esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 3 Discrete Time Fractional-Order Sine Chaotic Map (DFSCM)
### Discussion
#### 3.1.2 Complexity of the Proposed DFSCM  
The complexity of the proposed DFSCM is determined based on the results of approximate entropy. The approximate entropy method employed here is adapted from the work of [PERSON] in [45]. The approximate entropy is a valuable tool to measure the persistence, correlation, and regularity of the time series obtained for chaotic dynamic systems. The evolution of the complexity of the proposed DFSCM (8) is investigated under different circumstances, such as varying the fractional order and the value of parameter \(b\). The approximate entropy is estimated using the following  
\[ApEn(d,r,M)(\
u)=\Phi_{d}(r)-\Phi_{d+1}(r),d\geq 1,\]  
where \(\Phi_{d}(r)=\dfrac{1}{M-d+1}\sum\limits_{i=1}^{M-d+1}\log W_{i}^{d}(r)\), \(r\) is the tolerance factor or filtering noise, \(d\) represents the length of data comparison, and the value \(W_{i}^{d}(r)\) calculated from \(\dfrac{|\xi(i)-\xi(j)|}{M-d+1}\), \(\xi(i)\) is the \(i\)-th block of a sequence of numbers. Here, the analysis of the randomness of the data series obtained from the DFSCM (8) for \(b=12,16\)\(c=50\) and \(\omega=0.5,0.65,0.8\) is performed for different tolerance values \(r\) between \((0,1)\) for a fixed value of \(d=2\). The calculated values of approximate entropy are plotted as 2D plots in Figure 5 with values tabulated in Table 1. The observations from the simulations imply that the higher the value of entropy, the greater the randomness of the data series, and lower values explain the predictive nature of the time series considered for study. In order to gain a detailed insight into the approximate entropy results, we presented the results for different lengths of data, such as \(M=1500,\ 4500\). The variation in the values explains the choice of the tolerance factor for a better illustration of the complexity of the proposed DFSCM.  
Figure 3: Phase portrait of (8) with \(\omega=0.5\), \(b=12\), \(c=50\) illustrating the chaotic nature of the state variables.  
Figure 2: Comparison of the chaotic behaviour of different maps in (10)–(12) and (8) with bifurcation diagrams for state variable \(x(n)\) in (**a–d**); for state variable \(y(n)\) in (**e–h**); and corresponding largest Lyapunov exponents in (**i–l**).
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
## 2 Data and Methods
### Methodology  
#### 2.2.1 Evaluation of Remote Sensing Precipitation Precision  
(1) The TRMM products' precision in gauged grids:  
Figure 1: The research area of the Qaidam Basin.  
The correlation coefficient (R\({}^{2}\)), relative deviation (Bias) and root mean square deviation (RMSD) were calculated in the grids where a gauge station was located. The data series is from 2001 to 2016 and the temporal resolution is all on an annual scale. The formulas are as follows:  
\[R^{2}=\frac{\left[\sum\limits_{i=1}^{n}{{{\left({{P_{S}}_{i}-\overline{P_{S}}} \right)}}}\right]^{2}}{\sum\limits_{i=1}^{n}{{{\left({{P_{S}}_{i}-\overline{P_{S }}}\right)}^{2}}}\cdot\sum\limits_{i=1}^{n}{{{\left({{P_{i}}-\overline{P_{t}}} \right)}^{2}}}}, \tag{1}\]  
\[Bias=\frac{\sum\limits_{i=1}^{n}{{{\left({{Pt_{i}}-{P_{S}}_{i}}\right)}}}}{ \sum\limits_{i=1}^{n}{{{P_{S}}_{i}}}}\times 100\%, \tag{2}\]  
\[RMSD=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{{{\left({{Pt_{i}}-{P_{S}}_{i}} \right)}^{2}}}}}, \tag{3}\]  
where, \(P\)s is the gauged precipitation by the ground gauge stations (mm); \(Pt\) is the remote sensing precipitation retrieved from TRMM products (mm); \(\overline{P}\)s and \(\overline{P}\)t are the average value of \(P\)s and \(Pt\), respectively; \(n\) is the number of years (2001-2016). The \(R\) reflects the correlation between the gauged precipitation and the TRMM precipitation. The relative deviation (Bias) and root mean square deviation (RMSD) reflect the deviation degree between the gauged precipitation data and the TRMM precipitation.  
(2) The TRMM products' precision in ungauged grids:  
There are only 20 gauges in the study area which means the gauged grids are equal or less than 20. All the other grids are ungauged. According to [PERSON]'s achievements [9], the accuracy of the remote sensing precipitation on the grids without a gauge station can be evaluated by the criteria of the consistency rate (CR). The formulas are as follows:  
\[Count_{i}=\left\{\begin{array}{ll}1,&\text{if $Pt_{i}\in D$}\\ 0,&\text{if $Pt_{i}\
ot\in D$}\end{array}\right.i=1,2,\cdots,N, \tag{4}\]  
\[S=\sum\limits_{i=1}^{N}{Count_{i}}, \tag{5}\]  
\[CR=\frac{S}{N}\times 100\%, \tag{6}\]  
where, \(D\) is the precipitation-elevation mask (PEM) derived from the relationship of gauged precipitation and the elevation of the gauge stations; \(N\) is the total number of grids without gauge stations; \(S\) is the total number of grids in which remote sensing precipitation falls into the PEM. If the remote sensing precipitation value falls into the mask, then the remote sensing precipitation is considered consistent with the gauged precipitation at the same elevation region, that is, the remote sensing precipitation is reliable and vice versa.
# Review of Adaptive Shock Control Systems
## 2 Literature Review of Adaptive Concepts for Shock Control
### Using Pressure Differences in the Shock Region
#### 2.3.1 Thin Flexible Plate  
[PERSON] et al. [81] (2015) describe a passive concept as well as an additional actuated approach. For both concepts the authors performed simulations with coupled fluid-structure interaction (FSI) as well as wind tunnel measurements. Similar simulations using FSI have been performed by [PERSON] et al. [82; 83] and [PERSON] et al. [84; 85].  
The general idea of the passive concept is based on a flexible plate on an airfoil suction side that is deformed only by the depression due to the airflow aerodynamics in the transonic shock vicinity. Similar to the porosity that has been debated in Section 1, this concept applies the pressure differences in the shock regime. As depicted in Figure 11, the pressure levels in the supersonic region as well as in the direct shock vicinity are lower than the pressure downstream of the shock. By implementing a cavity beneath a flexible skin and by appropriately adjusting the cavity pressure, the pressure difference can cause a deformation of the flexible material. This deformation leads to a bump formation which is affecting the transonic shock similar to an adaptive SCB, as can be seen in Figure 11.  
For the wind tunnel model, [PERSON] et al. [81] use a 0.4 mm thin aluminum plate (Al 7075 T6) that reaches from 40 to 60% chord. Since the shock is located at about 50% chord it is almost in the middle of the flexible airfoil surface. The airfoil chord length is given as 1 m. The investigated cavity pressures range from 0.3 to 1.0 bar and for the experiments in a Mach 1.4 wind tunnel a value of about 0.7 bar has been chosen.  
Since the cavity pressure is the only control variable and the bump position cannot be controlled with such a passive approach, [PERSON] et al. [81] suggest an additional actuated concept which was originally proposed by [PERSON] et al. [10] in 1992. This SCB concept uses an actuated flexible plate which is described in the following section.
# Exploring the Benefits of Combining DevOps and Agile
## 2 Literature Review
### Similarities and Differences between DevOps and Agile
Therefore, the level of collaboration is elevated with professionals that are beyond the initially foreseen roles, for example, in the SCRUM approach that is currently used in Agile, promoting the creation of cross-functional teams in the context of the DevOps approach. The approach of development teams that implement Agile methodologies, to operations teams as proposed by DevOps, tends to accelerate the software release process, with studies [40] indicating that in addition to acceleration, there is an increase in software quality in terms of reliability and maintainability. As a result, the deliverable product meets the conditions foreseen in the essential objectives of the project, from software development \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Problem with Agile Development** & **DevOps Solution** \\ \hline Delivery of new features to the customer is often delayed. & DevOps tools are used to test and release new features as they are completed. \\ Completed software components are not compatible with each other. & Open interfaces and test automation make it possible to divide development into independent yet compatible parts. \\ Quality of the product is not ensured properly prior to release. & DevOps tools and practices help automating quality assurance and reduce the need for repetitive manual work. \\ New features break old functions. & The quality of existing functions is ensured quickly and automatically after each change. The tools and procedures of DevOps increase the transparency and predictability of the development work. \\ Developer teams and IT operations crews are not cooperating. & Developer teams and IT operations crews agree upon responsibilities together. Their goals are unified. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Problems with Agile development and DevOps solution (adapted from [35]). (Agile), as in deployment and testing in operations (DevOps). However, despite DevOps decreasing the gap between Developers and Operators, in terms of standardization, the DevOps methodology lacks a simple approach or a roadmap to be followed for its implementation in an organization [41], leaving it up to companies to define their standards and metrics. This can present as a complicating factor, very dependent on the maturity level of organizations and work teams, which will have to define their specific integration processes in a DevOps approach.
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
## 2 Satellite Laser Ranging Process
### Tracking Process
It is also possible to transfer to the hardware layer the new values of the position\({}_{i}\), speed\({}_{i}\), and acceleration\({}_{i}\), developed by the management software in pseudo-real-time based on the position\({}_{j:j<i}\), speed\({}_{j:j<i}\), and acceleration\({}_{j:j<i}\) (\(Az_{j:j<i}/El_{j:j<i}\)) values received at time t\({}_{j:j<i}\) from the drive controller for the previous designated position \(X_{j:j<i}\), Y\({}_{j:j<i}\), Z\({}_{j:j<i}\) (Figure 2). The advantage of this solution is full control over the control process, but the disadvantage is the required pseudo-real-time data exchange between the managing software and the drive controller. The data processing chain in this case is obligated to make software transformations in specific time intervals (\(\Delta t\sim 100\) ms), directly by correcting the drive control settings for the approaching moment of time \(t_{i}\). Therefore, the algorithm at time t\({}_{j:j<i}\) must estimate the new position of X\({}_{i}\), Y\({}_{i}\), Z\({}_{i}\) (\(Az_{i}/El_{i}\)), establish the current state of the drives (e.g., position\({}_{j:j<i}\), speed\({}_{j:j<i}\), acceleration\({}_{j:j<i}\)), correct these parameters based on the required trajectory, and finally, send the newly estimated parameters as settings to the telescope's drive controller. As mentioned, effective process control imposes theneed to make calculations in time intervals \(\Delta t\sim 100\) ms, including the time needed to transmit new motion parameters to the drive controller. It follows that designing a data processing algorithm requires defining time restrictions that determine the quality of the control loop operation.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 6 Detail Analysis of Track Condition Using Time-Frequency Analysis  
### Continuous Wavelet Transform (Cwt)  
The wavelet transform is a tool that divides data or a function into its different frequency components and examines each component at a resolution appropriate to its scale. This method is used in various fields of engineering. The wavelet transform is well-known technique for analysing non-stationary signals [36; 37].  
A CWT gives simultaneous detection of the frequency and time characteristics for non-stationary signals, \(s(t)\), using a wavelet, \(\psi(t)\), which is a function of zero average. The CWT is calculated using the mother wavelet \(\psi(t)\) as  
\[W_{\psi}(a,b)=\int_{-\infty}^{\infty}\frac{1}{\sqrt{a}}\psi^{*}\bigg{(}\frac{t -b}{a}\bigg{)}s(t)dt, \tag{3}\]  
Figure 16: Track fault identification with position correction.  
Figure 17: Comparison of track fault identification with and without position correction.  
where, \(a\) and \(b\) correspond to the dilatation and location parameters, respectively. \(\psi^{*}\) indicates the complex conjugate of \(\psi\).  
A CWT translates a source signal \(s(t)\) using the mother wavelet transformed by a time shift \(b\) in time and by \(1/a\) in frequency. In this study, we employed the _Morlet_ wavelet, which has a good performance between its localisation of time and frequency [38].  
The CWT is subject to the uncertainty principle in the time-frequency domain. In case of fault detection using the CWT, if we are focusing on the frequency related on the fault, the time when the fault occurred will be vague. If we are focusing on the time when the fault occurred, the frequency will spread widely on the time-frequency plane. The time and frequency variables cannot be treated independently. This fact is known as the uncertainty principle in the time-frequency plane.
## 5 Feasibility Study Through System Simulations
### Mechanisms to Optimize the Models and Minimize the Error Distance
#### 5.7.3 Mechanism 3: Selection of the Best Model Weights with Different Initial Weights  
Mechanism 3 consists of the selection of the best model weights among the model training iterations with different initial random weights. The goal is to select the model weights that minimize the positioning error, generalizing the model for moving tiles. The training process is repeated with different initial random weights for a number of \(N_{I}\) iterations. Then, the set of weights that produced the minimum error distance at the 95 th percentile (\(P_{R}\) cut) of the CDF of the error distances resulting from the MTV dataset is kept as the best model.  
This mechanism is explained in step 9 of the block diagram describing the model training execution flow in Section 5.6.3 and depicted in Figure 15.
## 9 Enabling Technologies for Energy Efficiency in UAV-Based Cellular Networks
### Energy Harvesting Technologies
A different tack was followed in [311] where the authors proposed an energy sharing scheme whereby high-capacity UAVs harvest solar energy and share it with smaller-capacity UAVs. In some implementations of energy harvesting to support UAV operations, the authors assume that the harvested energy is sufficient to support the communication functions of the UAV, whereas in others, harvested energy merely serves to support power provided by the UAV on-bard battery. In [312], Yang et al. studied the outage problem of an UAV that harvests energy from a ground-based BS and uses the harvested energy to relay data for user terminals on the ground.
## 1 Introduction  
Styrene-butadiene-styrene (SBS) is a common modifier with high molecular polymer, which could make the asphalt binder modified by miscible with asphalt binder [1]. The SBS-modified asphalt could improve the high temperature rutting resistance, low-temperature crack resistance and anti-fatigue performance of asphalt pavement [2]. The SBS-modified asphalt has been applied widely in many high-grade pavement in China at present to satisfy the increasing traffic. SBS modified asphalt has wide scope of application [3]. In recent years, many scholars in China and other countries research much about SBS-modified asphalt. [PERSON] (2009) conducted dynamic creep test on unmodified and SBS-modified samples, and the creep behavior of the samples was estimated by the three-stage creep model. The result showed that dense-graded mixtures had higher permanent deformation susceptibility than coarse graded mixtures, and lower stress levels in dynamic creep test could not show the actual behavior of asphalt mixtures and particularly the modified mixtures [4]. [PERSON] (2014) used the creep curves derived from the dynamic creep tests to investigate the effects of loading frequency and temperature on the moisture sensitivity of dense-graded polymer-modified asphalt mixtures. The results showed that both the variables of loading frequency and temperature had significant effects on the permanent strains of both the dry and wet asphalt mixtures [5]. [PERSON] (2015) used multiple stress creep recovery (MSCR) test to investigate the effect of cross-linking agent and SBS content on SBS-modified asphalt. The result showed the effect of increasing SBS content was more prominent for binders at lower SBS content. MSCR test failed to distinguish 5.0% and 5.5% SBS-modified asphalt in the study [6]. [PERSON] (2017) conducted three points bending test and deformations test to evaluate the low-temperature performance and fatigue resistance of recycled SBS-modified asphalt mixture. The results showed that fatigue resistance of modified recycling of asphalt mixture with different RAPs did not vary much under low-temperature while displaying an obvious difference under higher temperature [7]. However, the test index used to evaluate the performance of SBS-modified asphalt is diversified, and lack of unification. This study based on the Chinese test methods of penetration test, softening point test, ductility test, elastic recovery test and Superpave test methods of dynamic shear rheometer test (DSR), Brookfield rotary viscosity test, bending beam rheometer (BBR) test, to analyze the influence of SBS modifier on asphalt performance. By comparing each performance index, it provides theoretical support for unifying the performance evaluation index of SBS modified asphalt.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on MARS Dataset
#### 5.2.2 Score-Level Fusion
Table 9 presents the results of threshold-based conditional fusion on the MARS dataset, with the best performance achieved after optimizing \(w\) and threshold values. This approach adaptively combines gait and appearance data or relies solely on appearance when necessary. Our methodology yields modest but consistent improvements in re-identification accuracy across the tested architectures. * OSNet: The OSNet model, with an initial mAP of 81.64% and rank-1 accuracy of 86.20%, showed a slight improvement when using threshold-based conditional fusion. The mAP increased to 81.71% (+0.07%). While the rank-1 accuracy remained unchanged at 86.20%, the rank-5 accuracy improved from 95.71% to 95.76% (+0.05%). * MGH: The MGH model, initially recording a mAP of 85.52% and a rank-1 accuracy of 89.40%, saw its performance increase to a mAP of 85.57% (+0.05%) and a rank-1 accuracy of 89.46% (+0.06%) through the fusion process. A slight improvement was also observed at rank-5, increasing from 96.63% to 96.68% (+0.05%). These results indicate that gait information can contribute to performance enhancement, even marginally, under the complex conditions of the MARS dataset. These results demonstrate the potential of threshold-based conditional fusion in person re-identification tasks on the MARS dataset. The integration of gait characteristics with appearance data contributes to the improvement of the performance of the tested architectures, adapting well to the dataset's specific challenges.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Experiments on Multi-Object Detection and Tracking for Inland Ships
#### 6.5.1 Object Tracking in Good Visibility  
We set up our detection and tracking model to track in the daytime with good visibility. As shown in Figure 7, the proposed model can accurately track the target cargo ship and buoy. Figure 8 shows that the
# Additive Manufacturing of Bone Scaffolds Using PolyJet and Stereolithography Techniques
###### Abstract  
In this study, the printing capability of two different additive manufacturing (3D printing) techniques, namely PolyJet and micro-stereolithography (\(\upmu\)sLA), are investigated regarding the fabrication of bone scaffolds. The 3D-printed scaffold structures are used as supports in replacing and repairing fractured bone tissue. Printed bone scaffolds with complex structures produced using additive manufacturing technology can mimic the mechanical properties of natural human bone, providing lightweight structures with modifiable porosity levels. In this study, 3D scaffold structures are designed with different combinations of architectural parameters. The dimensional accuracy, permeability, and mechanical properties of complex 3D-printed scaffold structures are analyzed to compare the advantages and drawbacks associated with the two techniques. The fluid flow rates through the 3D-printed scaffold structures are measured and [PERSON]'s law is applied to calculate the experimentally measured permeability. The Kozeny-Carman equation is applied for theoretical calculation of permeability. Compression tests were performed on the printed samples to observe the effects of the printing techniques on the mechanical properties of the 3D-printed scaffold structures. The effect of the printing direction on the mechanical properties of the 3D-printed scaffold structures is also analyzed. The scaffold structures printed with the \(\upmu\)sLA printer demonstrate higher permeability and mechanical properties as compared to those printed using the PolyJet technique. It is demonstrated that both the \(\upmu\)sLA and PolyJet printing techniques can be used to print 3D scaffold structures with controlled porosity levels, providing permeability in a similar range to human bone.  
scaffold design; 3D-printed porous scaffold structures; trabecular bone; permeability; mechanical properties +
Footnote †: journal: Applied Sciences