esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Model Fusion
#### 3.1.1 Slerp  
Spherical Linear Interpolation (Slerp) [30] is a method for smoothly interpolating between two vectors, maintaining a constant rate of change and preserving the geometric properties of spherical space. First, the input vectors are normalized to unit length, ensuring they represent direction rather than magnitude. Next, the angle is computed using the dot product. When the vectors are nearly parallel, linear interpolation is applied for efficiency; when the angle is large, SLERP is used, with scale factors computed based on the interpolation factor t and the angle. These factors weight the original vectors and are then summed to obtain the interpolated vector. Slerp can be described as follows:  
\[\text{Slerp}(\text{p},\text{q},\text{t})=\frac{\sin[(1-\text{t})\theta]\cdot \text{p}+\sin(\text{T}\theta)\cdot\text{q}}{\sin\theta}, \tag{1}\]  
p, q are the two vectors to be interpolated; T is the interpolation factor, a value between 0 and 1 that specifies the degree of interpolation. Reinforcement learning dynamically adjusts  
Figure 1: Overall architecture diagram of the framework. The framework is built around a dynamic closed-loop optimization process, beginning with model fusion, where parameters from the source Large Language Model (LLM) are integrated to create an initial fused model. This fused model is then applied to domain-specific tasks (named entity recognition) and evaluated by a SOTA LLM, which provides performance scores and rankings. The evaluation results are fed into the reinforcement learning optimization module, where fusion parameters are dynamically adjusted. The updated parameters are then fed back into the model fusion phase, creating a continuous iterative loop that progressively enhances model performance. By leveraging dynamic parameter space exploration, real-time feedback-driven optimization, and a scalable closed-loop architecture, the framework significantly improves the model’s adaptability and task performance, while offering key advantages such as high automation, strong domain adaptability, and optimized computational efficiency.  
T based on the results of the model evaluation; \(\theta\) is the angle between the two vectors. SLERP is applied to each layer of the model to achieve overall model fusion. Reinforcement learning is used to obtain a new fusion model by adjusting the T.
###### Abstract  
Over the previous two decades, a notable array of space exploration missions have been initiated with the primary aim of facilitating the return of both humans and robots from Earth to the moon. The significance of these endeavors cannot be emphasized enough as numerous entities, both public and private, from across the globe have invested substantial resources into this pursuit. Researchers have committed their efforts to addressing the challenges linked to lunar communication. Even with all of these efforts, only a few of the many suggested designs for communication and antennas on the moon have been evaluated and compared. These designs have also not been shared with the scientific community. To bridge this gap in the existing body of knowledge, this paper conducts a thorough review of lunar surface communication and the diverse antenna designs employed in lunar communication systems. This paper provides a summary of the findings presented in lunar surface communication research while also outlining the assorted challenges that impact lunar communication. Apart from various antenna designs reported in this field, based on their intended usage, two additional classifications are introduced: (a) mission-based antennas--utilized in actual lunar missions--and (b) research-based antennas--employed solely for research purposes. Given the critical need to comprehend and predict lunar conditions and antenna behaviors within those conditions, this review holds immense significance. Its relevance is particularly pronounced in light of the numerous upcoming lunar missions that have been announced.  
lunar communication; sleeve-balanced antenna; lunar antennas; lunar surface propagation; lunar propagation channels; Rashid rover
Future Internet_**2009**, \(1\), 28-46; doi:10.3390/fi1010028  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**A Method for Automating Geospatial Dataset Metadata**  
**[PERSON] *, [PERSON] and [PERSON]**  
Institute of Geography, School of GeoSciences, University of Edinburgh, Drummond St., Edinburgh, EH8 9 XP, UK; E-Mails: [EMAIL_ADDRESS] (B.M.G.); [EMAIL_ADDRESS] (R.I.D.)  
* Author to whom correspondence should be addressed. E-Mail: [EMAIL_ADDRESS].  
_Received: 28 August 2009; in revised form: 1 November 2009 / Accepted: 6 November 2009 / Published: 10 November 2009_  
**Abstract:** Metadata have long been recognised as crucial to geospatial asset management and discovery, and yet undertaking their creation remains an unenviable task often to be avoided. This paper proposes a practical approach designed to address such concerns, decomposing various data creation, management, update and documentation process steps that are subsequently leveraged to contribute towards metadata record completion. Using a customised utility embedded within a common GIS application, metadata elements are computationally derived from an imposed feature metadata standard, dataset geometry, an integrated storage protocol and pre-prepared content, and instantiated within a common geospatial discovery convention. Yielding 27 out of a 32 total metadata elements (or 15 out of 17 mandatory elements) the approach demonstrably lessens the burden of metadata authorship. It also encourages improved geospatial asset management whilst outlining core requisites for developing a more open metadata strategy not bound to any particular application domain.  
**Keywords:** geospatial metadata; metadata automation; data documentation; discovery metadata; feature metadata
## 3 Proposed System
### Gradient Orientation Histogram Feature Extraction  
The gradient orientation histogram is a global descriptor commonly used in image analysis and recognition, which can provide information about the appearance and shape of an image by the distribution of local intensity gradients. We divided the image into an \(m\times n\) grid of sub-images, each no larger than the maximum pixel size. For each image block, the gradient orientation was quantified into 9 bins and weighted by the magnitude of the gradient.  
After performing the operations mentioned above, we obtain 9-element vectors to represent image blocks, which are then clustered using a density-based spatial clustering algorithm. This clustering algorithm is inspired by DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [27], which groups data points based on their proximity and density. An appropriate similarity measure is the cosine distance, which measures the similarity between two vectors by computing the cosine of the angle between them. The distance between two vectors \(A\) and \(B\), denoted as \(d(A,B)\), is calculated as:  
\[d(A,B)=1-\frac{A\cdot B}{\|A\|\times\|B\|}, \tag{1}\]  
this distance is a normalized measure with range [0, 1], which decreases as the similarity between the two vectors increases. The clustering algorithm can be divided into the following steps:  
* Parameter Selection: Choose the core distance, \(\epsilon_{c}\), and the reachability distance, \(\epsilon_{r}\).
* Core Object Identification: Identify objects whose distance to at least one of its neighboring objects (image blocks adjacent this block upper, lower, left and right) is less than \(\epsilon_{c}\). These objects are known as core objects.
* Reachable Object Identification: If the distance between an object and one of its neighboring objects is less than \(\epsilon_{r}\), the object is considered a reachable object of this neighboring object.
* Cluster Generation: Assign each core object and its reachable objects to a cluster. For each reachable core object, add its reachable objects to the cluster until no new objects can be added.
* Output: Return the clusters.  
An example of clustering image blocks using this algorithm is shown in Figure 2. By clustering similar image blocks, we extracted several image region features from the image. Each feature vector is obtained by summing the vectors of the image blocks in the region and normalizing the resulting vector to unit length.  
Figure 1: The pipeline of the loop closure detection mechanism.
###### Abstract  
This study addresses the camera placement problem for bridge surveillance and proposes solutions that minimize the cost while satisfying the minimum coverage level. We discuss the field of view of cameras in the three-dimensional space. We also consider occlusions, the characteristics of surveillance targets, and different pan-tilt-zoom cameras in the visibility test. To solve the camera placement problem while minimizing the total cost, we propose a genetic algorithm (GA) and a uniqueness score with a local search algorithm (ULA). Problem sets for a large-scale dimension scenario are generated based on the data of actual bridges in the Republic of Korea. For three simulation sets and a case study of Samoonjin Bridge, the proposed ULA yields better results than GA.  
camera placement; surveillance; pan-tilt-zoom cameras; local search; genetic algorithm 1
## 3 High Pressure Water Jet Experiments and Numerical Simulation
### High Pressure Water Jet Numerical Simulation  
From the high pressure water jet experiments we can only observe the final coating removal effect while the pressure and speed distribution of water during the jet process is hard to observe visually. In order to investigate and analyze the change of water flow in a more efficient way, the numerical simulation method based on the general fluid simulation software FLUENT is used in this section.  
It should be noted that there are 8 nozzles with the same diameters for the experimental coating removal device. To simplify the finite element simulation, only 1 nozzle is analyzed, so the equivalent diameter used in the finite element simulation can be calculated as follows,  
\[d_{e}=\sqrt{{d_{1}}^{2}+{d_{2}}^{2}+\ldots+{d_{8}}^{2}}=2\sqrt{2}d_{1} \tag{2}\]  
The diameter for each nozzle is 0.3 mm, so the equivalent diameter for the nozzle used in FEM is about 1 mm.  
In order to save the calculation time, the 2D finite element model is used. The diameter, length-diameter aspect and shrinking angle are 1 mm, 3, and 13o, respectively. The flow field model is  
Figure 5: High pressure water jet experimental platform.  
Figure 6: Coating thickness gauge (**a**) measurement of the residual coating thickness; (**b**) measurement of the initial coating thickness.  
a cylinder with diameter of 80 mm and height of 15 mm or larger, depending on the stand-off distance. To obtain accurate simulation results, it is necessary to define the proper physical model, materials and the corresponding boundary conditions. For this study, the VOF model is chosen as the multiphase model, where the first phase is air and the second phase is water. The realizable k-epsilon model is chosen as the turbulent model. The pressure-inlet is chosen for the inlet boundary condition and a standard atmosphere is chosen as the outlet boundary condition. The whole simulation process is that high pressure water forms into high speed jet through the nozzle, and impacts the target surface through air to induce impact. Here, the rigid wall means the ship-hull surface with coating. For the case with the stand-off distance of 30 mm and the inclined angle of 0\({}^{\circ}\), the number of the mesh for the fluid field is 23,690. Figure 7 shows the finite element model and the corresponding boundary conditions for the case with the stand-off distance of 30 mm and the inclined angle of 0\({}^{\circ}\).  
It should also be noted that, considering the existing experimental condition, the inclined angle is not easy to change, so the influence of the inclined angle on the coating removal effect is analyzed only based on numerical simulation.
## 3 Results and Discussion
### Determination of the Product Factor \(k\)  
[PERSON] and [PERSON] (2018) found a product factor \(k\) ranging from 2.00 to 2.20 in their study for calculating the airflow resistance of stored wheat bulks. However, a different wheat variety with different moisture content, physical properties, kernel size distribution and filling method has been employed in this study, therefore the value of \(k\) was tailored to the specific grain bulk and experimental conditions. Herewith for the determination of \(k\), the experimental data of \(\Delta P\) vs. \(\varphi_{a}\) for bulk depths of \(H_{b}\) 1.0, 2.0, 3.0 and 3.4 m and storage time \(t\) of 1 h were fitted by the Matthies and Petersen model (Equation (9)) and the pore volume \(\varepsilon\) at different depths \(H_{b}\) was determined, accordingly. The observed \(\varepsilon\) were afterwards fitted by a linear model to describe the relationship between \(\varepsilon\) and \(H_{b}\) and extrapolated to \(H_{b}\) = 0.1 m, which is the criterion for comparison with the default \(\varepsilon_{0}\) (uncompacted) ascertained in the laboratory. First, the reported \(k\) were used and afterwards \(k\) was iteratively adjusted by intervals of 0.01 until the predicted pore volume matched the default \(\varepsilon_{0}\) of 0.421 (Figure 2). By using this criterion, a value of \(k\) of 2.73 was found, which fell between values of 2.00 and 3.90 used for various grains by [PERSON] and [PERSON] (2018). This finding was consistent with findings of [PERSON] et al. (2018) and [PERSON] (2018), who adjusted the factor \(k\) for the specific settings of their experiment and found out higher values of \(k\) than those reported by [PERSON] and [PERSON] (2018).  
The linear models and the goodness of fit acquired from individual fittings at \(k\) of 2.00, 2.10, 2.20 and 2.73 are presented in Table 4. An accuracy of \(R^{2}\)\(\geq\) 0.985 was observed from fitting with the linear models, which indicated a high capability of the employed models to depict the relationship between \(\varepsilon\) and \(H_{b}\) at different \(k\). Figure 2 displays graphically the variation of the pore volume \(\varepsilon\) influenced by factor \(k\) with respect to the default \(\varepsilon_{0}\). It can be seen that the values of \(\varepsilon\) increased proportionally with the increase of \(k\). The values proposed by [PERSON] and [PERSON] (2018) resulted in underestimation of 7.31, 6.17 and 5.09% of default \(\varepsilon\) at \(H_{b}\) = 0.1 m for \(k\) of 2.00, 2.10 and 2.20, respectively. Moreover, these factor \(k\) yielded a _MAPE_ of 26.82, 23.17 and 19.51% for pressure drop in fitting the experimental data. Therefore, a product factor \(k\) of 2.73 was used for prediction of pressure drop of wheat bulk cv. 'Pionier'.
## 3 Methods
### Network Topological Metrics
#### 3.2.1 Service Efficiency Index
The service efficiency of a network is defined as the average service efficiency of all node pairs in the network model, and expressed as follows:  
\[R_{S}(G)=\frac{1}{N(N-1)}\sum_{i\
eq j,i,j\in V}R_{S,ij}. \tag{3}\]  
To eliminate the influence of network scale and make the service efficiency comparable across different networks, the service efficiency is further normalized:  
\[R_{N}(G)=\frac{R_{S}(G)}{\max\limits_{i\
eq j}R_{S,ij}}. \tag{4}\]  
In the recovery stage, it is important to determine the restoration sequence of failed nodes. Therefore, to determine the importance of different nodes, we propose a node importance indicator based on the normalized service efficiency by analyzing the relative change in network service efficiency before and after nodes fail. \[\kappa_{i}=\frac{R_{N}(G)-R_{N,i}(G)}{R_{N}(G)},i\in V, \tag{5}\]  
where \(\kappa_{i}\) represents the service efficiency importance index for node \(i\); \(R_{N,i}(G)\) represents the service efficiency of network \(G\) following the failure of node \(i\).
# Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
## 2 Principle of the VSJ
### Performance Analysis of Stiffness Adjustment Mechanism
The changes in the stiffness adjustment resistance torque of the VSM are shown in Figure 9. Evidently, the changes in the effective length of the leaf spring and the elastic deflection angle of the joint have significant nonlinear effects on the required stiffness adjustment resistance torque. The specific mechanism design of VSJs must reduce this adverse effect.
## 4 Creation of Models and Extraction of Geometrical Components
### Extraction of Geometrical Components
#### 4.1.1 Horizontal Planes' Extraction  
Horizontal plane detection and extraction is achieved using a method based on histograms. For instance, a table and a bed have differences in their horizontal planes. In order to capture the characteristics of a wide variety of horizontal planes, three specific tasks are performed:  
1. Considering that a robot and its sensors are correctly linked (i.e., between all reference frames, fixed or mobile), it is possible to obtain a transformation for a point cloud coming from a sensor in the robot's head into a reference frame to the base or footprint of the robot (see Figure 5). The TF package in ROS (Robotic Operation System) performs this transformation at approximately 100 Hz.
2. Once transformation between corresponding references frames is performed, a histogram of heights of the points in the cloud with reference to the floor is constructed. Over this histogram, horizontal planes (generally composed of a wide set of points) generate a peak or impulse. By extracting all the points that lie over those regions (peaks), the horizontal planes can be recovered. The form and characteristics of the peak or impulse in the histogram refer to the characteristics of the plane. For example, the widths of the peaks in Figure 6 are different; these correspond to: (1) a flat and regular surface for a chest of drawers (Figure 6b) and (2) a rough surface of a bed (Figure 6d). Nevertheless, there can be several planes merged together in a peak in a scene, i.e., two or more planes with the same height.
3. To separate planes merged together in a peak in a scene, first, all the points of the peak are extracted, and then, they are projected to the floor plane. A clustering algorithm is then performed in order to separate points corresponding to each plane, as shown in Figure 7.  
Figure 4: Example of the created models, for (**a**) a bed, for (**b**) a table and for (**c**) a couch.
## 3 Model Details
### Simulation-Optimization Model for Pollutant Traceability
The following code is used to create the solute transport model and compile the MT3 DMS input file:  
_10_\(mt\) = _flopy.mt3d.Mt3 dms(modelname ='mt', exe_name ='mt3 dms5b', modflowmodel = mod)_  
_11_\(btn\) = _flopy.mt3d.Mt3 dBtn(mt, muuit ='mg/L', obs = obs, nprs = nprs, timprs = timprs)_  
_12_\(adv\) = _flopy.mt3d.Mt3 dAdv(mt, mixelm = -1, percel = 1)_  
_13_\(dsp\) = _flopy.mt3d.Mt3 dDsp(mt, al = 20, trpt = 0.2)_  
_14_\(ssm\) = _flopy.mt3d.Mt3 dSsm(mt, stress_period_data = spd_mt)_  
_15_\(gcg\) = _flopy.mt3d.Mt3 dGcg(mt, mxiter = 1000, iter1 = 200, isolve = 1, close = 0.0001)_  
_16_\(mt\).write_input()_  
_17_\(mt\).run_model(silent = True)_  
Line 10 of the code initializes the MT3 DMS model. Line 11 of the code sets the MT3 DMS basic transmission package parameters, where _obs_ is a list containing the locations of concentration observation points, _nprs_ is the number of concentration observations, and _timprs_ is a list of observation times corresponding to the number of observations. _nprs_ and _timprs_ mainly reflect the preservation of the concentration values at each point at different moments in time. Line 12 of the code sets the parameters for the selection of the advection calculation scheme. Here, _mixelm_ is an integer flag for the advection solution option. When the value is \(-1\), it means that the third-order TVD scheme is used. For the third-order TVD scheme, _percel_ is a stability constraint that must not exceed one and will be automatically reset to one if a value greater than one is specified. In line 13 of the code, the _al_ is the longitudinal dispersivity. For every cell of the model grid; the _trpt_ is a real array defining the ratio of the horizontal transverse dispersivity to the longitudinal dispersivity. The _stress_period_data_ in line 14 of the code is a dictionary containing the location, concentration, and stress period of the pollution source. Line 15 of the code is used to set the relevant parameters in the gcg solution method. _mxiter_ is the maximum number of outer iterations; _iter1_ is the maximum number of inner iterations; and _isolve_ is the type of preconditioners to be used with other acceleration schemes. When the value is 1, the Jacobi scheme is used. Lines 16 and 17 of the code compile and run the solute transport model. The calculated concentrations can be loaded from the model results once the entire groundwater model is completed. This provides the value of the parameter \(C_{calc}\) in the objective function in Equation (7), which allows for the calculation of the objective function values for the optimization problem. This procedure allows for the groundwater pollution traceability problem to be transformed into an optimization problem, which can be solved using the SEGA algorithm.
# Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation
## 4 Optimization of Data Acquisition Parameters and Calibration of Lidar Data
### Estimation of Dead Time of Photon-Counting System for Corrections of Pile-Up Effects  
Due to the existence of dead time in a PC detection system, the observed count rate will be increased or decreased compared with the true count rate, resulting in pile-up effects of the electronic pulse in the data acquisition system. In a Licel transient recorder, the PC system is the non-paralyzable system [26], with a maximum count rate of 250 MHz and sample time of 25 ns. The relationship between the true photons \(N^{\prime}\) and observed photons \(N\) from a single photon pulse is:  
\[N^{\prime}=\frac{N}{1-N\frac{\tau_{d}}{\tau_{s}}} \tag{4}\]  
where \(\tau_{s}\) is the sampling time and \(\tau_{d}\) the dead time. When the lidar data is a summed result of laser echo signals excited by \(m\) laser pulses, the true photons \(n^{\prime}\) and the observed photons \(n\) have the following relationship:  
\[n^{\prime}=\frac{n}{1-\frac{n}{m}\frac{\tau_{d}}{\tau_{s}}} \tag{5}\]  
From the scatter plots of spatial variance and temporal variance of PC data, it can be seen that the variance values from the sample distributions of the PC data that suffered from the pile-up effect are out of the line of \(\mu=\sigma^{2}\). However, when the PC data is calibrated using Equation (5), the variance distribution is changed as well, which shows that the dead time of the PC system can be investigated by minimizing the differences between the variance and the mean as:  
\[\chi^{2}=\sum_{i}\left[\sigma^{2}(P_{i})-\mu(P_{i})\right]^{2} \tag{6}\]  
The calibrated PC data trace will have the maximum data distributions to be contributed Poissonian, which has the characteristics of the equality between the variance and the mean. The dead time of the PC system of the Licel TR40-160 transient recorder was estimated to be 3.488 ns by processing the 48 probing profiles from the scanning measurements of the ultraviolet elastic scanning lidar [27]. Figure 6 shows the estimations of the dead time of the PC system for the correction of pile-up effects. It can be seen that after the dead time correction (DTC), the PC data are mostly distributed according to a Poisson distribution, and more PC data points in near range can be used to express the atmospheric information.