esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 4 Discussion
However, these encounter issues when facing non-linear wakes, strong sea clutter, and any conditions that do not allow the formation or conservation of linear features in the remotely sensed scene. Nonetheless, these methods are still powerful and are often paired with other techniques and transforms, which can enhance their performance. Recently, DL-based methods have proven their capabilities in terms of overcoming some of the aforementioned weaknesses. Comparative studies have shown that DL-based methods outperform traditional techniques, particularly in complex and high sea state conditions. For example, [PERSON] et al. (2016) demonstrated that YOLOv5 could detect non-linear and weak wakes more effectively than RT-based methods. Similarly, the lightweight DL model proposed by [PERSON] et al. (2018) achieved real-time detection capabilities suitable for embedded systems, highlighting potential for practical, real-world applications. However, the lack of a well-recognized large benchmark dataset for ship wake detection hurts the field, as data availability is the most important factor for the development, training, and validation of learning-based methods. At the time of writing, each new work on the topic requires a significant expenditure of time and resources to develop a well-annotated dataset that suits its focus. Nonetheless, there is a clear growth of interest in the field, as maritime surveillance remains a topic of great strategical importance. The use of synthetic data is an interesting solution to the issue of low data availability, and represents a feasible and powerful approach that cab be paired with the usual techniques of data augmentation. Moreover, there is unexplored potential for the use of more advanced architectures and methodologies, such as vision-transformer-based detectors and physics-informed DL, which may incorporate hydrodynamic laws or information related to local meteo-marine conditions. As a final remark, hitherto, the multi-spectral (in electro-optical imagery) or the multi-polarization (in SAR data) content of wake images and features has remained largely unexplored or underutilized for both wake detection and false alarm reduction.
## 2 Materials and Methods
### Characterization  
Thermogravimetric analysis (TGA) was performed using a thermal gravimetric analyzer (Q50, TA Instruments, New Castle, DE, USA) on 4-5 mg of each sample. The analysis was carried out within a temperature range of 30-600 \({}^{\circ}\)C, employing a constant heating rate of 10 \({}^{\circ}\)C/min, under a nitrogen-controlled atmosphere. The primary objective of the TGA was to determine the decomposition temperature and the actual Fe content of the composite.  
Differential scanning calorimetry (DSC) analysis was performed using a DSC analyzer (Q20, TA Instruments, USA) to examine the thermal stability of the materials. The study was conducted over three cycles, involving a sequence of heating, cooling, and reheating to eliminate any prior thermal history on the materials. Each DSC measurement was conducted on 6 mg samples of the respective composites, covering a temperature range of 30 to 200 \({}^{\circ}\)C. The scanning rate during the analysis was set at 10 \({}^{\circ}\)C/min, and the entire process was carried out under a nitrogen atmosphere. With H\({}_{\text{mPLA}}^{0}=93.7\) J/g, which is the heat of fusion value if PLA was 100% crystalline, H\({}_{\text{ccPLA}}\) is the enthalpy of cold crystallization, and H\({}_{\text{mPLA}}\) is melting enthalpy [31].  
\[  
voids between the printed layers were detected, consistent with the inherent defects typical of material extrusion 3D printing, as reported in prior studies. In the PLA/Fe samples, iron particles were found to be well-dispersed within the polymer phase, with no signs of agglomeration. However, small gaps were observed around certain particles, suggesting potential sites for crack initiation and propagation [33]. Additionally, spherical voids were identified, likely corresponding to particle locations where separation occurred under the shear force during testing.  
Regarding the overall quality of the 3D-printed auxetic structures, smooth surfaces without visible defects were noted in both the neat PLA and PLA/Fe composites. This smoothness is attributed to the spherical morphology of the Fe powder particles, which prevented nozzle clogging and ensured consistent material flow and print quality throughout the fabrication process. These characteristics were crucial in achieving reliable and reproducible structures in the printed samples.
## 2 Materials and Methods  
### Hybrid Control System Architecture  
#### 2.1.1 Overall System Structure  
Most of the processes with various dynamic characteristics can be reduced to the SOPTD model by using the hybrid model reduction method. The proposed hybrid control system integrates LSTM networks with a traditional Smith predictor structure to enhance control performance and adaptability. Figure 1 shows the overall architecture of the proposed system.  
The proposed control system architecture consists of three primary components that work in conjunction. The first component is an LSTM-based real-time model identifier that continuously adapts to system dynamics. This is complemented by an enhanced Smith predictor incorporating Genetic Algorithm optimization for parameter tuning. The third component is an adaptive PID controller featuring dynamic parameter adjustment capabilities, which ensures optimal control performance under varying conditions. These components work synergistically to achieve superior control performance while maintaining computational efficiency.  
The paper consists of model reduction, controller design, runtime control, and performance evaluation phases; the flowchart is shown in Figure 2.  
The architecture of our hybrid control system is organized into four distinct phases, each contributing to the overall system performance and adaptability. These phases are systematically structured to ensure optimal control performance while maintaining computational efficiency. The complete process flow is illustrated in Figure 2, which provides a comprehensive visual representation of the system's operational sequence and interconnections.  
Each phase serves a specific purpose in the control system:  
1. Model Reduction Phase: transforms complex higher-order systems into manageable SOPTD models.
2. Controller Design Phase: establishes and optimizes control parameters.
3. Runtime Control Phase: executes real-time control with continuous adaptation.
4. Performance Evaluation Phase: assesses system performance and validates improvements.  
The flowchart in Figure 2 illustrates the logical progression and interaction between these phases, demonstrating the system's comprehensive approach to adaptive control.  
Figure 1: Block diagram of proposed hybrid control system architecture.
## 4 Applications in Medical Detection  
Currently, medical instruments that measure somatic function attract great interest of companies and individuals. The structure presented in this article is suitable for the detection of health indicators that are sensitive to refractive index. We can use the concentration of dry hemoglobin (g/L) at  
Figure 8: (**a**) Transmission spectrum of different lengths of the long axis of the inner elliptical resonator \(r_{1}\); (**b**) Transmission spectrum of different lengths of the short axis of the inner elliptical resonator \(r_{2}\); (**c**) Transmission spectrum of different lengths of the short axis of the outer elliptical resonator \(R_{2}\); (**d**) Transmission spectrum of different widths of the MIM waveguide \(w\).  
temperature T (\"C) to detect our health condition. According to the [PERSON]'s study, the refractive index has a linear relationship with C [39], which is depicted in Equation (5):  
\[n=n_{0}+\xi C \tag{5}\]  
where \(n_{0}\) is the refractive index when the concentration of hemoglobin (several nanometers) C is 0 and \(\xi\) is the specific refraction increment. According to the study by [PERSON] et al. [35], the measured values of the refractive index have a linear relationship with the concentration of dry hemoglobin at all wavelengths. In addition, the wavelength of the Fano resonance dip also has a linear relationship with the refractive index. Therefore, by means of measuring the wavelength of the Fano resonance of the blood group, researchers can calculate the refractive index and then observe C within the erythrocytes, which is a vital parameter to detect the blood health condition. In using the proposed structure as a health detection sensor, the blood sample is placed in the MIM waveguide. We can pass the blood sample through the filter to remove large cells before we place the blood in the waveguide. Then, researchers can calculate the concentration within the erythrocytes to detect the health condition of the patient. The Fano resonance relies on its advantage that it is sensitive to the changes of refractive indices, making it become a promising platform for designing highly integrated medical optical sensors to detect concentration of hemoglobin and monitor body health.
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 2 System Description
### Based Algorithm for Reative Power Allocation
This component also can be divided with several reactive power flow rates, as:  
\[\Delta Q_{req}=\Delta Q_{Gn}+\Delta Q_{n\cdot n+1}+\Delta Q_{Cn} \tag{8}\]  
where, \(Q_{Gn}\) is the reactive power reference of the wind turbine at node \(n\), \(Q_{n\cdot n+1}\) is the reactive power flow between node \(n\) and \(n+1\), and \(Q_{Cn}\) is the reactive power injection at node \(n\) from the line capacitance. When we ignore the reactive power generation from the cable, the rate of change of the required reactive power at a certain node can allocate by following previously calculated coefficient. In this case, if the turbine controller utilizes the previously defined coefficient, a wind turbine can solely update the additionally required reactive power reference, by following the main objective. The changed order has an interaction formula with the measured flow and the voltage variation at a subordinate bus, using a linearized component, as in Equation (9):  
\[Q^{\prime}_{Gn}=Q_{Gn}+\eta_{In}\left[\left(J_{QU}-J_{Q\delta}\frac{J_{PU}}{J_ {P\delta}}\right)\cdot\frac{\Delta U}{U}-\Delta Q_{n\cdot n+1}\right] \tag{9}\]  
This can lead to a prompt calculation process, which is a usable option in a quick response mechanism.
## 4 Experimental Validation
### Statistical Evaluation  
We evaluated the results statistically using Bayesian credibility assessment by Ref. [86] to assess the credibility of our results considering the existing evidence. The odds ratio had to be smaller than 0.96 so that our results could be considered credible. It specifically indicates if the new finding, when integrated with existing information, may be considered  
Figure 11: Summary of the performance measures.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value (Confidence Range) Italian Dataset** &
\begin{tabular}{c} **Value (Confidence Range) Lithuanian** \\ **Dataset** \\ \end{tabular} \\ \hline Sensitivity & 0.9543 (0.9283–0.9720) & 0.7671 (0.6914–0.8300) \\ False Negative rate (FNR) & 0.0457 (0.0285–0.0712) & 0.2329 (0.1706–0.3080) \\ Specificity & 0.8440 (0.8047–0.8771) & 0.8193 (0.7479–0.8753) \\ False Positive rate (FPR) & 0.1560 (0.1231–0.1951) & 0.1807 (0.1255–0.2513) \\ Positive Likelihood Ratio (PLR) & 6.1188 (5.7392–6.5236) & 4.2447 (3.6150–4.9842) \\ Negative Likelihood Ratio (NLR) & 0.0541 (0.0508–0.0577) & 0.2842 (0.0241–0.3338) \\ Precision & 0.8468 (0.8077–0.8796) & 0.7887 (0.7147–0.8489) \\ False discovery rate (FDR) & 0.1532 (0.1206–0.1921) & 0.2113 (0.1518–0.2847) \\ Negative Predictive Value (NPV) & 0.9534 (0.9272–0.9713) & 0.8000 (0.7269–0.8587) \\ False omission rate (FOR) & 0.0466 (0.0292–0.0724) & 0.2000 (0.1420–0.2724) \\ Accuracy & 0.8964 (0.8620–0.9235) & 0.7949 (0.7213–0.8543) \\ Mis-classification Rate (MCR) & 0.1036 (0.0768–0.1377) & 0.2051 (0.1464–0.2780) \\ Balanced Accuracy (BA) & 0.8992 (0.8651–0.9259) & 0.7932 (0.7195–0.8528) \\ F1-measure & 0.8974 (0.8631–0.9244) & 0.7778 (0.7029–0.8393) \\ G-measure (Fowlkes–Mallows index) & 0.8990 (0.8649–0.9258) & 0.7779 (0.7029–0.8394) \\ Matthews index: & 0.8996 (0.8656–0.9263) & 0.7938 (0.7201–0.8533) \\ Critical success index (CSI) & 0.8139 (0.7723–0.8496) & 0.6364 (0.5552–0.7110) \\ Cohen’s Kappa & 0.7935 (0.7351–0.8519) & 0.5874 (0.4599–0.7148) \\ Yule’s coefficient & 0.9825 (0.9628–0.9918) & 0.8745 (0.7453–0.9404) \\ Critical Diagnostic Odds Ratio (DOR) & 1.1326 & 1.2643 \\ Discriminant Power & 2.6066 & 1.4906 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the performance results.  
to have genuinely proved efficacy at the 95% confidence level. At the 95% confidence threshold, such a finding is deemed to be credible.  
For the ItalianPVS dataset, the Critical Error Odds Ratio (EOR) is 1.6841. Error Odds Ratio (EOR) is 3.8599. As \(EOR>COR\), the test is credible at the 95%. Critical Diagnostic Odds Ratio (DOR) is 1.1326. Diagnostic Odds Ratio (DOR) is 113.0458. As \(DOR>COR\), the test is credible at the 95%.  
For the Lithuanian voice dataset, Critical Diagnostic Odds Ratio (DOR) is 1.2643. Diagnostic Odds Ratio (DOR) is 14.9333--the classifier discriminates between diseased and not diseased. As \(DOR>COR\), the test is credible at the 95%.
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 3 Verification According to European Standards
### Resistance of Beam-Columns with RHS 200/100/10 Cross-Section
#### 3.2.3 Axial Compression and Minor-Axis Bending  
In the case of a weak axis bending scenario, similar behaviour to flexural buckling about the major axis was expected for double symmetric rectangular hollow sections. This is due to the fact that the shape of the cross-section and, therefore, cross-sectional properties remain similar for both axes. The best examples of such cross-sections are circular hollow sections (CHS) or square hollow sections (SHS) sections. To make this analysis complete, a comparison of design methods for weak axis flexural buckling is also presented. Therefore the Figures 43-47 presents the interaction curves for out-of-plane buckling mode with a RHS type of cross-section.  
Figure 42: Interaction curves for beam-columns with RHS cross-section without lateral restraints.  
Figure 43: Interaction curves for weak axis flexural buckling of beam-columns with RHS cross-section.  
Figure 44: Interaction curves for weak axis flexural buckling of beam-columns with RHS cross-section.  
Figure 45: Interaction curves for weak axis flexural buckling of beam-columns with RHS cross-section.  
It can be seen that in case of beam-columns with RHS cross-sections subjected to the axial force and bending moment acting in the same direction as the buckling mode, the GMNIA interaction curves had similar behaviour. However, the GMNIA curves showed lower plastic capacity, as the interaction curves were slightly concave for lower slenderness ratios. Therefore, the accuracy of design methods was different for lower slenderness ratios. This was particularly visible for Method 2 of Eurocode 3, which used the same value of \(ky\) and \(k\underline{z}\) factors for both axes. The resistances provided by Eurocode 9 for higher slenderness ratios remained too conservative, as in the previous loading conditions.
## 5 Performance Assessment
### Comparison between D-ST and D-TSN  
The simulations of D-TSN and D-ST were run with the network configuration parameters set as in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Flow**} & \multirow{2}{*}{**Deadline (ms)**} & \multicolumn{2}{c}{**D-ST**} & \multicolumn{2}{c}{**TSN-ST**} \\  & & **Max e2 eDelay (ms)** & **Max e2 eDelay (ms)** \\ \hline LiDAR & 10 & 0.01 & 0.01 \\ Ultrasonic & 20 & 0.01 & 0.01 \\ ADAS sensors & 1 & 0.45 & 1.56 \\ Video & 10 & 8.50 & 7.90 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results: maximum end-to-end delay.  
As expected, the _e2 eDelays_ of LiDAR and Ultrasonic flows in the D-TSN configuration are higher than those obtained using the D-ST configuration, as D-TSN does not support scheduled transmissions. The jitter for LiDAR and Video flows is equal to 40 \(\upmu\)s and 1 \(\upmu\)s, respectively; nevertheless, with D-TSN, no deadline miss are experienced. In fact, the maximum e2 eDelays of LiDAR and Ultrasonic flows are always lower than their relative deadlines. Moreover, the two approaches obtained the same maximum e2 eDelay values for the ED traffic (i.e., the ADAS sensors flows) and similar maximum e2 eDelays values for the Video flow.  
Summarizing, although D-TSN is able to serve jitter-sensitive flows without deadline miss, it is evident that the EDF schedule cannot avoid jitter, whereas D-ST offers a null jitter to ST flows at the expense of a negligible delay increase for the periodic RT flows.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 4 Privacy-Based Refinement
### Oblivious Mechanisms  
In the case of an _oblivious_ mechanism, a query \(f\colon\mathcal{X}\to\mathcal{Y}\) is first applied to database \(x\), and a noise mechanism \(H\) from \(\mathcal{Y}\) to \(\mathcal{Z}\) is applied to \(y=f(x)\), producing an observation \(z\). In this case, it is useful to study the privacy of \(H\) wrt some metric \(d_{\mathcal{Y}}\) on \(\mathcal{Y}\). Then, to reason about the \(d_{\mathcal{X}}\)-privacy of the whole mechanism \(H\circ f\), we can first compute the _sensitivity_ of \(f\) wrt \(d_{\mathcal{X}},d_{\mathcal{Y}}\):  
\[\Delta^{f}_{d_{\mathcal{X}},d_{\mathcal{Y}}}=\max_{x,x^{\prime}}\frac{d_{ \mathcal{Y}}(f(x),f(x^{\prime}))}{d_{\mathcal{X}}(x,x^{\prime})} \tag{16}\]  
and then use the following property [3]:  
\[\text{If $H$ satisfies $d_{\mathcal{Y}}$-privacy, then $H\circ f$ satisfies $\Delta^{f}_{d_{\mathcal{X}},d_{\mathcal{Y}}}\cdot d_{\mathcal{X}}$-privacy }\,. \tag{17}\]  
For instance, the geometric mechanism \(G^{\varepsilon}\) satisfies \(\varepsilon\cdot d_{\mathrm{E}}\)-privacy (where \(d_{\mathrm{E}}\) denotes the Euclidean metric), hence it can be applied to any numeric query \(f\): the resulting mechanism \(G^{\varepsilon}\circ f\) satisfies \(\Delta^{f}_{d_{\mathrm{H}},d_{\mathrm{E}}}\cdot\varepsilon\)-differential privacy. The sensitivity wrt the Hamming and Euclidean metrics reduces to \(\Delta^{f}_{d_{\mathrm{H}},d_{\mathrm{E}}}=\max_{x\sim x^{\prime}}|f(x)-f(x^{ \prime})|\) where \(x\sim x^{\prime}\) denotes \(d_{\mathrm{H}}(x,x^{\prime})=1\).
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 2 Materials and Methods  
The mathematical model of GNSS observations for relative positioning was first presented to obtain the GNSS ambiguity float resolution. Then, an IPSO algorithm was proposed and applied in GNSS AR. This section will focus on IPSO-AR, and its specific application will be illustrated by using a flow diagram.
# Multi-LEO Satellite Stereo Winds
## 1 Introduction
The same could be tried by pairing MISR with imagery from another LEO satellite covering the same area; however, the lack of a thermal IR channel limits the usefulness of MISR for polar observing, although interesting observations of Polar Stratospheric Clouds (PSCs) have been made during polar daylight [35]. In the present work, we consider other sensor combinations for polar stereo-wind observations. An especially promising LEO constellation involves the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and the Sea and Land Surface Temperature Radiometer (SLSTR) on the Sentinel-3A and -3B satellites, which offers both down- and aft-looking views of the Earth and thermal IR capability [36]. SLSTR is a successor to the Along Track Scanning Radiometer (ATSR) instrument flown on the European Remote Sensing-1 and -2 (ERS-1 and -2) satellites and the Advanced ATSR (AATSR) flown on the Envisat satellite. An operational multi-LEO SLSTR AMV product is under development at EUMETSAT, but its wind height assignments are based on IR methods rather than stereo [37]. [PERSON] et al. [38; 39; 40] and other investigators [41; 42; 43; 44; 45; 46; 47] have pioneered the LEO stereo method with ASTR, AASTR, and MISR and have proposed future stereo-observing systems; also studied is the use of cloud shadows in Sentinel-3 imagery to measure cloud heights [48]. The contribution of our work is twofold: (i) fusion of information across LEO platforms (i.e., Terra and Sentinel-3) to cleanly separate the cloud height and the cross-track wind and (ii) compensation of small relative image deregistration errors to further improve stereo-wind retrieval accuracy. Section 2 of this paper introduces the multi-LEO approach to stereo-wind observing and identifies several candidate configurations, including SLSTR-MODIS. The remainder of the paper focuses on the SLSTR-MODIS configuration and the polar regions. Results over both the Arctic and Antarctic are presented in Section 3 and the discussions in Section 4 provide validation and identify applications ranging from cloud analysis to polar circulation.
## 2 Study Area and Data
### In Situ ET Calculation  
_In situ_ ET measurements were obtained from AmeriFlux towers at the two U.S. sites and flux towers operated by the Hydrological Survey Centre at the South Korean sites. AmeriFlux is a network composed of over 110 sites across North and South America that continuously measure meteorological variables such as CO\({}_{2}\), water and energy fluxes using an eddy covariance technique. In this study, each U.S. site has an AmeriFlux tower station, namely US Atmospheric Radiation Measurement Program Southern Great Plains Main (US-ARM SGP Main) at Site 1, and US Twitchell Island (US-Twt) at Site 2 (Figure 2a,b). US-ARM flux tower is located at an open cattle pasture surrounded by a rain fed winter wheat field [34]. Summer crops such as maize, soybean, or sorghum are commonly planted after wheat is harvested [34; 35]. US-Twt site is a rice paddy field. Level 2 data with half-hourly flux measurements during 2013-2014 were downloaded and processed as higher level data is not available for recent years. Air temperature, relative humidity (RH) and air pressure (Pa) measurements were used to calculate water vapor for LST computation [36], and LE (W/m\({}^{2}\)) measurements were used to derive 8-day ET measurements (mm/8-day) following [10]. As the AmeriFlux tower observations are collected every 30 minutes, ET (mm/30 min) was first calculated as  
\[\lambda=(2.501-0.002361\times T_{n})\times 10^{6} \tag{1}\]  
\[ET_{n}=\frac{LE_{n}\times 60.0\times 30.0}{\lambda} \tag{2}\]  
where \(T_{n}\) is air temperature for \(n\)th 30-minute observation in degree Celsius and \(\lambda\) is the latent heat of vaporization (J/kg). When the number of valid 30-minute measurements of LE and \(T_{n}\) (\(N\)) is no less than 40, daily ET was calculated using Equation (3). When \(N\) is less than 40, daily ET was estimated by linear interpolation with the two nearest daily ET values. The 8-day ET is a sum of daily ET during an 8-day interval with the same starting date of MODIS ET product.  
\[ET=\frac{\sum_{n=1}^{N}ET_{n}\times 48}{N} \tag{3}\]  
Similar to the AmeriFlux system, two flux towers in South Korea (_i.e._, Seolmacheon in Site 3 and Cheongmicheon in Site 4) measure meteorological and soil parameters and use an eddy covariance method to calculate ET (Figure 2c,d). The Seolmacheon flux tower was designed to observe ET over a mixed forest with trees of 30-50 years at an altitude of about 420 m, while the Cheongmicheon was constructed to monitor ET over agricultural lands in a rice paddy area at an altitude of 310 m.