esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Although urban expansion statistics have been widely carried out, large-scale and rapid monitoring is still worth doing in order to improve the efficiency of statistics, as well as make up for the omissions and deficiencies of construction expansion statistics with multi-year intervals. This paper presents a study of urban expansion in the Beijing-Tianjin-Hebei plain based on ALOS Global Digital Surface Model \"ALOS World 3D-30 m\" (AW3D30 DSM), Shuttle Radar Topography Mission (SRTM) DSM, and Landsat 7 ETM+ images. Through the evaluation of errors and the elimination of non-building changes, a relatively objective result is derived. The neighborhood block statistics of the construction height expansion reveal that from 2000 to 2009, the largest centralized construction expansion mainly occurred between the Second Ring Road and the Fifth Ring Road of Beijing, followed by Yizhuang, Shunyi, Tianjin Central City, and Langfang. Zonal statistics also show a significant imbalance in the expansion of construction in the counties of the Beijing-Tianjin-Hebei plain. For example, Chaoyang, Dongcheng, Xicheng, Xuanwu, Chongwen, Nankai, Heping, and Hexi have a larger construction expansion; however, other counties present a relatively slow rate of building expansion. Furthermore, the correlation coefficient between the statistical average building height expansion per unit area (ABHE, by our method) and the actual average completed building floor space per unit area (ACBFS) derived from the Beijing Statistical Yearbook (BSY) is 0.9436, which proves that this method is feasible. With the continuous improvement of DSM data quality in the future, the method proposed in this paper can provide rapid and large-scale statistics to study more urban construction expansion in the world.  
three-dimensional urban expansion; building height; AW3D30 DSM; SRTM DSM; Beijing-Tianjin-Hebei plain +
Footnote †: journal: Applied Sciences
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 9 Analysis and Discussion
### Impact on VANETs Applications  
We analyze the impact of location privacy scheme on the vehicular network applications that may be safety-related and comfort applications. Achieving a higher level of privacy certainly will need to reduce its impact on road network applications. However, dummy data or wrong information is disseminated in the network for anonymizing a vehicle, which significantly reduces the application services of the vehicular network. Supposing inaccurate information is circulated, how would the vehicles efficiently utilize the applications of VANETs? The existing scheme IndMZ [24] takes the help of fake pseudonyms to anonymize the vehicles. This scheme falls into two problems. The first one is the computation burden on the vehicles. Secondly, it affects safety applications. The generation of fake pseudonyms for anonymisation reduces the application service quality. A large number of fake pseudonyms may also suffer communication among vehicles. The TAPCS [25] scheme is based on the radio silence period to preserve vehicles' location privacy. The use of silence periods during communication networks such as in VANETs is dangerous for vehicles' safety. During silent modes, mobile nodes do not broadcast road network information such as emergency, accident, the danger of lane changing, etc., and how the other vehicles will know about the road status information at a certain period. Safety application is critical, and delays in information dissemination compromise driver safety on the road. Thus, the TAPCS has a higher impact on road safety applications than the proposed scheme CMC. There is no concept of a silent period and generation of fake pseudonyms in our scheme CMC that impact road safety and comfort application. CMC disseminates road status information on a timely basis and utilizes road context information for vehicle anonymisation. CMC reduces privacy impact on safety applications compared with IndMZ and TAPCS.
## 3 Results and Discussion
### Physical Characterization  
Film morphology and uniformity has a significant impact on the performance of the OTFT devices, where uniform films exhibit low sheet resistance, which is highly desirable. Figure 4a-d display AFM images of the surface topography of the C\({}_{60}\) OTFT channel layer, graphene:PEDOT:PSS, rGO:PEDOT:PSS, and hydrogenated graphene: PEDOT:PSS, on a 50 \(\upmu\)m \(\times\) 50 \(\upmu\)m area, respectively. The AFM image reveals that the spin-coated C\({}_{60}\) has a smooth surface with a spot-like crystalline morphology with an RMS roughness (R\({}_{\rm d}\)) of 6.67 nm, and mean roughness (R\({}_{\rm a}\)) of 5.33 nm. Whereas graphene: PEDOT:PSS, rGO:PEDOT:PSS, and hydrogenated graphene:PEDOT:PSS have an RMS roughness of 7.77 nm, 7.28 nm, and 11.4 nm, and R\({}_{\rm a}\) of 5.68 nm, 4.24 nm, and 8.46 nm, respectively.  
The conductive/sensing layers show a relatively smooth surface with apparent peaks corresponding to the doping agent flakes (Gr, rGO, and hydrogenated graphene), indicating that PEDOT:PSS results in smooth conductive layers.  
Figure 4: (**a–d**) AFM 2D and 3D surface topology images of (**a**) C60, (**b**) graphene:PEDOT:PSS, (**c**) rGO:PEDOT:PSS, (**d**) Hydrogenated graphene:PEDOT:PSS, (**e**) SEM cross sectional images of spin-coated C\({}_{60}\) layer.  
Figure 3: Characterization results of hydrogenated graphene vs. pristine graphene: (**a**) XPS C 1s. (**b**) FTIR Spectra.  
The thickness of the OTFT individual structural layers was determined using SEM, as shown in Figure 4e, which displays the C\({}_{60}\) channel layer deposited on the SiO\({}_{2}\)/Si substrate. The thicknesses of the other structural layers was estimated using the same approach. The measured OTFT structural layers thicknesses were 120 nm, 125 nm, 200 nm, 1000 nm, and 500 nm for C\({}_{60}\), PVA, PMMA, PEDOT:PSS, and Gr:PEDOT:PSS, respectively.  
The thicknesses of the spin-coated rGO:PEDOT:PSS and hydrogenated graphene: PEDOT:PSS films were both measured at ~500 nm.
# Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues
## 2 Proposed Methodology  
The proposed architecture addresses the task of text-to-image matching. This task aims to retrieve the matching image/sentence that resides in a training set \(\mathcal{D}\) prepared offline.  
Let us assume a training set \(\mathcal{D}=\{X_{i},Y_{i}\}_{i=1}^{N}\) consisting of \(N\) images alongside their respective sentences. In the test phase, given a query sentence \(t_{q}\), we aim to retrieve the most relevant image from the training set \(\mathcal{D}\) (Figure 1a). On the side, in the image-to-text retrieval scenario, however, a query image is presented at the text-to-image matching model to retrieve the most likely textual description (Figure 1b). Figure 2 gives the detailed architecture of the proposed method, which is divided into two branches for learning appropriate image and text embedding, i.e., \(f(X_{i})\) and \(g(T_{i})\). Further details are provided in the subsequent sections.  
Figure 1: Bidirectional text-image retrieval: (**a**) text-image retrieval; (**b**) image-text retrieval.
# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Feature Extraction  
In total, 1376 features were extracted from all of the binaural audio excerpts. All the features could be divided into two categories: spatial and spectro-temporal. An overview of the extracted metrics is presented in Table 3. Regardless of the category, the features were extracted directly from the left and right channel signals--denoted as \(x\) and \(y\), respectively--as well as from the mid (\(m\)) and side (\(s\)) signals, where \(m=x+y\) and \(s=x-y\). Prior to calculating the features, the signals were divided into 20 ms time frames with a 10 ms overlap. A Hamming window was applied to each frame. Details of the extracted features are described in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Acronym** & **Description** & **Dummy Head** & **RT60 (s)** \\ \hline
1 & sbs & Salford, British Broadcasting Corporation & B\&K HATS Type & 0.27 \\  & & (BBC)–Listening Room [36] & 4100 & 0.27 \\
2 & mair & Huddersfield–Concert Hall [26] & Neumann KU100 & 2.1 \\
3 & thkcr1 & Westdeutscher Rundfunk (WDR) Broadcast & Neumann KU100 & 0.23 \\  & Studios–Control Room 1 [37] & & \\
4 & thkcr7 & WDR Broadcast Studios–Control Room 7 [37] & Neumann KU100 & 0.27 \\  & & WDR Broadcast Studios–Small Broadcast & Neumann KU100 & 1.0 \\
5 & thksbs & Studio (SBS) [37] & Neumann KU100 & 1.8 \\
6 & thklbs & WDR Broadcast Studios–Large Broadcast & Neumann KU100 & 1.8 \\  & & Studio (LBS) [37] & & \\
7 & calypso & Technische Universität (TU) Berlin–Calyposo & KEMAR 45 BA & 0.17 \\  & & Room [38] & & \\
8 & tvtui & TU Ilmenau–TV Studio (distance of 3.5m) [40] & KEMAR 45 BA & 0.7 \\
9 & tvtui2 & TU Ilmenau–TV Studio (distance of 2m) [41] & KEMAR 45 BA & 0.7 \\
10 & labtui & TU Ilmenau–Listening Laboratory [41] & KEMAR 45 BA & 0.3 \\
11 & rehabtui & TU Ilmenau–Rehabilitation Laboratory [41] & KEMAR 45 BA & NA \\
12 & tuburo250 & University of Rostock–Audio Laboratory & KEMAR 45 BA & 0.25 \\  & & & & \\
13 & tuburo310 & University of Rostock–Audio Laboratory (all & & \\  & & broadband absorbers) [42] & KEMAR 45 BA & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the binaural-room-impulse-response (BRIR) sets used in the study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Spatial Features**} & \multicolumn{2}{c}{**Spectro-Temporal Features**} \\ \hline
**Feature Acronym** & Root Mean Square (RMS) & Binaural Cues & Spectral Features &
\begin{tabular}{c} Mel-Frequency Cepstral \\ Coefficient (MFCC) \\ \end{tabular} \\ \hline
**Number of** **Features** & 8 & 504 & 224 & 640 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of the extracted features (1376 metrics in total).  
Figure 2: Examples of an intended placement of 20 foreground audio objects for: (**a**) \(FB\) scene; (**b**) \(BF\) scene; (**c**) \(FF\) scene.
# Mineralogical Transformations in Granitoids during Heating at Fire-Related Temperatures
## 3 Results
### Chemical and Mineralogical Composition
The peak A at approximately 709 eV of binding energy corresponds to Fe\({}^{2+}\) and the peak at approximately 711 eV to the oxidized form Fe\({}^{3+}\)[38] (Figure 5). The clearest way to confirm oxidation is the relationship between the A/B peak ratio (Fe\({}^{2+}\)/Fe\({}^{3+}\) compounds). If oxidation takes place, Fe\({}^{3+}\) should increase after heating and thus, Fe\({}^{2+}\) and the relationship Fe\({}^{2+}\)/Fe\({}^{3+}\) should decrease. Figure 5 has been normalized in the y-axis to better compare the iron behaviour and the differences between granitoids. In all of them, independently of the initial proportion Fe\({}^{2+}\)/Fe\({}^{3+}\), the heating produced a clear increase in Fe3+ curve in relation to the Fe\({}^{2+}\) one, as an indicator of an oxidation. GS showed a strong oxidation state at the fresh sample due to its higher initial alteration and enhanced after the temperature increase. ER, with a predominance of Fe\({}^{2+}\) at the fresh state, narrowed to the oxidized state \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **MgO** & **Al2O3** & **SiO2** & **K2O** & **CaO** & **TiO2** & **MnO** & **Fe2O3** \\ \hline \multirow{2}{*}{ER} & Q & & 2.78 & \(-\)3.74 & 0.39 & 0.56 & & & \\  & AF & 10.82 & \(-\)0.04 & 11.78 & \(-\)1.13 & & 0.48 & 0.06 & 1.6 \\  & B & & \(-\)0.02 & 1.87 & \(-\)0.44 & & \(-\)1.02 & 0.05 & \(-\)0.45 \\ \hline \multirow{3}{*}{G} & Q & & \(-\)1 & \(-\)0.47 & 0.24 & \(-\)0.02 & \(-\)0.08 & & \\  & AF & & \(-\)2.53 & \(-\)3.5 & 6.81 & \(-\)0.78 & 0.14 & & \(-\)0.14 \\  & B & & 1.35 & \(-\)6.45 & \(-\)1.19 & & 0.51 & 0.09 & 5.71 \\ \hline \multirow{3}{*}{GS} & Q & & 5.43 & \(-\)8.58 & 3.14 & & & & \\  & AF & & \(-\)0.34 & \(-\)0.84 & 1.8 & \(-\)0.57 & & & \\  & B & 15.26 & 4.2 & 12.8 & 1.01 & & \(-\)0.53 & \(-\)0.02 & \(-\)1.87 \\ \hline \multirow{3}{*}{NG} & Ms & & 0.18 & \(-\)0.23 & 0.19 & 0.04 & \(-\)0.08 & & \(-\)0.11 \\  & B & 3.57 & 0.48 & 2.56 & \(-\)0.07 & 13.49 & \(-\)0.19 & \(-\)0.02 & \(-\)0.38 \\ \cline{1-1}  & AF & & \(-\)1.01 & 0.66 & \(-\)0.25 & 0.67 & 0 & \(-\)0.08 & \(-\)0.13 & \(-\)2.37 \\ \cline{1-1}  & Ti & & 0.02 & 13.03 & & \(-\)7.14 & 9.81 & & \(-\)0.21 \\ \hline \end{tabular} Q: quartz; AF: alkali feldspar; P: plagioclase; B: biotite; Ms: muscovite; Ti: titanite.  
\end{table} 
Table 2: Absolute difference between heated and fresh stones. Red colours indicated a loss and green ones a gain. after heating. G and NG showed a closer proportion of both types of iron, although the proportion of Fe\({}^{3+}\)/Fe\({}^{2+}\) was higher after heating in both cases. Figure 4: Thermal XRD analysis of the four granitoids at temperatures increasing progressively from 25 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C and cooled down at the end to 25 \({}^{\circ}\)C. Q: quartz; AF: alkali feldspar; P: plagioclase; M: mica; H: hornblende.
## 3 Design Methodology of the Ideal Class-E PA with FDI
### Maple(tm) Implementation of Approach A and B  
This section describes briefly the Maple(tm) script used to design the PAs with the approaches A and B of the proposed methodology. The script was divided into segments. In the first segment, the PA model variables and its design set were defined as a function of the \(D\) and \(q\) variables while using the script summarized in Appendices A.1 and A.2 (described in Section 2.3). In the next segment, the input parameters and extended design set were defined according to the results of steps 1 to 3 of the proposed methodology. Subsequently, if the variables \(D\) and/or \(q\) are unknown, then a dedicated segment plots the search space (3D or 2D), which are required in step 4 of the methodology. In the next segment,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Approach A** & **Approach B** &
\begin{tabular}{c} **Optimization** \\ **Effect * [\%]** \\ \end{tabular} \\ \hline \(V_{p}\) [V] & 17.02 & 26.42 & 55.20 \\ \(V_{p_{m}}\) [V] & 2.00 & 0.08 & 96.12 \\ \(I_{p}\) [A] & 4.45 & 4.30 & 3.37 \\ \(I_{p_{m}}\) [A] & 189.00 & 6.72 & 96.45 \\ \(P_{im}\) [W] & 8.81 & 10.50 & 19.23 \\ \(P_{out}\) [W] & 8.68 & 10.43 & 20.18 \\ \(\eta\) & 0.99 & 0.99 & 0.79 \\ \(C_{p}\) & 0.0027 & 0.0588 & 2078.94 \\ \hline \multicolumn{3}{c}{* Optimization effect \(=\frac{|Approach A-Approach B|}{Approach A}\cdot 100\).} \\ \end{tabular}
\end{table}
Table 6: Simulated circuit parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Design Values** & **Commercial Values** \\ \hline \(R_{L}\) [\(\Omega\)] & 3.95 & 3.90 \\ \(L_{SH}\) [uH] & 7.51 & 7.50 \\ \(C_{SH}\) [nF] & 101.74 & 100.00 \\ \(C_{e}\) [nF] & 102.36 & 100.00 \\ \(V_{Cs_{\text{int}}}\) [V] & 24.37 & NA \\ \(D\) & 0.62 & 0.62 \\ \(q\) & 1.821 & 1.821 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Circuit components and restriction values of approach B.  
Figure 9: Waveforms of the Class-E PA designed in approach B. (a) \(v_{Cs_{H}}\). (b) \(i_{S}\).  
the script calculates the circuit components from the chosen values of \(D\) and \(q\). In the last segment, the script plots the main circuit waveforms, which are used to compare the model estimation with the simulated values. This script was summarized in Appendix B, and it could be adapted with little modifications to the specific requirements of a particular application. For instance, we use a modified script in order to develop the amplifier designs that are reported in the next section. Additionally, as a support tool, we developed a basic code to start a new design following the proposed methodology (i.e., supplementary materials BasicCode.mw file in the support tools).
# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 4 Experiment
### Comparison of Results of Different Segmentation Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Specificity** & **Sensitivity** & **Dice** & **BLE** \\ \hline Vessel Bend [43] & - & - & - & 0.7700/0.2000 & 30.510/24.800 \\ Superpixel [44] & - & - & - & 0.0800/0.1400 & 22.040/12.570 \\ Multiview [45] & - & - & - & 0.7900/0.1800 & 25.280/18.000 \\ U-Net [24] & 0.9700/ - & 0.9800/ - & **0.9600**/ - & 0.8500/0.100 & 19.530/13.980 \\ BCRF [46] & - & - & - & 0.8300/0.1500 & 18.610/13.020 \\ [PERSON] et al. [47] & - & - & - & 0.8260/ - & 21.980/ - \\ [PERSON] et al. [49] & 0.9712/ - & 0.9758/ - & 0.8598/ - & - & - \\ [PERSON] et al. [50] & - & - & - & 0.8110/ - & 23.335/ - \\ MPAR (ours) & **0.9950**/**0.0022** & **0.9980**/**0.0020** & 0.9365/ 0.0639 & **0.8921**/**0.0793** & **14.342**/**10.169** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the OC results of different baseline methods on the Drishti-GS1 dataset. The segmentation of retinal blood vessels is challenging because retinal blood vessels are too small and diverse in shape. In order to verify the effectiveness of the method in this paper, we carry out a push verification on the DRIVE dataset and compare it with several recent methods, as shown in Table 9. The accuracy of the vascular segmentation and F1 score of the method in this paper reached optimal values, which were 0.9642 and 0.8275, respectively. The specificity is equal to that of the method in [52], and the sensitivity is only 0.0109 smaller than that of the method in [53], but 0.0747 more than that of the method in [54]. The segmentation effect is shown in Figure 11. This article enlarges part of the peripheral blood vessels. It can be seen that the method in this paper is more accurate than the method in [11; 53] in segmenting small blood vessels and can effectively reduce the problem of small blood vessel rupture. In summary, the MPAR model in this paper can detect objects with variable sizes better than other methods.
## 2 Materials and Methods
### Methodology
#### 2.3.3 ITS in Leaf-On Conditions
We set the condition that the core cluster would be selected as the right tree from the initial tree points by the seed points based ITS method (Equation (1), Figure 7b). The other clusters and the noise points instead of the core cluster are given a new tree label in the 3D space based on the kNN (k-Nearest Neighbor classifier) classification algorithm. An iterative method according to the different values of _eps_ (0.4, 0.55, 0.7) in the DBSCAN method was applied to improve the new segmentation results k from the kNN classification (_k value_ choose 5 points). Each point was identified several times to determine  
Figure 6: Illustration of the two types of seed points using plot Y8-1 as an example. (**a**) The cluster created by the Mean Shift algorithm based on the LiDAR data from the leaf-off season; (**b**) the treetops of each tree cluster from the red rectangle from (**a**); (**c**) the reference tree positions (red), the Mean Shift center points (blue), and the treetop points (green) in the data view of the leaf-on season. whether to change its tree label. Finally, a result with higher segmentation accuracy was determined (Figure 7c). \[Tree=\left\{ \begin{array}{l}Ni>0.3\times N\\ Ai>0.3\times A\\ Si=S\_min\end{array} \right. \tag{1}\]  
where \(Tree\) is the core cluster from the initial individual tree points, \(Ni\) is the number of points of cluster \(i\), \(Ai\) is the crown area of cluster \(i\), \(Si\) is the crown shape index of cluster \(i\), \(N\) is the total number of the initial individual tree points, \(A\) is the total area of all clusters of the initial individual tree points, and \(S\_min\) is the smallest shape index among all clusters of the initial individual tree points. To evaluate the improvement of the segmentation results from a data perspective, we manually segmented 15 trees in sample plots of each stand age. Two quantitative indicators were extracted, including the approximate horizontal projected area of the canopy crown (using the product of \(x\) and \(y\)), and the vertical range of the tree (using the range of \(z\)). The results of manual segmentation are compared with segmentation with the refining process and without the refining process based on these two indicators (Figure 8). Figure 8: Evaluation parameters to quantify the process of refining results. The left part is the sample plot used for example (Y16-2). The right part is an individual tree example extracted manually (**a**), segmentation with the refining process (**b**), and without the refining process (**c**). The values \(x\) and \(y\) are the two sides of the horizontal projection of the tree, and \(z\) is the vertical range of the tree. Figure 7: The process of refining segmentation results using plot Y16-2 as an example. (**a**) The initial segmentation results using the Seeds + Silva2016 method; (**b**) core clusters (colored) and other points (black); (**c**) the final segmentation results after improving accuracy using the novel strategy proposed in this study.
###### Abstract  
Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO-GWO-LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features--time, energy, similarity, and second-order features--are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R\({}^{2}\)) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability.  
Keywords:lithium-ion batteries; state of health; features extraction; particle swarm optimization; grey wolf optimization; least squares support vector machine +
Footnote †: journal: Applied Sciences