esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Considering the increasing demand for titania nanoparticles with controlled quality for various applications, the present work reports the up-scalable synthesis of size-controlled titanium dioxide nanocrystals with a simple and convenient thermal treatment route. Titanium dioxide nanocrystals with tetragonal structure were synthesized directly from an aqueous solution containing titanium (IV) isopropoxide as the main reactant, polyvinyl pyrrolidone (PVP) as the capping agent, and deionized water as a solvent. With the elimination of the drying process in a thermal treatment method, an attempt was made to decrease the synthesis time. The mixture directly underwent calcination to form titanium dioxide (TiO\({}_{2}\)) nanocrystalline powder, which was confirmed by FT-IR, energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) analysis. The control over the size and optical properties of nanocrystals was achieved via variation in calcination temperatures. The obtained average sizes from XRD spectra and transmission electron microscopy (TEM) images showed exponential variation with increasing calcination temperature. The optical properties showed a decrease in the band gap energy with increasing calcination temperature due to the enlargement of the nanoparticle size. These results prove that direct calcination of reactant solution is a convenient thermal treatment route for the potential large-scale production of size-controlled Titania nanoparticles.  
titanium dioxide (TiO\({}_{2}\)) nanoparticles; thermal treatment; X-ray diffraction (XRD); transmission electron microscopy (TEM)
## 4 Marker Tracking
### Marker Tracking with Improved Kalman Filter
#### 4.2.2 Improved Adaptive Kalman Filter  
According to the body structure and movement model of the fish, it can be seen that, if the markers are attached to the fish, the swing amplitude of the fish side line is the largest, and the upper and lower areas of the line position are consistent with the movement frequency, and the fish body is symmetrical with respect to the dorsal fin. If the points are symmetrical, then their relative position does not change. Therefore, there is a constraint relationship between the position of the markers.  
The number of vertebrae of Chinese Cypriniformes is 30 to 52, with an average of \(39.5\pm 4.4\). Assuming that the maximal angle  
where \(\tilde{M}_{\text{kjk-1}}\) the is the estimation of the overall movement of fish and is the estimated coefficient of the overall motion.  
Figure 9 shows the spatial position and the position after Kalman filtering of a marker in the live fish experiment, where the blue one indicates the measurement position and green one indicates the position processed with adaptive Kalman filtering.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Long-Range Communications Technology
#### 5.3.2 5G-NR Technology: Evolution of C-V2X Technology  
The aim of the 5G-NR standard, developed by 3 GPP, is to provide higher data rates, lower latency and enable communication between multitude of devices [171; 172]. These requirements correspond to three different use cases. First, there is the massive machine type communication (mMTC), which requires low bandwidth, low energy consumption and high connection density to connect a large number of sensors [173]. Second, there is the enhanced mobile broadband (eMBB) for applications requiring extremely high data rates [174]. Third, there is the Ultra Reliable Low Latency Communication (uRLLC) required for applications that require extremely low delays along with low error rate [31; 165; 175]. The 5G-NR offers interoperability with the C-V2X technology and, as a result, vehicles in a given geographical region can access both technologies. The gains provided by densification of remote radio heads in cloud radio networks and radio head configuration schemes can further contribute towards ubiquitous coverage [128; 176]. The comparison between the two technologies is given in Table 7.
## 2 Test Setup
### Instrumentation Details  
A total of 2 tri-axial and 6 uni-axial piezoelectric type accelerometers, resulting in 12 measurement points, were used to capture the dynamic responses of the beams. The amplitude range, frequency bandwidth, and sensitivity of the accelerometers were \(\pm\)5 g, 0.25-3000 Hz, and 1000 mV/g, respectively. The data acquisition system employed was an NI-PXI system, which included a PXI-4472 A/D card having simultaneous sampling capability and anti-aliasing filters. The layout of the accelerometers, with their positive polarities, is presented in Figure 3a. At this stage, the mode shapes of a preliminary numerical model were analyzed, with the primary motivation of capturing as many dynamic responses as possible. In Figure 3a, Sta-1 and Sta-5 (where 'Sta' denotes accelerometer stations) each contains one tri-axial accelerometer, whereas Sta-2, Sta-3, and Sta-4 each has two uni-axial accelerometers placed in the vertical and horizontal (i.e., perpendicular to the beam axis) directions. Additionally, 3 Linear Variable Displacement Transducers (LVDTs), with a capacity of 100 mm, were used to measure the static responses during the quasi-static tests. Two of the transducers (i.e., one at each support) were positioned to monitor vertical movements at the supports, while the third transducer was employed to measure and cross-check the midspan displacement of the beam, which was imposed by a load cell with a pressure capacity of 300 kN. A four-point bending test setup was adopted in order to create a region in the center of the beam free from shear forces, allowing the experimental variables to be studied under the influence of bending moments alone. Since for an a/d (i.e., shear span/effective depth) ratio greater than approximately 6, the beam will fail in flexure before the formation of inclined shear cracks, and given that the main objective was to investigate the flexural response, the a/d ratio was set at 6.3, with a and d being 1350 mm and 215 mm, respectively. A schematic presentation of the static test system and the general view of the test setup are shown in Figure 3b,c.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Glass Fiber** & **Carbon Fiber** \\ \hline Weight & 330 g/m\({}^{2}\) & 330 g/m\({}^{2}\) \\ Poisson’s ratio & 0.21 & 0.30 \\ Shear modulus & 30 GPa & 45 GPa \\ Tensile strength & 1950 MPa & 4000 MPa \\ Young modulus & 72 GPa & 230 GPa \\ Weft direction fiber & 283 g/m\({}^{2}\) & 290 g/m\({}^{2}\) \\ Warp direction fiber & 37 g/m\({}^{2}\) & 35 g/m\({}^{2}\) \\ Sewing & 10 g/m\({}^{2}\) & 5 g/m\({}^{2}\) \\ Weaving type & Uni-directional & Uni-directional \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical and physical properties of the strengthening materials.
## 5 Conclusions and Perspectives  
This study conducted data preprocessing on the collected data to effectively utilize temperature rise features, along with column and vehicle temperature rise difference features for axle temperature alarms. It also integrated the existing method of axle temperature alarm-level discrimination based on these features. Addressing the challenges posed by the infrequent occurrence and difficulty in obtaining data related to intense heat axle temperature states in trains, which results in a scarcity of intense heat axle temperature feature data and hinders the training of a generalized axle temperature alarm-level discrimination model, this study developed an optimized generative adversarial network (GAN) for simulating the generation of limited intense heat feature data. Subsequently, the generated intense heat feature data were integrated with existing intense heat, mild heat, and normal feature data to generate sequence feature data for the warning-level discrimination model. Finally, using a transformer block, the alarm-level discrimination model enhanced the loss function by introducing cross-entropy loss based on the axle temperature states. The present study aims to provide comprehensive training, validation, prediction, and in-depth analysis on hyperparameter design, loss function optimization, and model integration.  
Conceptualization, W.L. and K.X.; methodology, W.L.; software, W.L.; validation, W.L. and K.H.; formal analysis, K.X. and J.Z.; investigation, W.L.; resources, F.M. and L.C.; data curation, K.H.; writing--original draft preparation, W.L.; writing--review and editing, K.X.; visualization, W.L.; supervision, K.X. and J.Z.; project administration, K.X. and J.Z.; funding acquisition, K.X. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Science and Technology Commission \"Belt and Road\" China-Laos Railway Project International Joint Laboratory (No. 21210750300).  
Not applicable.  
The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author/s.  
We would like to thank the editor and the anonymous referees for their valuable comments and suggestions that greatly improved the presentation of this work. This work was supported by various funding sources, as detailed in the funding section.  
The authors declare no conflicts of interest.
# Adhesion Behavior between Multilayer Graphene and Semiconductor Substrates
###### Abstract  
A high bonding strength between graphene and a semiconductor surface is significant to the performance of graphene-based Micro-Electro Mechanical Systems/Nano-Electro Mechanical Systems (MEMS/NEMS) devices. In this paper, by applying a series of constant vertical upward velocities (V\({}_{\mathrm{up}}\)) to the topmost layer of graphene, the exfoliation processes of multilayer graphene (one to ten layers) from an Si semiconductor substrate were simulated using the molecular dynamics method, and the bonding strength was calculated. The critical exfoliation velocities, adhesion forces, and adhesion energies to exfoliate graphene were obtained. In a system where the number of graphene layers is two or three, there are two critical exfoliation velocities. Graphene cannot be exfoliated when the V\({}_{\mathrm{up}}\) is lower than the first critical velocity, although the total number of graphene layers can be exfoliated when the V\({}_{\mathrm{up}}\) is in the range between the first critical velocity and second critical velocity. Only the topmost layer can be exfoliated to be free from the Si surface if the applied V\({}_{\mathrm{up}}\) is greater than the second critical velocity. In systems where the number of graphene layers is four to ten, only the topmost layer can be free and exfoliated if the exfoliation velocity is greater than the critical velocity. It was found that a relatively low applied V\({}_{\mathrm{up}}\) resulted in entire graphene layers peeling off from the substrate. The adhesion forces of one-layer to ten-layer graphene systems were in the range of 25.04 nN-74.75 nN, and the adhesion energy levels were in the range of 73.5 mJ/m\({}^{2}\)-188.45 mJ/m\({}^{2}\). These values are consistent with previous experimental results, indicating a reliable bond strength between graphene and Si semiconductor surfaces.  
exfoliation behavior; bonding mechanism; graphene; adhesion force; adhesion energy; MEMS/NEMS; molecular dynamics +
Footnote †: journal: Applied Physics Letters
# Learning Effective Feature Representation against User Privacy Protection on Social Networks
## 5 Experiments
### Experimental Results
While disconnected components in the original privacy-protected network worsen the quality of embeddings, our model can successfully alleviate the sparsity problem and generate better node embeddings. Because the learned node embeddings can be essentially treated as node feature vectors in a latent form, such visualization results can explain that the proposed SAEM can better transform the original social network data to effective feature representations, in which nodes with the same labels are close to one another. Figure 3: Results of Area Under Curve (AUC) scores on link prediction for three privacy settings and three datasets. **Hyperparameter Analysis. ** By varying \(k\) and \(\alpha\) that affect the effectiveness of SAEM, we show the results of hyperparameter sensitivity in Figure 6. There are two findings. First, the outcome informs us that under any fixed \(k\), the value of \(\alpha\) does not affect the performance of NC tasks. Second, choosing the most relevant nodes based on attribute-based similarity by \(k\) in Equation (2) has a crucial impact on the performance. The results indicate that selecting only a few highly similar nodes (i.e., smaller \(k\)) is sufficient to create useful ghost links when constructing SAEM. In practice, we would suggest the data scientists to choose smaller \(k\), which can select nodes with the most similar attributes to mitigate the sparsity of graph structure. Larger \(k\) may make irrelevant nodes to be connected with one another. Besides, as for choosing the value of \(\alpha\) that is in charge of enhancing the data incompleteness, we think the SAEM should blend graph structure and node attributes together. Hence, we would suggest the data scientists to choose \(\alpha\) to be around 0.5 to avoid the risk of extremely sparse graph structure and drastically incomplete user attributes. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Cora**} & \multicolumn{3}{c}{**Citeseer**} \\ \hline  & & **Random** & **Partial (0.5)** & **Invisible** & **Random** & **Partial (0.5)** & **Invisible** \\ \hline \multirow{2}{*}{CD} & n2v & 0.422 & 0.422 & 0.397 & 0.391 & 0.384 & 0.390 \\  & c2n2v & 0.434 & 0.432 & 0.407 & 0.396 & 0.388 & 0.395 \\ \hline \multirow{2}{*}{LP} & n2v & 0.846 & 0.850 & 0.848 & 0.913 & 0.912 & 0.914 \\  & c2n2v & 0.854 & 0.858 & 0.857 & 0.918 & 0.918 & 0.920 \\ \hline \multirow{2}{*}{NC} & n2v & 0.706 & 0.709 & 0.704 & 0.635 & 0.631 & 0.633 \\  & c2n2v & 0.711 & 0.710 & 0.714 & 0.640 & 0.638 & 0.638 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between n2v and c2n2v with structure-attribute enhanced matrix (SAEM). Figure 4: Results of Macro-F1 (MAF) scores on node classification for three privacy settings and three datasets.
# Sensitivity Estimation for Differentially Private Query Processing
## 4 Sensitivity Estimation for Join Queries
### Sketch-Based Sensitivity Estimation
#### 4.3.2 Sketching Sensitivity for Multi-Join Queries
The AGMS estimator \(\hat{J}\) satisfies \(\Pr\bigl{[}\left|\hat{J}-\hat{J}\right|\geq e\hat{J}\bigr{]}\leq\delta\), where the relative error bound \(\zeta\) is_  
\[\xi=\sqrt{\frac{2k\log(1/\delta)}{s}}\cdot\sqrt{\prod\limits_{i=1}^{k}\frac{U_{ i}}{\hat{J}}},\]  
_Here, \(s\) denotes the number of sketch buckets, and \(U_{i}\) is the self-join size of \(R_{i}\) (\(U_{i}=\sum_{v\in d\omega(A)}f_{v}^{2}\))._Similar to the elastic sensitivity and residual sensitivity, our sketching sensitivity (SKS) must be smoothed using smooth sensitivity before it can be used with the Laplace mechanism. \[SKS=\max_{k>0}e^{-\beta k}\hat{L}S_{q}^{(k)}, \tag{28}\]  
where  
\[\hat{L}S_{q}^{(k)}=\max_{I^{\prime}\subset\mathcal{I}^{k}}\max_{i\in\mathcal{P} }\frac{\underset{s\in[[s,i]]}{\sum}\left|\prod_{j\in[n]-\{i\}}sk_{s}^{(k)}(R_{ j})\right|}{1-\tau} \tag{29}\]  
We use the following example to show how to compute the sketching sensitivity. Consider the example database \(I\) in Figure 2; we define two families of four-wise independent random variables for the join attributes \(B\) and \(C\) as \(\xi_{1}\) and \(\xi_{2}\). We can construct sketches for \(R_{1}\), \(R_{2}\), and \(R_{3}\) as follows: \(sk(R_{1})=\sum_{t\in R_{1}}\xi_{1}(t[B])\), \(sk(R_{2})=\sum_{t\in R_{2}}\xi_{1}(t[B])\cdot\xi_{2}(t[C])\), and \(sk(R_{3})=\sum_{t\in R_{3}}\xi_{2}(t[C])\). The influence of adding a tuple to a database \(I^{\prime}\) with \(k\) distance to the original database can be estimated by  
\[\hat{L}S_{q}^{(k)}=\max_{\sum_{j=1}^{3}|k_{j}|=k}\max\{ \underset{s\in[s,i]}{\sum}\left|sk_{s}^{(k_{1})}(R_{1})\cdot sk_{s}^{(k_{2})} (R_{2})\right|\underset{s\in[s,i]}{\sum}\left|sk_{s}^{(k_{1})}(R_{1})\cdot sk _{s}^{(k_{3})}(R_{3})\right| \tag{30}\] \[\underset{s\in[s,i]}{\sum}\left|sk_{s}^{(k_{2})}(R_{2})\cdot sk _{s}^{(k_{3})}(R_{3})\right|\}\cdot\frac{1}{1-\tau}\]  
where \(sk_{s}^{(k_{i})}(R_{j})=sk_{s}(R_{j})+k_{j}\). Then, the sketching sensitivity of \(R_{1}\Join R_{2}\Join R_{3}\) can be computed according to Equation (28).
# Robust Static Structural System Identification Using Rotations
## 1 Introduction
### Research Objective  
The novelty of this method is that it can determine the observability of target parameters when a measurement set is given. It is highlighted that a systematic procedure to carry out statistical analysis of the distribution of parameter \(\theta\) using analytical expression is presented. The expression for parameter \(\theta\) is obtained under the framework of static structural system identification using the observability method. The distribution of the parameters can be determined by the analytical expression from OM and the theory of inverse distribution [45]. The advantage of this method is that it can obtain analytical expression of the target parameters which have clear physical meaning and mathematical elegance. It can avoid the heavy computation burden that normally occurs in the Bayesian method or optimization-based method.  
Although inclinometers have been used in SHM for a long time, the research on the use of rotations in structural system identification is very limited. In the case of static structural system identification for beam-like structures [25], it is found that the estimations of bending stiffness were more sensitive to the measurement errors in deflections than errors in rotations using Monte Carlo analysis. However, no theoretical advantage of using rotations can be found. A comparative study of the use of rotations and/or deflections was carried out to justify the theoretical advantage of using rotations over deflections regarding the unbiasedness and the extent of variation in the estimations. Different strategies to use redundant rotations with the aim of improving estimation accuracy are also discussed.  
The remainder of this paper is organized as follows. Section 2 describes the algorithm for structural system identification by constrained OM [46], which obtains the analytical expressions for the parameters. Then, the procedure to carry out statistical analysis with the resulting expression is also presented in this section. Section 3 adopts two illustrative examples to justify the theoretical advantages of using rotations over deflections. Section 4 investigates the effectiveness of four strategies to make use of redundant rotations in a high-rise building. Finally, some conclusions are presented in Section 5.
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 3 Interferometric Data Processing
### High-Accuracy InSAR Image Co-Registration  
Interferometric coherence can be guaranteed when the co-registration accuracy of the InSAR image pair is higher than 0.1 pixel [1]. However, to improve the accuracy of the interferometric phase as well as the elevation accuracy of the final reconstructed DEM as much as possible, the co-registration accuracy of the image pair in the experimental area is required to reach a level of 0.01 pixel. Although an average level of 0.1 pixel of the global registration accuracy of the image can be obtained using the classical InSAR image co-registration method [9], the accuracy is difficult to guarantee for local areas on the image, especially those with large topographic reliefs [20].  
The classical method is not recommended for implementing the co-registration of the experimental dataset. To achieve the 0.01 level of co-registration accuracy in both the range and azimuth, this study adopted the following co-registration method. Firstly, the Shuttle Radar Topography Mission (SRTM) DEM with a resolution of 1\" and the satellite observation geometric parameters were used to eliminate the non-translation relationships between images due to terrain reliefs and inconsistent observation states. Subsequently, the first co-registration was performed and the slave image was resampled. Afterwards, the image pair had simple geometric transformation relations and mainly constant offsets, which could be fitted by global co-registration rational expression with a certain number of offsets with high accuracy and reasonable distributions. In order to obtain a series of high-accuracy offsets, the high-quality co-registration points were screened out with the signal-to-clutter-ratio (SCR) and intensity information between the master and resampled slave images. The details are provided by [PERSON] [20], and the workflow is shown in Figure 2. The method was applied to conduct the co-registration of the Gaofen-3 experimental dataset in the Dengfeng area. The co-registration accuracy in range was 0.0121 pixel and that in azimuth was 0.0093 pixel, which satisfied the expected requirements.
# Applying Deep Learning to Medical Imaging: A Review
## 5 Evaluation Methods and Available Datasets
### Metrics for Performance Evaluation  
Various metrics have been proposed to evaluate the performance of DL methods for medical imaging. For image segmentation tasks, commonly used metrics include Dice coefficient, Jaccard index, and surface distance measures [157]. For image classification tasks, metrics such as accuracy, precision, recall, and F1 score are often used [158]. For image reconstruction tasks, peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are commonly used metrics [159]. In addition, some studies have proposed novel metrics specific to certain applications, such as registration accuracy and tumor size measurement in cancer imaging [160]. It is important to note that no single metric can fully capture the performance of a DL method, and a combination of metrics should be used for comprehensive evaluation. Moreover, the choice of metrics should depend on the specific application and clinical relevance.  
In the realm of medical imaging, various deep learning (DL) methods have been applied and compared in terms of their performance. For instance, a notable comparative study by [PERSON] et al. [161] explored the effectiveness of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in detecting tumors from lung CT scans. The study revealed that both models yielded commendable results, but CNNs outshined RNNs with an accuracy rate of 92% compared to 89%. Furthermore, the CNN model demonstrated superior sensitivity and specificity, underscoring the potential advantages of CNNs in tasks involving medical imaging. Another insightful comparison was presented by [PERSON] et al. [162], which contrasted the performance of deep belief networks (DBNs) and CNNs for the detection of breast cancer using mammograms. Although both models achieved impressive accuracy rates, the DBN demonstrated a superior area under the receiver operating characteristic (ROC) curve (AUC), scoring 0.96 compared to the CNN's 0.92. This finding suggests that DBNs might offer an edge over CNNs in distinguishing between malignant and benign cases in mammography.