esa-sceva/satcom-chunk-collection huggingface dataset:
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 5 Validation and Discussion
### Validation of Blower Fan MSET Modeling
#### 5.1.2 Dynamic Working Condition  
In order to further validate the effectiveness of the proposed method, the horizontal and vertical vibrations for bluer fan in dynamic work condition, named condition II, are applied. The comparison for proposed method and traditional method in condition II is shown in Figures 7 and 8.  
Figure 7: Comparison for proposed method and traditional method for horizontal vibration condition II.  
As shown in Figure 7, the horizontal vibration of test data set is change in the range of 1.144 mm/s to 1.767 mm/s, which is the normal vibrating range according to engineering handbook. Comparing the residual results, the proposed method achieves a higher accuracy, the residual is contained in \(-0.0243\) mm/s to 0.0274 mm/s, which for traditional method is \(-0.0411\) mm/s to 0.0756 mm/s.  
It should be pointed out that the condition changed obviously at certain samples, such as sample A and sample B marked in the Figure, and the condition changes do not reflect in residuals obviously, however, the similarity reveals the condition changes accurately, at the sample A and sample B, the vibrate condition changes and similarity descends correspondingly. Owning to the calculation of the other variables in observation vector, information except direct vibration variables is contained in similarity calculation, which is significant for a comprehensive vibration condition monitoring.  
As shown in Figure 8, the vertical vibration of test dataset is changed in the range of 1.573 mm/s to 3.273 mm/s, which is also a normal vibrating range, and the trend for vertical vibration is similar with horizontal vibration. The ranges for residual for proposed method and traditional method are \(-0.048\) mm/s to 0.1434 mm/s, \(-0.5495\) mm/s to 0.192 mm/s, respectively, and the results reveal that the proposed method achieve a higher estimation accuracy.  
The RMSE and MAPE for condition II is shown in Table 5. For horizontal vibration \(x_{1}\), the RMSE and MAPE of the
# The Additive Manufacturing of Glass: A Critical Review
## 4 Properties of AM Glass
### Optical Properties  
The optical properties of AM glass have been a primary interest for the direct ink writing, light-based, SLM, and DED methods [3]. This is due to the high resolution achieved with these techniques, with a focus on optical applications such as lenses and fibres. An AM process unlocks a new level of complexity for lenses and fibre preforms, allowing the precise control of geometry and even the possibility of varying material (and the associated refractive index) across a printed object [50; 56; 60; 66; 67; 68; 69; 82; 83; 84; 98; 111]. However, for glass EAM processes, optical properties have been less studied. This is because the resolution achieved using these techniques is more suited for alternative applications, such as architecture, art, prototyping, packaging, and so forth. Common interest does revolve around the layer lines and whether these can be altered to improve the optical quality of an object produced with glass EAM. Polishing and firing methods have been mentioned in Section 3.4 which show that optical properties can be altered. When it comes to exploring new forms and functions, there is also the possibility of exploiting the layer lines to achieve an aesthetic. For example, their unique interaction with light may be used to create caustic patterns in combination with a light source, such as those depicted earlier in Figure 14. More research is required to make definitive statements on the optical properties of additively manufactured glass.
# Tectonic Subsidence and Its Response to Geological Evolution in the Xisha Area, South China Sea
## 6 Conclusions  
Based on the information obtained from the wells and pseudo-wells extracted from seismic profiles, the tectonic subsidence of rifted basins surrounding the Xisha area was systemically recovered, and the following conclusions are drawn.  
1. The subsidence in the structural highs is much smaller than that in basins. The largest tectonic subsidence in the Xisha area is located in the Changchang Depression, with maximum subsidence of 5.4 km and maximum subsidence rate of 0.45 m/ky.
2. Subsidence history in the Xisha area, including two phases of relatively rapid subsidence, could be divided into five phases since the Eocene to the present: the slow subsidence phase (53.5-32 Ma), the rapid subsidence phase (32-23.3 Ma), the interim phase (23.3-15.5 Ma), the accelerated rise phase (15.5-5.5 Ma), and the transitional phase (5.5 Ma to present).
3. The characteristics of faults around the Xisha area and the anticlockwise splitting process of the Xisha Block suggest that the extrusion of the Indochina Block and the associated left-lateral slip of the Red River Fault Zone led to the stretching and thinning of the lithosphere and crust in the Xisha area, resulting in the rapid Oligocene subsidence (32-23.3 Ma) in the Xisha area.
4. From the beginning of the Middle Miocene to the end of the Late Miocene, the accelerated rise in tectonic subsidence was related to the release of regional stress, caused by the reversion of the Red River Fault Zone in the Xisha area. After the reversion of the Red River Fault Zone at 5.5 Ma, rapid subsidence occurred on the north slope of Qiongdongnan Basin. However, the fierce vertical intrusion of magma in the Xisha area hampered regional tectonic subsidence in the Pliocene to the present.  
Formal analysis, W.W. and D.S.; project administration, G.Z.; methodology, Z.Y. and Y.L.; writing--original draft, Z.Y.; writing--review and editing, S.L. and G.F. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Science and Technology Program of Guangzhou (grant 202201011171), Marine Economy Development Foundation of Guangdong Province (GDNRC [2022]44), National Natural Science Foundation of China (grant U20A20100), and Geological Survey Project of China Geological Survey (DD20221712).  
Not applicable.  
The data that support the findings of this study are available from the Guangzhou Marine Geological Survey. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors with the permission of Guangzhou Marine Geological Survey.  
The authors declare no conflict of interest.
## 2 Preliminaries
### Defensive Schemes
#### 2.3.2 Test-Time Processing  
While adversarial training tries to make the neural network robust at the training stage, the test-time processing does not change the training data and process. Instead, the input data or the model is manipulated at test-time to mitigate the effect of adversarial perturbation. In Figure 1, we can observe that in the adversarial images, noise is added either at the background or near the digit. Thus, an intuitive defense scheme is to try to remove the noise, by applying blurring or quantization [24]. Randomly transforming the input image could also help [25]. Especially for white-box attacks, it is possible that an adversarial sample is crafted so that it barely crosses the class boundary in the neural network. Random transformation could bring back the image to its original class. Generative Adversarial Networks (GANs) and autoencoders can be used in transforming the inputs. For example, a GAN is first trained to learn the distribution of clean images. At test time, a sample close to the image is searched in the learned distribution. Then, instead of the input, the image in the learned distribution is input to the network [26].  
Instead of manipulating the input image, it is also possible to process the neural network at test time. For example, some neurons from the intermediate layers of the network can be randomly pruned [27] or random noises can be added to the output of intermediate layers [28]. When adding randomness to the network, a typical technique is to combine results from an ensemble of multiple networks in order to normalize the effect of randomness. By applying randomization, we can investigate multiple points in the neighborhood of the input, instead of a single point. It may be helpful in defending against adversarial images, because the attacker now has to add more perturbation to the original input so that the adversarial sample and its random neighbors are all misclassified.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 4 Numerical Results
### Cross-Correlation Comparison  
As we know, the cross correlation is an essential indicator for a ground-based navigation system, which reflects the resistance to near-far effects. The following statistic histogram is drawn with the signals that modulated by the C/A code of satellite 1 and 2. The cross-correlation amplitudes are normalized with the value of auto-correlation peak. As we can see from Figure 10, BPSK(1) and ChPN(5,1) have the same maximum cross correlation, which is larger than O-ChPN(5,1). Furthermore, most samples of ChPN(5,1) and O-ChPN(5,1) are located in \(-0.02\) to \(0.02\), while BPSK(1) contains more samples in higher range, such as \(0.02\) to \(0.06\). The auto-to-cross-correlation peak ratio (ACCR) of BPSK(1) and ChPN(5,1) is \(24\) dB, while the ACCR of O-ChPN(5,1) is about \(26\) dB, \(2\) dB better than the above two.  
Figure 9: MSPRs VS various code rates and sweep bandwidths.
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 4 Enhanced Routing Solutions for Internet of Things Networks
### Different Traffic and Mode of Operations' Support  
IoT/LLN applications can require the use of different data traffic patterns during its execution. As an example, two nodes can exchange messages among them creating P2P traffic, and a few moments later, both need to send data to a central node generating MP2P traffic. Furthermore, the central node can send data messages to a specific group of nodes using multicast communication (or P2 MP). Thus, based on this requirement, the routing protocols should provide support for different traffic patterns, preferentially permitting that all these can be used simultaneously. The main feature of LOADng is P2P traffic support. However, the most significant limitation of the protocol is the lack of efficient support for MP2P and P2 MP. Seeking to reduce this drawback, [PERSON] and [PERSON] [82] proposed the LOADng Collection Tree Extension (LOADng-CTP). The introduced improvement allows the LOADng to function as a proactive routing protocol and to perform the creation of a routing tree for the collection of data messages from the leaf nodes to the root (similar to what happens in the RPL). The LOADng-CTP introduces two new flags on the RREQ messages (RREQ_TRIGGER and RREQ_BUILD) and a new HELLO message. At the begin of protocol functioning, the root node floods the network using an RREQ_TRIGGER message (RREQ with the RREQ_TRIGGER flag set to true) to indicate the interest in constructing the routing tree. The nodes that receive the RREQ_TRIGGER answer it using the HELLO messages. Each receiver of a HELLO should set the message sender as bidirectional on its routing set. After a predefined time, the root node floods the network again using an RREQ_BUILD message (RREQ with the RREQ_BUILD flag set to true). The receivers of an RREQ_BUILD verify whether the message was received from a neighbor set as bidirectional, and if true, a new route entry to the root is inserted in the routing set. After this procedure, the collection tree is built, and the upward traffic (MP2P) can be performed more efficiently. Optionally, LOADng-CTP also permits the construction of a reverse path to facilitate the downward traffic. For this, each node that receives an RREQ_BUILD should answer it using an RREP message. Thus, LOADng-CTP overcomes one of the main limitations of LOADng, allowing it to support different traffic patterns appropriately.
# Research on Risk Quantification Methods for Connected Autonomous Vehicles Based on CNN-LSTM
## 3 CAV Trajectory Prediction
### Cnn  
CNN is a deep-learning model widely applied in fields such as image processing, computer vision, and natural language processing. Inspired by the biological visual system, it mimics the way the human brain processes visual information to effectively extract features.  
The core component of a CNN is the convolutional layer, which processes input data using multiple learnable convolutional kernels. These kernels slide over the input data, extracting features through localized perception. This operation captures local features such as edges and textures in images, facilitating the learning of deeper-level features. The convolutional layer extracts local features through the convolution operation, which is computed as follows:  
\[S(i,j)=(C\cdot K)(i,j)=\sum_{m}\sum_{n}c(i+m,j+n)\cdot k(m,n)+a \tag{4}\]  
where \(C\) is the input feature; \(K\) is the convolutional kernel; and \(a\) is the bias term.  
The convolutional layer is typically followed by a pooling layer, which reduces the dimensionality of the data through sampling, thereby decreasing the computational complexity while preserving the information quality. It also enhances the model's robustness to small shifts and rotations in the input data. In this work, multiple features are mapped to a single feature, then weighted by \(k_{x+1}\), and a bias term \(a_{x+1}\) is added.  
The output of the kth convolutional layer can be calculated as follows:  
\[c_{k}^{\text{u}}=f\bigg{(}\sum_{i}x_{k-1}^{i}\cdot f_{k}^{ij}+b\bigg{)} \tag{5}\]  
Figure 1: CNN-LSTM framework.  
where \(f\) is the activation function; \(\hat{I}_{k}^{ij}\) is the convolutional kernel; \(b\) is the additive bias corresponding to the tth feature value; and \(x_{k-1}^{i}\) is the output of the \((k-1)\)th convolutional layer. Constraints: \(\mathrm{i}\in[1,\mathrm{N}]\), represents the number of input feature maps; \(j\in[1,\mathrm{M}]\) denotes the size of the convolutional kernel; \(u\in[1,\mathrm{U}]\) indicates the number of output feature maps; and \(k\) represents the current layer of the convolutional network.  
The rectified linear unit (ReLU) is a widely used nonlinear activation function that effectively reduces complexity while preserving most of the data's activation, thereby enhancing training performance. ReLU is defined as follows:  
\[\mathrm{ReLU}=\left\{\begin{array}{c}x,x>0\\ 0,x\leq 0\end{array}\right\} \tag{6}\]  
Since the gradient in the non-negative region is constant, the vanishing gradient problem does not occur with ReLU, which helps the model maintain a steady convergence rate.
## 2 Results
### Hydrolytic Activity of hNudt16 on Dinucleotides Containing Diphosphate Bridge
#### 2.2.2 Analysis of Protein Thermal Stability by DSF  
The structure of hNudt16 reveals two Mg\({}^{2+}\) ions, detailed analysis of their location in the structure suggests that a single mutation of E76Q may allow one of the magnesium ions to be retained, while a double mutation of EE-QQ and EE-KK is likely to affect the binding of both ions. Magnesium ions are essential for hydrolysis and are probably also important for substrate binding to the active site. We assumed that for further studies of the hNudt16-ligand interaction we require a mutant that would still contain magnesium ions, but only those that might be needed for substrate binding. We therefore tested the thermal stability of different mutants in the presence of magnesium ions, as their ability to bind these ions can be determined from T\({}_{\rm m}\) parameter.  
We tested the effects of mutations introduced into the hNudt16 amino acid chain at positions 76, 79, and 80 on the thermal stability of the protein in the presence and absence of magnesium ions. Wild-type hNudt16 without magnesium ions had a melting point of T\({}_{\rm m}\) = 52.8 \(\pm\) 0.1 \({}^{\circ}\)C. The introduced mutations resulted in a higher melting point for all mutants, with T\({}_{\rm m}\) of 56.0 \(\pm\) 0.1 \({}^{\circ}\)C for E76Q, 62.5 \(\pm\) 0.1 \({}^{\circ}\)C for EE-QQ, and 71.0 \(\pm\) 0.1 \({}^{\circ}\)C for EE-KK. A thermal stability study of hNudt16 and its mutants in the presence of increasing magnesium concentrations (Figure S2) showed that the EE-KK double mutation at positions 79 and 80 prevented magnesium binding (\(\Delta\)T\({}_{\rm m}\) = 0 \({}^{\circ}\)C at all magnesium concentrations used), whereas the EE-QQ mutation significantly reduced magnesium binding (\(\Delta\)T\({}_{\rm m}\) < 3 \({}^{\circ}\)C). The negative effect on magnesium ions binding, which is probably essential for substrate binding of the wild-type protein, led us to exclude the EE-QQ and EE-KK mutants as candidates to replace the wild-type protein for further ligand binding studies.  
On the other hand, in the E76Q mutant, increasing the magnesium concentration was found to gradually increase the melting temperature (\(\Delta\)T\({}_{\rm m}\) > 10 \({}^{\circ}\)C at a minimum of 10 mM MgCl\({}_{2}\)) and magnesium ion saturation occurred at higher concentrations (20 mM MgCl\({}_{2}\)) than for wild-type hNudt16 (Figure S2). This indicates that the E76Q mutant is likely to bind the magnesium ion responsible for the catalysis process weaklier or not at all, while the binding of the other magnesium ion is retained and is therefore a suitable candidate for further protein-ligand studies. The hydrolytic properties of E76Q, EE-QQ, and EE-KK towards GppG and the E76Q mutant towards m\({}^{7}\)GppG were tested by high-performance liquid chromatography (HPLC), and none of the mutants exhibited hydrolytic activity (Figure S1).
## 2 Materials and Methods
### State Estimation in Distribution Power Systems  
This section provides the mathematical formulation of the state estimation problem in unbalanced distribution systems, laying the foundation for the PINN approach developed in this work. By formalizing the relationship between nodal power injections and bus voltages, we clarify the physical model that underpins the data generation process and informs the physics-based loss function used during PINN training.  
In conventional DSSE, the system state comprises the complex voltage phasors at each bus. These are inferred from available measurements--such as power flows, voltage magnitudes, current values, or pseudo-measurements derived from historical data--using power flow equations and statistical estimation methods. These measurements are typically gathered from SCADA systems, smart meters, or forecasting models. In this work, however, we assume full knowledge of power injections per phase derived from simulated load profiles, which serve as direct inputs to the PINN.  
In unbalanced three-phase distribution networks, the physical relationships between voltages and power injections must be modeled per phase. The complex power injection at bus \(i\), phase \(\phi\in\{a,\ b,\ c\}\), is given by:  
\[S_{i}^{\varphi}=V_{i}^{\varphi}\cdot\left(\sum_{i}\sum_{\psi}Y_{ij}^{\varphi \varphi}\cdot V_{j}^{\psi}\right)^{*} \tag{1}\]  
where \(S_{i}^{\varphi}\) is the complex power injection, \(V_{i}^{\varphi}\) is the complex voltage at bus \(i\)--phase \(\varphi\), and \(Y_{ij}^{\varphi\psi}\) is the element of the three-phase nodal admittance matrix coupling phase \(\varphi\) of bus \(i\) with phase \(\psi\) of bus \(j\). The active and reactive power components are obtained from the real and imaginary parts:  
\[P_{i}^{\varphi}=Re\Big{\{}S_{i}^{\varphi}\Big{\}},\ \ Q_{i}^{\varphi}=Im\Big{\{}S_{i}^{ \varphi}\Big{\}} \tag{2}\]  
These equations are nonlinear in \(V\) and must be solved iteratively in traditional SE methods. Instead, the proposed PINN model learns an approximation of the inverse mapping \(S\to V\) directly from simulated data.
###### Abstract  
In this study, a set of benchmarks for object tracking with motion parameters (OTMP) was first designed. The sample images were matched with the spatial depth of the camera, the pose of the camera, and other spatial parameters for the training of the detection model. Then, a Fast Depth-Assisted Single-Shot MultiBox Detector (FDA-SSD) algorithm suitable for 3D target tracking was proposed by combining the depth information of the sample into the original Single-Shot MultiBox Detector (SSD). Finally, an FDA-SSD-based monocular motion platform target detection and tracking algorithm framework were established. Specifically, the spatial geometric constraints of the target were adapted to solve the target depth information, which was fed back to the detection model. Then, the normalized depth information of the target was employed to select the feature window of the convolutional layer for the detector at a specific scale. This significantly reduces the computational power for simultaneously calculating detectors of all scales. This framework effectively combines the two-dimensional detection model and the three-dimensional positioning algorithm. Compared with the original SSD method, the network model designed in this study has fewer actual operating parameters; the measured detection operation speed was increased by about 18.1% on average; the recognition rate was maintained at a high level consistent with that of the original SSD. Furthermore, several groups of experiments were conducted on target detection and target space tracking based on monocular motion platforms indoors. The root mean square error (RMSE) of the spatial tracking trajectory was less than 4.72 cm. The experimental results verified that the algorithm framework in this study can effectively realize tasks such as visual detection, classification, and spatial tracking based on a monocular motion platform.  
1 2022