esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion of the Results  
The results indicate that the maximum von Mises stress is located at the top dead center position. Additionally, it was noted that a tensile load may induce buckling in the  
Figure 27: Location of the minimum safety factor.  
Figure 26: Safety factor distribution.  
connection between the connecting rod and the piston rod. Future investigations could examine whether this connecting rod satisfies the buckling failure criterion.  
Regarding displacements, a scale factor of 2 was employed to more clearly visualize the deformed configuration of the steam engine at both critical positions (lower and upper dead center). It is logical that the maximum displacements occur at the observed location, as that portion of the steam engine functions as a cantilever. This observation suggests that potential optimization could involve enhancing the bedplate design by extending it to the valve chest area and securing it with cap screws. Nonetheless, the observed displacements are negligible, which supports the efficient operation of the steam engine under the given pressure load. This validates the hypothesis of small deformations, allowing for calculations to be performed in the undeformed state using the Eulerian formulation method for discretization, thereby reducing computational costs. Furthermore, the absence of significant deformations ensures minimal friction due to the relative movement between components.  
Moreover, the safety factor exceeds unity as intended and the optimal admissible steam pressure at the inlet (operating pressure) for this machine was identified as 0.320 MPa. This pressure ensures a safety factor within the range of 2 to 4. This value can be utilized to determine the operating pressure for other similar machines by relating the parameters of both models through dimensional analysis.  
In addition, this working pressure value, which was obtained by observing the upper dead center as it is the most restrictive for this value, is the maximum so that, in both compression and traction, a safety factor is achieved of higher than 2. Moreover, it was verified which pressure value produces a safety factor of less than 4 (3.96), so the range of working pressures was obtained. On the other hand, the minimum pressure value was obtained by observing the lower dead center, which was the most restrictive at this value of 0.165 MPa (Figure 28).  
Figure 28: Safety factor distribution at a pressure of 0.165 MPa at the lower dead center position.  
As a check, this pressure was also applied to the upper dead center, verifying that the safety factor is below 4 (3.77), as mentioned previously (Figure 29).  
Therefore, it can be stated that the operating range of steam intake pressure would be between 0.165 MPa and 0.320 MPa, so that the safety factor is always at a value of between 2 and 4.
# Flexible Phased Antenna Arrays: A Review
## 4 Major Challenges
### Environmental and Operational Challenges  
The real-world deployment of FPAs requires not only high electromagnetic performance but also robust operation under various environmental and mechanical stress conditions, including extreme temperatures, humidity, moisture exposure, and mechanical deformation from stretching and bending. So, a crucial area of research focuses on maintenance of consistent performance in face of such challenges. This has led to the exploration of environmentally resilient materials and mechanically adaptive design strategies. As an example, low-loss substrates have been utilized to reduce the dielectric losses under variation of environmental conditions such as moisture absorption and thermal fluctuations [61]. Real-time phase calibration techniques have been introduced to tackle the problem of environmental variability affecting array performance, specifically in beamforming and signal alignment. Such systems have continuous element-wise monitoring and phase readjustment in response to the changes in environmental conditions, leading to dynamic beam correction [50]. In recent years, FPA have been widely utilized in disaster response and field surveillance equipment. In such applications, self-sufficient power solutions are of high significance. One such innovative approach integrated Photovoltaic (PV) concentrators directly with power amplifiers. This enabled a standalone operation. This is considered as a significant advancement for long-duration deployments without access to conventional power infrastructure dedicated to remote deployment [51]. Although not for remote environments, another study was conducted for a smart and wearable textile-based antenna array. The solution incorporated textile-based microstrip antennas with direction of arrival estimation for dynamic beam correction, achieving a low-cost and scalable structure [74].
# An All-Batch Loss for Constructing Prediction Intervals
## 2 Materials and Methods
### All-Batch Loss Function
#### 2.3.2 Comparison with QD Loss  
Even if the AB loss and the QD loss are derived from the same assumption, there are many differences between the two methods.  
The main difference between the two methods is the different approximation methods for binomial distribution. The QD loss method applies the central limit theorem as an approximator, which assumes that the number of sample points is sufficient. Under such assumptions, each batch should have enough data in the training process, which may consume more computing resources. The correctness of the training results is questionable on the small batch of samples. The AB loss, by contrast, adopts the Taylor formula as an approximator, which may make the best use of the advantages of binomial distribution. Since AB loss only utilizes the second-order Taylor expansion formula during derivation, it may introduce numerical errors into the original NNL. However, QD loss approximates the binomial distribution to a normal distribution, which fundamentally changes the original assumption. Thus, AB loss may yield more reasonable results. Mathematically, the proposed method has no limitation on the size of the training set, which expands its potential for application in deep learning.  
Due to the introduction of the penalty term, the AB loss is more stable than the QD loss. During the training process, when the PICP is low, the use of the QD loss may cause the problem of gradient disappearance, which may prevent the training from continuing. The penalty term adopted in the AB loss is conducive to guiding the update of network parameters in the direction of improving PICP. Moreover, the penalty term ensures that the network continues training when the PICP is low, which may improve the stability of the training process.  
Another benefit of the penalty term in the AB loss is that it simplifies the training process. When adopting the QD loss for training, the output layer should be initialized according to the range of training data, which limits the application of the QD loss. Nevertheless, when using the AB loss for training, there is no need to independently initialize the output layer of the network. The neurons of the output layer only need to be distinguished logically, which simplifies the training process.
## 2 System Model
### Scenario Setting  
It was assumed that the multi-UAVs kept the flying formation during the execution of a task. The whole UAV network consisted of multi-UAVs and one base station (BS). The multi-UAVs were located at the eight vertices or edges of one cube. There were two data transmission modes, and the spectrum availability was limited. For the UAV network, \(M\) (\(M=1,2,\ldots,M\)) orthogonal uplinks were pre-assigned to M UAVs to perform the UAV to base station (U2B) data transfer, and \(N\) (\(N=1,2,3,\ldots,N\)) pairs of UAV to UAV (U2U) links performed the U2U data transfer. The U2B links could be reused by the U2U links to make full use of the frequency. In one time slot, as agreed, one U2B link only used one uplink and multiple U2U links could reuse the same uplink; however, one U2U link could only occupy one uplink. The value \(\rho_{n,m}\) was used as the \(n*m\) binary indicator matrix and \(\rho_{n,m}=1\) if the \(n\)th U2U link reused the \(m\)th uplink resource, otherwise \(\rho_{n,m}=0\). \((x_{k},y_{k},z_{k})\), \(x_{j},y_{j},z_{j}\) and \((0,0,H)\) were used to represent the locations of the \(k\)th UAV, the \(j\)th UAV and the BS, respectively. Then, the distance between the \(k\)th and the \(j\)th UAV is calculated as follows:  
\[d_{k,j}=\sqrt{(x_{k}-x_{j})^{2}+(y_{k}-x_{j})^{2}+(z_{k}-z_{j})^{2}}. \tag{1}\]  
Similarly, the distance between the \(k\)th UAV and the BS is calculated as  
\[d_{k,BS}=\sqrt{(x_{k}-0)^{2}+(y_{k}-0)^{2}+(z_{k}-H)^{2}}=\sqrt{x_{k}^{2}+y_{k }^{2}+(z_{k}-H)^{2}}. \tag{2}\]
## 3 Methods
### Network Layer  
The network layer includes the network of sensors controller composed of sensor node and synthetic sensor nodes. The internal communication between them relies on WiFi devices and their protocols. The network of sensors controller consists of sensor nodes that integrate individual sensors. The arrangement is called a tree topology network. The network of sensors controller includes three components: process control, runtime configuration, and a local database for sensor storage. It acts as a communication and  
Figure 1: EU-SENSE system architecture [36].  
direct management tool. Specifically, it collects and decodes raw streams from connected sensor nodes; transits data from the sensor nodes to the local database, configures and reconfigures sensor nodes; and checks network component status. The network controller provides both live and historical data. An SD card stores all information related to the node configuration (e.g., addresses of Bluetooth devices or node identification parameters).  
A heterogeneous sensor node contains a variety of chemical sensors based on different technologies that function within the same node. With this solution, a large amount of data are gathered. Data fusion algorithms are simultaneously responsible for detecting and identifying chemical agents, and reducing the false alarm rate. The sensor node unit comprises sensors for chemical detection, a node controller based on an STM32F4 microcontroller, pre-processing algorithms and configuration components [36]. The sensors can be person-worn or stationary.  
It must be stressed that for the EU-SENSE system, ensuring sensor node interoperability is the challenge. Data in different formats, schemas, models, and discrepancies in third party devices (e.g., Proengin FDP detector and TN MO detector) require the creation of a unified data model. This model involves two elements, a network configuration standard (implemented as a set of XML files) and a set of files describing communication between the system components (mainly protobuf format files) [36].
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 5 Progress in Biomass Chemical Looping Gasification
### Pilot-Scale Investigations
The operating parameters in the BCLG in those studies were found that a high content of syngas components was produced during the process with 27-30% of H2, 17-21% of CO for ilmenite and 37% of H2, 21% of CO for Fe2O3/Al2O3 at autothermal conditions. It is noteworthy that tar generated in the studies was in the range of 0.9-3.0 g/Nm3, being lower than that reported by other gasification technologies. [PERSON] et al. [71] indicated that operating parameters strongly influence tar reforming in a BCLG process. The research results showed that tar decomposition is promoted at higher gasification temperatures, steam-to-biomass ratios and oxygen carrier-to-biomass ratios, resulting in the conversion of large molecular compounds into small ones. They also found that the presence of Cu in the oxygen carrier could enhance the decomposition of small molecular compounds in tar, while the Fe composition is favorable for a reduction in the yield of large molecular compounds in tar. A novel oxygen carrier was synthesized for BCLG with additional functionalities of its reduced form acting as a catalyst for tar decomposition and a CO2 adsorbent in syngas [130]. Fe2O3 is supported on silicalite-1 (Fe/S-1) was synthesized and characterized with different loadings as multi-functional materials to improve the quality of syngas and the efficiency of BCLG. The results found that the iron supported on silicalite-1 performed high thermal/chemical stability in a multi-cycle process and better catalytic activity in tar decomposition compared to the iron on a conventional silica support. An experimental investigation of CLG of torrefied woodchips using iron-based oxygen carriers was conducted in a bubbling fluidized bed reactor to evaluate the effects of operating parameters and the reactivity of oxygen carriers during the process [69]. The authors found that the operating conditions strongly influence the process performance in terms of process efficiencies, gas yields. It was noted that the reactivity of iron-based oxygen carriers with different gaseous fuels follows the order H2 > CO > CH4. Additionally, the calcination temperature of oxygen carriers plays a key factor in the reactivity of oxygen carriers in the BCLG process. The other recent experimental studies of BCLG in the pilot-scale reactors are summarized and tabulated in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 1 Introduction  
Indoor disaster sites urgently requiring emergency services can be very dangerous; rescuers require data about the indoor conditions to efficiently perform their rescue mission and mitigate losses [1] AR(Augmented Reality) devices allow rescuers to evaluate indoor disaster sites quickly, thus improving rescue efficiency [2].  
AR navigation considers the structure of the building and the locations of all rescue personnel. However, not only is the existing GPS focused on the outdoors [3, 4], but also indoor navigation using GPS is difficult because GPS signals are weak or blocked in urban areas and inside buildings [5, 6, 7, 8]. In addition, indoor disasters may create new obstacles such as collapses [9].  
We thus generated topological maps from image-based maps and updated obstacles in real time so that rescuers could quickly understand the building scenario. We also are developing AR navigation systems for indoor disaster sites. A schematic of topological map generation is shown in Figure 1.  
The A* algorithm [10] has been available for some time, and is widely used because it is easy to implement and tends to generate accurate routes. Nevertheless, efforts are being made to increase the efficiency of pathfinding by improving heuristic methods [11, 12, 13, 14, 15, 16].  
A critical disadvantage of the A* approach is that it consumes a considerable amount of memory and computational power when the resolution of the map image is high [15, 16]. As such, here we applied the corner node algorithm (CNA) in combination with A* forpathfinding; this approach requires less computational power and memory by preprocessing the data to generate the topology map.  
CNA generates a topology map based on a two-dimensional (2D) array of map data via two steps: creating nodes by searching a physically inaccessible space, such as a wall, and determining whether individual nodes can be connected in a straight line. The topology map is then generated by connecting the valid nodes.  
Because the CNA predefines the connections between paths by generating a topology map, it shortens the path search computation process, thus improving the efficiency and accuracy of the identification of the optimal path. The combined algorithm is suitable for indoor disaster-related applications, such as in situations in which routes must be found quickly, due to its significantly shorter computational time.  
Section 2 of this paper introduces the A* algorithm. Section 3 discusses the CNA. Section 4 presents our experimental results, Section 5 introduces AR navigation implemented using the algorithm, and Section 6 provides a summary of our findings and concluding remarks.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 5 Software Simulation
### Validation of Dynamic Stress Error  
As shown in Equation (25), the dynamic stress error is related to the residual vehicle dynamic, integration time, noise bandwidth and update time of aiding information. Therefore, we can compare the mean of the output of phase discriminator with the theoretical value by setting different vehicle dynamic, noise bandwidth and integration time.  
Figure 8: Mean of the output of PD when \(p=200\) m/s\({}^{3}\).  
Figure 6: The block diagram of software simulation platform.  
Figure 7: Mean of the output of PD (phase discriminator) when \(p=50\) m/s\({}^{3}\).  
To analyze the dynamic stress error, single-channel simulations are conducted with \(C/N_{0}=50\) dB-Hz and LOS jerk \(p=50\) m/s\({}^{3}\). The aiding information update timeis set as 10 ms which is the INS prediction update time. The mean of the output of phase discriminator for different bandwidth and integration time is shown in Figure 7. The result when the jerk is increased to 200 m/s\({}^{3}\) is shown in Figure 8. In Figure 8, when \(T\) is 20 ms and the noise bandwidth is 4 Hz, the theoretical value exceeds the stable tracking threshold, so it is not taken into consideration. Figures 7 and 8 demonstrate that the dynamic stress error of INS-aided PLL increases with the vehicle LOS jerk, and it also increases with the narrower noise bandwidth and longer integration time. The simulation results fit well with the theoretically calculated ones, and show that the theoretical analysis of the dynamic stress error is correct.
## 4 Performance Evaluation of the Active Support System for the 4m SiC Primary Mirror
### Correction of the Primary Mirror Surface Shape Errors Caused by Thermal and Wind Loads  
In addition to gravity, thermal and wind loads could deform the primary mirror, thereby affecting its surface accuracy and the imaging quality of the telescope. This section analyzes the effects of the thermal and wind loads, and corrects the corresponding surface shape errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Elevation Angle**} & \multicolumn{2}{c}{**Primary Mirror Surface Shape rms/nm**} & \multicolumn{2}{c}{**Amplitude of the Correction Force/N**} & \multicolumn{2}{c}{**Maximum Von Mises Stress of the Primary Mirror/MPa**} \\ \cline{2-5}  & **Before Calibration** & **After Correction** & **Correlation Force/N** & **Maximum Von Mises Stress of the Primary Mirror/MPa** \\ \hline
90\({}^{\circ}\) & 64.8 & 14.8 & 51.3 & 0.51 \\
45\({}^{\circ}\) & 165.3 & 14.5 & 59.6 & 0.96 \\
0\({}^{\circ}\) & 211.0 & 15.8 & 57.2 & 1.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relevant data of the primary mirror surface shape before and after correction for gravity deformation.  
Figure 16: Results of correcting the gravity deformation of the primary mirror using the first 30 orders of bending modes. (**a**) Elevation angle of 90\({}^{\circ}\); (**b**) Elevation angle of 45\({}^{\circ}\); (**c**) Elevation angle of 0\({}^{\circ}\).  
Figure 15: Results of correcting gravity deformation of the primary mirror using different orders of bending modes. (**a**) Results at elevation angle of 90\({}^{\circ}\); (**b**) Results at elevation angle of 45\({}^{\circ}\); (**c**) Results at elevation angle of 0\({}^{\circ}\).
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation  
The FEM has long been a cornerstone for simulating crack growth and understanding the complex stress and strain fields around crack tips. However, traditional FEM approaches have limitations in addressing the intricate phenomena associated with fatigue crack growth, such as variable amplitude loading, crack closure effects, and environmental interactions. In recent years, advanced techniques in FEM have emerged to overcome these challenges, providing more accurate and efficient simulations of fatigue crack propagation. The following sections outline the latest advancements in FEM development, highlighting their key features.
# A Combinatory Framework for Link Prediction in Complex Networks
## 3 System Model  
We consider a network as a graph \(G=(V,E)\), where \(V\) is the node set and \(E\) is the edge set. For two nodes \(u,v\in V\), \(e(u,v)\in E\) represents a link between nodes \(u\) and \(v\). A link prediction algorithm over \(G\) attempts to determine whether a link \(e(u,v)\) will exist between nodes \(u\) and \(v\), in the future, given that it is not present at the moment.  
We considered networks as undirected graphs, where the nodes are represented by integers and the edges connecting them are tuples of these node numbers. The employed networks ranged between 600 and 2500 nodes and edge count varied between 5500 and 20,000. Larger networks can be analyzed straightforwardly. We consider the processing time less critical and target a more accurate predictor for missing edge estimations.  
We propose a framework that concerns the composition of various link prediction algorithms in order to obtain the best possible result from this composition. According to our approach, we rely on two or more well-known and basic algorithms that perform link prediction and have good results, that is, they make good estimates for edges that are missing from a network and will probably be added to it in the future. These estimates are combined and a new estimate results from their composition. The new estimate is essentially a more enhanced evaluation (rank) of how much a particular missing edge in a network could be added to it in the future. With the enhanced estimation, we attempt to promote the best possible estimation from each basic algorithm. This idea follows the principle that there is no link prediction algorithm which is the best for every type of network, but there are link prediction algorithms which, depending on the type of network to which they are applied [30; 31; 32], return good or bad results or estimates.  
In our approach for link prediction, we have used five well-known algorithms. These are Resource Allocation Index, Adamic-Adar Index, Common Neighbor Centrality, Jaccard Coefficient, and Preferential Attachment [14; 15; 16; 17; 18; 19; 20; 21]. Section 2 described the operation of each one. The use of these algorithms is not limiting. We could have used any  
we have used and which expresses the weight of its estimate in the enhanced estimate we will construct.  
Having this weights list, we can now make our own assessment (prediction) of how likely it is that an edge is missing from our original graph (or added in the near future). The procedure we follow is to take the estimate of each basic algorithm for some missing edge, normalize these estimates, and then compose a new estimate (on any scale we want; here we have used the scale 0-1,000,000) based on genetic algorithm output weights. In the tests found in the following sections, we experimentally prove that in networks where the algorithms selected in this paper work well, the enhanced estimate we create in this way for a network is more reliable than the estimate returned by the best basic algorithm that we have applied for this network.