esa-sceva/satcom-chunk-collection huggingface dataset:
# A Conceptual Design Methodology for e-VTOL Aircraft for Urban Air Mobility
## Appendix A  
The requested power during take-off phase is given by the sum of different components (e.g., induced power, profile power, tip losses). In conceptual design phase, a common approach is to evaluate induced power by using the classical disk actuator theory, then a figure of merit is introduced in order to consider all the other components (e.g., fraction of power due to parasitic drag of the blade, tip losses, etc.). According to the disk actuator theory, induced power (\(P_{i}\)) can be evaluated by Equation (A1)  
\[P_{i}=T\;g\;v_{i}.\] (A1)  
\(T\) is the thrust generated by the single propeller, \(g\) is the acceleration gravity, and \(v_{i}\) is the induced speed. Induced speed can be computed by Equation (A2).  
\[v_{i}=\sqrt{g\frac{T}{2\rho A}}\] (A2)  
Combining Equations (A1) and (A2) and considering the definition of figure of merit (as the ratio of induced power \(P_{i}\) and the total take-off power \(P\)), the Equation (A3) is derived  
\[\left(\frac{P}{W}\right)_{TO}=\frac{T}{W}\sqrt{g\frac{T}{2\rho A}}/FoM\] (A3)  
During take-off, propeller thrust is higher than the aircraft weight, so the term \(k_{T}\) (the ratio of \(T\) and \(W\)) is introduced; moreover, the power supplied by the electric motordepends also on the propeller efficiency (\(\eta_{prop}\)). Introducing the above parameters, the Equation (A4), has been obtained  
\[\left(\frac{p}{W}\right)_{\text{TO}}=\sqrt{\frac{Tg}{2\rho A}}\frac{k_{T}}{FoM \ \eta prop}\] (A4)  
The requested power during climb can be easily derived by using classical flight mechanic equations. Considering the assumption of quasi-steady climb, the Equation (A5) can be written  
\[\left\{\begin{array}{c}L=W\ g\\ D=W/E_{CL}\\ T=D+W\gamma\end{array}\right.\] (A5)  
\(D\) is the total drag of the aircraft, \(E\) is the aerodynamic efficiency of the aircraft, and \(\gamma\) is the trajectory slope. By multiplying the second relation of Equation (A5) by the aircraft speed and dividing by the aircraft weight and the propeller efficiency, the Equation (A6) is obtained  
\[\left(\frac{P}{W}\right)_{CL}=\frac{1}{E_{CL}}\frac{V}{\eta_{prop}}+\frac{V_{z }}{\eta_{prop}}\] (A6)  
Explicating the first relation of the Equation (A5) and substituting in Equations (A6) and (A7) is obtained  
\[\left(\frac{p}{W}\right)_{CL}=\frac{1}{\eta_{prop}E_{CL}}\sqrt{\frac{2g}{\rho CL _{CL}}\frac{W}{S}}\ +\frac{V_{z}}{\eta_{prop}}\] (A7)  
It is worth to note that aerodynamic efficiency depends on the wing loading, but no explicit relationship can be derived. The cruise equation can be easily obtained by imposing aircraft rate of climb equal to zero.  
To compute maximum wing loading, two assumptions have to be introduced: (i) the stall speed (\(V_{stall}\)); (ii) the maximum lift coefficient with flap deployed (\(CL_{max}\)). Considering the aircraft in level flight and exploiting the first relation of Equation (A5), the Equation (A8) is obtained.  
\[\left(\frac{W}{S}\right)_{max}\ =\ \frac{\frac{1}{2}\rho V_{stall}^{2}CLmax}{g}\] (A8)
# Model Test on Grouting Properties of Alluvial Filler Soil
## 7 Engineering Characteristic Analysis of Grouting Soil
### Geological Radar Scanning  
7.1.1. Role of Geological Radar  
Ground-penetrating radar (GPR) is a valuable tool in geotechnical engineering, especially when dealing with alluvial soils. GPR uses electromagnetic waves to penetrate the ground and create subsurface images. It helps in identifying soil stratification, the presence of voids, and potential weak zones. By understanding the subsurface conditions, engineers can plan the grouting process more effectively. GPR surveys can aid in identifying areas where grouting is needed the most and help optimize grout injection points.
# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 6 Experimental Results
### CapNet-Based Anomalous Behavior Interpretation  
In the experiment, a subset of data from NGSIM is extracted, and vehicle trajectory time series data are from five channels: local_x, local_y, vehicle velocity, vehicle acceleration, and vehicle movement direction.  
Figure 8: ROC curve.  
Figure 7: Detection accuracy for different length \(N\).  
There are nine sample trajectories in Figure 9 and the legend on the right is vehicle ID. The \(x\)-axis and \(y\)-axis are the coordinates of vehicle locations within the image. The moving direction is from the top to the bottom. In the recorded video, vehicle 343 made a right turn and vehicles 332, 338, 340, 342, 347, 354, and 355 are going forward in different lanes. Vehicle 345 is the anomalous one. In the video stream, it is observed that vehicle 345 turned left to merge into the lane from a left-turn only exit, which is illegal.  
Therefore, the entire subset of trajectories is labeled as five behaviors: turning right, moving forward at lane one, lane two, and lane three, and the last behavior is the abnormal one which is turning left from a wrong exit. There are two capsules networks trained. The CapNet-1 takes individual trajectories as the input, and its training dataset is for four legal driving behaviors except the abnormal one. The goal is to check whether the detected behavior using mSSA can be recognized as any of the normal driving behaviors. The CapNet-2 takes multiple trajectories as the input and is to predict if, with the existence of the detected behavior, it will be agreed by other vehicles.  
The time window length is 12.5 s, and there are five channels for each vehicle with different characteristics. The overall length of a trajectory is 625, which will be converted into a \(25\times 25\) matrix as the model input. There are 8000 trajectories in the training dataset while the test dataset contains 2000 trajectories. The number of Epoch is six, and the kernel size of the convolutional layer is six with stride one. In the primary capsules layer, there are 32 capsules, and each one contains eight convolutional units. At the behavior capsules layer, there are eight capsules and four classes are for prediction. The model is trained at a virtual machine with 4 GB memory.  
Figure 10 shows the training and testing time that is consumed per piece of trajectory data. It takes around 0.225 s to train the model with a trajectory while testing only takes under 0.050 s.  
Figure 11 shows the training and testing accuracy. After three epochs, the training accuracy can achieve 100% accuracy. The testing accuracy after epoch three becomes 90% to 100% as well. The experimental results show that the proposed approach can be deployed on edge nodes with limited computational resources and achieve a good performance.  
Figure 9: Sample trajectories from the NGSIM data set.
## 4. Constructional and Functional Aspects of Chains
### Requirements and Dimensioning
Tensile testing is performed using specialized testing equipment compliant with ISO 7500-1, equipped with a tensioning mechanism of sufficient length to avoid the need for re-clamping during testing. Fatigue and impact tests are conducted on specimens extracted from selected batches, and their performance is evaluated against the criteria defined for each quality class. These tests are designed to confirm that the chain meets the required strength characteristics, ensuring safe and long-lasting operation under actual working conditions. The research framework is illustrated in Figure 8.  
The elongation test under the proof force should be performed by inserting the chain specimen into the gripping points of a tensile testing machine and subjecting it to a force equal to half of the specified proof force. This force is then reduced to the setting force, ranging between 8 and 35 kN, depending on the diameter of the chain link wire/rod. While holding the specimen under this setting force, the gauge length is marked and, if applicable, a strain gauge is attached to the sample. Next, the applied force is increased up to the designated proof force at a maximum rate of 20 kN/s. Upon reaching the test force, the elongation is recorded. The total percentage elongation is calculated by dividing the measured elongation by the gauge length and multiplying by 100%. The resulting elongation must not exceed the allowable value for the given chain class. After the proof force is applied (and the strain gauge, if used, is removed), the tensile force is increased further until the sample breaks. The force at fracture constitutes the breaking force and must not fall below the minimum threshold for the respective chain type. The total elongation at break must also be measured and meet the minimum required value. At present no international standard stipulates design loads for polymer or composite link chains. Consequently, dimensioning must revert to limit-state principles adapted from metallic norms. In the LIDER project we calibrate material partial factors (\(\gamma_{\text{M}}\) = 1.5) [109] against \(>\)200 tensile tests on PET/GF prototypes. For example, 10 mm PET/GF link with a net section of 78 mm\({}^{2}\) and f\({}_{\text{u}}\) = 390 MPa [110] thus delivers a design resistance R\({}_{\text{d}}\) = 20.3 kN, comfortably exceeding the 8.3 kN working load limit (utilization = 0.41) [111]. Injection-molding parameters [112] -- melt temperature (250 \(\pm\) 5 \({}^{\circ}\)C), clamping force (75-80 kN), mold temperature (80 \(\pm\) 2 \({}^{\circ}\)C), cooling time (45-60 s) and compatibilizer level (\(\leq\)5 wt %) -- were found to shift ultimate strength by up to 12% and impact strength by +66%, underlining the necessity of process-structure-property-based design rules.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Proactive
#### 5.2.3 Enhancing Routing Performance Using Probability of Link Disconnection  
[PERSON] et al. [125] propose a proactive routing scheme to select routes based on the probability of link disconnection between nodes in a route as a result of high mobility in order to prolong route lifetime. The main objectives are to enhance route stability (O.1) as well as to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses two challenges, namely highdynamicity (X.1) of network topology as well as low residual energy (X.3) due to retransmission. The routing scheme is designed for source routing (C.1.2).  
During the neighbor and route discoveries stage, the source node periodically broadcasts RREQ messages that contain mobility metrics (i.e., nodes' velocity) (M.1) and geographical location (M.3) of one-hop neighboring nodes to form a neighbor set comprised of two-hop neighboring nodes and to gather node information along potential routes. Intermediate nodes that receive RREQ calculates the link expiration time (M.2) and the probability of route breakage based on mobility of nodes. During the route selection stage, the destination nodes, which receive RREQ messages via different routes, select a route that provides higher link expiration time (i.e., higher connectivity time between source and destination) and lower probability of route breakage in order to prolong route lifetime and to reduce delay. The destination node sends a RREP message to the source node using the same route that the RREQ message traverses. During the route maintenance stage, link disconnection occurs due to the dynamicity of network topology and so the source node determines an alternative route before the expiration of the link expiration time.  
The routing scheme is compared with a state-of-the-art scheme, which is a conventional routing scheme that establishes routes to destinations in an on-demand manner and supports both unicast and multicast routing. Random waypoint mobility model (A.2) is used in simulation. The routing scheme has been shown to increase route setup success rate (P.4) and to reduce energy consumption (P.2).
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 3 Proposed Approach
Node \(\boldsymbol{f}\), \(\boldsymbol{1}\leq\boldsymbol{f}\leq\boldsymbol{m}\), in this layer calculates the weighted sum \[s_{f}=w_{f,1}h_{1}+w_{f,2}h_{2}+\cdots+w_{f,l}h_{I}+b_{f}\] (4) where \(w_{f,1},w_{f,2},\cdots,w_{f,l}\) are the weights and \(b_{f}\) is the bias associated with it. Then it provides the network output \(\hat{y}_{f}\) by \[\hat{y}_{f}=\text{sign}\big{(}s_{f}\big{)}\] (5) where sign \((\cdot)\) is the sign function defined as \[\text{sign}\big{(}s_{f}\big{)}=\left\{ \begin{array}{l}+1,\text{if}_{f}\geq 0 ;\\ -1,\text{if}_{f}<0.\end{array} \right.\] (6) Therefore, the network output \(\overset{\wedge}{\mathbf{y}}\) for the input vector \(\mathbf{x}\) is \[\overset{\wedge}{\mathbf{y}}=\left[ \begin{array}{cccc}&\hat{y}_{2}&\hat{y}_ {2}&\cdots&\hat{y}_{m}\end{array} \right]^{T}\] (7) Each component in \(\overset{\wedge}{\mathbf{y}}\) is either \(+1\) or \(-1\). If component \(f\) is \(+1\), \(1\leq f\leq m\), \(\mathbf{x}\) is predicted to belong to category \(f\). Note that one component or more in \(\overset{\wedge}{\mathbf{y}}\) can be \(+1\). Therefore, the proposed network architecture is applicable to solving both single-label and multi-label pattern classification problems. We describe below how the network of Figure 1 is created from the given training set. Two phases, network setup and parameter refinement, are involved. In the first phase, the network structure is built and the initial values of the parameters are set. Then the parameters of the network are optimally refined in the second phase.
# Rearrangeable and Repackable S-W-S Elastic Optical Networks for Connections with Limited Bandwidths
## 5 Conclusions  
Elastic optical networks flexibly allocate bandwidth to a connection to improve utilization efficiency. The paper considers an S-W-S switching fabric, represented as SWS1(\(q\), \(p\), \(n\)), for elastic optical networks. RNB and RPNB SWS1(\(q\), \(p\), \(n\)) switching fabrics are proposed in this paper. The number of middle switches is calculated and evaluated. Note that for an SWS1(\(q\), \(p\), \(n\)) switching fabric with given parameters \(q\) and \(n\), reducing the value of \(p\), namely, the number of middle switches, leads to lowering the hardware cost, area requirements, and power consumption. The proposed RNB and RPNB SWS1(\(q\), \(p\), \(n\)) switching fabrics for \(m\)-slot connections where \(1\leq m\leq m_{\max}\) with either \(m_{\max}=1\) or \(\frac{q^{2}-1}{q-1}\leq m_{\max}\leq\frac{nq}{2q-1}\) consume around half the number of middle switches compared to the SNB SWS1(\(q\), \(p\), \(n\)) switching fabric given in [7]. Different from the SNB SWS1(\(q\), \(p\), \(n\)) switching fabric, connection rearrangements are required for both RNB and RPNB SWS1(\(q\), \(p\), \(n\)) switching fabrics. Though the RPNB SWS1(\(q\), \(p\), \(n\)) switching fabric has the same result in terms of \(p\) as the RNB SWS1(\(q\), \(p\), \(n\)) switching fabric, the former does connection rearrangements during departures, which requires less critical time than during arrivals. It is worth noting that some rearrangement methods adopted for the RNB and RPNB SWS1(\(q\), \(p\), \(n\)) switching fabrics could cause connection delays, and they may need some kind of setup network that would consume more power. All of these factors could affect the performance and throughput of the proposed switching fabrics. These factors, as well as further improvement of the upper bounds for RNB and RPNB SWS1(\(q\), \(p\), \(n\)) switching fabrics, will be investigated in the future.  
This work was supported by the Ministry of Science and Technology, Taiwan, under Contract MOST 108-2221-E-024-002-MY2.  
The authors declare no conflict of interest.
## 4 Proposed HE-DEPSO Algorithm  
Despite the development of several advanced versions of the DE algorithm in recent years, its performance still needs to improve in optimization problems with multiple local minima. To address these issues, the design of effective mutation operators and parameter control are two key aspects to improve the performance of the DE algorithm. In the proposed HE-DEPSO algorithm, an adaptive hybrid mutation operator is developed, which takes the self-adaptive mutation strategy from the DEPSO algorithm [39] as a basis and incorporates the parameter control mechanism from the SHADE algorithm [32]. This adaptive hybrid mutation operator introduces a new mutation strategy called \"DE/current-to-EHE/1\", which utilizes the historical information of the elite individuals in the population to enhance the optimization of the \(\chi^{2}\) function.
## 7 Conclusions  
In this paper, a comparison between strain measurements and FE calculations, carried out on a full-scale girder in a precast plant during normal production, is presented. The girder was instrumented with strain gauges on three different strands and on vertical reinforcement bars in the end zone. Moreover, also DEMEC mechanical strain measurements were carried out on the end zones lateral faces. These experimental results are compared with 3D nonlinear FE models. In these models, the material properties were based on concrete specimens from the corresponding batch and stored next to the girder. When analyzing these results, a good fit between these experimental tests and the numerical results is noticed, proving the validity of the FE model.  
The previously numerically encountered effect where prestress transfer is influenced by the location of the strands and whether the strand is surrounded by other strands is experimentally demonstrated.  
From the test data, a friction coefficient of 0.5-0.9 can be recommended. However, it should be noted that compared to other relevant literature a different way of strand modelling, i.e., a hollow and not a solid cylinder, is used, and as a result the proposed coefficients should be interpreted in this way. The authors nevertheless believe that this way of modelling is more consistent with reality, and is preferable.  
It can be concluded that the FE model is validated by experimental results and can further be used to optimize the reinforcement in the anchorage zones of pretensioned girders. This is an important step into the optimization of anchorage zones of pretensioned concrete members.  
Figure 17: Longitudinal stresses obtained from the FE model and experimental results: (**a**) Strand A (**top**); (**b**) Strand B (**middle**); (**c**) Strand C (**bottom**).  
**Author Contributions:** Conceptualization, methodology, supervision, funding acquisition, W.D.C., V.B. and L.T.; testing, software, validation, formal analysis, K.V.M.; writing--original draft preparation, W.D.C.; writing--review and editing, W.D.C., K.V.M., V.B. and L.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Agency for Innovation by Science and Technology (IWT) and the companies Structo+ and Megaton.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Case Study of Interworking of QKD Networks and the Role of SDN Orchestrator  
This study pertains to the integration of operational and control mechanisms aimed at ensuring the efficient operation and maintenance of heterogeneous QKD equipment and transmission equipment. This research is based on a practical case study conducted within the Korea Research Environment Network (KOREN) in South Korea. The objective of this research is to not only manage individual SDN controllers for QKD equipment and transmission equipment but also to enable the exchange of keys between different domains, i.e., networks, that are associated with disparate QKD equipment.
## 3 Denoising Diffusion Probabilistic Model (DDPM)  
### Overview of DDPM  
The DDPM is a powerful generative model designed for image generation and denoising tasks. Developed to address challenges in capturing complex data distributions and handling noise in images, the DDPM has demonstrated significant success in various domains, including computer vision and biomedical image processing.  
At the heart of the DDPM is the denoising diffusion process. As shown in Figure 1, the model leverages the insight that a sequence of noisy images can be generated by applying a diffusion process, where each step corresponds to introducing controlled noise. By training the model to reverse this process and denoise the images, DDPM learns to capture the underlying probability distribution of the clean data [13].  
DDPM is inherently rooted in probabilistic modeling, aiming to capture the distribution of realistic data. The denoising diffusion process is formulated as a probabilistic generative model, allowing for the estimation of intricate data distributions and enhancing the model's capability to generate high-quality samples. DDPM employs a reversible diffusion process, where noise is incrementally added to an image in a series of steps. This process allows the model to learn both the forward and inverse transformations, enabling effective denoising during the training phase. DDPM is trained using a maximum likelihood estimation (MLE) objective, seeking to maximize the likelihood of observing clean images given the noisy counterparts generated by the denoising diffusion process. This  
Figure 1: Graphical model of DDPM.  
training objective ensures that the model captures the underlying distribution and can effectively denoise input images [42; 43; 44].  
DDPM's denoising capabilities make it particularly valuable in medical imaging, where the accuracy of segmentation tasks is heavily influenced by the quality of input images. By effectively removing noise, DDPM enhances the robustness and reliability of segmentation models in biomedical applications. Beyond denoising, DDPM's generative nature allows for probabilistic image generation. In biomedical imaging, this capability can be harnessed for generating synthetic datasets, augmenting training data, or simulating various imaging conditions to improve the robustness of segmentation models.  
In conclusion, the DDPM represents a cutting-edge approach to image generation and denoising, rooted in the principles of probabilistic modeling and the reversible denoising diffusion process. Its applications in biomedical image segmentation, particularly in denoising medical images and generating synthetic datasets, showcase its potential impact on advancing the field. As research in probabilistic models continues to evolve, DDPM stands out as a promising tool for improving the reliability and quality of biomedical image segmentation tasks.