esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Knowledge
### Discrete Wavelet Transform  
Most SE methods employ short-time Fourier transform (STFT) to create a spectrogram for input signals and conduct de-noising on the spectrogram or its variants. However, as mentioned in previous sections, STFT is not necessarily an optimal choice for processing non-stationary signals such as speech. Comparatively, discrete wavelet transform (DWT) is one of the emerging technologies in signal processing in recent years. Relative to discrete Fourier transform (DFT), DWT can highlight some crucial information about a signal within a short-time period. We briefly review the discrete wavelet transform (DWT) in the following.  
Mathematically, the DWT of a signal \(f[n]\) is the outcome of passing \(f[n]\) through a series of filters. The resulting signals represent different frequency components of \(f[n]\). First, the signal \(f[n]\) is passed through two filters (called analysis filters) with impulse responses \(g[n]\) and \(h[n]\) in parallel to obtain two signals. The two filters are low-pass and high-pass, respectively, and are related to each other by  
\[h[n]=(-1)^{n}g[N-1-n], \tag{3}\]  
where \(N\) is the filter length, and Equation (3) shows that \(g[n]\) and \(h[n]\) are quadrature mirror filters to each other. Since the bandwidth of the signal \(f[n]\) is halved in the output signals of two filters, we can discard half the points according to Nyquist's rule. The outputs of filters \(h[n]\) and \(g[n]\) are then down-sampled by 2:  
\[f_{A_{1}}[n]=\sum_{k}f[k]g[2n-k], \tag{4}\]  
and  
\[f_{D_{1}}[n]=\sum_{k}f[k]h[2n-k]. \tag{5}\]  
Compared with the original signal \(f[n]\), the two sub-band signals \(f_{A_{1}}[n]\) and \(f_{D_{1}}[n]\) have a half time resolution but twice frequency resolution. In addition, \(f_{A_{1}}[n]\) and \(f_{D_{1}}[n]\) are usually termed the one-level approximation and detail parts of \(f[n]\), respectively, in wavelet analysis. The operation mentioned above is called one-level DWT, and we depict it in Figure 4.  
Figure 3: A schematic diagram of the DPTNet system.  
Moreover, we can expand a one-level DWT to multiple-level DWT by repeatedly employing sub-band filtering and factor-2 downsampling. In practice, an \(L\)-level DWT of \(f[n]\) is created by performing a one-level DWT as shown in Figure 5 on the approximate (low-frequency) part obtained from the preceding level (the \((L-1)\)-level) of DWT of \(f[n]\), where \(L>1\). In the multiple-level DWT, the decomposition, which contains low-pass and high-pass filtering together with down-sampling, is repeated to increase the frequency resolution further. Therefore, the ultimate sub-band signals exhibit a different time-frequency localization. Figure 6 depicts the flowchart of a three-level DWT.
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 3 Interferometric Data Processing  
This section describes the major processing steps in obtaining a DEM with InSAR technology, using the interferometric data from Gaofen-3 in the Dengfeng area, and provides the basis for evaluating the interferometric imaging and measurement capabilities of Gaofen-3 satellite data. Regarding the application, in order to reduce the errors in data processing as much as possible, this study focused  
Figure 1: Geographical coverage (Dengfeng area, China) of experimental dataset of the Gaofen-3 (GF3) satellite (**a**), single look complex intensity image from the GF-3 satellite in the Dengfeng area (**b**), and the corresponding distribution of elevation acquired by the Shuttle Radar Topography Mission (**c**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Master Image** & **Slave Image** \\ \hline Date & 29 November 2016 & 28 December 2016 \\ Mode & Fine strip I \\ Resolution & 3 m \\ Wave length & 5.56 cm (C band) \\ Path direction & Descending \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gaofen-3 satellite data of experimental area (Dengfeng area, China).  
on the interferometric image co-registration with a 0.01-pixel (equal to 3 cm) accuracy level and high-accuracy phase filtering methods to obtain higher accuracy for elevation measurements.
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 2 Formulas of the Proposed Approach
 \tag{22}\]  
Based on the principle of EKF, the gain matrix can be determined by the following equation:  
\[\frac{\partial\textit{tr}\Big{(}\mathbf{P}_{k+1|k+1}\Big{)}}{\partial\mathbf{ G}_{k+1}}~{}=~{}2\mathbf{G}_{k+1}\mathbf{\[PERSON]}\Big{(}\mathbf{H}_{k+1|k}\mathbf{P}_{k+1| k}\mathbf{H}_{k+1|k}^{\mathrm{T}}+\bar{\mathbf{R}}_{k+1}\Big{)}\mathbf{\Phi}^{ \mathrm{T}}-2\mathbf{P}_{k+1|k}\mathbf{H}_{k+1|k}^{\mathrm{T}}\mathbf{\Phi}^ {\mathrm{T}}~{}=~{}0 \tag{23}\]  
where _tr_(\(\cdot\)) denotes the trace operator. Then, one can obtain:  
\[\mathbf{G}_{k+1}~{}=~{}\mathbf{P}_{k+1|k}\mathbf{H}_{k+1|k}^{\mathrm{T}}\mathbf{ \Phi}^{\mathrm{T}}\Big{[}\mathbf{\Phi}\Big{(}\mathbf{H}_{k+1|k}\mathbf{P}_{k+ 1|k}\mathbf{H}_{k+1|k}^{\mathrm{T}}+\bar{\mathbf{R}}_{k+1}\Big{)}\mathbf{\Phi }^{\mathrm{T}}\Big{]}^{-1} \tag{24}\]  
Once the posteriori state estimate \(\hat{\mathbf{Z}}_{k+1|k+1}\) is determined through Equation (20), the unknown NRF can be determined by Equation (9) as follows:  
\[\mathbf{R}_{k+1}^{non}~{}=~{}\Big{(}\mathbf{D}^{\mathrm{T}}\mathbf{D}\Big{)}^ {-1}\mathbf{D}^{\mathrm{T}}\Big{[}\mathbf{h}\Big{(}\mathbf{Z}_{k+1|k+1}\Big{)} -\mathbf{y}_{k+1}\Big{]} \tag{25}\]  
It is clear that the recursive solution for the proposed approach includes priori estimation through Equations (12) and (19), posteriori estimation through Equations (20) and (22), gain matrix calculation by Equation (24), as well as NRF identification by Equation (25). The effectiveness and robustness of the proposed method will be verified via several numerical examples in the following section.
## 3 Results and Discussion
### Gas-Sensing Properties  
The gas detection performance of TiO\(\mathrm{x}^{\mathrm{ORC}}\) is shown in Figure 8. In ambient air, the resistance of the coating was above the measurement range of the used ohmmeter, which was related to the known high resistivity of titanium-based oxides. After the injection of the reducing gas, the resistance of the sample decreased. This nature of the sensor shows that the coating prepared in the ORC mode was characterised by n-type conductivity, which is characteristic of TiO\({}_{2}\)-based films [2,34]. Additional annealing at 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C led to an increased sensor response to H\({}_{2}\). The best effect was found after annealing at 400 \({}^{\circ}\)C. The use of a higher annealing temperature (600 \({}^{\circ}\)C) most probably resulted in an oxidation of the film that was too strong; additionally, as a result of the significant increase in its resistance (above the level of measurability in the used measurement system), changes due to the presence of hydrogen were no longer observed. An opposite effect was obtained for the second series of coatings prepared in the ODC mode of the GIMS process (Figure 9). The resistance of the TiO\(\mathrm{x}^{\mathrm{ODC}}\) film to exposure to ambient air was in the range of 0.01 to 0.15 M\(\Omega\). After the introduction of H\({}_{2}\), its resistance increased. Thus, we have demonstrated the p-type conductivity of the TiO\(\mathrm{x}^{\mathrm{ODC}}\) film, which was challenging. The p-type conductivity of Ti-based oxide coatings is usually achieved by doping (e.g., with Cr [34]). In the case of undoped TiOx films, similar results were observed by [PERSON] et al. [35] and [PERSON] et al. [36], but they were achieved for nanoparticles (synthesised by the hydrothermal method) or films based on nanoparticles (prepared by the sol-gel technique), respectively. To characterise the properties of the TiO\(\mathrm{x}^{\mathrm{ODC}}\) coating, the sensor response, the response time, and the recovery time were calculated. For TiO\(\mathrm{x}^{\mathrm{ORC}}\), these
# Modification of Nonlinear Controller for Asymmetric Marine Vehicles Moving in Horizontal Plane
## 3 Controller in Terms of Quasi-Velocities
### Differences Compared to the Original Trajectory-Tracking Algorithm
In the particular case, the relative velocities \((\xi_{1},\xi_{2},\xi_{3},\xi_{4})\) tend globally exponentially to the corresponding set velocities \((\xi_{1_{d}},\xi_{2_{d}},\xi_{3_{d}},\xi_{4})\), while \((z_{1},z_{2})\) are only globally ultimately bounded. In addition, the steady-state values \(\hat{V}_{x},\hat{V}_{x}\) of the integral variables provide estimates of the ocean current. _Comment_. Condition \(Y_{23}=0\) is necessary due to the fact that only partial diagonalization is needed to take advantage of the similarity to the original method. The quantities \(X_{1},X_{2},Y_{1},Y_{2}\) are determined from (A21). Moreover, the positive constants \(\underline{a},\underline{c},\underline{d}\) are defined in [38]. **Sketch of proof**. Consider the relationships shown in Section 2.3 and the results shown in Appendix A, in particular the revised values of \(X_{1},X_{2},Y_{1},Y_{2}\). Using the similarity to the results shown in [38], the proof of the modified theorem would be analogous.
## 3 Feature Extraction
### Multi-Resolution Analysis  
The MRA is the process that uses the scaling function and orthogonal wavelet function to decompose and reconstruct signals at various resolution levels. This process benefits for filtering the propitious information of input signals, so the resulting signals are easier to be further implemented and require less execution and memory [22]. In this work, we used the MRA for decomposing the PQD signals before constructing the feature vectors. The procedure of MRA can be described as follows; the continuous-time PQD signals are passed through the filtering system that consists of low-pass (LP) and high-pass (HP) filters at different resolution levels. The high-frequency component having the first detail coefficient (\(D1\)) was obtained by HP filtering the down-sampling signal. Likewise, a low-frequency component having the first approximation coefficient signal (\(A1\)) is obtained by LP filtering the down-sampling signal. Afterwards, the output of \(A1\) is decomposed before calculating the second detail coefficient (\(D2\)) and approximation coefficient (\(A2\)). This process was repeatedly operated until eight decomposition levels were achieved since it was confirmed in the previous study that this desired value is appropriate for extracting the characteristics of the PQD signals [53]. The feature vector of PQD signals, \(F(k)\), is then constructed from the obtained coefficients, as written in Equation (2)  
\[F(k)=\ \ [D_{1}\ \ \ D_{2}\ \ \ D_{3}\ \ \ D_{4}\ \ \ D_{5}\ \ \ D_{6}\ \ \ D_{7}\ \ \ D_{8}\ \ \ A_{8}] \tag{2}\]
## 2 Electromechanical Model
The frequency response function (FRF) is  
\[H(\omega)=\sum_{r=1}^{2}\frac{\chi_{r}\phi_{r}(x)}{\omega_{r}^{2}-\omega^{2}+ j2\zeta_{r}\omega_{r}\omega} \tag{10}\]  
The anti-resonance occurs when the contributions from the two modes cancel out:  
\[\sum_{r=1}^{2}\frac{\chi_{r}\phi_{r}(x)}{\omega_{a}^{2}-\omega_{r}^{2}}=0 \tag{11}\]  
At this anti-resonance frequency \(\omega_{a}\), destructive interference causes a dip in the response. This dynamic behaviour is essential for optimizing energy harvesting in slitted beams. In combining the above equations, the electromechanical model for the slitted beam piezoelectric energy harvester, including the coupling term, is  
\[ \begin{split} YI\frac{\partial^{4}w_{slit}(x,t)}{\partial x^{4}}+ c_{s}I\frac{\partial^{5}w_{slit}(x,t)}{\partial x^{4}\partial x^{4}}+c_{a} \frac{\partial w_{slit}(x,t)}{\partial t}+m\frac{\partial^{2}w_{slit}(x,t)}{ \partial t^{2}}+\theta_{eff}\frac{\partial V(t)}{\partial t}\delta(x)\\ =-m\frac{\partial w_{slit}(x,t)}{\partial t}\delta(x)-c_{a}\frac{ \partial w_{slit}(x,t)}{\partial t}-\frac{\partial^{2}w(x,t)}{\partial t^{2}} \end{split}  \tag{12}\]where \(YI\) is the bending stiffness of the beam, \(C_{s}I\) is the internal strain rate, \(C_{a}\) is the external air damping coefficient, \(m\) is the mass per unit length, \(\delta(x)\) is the Dirac delta function representing the location of the piezoelectric layer, and \(w_{b}\) is the motion of the beam carrying the piezoelectric layer. The voltage across the piezoelectric layer can be derived from the piezoelectric constitutive equations and the electromechanical coupling. For a piezoelectric cantilever beam with a tip mass, the voltage \(V(t)\) generated due to mechanical strain can be expressed as [48]  
\[V(t)=-R_{L}\int_{0}^{L}d_{31}Y_{p}h_{p}b_{p}\frac{\partial^{3}w_{rel}(x,t)}{ \partial x^{l}\partial t}\ dx-\frac{1}{e_{33}^{s}}\int_{0}^{L}\frac{\partial v( t)}{\partial t}b_{p}h_{p}\ dx \tag{13}\]  
where \(R_{L}\) is the load resistance, \(d_{31}\) is the piezoelectric strain constant, and \(e_{33}^{s}\) is the permittivity at constant strain. The current \(i(t)\) through the load resistance due to the voltage generated can be derived using [PERSON]'s law and the relationship between the voltage and the charge generated in the piezoelectric layer and is given by  
\[i(t)=\frac{v(t)}{R_{L}} \tag{14}\]  
The relative motion of the beam \(w_{rel}(x,t)\) can be described as a series of mode shapes and corresponding modal coordinates. This approach uses the orthonormal mode shapes of the beam, which are solutions to the homogeneous part of the differential equation representing the beam's movement, and is given by  
\[w_{rel}(x,t)=\sum_{r=1}^{\infty}\phi_{r}(x)\eta_{r}(t) \tag{15}\]  
where \(\eta_{r}(t)\) is the modal coordinate of a clamped-free beam for the \(r\)th mode, while \(\phi_{r}\) is the orthonormal mode shape of the free vibration of a cantilever beam, which is governed by the following equation [50].
# DMS-YOLOv5: A Decoupled Multi-Scale YOLOv5 Method for Small Object Detection
## 4 Experiments  
### Datasets  
For small object detection tasks, it is important to choose an appropriate dataset for training. Although the COCO dataset [9] and PASCAL VOC dataset [35] have significant advantages in terms of object categories and dataset size, they are not suitable for small object detection tasks because the proportion of small objects in these datasets is relatively small and their distribution is uneven.  
In this paper, we use the VisDrone2019-DET dataset [36] collected and produced by the AISKEYEYE team at Tianjin University as the dataset for small object detection. This dataset includes aerial images captured by various styles of unmanned aerial vehicles in different cities. Some sample images from the dataset are shown in Figure 10.  
The VisDrone2019-DET dataset is composed of 10 distinct object categories, which include pedestrians, people, bicycles, cars, vans, trucks, buses, tricycles, motorized vehicles, and motorcycles. In order to more effectively distinguish between object sizes, the dataset has classified objects into three distinct categories, namely small, medium, and large, based on the size of the pixel side length within the image. Objects with a pixel side length less than 32 pixels are classified as small, while objects with a pixel side length between 32 and 96 pixels are categorized as medium. Objects with a pixel side length greater than  
Figure 10: Sample images from the dataset.  
96 pixels are classified as large objects. Among these categories, small objects are the most prevalent in the dataset, accounting for up to 61% of the total objects. Medium and large objects account for 34% and 5% of the dataset, respectively. Upon analysis of the dataset, it can be observed that it is primarily composed of small objects, rendering it particularly well-suited for small object detection tasks. As such, this dataset holds high potential value in the development of accurate and efficient small object detection algorithms. The object count for each category is depicted in Figure 11.  
The VisDrone2019-DET dataset also has significant issues, one of which is the large discrepancy in the distribution of object categories. Specifically, the number of objects in the car and pedestrian categories is an order of magnitude higher than that in the truck, bus, and other categories. Such an imbalance in the number distribution may lead to a situation where the model's detection performance for categories with fewer objects is inferior to that for categories with more objects in some object detection tasks. The distribution of object numbers in each category in the VisDrone2019-DET dataset is shown in Figure 12.
###### Abstract  
The production of Portland cement (PC) is associated with carbon emissions. One-part geopolymer \"just add water\" is a user- and environmentally-friendly binder that can potentially substitute PC. However, there is limited research on the setting time, fresh, and strength properties of one-part metakaloin (MK)-based geopolymer concrete (OMGPC) incorporating recycled aggregates. Hence, the study explored the fresh, mechanical (compressive, flexural, splitting tensile, and E-modulus) and microstructural properties of ambient cured (7-, 28-, and 90-day) OMGPC containing recycled waste plastics (RESIN8) and recycled fine waste glass aggregate (FWG) at 5% and 10% by volume of the sand. The study result shows that 2% trisodium phosphate by wt. of the binder retard the initial and final setting times of OMGPC. At the same time, the incorporation of RESIN8 and FWG aggregates improved the workability of geopolymer concrete. The lightweight properties of RESIN8 aggregate reduce the hardened density of OMGPC, while the FWG specimens show a similar density to the control. The compressive strength of RESIN8 and FWG OMGPC range from 19.8 to 24.6 MPa and 26.9 to 30 MPa, respectively, compared to the control (26 to 28.9 MPa) at all curing ages. The flexural and splitting tensile strength of the OMGPC range from 2.2 to 4.5 MPa and 1.7 to 2.8 MPa, respectively. OMGPC is a viable alternative to Portland cement, and FWG can substitute sand in structural concrete by up to 10% and RESIN8 aggregate at 5% by volume of the natural sand.  
geopolymer concrete; fine waste glass; RESIN8; metakaloin; slag; mechanical properties +
Footnote †: journal: Applied Sciences
# Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
## 4 Experiments  
### Datasets and Evaluation Metrics  
Both of the datasets THUMOS'14 [40] and ActivityNet1.3 [41] can be used to evaluate multiple action-related tasks, including action detection. THUMOS'14 is the most widely used benchmark in the action detection works, and to the best of our knowledge, the ActivityNet1.3 is the largest existing dataset for action detection task. THUMOS'14 includes the training set, validation set, background set, and testing set. Training set and validation set consist of trimmed and untrimmed videos, respectively, with 20 action categories. Background set contains videos that do not include any action. Testing set includes untrimmed videos. Some of the videos might contain one or multiple action instances from one or multiple action categories. ActivityNet1.3 contains 19,994 untrimmed videos in training set, validation set and testing set, with 200 action categories.  
In the experiments, we perform the evaluation of binary and multi-class Res3D classifiers with the metric: segment-level recognition accuracy. For the action proposal evaluation, we follow the standard metrics in the temporal action proposal task of ActivityNet Challenge [41]: Average Recall vs. Average Number of Proposal per Video (AR-AN) Curve and Metric Score, which is the area under the resulting final curve. We port the official evaluation toolkit of ActivityNet to THUMOS'14. We also use the metric: Recall @ 1K proposal vs. tIoU (Recall@1K-tIoU) plot [26] to evaluate the action proposal. The action detection is evaluated using Mean Average Precision (mAP) at 0.5 tIoU. We use the official evaluation toolkit provided by THUMOS'14 challenge.
## 3 Results  
Exploration of the complete data collection distribution is partially detailed in Table 2, to gain insights into its characteristics. The distribution is visualized in Figure 1, while astatistical summary is provided in Table 3. The complete data set comprises an extensive collection of 157,135 feature combinations, a substantial quantity that has sparked interest in applying association rules to our specific case study. This large data set size further fuels our curiosity and motivates us to investigate the application of association rules in this context. The RMSE mean of all these quantities is 0.63, while the whole interval is defined by the minimum and maximum RMSE values of 0.55 and 0.66, respectively. Following the interpretation of Table 3, the mean of feature quantities of 6.54, the percentile of 25-100% of six and seven features, respectively, indicates that the majority of feature combinations contains between six and seven features.  
It is also important to analyze the dependence and independence relationships between variables. Therefore, correlation relations between all features (including the target \(BBCH\) and the mandatory input parameter \(DOY\)) are depicted in Figure 11, where two main highly correlated feature groupings appear: \(BBCH\), \(DOY\), \(mean\)\(temp\), \(max\)\(temp\), \(min\)\(temp\), and \(dewp\)\(temp\), most of them temperature-derived features. On the other hand, accumulative quantities (\(precip\)\(cum\), \(GDD\), \(NDVIcum\), \(EVIcum\), \(REDcum\) and \(NIRcum\)) are highly related between them, as well as to \(BBCH\) and \(DOY\) (both of them have an increasing trend), and slightly less correlated to temperature-derived features. Low correlation quantities are equally important, as a result of the independence between features.  
Figure 1: Complete data distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **RMSE** & **Feature Quantity** \\ \hline count & - & 157,135 \\ mean & 0.63 & 6.54 \\ std & 0.02 & 0.63 \\ min & 0.55 & 3 \\
25\% & 0.61 & 6 \\
50\% & 0.63 & 7 \\
75\% & 0.64 & 7 \\ max & 0.66 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of complete data collection.