esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusion-Based Localization System Integrating UWB, IMU, and Vision
## 2 Related Work  
### Visual-Inertial Odometry  
In GNSS-denied environments, VIO has become one of the most prominent solutions for motion estimation and mapping. By fusing the complementary information of visual and inertial sensors, VIO provides robust and accurate short-term localization [6]. [PERSON] et al. [9] introduced OKVIS, a keyframe-based VIO system that utilizes non-linear optimization. Although it achieves high-quality localization and mapping, it lacks loop closure mechanisms, which results in the accumulation of drift over time. Similarly, [PERSON] et al. [10] developed the Multi-State Constraint Kalman Filter (MSCKF), a filtering-based approach that reduces computational complexity by limiting state dimensionality, demonstrating good performance in large-scale real-world environments. [PERSON] et al. [11] proposed a tightly coupled optimization-based VI-SLAM system that integrates loop closure to address the significant drift present in earlier VI systems. This system is capable of achieving unbiased pose estimation in looped trajectories and has been widely adopted in the research community. [PERSON] et al. [12] from the Hong Kong University of Science and Technology developed the Visual-Inertial Navigation System-Mono (VINS-Mono), a robust monocular VIO system. It employs KLT optical flow tracking for front-end feature management and a sliding-window nonlinear optimization backend, while also integrating loop closure detection. This design offers excellent accuracy in diverse indoor and outdoor scenarios. [PERSON] et al. [13] presented a VIO method based on an iterated extended Kalman filter (EKF), which minimizes photometric error from image patches. This approach achieves lightweight, real-time fusion of inertial and visual measurements and provides reliable localization performance even in the absence of loop closure. [PERSON] et al. [14] introduced ORB-SLAM3, a feature-based SLAM framework that supports monocular, stereo, and inertial configurations. However, it is sensitive to environmental variations such as lighting changes. VINS-Mono [13] combines visual and inertial data using a sliding-window filter and requires careful initialization. Another notable contribution is OpenVINS [15], an open-source, real-time visual-inertial odometry system developed by [PERSON] et al. It supports monocular, stereo, and multi-camera configurations and is designed for easy integration and deployment in resource-constrained embedded systems. OpenVINS features a tightly coupled EKF backend and emphasizes repeatability, robustness, and portability, making it an attractive baseline system for real-time robotics applications.  
Despite these significant advancements, current VIO systems still suffer from limitations such as accumulated drift, sensitivity to visual degradation (e.g., poor lighting, motion blur), and the lack of absolute positioning references.
## 2 Materials and Methods
### Representing SIF Signals  
Where a representation of a SIF signal was required, it was modelled based on the properties of the SIF signal displayed in [PERSON] et al., 2019 [PERSON] et al. (2019). The SIF signal was represented as the sum of two normal distributions representing photosystem II (with standard deviations of 10 nm, and 18 nm), and a further normal distribution representing photosystem I (with a standard deviation of 20 nm). The overall and relative scaling of the resulting SIF signal was controlled by three parameters: a value of SIF for the apex of the 740 nm peak; a scaling factor for photosystem I (0-1); and a scaling factor for photosystem II (0-1). For more details on the SIF simulation used here see Supplementary Materials and Appendix A.2, Table A1.  
Figure 3: (**a**) Different types of influences (top row, blue line) that can confound SIF retrievals: uniform (left column), and non-uniform sloped (center column) and random (right column). Each of these influences is applied (middle row) to a sample of simulated FIREFLY spectra (black line), resulting in an adjusted spectrum (blue line). Valleys for indirect SIF retrieval (red x) are shown for seven absorption features. The resulting error in the SIF retrieval (bottom row) is shown for direct retrievals from every band (green line), and indirect retrievals from the absorption features (red x). (**b**) One absorption feature (identified by the red boxes in (**a**)) is simulated over a range of proportional depths to show the reaction of indirect SIF retrievals to uniform (left), sloped (middle), and random (right) influences.
## 1 Introduction
However, most of the above studies provide steady-state analytical solutions for grout pressure distribution, lacking an analysis of the grout diffusion process. In this study, the level set method is employed to simulate the interface evolution between grout and air. Through the integration of the discrete element method (DEM) with the finite element method (FEM), a heterogeneous pore structure model of the pea gravel backfill layer is constructed. The grout infiltration behavior within the backfilling layer during the injection process is systematically analyzed, addressing a research gap in the numerical simulation of grout infiltration in the backfill layer of double-shield TBM projects. This study implemented grout injection using both pressure boundary conditions and velocity boundary conditions, obtaining the permeability characteristics of grout within gravel pores and pressure distribution patterns. Building upon previous research, the basic principles of fluid mechanics and the limit equilibrium method are used to establish a mathematical model for synchronous grouting in shield tunnel construction. An expression for grouting pressure is derived and compared with the simulation results to verify the reliability of the model.
## 4 Basic Correlations for Reference Conditions
### Minimum Values of Incidence and Deviation Angles  
The variable \(i_{\text{min}}\) is also intended to be modeled by the correlation of conventional blades \(f_{\text{ref}}\). As shown in Figure 10a, the variable \(i_{\text{min}}\) can be obtained from the correlation of conventional blades \(f_{\text{ref}}\), which has a special solidity, \(\sigma_{\text{se}}\). Therefore, the variable \(i_{\text{min}}\) can be expressed as in Equation (14). It is found that the special solidity can be expressed linearly with \(\sigma_{\text{cs}}\) and \(\sigma_{\text{eq}}\), as shown in Equation (15), where the constant coefficients \(k_{\text{cs}}\) and \(k_{\text{eq}}\) have been previously listed in Table 4.  
Figure 9: Validations of equivalent (**a**) incidence and (**b**) deviation angles for conventional blade correlations (symbols denote CFD data).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficients** & \(\mu_{1}\) & \(\mu_{2}\) & \(\mu_{3}\) \\ \hline for \(i\) & \(0.12419\) & \(0.96242\) & \(1.25391\) \\ for \(\delta\) & \(-0.43210\) & \(-0.58577\) & \(1.18477\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Constant coefficients for the correction functions of equivalent conventional blades.  
The special solidity can be graphically derived through Figure 10a. It is necessary to note that the values of the geometry parameters substituted into the conventional blade correlation \(f_{\text{ref}}\), including the camber angle, inlet metal angle and maximum thickness-chord ratio, are the same as those of the corresponding PBs. The predictions of the minimum incidence values based on the effects of solidity of PBs are in good agreement with the CFD database, as presented in Figure 10b.  
\[i_{\text{min}}=f_{\text{ref}}(K_{\text{imod}}\varphi,\kappa_{1}+i_{\text{min}}, \sigma_{\text{se}},t_{\text{max}}/C) \tag{14}\]  
\[\sigma_{\text{se}}=k_{\text{cs}}\sigma_{\text{cs}}+k_{\text{eq}}\sigma_{\text {eq}}. \tag{15}\]  
Similarly, the variable \(\delta_{\text{min}}\) can be derived by a liner combination of \(\delta_{\text{cs}}\) and \(\delta_{\text{eq}}\), as given in Equation (16). The variable \(\delta_{\text{cs}}\) equals the deviation angle of conventional blades but with the solidity replaced by \(\sigma_{\text{cs}}\), as shown in Equation (17):  
\[\delta_{\text{min}}=\delta_{\text{eq}}-k_{\delta}\big{(}\delta_{\text{cs}}- \delta_{\text{eq}}\big{)} \tag{16}\]  
\[\delta_{\text{cs}}=g_{\text{ref}}(K_{\beta\text{mod}}\varphi,\kappa_{1}, \sigma_{\text{cs}},t_{\text{max}}/C). \tag{17}\]  
Figure 11a illustrates the graphical derivation. Figure 11b gives a comparison between the correlation values and CFD database, which also shows good agreement.  
Figure 11: (**a**) Re-illustration of Figure 8b with solidity of PBs and (**b**) comparison between model predicted \(\delta_{\text{min,cor}}\) and CFD calculated \(\delta_{\text{min,CFD}}\).  
Figure 10: (**a**) Re-illustration of Figure 8a with solidity of PBs and (**b**) comparison between model predicted \(i_{\text{min,cor}}\) and CFD calculated \(i_{\text{min,CFD}}\).
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 4 Execution of SF Chart and Operation Modes of HES
### The Islanded_Mode  
The Islanded_Mode is triggered when load shedding occurs. At this time, no electric power is supplied from the grid. The SF chart of the Islanded_Mode during RES operation is shown in Figure 11b. During this period, the load demand is met either by the ESU and PV, or if necessary, using a diesel generator. The objective is to ensure the availability and continuity of supply and to optimize the utilization of the remaining energy from the PV (if available). The operations of RES and generator are represented by the child states RES_Mode and Gen_Mode, respectively. Since the PV is not always available and intermittent (weather dependent in nature), the generator is needed to sustain the load demand. However, the extraction of energy from PV is given priority due to the much higher operational cost of the latter.  
Figure 11: (**a**) The state chart for Grid_Connected_Mode. (**b**) The state chart for Islanded_Mode.  
To complement the energy excess/deficit, the ESU is charged or discharged. For example, if there is a surplus of PV power, the excess is used to charge the ESU. Accordingly, ESU delivers the deficit power to the load if the PV energy is not sufficient. These events take place in the child states, namely PV_Mode and ESU_Mode, respectively. In addition, the generator operation is performed using two generators of different sizes. The small generator Gen1 is activated first to supply the energy deficit between RES and load. However, Gen2 contributes if the load exceeds the power supply. The child states Gen1_Mode and Gen2_Mode represent the generator operation of HES. The parent-child relationships for Grid_Connected_Mode and Islanded_Mode, along with their respective CFT, are summarized in Table 3.
## 2 Signal Modeling and CFR Method  
A block diagram of an SDR downlink is depicted in Figure 1, which presents a system able to support the real-time baseband signal generation with several bandwidths. The front-end system-level analysis for specific application and implementation with a digital signal processing approach, provides the flexible signal treatment between domains (Digital-FPGA to RF) as shown in Figure 1. Furthermore, more issues related to complex gain variations (amplitude and phase), in the RF front end (RFFE) performance are considered in three effects: the amplitude and frequency distortion, and memory effects to modeling the signal properties. So that, in this investigation the SDR transceiver implementation consider three key aspects: (_i_) modulated signal generation, (_ii_) feedback response of the DUT, and algorithm correction for (_iii_) CFR and offline predistortion (PD) calculation. Thus, the input \(x(n)\) and output \(y(n)\) of the DUT are used to calculate the signal properties such as amplitude and phase distortions added in the RF domain. Thus, the signals are calculated to evaluate the figures of merit of the system and are generated by the DUT in order to adapt the algorithms in terms of CFR, PAPR, gain, and further to obtain the predistortion function.  
Figure 1: Block diagram for the SDR downlink system process with the feedback observation path up-conversion/down-conversion.
## 3 Results and Discussion
### Effects of Oxygen Equivalence Ratio \(R_{o}\)  
Oxygen equivalence ratio, \(R_{o}\), refers to the amount of O\({}_{2}\) supplied to the gasifier, and it is also a crucial parameter that effects the syngas composition. In this simulation, the \(R_{o}\) was varied from  
Figure 3: Effect of gasification temperature \(T\) on raw syngas composition and system performance (\(R_{o}\) = 0.2 and \(R_{c}\) = 0.5). (a) BC; (b) MSW; (c) \(R_{M}\) = 0.6  
0.1 to 1.0 by adjusting the oxygen feed rate, and the effects of \(R_{\rm o}\) on the syngas composition and system performance are shown in Figure 4. It can be found that the mole fraction of CO, H\({}_{2}\), and CH\({}_{4}\) gradually decreased, whereas that of CO\({}_{2}\) had a large upward trend, which implies that the increase of supplied oxygen contributes to the enhancement of the R7 and R8 reactions. [PERSON] et al. [46] carried out the co-gasification experiment of coal and biomass to research the effect of equivalence ratio and obtained the similar conclusion. Based on Figure 4a, the CGE and OEE of system showed a trend of increasing, decreasing, and then reaching a maximum at \(R_{\rm o}\) = 0.2. This indicates that an equivalence ratio of 0.2 is very suitable for the gasification characteristics of individual BC. In addition, the CGE and OEE for MSW and the blends with \(R_{\rm M}\) = 0.6 consistently decreased with increasing \(R_{\rm o}\). This trend might occur because the presence of oxygen promotes the combustion reaction of combustible gases, resulting in the reduction of the syngas heating value. In addition, it can be found that the OEE reduction extent of the system was less than that of CGE for three samples. This is because the amount of syngas production decreases at higher equivalence ratio, but the released heat \(Q_{5}\) of the entail system increases, which eventually leads to slower decline of the OEE.  
Figure 4: \(Cont.\)
# Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data +
## 2 Materials and Methods
### Training and Testing of the DNN  
The studied 20,000 patients were divided randomly into 3 sets: a training set (70%), a validation set (10%), and a testing set (20%). The training set was used to build the prediction model, during which all the parameters (i.e., the weights and biases) in the network were updated, so that the prediction outputs approximated the true outcomes. The parameters were initially set to be random values and were updated iteratively using a common approach called mini-batch stochastic gradient descent. Specifically, the training set was randomly divided into small groups of 100, with each group called a mini-batch. The temporal data of each mini-batch (i.e., 100 temporal images) were supplied to the input layer to compute 100 output probabilities, which were compared with the corresponding 100 binary outcomes to calculate a distance. The distance was calculated using a binary cross-entropy metric. We then added L1 and L2 regularizations to the distance to make an objective function. The purpose of the added L1 and L2 regularizations was to make the parameters sparse and to prevent the parameters from being too large (in absolute value), respectively. Then, the parameters were updated to reduce the objective function. One pass over the training set is called an epoch. Since the training set had 14,000 patients, the parameters would be updated 140 (=14,000/100) times within one epoch.  
After one epoch ends, the training could be continued on another epoch. However, the training must be stopped at some point because the overfitting effect would appear if training continued without stopping. A common approach to prevent overfitting is to use a validation set. In this study, we used the validation set to determine after how many epochs the training should stop. After each epoch of training, we applied the trained model to the pre-set validation set to calculate the performance of the prediction. We chose AUC to measure the performance. Initially, the AUC on the validation set usually showed an increasing trend from one epoch to the next. After some epochs of training, the increasing trend stopped, and a decreasing trend would appear. Note that an increasing trend does not mean the value is always increasing. Occasionally, the value would decrease slightly during one or more epochs but would increase again after that and reach a higher level than before, and that is still counted as an increasing trend. To find when the decreasing trend started, we looked for the number of epochs such that the AUC at that point was higher than all the AUCs before and also higher than 10 consecutive AUCs afterward. Then, we selected the model corresponding to the AUC value and thus found the final model, which was then applied to the testing set to report the performance in terms of AUC and accuracy.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 3 ISLs Design
### Shortest Path Transmission  
Determining the data transmission path between two satellites is essential for the LEO mega-constellations system. According to graph theory, the topology of a mega-constellation can be abstracted as a time-varying undirected graph, represented as:  
\[\left\{\begin{array}{c}G=(V,E)\\ V=\left\{v_{i}|v_{i}\in(v_{1},\,v_{2},\ldots,v_{N_{k}})\right\}\\ E=\left\{\left(v_{i},v_{j}\right)\middle|v_{i},v_{j}\in V\right\}\end{array}\right.\]  
where \(V\) denotes the set of satellite nodes, and \(E\) is the set of edges between satellite nodes.  
The widely used shortest path first protocol (SPF) is the basis of end-to-end low-latency transmission. Dijkstra algorithm [PERSON] (1996) is a well-known and effective method for finding the shortest path in the network. Figure 5 shows the pseudo-code of the algorithm.
# Reverse Time Migration Imaging Using SH Shear Wave Data
###### Abstract  
In this paper, we discussed the reverse time migration imaging of compressional wave (P-wave) and horizontally polarized shear wave (SH shear wave) seismic data, together with P- and SH shear wave constrained velocity model building. In the Sanhu area in Qaidam Basin, there are large areas of gas clouds, which leads to poor P-wave seismic imaging. The P and SH shear wave seismic data of a co-located survey line with the same acquisition geometry were used to access their imaging capability using reverse time migration. We first estimated the change in P-wave and SH shear wave velocity ratio using pre-stack time migration (PSTM) for constraining the overall depth domain velocity model. Additionally, we then used an eighth-order finite difference scheme for P-wave reverse time migration on a variable grid and used the sixth-order combined compact difference (CCD) wave field simulation method for SH shear wave reverse time migration on a regular grid. The results show that the constrained velocity model produces a good match in the overall geological structure shown in the P-wave and SH shear wave reverse time migration results. However, in the gas cloud areas, SH shear wave reverse time migration has obvious imaging advantages, which can clearly image the structure inside the gas clouds.  
shear wave; reverse time migration; numerical simulation; Sanhu depression; Qaidam Basin Article  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; Reverse Time Migration Imaging Using SH Shear Wave Data  
Asciences 2022  
1 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China 12 BGP Inc., China National Petroleum Corporation, Beijing 100083, China 2  
* Correspondence: xy1962 [EMAIL_ADDRESS]; Tel.: +86-10-8973-2259
# \(K\)-Means Cloning: Adaptive Spherical \(K\)-Means Clustering
## 2 K-Means Cluster Cloning
### Clusters' Fitness Function  
To measure the quality of the proposed clustering, we exploit the silhouette as a fitness function [70; 71; 72; 73]. In order to calculate the final silhouette fitness function, two inputs should be firstly computed for all data objects as shown in the following.  
* **Inner Dissimilarity (\(\alpha(i)\)):** is the average dissimilarity between a data object \(i\) and other objects in its cluster \(C_{j}\), which can given by \[\alpha(i)=\frac{1}{(n_{j}-1)}\sum_{\begin{subarray}{c}l\in C_{j}\\ l\
eq i\end{subarray}}d(i,l),\] where \(d(\cdot,\cdot)\) is the classical Euclidean distance used in \(K\)-means, and \(n_{j}\) is the number of objects in cluster \(C_{j}\).
* **Outer Dissimilarity (\(\beta(i)\)):** is the minimum distance between a data object \(i\) to cluster centers except its cluster \(C_{j}\), this can be computed in two steps as follows. First, we compute the average dissimilarity (\(d(i,C_{\kappa})\)) between \(i\) and other cluster \(C_{\kappa},\kappa\
eq j\), \[d(i,C_{\kappa})=\frac{1}{(n_{\
u})}\sum_{\begin{subarray}{c}l\in C_{\kappa} \\ k\
eq j\end{subarray}}d(i,l),\] where \(n_{\kappa}\) is the number of objects in cluster \(C_{\kappa}\). Then, \(\beta(i)\) can be computed as, \[\beta(i)=\min_{\begin{subarray}{c}\kappa=1,\dots,K\\ k\
eq j\end{subarray}}d(i,C_{\kappa}).\]For each data object \(i\) in all clusters, the overall dissimilarity \(s(i)\) is expressed by [73]:  
\[s(i)=\frac{\beta(i)-\alpha(i)}{max\{\alpha(i),\beta(i)\}}. \tag{1}\]  
From Equation (1), the cluster silhouette \(S(\kappa),\kappa=1,\dots,K\), is the average dissimilarity of its data objects.  
\[S(\kappa)=\frac{1}{n_{\kappa}}\sum_{i\in\mathcal{C}_{\kappa}}s(i). \tag{2}\]  
Finally, the clusters fitness \(SF(K)\) function is the average of all cluster silhouettes.  
\[SF(K)=\frac{1}{K}\sum_{\kappa=1}^{K}S(\kappa). \tag{3}\]  
It is worthwhile to mention that the object dissimilarity \(s(i)\) satisfies the condition,  
\[-1\leq s(i)\leq 1.\]  
This condition is still valid for \(S(\kappa)\) and \(SF(K)\) since they are computed as averages of \(s(i)\). Moreover, each different value of \(K\) generates a different \(SF(K)\). The higher value \(SF(K)\) can achieve, the better clustering can be obtained.