esa-sceva/satcom-chunk-collection huggingface dataset:
# Light-Matter Complex Interactions in Stereolithographies
## 2 Resins and Additive Manufacturing
### Polymerization  
In the presence of light, free radicals are produced; they consume the inhibitors and then react with the monomers to create macro-radicals which grow by reaction with \"fresh\" monomers with the risk of trapping free radicals in the polymer matrix. Given the concentration of monomers (quasi-pure monomer medium at the time of initiation), the polymerization takes place from close to close, on a molecular scale as shown in Figure 5. In addition, if multifunctional monomers are available, the polymer that is built up is normally insoluble in the monomer that gave rise to it [14].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Some Commercial Resins** & **References** \\ \hline
**Monomers, initiators:** Acrylic resins and photochemical & \\ initiators (aromatic ketones). Examples: 2-hydroxyethyl & Cf. Fluka; Sigma-Aldrich (St. Louis, MO, USA); Quick-Parts; \\ methacrylate, 1,6-hexanediol diacrylate, pentaerythritol & Merck; Arkema; Norland; Yosra; Nanoscribe catalogs (2022) \\ \hline
**Specific monomer**: Ionic polymerization monomer & [5] \\ \hline
**Commercial resins:** Materialise, Acura AMX, Durable natural & Materialise (2022); [6]; Additive 3D (2022); Evonik catalog; 3D \\
3D printing resin (3D Systems), etc. & Ceram (2022) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some resins applicable in stereolithography.  
Figure 4: Radical photopolymerization of acrylic resins.  
From a chemical kinetics point of view, the transformation follows a sigmoidal law, as presented in Figure 2, with an initially slow kinetics linked to the consumption of reaction inhibitors (hydroquinone for example to allow the conservation of monomers, oxygen in solution, etc.), i.e., a \"free\" polymerization reaction, but increasingly hindered by the three-dimensional polymer that forms, which limits the space available for the reaction of macro-radicals with the remaining monomers and thus the reaction kinetics [15]. Experimentally, it is possible to show that then free radicals are present but are trapped in the polymeric matrix. To summarize, there are essentially three zones in such polymerization kinetics:  
* a zone (I) with very slow kinetics where the free radicals formed in the initiation step consume the inhibitors (oxygen);
* the zone (II) of the polymerization itself;
* the terminal step, linked to the imprisonment of the free macro-radicals which can no longer reach the reactive bonds of the remaining monomers because of the passage from a fluid material to a highly polymerized entity in which the diffusion of the reactive species is strongly hindered (cf. Figure 5).  
From an epoxy composition (cationic monomer) and a photo-initiator, polymerization under light irradiation leads to the desired liquid/solid transformation. Some compositions suitable for one-photon additive manufacturing are commercially available. These compositions include monomers, typically epoxies, and the initiator. In addition to the epoxy family of compounds, a large number of monomers are described synthetically in [16; 17].
# Virtual Validation of In-Flight GNSS Signal Reception during Jamming for Aeronautics Applications
## 2 DLR's Measurement Campaign  
For the test scenario, flight data from the measurement campaign in Cochstedt, Germany, were taken as a reference. The flight route considered for the analysis is depicted in Figure 1. The measurements took place on 7 October 2022, between 12:26:26 and 12:29:48 UTC. The measurement campaign comprised the antenna described in [15] and is shown in Figure 2, which was installed on the DLR aircraft Do 228-101 D-CODE for flight tests.  
Figure 1: Flight route with jammer as transmitter and DLR’s antenna as receiver.  
For this particular setup, the airborne antenna configuration consists of a compact array comprising four L1/E1 elements and one L5/E5a element. This compact array, with a 3.5-inch footprint, is well-suited for safety-of-life airborne applications due to its small size and the dual frequency capability it offers. For the current work, only signals from the four L1 elements are used (as single antennas, i.e., without any array processing).  
A high gain GNSS helix antenna (MAT-18X8.5H1.1-1.6R-P-XN1), covering the full GNSS band from 1.1 to 1.66 GHz, from Matterwaves, Torrance, CA, USA, was used to radiate jamming signals. The antenna was placed at 51.86020550\({}^{\circ}\), 11.43341470\({}^{\circ}\), 225.27 m (or 51\({}^{\circ}\)51\({}^{\circ}\)36.7\({}^{\prime\prime}\) N 112\({}^{\circ}\)600.3\({}^{\prime\prime}\) E 180.490 MSL) and was transmitting with a power of 10.5 dBm. It was installed on the ground using tripod as shown in Figure 3.
# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
###### Abstract
[31] proposed an improved multi-objective CS, applying it to the challenge of multiple UAV obstacle avoidance, optimizing coordinated formation control to identify safe routes. [PERSON] et al. [32] suggested a hybrid PSO and CS (PSO-CS) algorithm, enhancing the convergence speed of the algorithm and deploying it to engineering optimization problems. This manuscript discusses an adaptive CS algorithm, known as BASCS, which refines CS by integrating chaotic mapping with random migration, infusing Levy flight with the directional judgment of Beetle Antennae Search (BAS), and adapting parameters along with the local exploitation of the Sine Cosine Algorithm (SCA). The evaluation of BASCS is structured into three phases. Initially, ablation experiments employ the tent chaotic mapping and random migration strategy, establishing the feasibility of their combined approach. Subsequently, BASCS is benchmarked against CS and its four variants--ICS [25], ECS [26], ASCSA [27], and MCSA [28]--using diverse indicators such as the Wilcoxon rank-sum test, mean, and variance. The results indicate that BASCS excels in convergence and robustness compared to other algorithms. Lastly, BASCS is implemented in a robot path planning scenario to affirm its practical applicability and effectiveness in addressing real-world challenges. The manuscript is structured as follows: Section 2 reviews the relevant basic theories. Section 3 introduces the improvement strategies, procedural steps, and complexity analysis of BASCS. Section 4 presents ablation studies on 10 benchmark functions using the tent chaotic map and random walk strategy, followed by a comparative analysis of BASCS against CS and its four variants. Section 5 applies BASCS to a robot path planning problem, showcasing its practical utility. Section 6 concludes the study.
# A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images
## 2 Materials and Methods  
### Study Area and Datasets  
In this paper, we use the visible spectral Google Earth remote sensing image dataset of lake waters on the Tibetan Plateau, which consists of 6773 RGB images each of size 256 \(\times\) 256 [24]. The dataset was randomly divided into 5419 training set data and 1354 test set data according to the ratio of 8:2. The lakes of the Tibetan Plateau can be divided into saltwater lakes and fresh water lakes in terms of type. Saltwater lakes have salt belts along their shores, which show a complex structure. The spectral characteristics of freshwater lakes are white, light blue, dark blue and black. Black lakes have features similar to mountains and cloud shadows, while white lakes have features similar to snow. In terms of state, there are frozen, semi-frozen and non-frozen lakes. The study area of lake water bodies is shown in Figure 1.
# Experimental PIV Radial Splitting Study on Expansive Soil during the Drying Process
## 4 Conclusions  
A PIV test system was used to conduct radial splitting tests on undisturbed and remolded expansive soils with different water contents during the drying process, and the internal mechanism was analyzed according to the microstructure. The main conclusions are as follows.  
1. During the drying process, the water content has an important influence on the splitting strength of expansive soil specimens. The load-displacement relationship curve shows the strain-softening phenomenon and, with decreasing water content, the peak strength increases.
2. In the process of the radial splitting test, there was an obvious one-to-one correspondence between the load-displacement relationship curve, the displacement vector field, and the crack characteristics in the stage division. With the help of PIV technology, the compression deformation stage of the radial splitting test, the stage of crack development after the peak load, and the stage of crack development maturity until breakthrough failure could be observed.
3. The undisturbed soil has a primary structure, and there is a certain proportion of large pores or primary cracks between the aggregates. After the remolded soil was crushed and sieved, the large pores between the aggregates disappeared and the pores in the aggregates gradually decreased or even disappeared during the drying process. Because of their inherent structural and fissure characteristics, the peak  
Figure 7: Comparison of the undisturbed and remolded specimens with \(w=17.5\%\): (**a**) undisturbed specimens; (**b**) remolded specimens.  
loads of undisturbed expansive soil specimens were lower than those of remolded specimens and the crack characteristics of remolded specimens were more regular than those of undisturbed specimens under the same water-content conditions.
* The above research results provide the scientific basis for the construction of geotechnical engineering related to expansive soil.  
Conceptualization, S.Y., F.H. and J.Z.; methodology, S.Y., F.H. and J.Z.; data curation, S.Y., F.H. and J.Z.; writing--original draft preparation, S.Y. and F.H.; writing--review and editing, J.Z.; visualization, S.Y. and F.H.; supervision, J.Z.; project administration, S.Y., F.H. and J.Z.; funding acquisition, S.Y., F.H. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Natural Science Foundation of China (Grant No. 41602295), the Foundation for University Key Teachers by the Ministry of Education of Henan Province (Grant No. 2020 GGJS-094), the Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2023 AL004) and the Water conservancy science and technology project of Henan Province (Grant No. GG202240).  
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.
## 2 Methodology  
### Three-Dimensional Reconstruction Method for Road Structural Defects  
Based on simulated and measured GPR detection data, this study adopted the following steps for the 3D reconstruction of road structural defects. The flowchart is shown in Figure 1.  
(1) Preprocessing: direct wave removal and wavelet denoising were carried out on the GPR detection data of each section to remove these noises to the maximum extent, improve the signal-to-noise ratio of the image, and retain effective target reflection information [33]. (2) BP imaging: A BP (back-projection) imaging algorithm was used to process the preprocessed GPR data to realize the return of the reflected wave and the convergence of the diffraction wave, and then effectively restore the location and size of the defect to ensure the accuracy of the 3D reconstruction. (3) Construct a 3D data set: through interpolation, the collected data of each section were combined into a 3D data set.  
Figure 1: Three-dimensional reconstruction process for road structural defects.  
Calculate the threshold: the K-means-AHC algorithm was used to calculate the threshold of distinguishing background data and effective reflection data, and all the former were set to 0, while only the latter were retained.
5. Three-dimensional imaging: the effective reflection data's upper and lower surfaces were extracted and processed in MATLAB 2022 [34] to realize the 3D imaging.
6. Evaluation accuracy: according to the location and size of the reconstructed disease and its actual location and size, the _IoU_ (Intersection Over Union) was calculated to evaluate the accuracy of the 3D reconstruction.
# Acoustic and Flow Aspects of Synthetic Jet Actuators with Chevron Orifices
## 3 Results and Discussion
### Centerline Velocity in Time Function  
Velocity as a function of time is shown in Figure 6. The absolute velocity is presented because the used CTA probe does not recognize the flow direction and measures only the velocity's magnitude. The centerline velocity at ratio \(x/d=0\) is presented in Figure 6a. There are two stages in the course of the function. The stage of injection is when the air is sucked into the cavity (\(0\leq\tau/T\leq 0.5\)), and the stage of expulsion is when the air is blown out of the actuator cavity (\(0.5\leq\tau/T\leq 1\)).  
Figure 4: The local centerline time-averaged velocity (**a**) and local centerline maximum velocity (**b**) as a function of frequency at \(x/d=0\) and real power \(p=3\) W.  
Figure 5: The electrical efficiency of the SJA as a function of real power.  
In the stage of injection, the highest velocity was obtained in Case 0; the higher the chevron cutouts in the orifice, the lower the velocity. The air is injected through the surroundings of the orifice, and, in the case of a circular nozzle, the volume of air flows through the circular area of the orifice exit. In the center point of this area, the velocity is measured with the CTA probe. In the case of the chevron orifice, the air is also injected through chevron cutouts in the orifice wall, and the airflow area is much bigger than in the case of the circular orifice. Additionally, the air is drawn in from a larger area around the orifice than in the case of the circular orifice. The injected volume of flow and the injection time are the same, but the flow area increases for each following case. For these reasons, the measured velocity is smaller in each following case.  
The blow velocity of SJs was highest in Case 3 and lowest in Case 0. The difference was around 15%. It confirms the results presented in Figure 4b. The bigger the chevron cutouts, the higher the velocity. This may be due to the reduction of viscous forces and more harmonious separation of the stream from the orifice wall. However, the exact cause of this is unclear and should be additionally investigated by the PIV or a numerical method.  
Figure 6: Centerline velocity as a function of time at real power \(p=3\) W and \(x/d=0\) (**a**) or \(x/d=3\) (**b**).  
Centlerline velocity at ratio \(x/d=3\) is presented in Figure 6b. For this distance from the orifice, the injection stage is not visible at the measured velocity--[PERSON] et al. [30] showed that the change of velocity vector direction in the centerline no longer occurs at \(x/d=2\). The expulsion stage is still visible as a peak of velocity. The highest velocity was obtained in Case 3 and the lowest in Case 0. The results coincide with the results discussed previously.
## 1 Introduction
In this paper, we employ the hybrid concept by combining the profile-based and model-based methods for tunnel deformation monitoring. More specifically, based on the concept of the profile-based method, we propose a novel cross-sectional extraction method, which not only gets the continuous points within each cross section, but also more importantly has the capability to generate continuous profiles over the entire metro tunnel. Utilizing these continuous points and profiles, the entire tunnel model can be easily generated by triangulation. Thus, if we have two-phase TLS tunnel data acquired at different times, we can evaluate the whole differences by the superposition of two phase tunnel models. Given that our work is built on profile-based framework, we explicitly state our original contributions as follows:  
* **Tunnel Central Axis Extraction:** we present an algorithm for tracking the tunnel central axis in topologically-ware and geometrically-aware manners. That is, we first propose a slice-based algorithm for tracking the initial central axis, which is further refined by integrating a Minimum Spanning Tree (MST) and Savitzky-Golay smoothing. * **Tunnel Central Axis Segmentation:** we propose an enhancement to the traditional Random Sample Consensus (RANSAC) algorithm. The enhanced algorithm translates the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying segmentation problem. * **Tunnel Profile Determination:** we introduce a high-accuracy interpolation and filtering algorithm for extracting continuous tunnel profiles over the entire tunnel. The generated profile points are determined by using interpolation, and further densified via solving a constrained nonlinear least squares formulation. This paper is organized as follows: Section 1 reviews profile-based and model-based methods in tunnel deformation. Section 2 describes the detailed methodology including tunnel centerline extraction and cross section determination. In Section 3, the experimental dataset, the performance evaluation results of geometric and topological accuracy based on actual TLS tunnel data are presented, analyzed, and discussed. Finally, Section 4 concludes the paper along with a few suggestions for future research topics.
## 4 Results' Analysis
### Shortage Risk Segregation  
The final analysis focused on separating the shortage risk formulation into its three components and using them independently when running the optimization. The formulation of this problem became as shown in expression (19). In terms of algorithm, the only change required in Algorithm 1 was to disregard row 12.  
\[\begin{array}{ll}\min_{\mathbf{X}}&F_{1}=E&[EU]\\ &F_{2}=C_{Hold}&[CU]\\ &F_{3}=\sum Sh1_{i}\\ &F_{4}=\sum Sh2_{i}\\ &F_{5}=\sum Sh3_{i}\\ s.t.&\mathbf{X}\geq 0\\ &S_{k}\leq Lim_{k}&[IU],\quad\forall_{k\in\{1,\cdots,n_{k}\}}\end{array} \tag{19}\]  
The only alterations to the parameters presented in Table 4 were an increase in the number of reference direction dimensions from three to five and an increase in the population size from 200 to 2000. The optimization was then run 10 times to allow for a better representation of the solution space. As for the algorithm analysis, the average execution time was 453.4 s (358.5 s-524.4 s), the average final population size was 453.5 (410-514), and the average total number of iterations was 329 (270-390). The parallel plot of the complete solution space (including the unique solutions from the 10 optimization runs) is shown in Figure 5.  
The parallel plot is a very complex display of information, showing an aggregation of many varied optimal solutions, each prioritizing a different set of objectives. Since the colour coding simply scales the energy consumption of the solution it regards, so as to allow for better tracking of solution along the other objectives, it can be seen that the first component of the risk is the one which is most closely related to the energy consumption, in an inverse proportion. This means that the higher the energy consumption, the lower the risk of type 1--since the larger the total stock the higher the energy consumption.  
A many-objective optimization such as the one presented can be extremely useful for decision-makers to make their decision in a more informed way. A decision could determine that only solutions with a type 1 risk smaller than 400 would be acceptable. The result to this requirement would be the solutions shown in Figure 6.  
After obtaining this subset of parallel plots, the decision-maker could specify that only solutions with a holding cost higher than 4.8 are acceptable. The results would be as shown in Figure 7.  
As can be seen, sequentially combining different requirements for the objectives allows the decision-makers to arrive at a restricted set of solutions that best fit the company's requirements.  
Figure 5: Parallel plot of the separated risk optimization for the complete solution space.  
Figure 6: Parallel plot of the separated risk optimization for the complete solution space, only considering solutions with a risk of type 1 smaller than 400.
## 2 G-LiHT Design and Instrumentation
### GPS and Inertial Navigation System (INS)  
A single-solution GPS-INS (RT-4041, Oxford Technical Solutions, Oxfordshire, UK) is used to obtain high precision position and attitude measurements for all G-LiHT sensors. The unit is directly attached to the airborne laser scanning (ALS), which shares the same mounting plate as the optical breadboard for the imaging spectrometer and thermal camera (Figure 2). The GPS-INS incorporates a six-axis inertial navigation system (three gyroscopes and three servo-grade accelerometers) and an L1/L2 GPS (Global Positioning System) and GLONASS (Global Navigation Satellite System) receiver with an OmniStar decoder to deliver 10 cm positioning, 0.1\({}^{\circ}\) heading and 0.03\({}^{\circ}\) roll and pitch accuracies. Measurements are acquired at 250 Hz, which is required to geolocate data from the optical imagers (25 to 50 Hz) and ALS (300 kHz laser). The real-time internal processing includes strap down algorithms, a WGS-84 and EGM96 (Earth Gravitational Model 1996) Earth model, Kalman filtering and in-flight alignment algorithms. The Kalman filter and in-flight algorithms monitor the performance of the system and update the measurements to correct for inertial sensor errors and maintain high positional accuracy.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### Robustness Enhancement of Kalman Prediction  
#### 4.3.1 Kalman Filtering Prediction  
The Kalman filter is an algorithm for optimal prediction of dynamic systems through recursive state estimation. Its core idea is based on the \"prediction-correction\" cycle. In satellite positioning, the algorithm suppresses random noise and improves positioning accuracy by fusing the dynamic characteristics of historical trajectories with current observations. In this design, the Kalman filter is innovatively extended from the traditional real-time positioning optimization to the trajectory prediction after signal loss. The state equation is:  
\[\left\{\begin{array}{rcl}X_{k}&=&F_{k-1}X_{k-1}+\omega_{k-1}\\ Z_{k}&=&HX_{k}+v_{k}\end{array}\right. \tag{9}\]  
\(\bullet\)\(X_{k}\) is a state vector including longitude, latitude, meridional velocity, and zonal velocity. \(\bullet\)\(F\) is the state transition matrix:  
\[F\ =\ \begin{bmatrix}1&0&\Delta t&0\\ 0&1&0&\Delta t\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{10}\]  
\(\bullet\)\(\omega_{k}\) is the process noise, which conforms to the normal distribution (0, Q), and the process noise co-square is \(Q\ =\ \mathrm{diag}(0.2^{2},0.2^{2},0.1^{2},0.1^{2})\).  
\(\bullet\)\(Z_{k}\) is the observation value (satellite positioning coordinate), and \(v_{k}\) is the observation noise.