esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadrotor Formation Strategies Based on Distributed Consensus and Model Predictive Controls
## 1 Introduction  
As the quadrotor becomes increasingly popular and easy to acquire, it has gained much attention from researchers and manufacturers. Along with the various well-developed control algorithms of a single quadrotor, researchers have shifted their attention toward formation-control algorithms in recent years. A survey [1] classifies the formation flying control algorithms of spacecraft into five architectures: multiple-input and multiple-output, leader-follower, virtual structure, cyclic, and behavioral. The above classifications can be extended to the formation-control algorithms of the quadrotor. Among the above formation-control architectures, the leader-follower architecture [2; 3; 4; 5; 6] has received most attention from researchers.  
In this study, the distributed consensus control and model predictive control (MPC)-based formation strategies considering the leader-follower architecture are proposed. First of all, the formation-control problem is decoupled into horizontal and vertical motions. In the horizontal motion, the leader tracks the given waypoints by using the MPC, and generates the desired formation trajectory for each follower based on its flight information, predicted trajectory, and the given formation pattern. The followers carry out the formation flight based on the proposed horizontal formation strategy and the desired formation trajectories generated by the leader. The follower's horizontalformation strategy is set up based on the distributed consensus control and the MPC. Moreover, the optimized problem, which is considered in the MPC as adopted by the leader's tracking control and the follower's formation strategy in the horizontal motion, is formulated as a quadratic programming (QP) problem. In the vertical motion, the leader tracks the desired altitude and climb rate and generates the followers' desired formation trajectories and the followers track the desired formation trajectories generated by the leader using the MPC. The optimized problem, which is considered in the MPC as adopted by the leader and the follower in the vertical motion, is formulated as a linear quadratic tracker (LQT).  
Section 2 provides the background of this study. Both the linear and nonlinear equations of motion (EOMs) of the quadrotor are derived in Section 3. Section 4 briefly introduces the control laws that are adopted in the formation strategies. The full map of the formation strategies for the leader and the follower are carried out in Section 5. In Section 6, the preparation for the simulation, including the gains for the control laws as well as the Matlab/Simulink environment setup (Matlab R2016b, The MathWorks, Inc., Natick, MA 01760, USA), is provided. A simulation of the comprehensive maneuver was carried out to examine the performance of the formation strategies, and simulation results are illustrated in Section 7. Section 8 discusses the simulation results and the performance of the proposed formation strategies. The conclusions of the study are summarized in Section 9.
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
## 3 Results  
### Half-Space Model  
#### 3.1.1 Electric Fields in the Earth  
To comprehend the magnetic field distribution characteristics, it is essential to first analyze the electric field distribution patterns. Figure 3 illustrates the electric field distribution on the surface (oxy plane) at different time instants for both linear and curved sources. Figure 3a-c represent the curved source responses, while Figure 3d-f depict the linear source responses.  
When the transmitting current is abruptly terminated, induced currents concentrate in proximity to the wire to maintain spatial consistency in the magnetic field distribution, resulting in corresponding spatial patterns in the electric field. During the early transient  
Figure 3: Electric field (V/m) in oxy plane for the half-space model at different time instants. Panels (**a–c**) represent the curved source response, while panels (**d–f**) illustrate the linear source response.  
Figure 2: Schematic representation of the 3D model and SATEM transmitter and receiver distribution.  
stage, the electric field distributions in the immediate vicinity of the wire differ significantly between the two source geometries; however, the distributions in regions distant from the wire remain essentially identical, as demonstrated in Figure 3a,d. As the transient process evolves, the current begins to diffuse downward, diminishing the influence of the source geometry and reducing the disparities in electric field distributions near the wire, as illustrated in Figure 3b,e. In the late transient stage (t = 0.01 s), as shown in Figure 3c,f, the electric field distributions for both curved and linear sources become virtually indistinguishable.  
Figure 4 presents the electric field distribution in the oyz plane intersecting the central point of the wire source at various time instants. Figure 4a-c display the curved source responses, while Figure 4d-f show the linear source responses. Due to the lateral displacement in the y-direction between the central points of the curved and linear sources, their respective maximum electric field positions exhibit a spatial offset during the early transient stage, as evident in Figure 4a,d. With increasing time, the current diffuses progressively downward, the influence of the wire geometry diminishes, and the electric field distributions gradually converge. As illustrated in Figure 4c,f, by t = 0.01 s, the electric field distributions for both curved and linear sources become nearly identical.
## 5 Numerical and Experimental Results
### Cross-Bridge Wind Speed at Main deck Level
Thus, if the there is no improved BTS in front of the terrain model, the cross-bridge design wind speed of the bridge deck may be underestimated. It is recommended to use the improved BTS for terrain model boundary correction. The statistical values of numerical results and experimental results of non-dimensional cross-bridge wind speed at main deck level under different cases are shown in Figure 14. It can be seen from Figure 14 that though there are some discrepancies between the wind tunnel tests results and numerical simulation results, the majority of the data is located within relative errors of 30%. This kind of error is indeed unavoidable because the scale of terrain model was small which made it a challenge to place the model in the exact center of the test section and find the accurate monitoring point positions as CFD simulation did. Thus, the wind tunnel test results can be regarded as accurate ones, and the results from CFD simulations can be used for qualitative analysis.
###### Abstract  
This research presents the numerical evaluation of fatigue crack growth of structural steels S355 and S960 based on Paris' law parameters (C and m) that are experimentally determined with a single edge notched tension (SENT) specimen using optical and crack gauge measurements on an electromotive resonance machine at constant amplitude load. The sustainable technique is replacing destructive, time-consuming and expensive approaches in structural integrity. The crack propagation is modelled using the 3D finite element method (FEM) with adaptive remeshing of tetrahedral elements along with the crack initiator elements provided in simulation software for crack propagation based on linear elastic fracture mechanics (LEFM). The stress intensity is computed based on the evaluation of energy release rates according to [PERSON]'s crack closure integral with applied cyclic load of 62.5 MPa, 100 MPa and 150 MPa and stress ratios of R = 0 and 0.1. In order to achieve optimized mesh size towards load cycle and computational time, mesh and re-mesh sensitivity analysis is conducted. The results indicate that the virtual crack closure technique VCCT-based 3D FEM shows acceptable agreement compared to the experimental investigation with the percentage error up to 7.9% for S355 and 12.8% for S960 structural steel.  
crack growth; structural steel; fracture mechanic; finite element model +
Footnote †: journal: Applied sciences
###### Abstract  
Quantitationally describing weathering process and characterizing particle breakage are important in predicting the behavior of coarse-grained soil (e.g., weathered granite). In order to comprehensively understand the mechanical properties of weathered granite and provide references for reasonable evaluation of the engineering properties of subgrade filling in mountains, a series of tests--X-ray diffraction, sieving, heavy compaction, large-scale triaxial, and field compaction tests--were carried out in this research. Based on the weathering process parameters (\(m\) and \(r\)) of in-situ granite samples at two typical sections of weathered granite mountains obtained by using previous grading equation, the mathematical model of weathering process with depth was proposed and the variation laws of weathering process with depth were described. The results show that, with an increase in burying depth of mountain profile (\(h\)) of weathered granite, the geological year's parameter (\(m\)) decrease by power function, but the geometric progression constants (\(r\)) increase by power function. Some factors affecting the particle breakage properties of compacted weathered granite were evaluated by using relative breakage (\(B_{r}\)). Especially, the effect of field compaction condition (such as thickness of loose paving layer and number of vibratory rolling passes) on particle breakage was analyzed creatively. Through laboratory and field tests, it was found that particle gradation, mineral composition, compaction effect, stress level, and number of rolling passes significantly affected the particle breakage characteristics of weathered granite. Furthermore, it is worthy of attention that the weathered granite in this paper showed obvious particle breakage characteristics under weak compaction effect and at low stress levels and over-compaction could result in a decrease in the degree of compaction of a certain thickness of loose paving layer filled with weathered granite. Findings in this research can provide a theoretical basis for evaluating the physical properties and mechanical behavior of weathered granite as subgrade fillings.  
weathered granite; weathering process; particle breakage; compaction effect; stress level; field compaction; large-scale triaxial test 2018 8 1108; doi:10.3390/app8071108
## 6 Performance Evaluation
### Simulation Setting and Performance Metrics
#### 6.1.2 Experimental Cases  
Our study examines the relative performance between the MPMF, Epidemic, GSaR, TCCB, TC, and PRoPHETv2 routing policies in OppNets. The performance results by the variances of the TTL period are investigated. Epidemic duplicates a message copy to each encountered node. PRoPHETv2 calculates the DP estimates of directly encountered nodes with the destination and avoids blind replication. GSaR uses historical information such as location, speed, and time duration to estimate the movement range of the destination node and sprays a limited number of copies of a message in the network. TCCB exploits the social contact patterns from the temporal perspective. It predicts temporal closeness by considering the average time span of a node pair, i.e., the duration plus inter-contact time between two nodes in a network. TC computes the probability/possibility of a node that will appear in a destination community, so as to forward a message in a network. Communities are formed by exploiting pairwise contacts, where the regular appearance of a contact pattern between a node pair is emphasized. All the MPMF, PRoPHETv2, GSaR, TCCB, TC, and Epidemic policies adopt the FIFO dropping policy as a plain comparative base.
# Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network
## 4 Conclusions  
In conclusion, we have realized a real-time demonstration of a secure relay for the QSDC network. The post-quantum algorithm CRYSTALS-KYBER is used to protect the security of the message at intermediate nodes. Only the intended receiver can decrypt the ciphertexts. The results pave the way to realizing a practical QSDC network using existing technology.  
It is worth noting that the performance of the QSDC network can be significantly improved further by applying several optimizations. First, it is possible to use a QSDC system with fiber links of over 100 km with the masking technology and superconducting nanowire single-photon detectors (SNSPDs). Second, we take advantage of the software implementation of KYBER directly in our experiment. It is believed that the hardware implementation can further optimize the efficiency.  
Our work provides a solution to construct large-scale quantum networks using secure relays. On the other hand, since NIST has announced the standardization of post-quantum algorithms, we can see the widespread application of PQC in communication networks in the future. However, the PQC algorithm may face store-now-decrypt-later (SNDL) [25] attacks. The QSDC transmission of the ciphertexts prevents eavesdroppers from obtaining them in the communication, thus reduces the risks.  
Conceptualization, M.W. and G.L.; methodology, M.W., W.Z., J.G. and X.S.; software, J.G.; validation, M.W., W.Z. and X.S.; formal analysis, M.W.; data curation, M.W. and W.Z.; writing--original draft preparation, M.W.; writing--review and editing, G.L., M.W., W.Z., J.G. and X.S.; supervision, G.L. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Communication rate of the relayed QSDC system. Communication rate (**a**) from Alice to relay; (**b**) from relay to Bob. Insets are the typical QBER of the QSDC system.  
**Funding:** This research was funded by the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2022 QNRC001), National Natural Science Foundation of China (62131002), and China Postdoctoral Science Foundation (2022M710401).  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors would like to thank [PERSON] for his help in the post-quantum cryptography.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dispatchable Substation for Operation and Control of Renewable Energy Resources
## 3 Operation of Dispatchable Substation with Renewable Generator Mix
### Operation of a Dispatchable Substation  
Lithium-ion or lead-acid type batteries are the most commonly used batteries in ESSs; the expected battery life is directly related to the number of charge and discharge cycles that a battery can endure [26]. It is recommended to continuously operate under the charge mode or discharge mode until the state of charge (SOC) of an ESS reaches the upper or lower limit of its hardware specification. Accordingly, the operation mode of the DSS is bimodal for the charge and discharge of the ESS, (Figure 4). The forecast information reported to the TSO every 15 min is compared with the real-time measurement data to determine whether it stays within the DNE limit. In this case, 10% of the DNE boundary was adopted for both charge and discharge modes. The measured data is compared with upper bound (depicted as U.B) and lower bound (L.B) that calculated from DNE boundary (\(\pm\)10% of forecast value) and decide the charge or discharge operation of ESSs.  
In the discharge mode, the ESS attempts to discharge as much as possible, while the charge operation is replaced by the curtailment of renewable generation under its interconnection. On the other hand, for the charge mode, the forecast information reported to the TSO is slightly decreased to make the charge operation occur more frequently. The charge offset for the charge operation should be properly assessed to avoid a situation that would require discharging of the ESS. Depending on the violation penalty by TSO for exceeding the DNE limit, the discharge operation can be neglected or conducted inevitably. Figure 5 shows the operation of the ESS under the charge mode with charge offset. The charge offset prevents the operation of the ESS from the mode changing at  
Figure 4: Operation of ESS in a dispatchable substation.  
around 11:40 in the simulation time. The charge offset should be adjusted in real time if mode change occurs frequently, because of the small offset value.
# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 4 Experiments
### Methodology
#### 4.1.2 Metrics  
Following [4; 45] we used three metrics to assess the plots obtained from dimensionality reduction: Trustworthiness, Continuity and KNN classification error.  
* **Trustworthiness**: this metric measures the rank of points in a neighborhood of a given point in the reduced space which are not really neighbors in the feature space: \[T(k)=1-\frac{2}{Nk(2N-3k-1)}\sum_{i=1}^{N}\sum_{j\in U_{i}^{(k)}}(r(i,j)-k),\] (1) where \(k\) is the number of points in the neighborhood, \(N\) is the number of points, and \(r(i,j)\) is the rank of point \(j\) with respect to \(i\) according to their distance in the 2D space. \(U_{i}^{(k)}\) indicates the set of points that are among the k-nearest neighbors of \(i\) in the 2D plot but not in the original feature space. Trustworthiness can be related to the idea of precision in information retrieval evaluation. If the plot is _trustworthy_, sounds that were originally distant should not appear close in the 2D plot.  
* **Continuity**: this metric measures the complementary case, which can be related to recall in information retrieval. Points that are originally close should also be close in the 2D plot: \[C(k)=1-\frac{2}{Nk(2N-3k-1)}\sum_{i=1}^{N}\sum_{j\in V_{i}^{(k)}}(\hat{r}(i,j)-k),\] (2) where \(\hat{r}(i,j)\) is the rank of point \(j\) with respect to \(i\) according to their distance in the original feature space, and \(V_{i}^{(k)}\) indicates the set of points that are among the k-nearest neighbors of \(i\) in the feature space but not in the 2D plot.
* **KNN classification error**: the performance of a K-Nearest Neighbors (KNN) classifier in the 2D space (according to the labels of each dataset) also gives an indication of the quality of the plot. Sounds of the same class should appear close in the plot and thus a KNN classifier should be able to generalize from some training samples. We measured the accuracy of a 2-nearest neighbors classifier when varying the fraction of points used for training. In all cases, we used a random partition to label the data, and averaged the results over 10 runs.
## 4 Results and Discussion
### Limitations  
Despite our efforts to address the research objectives, we acknowledge the following limitations that may have influenced the findings and interpretations of our study:  
* This work is limited to ASD and non-ASD (TD) classification tasks, and the accuracy and classification of ASD subgroups are open questions.
* Several algorithms have been evaluated for classifying ASD based on age, sex, and brain morphological features; behavioral features or clinical test results that could be informative are not included.
* Limited availability of sMRI images for children in the studied age group poses challenges for effective ML model training and increases the risk of overfitting.
* The findings are limited to the age group of 5-10 years, and applying them to different age groups may impact accuracy due to age-related brain differences. The complexity of selecting stable and discriminating biomarkers between age groups further contributes to these limitations.
* Relying on specific brain segmentation methods such as our method of segmentation according to the DK atlas, may lead to biases and limitations. It should be emphasized that our findings can be replicated using different data or atlases.
* The reported accuracy may be insufficient for clinical use due to data variability, heterogeneity, and limitations of multi-site datasets used. However, we followed ML best practices to the best of our knowledge. As data heterogeneity increases, the training, validation, and testing folds used to evaluate a model's performance can diverge significantly. This divergence can lead to poor performance in fold testing, ultimately reducing the cross-validated estimated generalized predictive performance of the model.  
* An inherent limitation is the cross-sectional design chosen, which limits understanding of potential changes over time. Longitudinal designs can provide a more nuanced understanding of ASD.  
Considering these limitations, future research should address these challenges to refine ML-based diagnostic systems for ASD.
# Uncertainty Propagation through a Point Model for Steady-State Two-Phase Pipe Flow
## 2 Materials and Methods  
### Flow Model  
We analyzed the model developed in [PERSON] et al. (2018), only with a small change to turbulent interpolation. This section briefly outlines their model, which considers two-phase flow in a circular pipe and predicts liquid holdup \(H\) and pressure drop \(-p^{\prime}=-\partial p/\partial x\) in the longitudinal direction \(x\). The model predictions are based on ten measured input variables listed in Table 1. In contrast to the presented approach, _system identification_ with a backward elimination method ([PERSON] et al., 2017; [PERSON] et al., 2018), forward selection ([PERSON] et al., 2017) or stepwise regression ([PERSON] et al., 2017) can be used for the same purpose. This would raise the model quality, but also increase the experiment cost and computational time significantly, and is not applied in this paper.  
The flow is categorized as either stratified, bubbly or slug flow. All cases are covered by a unit-cell model, as introduced in [PERSON] and [PERSON] (2017). The holdup is the weighted average  
\[H=sH_{\text{s}}+(1-s)H_{\text{l}}, \tag{1}\]  
where \(s\) is slug fraction, \(H_{\text{s}}\) is the slug zone holdup and \(H_{\text{l}}\) is the bubble zone holdup. Slug flow is illustrated in Figure 1. The model allows for gas bubbles in the slug. Note that \(s\leq 0\) is stratified flow and \(s\geq 1\) is bubbly flow, and \(s\) is then limited to 0 and 1, respectively, in the weighting.  
The estimation procedure consists of two main steps, deciding the flow regime and computing the holdup conditional on that regime. In general, we need the conditional holdups in order to decide on the regime.