esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Finite Element Formulation
### Time Discretization: Lie Group Generalized-\(\alpha\) Method  
The dynamics equilibrium equations can be expressed by a set of Differential Algebraic Equations (DAEs), giving:  
\[\left\{\begin{array}{c}\mathbf{F}^{\mathrm{int}}(\mathbf{q})+\mathbf{F}^{ \mathrm{ine}}(\mathbf{q})-\mathbf{F}^{\mathrm{ext}}(\mathbf{q})+\mathbf{ \Phi}_{\mathbf{q}}^{\mathrm{T}}\mathbf{\lambda}=0\\ \mathbf{\Phi}(\mathbf{q},t)=0\end{array}\right. \tag{48}\]  
where \(\mathbf{F}^{\mathrm{ext}}\) is the generalized external force; the notation \(\mathbf{\Phi}\) and \(\mathbf{\Phi}_{\mathbf{q}}\) denote the constraint equations and its Jacobian matrix relative to the generalized coordinates; and \(\mathbf{\lambda}\) is the Lagrange multiplier vector.  
For time discretization, the generalized-\(\alpha\) method is employed to solve the DAEs. The displacement field is handled with the conventional generalized-\(\alpha\) method [30] while the Lie group time integrators [31] are adopted to solve the rotation field. The solvers of the rotation field are based on the equations:  
\[\left\{\begin{array}{l}\mathbf{R}_{n+1}=\mathbf{R}_{n}\exp(\mathbf{\vec{\Theta}} _{n+1})\\ \mathbf{\Theta}_{n}=h\mathbf{\Omega}_{n}+h^{2}(0.5-\beta)\mathbf{a}_{n}+h^{2} \beta\mathbf{a}_{n+1}\\ \mathbf{\Omega}_{n+1}=\mathbf{\Omega}_{n}+h(1-\gamma)\mathbf{a}_{n}+h\gamma \mathbf{a}_{n+1}\\ (1-\alpha_{m})\mathbf{a}_{n+1}+\alpha_{m}\mathbf{a}_{n}=(1-\alpha_{f})\mathbf{ \alpha}_{n+1}+\alpha_{f}\mathbf{\alpha}_{n}\end{array}\right. \tag{49}\]  
in which \(\mathbf{\Theta}_{n}\) represents the rotational vector from time \(t_{n}\) to time \(t_{n+1}\); \(h=t_{n+1}-t_{n}\) means the time step; \(\alpha_{m}\), \(\alpha_{f}\), \(\gamma\), and \(\beta\) are algorithmic parameters for the generalized-\(\alpha\) method. According to the simultaneous Equations (48) and (49), the rotation matrix \(\mathbf{R}_{n+1}\), angular velocity \(\mathbf{\Omega}_{n+1}\), and angular acceleration \(\mathbf{\alpha}_{n+1}\) at time \(t_{n+1}\) can be obtained through newton iterations.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 2 Materials and Methods
### UAV Modeling
#### 2.4.2 Magnetic Detector Model  
The field of view of the magnetic detector is an important factor for measuring the instantaneous search area of the UAV [17]. The airborne sensor detection model is shown in Figure 3. In the relative coordinate system, the detection width is established as follows:  
\[d_{u}=2\cdot h\cdot\frac{\tan\gamma_{u}}{\sin\alpha_{u}} \tag{16}\]  
where \(h\) is the flight height of the UAV, \(\alpha_{u}\) is the installation angle of the magnetic detector, and \(\gamma_{u}\) is the horizontal field of view of the magnetic detector.  
Figure 3: Airborne magnetic sounder detection model.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 4 Airspace Clustering Steps Based on Multi-A \({}^{*}\) Algorithm
### Clustering Data Processing  
In Section 4.3, the preliminary clustering results have been obtained by observing the airspace clustering map. However, in the specific implementation process, the computer cannot directly observe the image to obtain the results, but needs to store, calculate, and compare the data to obtain the final results. Therefore, the images and information after the initial clustering need to be sorted and processed. Airspace clustering data processing is mainly divided into two parts: one is grid representation of clustering lines, and the other is definition of elements in each cluster. The following describes the methods and steps of clustering data processing based on the battlefield airspace case.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 1 Introduction  
Fiber laser has a wide range of application prospects in industrial processing, advanced manufacturing, national defense, and other fields due to its advantages of high efficiency, high stability, high beam quality, and easiness to expand to high power. Photodarkening (PD) is an important problem in fiber lasers. PD is characterized by broadband absorption centered on the wavelength of visible light, which increases with working time, and the performance of PD is also related to the preparation method of active optical fiber [1; 2; 3; 4; 5; 6; 7]. Aging is based on the continuous competition between PD and bleaching.
# Design and Application of a Distribution Network Phasor Data Concentrator
## 3 DPDC Design
### Hardware Platform  
DPDC hardware adopts platform hardware architecture design, with high-level electromagnetic compatibility (EMC) protection, its operating system adopts Linux real-time multitasking operating system with independent cutting and packaging. The DPDC front and rear panels are shown in Figure 5. The DPDC model is PDC-2018, with a width of 19 inches and a height of 2 units (U) and a length of 12 inches. U is a unit representing the external dimensions of the server, 1 U = 1.75 inches.  
In order to meet DPDC's multi-tasking processing capabilities, the device uses a dual-core dual-threaded CPU, model Intel(R) Celeron(R) 2980 U, with 2 M cache, clocked at 1.60 GHz. On the storage, DPDC needs to store more than 14 days of RT files and more than 1000 HD files. The device uses a 480 GB solid-state drive to meet the requirements of fast reading and writing, low power consumption, shock resistance, and anti-fall. In addition to the space occupied by the system and software, the remaining space of the solid-state drive is greater than 420 GB. The running memory type is DDR3L and the size is 4 GB, which can meet the need to set a large buffer when DPDC accesses a large number of \(\upmu\)MPMU. In order to meet the communication with a large number of \(\upmu\)MPMU, the DPDC is equipped with six Gigabit adaptive Ethernet ports. The timing interface is RS-485, and InterRange Instrumentation Group Time Code Format B (IRIG-B) is used. The DPDC is connected to a synchronous clock source through this interface and receives a synchronous clock signal. The DPDC supports dual power supply operation and can be powered by direct current or alternating current. Maintenance of the DPDC can be remotely logged in through the network port, or directly connected to the display and keyboard. There are multiple LED indicators on the front panel of the device. The indication information includes the status of the system operation, alarms, timing anomalies, communication anomalies, and data link status.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 1 Introduction
Adaptive fuzzy logic is another effective way to improve the performance of neural networks [21]. The fusion of mechanism and data can improve the stability, generalization, and accuracy of the model. Based on flow pattern recognition, [PERSON] et al. [25] developed the neural network models of circulating pressure drop under different flow patterns and supplemented the multiphase flow formula to realize the separate calculation of BHP drop in different well sections, extending the applicable conditions of the intelligent method. [PERSON] et al. [26] proposed the grey box theory and innovated the combination method of mechanisms and the intelligent model. MPD data have both strong and weak time-series feature parameters, such as drilling fluid rheological parameters, which play a key role [27], and the single intelligent prediction models, such as BP, LSTM, and CNN, lack the ability to capture potential information, while the parallel dual-branch networks can simultaneously capture the strong and weak features of time-series data in order to mine more related information of time-series data, thereby reducing information loss. Based on the advantages of the single intelligent models to capture different characteristics, hybrid optimization parallel models are established in this paper. This method can fully identify the time-series characteristics of the data and effectively capture the subtle changes behind the data. This research can be applied to optimally solve temporal problems and provides a reference for precise pressure control in drilling.
## 4 Designing an Ontology Framework: A Case Study on Asthma
### Validation for the Extended Ontology of Asthma Based on Specific Metrics
#### 4.9.3 Completeness  
Completeness refers to an ontology's ability to comprehensively represent essential domain knowledge and infer the necessary information to address competency questions [76]. However, in our case, we refer to relative completeness, as it does not represent general completeness, meaning that the ontology is complete with respect to the available dataset, but its scope remains specific rather than general. The extended asthma ontology incorporates key features from the asthma dataset, such as DietQuality, LungFunction FEV1, PhysicalActivity and Smoking. Additionally, the ontology includes hierarchical structures to represent Symptoms, Triggers, Treatments and Diagnosis, ensuring logical representation of asthma-related data. The extended ontology ensures sufficient completeness to support decision-making and reasoning for asthma. However, gaps in representing rare or less-studied aspects may limit its applicability for edge cases or advanced medical research. These gaps can be addressed by integrating additional datasets or collaborating with domain experts to further refine the ontology.
## 3 Results and Discussions
### Mechanical Property Test Results
#### 3.1.2 Direct Shear Test Results
The influence of moisture content on the cohesion of reinforced sand is shown in Figure 8. The soil with 10% moisture content exhibits the highest cohesion. When the moisture content increased from 10% to 12.5%, the cohesion of the sample decreased. This indicates that, when the critical moisture content is around 10%, the highest cohesion is  
Figure 6: Shear strength of fibre-reinforced sand with different moisture contents. Figure 7: Effect of fibre length and content on internal friction angle of samples with different moisture contents. achieved. In addition, when the moisture content was larger than the critical content, the soil cohesion decreased faster than when the moisture content was less than the critical content. The fibre content and length also affected the cohesion of reinforced sand. The cohesion of reinforced sand increases with the fibre content and length. Comparing Figure 8a,b, with increasing moisture content, the cohesion of the sample with 12 mm-long fibres exceeds that of the sample with 6 mm-long fibres. This indicates that the sensitivity of sand to moisture content increases with the fibre length.
# Mobility-Multihoming Duality
## 4 ILNP Implementation in Linux
### Route Selection Mechanism and Transport Layer Packet Processing  
In order for ILNP to select a source L64 for multipath transport, the outgoing interface must correspond to the L64 selected by ILNP on a per-packet basis. However, the two distinct transport layer packet processing mechanisms for TCP and UDP select the outgoing interface at the early phase of their packet processing in the Linux kernel.  
The packet processing path involves route lookup when the UDP and TCP packet headers are being constructed _before_ they are passed onto the network layer packet processing mechanisms. This is to ensure that the pseudo-header source address (and Checksum calculation) matches with the actual source address and the outgoing interface. In the context of a conventional IPv6, the actual source address and the respective outgoing interface do not change; therefore, they are cached after the initial lookup. However, that is not the case with ILNP, especially in the context of multihoming, where every packet may leave from a different interface to the previous packet for the same flow.  
To allow ILNP to select the correct outgoing interface, the ILNP code needs to modify the logic for the route selection mechanism for the IPv6. So, both the UDP and TCP packet processing mechanisms were modified to correctly set the outgoing route and the respective interface. Specifically, by preventing the caching of routes and instead triggering route lookup for every packet, ILNP allows for selection of the outgoing L64/interface for each packet. This in turn allows for maximum flexibility for packet forwarding across multiple interfaces. Figure 5 shows a simplified diagram illustrating how the UDP and TCP packet processing were modified to enable source L64 selection and respective outgoing interface selection.
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 2 Materials and Methods
### Statistical Analysis and Model Fitted  
To reduce the confounding due to backgrounds, such as soil, and the effect of scattered light ratios from direct light, only spectral data with NDVI > 0.5 and SZA < 80\({}^{\circ}\) were used.  
\[NDVI=\frac{R_{NIR}-R_{Red}}{R_{NIR}+R_{Red}} \tag{2}\]  
where \(R_{NIR}\) and \(R_{Red}\) represent the reflectance at near-infrared (NIR) and red bands. Here, the average reflectance of 780-800 nm and 681-685 nm was used to stand for the NIR and red band reflectance, respectively.  
The existence of coupling between SIF and GPP has been illustrated in a variety of studies on various platforms, scales, and ecosystems, but whether the link between SIF and GPP is linear or nonlinear remains inconclusive [28; 53]. Previous studies indicated that the SIF-GPP relationship is linear [26; 30], but mounting studies have shown that the SIF-GPP relationship tends to be more nonlinear as the data temporal resolution improves [54; 55; 56]. Because of this, two fitted models, linear and nonlinear, were used to depict the SIF-GPP relationship for the 30-min averaged data, while for the daily averaged data, only the linear fitted model was used.  
\[GPP_{Linear}=k\times SIF \tag{3}\]  
\[GPP_{Nonlinear}=\frac{p_{1}\times SIF+p_{2}}{SIF+p_{3}} \tag{4}\]  
where \(GPP_{Linear}\) stands for the fitted results of SIF with the linear model, \(GPP_{Nonlinear}\) stands for the fitted results of SIF with the nonlinear model, and \(k\), \(p_{1}\), \(p_{2}\), and \(p_{3}\) stand for the fitted parameters of the models.  
The merits of the fitted models were then evaluated using R\({}^{2}\) and the root mean square error (RMSE) for a comprehensive evaluation.
## 3 Proposed Model
The attention weights are the key part of the interpretable model since they can analyze the MSFs learned from the MSFL and monolingual features learned from the single-task version of MSFL (MSFL_ST) to explain why the proposed model can learn generalized features representation and improve the recognition  
Figure 2: The overview of the MSFL model. effect of multiple languages. Finally, \(\mathbf{Z}\) is transformed by two fully connected layers and inputs the task-specific module for task classification. The loss function of our model is shown in Equation (4), where \(L_{e}\) represents the loss function of the emotion recognition task shown in Equation (5), while \(L_{l}\) represents the loss function of the language task shown in Equation (6). Note that considering the unbalanced samples of emotion categories and language categories, the weighted cross-entropy is used to perform emotion and language loss functions so that the network places equal emphasis on the categories with smaller sample sizes. \[L=w_{1}\times L_{\text{e}}(x_{\text{e}},y_{\text{e}})+w_{2}\times L_{l}(x_{l}, y_{l}) \tag{4}\]  
\[L_{e}(x,y_{e})=-w_{y_{e}}\log\biggl{(}\frac{\exp(x[y_{e}])}{\sum_{c_{\text{e}}} \exp(x[c_{\text{e}}])}\biggr{)} \tag{5}\]  
\[L_{l}(x,y_{l})=-w_{y_{l}}\log\biggl{(}\frac{\exp(x[y_{l}])}{\sum_{c_{\text{1}}} \exp(x[c_{\text{1}}])}\biggr{)} \tag{6}\]  
Generally, the weight of the \(i\)-th task \(w_{i}\) in the multi-task learning model can be decided by experience adjustment. However, due to imbalances between backpropagated gradients, multi-task networks are difficult to properly train. The task with the larger gradient magnitude will dominate the training process of the model and makes the model inadequate for the learning of other tasks. To obtain robust shared features that are useful across all tasks, an adaptive loss balancing method called grad normalization (GradNorm) is introduced, which controls the training of multi-task networks by adjusting the magnitude similarity of task feature gradients during the training process, thereby encouraging the network to learn all tasks at the same speed [67]. In this method, the \(w_{i}\) is no longer a fixed value but varies with the training step \(t\): \(w_{i}=w_{i}(t)\). Therefore, it aims to find the optimal \(w_{i}(t)\) for each training step \(t\), and results in two optimization goals. One is to optimize \(w_{i}(t)\), the other is to optimize the label loss \(L\). For the optimization of \(w_{i}(t)\), it can be divided into two aspects: balancing the different gradient magnitude \(\mathrm{G}_{W}^{(i)}(t)\) and balancing the different learning rate \(r_{i}(t)\) of each task \(i\) in each training step \(t\). The specific calculation formula can be found in [67].
# Detection and Monitoring of Fitting Progression on Gear Tooth Flank Using Deep Learning
## 2 Method Description  
To detect and monitor pitting on gear teeth, we used a method that is based on integrating domain knowledge with the faster-RCNN deep learning model [27] trained using bounding box annotations of the defects and teeth. The output of the faster R-CNN is an image with the bounding box of the area that has the shape of the defect and teeth, and the corresponding prediction confidence. This approach was used in [17] on a similar problem, where it gave satisfying results from a practical point of view, which is why the authors decided to use this neural network, as it is a tool that can lead to the practical contribution of this research.  
Faster-RCNN [27] is a CNN-based model whose input is an image. During processing, it proposes a bounding box localization of target objects and corresponding class probabilities, and results in an image of a desired region of interest as its output. Faster R-CNN (Figure 1) consists of three network components: a Feature Pyramid Network (FPN), a Regional Proposal Network (RPN) and a Fast R-CNN.  
The Feature Pyramid Network is a backbone network for feature extraction. The network input is an RGB image from which the Feature Pyramid Network extracts features that can be used for object localization and classification. These features are taken in the form of width \(\times\) height \(\times\) depth, where the depth is always 256 channels and it is constant across the extracted layers, which are generated in different scales depending on the layer in the network.  
The Regional Proposal Network identifies interesting areas on an image. It takes the feature maps from the backbone network through a 3 \(\times\) 3 convolutional filter to generate bounding boxes over the image along with their prediction confidence. Anchors are generated at each position with different scales and aspect ratios so that they can be considered as the foreground or background class. For the balanced dataset, the network reduces the number of labels to the level that they are equal to the foreground class by randomly selecting labels from the background class.  
The Fast R-CNN is used for object classification and bounding box regression by taking both the output feature map from the FPN and the prediction objects from the RPN. The network classifies the predicted object as being either an exact tooth on the gear or pitting on a tooth or a background class, and also outputs the corner coordinates of the bounding box that contains the object.
## 3 Results
### Post-COVID-19 Modifications
#### 3.4.2 Monitoring Approaches  
Evolution in measurement strategies:  
* Real-time monitoring: 41.4% of studies.
* Multi-parameter integration: 24.2% of studies.
* Remote monitoring capabilities: 34.2% of studies.  
The pie chart in Figure 5 summarizes the post-COVID-19 modifications observed in the studies, including real-time monitoring, multi-parameter integration, and remote monitoring capabilities.
## 3 Results and Discussion
### E-Nose  
As shown in Figure 5a, the sensor response curve of the sample on day 4 almost overlapped with that of day 0, indicating that potatoes maintained a fresh-like flavor during the first 4 days. With the extension of storage time, the values of the W5S (highly sensitive to nitrogen oxides, furans), W1S (sensitive to hydrocarbons), W1W (sensitive to sulfides and pyrazine), W2S (sensitive to alcohols, aldehydes and ketones) and W2W (sensitive to organic sulfides) sensors increased significantly, which indicates that various compounds, such as nitrogen oxides, furans, sulfides, alcohols and aldehydes, were formed during storage. [PERSON] et al. [43] also found that alcohols, aldehydes and sulfides increased during the storage of fresh-cut bell pepper.  
A principal component analysis (PCA) was used to analyze the differences of the flavor profiles of samples from different storage periods (Figure 5b). PC1 and PC2 explained 87.43% and 11.07% of the total variance, respectively. The cumulative variance contribution rate of the first two principal components reached 98.5%, which explained a large fraction of the overall variability. As the storage period was prolonged, the distance of day 4-12 from day 0 became longer. The PCA profile of day 4 almost overlapped with day 0, while those of day 8 and day 12 were clearly separated from day 0-4, indicating that the flavor quality deteriorated significantly after 8 days of storage and that the e-nose could be used to discriminate the freshness of peeled potatoes. Furthermore, the off-odor may be caused by spoilage bacteria (_Enterobacteriaceae_, _Erwinita_, _Luconostoc_ and _Lactococcus_) due to fermenting sugar [24; 28; 44].  
The loading plot (Figure 5c) showed a relationship among the variables of the e-nose sensors. The loading vectors of W3S, W1C, W3C and W5C had positive scores, and the other loading vectors had negative scores in the PC1 direction. The sensors of W3S, W6S, W2S and W1S had positive scores, and the remaining sensors had negative scores in the PC2 direction. All sensors were far from the origin, indicating that the response values of all sensors had effects on this PCA [45].
# Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
## 5 Path Loss Model for Multi-Frequency  
Multi-frequency path loss models were proposed to broad the frequencies and measurements range. The ABG model was used for path losses at various frequencies (multi-frequency model). Additionally, the CI and PLM models were used for both single and multi-frequency data sets. In our proposed study, the ABG model had a 1 m reference distance and 1 GHz reference frequency (\(f_{0}\) set to 1 GHz). The ABG model equation is given as [34]:  
\[PL^{ABG}(f,d)[dB]=10\alpha\log_{10}\left(\frac{d}{d_{0}}\right)+\beta+10\gamma \log_{10}\left(\frac{f}{f_{0}}\right)+\chi_{\sigma}^{ABG} \tag{11}\]  
where \(\alpha\) and \(\gamma\) are constant coefficients; these coefficients are majorly used to indicate the dependencies of frequency and distance on path loss, \(\beta\) is referred to as offset in path loss, \(f\) is frequency in GHz and \(\chi_{\sigma}^{ABG}\) is a Gaussian random variable. As for the CI model, the ABG model parameters were calculated using the MMSE approach.