement.\"  
Error explanation: The assumptions and results in this original text that relate to the roll angle of the unmanned aerial vehicle (UAV) are incorrect. The reason is explained as follows, and it is shown that the roll angle cannot be calculated analytically but must be measured with additional sensors. To determine the roll angle, the paper pursued the idea of using a third magnetometer \(k\) rotated 90 degrees around its \(y\)-axis relative to the UAV body frame \(L_{u}\), which should lead to a third linearly independent equation. In this configuration, when the UAV body frame is aligned with the global frame \(\{G\}\), the magnetometer measures a zero value on its \(z\)-axis due to the zero-field property (2). Linking this to the sensor measurement via a coordinate transformation then leads to Equation (13), which appears to yield the roll angle as the only unknown quantity. However, this result is incorrect, because in derivation (12), the coordinate transformation \(A\) was set incorrectly, namely \(A=R_{G}^{L_{u}}R_{y}^{T}R_{L_{u}}^{C}\), where \(R_{y}\) represents the rotation of the magnetometer by 90 degrees around its \(y\)-axis. The correct transformation is \(A=R_{y}^{T}R_{G}^{L_{u}}R_{y}\), which can be derived as follows: the rotated magnetometer \(k\) measures \(\overrightarrow{B}=R_{G}^{L_{u}}R_{y}\overrightarrow{v}_{k}\) referred to the global frame according to Equation (5), where \(\overrightarrow{v}_{k}\) is the measurement in the local sensor frame. If the UAV has no orientation, i.e., \(R_{G}^{L_{u}}=I\), it is \(\overrightarrow{B}_{k}=R_{y}\overrightarrow{B}_{k}\), where \(\overrightarrow{B}_{k}\) has a zero \(z\)-component (due to its 90-degree rotation). From this follows the correct \(A\) and further \(\overrightarrow{B}_{k}=R_{y}^{T}R_{G}^{L_{u}}R_{y}\overrightarrow{v}_{k}\). Now, following the same approach as in the paper, the calculation of \(\overrightarrow{B}_{k}\cdot\overrightarrow{e}_{z}=\left(R_{y}^{T}R_{G}^{L_{u} }R_{y}\overrightarrow{v}_{k}\right)\cdot\overrightarrow{e}_{z}=0\), fails, because the corrected \(A\) now has no roll angle variable in the \(z\)-row.
# Enhancing the Recommendation of Learning Resources for Learners via an Advanced Knowledge Graph
## 2 Related Work
### Recommendation Algorithms Based on Knowledge Graph Representation Learning  
With the rapid development of artificial intelligence and big data technologies, recommendation systems have been widely applied in various fields. Knowledge graphs, as an efficient method of knowledge representation and organization, provide rich semantic information and structured knowledge for recommendation systems, significantly enhancing the accuracy and interpretability of recommendations. In recent years, recommendation algorithms based on knowledge graph representation learning have gradually become a research hotspot. These algorithms enhance the representation capabilities of users and items by integrating entity and relationship information from knowledge graphs, thereby achieving more precise personalized recommendations. [PERSON] et al. [5] proposed Ripple Network, an end-to-end framework that naturally integrates knowledge graphs into recommendation systems. It automatically and iteratively expands users' potential interests along the links in the knowledge graph, stimulating the propagation of user preferences, and superimposes multiple \"ripples\" activated by users' historical clicked items to form a preference distribution of users for candidate items, thereby predicting the final click probability. These algorithms can be classified into three categories based on the ways in which knowledge graph information is utilized in existing research: embedding-based methods, path-based methods, and propagation-based methods.
## 3 Proposed Method
### Feature Fusion Module  
SSD7 [33] uses four feature maps of different sizes to predict objects independently, and there is no connection between each feature map. The deep low-resolution feature map has undergone many convolution operations and can extract rich semantic information, which helps distinguish the object and the background. However, due to excessive downsampling, a lot of detailed information is lost. The shallow, high-resolution feature map contains detailed information of the object, which is conducive to accurate object positioning. However, because it performs fewer convolution operations, the shallow feature map cannot extract enough high-level features, and the semantic information is insufficient. Therefore, combining shallow feature maps and deep feature maps to generate a feature map with high resolution and full semantic information can be beneficial to the detection of small targets.  
The schematic diagram of the semantic interpolation processing proposed in FPN [7] is shown in Figure 3. The dark color indicates that it contains richer semantic information. The low-level feature maps are passed to high-level through convolution, and the high-level feature maps are given to the low-level through upsampling, and then the new feature maps generated by the element-sum module stitching between the two are used to predict the object.  
Figure 3: The schematic diagram of the semantic interpolation processing, where \(\oplus\) represents the element-wise sum operation.  
Unlike FPN, in SSD7-FFAM, we use 2 \(\times\) 2 deconvolutions instead of nearest-neighbor interpolation for upsampling. The deconvolution layer introduces non-linearity and therefore helps to represent the ability of network feature expression. In order to make the size of the feature map consistent, some deconvolutions are followed by zero-padding, which is to add pixels with a value of zero to the row or column. Similarly, the two adjusted feature maps are spliced by element-wise summation so that the in-depth semantic information is propagated to the shallow feature maps. An operation called Rectified Linear Unit (ReLU) follows the element-wise summation module. At the same time, the smooth convolution with the size of the 3 \(\times\) 3 convolution kernel is used to reduce aliasing caused by the enlargement of the feature map size. Figure 4 shows an example of the structure of the feature fusion module used in the proposed SSD7-FFAM. The new Conv4 feature map is passed to the attention module along with the other two feature maps.
# Evaluation of Metal-Organic Frameworks as Potential Adsorbents for Solar Cooling Applications
## 4 MOF-Based Adsorption Cooling
In contrast, an uptake capacity of greater than 14 mmol g\({}^{-1}\) is observed for mesoporous MOFs at a P/P\({}_{\mathrm{o}}\) of 0.4. It can be concluded that, depending upon the required water uptake, the desired framework of material can be employed which can be very advantageous for adsorption-based cooling applications [79; 80; 81; 82]. The specific pore volume and surface area of MOFs strongly affect their water adsorption properties for adsorption or desiccant cooling. MIL-100(Cr) and MIL-101(Cr) have been identified to possess large water adsorption quantity and exceptional hydrothermal stability by numerous researchers, as can be observed from Figure 8[83]. The high adsorption uptake makes these MOFs a feasible adsorbent for adsorption-based cooling systems. Better adsorption characteristics, lower required heat, and stable nature have increased the interest of these materials to be used as adsorbents for cooling applications.
## 2 Material and Methods
### Sample Preparations  
Original and alkali treated flax fibers were added to the cement mixture at weight fractions of 0 wt %, 0.5 wt %, and 1 wt %. The water/cement ratio of the different groups was 0.2. Superplasticizer and liquid tributyl phosphate were added at 0.4% and 0.13% of the mass fraction of cement, respectively. The plain cement paste, untreated flax fiber, and modified flax fiber reinforced cement pastes were designated as C, F, and MF, respectively. Each treatment group with different concentrations of flax fibers were referred to as C0, F0.5, F1, MF0.5, and MF1.  
Untreated and treated fibers were mixed with the cement for 10 min to ensure that fibers were uniformly distributed within the cement. Water, water reducer, and defoamer were then mixed thoroughly with the fiber mixture for 20 min. The fresh paste was cast in 40 mm \(\times\) 40 mm \(\times\) 160 mm molds for evaluation of mechanical properties, and 60 mm \(\times\) 12 mm \(\times\) 4 mm molds for evaluation of damping properties. Specimens were demolded after 48 h and placed in a curing chamber at a temperature of 20 \({}^{\circ}\)C and relative humidity of 95%. Specimens were cured for a total of 28 days.
## 3 Results
### Narrowest Observation Window for Detecting AKI and Associated Clinical Outcomes  
Of the 21,894 cases of AKI detected using a 172-h observation window (largest window evaluated), 42.7% of cases were detected using a window of \(\leq\)24 h and 74.5% of AKI cases were detected using a window of \(\leq\)48 h (Figure 6). Compared to patients who did not develop AKI, total hospital LOS increased in a stepwise fashion for narrowest observation windows (all \(p<0.001\)) (Supplementary data, Table S1). However, this trend was driven by increased days before AKI through the detection of additional cases of AKI that occurred later during the hospital course (Figure??). The average hospital LOS following detection of AKI was \(\geq\)3.4 days for all narrowest observation windows.  
Figure 6: **New cases of AKI detected through stepwise expansion of observation window from 24 h to 172 h (\(n=\) 21,894).** AKI, acute kidney injury. The bars display the number of new cases of AKI that are detected as the rolling observation window (used to calculate the increase from lowest to highest serum creatinine) expanded from 24 h to 172 h. Count data are shown on the left y-axis. For example, 9357 cases of AKI that were detected using a rolling 24-h window and are shown in the first bar. The second bar at 28 h shows the 3146 new cases that were detected using a 28-h rolling window that were not previously detected using the 24-h rolling window. By moving right on the x-axis, each bar shows the number of new cases that were not previously detected by narrower observation windows. Cumulative percentage is displayed on the right y-axis. The solid line represents the cumulative percentage of the 21,894 cases of AKI detected at each specific observation window using data from that observation window and all narrower observation windows.  
Figure 7: Days of hospital care before and after detection of AKI by narrowest observation window for detecting AKI (\(n=\) 126,367). AKI, acute kidney injury. Days before AKI was calculated as daysfrom hospital admission to first detection of AKI. If AKI was not detected, days before AKI was equal to the entire length of hospital stay. Days after AKI was the number of days from first detection of AKI to hospital discharge. Days prior to hospital AKI do not exist for patients who did not develop AKI; therefore, adjusted regression could not be used, and unadjusted data are presented in this figure. Differences in total length of stay were driven by differences in days before AKI detection. As the observation window for detection of AKI increases from 24 h to 172 h, additional cases of AKI were detected later during hospital admission. The average hospital length of stay following the detection of AKI was 3.4 days or longer for all observation windows.  
Detection of AKI using a 24-h observation window was associated with tremendously increased risk for in-hospital death (adjusted OR = 17) (Supplementary data, Table S2 and Figure 8). The detection of AKI using narrowest observation windows between 28 and 120 h was associated with increased risk for death (adjusted OR from 1.9 to 6.5).
###### Abstract
The utilization of energy storage systems, especially in renewable microgrids, has a significant impact on the reliability of the electric power as it can smooth the power fluctuation, reduce power quality problems, control the microgrid frequency and voltage, deliver initial energy when there is a transition between grid connection and islanded mode operation of microgrids, provide ride-through capability in the case of dynamic variations in intermittent energy sources, and enable distributed generations to operate as dispatchable units [7; 8]. Three different types of microgrids exist, namely, the AC microgrid, the DC microgrid, and the hybrid microgrid [9]. This paper considers a DC microgrid. The rapid increase in renewable energy sources requires a robust energy management system to connect these renewable energy sources, including energy storage systems. Based on the inexact models, deterministic estimations, and balance conditions, different researchers have reported findings which focus on enhancing the efficient action and operation of EMS, which requires much more improvement. Thus, new research needs to be performed to improve the reliability of microgrid systems. The aim of this paper was to develop an energy management system for DC microgrids, which contributes to an increase in the deployment of renewable energy sources (RESs). The principal contribution of this research was the implementation of an energy management strategy that attempts to ensure the proper operation of a DC microgrid system with the deep penetration of renewable energy sources. The developed DC microgrid model was created using MATLAB/Simulink software. The rest of the paper is presented as follows. The concept of microgrid systems is presented in Section 2. Section 3 discusses the mathematical modeling of the system components. In Section 4, the developed energy management system is presented. Section 5 explores and evaluates the simulation results of the developed method, and finally, Section 6 presents the conclusion of the paper.
# Comparison of Measurement Possibilities by Non-Invasive Reflectometric Sensors and Invasive Probes
## 4 Discussion
### Discussion of the Calibration Results and Uncertainty Calculations
It was also found that the uncertainties read at low and high material moisture levels were greater than those obtained at middle moisture levels, which is a typical observation for many measuring devices. It also resulted from the adopted regression model [11; 27]. In turn, in the case of the measurement uncertainty, it can be referred to the expanded uncertainty U. As its value is mainly influenced by the measurement uncertainty of type A, related to the quality of fit of the adopted regression model, the correlation between the U and the RMSE values was clearly visible. Only the U values from the middle measuring range were analyzed. The lowest value of expanded uncertainty was observed for the S2 sensor (concrete 400 class) corresponding to 1.799% mass. In the case of the measurement in concrete 600, the same values were also obtained with the S2 sensor, i.e., 2.468%mass. Various literature sources indicate the following values of measurement uncertainties of models developed for soil media and invasive probes: according to [52] and [53], this uncertainty was in the range of 0.022-0.023 cm\({}^{3}\)/cm\({}^{3}\); according to [54], it was 0.0269 cm\({}^{3}\)/cm\({}^{3}\), according to [55], it was 0.004-0.018 cm\({}^{3}\)/cm\({}^{3}\), and according to [50], it was 0.011-0.013 cm\({}^{3}\)/cm\({}^{3}\). Most of these values are higher or comparable to those obtained in the course of the experimental research performed in this work.
# An Improved Pied Kingfisher Optimizer for Maritime UAV Path Planning
###### Abstract  
Maritime activities have become increasingly frequent with the deepening of economic globalization, highlighting the burgeoning significance of maritime rescue. However, in practical applications, UAVs for maritime rescue faces numerous challenges, such as limited endurance and inadequate autonomous planning capabilities. To optimize flight routes and circumvent adverse sea conditions, an improved Pied Kingfisher Optimizer (IPKO) that incorporates refraction reverse learning, variable spiral search, and Cauchy mutation strategies was proposed. Comparative experiments conducted on CEC2005 and CEC2022 datasets with seven traditional algorithms demonstrate that the proposed algorithm exhibits superior precision and convergence speed. Subsequently, a path planning objective function was constructed based on trajectory cost and threat cost to simulate a 3D space for UAV maritime rescue missions, and the IPKO algorithm was applied to address the UAV path planning problem. The results showed that the total cost incurred by the IPKO algorithm decreased by 5.77% compared to the PKO algorithm and by 51.19% compared to the SCA algorithm. Finally, through UAV flight tests validating its practical applicability, it is ascertained that IPKO can enhance rescue efficiency in complex maritime rescue environments.  
maritime search and rescue; path planning; pied kingfisher optimizer; intelligent transportation Article
# A Case Study on the Rehabilitation of a Fire-Damaged Structure
## 1 Introduction  
Fire is one of the most severe hazards that building structures may experience during their lifetime [1]. If a structure is damaged by fire, it is necessary to investigate the cause of the fire and evaluate the reusability of the damaged structure. In terms of economic efficiency, it may be a better approach to retrofit the damaged components of the structure, instead of demolishing it partially or completely. This decision must be made based on the result of investigations such as the visual inspection of the damaged structure, tests on the remaining material, and finite element simulations of the structure or its structural components.  
Concrete is a structural material, which performs well under fire conditions due to its low thermal conductivity and incombustibility. Furthermore, a reinforced concrete (RC) structure with proper reinforcement details can effectively redistribute loads from damaged region to undamaged components even if the part of the structure is severely damaged by fire. Therefore, it is not common for fire-damaged RC structures to completely collapse during or after an event of fire [2].  
For this reason, fire-damaged RC structures can be generally rehabilitated by applying proper retrofitting methods. Representative examples of such methods include the strengthening of damaged RC beams and columns with fiber-reinforced polymer (FRP) sheets [3; 4] and the repair of damaged RC slabs with near-surface mounted carbon FRP rods [5]. They are sufficiently effective to restore the structural functions of the damaged structural components. In addition, thermo-mechanical finiteelement analysis is a useful tool to predict the structural behavior and performance degradation of RC structures subjected to fire [6; 7; 8]. In the case of a special event such as a fire occurrence caused by earthquakes, incremental dynamic analysis can also be performed to evaluate the nonlinear seismic response of fire-weakened RC structures [9; 10].  
Based on this discussion, this paper presents a case study to provide a reference for the rehabilitation of fire-damaged RC structures. The target structure is the main control building of a thermal power plant. The fire broke out in one of battery rooms in the building for an unknown reason and lasted for three hours before it was extinguished. The fire was contained within the battery room and did not spread to the other part of the building. As the power plant was under construction at the time of fire, the building was not in use and there were no casualties due to the fire other than to the building structure. A team of structural and material researchers/engineers was dispatched to the site to evaluate the condition of the structure and to set up appropriate corrective measures. The topics discussed in this paper include the results of the site investigation and tests, and a tentative plan for the rehabilitation of a structure damaged by fire.