esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Permanent Facility for Altimeter Calibration
### Infrastructure and Instrumentation
#### 2.1.5 The GNSS Monitoring Network
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Ell.Height** & **\(\sigma\)(lat)** & **\(\sigma\)(lon)** & **\(\sigma\)(h)** & **Time Span** \\  & **(Deg)** & **(Deg)** & **(m)** & **(mm)** & **(mm)** & **(mm)** & **(Years)** \\ \hline \multirow{2}{*}{CDN0} & N 35 20 & E 23 46 & \multirow{2}{*}{1049.5183} & \multirow{2}{*}{1.9} & \multirow{2}{*}{2.5} & \multirow{2}{*}{7.6} & \multirow{2}{*}{2014.49–2018.44} \\  & 16.024403 & & & & & & \\ \hline \multirow{2}{*}{CDN2} & N 35 20 & E 23 46 & \multirow{2}{*}{1050.4080} & \multirow{2}{*}{1.7} & \multirow{2}{*}{1.9} & \multirow{2}{*}{8.0} & \multirow{2}{*}{2016.40–2018.44} \\  & 16.291142 & & & & & & \\ \hline \multirow{2}{*}{CRS1} & N 35 18 & E 23 31 & \multirow{2}{*}{21.2075} & \multirow{2}{*}{2.0} & \multirow{2}{*}{1.8} & \multirow{2}{*}{5.4} & \multirow{2}{*}{2008.18–2018.44} \\  & 12.649002 & & & & & & \\ \hline \multirow{2}{*}{GVD0} & N 34 50 & E 24 6 & \multirow{2}{*}{123.8719} & \multirow{2}{*}{2.2} & \multirow{2}{*}{1.6} & \multirow{2}{*}{5.6} & \multirow{2}{*}{2003.04–2017.35} \\  & 18.578282 & & & & & & \\ \hline \multirow{2}{*}{GVD7} & N 34 50 & E 24 7 & \multirow{2}{*}{20.1685} & \multirow{2}{*}{1.5} & \multirow{2}{*}{1.8} & \multirow{2}{*}{5.8} & \multirow{2}{*}{2009.37–2018.44} \\  & 52.744567 & & & & & & \\ \hline \multirow{2}{*}{GVD8} & N 34 50 & E 24 7 & \multirow{2}{*}{22.2757} & \multirow{2}{*}{1.6} & \multirow{2}{*}{1.9} & \multirow{2}{*}{6.2} & \multirow{2}{*}{2010.50–2018.44} \\  & 52.612206 & & & & & & \\ \hline \multirow{2}{*}{IMS1} & N 35 22 & E 24 28 & \multirow{2}{*}{35.9138} & \multirow{2}{*}{1.8} & \multirow{2}{*}{1.6} & \multirow{2}{*}{5.0} & \multirow{2}{*}{2010.19–2015.19} \\  & 12.547506 & & & & & & \\ \hline \multirow{2}{*}{MEN2} & N 35 40 & E 23 44 & \multirow{2}{*}{265.7064} & \multirow{2}{*}{2.9} & \multirow{2}{*}{3.1} & \multirow{2}{*}{5.9} & \multirow{2}{*}{2013.26–2018.25} \\  & 12.897887 & & & & & & \\ \hline \multirow{2}{*}{RDK1} & N 35 11 & E 24 19 & \multirow{2}{*}{25.5337} & \multirow{2}{*}{3.7} & \multirow{2}{*}{1.9} & \multirow{2}{*}{9.0} & \multirow{2}{*}{2009.18–2017.44} \\  & 15.375737 & & & & & & \\ \hline \multirow{2}{*}{SELI} & N 35 21 & E 23 50 & \multirow{2}{*}{1038.0693} & \multirow{2}{*}{1.9} & \multirow{2}{*}{2.1} & \multirow{2}{*}{4.7} & \multirow{2}{*}{2009.52–2013.80} \\  & 14.334437 & & & & & & \\ \hline \multirow{2}{*}{TUC2} & N 35 31 & E 24 4 & \multirow{2}{*}{14.015013} & \multirow{2}{*}{160.8894} & \multirow{2}{*}{2.0} & \multirow{2}{*}{1.9} & \multirow{2}{*}{4.6} & \multirow{2}{*}{2004.47–2018.44} \\  & 59.482697 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coordinates and uncertainties (weighted root mean square error) for 11 GNSS stations in western Crete from the established monitoring network in support of the Permanent Altimeter Calibration Facility (ITRF 2008).
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.4 Integrating Large Language Models for Underwater SLAM  
LLMs have emerged as powerful tools for semantic understanding and natural language interactions. In the broader SLAM domain, LLMs have already been employed to enhance visual place recognition and localization. For example, LP-SLAM [178] leverages LLMs to detect text in scenes and use it as landmarks for mapping and localization. Similarly, FM-Loc [179] integrates foundation models, including LLMs, to improve place recognition by incorporating semantic reasoning into the SLAM pipeline, enhancing robustness in complex environments.  
In underwater contexts, LLMs have demonstrated significant promise. Marine-Inst [180] employs vision-language models to achieve semantic instance understanding, enabling underwater robots to interpret their surroundings at a semantic level, such as identifying objects or features in the environment. ChatSim [181] integrates LLMs with underwater simulations, providing intuitive natural language control of simulated environments, which could be extended to real-world underwater systems.  
These examples collectively illustrate how LLMs can improve underwater SLAM by enabling semantic understanding, supporting natural language interfaces, and facilitating data integration. Future research should focus on the following aspects:  
* **Efficient Deployment Strategies:** developing compact representations of LLMs through model distillation and quantization to enable their deployment on resource-constrained underwater SLAM systems;
* **Multimodal LLMs:** advancing multimodal LLMs that integrate acoustic, visual, and textual data for a holistic understanding of underwater environments;
* **Natural Language Interfaces:** exploring the use of LLMs to enable natural language control and interaction with underwater SLAM systems, improving usability and accessibility.
## 1 Introduction
This is of particular importance for the safe and reliable operation of electric vehicles. The proposed algorithm makes use of the dual-filter architecture to allow access to the model's parameters and combines it with the IMM method to allow access to different aging rates of the parameters. The resulting algorithm is then combined with a KF and an ECM to estimate the SOC and parameters of the battery. The final algorithm is referred to as a dual-KF-IMM if a KF was implemented. Furthermore, the proposed method is combined with a sliding innovation filter (SIF) in an effort to improve robustness to modeling uncertainties. The SIF is a predictor-corrector filter with similar estimation capabilities to the KF. However, the SIF offers robustness to modeling uncertainties at the cost of estimation accuracy (i.e., it is sub-optimal in its standard formulation). Therefore, the well-known KF and robust SIF are implemented and utilized by the IMM in this study. We were able to combine the best elements of the KF (optimality) and the SIF (robustness) for strong estimation results. This is of particular use for electric vehicles when dealing with battery health estimation for improved safety and reliability. The first main contribution is the study of the SIF in a dual-IMM setting, which leads to a new strategy called the dual-SIF-IMM. The second main contribution is the study of the IMM algorithm when combined with the dual-filter architecture to estimate the SOH of an LiB subject to different cycling conditions. The third main contribution is the detailed comparison between the dual-KF-IMM and the dual-SIF-IMM, the results of which were obtained based on a well-known dataset used for benchmarking. This paper is structured as follows: Section 2 presents the battery and parameter models. Section 3 details the experimental data and estimation algorithms. Section 4 covers the artificial measurements. Section 5 describes the model parameter identification results. Section 6 presents the experimental setup and details the results of the proposed strategy. Section 7 presents the concluding arguments of the work and proposes future research that may be explored in the area.
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 4 Discussion
### Limitations  
This study proves that it is a worthwhile endeavor to evaluate the forest ecological function on the basis of administrative regions. This work contributes to a more systematic understanding of interactions between human and nature, and it should be an indispensable foundation for all the forest-related discussions. We also gain some interesting findings through exploring the influence of human activities on forest ecological function. It is well-known that natural factors have greater impacts on forest ecological function than human activities in untrod areas of high altitude. Surprisingly, this study confirms that in such regions with considerable human activities, the level of forest ecological function may still ultimately depend on natural factors. This is due to the fact that the positive or negative effects of different human activities on forest systems constrain or counteract each other, but the final effect of the counteracting does \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{6}{c}{**Coefficient**} \\ \cline{2-9}  & **I** & **II** & **III** & **IV** & **V** & **VI** & **VII** & **VIII** \\ \hline ECO & 0.029 & 0.208 & 0.382 & \(-\)0.001 & \(-\)0.030 & \(-\)0.196 * & \(-\)0.077 & 0.093 \\ AGRI & 0.062 & \(-\)0.143 & \(-\)0.445 *** & \(-\)0.184 ** & \(-\)0.127 & \(-\)0.107 ** & \(-\)0.065 & 0.001 \\ INDS & 0.102 & \(-\)0.091 & \(-\)0.063 & \(-\)0.087 & \(-\)0.035 & 0.056 * & 0.031 & 0.088 \\ PDS & 1.686 & \(-\)1.382 & \(-\)1.727 & \(-\)0.787 & 0.120 & 0.073 & 0.122 & 0.636 \\ URB & 0.105 & \(-\)0.083 & \(-\)0.386 ** & \(-\)0.008 & \(-\)0.070 & \(-\)0.015 & 0.039 & 0.233 ** \\ PDUC & 0.193 & \(-\)0.099 & \(-\)0.790 *** & 0.020 & 0.129 & \(-\)0.036 & 0.055 & 0.206 ** \\ LIVE & \(-\)0.420 & \(-\)0.017 & \(-\)0.024 & 0.035 & 0.131 & 0.075 & 0.092 & \(-\)0.010 \\ PROT & 0.124 & 0.142 & 0.258 * & 0.130 & 0.034 & 0.030 * & 0.008 & 0.032 \\ TEMP & 0.006 & 0.091 & \(-\)0.557 *** & 0.285 & 0.479 ** & 0.602 *** & 0.160 & \(-\)0.116 \\ PREC & 0.363 & 0.319 ** & 0.487 *** & 0.332 ** & 0.239 * & 0.463 *** & 0.059 & \(-\)0.005 \\ ELEV & \(-\)0.233 & \(-\)0.141 & \(-\)1.218 *** & 0.406 ** & 0.791 ** & 0.889 *** & 0.302 & 0.179 \\ SLOP & \(-\)0.006 & 0.090 & 0.155 & 0.115 * & 0.081 & 0.138 * & 0.202 ** & 0.199 \\ SOIL & \(-\)0.039 & \(-\)0.126 & \(-\)0.248 * & \(-\)0.113 & \(-\)0.006 & 0.074 * & 0.058 & 0.052 \\ \hline \hline \end{tabular} Note: **, *, and * indicate significance at 0.01, 0.05, and 0.1 levels, respectively (two-tailed). Refer to Table 2 for definitions of the acronyms.  
\end{table} 
Table 6: Robustness test results for different physical geographic regions.
## 3 Results and Discussion
### Optimization Results and Optimal Wing Performance
However, the rotational and added-mass components contribute little to flapping power consumption during the supination and pronation of each half-stroke, respectively. Due to the deceleration of pronation and the acceleration of supination, during these two fast rotations, the added-mass forces have different directions (see Figure 8c), always opposite to the normal acceleration of the point used for reference in each spanwise element. The same is true for the rotational force, as it is in the opposite direction to each rotation. These are why the flapping power associated with them has opposite signs during these two rotations. Thus, the added-mass component is dominant during pronation, but the rotational component, by contrast, plays a dominant role in the flapping power consumption during supination. As for the power along the pitching axis in Figure 8e, only the rotational component has positive portions. Because the normal velocity and the rotational force component of a wing section induced by rotation motion are opposite, two positive local peaks occur during fast rotations. However, all other power components have negative values over stroke duration. These results mean that the pitching of this wing does not require much power consumption to overcome the moment around the pitching axis. Compared with the flapping power consumption, it has a relatively smaller magnitude. These are also why subtle differences in the chord-wise location of the centers of these forces have a weak influence on total power consumption, as described earlier. As seen in Figure 8f, the contribution of the aerodynamic component to the mechanical power output has three local peaks similar to those in the curve of the total vertical force (Figure 8g). When the wing decelerates, the negative portion can be dissipated. As a result, the mechanical power out is concentrated mainly during the earlier phase of each half-stroke. The synergistic effect between aerodynamic and inertial contributionsleads to three positive peaks in the total mechanical power output. The cycle-averaged power is calculated to be approximately 0.99 W, and the power required to overcome the aerodynamic load is about 1.8 times that of the inertial load. The inertial contribution is not as significant as the aerodynamic contribution, due to the small wing density in this study. Figure 8: The objective function values in the current optimization, the wing kinematics, vertical forces, flapping, and rotational power outputs of a single wing. (**a**) The logarithmic objective function values obtained at 600 design points, (**b**) the flapping angle and the rotation angle at the root and tip of the wing, (**c**) the total vertical force and its individual components, and the (**d**) flapping, (**e**) rotational, and (**f**) mechanical power output.
# Model Updating Using Frequency Response Functions Based on Sherman-Morrison Formula
## 2 Theoretical Analysis
### Frequency Response Function (FRF)'s Model Updating Based on Sensitivity Analysis
Therefore, the core of the model updating is how to establish the sensitivity matrix \(S\). It is not difficult to find from Equations (4) and (5) that for multi-degree-of-freedom systems, the solution of the sensitivity matrix problem finally boils down to the sensitivity of the system mass and stiffness to the design parameters. Conducting the model updating progress by the obtained sensitivity \(\mathbf{S}\), and the FRFs was iteratively updated by using Equation (27) to obtain the finite element model consistent with the actual structure. A simulation example of a truss and a solar wing is used to verify the effectiveness of this method in this paper.
## 2 Channel Estimation in OFDM
While simulating with stochastic channels, users should choose a group of channels by clicking 'Add Channel' button. After that, curves of BER and MSE will be plotted by clicking the 'Generate Figure' button. Repeating the progress of modifying parameters and plotting curve, result of performance comparison will be produced. Eventually, the simulation figures can be saved in high-resolution by clicking 'Save Figure' button. The design of simulation platform puts forward a high-efficiency approach to the research on UWA channels. One of the distinct advantages lies in the reduction of workload for finding real data, building input files, and processing output data. The users using simulation platform without any previous experience in BELLHOP model or MATLAB programing can still perform simulation for research. Besides, the GUI of simulation platform makes using BELLHOP model more intuitive and provides users with immediate visual feedback about the results of the program. Moreover, based on the real environment data acquired from open databases, the simulation platform provides users a flexible way to choose parameters. In sight of the accuracy and convenience of simulation platform, users can modify position, depth, frequency, SWH, and even the number of beams to study the UWA channel. At the same time, the factors of OFDM modulation, pilot pattern, and estimation algorithms can also be analysed by changing these values. In addition, by combining UWA channel modelling with OFDM channel estimation, the simulation platform helps users analyse characteristics of UWA channel and pick the appropriate OFDM implementation scheme for UWA communication device. Figure 2: The user interface of simulation platform. (**a**) underwater acoustic (UWA) channel modelling. (**b**) OFDM channel estimation.
## 4 Results and Discussion  
### Parametric Study on Breakthrough Curves of AC/Zeolite 13X Layered Bed  
#### 4.1.1 Effect of Feed Pressure on Breakthrough Curves  
Figure 3 shows the effect of different feed pressures on the breakthrough curve of the layered bed. It can be observed that as the feed pressure increases, the breakthrough of each impurity gas is progressively delayed. Notably, the difference in the breakthrough time between 8 bar and 10 bar is slightly greater than the difference between 10 bar and 12 bar. According to the adsorption isotherm model, higher pressure corresponds to a higher adsorption amount. With a constant feed flow rate, it takes more time for the gas to reach adsorption saturation. Therefore, as the pressure increases, the breakthrough time of impurity gases also increases. Similarly, this reason explains why the roll-up of CO also rises with increasing pressure. However, the variations in breakthrough times across different pressures suggest that, as the pressure increases, its effect on gas adsorption gradually diminishes.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 6 Numerical Experiments
### Algorithm Parameter Setting  
The self-learning mechanism is employed to dynamically adjust parameters through population evolution information. However, the initial values of key parameters are determined using the Taguchi method, such as population size _pop_, base learning rate \(B\), initial inner-loop iterations \(L\), and outer-loop iterations \(K\). Taking example C10, each parameter is set to three levels: 50, 80, and 110 for population size; 0.4, 0.5, and 0.6 for base learning rate; 20, 50, and 80 for initial inner-loop iterations; and 20, 60, and 100 for outer-loop iterations. An orthogonal array L27 is used for the analysis of the four parameters with three levels in Minitab 22 software. The Taguchi method is one of the main tools used for this, and since the objective is minimization, the small-type Taguchi method is applied to identify the best parameter combination for achieving the minimum. The results, as shown in Figure 8, indicate that the optimal parameter combination is 80, 0.4, 50, and 60. The remaining parameters are set as follows: \(P_{c1},P_{c2},P_{m1},P_{m2},M\) are set to 0.4, 0.8, 0.03, 0.08, and 1000, respectively.
## 4 Three-Dimensional Modelling by the SfM-MVS Approach
### Three-Dimensional Accuracy Assessment Based on GCPs in SfM Photogrammetry  
The evaluation of the accuracy of the point cloud obtained from the photogrammetric process is carried out using check points (CPs), which, in the same way as GCPs, are materialised on the area to be surveyed. Indeed, GCPs and CPs can be acquired by GNSS survey or Total Station (TS).  
TS is an electronic surveying instrument that combines an electronic distance measuring device (EDM) with an electronic theodolite, integrated with a microprocessor, electronic data collector, and storage system. The TS measures horizontal angles, vertical angles, and slope distance from the TS to the reference point.  
Accuracy measurements are based on the variation between the value obtained from the UAV photogrammetric solution and the reference value at the CP considered.  
The parameter that defines the deviations between the (more accurate) reference data and the UAV-derived data is defined as root mean square error (RMSE), which is a statistical index that defines the internal variance provided by the ratio of the internal deviance (or deviance within groups) to the total numerosity.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Software** & **Description** & **Reference** \\ \hline \multirow{6}{*}{**Regard 3D**} & \multirow{6}{*}{Regard 3D is an SfM programme that can create 3D models of objects using a series of photographs taken from different viewpoints. The free, open-source software is available on various platforms (Windows, OS X, and Linux) and uses third-party tools and libraries. Several parameters enable the processing of models in a controlled and accurate manner. Furthermore, the actions necessary for processing the 3D model are implemented within the software in a logical and sequential manner. & \\ \cline{1-1}  & \multirow{6}{*}{[112,113]} \\  & & \\ \cline{1-1  
In UAV photogrammetry, the evaluation of accuracy is defined by the following relationships [125]:  
\[RMSE_{x}=\sqrt{\frac{\sum_{i=1}^{n}(x_{UAS}-x_{S})^{2}}{n}}, \tag{11}\]  
\[RMSE_{y}=\sqrt{\frac{\sum_{i=1}^{n}(y_{UAS}-y_{S})^{2}}{n}}, \tag{12}\]  
\[RMSE_{z}=\sqrt{\frac{\sum_{i=1}^{n}(z_{UAS}-z_{S})^{2}}{n}}, \tag{13}\]  
where:  
\(n\)--number of CPs tested for each project;  
\(x_{UAS},~{}y_{UAS},~{}z_{UAS}\)--coordinates estimated in the bundle adjustment for the \(i^{th}\) CP;  
\(x_{S},~{}y_{S},~{}z_{S}\)--coordinates obtained from the topographic survey for the \(i^{th}\) CP.  
These RMSE values (see Equations (11)-(13)) show the accuracy assessment in East (X), North (Y), and height (Z) and are carried out on surveyed points that were not used for georeferencing.  
The horizontal error (XY), i.e., the error distributed in the two dimensions, is defined by the relationship [126]:  
\[RMSE_{xy}=\sqrt{\left(RMSE_{x}\right)^{2}+\left(RMSE_{y}\right)^{2}}. \tag{14}\]
## 2 Measured Parameters
### HF Channel Parameters
Finally, we have measured the Doppler frequency shift caused by the continuous movement of the reflective layers during the day, so we can measure the absolute speed of the layers.
# Augmented Reality Assisted Assembly Training Oriented Dynamic Gesture Recognition and Prediction
## 3 Dynamic Gesture Recognition in Augmented Reality Assisted Assembly Training
### Action Categories  
According to the American Society of Mechanical Engineers (ASME) standard operations [28], there are five typical types of assembly tasks in practical industrial assembly: \"matching\", \"conjugating\", \"joining\", \"fastening\", and \"meshing\", as shown in Figure 3. The essential operations for conducting these assembly tasks can be categorized into \"inserting\", \"fastening\", and \"screwing\", presented in Figure 4. Based on these typical tasks and operations, we conclude six basic actions [29]:  
* \"Rotating\": trainees can change the orientation of objects;
* \"Moving\": the movement of an object or a tool;
* \"Grasping\": trainees can gain an object or a tool;
* \"Releasing\": trainees can put down an object or a tool;
* \"Pointing\": the selection action of an option or a virtual workpiece in AR environment;
* \"Scaling\": trainees can resize objects.  
The actions are demonstrated in Figure 5:  
Figure 4: The types of typical assembly operations: (**a**) inserting, (**b**) fastening, and (**c**) screwing.  
Figure 3: The types of typical assembly tasks: (**a**) matching, (**b**) conjugating, (**c**) joining, (**d**) fastening, and (**e**) meshing.  
In ARAAT, trainees usually complete assembly tasks with their hands and several virtual tools, similar to practical industrial assembly. Thus, the above six standard actions are also the basis in ARAAT. To evaluate the standard and achievement of assembly tasks, we first need to recognize trainees' gestures of basic actions.  
Different from single action recognition, meaningless transition actions between important actions in practical assembly operation are unavoidable. The transition actions make no sense and may impact negatively on the output in gesture recognition. To solve the disturbance of transition actions, a \"Null\" action is added in the standard basic action set, that is,  
\[A=\{\text{Pointing, Moving, Grasping, Releasing, Scaling, Rotating, Null}\} \tag{5}\]