esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### System Propagation Model
#### 3.3.4 Transmission Delay Model of Satellite Channel  
As shown in Figure 4c, when the distance between user and satellite is 1500 km in scenario (a) in Figure 3, the transmission delay at the same distance is 34 ms. In scenario (b) in Figure 3, when the distance between satellites and users is 1700 km and 1500 km, the transmission delay is 56.5 ms and 50 ms, respectively. The transmission delay can be simplified as:  
\[\delta_{a_{i}^{c}s_{p}}=\frac{d_{a_{i}^{c}s_{p}}}{c}\text{tan}(\theta). \tag{5}\]  
where \(d_{a_{i}^{c}s_{p}}\) is the distance between the satellite \(s_{p}\) and the user \(u_{q}\) which generate the packet \(a_{qi}^{c}\): \(c\) is the velocity of light. The value of \(\theta\) is as described above. As the distance of the satellite increases, the transmission delay increases.  
Multi-Beam Satellite Cooperative Transmission Scheme Based on Cross-Layer and Cross-Dimension Resources Optimization and Irregular Gradient Adaptive Packets Segmentation  
In this section, at first, the multi-beam satellite cooperative transmission scheme based on cross-layer and cross-dimension resources optimization and irregular gradient adaptive packet segmentation is mathematically expressed. Then, the cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm and the adaptive packet segmentation scheme based on the irregular gradient algorithm are introduced. Finally, we put forward the user satisfaction index and evaluate the proposed algorithm.
# A Physical Model Test of Coal-Mining-Induced Deformation Mechanisms in a Canal
###### Abstract  
The route of the South-to-North Water Diversion channel strides across part of the coal mine goaf in Yuzhou County, Henan Province, China, and long-term deformation due to coal seam recovery poses a threat to the safe operation of the main canal. Therefore, the study of the deformation mechanisms induced by coal seam recovery is of great significance to the canal's safe operation, as well as to deformation monitoring and to the development of early warnings. The geologic model was established based on the geological engineering conditions of the Yuzhou Gongmao mining area, spanning the main canal of the South-to-North Water Diversion Project; then, the physical model test was carried out according to similar theories. The deformation characteristics of the rock overlay and the channel above the goaf were analyzed, and failure criteria for overburdened rock and the channel were proposed. The results showed that horizontal fissures were gradually observed in the overlying rock as the coal mining progressed, extending and widening. When the goaf was excavated to 76 cm, the overlying rock body suddenly collapsed as a whole, and the channel collapsed and was destroyed. During the formation of the goaf, there was a critical span ratio (R): When the height-to-span ratio was greater than 0.039, the collapse of overlying rock occurred only within a certain range above the goaf. When the height-to-span ratio was less than 0.039, the overlying rock body collapsed in a wide area, and the soil on both sides of the channel collapsed to the center of the channel, presenting a \"V\" glyph collapse. The sediment in the center of the channel measured 22 mm, and there were multiple tensile cracks on both sides of the embankment, with a width of 5-10 mm. The vertical deformation of the channel went through three stages, namely, the initial deformation stage, the deceleration deformation stage, and the stability stage. This study can provide scientific guidance for early warnings of channel deformation and safe operation across the goaf.  
South-to-North Water Diversion Project; coal mining; deformation mechanism; height-to-span ratio +
Footnote †: journal: Applied sciences
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 4 Simulation and Performance Evaluation
### Simulation Results
#### 4.2.3 Number of Successful Packet Transmissions  
To evaluate the packet transmission of HECRA in different network environments, experiments involving different numbers of nodes and transmission ranges are conducted in this paper. In addition to the network lifetime and residual energy, the number of successful packet transmissions is also an important indicator of network performance. An efficient network needs to save energy while maintaining a high data transmission success rate.  
Figure 7,b show the number of successful packet transmissions when the transmission range is 200 m and 150 m, respectively, with the number of sensor nodes varying between 50 and 450 and the number of sensor nodes varying every 1000 rounds of operation. From the graphical results, it can be seen that when the transmission range decreases, the number of successful packet transmissions for the four protocols also decreases, while HECRA still shows better performance overall; as the number of nodes varies, the number of successful packet transmissions for a transmission range of 200 m compared to EECMR, EERBLC, and LEACH is improved by 2.61%, 42.89%, and 58.47%, respectively. The number of successful packet transmissions for a transmission range of 150 m is improved by 6.8%, 45.17%, and 47.82% compared to that of EECMR, EERBLC, and LEACH, respectively. This is mainly because HECRA introduces an AUV as a dynamic relay node, which enables it to effectively assist in data transmission when the CH is unable to communicate. As a result, HECRA significantly improves the success rate of packet transmission and enhances the throughput and stability of the network.  
Figure 7: Received packets at the sink. (**a**) Transmission range 200 m, nodes 50 to 450. (**b**) Transmission range 150 m, nodes 50 to 450.
## 2 Introduction and Definition of Multi-Modal Fusion
### Multi-Modal Fusion
#### 2.3.3 Relevance to the PRI-SWT Model  
In this study, feature-level fusion is applied to integrate the real and imaginary components of voltage signals obtained from the eddy current testing (ECT) of carbon fiber-reinforced polymer (CFRP) composites. The real and imaginary components contain complementary information, with the real component being more sensitive to surface features and the imaginary component being better suited for detecting subsurface defects.  
By employing feature-level fusion, the proposed model independently extracts high-level features from both signal components and combines them to form a comprehensive representation of the material. Single-modal approaches often miss critical information that can be captured by other modalities. For example, relying solely on visual data may lead to researchers overlooking key defect details that could be better identified through accompanying textual or audio data analysis. This limitation can result in incomplete analysis and limit the accuracy of decision-making. Dual-modal fusion mitigates these limitations by integrating diverse information sources, allowing for a more comprehensive understanding of the subject matter. This fusion not only enhances the robustness of defect detection but also enriches the context by providing multiple perspectives on the same phenomenon. This approach enhances the model's ability to detect and characterize various types of defects, particularly those that manifest differently in the real and imaginary signal spaces. By leveraging the strengths of each modality, the model improves detection accuracy and robustness, even in the presence of noise or incomplete data. Moreover, the modular architecture of feature-level fusion allows for future extensions, enabling the integration of additional modalities or more advanced feature extraction techniques, making it highly adaptable to evolving nondestructive testing requirements.
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint Meta-Learning
#### 3.1.2 Self-Supervised Keypoint Learning  
Our network undergoes self-supervised training, employing a multi-step process. Initially, we obtain the homography-warped images from current training images. To train a keypoint detector, we leverage the pre-trained MagicPoint network [14] to acquire keypoint pseudo labels for both the original and warped images. In both images, keypoint predictions and pseudo labels contribute to the formation of detector loss, denoted as \(L_{p}\) and \(L_{wp}\), with CrossEntropy serving as the loss metric. \(L_{p}\) represents the detector loss of the original image, while \(L_{wp}\) represents the detector loss of the warped image. Moving on to descriptor training, we utilize the sparse descriptor loss \(L_{d}\) proposed in [47]. This involves obtaining both the warped image and pixel-wise correspondences. Descriptor loss functions are then constructed solely for pseudo-labeled keypoints, employing triplet loss. The sparse descriptor loss is composed of two components: homography-matched descriptor loss  
Figure 1: An overview of our proposed DK-SLAM framework with deep keypoint meta-learning, two-stage coarse-to-fine keypoint tracking, and online learning-based binary BoW for loop closing.  
\(L_{dm}\) and non-matched descriptor loss \(L_{dn}\). The homography-matched descriptor loss, represented by Equation (1), involves a subscript \(i\) ranging between \((0,1,...,N)\), where \((\mathbf{u}_{i},\mathbf{\hat{u}}_{i})\) denotes the \(N\) matched keypoint position pairs.  
\[L_{dm}(\mathbf{u}_{i})=\|\mathbf{d}_{1}(\mathbf{u}_{i})-\mathbf{d}_{wl}(\mathbf{\hat{u}}_{i})\|^{2} \tag{1}\]  
As depicted in Equation (2), we use \(M\) pairs of descriptors from the vicinity of matched descriptors to formulate a non-matched descriptor loss. The subscript \(j\) ranges from \(0\) to \(M\), where \((\mathbf{u}_{i},\mathbf{u}^{\prime}_{j})\) represents unmatched keypoint position pairs.  
\[L_{dn}(\mathbf{u}_{i})=\frac{1}{M}\sum_{j=1}^{M}(\|\mathbf{d}_{1}(\mathbf{u}_{i})-\mathbf{d}_{ wl}(\mathbf{u}^{\prime}_{j})\|^{2}) \tag{2}\]  
The descriptor loss \(L_{d}\), illustrated in Equation (3) with \(z\) representing the boundary margin, is expressed as  
\[L_{d}=\frac{1}{N}\sum_{i=1}^{N}\max(0,z+L_{dm}-L_{dn}) \tag{3}\]  
This descriptor loss \(L_{d}\) aims to minimize the feature distance between matching descriptors while maximizing the feature distance between non-matching descriptors. The overall training loss is the sum of the aforementioned detector loss and descriptor loss. Here, \(\lambda\) acts as a parameter to balance these different losses.  
\[L_{\text{all}}=(L_{p}+L_{wp})+\lambda L_{d} \tag{4}\]
## 2 In Situ Acoustic Emission (AE) Monitoring--Fundamentals
### Applications and Characteristics of In Situ AE Monitoring  
Many examples of application of in situ AE monitoring in mines as found in the literature are given in Table 2. This table shows the test site of the project, the resulting publications, the type of AE networks with source-receiver distance and mode of recording, and the rock type with number of recorded events. Above the frequencies of microseismic measurements, the lower frequency of the in situ AE method begins at 1 kHz and ranges up to approximately 200 kHz. Due to the high sensitivity of the AE sensors, the in situ AE method allows to monitor fractures in the millimeter scale to decimeter scale (see Figure 2). This is important information because such small events indicate weakening in the rock long before macroscopic fractures occur [32; 33; 34]. Due to the high frequencies, the signals of AE events attenuate more than signals of microseismic events [35; 36]. Thus, in situ AE monitoring is limited to a few tens of meters in hard rock or a few meters only in soft rock like clay rock or sedimentary rock. However, if the rock is very homogeneous and the attenuation of the seismic waves is low, larger rock volumes over 150 m in length can be monitored [12; 33; 34; 35; 36; 37; 38]. The applications in Table 2 can be roughly divided into three groups. The first group shows applications from underground laboratories in Switzerland and Sweden. The second group deals with the application of in situ AE monitoring in gold mines in South Africa. The third and biggest group shows applications in salt mines in Germany. These in situ measurements in rock salt often focused on monitoring AE activity caused by work like backfilling [39; 40], excavation [41] or gas and fluid injection [42]. Early projects in in situ AE monitoring were limited to small rock volumes.
# Industrial Wireless Sensor Networks: Protocols and Applications
## 1 Introduction  
With the help of technological advances and standardization activities, we are witnessing the growth of wireless sensor network applications in many areas, such as home appliances, agriculture, transportation, and manufacturing. Recently, the adoption of wireless sensor networks (WSNs) in industrial areas has increased due to matured technologies such as transceivers and protocols. Industrial wireless sensor networks (IWSNs) require reliability, robustness, and timeliness in the information exchange between devices, and they need more sophisticated protocols that many researchers and developers are working on.  
This Special Issue focuses on the latest research, application, and adoption of wireless sensor networks in industrial fields from the perspective of protocols and applications, requiring high reliability and real-time packet delivery. In such a context, this Special Issue invited contributions in the following topics (though without being limited to them):  
* Protocols for industrial wireless sensor networks
* Wireless network control systems
* Real-time communications in IWSNs
* IWSN testbeds and applications such as smart factories
* Edge and fog computing for IWSN
* Any subjects relevant to IWSN.  
There were number of submissions and two to four reviewers evaluated each submitted article and finally six outstanding papers were accepted and published in this Special Issue. We present the brief summary of each accepted paper in the following section.
# Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande +
## 2 Materials and Methods
### GNSS Receiver  
As said before, a GNSS antenna and receiver are used. Once the exact position of the antenna has been established, it can be used as a time source. In this configuration, the selected receiver (PolaRX5 TR) will report on tracking all available satellites for 13 min and then directly compute a time difference between its local oscillator and the GPS time (only tested constellations in this study), taking into account all appropriate propagation delays (atmospheric, ionospheric, calibrated cables, antenna, etc.) and performing fits. An additional feature of particular interest for our system is that an external frequency (10 MHz and 1 pps signals) can be given as an input to this receiver so that it bypasses the local oscillator and directly returns the difference between that input signal and the GPS time, using the 10 MHz signal for the computation.
# Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea
## 1 Introduction
In the present paper, we propose to improve the regional TEWS by installing real-time continuous BDS-network at the Luzon Island, Philippines. Being integrated with traditional seismic networks, the proposed GNSS-array will contribute to fast and reliable source inversion, which is one of the most important tasks in the early warning and forecasting of tsunamis. Figure 1: Seismotectonic map of the Southern China Sea. The red thick line represents the Manila trench of the seismogenic Manila subduction zone. The red points show the earthquakes that have occurred from 1976 to 2015 with a magnitude larger than Mw = 6.5 (GCMT catalog). The yellow line E2 indicates the rupture area used in this study. The two multi-GNSS stations used in Section 2 are also shown by green triangles. In Section 2, we test the real-time precise positioning performance of BDS and compare it with GPS and joint BDS/GPS in the SCS region. In Section 3, we simulate a test scenario of an Mw = 8.0 earthquake along the Manila trench and its corresponding tsunami to demonstrate the feasibility of incorporating BDS, BDS/GPS into the regional TEWS. In Section 4, we discuss additional issues related to the performance of BDS, BDS/GPS based TEWS. Finally, Section 5 summarizes results and presents an outlook.
###### Abstract
These methods can estimate the errors of redundant INSs and compensate for them during navigation calculations individually, thereby improving navigation accuracy. To obtain comprehensive position information, [PERSON] designed a multi-INS data fusion method based on confidence analysis, which improved navigation accuracy and system reliability [17]. [PERSON] developed a velocity error prediction model that predicts and compensates for the velocity errors caused by inertial sensor errors, in order to enhance the overall positioning accuracy [18; 19; 20]. The aforementioned studies assume that the lever arms between multiple RINSs are constant. However, [PERSON] found that the ship deformation affects the navigation accuracy of multi-INS data fusion [21]. As a result, it is necessary to analyze the function of flexible lever arms to improve overall fusion accuracy. Additionally, [PERSON] and his colleagues discovered that rotational modulation technology cannot modulate the equivalent gyroscope geographic zero bias caused by the Earth's gravity and magnetic fields [22; 23]. Therefore, this error must be considered when establishing the error equations for RINSs. This study focuses on three sets of SRINSs and proposes a Kalman data fusion filtering equation for multiple INSs under the constraint of topologically distributed flexible arms. The major contributions of this study are as follows. (1) Considering the equivalent gyro geographic zero bias that cannot be modulated by the SRINS, the state equation and state transition matrix are improved and optimized. (2) A model is established between the flexible lever arms and dynamic deformation angles of RINSs installed at different positions, laying a theoretical foundation for the derivation of the measurement equation. (3) A data acquisition device is used to send One Pulse Per Second (1 PPS) synchronization signals to multiple RINSs and simultaneously collect measurement data from these RINSs, ensuring the time consistency of the measurement data. The novelty of the proposed method lies in using the flexible lever arms between three SRINSs as a constraint condition and the position difference between these RINSs as a measurement, and thereby improving the overall positioning accuracy of the RINSs. The remainder of this paper is organized as follows. Section 2 defines the coordinate systems and proposes the state equation of the SRINS, including the equivalent gyro geographic zero bias. The relationship between the flexible lever arms and the flexural deformation angle is derived in Section 3. Section 4 establishes a 63-dimensional Kalman filtering equation by using the 45-dimensional state quantities of the three SRINSs, as well as the lever arms, flexural deformation angles, and flexural deformation angle rates between RINSs. Section 5 verifies the Kalman filtering equation established in Section 4 through car trials, and analyzes the lever arm estimation results and positioning errors in the data fusion process. Finally, the conclusion based on this work is given in Section 6.
###### Abstract
A revised entropy criterion for the detection of oscillations has been developed to choose the most optimal numerical solution. Additionally, some researchers applied the finite element method [18; 19]. The discontinuous Galerkin method has also been applied to solve the shallow water equations coupled with the diffusion transport equation. All these methods are known for their high accuracy, but in practice they are still very complex. In addition, their results are dependent on the mesh quality, particularly for unstructured meshes, and the computation costs are generally high. In this paper, we develop a meshless local method based on radial basis functions (LRBFs) for the transport of non-active pollutants by shallow water. Typically, in the references mentioned above, LRBF methods are implemented for the simulation of water flows. Due to their flexibility and simplicity of implementation, these methods can be very useful in treating cases of high geometric and bathymetric complexity. Our objective in the present study is to extend the LRBF method for the numerical simulation of two-dimensional pollutant transport problems to real geometries. The main advantages of the proposed LRBF method are (i) the simplicity of implementation for the system of shallow water equations coupled with the advection-diffusion equation, (ii) the simultaneous resolution of both problems in time with the same accuracy, (iii) the ability to handle calculations of slowly varying flows or concentrations, as well as rapidly varying flows containing shocks or discontinuities, (iv) the ability to satisfy the C-property, and (v) guaranteeing positive values of both water level and pollutant concentration in transient simulations. The results presented in this paper demonstrate the high resolution of the proposed method and confirm its ability to provide accurate and efficient simulations for the transport of pollutants by shallow water flows, including complex topography and geometry. The rest of the paper is structured as follows: in Section 2, the mathematical model is presented. In Section 3, the numerical tools, including the LRBF method and its implementation, are developed. Section 4 discusses one-dimensional and two-dimensional experiments, demonstrating excellent results.