esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Bionic Type
#### 3.3.3. Bionic Beetle Structure  
There are a large number of insects in nature that can carry loads far beyond their own weight, providing much inspiration for the design of vibration isolators. [PERSON] et al. [72] designed a cockroach-like structure, where the exoskeleton is mimicked by a diamond-shaped cavity, the restoring force is provided by a spring, and the mimicry of the front legs relies on rods and sliders. The resonance frequency of this structure is as low as 1.4 Hz, and its nonlinear characteristics can significantly reduce the resonance frequency and peak transfer rate, and an anti-resonance phenomenon occurs at large inertia. And later, as shown in Figure 12, [PERSON] et al. [73], inspired by the three postures of the beetle, namely the unyielding, downward bending, and upward bending postures, proposed a vibration isolation structure consisting of three rods and two springs, which can change the stiffness by adjusting the initial assembly angle, length ratio, and linear stiffness, and its transmission rate can be reduced to 7 dB and the resonance frequency can be less than 2 Hz.  
Figure 11. Diagram of multi-layer QZS isolation system model [69]: (**a**) Overall model diagram; (**b**) Nth layer structure diagram.  
Although bionic design has great potential in theory, how to effectively apply it to practical engineering is still a relatively less studied topic. Current research has focused on extracting nonlinear properties from nature and using them to improve the efficiency and stability of vibration isolation performance. Future research should further explore how to optimize the design of bionic QZS isolators, select appropriate materials, and better construct abstract models to promote their application in more complex engineering environments.
## 6 Empirical Research on New Heterogeneous Traffic Control Mechanisms
### Exploration of Control Mechanisms for Future Traffic Environments
#### 6.3.1 Autonomous Intersection Management Without Signals  
In high CAV penetration rate environments, autonomous intersection management (AIM) without signals is expected to replace traditional traffic signal control, achieving more efficient intersection operations. The Trajectory-based Traffic Management (TTM) model proposed by [PERSON] et al. [72] and the Decentralized Coordination Learning method (DCL-AIM) developed by [PERSON] et al. [73] represent AIM technologies with two different approaches. The TTM model directly optimizes multi-vehicle trajectories through mixed integer programming and can be extended to consider scheduling decisions and fairness constraints; DCL-AIM models intersection control as a multi-agent collaborative decision-making problem, effectively reducing computational complexity through decomposing vehicle state spaces and dynamically adapting coordination needs. Both methods significantly outperform traditional control methods in different test scenarios, validating the enormous potential of AIM in future high CAV environments.  
To further enhance the practicality of AIM, [PERSON] et al. [74] and [PERSON] et al. [75] optimized control architectures and algorithm efficiency from different perspectives. The bi-level model predictive controller developed by [PERSON] et al. separates coordination control (time slot allocation) from vehicle control (trajectory planning), achieving control efficiency while ensuring safety. Experimental results prove that this controller can ensure safe coordination even under larger positioning uncertainties. [PERSON] et al. focused on real-time performance, with their graph-based three-stage optimization method (target velocity optimization, vehicle subgraph extraction, and velocity profile synchronization) reducing single-step computation time to 0.02 s while maintaining control effectiveness, demonstrating the feasibility of AIM in real-time applications.  
These studies collectively indicate that as CAV penetration rates increase, autonomous intersection management without signals will become an effective means to enhance intersection efficiency. The challenges lie in how to balance computational complexity, real-time performance, and control effectiveness, as well as how to handle practical issues such as positioning errors and communication delays. Methods such as hierarchical control, multi-agent learning, and graph theory optimization provide possible pathways to address these challenges.
## 2 Materials and Methods
### Experimental Methods and Testing Set-up
#### 2.2.1 Countered Double Cantilever Beam (CDCB) test
The CMOD (Figure 1c) was measured with a gauge transducer measuring the relative displacement near the base of the notch and was also monitored by means of the 2D Digital Image Correlation (DIC) technique (Figure 2) [22; 23]. The values obtained with the sensor and with DIC were consistent. For the data acquisition using DIC, a high-speed 9-megapixel mvBlueFox camera in combination with the GOM SNAP software were adopted; the collected data was processed using the GOM Correlate software. The camera was placed at a distance of 1.7 m from the specimen. Beforehand, a speckle pattern of black dots over a white background was applied on the surface of specimens by means of spray painting. The light was adjusted at every test to guarantee an even illumination of the specimen and to avoid over-exposure. Since the crack propagation test needs to be performed at a slow displacement rate to allow stable crack propagation, the maximum acquisition frequency was set to 0.5 Hz. Figure 1: (**a**) Countered double cantilever beam (CDCB) specimen geometry and (**b**) adaptation for testing of bi-material systems, (**c**) parameters for crack growth resistance analysis, (**d**) calculation of forces applied, and (**e**) test setup [15]. Figure 2: Representative black dot pattern, with location of the region of interest (ROI) around the interface for Digital Image Correlation.
## 2 Geo-AI Applied to Atmospheric Density Measurement  
Understanding the thermal layer density is of utmost importance. Yet, simulating it comes with a high degree of uncertainty. Among many methods for measuring atmospheric density, satellite accelerometer data stand out and have been extensively utilized.  
In the study titled 'Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer', [PERSON] et al. [4] used Swarm-C accelerometer data to invert orbital atmospheric density. While the authors built upon the calibration method employed with GRACE-A satellite accelerometer data, they introduced a pivotal modification: a linear temperature correction on this basis. Subsequently, they established a satellite surface shape model. The simplified conical shadow model replaced the traditional cylindrical shadow model for optical radiation pressure modeling. The authors retrieved atmospheric density by calculating the atmospheric drag coefficient of the Swarm-C satellite and conducted tests during geomagnetic storms. The comparison with existing research data demonstrated the necessity of temperature correction and the effectiveness of the proposed method.
## 5 Research on the Frequency-Domain Characteristics of AE Signals  
The inhomogeneity and defects in the rock cause stress concentration in the loading process. Internal microcracks cause and expand or intensify plastic deformation, resulting in the rapid release of strain energy stored in the rock and subsequent AE. In practice, thousands of AE events occur when a sample is loaded until it breaks. Thus, the experimental and practical construction efficiency could be greatly improved by extracting characteristic signals from many AE signals that explain different degrees of rock fracture and determining the AE frequency-domain precursors of rock fracture.
## 3 Results and Discussion
### Evaluation of the Proposed Robotic System Setup and Its Movement's Accuracy and Repeatability
This indicates a standard deviation along the Y-axis within this range, which can be attributed to minor misalignments in the junctions of the three V-Slot 2060 aluminum beams that are connected together. Despite securely attaching the beams on both sides with tee nuts, it was not possible to completely eliminate this offset. A potential solution would involve replacing the three aluminum beams, measuring 1.5 m and 1 m in length, with a single piece of aluminum beam measuring a total length of 3.5 m. However, this suggestion is reserved for a future update, as the one-piece beam was not available at the time of implementation. Furthermore,  
Figure 14: In the upper graph (**a**), the mean distance in mm (accuracy) from the ideal positions (targets expressed in steps from “home”) is shown with circles, as well as the standard deviation (repeatability), for 15 iterations, covering the error trends in the whole extent of the possible linear movement along the X-axis. In the lower image (**b**), the mean displacement error trends (system’s offset along the Y-axis) are shown for the same positions. In both figures, the standard deviation (where is different than zero) is shown visually with small black and red squares (extremity points) and the black line between them. It is evident from the plot that in case of zero standard deviation, both squares coincide with the respective circle’s center in that position (colored figure online). since this observation pertains to a standard offset, it can be compensated for through software means. Nevertheless, the primary objective of the proposed linear robotic camera system is to monitor plant growth through photography. While it is desirable to achieve a high accuracy in the movement of the carriage, there is no requirement for sub-millimeter precision as would be necessary in medical interventions, for example. We believe that procedures and applications of this nature can be approached with a certain level of tolerance.
## 2 Geodetic Methods  
GNSS data from a continuous station (THIV), which is located in the city of Thiva (at 38.3166\({}^{\circ}\) N, 23.3183\({}^{\circ}\) E), about 11 km west of the epicenter, and belongs to the METRICA S.A. commercial network (HexagonSmartNet) were processed (Appendix A). The formed time-series for THIV station (Figure A1) showed that the moderate mainshock did not caused any observable ground deformations in Thiva. The fluctuation of the two horizontal components (north and east) prior to and after the earthquake remained on the same limits, of about \(\pm\)3 mm from an average value, whereas the vertical component varied at \(\pm\)10 mm. There was no evident static co-seismic displacement on the station after the 2 December event, or any alteration on the motion pattern for the period following the event.  
We used synthetic aperture radar (SAR) C-band acquisitions in the IW mode, acquired over the study area by the Copernicus European satellite Sentinel-1 from the ascending tracks 102 and the descending track 7. We calculated with ENVI(r) SARscape(r) software version 5.5 the interferograms between 21 November 2020 and 3 December 2020 in both orbital directions (Figure 2, Figure 6, and Figure A3) and having a LOS unit vector of [\(-\)0.61, \(-\)0.14, 0.78] and [0.59, \(-\)0.14, 0.80] for the ascending and descending geometry, respectively. Precise orbits and the 30-m NASA shuttle radar topography mission (SRTM) 1-s digital elevation model (DEM) were used [26]. The interferograms that we processed had the most coherent deformation patterns, with slightly more than two fringes in the ascending and almost two in the descending geometry. The deformation lobes and the similarity for both orbits declare typical subsidence with a preference to normal mechanism. In addition, the increased density at the NE suggests that the deformation source is located on this side.  
As InSAR provides information on only two independent LOS measurements (Figure 2), in order to retrieve the E-W and vertical components (but not for the N-S one, due to the small sensitivity along that direction, caused by its close alignment with the orbit inclination), the interferograms were unwrapped. Furthermore, their planar trends were removed and their mean value (excluding the deformed area from both tasks) was set to zero. Finally, by using the equation provided in [27], we obtained their components (Figure 3a,b).  
Figure 3: The decomposed deformation (measured, modeled and residual) field. (**a,d**) The vertical and east components, respectively, obtained from the decomposition of the single ascending and descending interferograms. (**b,e,g**) The modeled vertical, east and north components. (**c,f**) The residuals between observed and modelled data.  
Figure 2: The interferograms used, along with their synthetic interferograms, calculated from the inferred model and the residuals: (**a**) The ascending interferogram of track 102 with (**b**) their corresponding modeled synthetic one and (**c**) the residuals, (**d**) the descending interferogram of track 7 and similar for (**e,f**).
## 3 Methodology
### MDDA Augmented GNSS/INS Integration
#### 3.6.3 Extended Kalman Filter (EKF)  
The EKF is a Kalman Filter modification that manages nonlinear systems' state estimation [49; 50]. A nonlinear system is linearized by the EKF with respect to its current states covariance and mean; hence, the state errors can represent the linear system (rather than the system's states), which thus permits the use of the linear KF. The KF makes the assumption that each state's time derivative is a function that is linear with the others and the white noise sources [49]. The first-order Taylor series approximation is applied to derive the linearized error-state model \(\delta\dot{x}(t)\), expressed as  
\[\delta\dot{x}(t)=F(t)\delta x(t)+G(t)w(t) \tag{31}\]  
where \(\delta x=x-\dot{x}\) represents the estimation error between the true state vector \(x\) and its estimate \(\dot{x}\) and consists of the position errors, velocity errors, attitude errors, and errors in the inertial sensors. Here, \(w(t)\) denotes zero-mean, unit-variance white, Gaussian-process noise; \(G(t)\) is the noise distribution matrix; and \(F(t)\) is the system dynamics matrix. The measurement model is similarly linearized as  
\[\delta z(t)=H(t)\delta x(t)+v(t) \tag{32}\]where \(H(t)\) is the measurement matrix, determined based on known system parameters, and \(v(t)\) is the measurement noise vector, assumed to be zero-mean white Gaussian noise.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 4 Results and Analysis
### Frost Resistance  
The residual UCS ratio and splitting strength ratio (BDR) of stabilized soil after different numbers of freeze-thaw cycles are shown in Figure 5.  
It can be seen from Figure 5 that the UCS and splitting strength residual strength value BDR of saline, lime, FA, and PAM-stabilized saline soils decreased and then stabilized as the number of freeze-thaw cycles increased. Among them, the decreasing trend of the residual value of the UCS is the most obvious, indicating that the freeze-thaw cycles have the greatest influence on the UCS. A comparison of the effects of three binders on the frost resistance of saline soils showed that the BDR of UCS decreased more rapidly than the BDR of splitting strength. Among them, lime-stabilized soil showed the smallest decrease in residual strength ratio (BDR); PAM-stabilized soil showed the largest decrease, which was close to that of saline soils; and FA-stabilized soil showed a decrease in residual strength ratio (BDR) between that of lime and PAM.  
Figure 4: Mechanical properties of stabilized soil at 28 d: (**a**) UCS; (**b**) splitting strength.  
When the number of freeze-thaw cycles is low, the water film bonding between soil particles is transformed into ice bonding, which enlarges the volume of the ice crystals and reduces the contact area between soil particles around the ice crystals. This leads to an increase in porosity and the first disintegration of the bonded material [39]. After more than three freeze-thaw cycles, the stabilizing material between the soil particles is continuously stretched during the ice crystal expansion and contraction cycles, resulting in continuous damage to the gel film and the soil particle material, a decrease in the bond strength, an increase in the number of voids and cracks, and a macroscopic manifestation of a rapid decrease in strength. After five freeze-thaw cycles, most of the PAM- and FA-stabilizing fractions between soil particles were destroyed, and the rate of decrease in the strength of stabilized soil slowed down. After 10 cycles of freeze-thaw cycles, the gel at the edges of soil particles decreased, while the pores increased and penetrated each other, destroying the integrity of the soil [40]. The gel material between the soil particles was repeatedly torn and destroyed by the contraction and expansion of the ice crystals, leaving the soil particles completely exposed and leading to a decreasing trend in the strength of stabilized soils. In addition, the solubility of Na\({}_{2}\)SO\({}_{4}\) is temperature-dependent. The solubility decreases with decreasing temperature. Na\({}_{2}\)SO\({}_{4}\) crystallizes into Na\({}_{2}\)SO\({}_{4}\)\(\cdot\)10H\({}_{2}\)O crystals when it does not react with lime and FA. These crystals along with ice crystals break the gel between the soil particles, leading to a strength failure of the soil.
# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 3 Methodology
### Problem Formulation  
Problem Definition. Cross-modal hashing methods aim to map multimodal data (such as images and text) into a shared hash space to enable efficient cross-modal retrieval. Given an image or a text query, the goal is to generate binary hash codes through the learned hashing functions and retrieve the most relevant corresponding modality (for example, retrieving the corresponding text from an image or vice versa) based on their semantic similarity. This paper focuses on image-text cross-modal retrieval. Given a training dataset \(\mathcal{D}=\{\mathcal{D}_{l},\mathcal{D}_{u}\}\) containing \(N\) image-text pairs, \(\mathcal{D}_{l}=\left\{\mathbf{X}^{l},\mathbf{Y}^{l},\mathbf{L}^{l}\right\}\) represents the labeled sample set with \(N_{l}\) labeled image-text pairs. The labeled images and texts are denoted as \(\mathbf{X}^{l}\in\mathbb{R}^{N_{l}\times d_{x}}\) and \(\mathbf{Y}^{l}\in\mathbb{R}^{N_{l}\times d_{y}}\), respectively, and \(\mathbf{L}^{l}\in\{0,1\}^{N_{l}\times C}\), where \(C\) is the number of categories. If the \(i\)-th sample belongs to the \(j\)-th class, then \(\mathbf{L}^{l}_{ij}=1\); otherwise, \(\mathbf{L}^{l}_{ij}=0\)[20]. On the other hand, \(\mathcal{D}_{u}=\{\mathbf{X}^{u},\mathbf{Y}^{u}\}\) represents the unlabeled sample set, where \(\mathbf{X}^{u}\in\mathbb{R}^{N_{u}\times d_{x}}\) and \(\mathbf{Y}^{u}\in\mathbb{R}^{N_{u}\times d_{y}}\) denote the unlabeled images and texts, respectively, with \(N_{u}\) being the number of unlabeled samples. Finally, the total number of samples is \(N=N_{l}+N_{u}\). Our goal is to learn the hashing functions \(\mathcal{H}^{x}\) for the image modality and \(\mathcal{H}^{y}\) for the text modality, and to obtain high-quality hash codes \(B^{x}\in\{-1,1\}^{K}\) and \(B^{y}\in\{-1,1\}^{K}\) through these hashing functions, where \(K\) is the number of bits in the hash code.  
Notations. Without loss of generality, sets are denoted by math script upper case letters (e.g., \(\mathcal{D}\)). Scalers or constants are signed by uppercase letters (e.g., C). Matrices are represented in uppercase bold letters (e.g., \(\mathbf{A}\)), and \((i,j)\)-th element of \(\mathbf{A}\) is denoted by \(A_{ij}\)[31]. Vectors are denoted by lowercase bold letters (e.g., \(\mathbf{a}\)), and \(i\)-th element of \(\mathbf{a}\) is represented by \(a_{i}\). Transpose of a matrix or a vector is denoted by a superscript T (e.g.,\(\mathbf{A^{\text{T}}}\)). Functions are denoted by calligraphy uppercase letters (e.g., \(\mathcal{L}\)). The frequently used mathematical notations are summarized in Table 1 for readability [31].
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis
### Experimental Comparison and Analyses
#### 4.3.3 Ablation Experiments for Different Modules  
To verify the effectiveness of each module, a set of ablation experiments are conducted. The baseline is BBAVectors and the methods '+FEFN', '+HAM', and '+HAM + FEFN' represent the addition of FEFN, HAM, as well as HAM and FEFN to the baseline, respectively. For all methods, all trainings converge at 70 epochs and the convergence curves of the loss function are shown in Figure 8. The '+HAM' or '+FEFN' method converges faster and performs better than the baseline, which illustrates that both '+HAM' and '+FEFN' can contribute to the convergence process. Further, regardless of the converge speed or losses, '+HAM + FEFN' exceeds the above methods in terms of performance. This means that the '+HAM + FEFN' method converges faster and performs better than the baseline and other methods. Therefore, '+HAM + FEFN', integrating '+HAM' and '+FEFN', jointly improves performance.  
The experimental results of various methods of adding different modules are shown in Table 3. By including FEFN, the method 'Baseline+FEFN' achieves a 1.51% mAP improvement. By adding HAM, the method 'Baseline+HAM' achieves a 2.48% mAP improvement. These improvements have been discussed earlier. Finally, the method 'Baseline+HAM+FEFN' achieves a 2.81% mAP improvement. FEFN enhances multi-scale features, and HAM learns more object features to weaken the inference of background noises. The combination of the two improves the performance of object detection.  
Figure 8: Training loss diagram.  
Figure 7: Visualization results comparison of different methods with or without HAM.  
Some typical detection results of the baseline (in the first row) and the proposed method (in the second row) are shown in Figure 9. Baseline tends to produce incorrect detection under different scenarios. In the first sample image shown in Figure 9a, many small vehicles in the red dashed box, as well as the vehicles indicated by red arrows, are not detected. The aircraft wings in the lower left part of image are not accurately located. In the second sample image shown in Figure 9a, for the two ships alongside each other, only one ship is detected and the other ship is missed for baseline, because the noise of sea ripples causes blurred edges of ships. In the last sample image shown in Figure 9a, the baseline method mistakenly deems some parking lines as cars, because the vehicles have very low contrast with the background and are thus easily confused with parking lines. In contrast, the proposed method detects the missed objects and reduces false detection under various adverse scenarios, as illustrated in the second row of Figure 9b. Excellent results are largely attributed to the inclusion of FEFN and HAM within the proposed method.
# K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony
## 3 Chaotic Adaptive Artificial Bee Colony (CAABC) for Clustering
### New Fitness Function  
Fitness function is the crux of population evolution, which determines the solution quality directly. It is the key factor which affects the stability and convergence of the algorithm. Based on the characteristics of the iteration of ABC and the basic idea of the K-means algorithm, a new fitness function is adopted in this work:  
\[fitness_{i}=CN_{i}/J_{i};i=1,2,\cdots N \tag{7}\]  
where \(CN_{i}\) represents the number of samples in the \(i\) -th cluster.  
\[J_{i}=\sum_{x_{j}\in C_{j}}d(x_{j},C_{i}) \tag{8}\]  
\(J_{i}\) denotes the sum of the distance between the sample points among the centers. The new fitness value can give rise to equilibrate the influence of the numbers and distance of samples. The phenomenon of inaccurate judgment caused by the same value of \(CN_{i}\) or \(J_{i}\) is avoided, which improves the adaptability of the function and makes the iterative process more accurate.