esa-sceva/satcom-chunk-collection huggingface dataset:
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
## 2 Signal Model for Small Time-Variant Scattering Objects  
By restricting ourselves to small objects of interest, which are sufficiently far from the antennas, the set of differential equations (i.e., Maxwell equations) describing the scenario may be approximated by a simple transmission model. It will be finally based on the Friis transmission formula and the radar equation, which we extend to time-domain conditions here. In order to make that approximation already valid for short distances, the involved antennas should be quite small.  
In what follows, we first introduce this transmission model for the simple case of free space propagation and summarize some methods of localizing/imaging of small, invariant objects without the need of an inversion of Maxwell's equations. We then discuss variant objects under free space conditions and some methods are introduced to detect them under noisy conditions. For the sake of brevity, we will only refer in this connection to the time variance of those objects. But, this approach will also include the \"targeted object modification\" (which is typically time-dependent) and the \"difference between two scenarios\"-method, if we regard the related measurements as sequentially done in (observation) time \(T\). Finally, we will extend the scenario model to strong multipath conditions (i.e., the case will be considered in which a weak time-variant object is embedded in a strong multipath environment).
## 4 Improved WRIM with Optimal IDS
Compared with the traditional single-scale method, the inversion results of the optimal IDS are significantly closer to the true PSD. A large number of the experiments show that the practice optimal IDS is mostly consistent with the true optimal IDS. However, in a few cases, there are inconsistencies. The inversion results can be seen \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Particles**} & \multirow{2}{*}{**Inversion Range**} & \multicolumn{2}{c}{**WRIM-\(RE\)**} & \multicolumn{2}{c}{**Improved WRIM**} \\ \cline{3-6}  & & **True Optimal IDS** & \(RE\) & **Practice Optimal IDS** & \(RE\) \\ \hline
10 nm\(\sim\)150 nm & [1, 600] & 3 & 0.0672 & 3 & 0.0672 \\
100 nm\(\sim\)600 nm & [1, 2000] & 5 & 0.1325 & 5 & 0.1325 \\
90 nm\(\sim\)800 nm & [1, 2000] & 3 & 0.3181 & 3 & 0.3181 \\
50 nm\(\sim\)500 nm & [1, 1500] & 3 & 0.3879 & 5 & 0.4015 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inversion data of the optimal IDS determined by the improved WRIM and WRIM-RE. Figure 7: Flow chart of the improved WRIM. from Table 3. For 50 nm\(\sim\)500 nm particle system, the practice optimal IDS 5 is inconsistent with the true optimal IDS 3. However, the difference between the two \(REs\) is relatively small--only 0.0136. Moreover, the inversion PSD of improved WRIM is also obviously better than that of IDS 1. Overall, improved WRIM can be used for DLS inversion.
## 3 Numerical Results and Discussion
### Dependence of F-Ratio and Algorithm Speed on Dataset Size  
To vary the size of the dataset, the parameter \(\mu\) (0.01,..., 1) was introduced. It determines the fraction of the database usage. Figure 12 shows the dependences of F-ratio and calculation time of one time series on \(\mu\).  
NNetEn with Dataset 1 shows a gradual decrease in F-ratio for \(\mu>0.2\) and an intense decrease for \(\mu<0.2\). Dataset 2 already at \(\mu<0.95\) has low F-ratio values, with the presence of local maxima. As a result of different database sizes, the algor  
Figure 11: Feature combination diagram [NNetEn(D1, \(Nset=2\)), \(\mathrm{SampEn}(m=2\), \(r=0.2\mathrm{d})\)]. The figure demonstrates the effectiveness of NNetEn and SampEn pairing, as a slanted blue line separates classes better than a vertical or horizontal one.  
Figure 12: Dependences of F-ratio and calculation time of one time series on \(\mu\). The figure shows that decreasing \(\mu\) can significantly reduce entropy calculation time. For some settings, time is reduced without significant loss in classifying accuracy (F-ratio).  
approximately an order of magnitude less time to execute than Dataset 1. With a decrease in \(\mu\), calculation time decreases. Based on the F-ratio of 30 and the average calculation time of 0.2 s, datasets have approximately the same efficiency regarding time costs. If we compare the time costs at the F-ratio \(\sim\)15 level, then using Dataset 2 speeds up calculations per order, due to the presence of local maxima at a low value of \(\mu\)\(\sim\)0.03, where the calculation time for NNetEn(D1) is \(\sim\)0.2 s and for NNetEn(D2) \(\sim\)0.02 s.  
In Figure 13, the dependences between NNetEn\({}_{\text{uv}}\)(r1), a difference (NNetEn\({}_{\text{uv}}\)(r2)--NNetEn\({}_{\text{uv}}\)(r1)), and the standard deviation S(r1) are plotted to reveal the reason for the sharp change in F-ratio. Here, \(r1\) is 1.1918 and \(r2\) is 1.2243. It can be seen that for Dataset 1 at \(\mu>0.2\), the standard deviation is approximately at the same level, and the difference in entropy slowly decreases. At \(\mu<0.2\), there is a sharp increase in S(r1) and a sharp decrease in the difference of entropies. For Dataset 2, a sharp decrease in F-ratio at \(\mu=095\) is associated with a sharp decrease in the value of the entropy difference, which changes its sign for the first time at \(\mu=0.8\). At \(\mu<0.2\) for Dataset 2, there is a sharp increase in the difference between entropy and standard deviation.
## 3 Results and Discussions
### 3.11 Stirring Speed Effect  
The stirring speed is a vital factor influencing the adsorption rate [59]. The stirring speed effect was studied using 50 mL of solution for 90 min at 25 \({}^{\circ}\)C, and different stirring speeds ranging from 50 to 250 rpm. The adsorption capacity versus different stirring rates is shown in Figure 10. The data showed that the adsorption capacity increased as the stirring speed increased from 50 to 150 rpm. A reasonable explanation for this is that the removal efficiency increased due to the increased diffusion rate of IBP molecules from the liquid to the liquid boundary layer surrounding the Raw-RGL particles because of the turbulence formed inside the solution with a decrease in thickness of the boundary layer [60]. The sorption capacity decreased when the stirring speed was above 150 rpm due to reduced adsorbent-adsorbate interaction [61].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Raw-RGL** & **AC-RCL** \\ \hline \(\Delta\)H\({}^{\circ}\) (KJ mol\({}^{-1}\)) & \(-\)2.39 & \(-\)2.34 \\ \(\Delta\)S\({}^{\circ}\) (KJ mol\({}^{-1}\)K\({}^{-1}\)) & 3.23 & 4.78 \\ \(\Delta\)G\({}^{\circ}\) (KJ mol\({}^{-1}\)) 298 K & \(-\)4.67 & \(-\)3.80 \\ 308 K & \(-\)5.65 & \(-\)4.89 \\ 318 K & \(-\)6.45 & \(-\)5.23 \\ 328 K & \(-\)7.55 & \(-\)6.01 \\ 338 K & \(-\)8.43 & \(-\)6.89 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Thermodynamic studies of Raw-RGL and AC-RGL and their parameters.  
Figure 9: Effect of pH on ibuprofen uptake by Raw-RGL and AC-RGL adsorbents.
## 3 Analysis of Factors Influencing the Results
### Effect of ROI in Slanted-Edge Images on the Slanted-Edge Method  
This section analyzes the impact of slanted-edge height on the slanted-edge method under a specific slanted-edge angle of 7\({}^{\circ}\). Examination of Figure 10 reveals that the continuous signal becomes discretized following the sampling process. Subsequently, the signal's representation on the detector resembles a stair-step distribution. The height of the slanted edge, corresponding to a varying number of steps for a constant angle, introduces result uncertainty. With a slanted-edge angle fixed at 7\({}^{\circ}\), the height fluctuates within a 9-pixel range. Table 3 presents the varied slanted-edge heights for each step. Consequently, simulation experiments were conducted to assess the relationship between slanted-edge height and step distribution.  
Figure 14 vividly illustrates the convergence of data, highlighting the correlation between the number of steps in the slanted edge and the precision of experimental results. As the number of steps increases, there is a notable improvement in convergence, with the root-mean-square error remaining below 0.1 when the step count exceeds three. Therefore, an image of the slanted edge should include a minimum of three step structures. Additionally, considering the relationship between the number of slanted-edge steps and their height, as detailed in Table 3, the slanted edge's height within the ROI should be at least 30 pixels for an ideal slanted angle range of \(\theta\in[6^{\circ},12^{\circ}]\).  
Figure 13: Nyquist MTF for different angles: (a) numerical curve at Nyquist frequency; (b) relative error curve.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Steps** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Rows & 2\(\sim\)18 & 11\(\sim\)27 & 20\(\sim\)36 & 29\(\sim\)45 & 38\(\sim\)54 & 47\(\sim\)63 & 56\(\sim\)72 & 65\(\sim\)81 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Height of slanted edge for different steps.
## 1 Introduction
However, it is noteworthy that these advanced signal processing methods have not yet been fully utilized to interpret the pavement texture cloud point data obtained during pavement compaction. In recent years, there has been a growing trend toward the use of intelligent compaction control techniques in pavement field compaction. To measure the compaction of asphalt pavement, the non-nuclear density gauge has gained widespread acceptance due to its efficiency, accuracy, and harmlessness. However, this instrument is limited to measuring the compactness of the pavement and cannot provide information on its texture. Moreover, the advent of Continuous Compaction Monitoring (CCM) and Intelligent Compaction (IC) technologies has presented new opportunities for assessing pavement quality and monitoring temperature in real time [27; 28; 29]. IC rollers utilize a combination of Global Positioning System, infra-red, and accelerometer sensors to capture and record the speed of the rollers, number of roller passes, temperature of the asphalt pavement, effort applied to compaction, and material response [30]. The CCM method allows for automatic feedback control by modifying the frequency and amplitude of roller vibrations based on real-time measurements [31]. In practical engineering applications, various post-construction acceptance methods, such as the sand patch test, pavement evenness test, and penetration test, are commonly used to monitor pavement compaction [32; 33; 34]. Despite the benefits of intelligent compaction control techniques, they mainly focus on controlling the rollers and measuring the compactness of the pavement, overlooking the importance of monitoring texture, which is closely related to slip and safety. This study presents an investigation into the evolution of pavement texture characteristics for five different asphalt mixture types: AC-13, AC-16, AC-25, SMA-13, and OGFC-13. These mixtures were prepared through laboratory compaction experiments, and high-precision pavement texture data were collected using 3D laser measurements. Macrotexture indicators were calculated based on the geometric parameters obtained during laboratory compaction. A 2D wavelet transform approach was then used to separate the micro-texture of each mixture into six levels, with the total energy of each level and the relative energy serving as wavelet-based indicators. The goal of this study is to provide insights into pavement texture evolution during compaction by analyzing the geometric parameters and decomposition results for the five different asphalt mixture types. Figure 1 illustrates the outline of this study. The remainder of this paper is organized as follows. The materials and test method are presented in Section 2. The methodology is presented in Section 3. The results and discussion are presented in Section 4. The conclusions are presented in the final section.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 3 Results
### Few-Shot Performance of the Methods under Study  
To evaluate the performance dynamics of the proposed method in the few-shot learning setting we split the dataset into two parts. The training set contains 13 images and the test set three: one from the Suez canal, a second from Brest and the last one from Rotterdam. We vary the number of training images from 1 to 13, which corresponds to a variation in the number of distinct ships from 42 to 742. This experiment aims to highlight the network's robustness towards training dataset size and diversity (sun glint, water color, etc.).  
Varying the number of training samples shows that SSL methods trained on large datasets, especially with the region based pretext task, achieve competitive and even better results than ImageNet pretrained networks fine-tuned on a small amount of data. Indeed, in Figure 9, we see that having only between 200 and 300 training examples is sufficient for SEN12 MS-RA method to get close to a F1 score of 35%, while ImageNet network needs at least 350 samples to reach this performance. This experiment also confirms the importance of the pretext task: the region invariances induced by SEN12 MS-RA method increased considerably the performances. Indeed, only 290 ships are needed by SEN12 MS-RA to get 92% of the best performances obtained with 750 training ships, while other methods need at least a third more training ships.  
Moreover, in a few-shot setting, with 126 training examples, Figure 10 analyzes the performance of the methods under study on different sub-classifications of the ships: small vs large and moored or at sea. SEN12 MS-A and SEN12 MS-RA have a better recall than ImageNet or training from scratch by far. SEN12 MS pretraining is significantly better at detecting moored ships than other methods, including ImageNet.  
The weakness of ImageNet may come from the fact that it needs to fine-tune its weights further, because of the large domain difference that exists when transferring the knowledge learned on object-centric datasets to remote sensing images. Therefore, robust SSL methods give a good boost when having few training samples, unlike networks trained from scratch or pretrained on ImageNet.  
Figure 9: Impact of the number of training ships variation on mean F1 score calculated on Brest, Rotterdam, and Suez canal.
# Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight
## 5 Optimization Problem's Modeling and Formulation  
### Power Density Model  
Before the development of optimization analysis, three assumptions are made for the power density model [8]. Firstly, the energetic cost consumed by hovering insect only considers the time-averaged positive mechanical power output used to overcome aerodynamic damping and inertial power [54; 55; 56]. Secondly, the elastic storage can be recovered completely and thus be treated as a part of the negative power of the system [55; 56; 57]. Thirdly, the wing motions are powered by actuators located at the wing shoulder. Based on these assumptions and Euler equations of a rigid body, the power output from two DOF wing motions about angle \(i\) can be written as  
\[P_{i}\left(t\right)=\varXi\left\{\Omega_{i}\left[I_{i}\hat{\Omega}_{i}-M_{i}^{ \text{aero}}\right]\right\}, \tag{41}\]  
where \(\left[i,j\right]\) is a sequence of \(\left[\phi,\psi\right]\), \(I_{i}\) and \(\Omega_{i}\) are moment of inertia and angular velocity when rotating in angle of \(i\), respectively. The moments of inertia of dynamically scaled wing planform have been expressed in Section 2.3. \(\varXi\left\{\cdot\right\}\) is a predefined function with the value of \(1\) for positive internal variable and with the value of \(0\) for non-positive ones, which is adopted to harness the assumption for completely positive power consumption by taking no account of negative power stored in the system and recovered in the later stroke. \(M_{i}^{\text{aero}}\), which is the component of aerodynamic moment in \(\hat{\phi}\)- or \(\hat{\psi}\)-directions, is defined as  
\[M_{i}^{\text{aero}}=\begin{cases}3M_{\text{total},x}^{\text{pitch}}&i=\psi\\ 1M_{\text{s}}^{\text{stroke}}&i=\phi\end{cases}. \tag{42}\]  
Figure 6: Comparison between calculated results (dash lines) and experimentally measured results (solid lines). Normalized time history of normalized forces (**a**); and pitch torques (**b**). The black horizontal lines denote the zero value.  
The inertial power is also included in the later optimization problem [58]. Similarly, take the consistency with much of reported literatures and convenience of measuring optimization objective into consideration [8; 34; 56; 54], we present specific total power \(P^{*}\), i.e., power normalized to mass of insect body:  
\[P^{*}=\frac{\Xi\left(\overline{P}_{\phi}+\overline{P}_{\psi}\right)}{M_{\rm insect }}, \tag{43}\]  
where \(\overline{P}_{\phi}\) and \(\overline{P}_{\psi}\) separately include the aerodynamic power and inertial power (their derivation are detailed in Appendix A-C, respectively). Here, \(M_{\rm insect}\) is not limited to the body mass of the fruit fly (Table S1), and some other mass arguments can be chosen for the particular design of hovering FWMAV with demand of minimum energy consumption.
## 3 Results
### Electricity Potential  
Division-wise electricity potential from biogas in Bangladesh is illustrated in Figure 11. Among the eight divisions, Rangpur ranks highest in its electricity production potential, generating 4.04 million MWh/year from livestock manure, which could meet 59.97% of the total electricity demand in 2024. In contrast, the Chattopram division was estimated to produce about 3.24 million MWh/year of electricity from biogas but can only satisfy 17.93% of its electricity demand due to significantly high industrial electricity needs. Although the Barisal division displays relatively low electricity production potential, it can still replace more than 50% of its demand due to lower electricity demand in this region. Similarly, the Sylhet division exhibits low electricity potential, but its electricity coverage from biogas is comparatively higher.  
The spatial assessment of electricity generation reveals nearly identical distribution patterns with those of biogas production potential. Six districts (Dinajpur, Boggra, Naogano, Siraigani, Mymensingh, and Chittagong) fall into the very high category (806,548-984,928 MWh/year), while eight districts (Nilphamari, Rangpur, Gaibandah, Tangail, Pabna, Jessore, Comilla, and Patuakhali) are categorized as high (628,168-806,547 MWh/year) for electricity production (Figure 12a). From the upazila-wise biogas potential map, the western-northern part of the country displays significant electricity potential, ranging from 54,549 to 137,323 MWh each year (Figure 12b). However, the existing electricity coverage map (Figure 12c) indicates where electricity is needed for economic development. The upazila-wise analysis of electricity potential can provide insights into locations where the electricity demand could be met by bioenergy production from livestock manure.  
Figure 11: Division-wise electricity potential from biogas in Bangladesh: (**a**) electricity production from biogas; (**b**) replacement of electricity demand by biogas.
## 3 The Proposed Method
### The Constraints for Particularity Representation  
#### 3.2.1 The Label-Aware Constraint  
To obtain the particularity representation for the classification, we will assign the labels to the atoms of the class-specific dictionary, as presented in [7,19]. If an atom \(d^{t}\in D_{i}\,(i=1,\cdots,C)\), the label \(i\) will be assigned to the atom \(d^{t}\) and kept invariant in the iterations. We take \(\mathcal{S}_{i}\) as the index set for the atoms of \(i\)th class sub-dictionary \(D_{i}\), and \(\mathcal{S}\) as the index set for all the atoms of the class-specific dictionary. For the particularity representation of samples \(X_{i}\), it is desirable that the large coefficients should mainly occur on the atoms in \(\mathcal{S}_{i}\). In other words, the sub-profiles associated with the atoms in \(\mathcal{S}\backslash\mathcal{S}_{i}\) need to be suppressed to some extent.  
For the \(i\)th class samples, we construct a matrix \(P_{i}\in R^{K\times K}\) to pick up the sub-profiles from the representation \(Z_{i}^{\mathrm{T}}\), which locate at the atoms in \(\mathcal{S}\backslash\mathcal{S}_{i}\) rather than the atoms in \(\mathcal{S}_{i}\)\((i=1,\cdots,C)\), so that we can define a label-aware constraint term as follows.  
\[\|Z_{p}^{i\,\mathrm{T}}P_{i}^{\mathrm{T}}\|_{F}^{2}=\|P_{i}Z_{p}^{i}\|_{F}^{2}, \tag{8}\]  
and the matrix \(P_{i}\) can be written as  
\[P_{i}(t_{1},t_{2})=\begin{cases}1,&t_{1}=t_{2}\text{ and }t_{2}\in\mathcal{S} \backslash\mathcal{S}_{i};\\ 0,&\text{else},\end{cases} \tag{9}\]  
where \(P_{i}(t_{1},t_{2})\) is the \((t_{1},t_{2})\)th entry of matrix \(P_{i}\). For the particularity representation \(Z_{p}^{i}\), minimizing the label-aware constraint can suppress the large values in the sub-profiles associated with the atoms in \(\mathcal{S}\backslash\mathcal{S}_{i}\), as well as encourage the large ones in the sub-profiles associated with \(i\)th class atoms. Therefore, it is expected that this constraint with a proper scalar can make the particularity representation approximately sparse. Besides, once a series of matrices \(P_{i}\)\((i=1,\cdots,C)\) are constructed, they will be unchanged in the iterations. Thus it is very efficient for coding over the class-specific dictionary.