esa-sceva/satcom-chunk-collection huggingface dataset:
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 3 Impulse Modeling
### Condition for Scattering and Pushing  
In this section, the condition for scattering and pushing are stated. During collision, due to actuation of the manipulator, the change in velocity of the manipulator is small compared to change in velocity of the object. Consider that \(\Delta v_{l}<<\Delta v_{inv}\), Equation (8) will be reduced to:  
\[\hat{F}_{ext}=-(1+e)m_{a}(v_{l}-v_{a}). \tag{24}\]  
which implies that the velocity of the manipulator before and after collision will remain the same (\(\mathbf{v^{\prime}}_{l}=\mathbf{v}_{l}\)), where \(\mathbf{v}_{l}\) and \(\mathbf{v^{\prime}}_{l}\) denotes the velocities of the manipulator before and after collision. The velocity of the object after collision is given as follows:  
\[\mathbf{v_{a}}^{\prime}(t_{o})=\mathbf{v}_{a}(t_{o})-(1+e)\mathbf{v}_{l}. \tag{25}\]  
where \(\mathbf{v}_{a}(t_{o})\) denotes the initial velocity of the object at \(t_{o}\) before collision which is always zero in our application. \(\mathbf{v_{a}}^{\prime}(t_{o})\) denotes the initial velocity of the object after collision with the robot manipulator.  
Finally, the object will come to rest with the following velocity:  
\[\mathbf{v_{a}}^{\prime}(t)=\mathbf{v_{a}}^{\prime}(t_{o})-sign(\mathbf{v})\mu_{g}gt \tag{26}\]  
The condition for scattering and pushing is associated with relative velocities of robot manipulator and object after collision. For the case of pushing (quasi-static manipulation), the condition states that:  
\[\mathbf{v^{\prime}}_{l}\geq\mathbf{v}_{a}(t_{o}),\text{ Object sticking} \tag{27}\]  
Similarly for the case of scattering (dynamic manipulation), the condition will be formed as follows:  
\[\mathbf{v^{\prime}}_{l}<\mathbf{v}_{a}(t_{o}).\text{ Object sliding apart} \tag{28}\]  
For the case of pushing (quasi-static manipulation), the object motion will be determined by the motion of the manipulator only. Accordingly, the dynamics of the object will not affect the motion of the object. This paper deals solely with the dynamic manipulation (scattering) of the objects, where the motion of the object is determined by the motion of the manipulator and dynamics of the object. Accordingly, the dynamics of the object, coefficient of friction, and coefficient of restitutions should be known to estimate the final position of the objects.
## 5 Adaptive Quadrature Near Immersed Boundaries
### Single Inclusion with Spherical Boundary  
The single-inclusion geometry shown in Figure 20 is used to verify the accuracy and convergence of the kd-tree data structure by comparing the estimated stress concentration factor against its theoretical value. The spherical inclusion is immersed in a finite domain with a uniform fraction of unit magnitude, as shown in Figure 20. The theoretical limit of the stress concentration factor (SCF) near the equator for a single spherical void in an infinite underlying domain is \(\dfrac{\sigma_{max}}{\sigma}=2\)[49]. A two-dimensional view of the quadrature points obtained using a kd-tree cell division is shown in Figure 21, revealing hierarchical refinement of quadrature cells near the immersed boundaries. In Figure 22, the contours of the Von Mises stress around the equator of the spherical void are shown. The convergence of the stress concentration factor is plotted in Figure 23 and listed in Table 7. Convergence to the theoretical limit is obtained with a decrease in the size ratio \(\frac{l}{d}\), provided that sufficient degrees of freedom (control grids of \(20\times 20\times 20\) or \(30\times 30\times 30\)) are used.  
(Figure 24b). The elliptical/ellipsoidal voids are immersed in the underlying domain, and both kd-tree and quad/oct-tree quadrature cell refinement are implemented (see Figures 25 and 26) to estimate the volume of the solid region. The estimated volume listed in Tables 8 and 9 indicates that kd-tree cell division achieves the same degree of accuracy as quad/oct-tree sub-division, with both methods resulting in an error of \(1.70\times 10^{-3}\) in the two-dimensional example and \(4.77\times 10^{-5}\) in the three-dimensional example. For two-level refinement, in the two-dimensional example kd-tree uses 3276 quadrature points, while quad-tree uses 4032 quadrature points. In the three-dimensional example, kd-tree uses 118,476 quadrature points, nearly one-third fewer than oct-tree subdivision, which results in 161,244 quadrature points.
## 2 Principle and Modeling
### Dynamic Model of a Tracked Vehicle  
In order to achieve accurate load simulation, firstly, the dynamics model of the tracked vehicle needs to be analyzed to obtain the relationship between the torque and rotation speed of the driving wheel.  
Tracked vehicles are subjected to the interaction of driving force, inertia force, and driving resistance in the course of driving. The force exerted on the body reaches the moment balance at the active wheel [31]. By identifying the equivalent of the force to the driving wheel, the torque formula of the driving wheel can be obtained as follows:  
\[T_{e}+T_{a}+T_{s}+T_{w}+T_{f}=0 \tag{1}\]  
\[T_{a}=J_{e}\alpha_{d} \tag{2}\]  
\[T_{s}=Rmgsinv \tag{3}\]  
\[T_{w}=\frac{RC_{D}AV_{v}^{2}}{21.45} \tag{4}\]  
\[T=T_{f}+T_{s} \tag{5}\]  
where, \(\alpha_{d}\) is the angular acceleration of the vehicle output axle, \(\gamma\) is the vehicle inclination, \(C_{D}\) is the wind resistance coefficient, \(A\) is the vehicle forward projection area, \(T_{e}\) is the vehicle driving moment, \(T_{a}\) is the vehicle inertia moment, \(T_{s}\) is the slope resistance moment, \(T_{w}\) is the wind resistance moment, \(T_{f}\) is the ground resistance moment, T is the road resistance moment, and R is the effective radius of the driving wheel.  
Because tracked vehicles often travel on complex roads, it is necessary to study their driving conditions when switching between different slopes in body dynamics modeling. In this paper, the driving conditions of tracked vehicles are divided into six processes, namely, the horizontal slope, increase of slope, downhill slope, decrease of slope, increase of slope, and decrease of slope, respectively, corresponding to stages 1 to 6 in Figure 2.  
Figure 1: Simulation test bench for tracked vehicles.  
Figure 2: Diagram of a tracked vehicle on a slope.  
Detailed slope decomposition for each stage is shown in Figure 3. In this figure, s means the up (down) slope distance, \(\theta_{i}\)(i=1,2,3,4) means the slope angle, \(h_{c}\) means the centroid height, and \(l_{d}\) means the relative horizontal distance between the centroid and geometric center.  
According to the above figure, the inclination angle and road resistance of the tracked vehicle at a certain instantaneous stage can be obtained. The judgment process is shown in Figure 4.  
By Laplace transformation of Formula (1) to Formula (5), the vehicle dynamic equation is obtained as follows:  
\[G_{em}(s)=\frac{\omega_{em}(s)}{T_{e}(s)-T(s)-T_{w}(s)}=\frac{1}{J_{e}s} \tag{6}\]  
where \(\omega_{em}(s)\) means the speed of the driving wheel and \(J_{e}\) means the vehicle equivalent inertia.  
Figure 3: Force analysis of a tracked vehicle.
## 3 Materials and Methods
### Adjustment of a Process Simulation Model with Actual Real-Time Project Data
#### 3.1.2 Data Pre-Processing  
For the use of real-time data for simulations and for the achievement of reliable simulation results, the quality of the collected data is decisive. Real-time data must meet requirements to ensure an adequate representation of reality. The important requirements formulated by the German Center for Resource Efficiency (2021) are the actuality, comprehensibility, consistency, unambiguity, and comparability of the data [36]. Furthermore, the recorded data must be pre-processed, since field data may be incomplete and erroneous due to sensor failures or erroneous sampling. Data pre-processing, defined as the handling of raw data, is a critical component of data science ensuring the quality of collected data. It has been studied mainly in the context of data mining and machine learning and is part of the process of knowledge discovery in databases [37; 38]. In the context of this study, data pre-processing is performed. Preferentially, simple methods are chosen to speed up the process of pre-processing to enable a nearly real-time simulation. The following criteria are relevant in the context of this study: missing values, outliers, and noise in time series. For more information on data analysis methods in this context, please see [34].  
Missing values represent data that were not stored or recorded due to faulty sampling caused by human or computer errors [38]. Ignoring missing values can lead to inaccuracies in data analytics or to misleading prognoses. In the literature, approaches can be found where missing values are either replaced by representative values or the corresponding dataset is discarded. However, discarding the entire dataset should only be considered if the amount of affected datasets is relatively small, since decreasing the total amount of data might lead to serious bias during data analytics [39].  
Replacing missing values with imputation values has been extensively examined in the literature, especially in the context of data mining and machine learning [40]. The simplest imputation method is mean or median substitution. Further imputation methods include, for example, the k-nearest neighbor or maximum likelihood methods. Depending on the context, the best fitting method can be selected [38; 39].  
With regard to tunneling, values that can be derived from the remaining, non-missing values can also be used for imputation. Using the elements of a cycle time, which is composed of the duration of the ringbuild, the advance duration, and the duration of the standstills, the missing value can be determined using back analysis (tcycle = tringbuild + tadvance + tstandstill). This method delivers the most likely results and is therefore chosen for the presented approach. Many cases can be covered in this way with little effort and good accuracy. However, depending on the type of missing parameter or due to several missing values, back analysis is not applicable in every case. This method is characterized by its simplicity and robustness.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications
### Intracranial Pressure  
Figure 15 shows a miniaturized planar implantable antenna proposed by [PERSON] et al. for intracranial pressure monitoring at 915 MHz and 2.45 GHz [95]. The proposed antenna has a size of 8 mm \(\times\) 6 mm \(\times\) 0.5 mm and utilizes Rogers 6010 as the substrate with \(\varepsilon_{r}\) = 10.2 and tan \(\delta\) = 0.0023. Biocompatibility is confirmed by the ceramic alumina encapsulation. The antenna achieved a gain and bandwidth of \(-\)28.5 dBi and 9.84% at 915 MHz, respectively, and \(-\)22.8 dBi and 8.57% at 2.45 GHz, respectively. To achieve the safety limit of 2 W/kg for SAR\({}_{10\text{g}}\), the maximum allowable input power is 17.12 mW at 915 MHz and 20.6 mW at 2.45 GHz.  
A coplanar miniature antenna with the size of 6 mm \(\times\) 5 mm \(\times\) 1 mm is presented in Ref. [96] with an operation frequency of 2.45 GHz, as shown in Figure 16. [PERSON] et al. claimed to use low-permittivity polyimide as a flexible substrate for the proposed antenna. This antenna can provide a peak gain of \(-\)19.63 dBi and maximum SAR of 10 mW/kg in brain tissue.  
Figure 16: Coplanar miniature antenna for intracranial pressure tested in liquid phantom [96]. Reproduced with permission from an open-access article from the _International Journal of Antennas and Propagation_ (CC-BY).  
Figure 15: Miniaturized planar implantable antenna for intracranial pressure [95]. Copyright © _IEEE_.  
In Ref. [97], a PIFA antenna design technique was explained for head-implanted medical devices, including intracranial pressure application. In this work, Rogers RO 3210 is selected as the substrate with \(\varepsilon_{r}=10.2\) and tan \(\delta=0.003\). The antenna is 12 mm in diameter and 1.8 mm in width. It was designed to operate at 402, 433, 868 and 915 MHz. It can achieve a SAR of 2 W/kg for 10 g of tissue when the input power is 4.927 mW. It can achieve a peak gain of \(-36.90\), \(-35.99\), \(-35.14\) and \(-32.94\) dB at 402, 433, 868 and 915 MHz, respectively.  
A wireless power receiver spiral antenna with dimensions of 12.88 mm \(\times\) 13.46 mm \(\times\) 0.05 mm is presented by [PERSON] et al. in Ref. [98] for an intracranial pressure implant. The antenna is made on a flexible polyimide substrate with \(\varepsilon_{r}=3.3\) and tan \(\delta=0.002\). The operating frequency of the antenna was selected as 11 MHz, where a \(-2.17\) dB measurement peak gain was achieved. The wireless power transfer efficiency was 1.18%.
## 6 IoRT Systems Based on Hybrid NTN Networks
### Enhanced Throughput/Transmission Rate
Table 11 summarizes research on IoRT systems based on Hybrid NTN Networks to enhance throughput and transmission rate. These studies propose novel frameworks and resource allocation strategies to address heterogeneity challenges and maximize network performance.
## 4 Numerical Simulation and Analysis
### Soil Impact Zoning
Below 0.9 \(H_{\text{m}}\) is the heave zone,  
Figure 13: Vertical deformation of the soil behind the shaft at a depth of 40.14 m and the delineation of the affected area: (**a**) vertical displacement distribution curve; (**b**) influence zoning. where the soil experiences rebound due to unloading, causing heave in the surrounding area of the support structure. The amount of heave decreases with increasing distance (\(L\)). Figure 14a shows the distribution curves of horizontal displacement in the soil at different distances, with displacement values represented numerically, where positive values indicate displacement towards the excavation. When the shaft sinks to \(H_{\text{m}}=40.14\) m, the horizontal displacement of the soil decreases gradually as the distance from the shaft increases. On the basis of the pattern of soil layer changes, the soil outside the shaft is divided into three zones: the arch-shaped zone, the deformation transition zone, and the cantilever bending zone, as shown in Figure 14b. [PERSON] et al. [38] conducted a zonal analysis of the soil deformation caused by excavation, which is largely consistent with the findings of this study. However, there are significant differences in regional scope due to various factors, such as excavation methods and excavation depth. When the ratio of \(L/H_{\text{m}}\) is less than 0.1, the horizontal displacement deformation curve in this area shows an arch-shaped distribution, with the maximum displacement occurring in the middle, whereas the soil at the top is influenced by the reinforcement of jet grouting piles and the load from the upper ring beam, resulting in relatively small horizontal displacement. As the distance from the support structure increases, when \(L/H_{\text{m}}\) falls between 0.1 and 0.3, a phenomenon  
0.7 to 0.9 \(H_{\rm m}\), excavation disturbances should be minimized as much as possible, and overex excavation should be strictly avoided. In the region where \(L/H_{\rm m}\) is less than 0.3, the number of environmental monitoring points should be appropriately increased to enable the more precise control and the optimization of deformation in the area outside the shaft.
## 2 Formal Analysis
### Perturbative Treatment of the Dressing Pulse  
Using perturbation theory, the time-evolution operator \(\hat{U}_{d}(t,t_{0})\) in the presence of the dressing field [see Equation (4)] can be written as follows:  
\[ \begin{split}\hat{U}_{d}(t,t_{0})&=e^{-i(\hat{H}_{ 0}-E_{0})(t-t_{0})}\left[1-i\int_{t_{0}}^{t}dt^{\prime}\hat{V}_{d}(t^{\prime}) \hat{U}_{d}^{I}(t^{\prime},t_{0})\right]\\ &=e^{-i(\hat{H}_{0}-E_{0})(t-t_{0})}\left[1-i\int_{t_{0}}^{t}dt^{ \prime}\hat{V}_{d}(t^{\prime})-\int_{t_{0}}^{t}dt^{\prime}\int_{t_{0}}^{t^{ \prime}}dt^{\prime\prime}\hat{V}_{d}(t^{\prime})\hat{V}_{d}(t^{\prime\prime}) +...\right],\end{split}  \tag{13}\]  
where  
\[\hat{U}_{d}^{I}(t,t_{0})=1-i\int_{t_{0}}^{t}dt^{\prime}\hat{V}_{d}(t^{\prime}) \hat{U}_{d}^{I}(t^{\prime},t_{0}) \tag{14}\]  
is the time-evolution operator in the interaction picture and  
\[\hat{V}_{d}(t)=-\varepsilon_{d}(t)e^{i\{\hat{H}_{0}-E_{0}\}(t-t_{0})}\hat{Z}e ^{-i\{\hat{H}_{0}-E_{0}\}(t-t_{0})} \tag{15}\]  
is the perturbation by the optical dressing field. When evaluating Equation (9), we make use of a complete set of eigenstates of \(\hat{H}_{0}\). Because the excited states accessed may lie in the electronic continuum, this involves, strictly speaking, an integration over states. However, by focusing on bound states and autoionizing states only, we replace the integration by a summation over a discrete set of states \(|\Psi_{f}\rangle\) with a complex energy:  
\[\hat{H}_{0}|\Psi_{f}\rangle=(E_{f}-i\frac{\Gamma_{f}}{2})|\Psi_{f}\rangle. \tag{16}\]  
Here \(E_{f}\) is the real part of the energy and the non-negative real number \(\Gamma_{f}\) is the decay rate of the state \(|\Psi_{f}\rangle\) (\(\Gamma_{f}=0\) for bound states). As a consequence of this choice, \(\hat{H}_{0}\) is not Hermitian, and, therefore, the time-evolution operator \(\hat{U}_{d}\) is not unitary, i.e., \(\hat{U}_{d}^{-1}(t,t_{0})\
eq\hat{U}_{d}^{\dagger}(t,t_{0})\). This is important, because in the analytical treatment that follows, we will require the inverse of \(\hat{U}_{d}(t,t_{0})\):  
\[ \begin{split}\hat{U}_{d}^{-1}(t,t_{0})&=\big{[}1+i \int_{t_{0}}^{t}dt^{\prime}(\hat{U}_{d}^{I}(t^{\prime},t_{0}))^{-1}\hat{V}_{d} (t^{\prime})\big{]}e^{i(\hat{H}_{0}-E_{0})(t-t_{0})}\\ &=\big{[}1+i\int_{t_{0}}^{t}dt^{\prime}\hat{V}_{d}(t^{\prime})- \int_{t_{0}}^{t}dt^{\prime}\int_{t_{0}}^{t^{\prime}}dt^{\prime\prime}\hat{V}_ {d}(t^{\prime\prime})\hat{V}_{d}(t^{\prime})+...\big{]}e^{i(\hat{H}_{0}-E_{0} )(t-t_{0})},\end{split}  \tag{17}\]  
which differs from \(\hat{U}_{d}^{\dagger}(t,t_{0})\) as conjugation is not applied to the Hamiltonian \(\hat{H}_{0}\). We now apply Equations (13) and (17) to Equation (10). Resonant excitation involving XUV light tends to lead to the formation of an inner-shell hole accompanied by an excited electron (for atoms heavier than helium). For a given inner-shell hole, the decay rate is relatively insensitive to the state of the excited electron. Thus, we assume the decay rates to be equal to each other for all states (\(\Gamma_{n}=\Gamma_{f}\)), except for the ground state (\(\Gamma_{0}=0\)).
# Wind Pressure Coefficient on a Multi-Storey Building with External Shading Louvers
## 4 Results and Discussion
### Features of Local Wind Pressure Coefficient along Measurement Lines  
The windward, lateral and leeward facades are divided into seven equal rows and five equal floors to ensure that the outside surfaces of rooms have the same area in the same direction. The rows are marked from R1 to R7, and the floors are marked from F1 to F5, as shown in Figure 10(a). Note that the area for calculation is half of the real area for R1 and R7, considering the usage of the symmetry building model and symmetry domain in simulations. The distribution of wind pressure coefficient are investigated on two W-R-L lines, five W-S-L lines and three R-S lines. All these lines are located on the center of their rows or floors, which are also shown in Figure 10(b)-d. The impact of shading louvers with various rotation angle \(\theta\) on \(C_{\text{p}}\) at a specific point is evaluated by the range \(R\) as shown in Equation (9).  
\[R=C_{\text{p-max}}-C_{\text{p-min}} \tag{9}\]  
where \(R\) is the range of wind pressure coefficient \(C_{\text{p}}\) at a specific point on a measurement line for different shaded conditions. \(C_{\text{p-max}}\) and \(C_{\text{p-min}}\) are the maximum and minimum \(C_{\text{p}}\) at that point in these shaded conditions. Note that \(C_{\text{p-max}}\) and \(C_{\text{p-min}}\) only represent the local values of a special point on a line in all shaded cases, which are different from the maximum and minimum \(C_{\text{p}}\) on a whole line.  
Figure 10: Airflow pattern and wind pressure distribution. (**a**) Airflow towards the central rows; (**b**) Airflow towards the side rows.
## 2 Materials and Methods
### Data Collection
#### 2.2.3 Meteorological Data  
Meteorological data, including temperature, precipitation and radiation, were obtained from the China meteorological forcing dataset (CMFD). This comprehensive dataset comprises seven key elements: near-surface air temperature, near-surface air pressure, near-surface air specific humidity, near-surface total wind speed, surface-down short-wave radiation, surface-down long-wave radiation, and surface precipitation rate. The data are formatted in NetCDF, providing a temporal resolution of 3 h and a horizontal spatial resolution of 0.1\({}^{\circ}\). Notably, the CMFD dataset exhibits superior accuracy compared to existing reanalysis data [20].
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 2 Methods and Equipment
### Data Analysis
#### 2.3.1 Integral DHC data
The gas flow delivered by the bubble, estimated at a given depth of the probe position, can be calculated using the following formula:  
\[p=\frac{\sum_{i=0}^{n}\frac{1}{6}\pi H_{i}^{3}\cdot v_{i}}{V_{DHC}}S, \tag{5}\]  
where \(v_{i}=\frac{g\cdot H_{i}^{2}}{18v_{S}}\)--ascending speed of \(I\) bubble [53], \(S=\)1 m\({}^{2}\)--sea water surface area accepted for analysis, \(v_{S}=1.787\cdot 10^{-6}\) m\({}^{2}\)/s--sea water kinematic viscosity for 0 \({}^{\circ}\)C temperature [54], and \(g=9.8\) m/s\({}^{2}\)--gravity acceleration. It should be noted that the conversion of the bubble gas flow (a mixture of methane, nitrogen, and oxygen) into methane requires independent studies of the composition of bubbles coming from various types of bottom sediments in the Eastern Arctic Seas, as well as the study of the methane fraction dynamics in floating bubbles of different sizes under various hydrological conditions [47].