esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Formulation
### Rayleigh-Ritz Solution and Response
\right\}\]  
where \({\bf 0}\) is the \(N\times N\) zero matrix, over-double-dot denotes double differentiation with respect to time, \({\bf w}=[w_{11}\ w_{12}\ \ldots\ w_{1N}\ w_{21}\ \ldots\ w_{NN}]^{T}\), \({\bf p}=[p_{11}\ p_{12}\ \ldots\ p_{1N}\ p_{21}\ \ldots\ p_{NN}]^{T}\), \({\bf q}=[q_{11}\ q_{12}\ \ldots\ q_{1N}\ q_{21}\ \ldots\ q_{NN}]^{T}\), \({\bf r}=[r_{11}\ r_{12}\ \ldots\ r_{1N}\ r_{21}\ \ldots\ r_{NN}]^{T}\), \({\bf s}=[s_{11}\ s_{12}\ \ldots\ s_{1N}\ s_{21}\ \ldots\ s_{NN}]^{T}\) and  
\[({\bf M}_{11}+{\bf M}_{11}^{*}){\bf\tilde{w}}+{\bf C}_{11}^{*}{\bf\dot{w}}+({ \bf K}_{11}+{\bf K}_{11}^{*}+{\bf K}_{11}^{\prime}+{\bf K}_{\rm inv}){\bf w}={ \bf F}_{z}(t) \tag{22}\]  
Here:  
\[{\bf K}_{\rm inv}=\left[ \begin{array}{cccc}{\bf K}_{12}&{\bf K}_{13}&{\bf K }_{14}&{\bf K}_{15}\end{array} \right]\left[ \begin{array}{cccc}{\bf K}_{22}+{ \bf K}_{22}^{\prime}&{\bf K}_{23}&{\bf K}_{24}&0\\ &{\bf K}_{33}+{\bf K}_{33}^{\prime}&0&{\bf K}_{35}\\ {\rm sym.}&&{\bf K}_{44}&{\bf K}_{45}\\ \end{array} \right]\left[ \begin{array}{cccc}{\bf K}_{12}&{\bf K}_{13}&{\bf K }_{14}&{\bf K}_{15}\end{array} \right]^{T} \tag{23}\]  
Equation (22) can also be written in the compact form:  
\[{\bf M}(t){\bf\tilde{y}}(t)+{\bf C}(t){\bf\dot{y}}(t)+{\bf K}(t){\bf y}(t)={ \bf F}(t) \tag{24}\]  
where \({\bf y}(t)={\bf w}(t)\) and \({\bf F}(t)={\bf F}_{z}(t)\) are displacement vector and force, and mass \({\bf M}(t)\), damping \({\bf C}(t)\) and stiffness \({\bf K}(t)\) matrixes are of the order (\(N^{2}\times N^{2}\)). The detailed expansion formula is shown in Appendix A. Mathematically, Equation (24) is a system of coupled ordinary differential equations of second-order in time, which can be solved using various explicit or implicit time step methods. In this study, Equation (24) is solved by using the differential quadrature time integration scheme [39]. In addition, the natural frequency \(\omega\) and the damping loss factor \(\eta\) can be obtained by solving the eigenvalue Equation (24) when the force is zero.
## 3 Results and Discussion
### MSCs Migrate in Response to IL-6 in Three-Dimensional Spheroid Culture  
Following observations that MSCs migrated in response to an IL-6 gradient in 2D culture, MSC spheroids were observed for cell migration in response to IL-6 over a 3-day period. MSCs were developed into three-dimensional spheroids and levitated in collagen gels, using mNPs, to produce an in vitro culture model the bone marrow microenvironment [12; 38]. As noted in Figure 6, following IL-6 exposure, initial MSC migration was observed after 3 h, with very apparent cell migration after 24 h IL-6 exposure.  
In vivo, cells would migrate toward the source of IL-6 [39], in the case of this in vitro experiment, there is no singular source of IL-6 (such as co-cultured cells) and concentration is uniform across  
Figure 5: MSC spheroid area derived from cells seeded at (**A**) 6 \(\times\) 10\({}^{3}\), (**B**) 1 \(\times\) 10\({}^{4}\), and (**C**) 2 \(\times\) 10\({}^{4}\) cells/mL. Note the increasing range of spheroid sizes with increased cell density. Scale bar = 10 \(\upmu\)m. (**D**) Box and whisker plot of spheroid areas (spheroid \(n\geq\) 15). (**E**) False colour scanning electron microscopy of MSC spheroid using JEOL JSM-6400 scanning electron microscope at 10 kV. (**F**) Tunnelling electron microscopy of MSC spheroid using JEOL 1200 tunnelling electron microscope at 80 kV; scale bar = 1 \(\upmu\)m. Cells labelled with green fluorescent mNPs were levitated in magnetic field for 24 h before measurement. Diameter measured using ImageJ.  
the whole culture. MSCs will, therefore, migrate out of the central mass in all directions. Indeed, fluorescent images of these spheroids indicate a migration of cells from the central mass following IL-6 treatment compared with a retention of spheroid shape in the control culture, not treated with the cytokine (Figure 6). Cells within the spheroid therefore appear to sense IL-6, most likely via the cognate cell surface receptor, IL6R [21], and the peripheral cells will transition from adherent cells to a migratory cell phenotype, to move toward the source of IL-6. This was observed in spheroids treated with IL-6, where peripheral cells increase in size as they change morphology to facilitate migration (Figure 6D). It should be noted that MSC spheroids may react to the presence of other MSC spheroids in their vicinity within the collagen gel, so even in control conditions cells may begin to migrate from the mass, but the effect of IL-6 on the migration of MSCs is much more pronounced.
## 3 The AgroTRACE Infrastructure
### Standards Followed by the IoT Applications Focused on Information Management  
Finally, as far as the IoT applications are concerned, specific standards are followed for the information management, namely:  
* Message Queuing Telemetry Transport (MQTT): This lightweight standard messaging protocol allows for both recording and publishing of messages with considerably small volume of data, while it is very useful for connections to remote sites with a small code footprint as well as minimal network bandwidth [66].  
Extensible Messaging and Presence Protocol (XMPP): Based on its abbreviation, going from \"P\" back to \"X\", XMPP is a \"Protocol\" allowing systems to communicate to each other; \"Presence\" shows the state of an XMPP entity (online, offline or busy); the \"Messaging\" refers to the part that clients can see; XMPP is designed so as to be \"eXtensible\", namely able to grow and accommodate alterations. In other words, XMPP is a set of open protocols for real-time communication, which supports a variety of applications, including content sharing, instant messaging, voice and video calling, presence and collaboration [67].
# Machine-Learning Application for a Likelihood Ratio Estimation Problem at LHC
## 3 Particle Physics Analyses Application  
This methodology for data-driven likelihood ratio estimation has been adopted by two ATLAS analyses: one is a search for heavy resonances Y decaying into a Standard Model Higgs boson H and a new boson X in the fully-hadronic final state [6], the other is a search for resonant Higgs boson pair production in the \(bbb\bar{b}\) final state [7]. The purpose of both analyses is the estimation of the _background_ distribution for the final fit discriminant variable, performed in SR.  
Since the hadronic final state is common to both the analyses, it is useful here to introduce the concept of jets, which are referred to frequently in the following sections. After high-energy collisions in a particle collider, free quarks or gluons are created. Due to a particular property of QCD (the _colour confinement_), these cannot exist individually, rather, they interact with each other to form composite particles, called _hadrons_. In the _hadronization_ process, all the particles created leave traces within the detector, resulting in conic agglomerates of tracks and energies deposits, called _hadronic jets_. Once data are analyzedoffline, these hadronic jets are reconstructed with a dedicated algorithm [8] with different cone opening angles, labeled by the parameter \(R(R=\sqrt{(\phi_{1}-\phi_{2})^{2}+(\eta_{1}-\eta_{2})^{2}}\), which is a measure of the angular distance between two vectors in cylindrical coordinates. The formula \(\phi\) indicates the angle in the plane x-y in the ATLAS coordinate system, transverse to the beam axis z; \(\eta\) is the pseudo-rapidity, defined as \(\eta=-\ln\tan(\theta/2)\), and correlated to the angle \(\theta\) between the particle trajectory and the \(z\) axis. In the definition of a jet, it refers to the angular opening of the cone enclosing all the particles associated with the jet.). Jets with R=0.4 are called _small-R jets; large-R jets_ are, alternatively, those with R = 1.0.  
This section is divided into two parts, with the two applications described separately.
# Each-Phase Metering with Gas-Liquid Stratified Flow Based on the Multi-Frequency Coriolis Principle
###### Abstract  
Gas-liquid two-phase flows generally have the characteristics of complex and variable flow patterns and flow rate uncertainty of each phase. The entrainment of gas increases errors of the existing non-separated multiphase metering. A novel metering method based on the multi-frequency Coriolis principle is proposed to solve the above problems. Compared to the conventional Coriolis mass flowmeter, the third-order mode of the measuring tube is used to improve the accuracy of the measurement. The influences of bubble effect and resonance effect on vibration responses in different vibrational modes were studied to determine the deviations of the apparent values of total density and mass flowrate by simulation. Simulation results with a single-frequency Coriolis flowmeter show that the maximum relative deviations of total density and total mass flowrate are \(-\)37.3% and \(-\)9.3%, respectively. Driven by different frequencies, the same two phase fluid in the measuring tube can have different responses of the primary mode and the higher vibrational modes. The vibrational responses characteristics corresponding to the first-order and third-order modes of measuring tube were selected and analyzed. Combined with advantages of high precision and multi-parameter measurement of traditional single-frequency Coriolis flowmeters, a multi-frequency correction model suitable for stratified flow was proposed. The results show that the corrected total density and mass flow deviations of gas-containing fluid are within \(\pm\)4% and \(\pm\)3%, respectively, which are significantly reduced. Corrected flowrate deviations of the gas-phase and liquid-phase are \(\pm\)9.1% and \(\pm\)7.2%, correspondingly, which also meet the metering requirements of the wellhead.  
multi-frequency coriolis principle; stratified flow; bubble effect; resonance effect +
Footnote †: journal: Applied Physics Letters
###### Abstract  
Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries.  
meteorological data; missing value imputation; machine learning; reconstruction +
Footnote †: journal: _Algorithms_  
**Citation:** [PERSON]; [PERSON]; [PERSON] Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observations Data. _Algorithms_**2023**, _16_, 422. [[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))
# Three-Dimensional Wind Measurements with the Fibered Airborne Coherent Doppler Wind Lidar LIVE
## 4 Conclusions  
An airborne 3D wind profiling lidar based on the latest 1.5 \(\upmu\)m fiber laser development at Onera has been designed to be operated on-board an ATR42 aircraft from SAFIRE. The lidar includes a conical scanner aiming at the nadir. The horizontal resolution is 3 km for a flight altitude of 5000 m, the vertical resolution is 100 m and the designed accuracy on each three wind vector components is better than 0.5 m.s\({}^{-1}\). The CNR and line of sight velocity are displayed on-board in real time, and 3D wind vector retrieval is obtained from data post processing using the best low CNR algorithms.  
A flight test campaign was successfully performed during February 2019. Airborne wind measurements are in good agreement with measurements from a WindCube lidar operated at Onera center in Fauga. Measurements from different headings and angles of attack are also consistent, showing that all procedures to transpose wind measurements relative to the aircraft into wind measurements relative to the ground are successful thanks to the use of aircraft speed extracted from on-board lidar measurements. Lidar measurement comparison with radar wind profile measurements from CRA are currently in progress.  
This airborne wind fibered lidar is now available for meteorological and atmosphere dynamics scientific studies.  
Conceptualization, B.A., M.V., A.D., A.D.-B., F.G. and C.B.; methodology, B.A., M.V., A.D., A.D.-B. and F.G.; software, D.G. and C.P.; formal analysis, M.V., A.D.-B., D.G. and T.H.; investigation, B.A., M.V., A.D., D.G., F.G. and T.H.; writing--original draft preparation, M.V., A.D. and A.D.-B.; writing--review and editing, B.A.; supervision, A.D.-B. and C.B.; project administration, B.A.; Resources, D.G., F.G., D.F. and T.H.; funding acquisition, B.A., A.D.-B. and C.B.  
Figure 9: (**a–c**) respectively x (north), y (west) and z (zenith) components of wind measured by airborne lidar LIVE at three different altitudes and each altitude at two different headings.  
**Funding:** The lidar LIVE is part of TERRISCOPE which is a shared research platform in airborne optical remote sensing. The scope is the characterization of the environment and continental surfaces from aircraft and drones. It was co-funded by the European Union (ERDF), the Occitanie French region, Onera, and the companies SOFRADIR, BOREAL, Leosphere and M3 Systems.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
###### Abstract  
China's accelerating industrialization has led to worsening air pollution, characterized by recurrent haze episodes. The accurate quantification of PM\({}_{2.5}\) distribution is crucial for air quality assessment and public health management. Although traditional prediction models can effectively identify PM\({}_{2.5}\) concentration fluctuations with moderate accuracy, their dependence relies heavily on extensive ground-based monitoring station data, limiting their applicability in areas with sparse monitoring coverage. To address this limitation, this study proposes a novel algorithm for high-precision PM\({}_{2.5}\) concentration prediction, termed VDMS (Vision Transformer with DLSTM Multi-Head Self-Attention and Self-supervision). Based on the traditional Vision Transformer (ViT) architecture, VDMS incorporates a Double-Layered Long Short-Term Memory (DLSTM) network and a Multi-Head Self-Attention mechanism to enhance the model's capacity to capture temporal sequence features and global dependencies. These enhancements contribute to greater stability and robustness in feature representation, ultimately improving prediction performance. Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM\({}_{2.5}\) concentration prediction tasks, achieving a coefficient of determination (R\({}^{2}\)) of 0.93, a root mean square error (RMSE) of 4.05 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{3}\), and a mean absolute error (MAE) of 3.23 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{3}\). Furthermore, experiments conducted in areas with sparse ground monitoring stations demonstrate that the model maintains high predictive accuracy, further validating its applicability and generalization capability in data-limited scenarios. Moreover, the VDMS model adopts a modular design, offering strong scalability that allows its architecture to be adjusted according to specific requirements. This adaptability renders it suitable for monitoring various atmospheric pollutants, providing essential technical support for precise environmental management and air quality forecasting.  
PM\({}_{2.5}\) prediction; deep learning; ViT; contrastive learning; multi-head self-attention; environmental management
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 4 Iterated Orthogonal Simplex Cubature Kalman Filter  
### Orthogonal Transformation of Cubature Points  
It is suggested from the invariant theory and the orthogonal transformation property that the transformed points \(\mathbf{O}\times\left[\mathbf{a}\quad-\mathbf{a}\right]\), with weights shown in Equation (23), remain to constitute a numerical cubature rule if \(\mathbf{O}\) is an \(n\times n\) orthogonal matrix. The orthogonally transformed points are able to reduce the higher-order terms of the Taylor series expansion that appeared while approximating the mean and covariance [20], and it is observed that more accurate estimation is obtained with the transformed points compared to the ordinary cubature points, which lead to the approximation accuracy of the cubature rule becoming further enhanced. In other words, the new set of cubature points can be modified as follows:  
\[\overline{\mathbf{x}}^{(i)}=\left\{\begin{array}{ll}\overline{x}+\sqrt{\left(n +2+\sqrt{2n+4}\right)\mathbf{P}_{x}}\mathbf{\gamma}_{i},i=1,2,\cdots,2n+2\\ \overline{x}+\sqrt{\left(n+2-\sqrt{2n+4}\right)\mathbf{P}_{x}}\mathbf{\gamma}_{i},i=2n +3,\cdots,4n+4\end{array}\right. \tag{23}\]  
where \(\mathbf{\gamma}=\left[\mathbf{\gamma}_{1}\quad\mathbf{\gamma}_{2}\quad\cdots\quad\mathbf{ \gamma}_{2n+2}\right]=\mathbf{O}\times\left[\mathbf{a}\quad-\mathbf{a}\right]\), and the orthogonal matrix \(\mathbf{O}\) can be designed using different methods. As an effective design method, the \(n\times n\) matrix \(\mathbf{O}\) is constructed in this paper as \(\mathbf{O}=\left[\mathbf{O}_{1}\quad\mathbf{O}_{2}\quad\cdots\quad\mathbf{O}_{n}\right]\), and \(\mathbf{O}_{i}=\left(O_{i,1}O_{i,2}\dots O_{i,n}\right)^{T}\), whose elements are defined as follows:  
\[\left\{\begin{array}{ll}O_{i,2r-1}=\sqrt{\frac{2}{n}}\cos\left[\frac{\left(2r -1\right)ir}{n}\right]\\ \ O_{i,2r}=\sqrt{\frac{2}{n}}\sin\left[\frac{\left(2r-1\right)ir}{n}\right] \end{array},r=1,2,\cdots,\max\left(p\in\mathbf{Z}|p\leq\frac{n}{2}\right) \tag{24}\]  
where \(O_{i,n}=(-1)^{i}/\sqrt{n}\) if \(n\) is odd.  
The information of high-order terms can be reduced to a low level using the orthogonal matrix, which means the non-local sampling problem can be significantly reduced with a rare computational complexity increase.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 3 TCS Design Proposal  
According to the previous analysis, taking all features including structure characteristics, thermal control requirements and heat flux of SSO into consideration, a technical proposal for the TCS was formed. Apart from the camera and battery, the rest of the satellite instruments are subject to all-passive thermal control. This proposal incorporates heat insulation design, heat conduction design, heat dispersion design and heating design. The general description of the technical proposal is stated below:  
* The battery is mounted to the panel in a heat insulation way adopting an active heating circuit. Apart from the mounting surface to the panel, all surfaces are covered with MLI.
* OBDH is mounted to the panel via the thermal conductivity of the membrane.
* The brackets of star trackers are mounted to the panel in a heat insulation way. Brackets and star trackers are covered with MLI.
* The camera is mounted to the satellite structure through the camera holder in a heat insulation way. The active thermal control circuit and MLI are implemented on it.
* The baffle of the camera is covered with MLI and it's connected to the camera in a heat insulation way.
* The focal plane unit is connected to the camera in a heat insulation way.
* Reaction wheels are mounted to the bracket in thermal conductive way using conductive grease in between the contact surfaces. The brackets are mounted on the panel in a heat insulation way.
* The navigation unit is mounted on the panel with thermal conductive grease in between the contact surfaces.
* The internal surface of the +X panel is covered with MLI. The external surface of the +X panel is covered with a thermal control coating named S781.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Surface**} & \multicolumn{3}{c}{**December Solstice**} & \multicolumn{3}{c}{**June Solstice**} \\  & **Sun** & **Albedo** & **Infrared** & **Sum** & **Sun** & **Albedo** & **Infrared** & **Sum** \\ \hline +X & 0 & 37.8 & 76.2 & 114.0 & 0 & 34.5 & 76.1 & 110.6 \\ \(-\)X & 0 & 37.8 & 76.0 & 113.8 & 0 & 34.4 & 76.1 & 110.5 \\ +Y & 0 & 46.2 & 49.4 & 95.6 & 0 & 46.8 & 52.2 & 99.0 \\ \(-\)Y & 0 & 20.2 & 43.3 & 63.5 & 0 & 14.8 & 44.1 & 58.9 \\ +Z & 0 & 77.1 & 72.1 & 149.2 & 0 & 64.1 & 69.1 & 133.2 \\ \(-\)Z & 891.5 & 5.3 & 74.6 & 971.4 & 859.3 & 5.4 & 72.9 & 937.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average heat flux density (\(\mathrm{W/m^{2}}\)).  
Figure 4: Variation curve of heat flux density on the satellite’s surface.