esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The stability of a TB-TSS, with a fixed-length tether and in-plane/out-of-plane motion in elliptic and inclined orbits, was analyzed by the monodromy matrix derived by the Floquet theory [25]. In order to understand the influence of the flexible characteristics of a tether on swing angles, [PERSON] and [PERSON] [26] presented a high-fidelity and high-accuracy dynamic model of a TB-TSS. In addition, a flexible-tether model of a TB-TSS using an absolute nodal coordinate formulation was set up to examine the dynamic influence caused by the distributed mass and elasticity of the tether in [27]. Although the modeling and dynamic analysis of TB-TSSs have been extensively researched, the existing literature mentioned above is based on the assumption that the out-of-plane motion is nonexistent [16; 19; 21; 22] or that the system is station-keeping [4; 23; 24]. To the best knowledge of the authors, there have been few published works that analyze the out-of-plane dynamics of a TB-TSS with the deployment or retrieval phases occurring in three dimensions; however, the out-of-plane response is inevitably caused by the Coriolis force when deploying or retrieving a space tether. Therefore, it is still necessary to establish a TB-TSS dynamic model considering libration motion and variable-length tethers in three dimensions. Moreover, analyzing the strongly coupled dynamic characteristics, especially the influence of out-of-plane motion on the dynamic response of a TB-TSS, is still challenging. In order to describe a three-dimensional space dynamic model of a TB-TSS and analyze the coupling dynamic characteristics of the system, a six-DOF dynamic equation of the system based on [PERSON]'s law was established. The proposed dynamic model is an extension of the existing TB-TSS dynamic model, which accounts for three-dimensional deployment/retrieval dynamics variation as well as in-plane and out-of-plane dynamics behavior. With the proposed model, it is easy to derive the explicit physical meaning of each equation and reveal the physical coupling properties between the degrees of freedom. Furthermore, compared to the Lagrange method, the proposed method avoids the complicated decoupling process and facilitates dynamic analysis. To analyze the influence of out-of-plane motion on the dynamic response of the TB-TSS, the equilibrium configuration and coupling characteristics of libration angles with fixed-length tethers are studied using mechanical features and simulation. Additionally, the dynamic effect of out-of-plane motion on the TB-TSS with variable-length tethers is studied using a predesigned deployment strategy. The paper is organized as follows: in Section 2, a novel dynamic model that has simple and explicit physical significance is modeled using a two-piece dumbbell model, and dynamic equations are derived by applying [PERSON]'s law. Additionally, we analyze and discuss the equilibrium and coupling characteristics of the libration angles. In Section 3, we investigate the dynamic performance of the TB-TSS and analyze the simulation results. Finally, Section 4 concludes the presented work.
## 2 Refrigeration Cycles, Models and Assumptions
### Cascade Refrigeration Cycle  
Cascade refrigeration cycle corresponds to the combination of two main refrigeration cycles, one cycle working with CO\({}_{2}\) in the low temperature level, which is condensed and maintained in subcritical, by another cycle that uses a refrigerant with good performance at high evaporation temperatures. In this case, both cycles are necessary, since the operation of the low temperature cycle depends on the operation of the high temperature cycle. In addition in this case, the high temperature cycle has similar or higher cooling capacity than the low temperature cycle.  
Figure 4 represents the operation of the CO\({}_{2}\) cycle in a cascade system. This is the mode of operation if the condition established by Equation (3) or Equation (4) is satisfied. That is, when the pressure established by the back-pressure (if present) or by the thermal equilibrium of condensation (P\({}_{\rm high}\)) is lower than the CO\({}_{2}\) saturation pressure at T\({}_{\rm H}\), Equation (3). As established in Equation (4), if T\({}_{\rm H}\) is higher than the critical temperature of CO\({}_{2}\), the high-pressure (P\({}_{\rm high}\)) must be lower than the critical one to satisfy the condition.  
\[{\rm P}_{\rm high}<{\rm P}_{\rm sat,CO_{2}}({\rm T}_{\rm H})\ {\rm if}\ {\rm T }_{\rm H}\leq{\rm T}_{\rm crit,CO_{2}},\ {\rm OR} \tag{3}\]  
\[{\rm P}_{\rm high}{\rm<P}_{\rm crit,CO_{2}}\ {\rm if}\ {\rm T}_{\rm H}>{\rm T}_{ \rm crit,CO_{2}} \tag{4}\]  
In the subcritical mode, the gas-cooler performs a small heat rejection to T\({}_{\rm H}\) and then the high-temperature cycle condenses CO\({}_{2}\) until saturated liquid. Subcooling is possible, but it offers worse results than the exit in saturation conditions because the intermediate temperature will need to descend.  
This cycle was experimentally investigated by [PERSON] et al. [24] using NH\({}_{3}\)/CO\({}_{2}\) and [PERSON] et al. [12] using HFC134a/CO\({}_{2}\).
# Design and Construction of a Low-Cost Test Bench for Testing Agricultural Spray Nozzles
## 2 Materials and Methods
### First Experimental Spraying Tests
The ultimate goal will be to attempt to implement a segmentation procedure based on the correlation between some features of the images and the optimal threshold value, similar to that discussed in [57], in order to make the segmentation process as less subjective as possible. After segmentation, images were processed with the watershed binary filter to separate some touching drops. The area (pixel) of the drops detected by ImageJ in each image was exported in text files for the subsequent analyses. Assuming that the measured area was circular, the droplet diameter was computed according to Equation (2):  
\[D_{i}=\sqrt{\frac{4A_{i}}{\pi}} \tag{2}\]  
Figure 6: Droplets trapped into the silicone oil. where \(A_{i}\) (pixel) is the area of drop \(i\). Particles with an area lower than 5 pixels were ignored. Droplet diameters expressed in pixels provided by Equation (2) were converted into real values by applying the proper scale factor, and then all the drop size distribution parameters were computed. The following quantities were calculated:  
* \(D_{10}\), as the arithmetic mean diameter;
* \(D_{20}\), as the surface mean diameter;
* \(D_{30}\), as the volume mean diameter;
* \(D_{32}\), as the Sauter mean diameter (SMD), i.e., diameter of a drop having the same volume to surface area ratio as the total volume of all the drops to the total surface area of all the drops;
* \(D_{v0.1}\), \(D_{v0.5}\), and \(D_{v0.9}\), as volumetric diameters, below which smaller droplets constitute, respectively, 10%, 50%, and 90% of the total volume;
* relative span factor (RSF), a dimensionless parameter indicative of the uniformity of the drop size distribution, defined as: \[\text{RSF}=\frac{D_{v0,9}-D_{v0.1}}{D_{v0.5}}\] (3)
* number mean diameter (NMD), which is the droplet diameter below which the droplet diameter for 50% of the number of drops are smaller;
* \(V_{100}\) and \(V_{200}\), as percentages of total volume of droplets smaller than, respectively, 100 and 200 \(\mu\)m in diameter. All computations were carried out by means of custom functions written in R [58].
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 3 Materials and Methods
### Materials  
For the process of swarming detection, appropriate IoT devices have been constructed. The IoT device includes a single-board ARM CPU with three peripherals attached to each beehive's lid: a microphone to record bee sounds, a temperature sensor to measure the temperature inside the beehive, and a temperature and humidity sensor to provide information about the conditions in the beehive. This device is powered using a 12 V/60 Ah battery placed next to the beehive. The raw sound audio recordings were recorded at a sampling rate of fs = 22,400 Hz. The raw sound data and hourly sound and humidity measurements are stored on the CPU's SD card. These measurements were then extracted periodically for further processing, using Wi-Fi point-to-point connectivity.  
The software used for raw sound data processing and training has been developed in Python. U-Net CNN, SVM, and k-NN classifiers were trained and tested on a local 16-core 64-bit Linux server with 64 GB RAM. Specifically, SVC was used for the SVM and KNeighborsClassifier for the k-NN, both from the scikit-learn library. In addition, Python's deep learning frameworks of Keras and TensorFlow were used to train and test the CNN classifier.
###### Abstract  
Background. Preoperative planning and 3D printing can be used to treat pelvic bone fractures using pre-contoured surgical plates, in particular complex, comminuted fractures involving the acetabulum and quadrilateral plate. The aim of the study was to develop a Fast-Track-Protocol (fast track methodology) for creating 3D anatomical models, that could be used to shape surgical plates, using open-source software and budget 3D printers. Such a 'low-budget' approach would allow a hospital-based multidisciplinary team to carry out pre-surgical planning and treat complex pelvic fractures using 3D technology. Methods. The study included 5 patients with comminuted pelvic fractures. For each patient, CT (computed tomography) data were converted into two 3D models of the pelvis-injured side and mirrored model of the contralateral, uninjured hemipelsvis. These models were 3D printed and used as templates to shape surgical plates. Results. A Fast-Track-Protocol was established and used to successfully treat 5 patients with complex, comminuted fractures of the pelvis. Conclusion. Using the Fast-Track-Protocol it was possible to prepare 3D printed models and patient-specific pre-contoured plates within 2 days of hospital admittance. Such an approach resulted in better surgical technique and shorter operative times, while incurring relatively low costs.  
virtual planning; 3D printing; custom-made implants; patient-specific implants and acetabular fractures +
Footnote †: journal: Applied Sci. 2022, 12, 3492. [[https://doi.org/10.3390/app12073492](https://doi.org/10.3390/app12073492)]([https://doi.org/10.3390/app12073492](https://doi.org/10.3390/app12073492))
## 4 Discussion
### Limitations and Prospects  
In this study, the fusion of GF-6 and Landsat 8 images using the STARFM algorithm solves the problem of accuracy degradation caused by the low temporal resolution in constructing the time series dataset, which requires a low amount of data. However, since  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Range** & **Farmland Area/km\({}^{2}\)** & **Cultivation Area/km\({}^{2}\)** & **Abandonment Area/km\({}^{2}\)** & **Abandonment Rate/\%** \\ \hline \multirow{4}{*}{Elevation} & \textless{400 m} & 0.42 & 0.41 & 0.01 & 2.77 \\  & 400–600 m & 122.66 & 115.75 & 6.91 & 5.63 \\  & 600–800 m & 99.75 & 94.14 & 5.61 & 5.62 \\  & \textgreater{}800 m & 9.58 & 9.37 & 0.21 & 2.15 \\ \hline \multirow{4}{*}{Slope} & \textless{5-10}\({}^{\circ}\) & 98.81 & 92.45 & 6.36 & 6.44 \\  & 5–10\({}^{\circ}\) & 82.55 & 77.81 & 4.74 & 5.74 \\  & 10–15\({}^{\circ}\) & 36.71 & 35.37 & 1.34 & 3.65 \\  & 15–20\({}^{\circ}\) & 11.66 & 11.41 & 0.25 & 2.14 \\  & 20–25\({}^{\circ}\) & 2.26 & 2.23 & 0.03 & 1.31 \\  & \textgreater{}25\({}^{\circ}\) & 0.42 & 0.39 & 0.03 & 7.04 \\ \hline \multirow{4}{*}{Distance to residential area} & \textless{0-200 m} & 96.94 & 90.77 & 6.17 & 6.36 \\  & 200–400 m & 71.82 & 67.82 & 4.00 & 5.57 \\  & 400–600 m & 31.94 & 30.21 & 1.73 & 5.42 \\  & 600–800 m & 12.26 & 11.66 & 0.60 & 4.89 \\  & \textgreater{}800 m & 19.45 & 19.21 & 0.24 & 1.23 \\ \hline \multirow{4}{*}{Road density} & Extremely low & 112.73 & 108.17 & 4.56 & 4.05 \\  & Low & 79.85 & 74.91 & 4.94 & 6.19 \\  & Medium & 33.04 & 30.74 & 2.30 & 6.96 \\  & High & 5.57 & 4.92 & 0.65 & 11.67 \\  & Extremely high & 1.22 & 0.93 & 0.29 & 23.77 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Farmland area and abandonment rate in different ranges for different features.  
the predictive accuracy of the STARFM algorithm decreases as the difference between the prediction date and the base date increases [48], it may be difficult to generalize when only GF-6 and Landsat 8 data are used. Additionally, while an NDVI time series analysis is a commonly used method, it is difficult to capture vegetation features that are insensitive to the linear relationship between NIR and R bands using this approach, resulting in decreased accuracy [49]. In subsequent studies, we can continue to improve or try other spatial-temporal fusion strategies, combine more types of remote-sensing data (e.g., GF-1, GF-4, and GF-6 grouped data), and combine multiple vegetation indices for time series analysis in order to continue to improve the accuracy of the results.
## 5 Silicon Accumulation Improves Plant Disease Resistance
### Formation of Physical Barriers as Influenced by Si  
The formation of physical barriers is one of the mechanisms by which plants control diseases. Precipitation of amorphous silica in plants is a mechanical barrier [87; 88].The hypothesis of potential physical barrier formation depends on the type of Si depositions in the leaves, especially in the cell walls. In fact, it was widely believed for many years that polymeric Si in plant cell walls and apoplast can prevent the penetration of pathogens [69]. Sis are mainly deposited in the epidermal cells of leaves and leaf sheaths, forming silicified cells and a cuticle-Si double-layer structure, which can enhance the mechanical strength and stability of the host plant cell wall, thereby delaying and resisting the invasion and expansion of pathogens. Studies on the resistance of tomato to root rot disease caused by _Fusarium_[64], of oil palm to stem rot [60], of coffee to leaf rust [67], of pepper to _Phytoophthora capsici_[68], are all examples of Si increasing plant disease resistance using physical mechanisms.
# A Simulation Study of Global Evapotranspiration Components Using the Community Land Model
## 2 Methods  
### Brief Model Introduction  
The CLM land surface model series is a global dynamic land surface simulation system developed primarily by the National Center for Atmospheric Research (NCAR) based on the Community EarthSystem Model (CESM) framework program. CLM integrates the advantages of several land surface models, such as the Biosphere-Atmosphere Transfer Scheme (BATS) [24], the 1994 version land surface model of the Chinese Academy of Sciences Institute of Atmospheric Physics (IAP94) [25], and Bonan's Land Surface Model (LSM) and has become one of the most developed and promising land surface models in the world. It has been coupled with the CCSM climate model, CESM earth system model, mesoscale models (such as WRF), and regional climate models (such as RegCM). CLM4.5 is the land surface component of the new version of CESM1.2 released by NCAR. CLM4.5 contains 15 layers of soil that are in total 42.1 m deep, but only 10 layers (down to 3.8 m) are hydrologically active with soil properties. The bottom five layers have bedrock properties. The soil is divided into 20 categories, and plant functional types (PFT) are divided into 17 categories. [PERSON] et al. [14] gave a detailed description of the differences between CLM4.5 and CLM4.0.
## 3 Magnetic Characterization Instruments
### Optically Pumped Magnetometers (OPM)  
Using this device, the Larmor precession of spin-polarized atoms in a magnetic field is measured. The discovery of a spin-exchange relaxation-free (SERF) mode and the availability of solid-state lasers make its sensitivity level similar to the one that SQUIDs provide [76]. In recent years, OPMs have gained more and more attention in the fields of magnetorelaxometry and magnetorelaxometry imaging, due to their abilities to provide reduced target-field distance, the flexibility in the positioning, and the absence of cryogenic cooling [1, 77; 78]. Moreover, the total field OPM and the fluxgate magnetometers intrinsically measure the absolute magnetic field, compared to the SQUIDs that, during the nanoparticle excitation, have to be switched to an insensitive mode to prevent the saturation of the electronics. Nevertheless, when this device is used for magnetorelxometry, one main drawback is the recovery time after the shut-off of the external magnetic field for the alignment of the nanoparticles, which leads to slow characterization [1].  
The main studies in the field of the OPM for nanoparticle properties detection are summarized below. More specifically, in [1], a novel power pulsed-pump optically pumped magnetometer is presented, which is used for magnetorelxometry measurements. This device has improved recovery time, high dynamic range, and high bandwidth and allows measurements without shielding. In [77], an OPM is used for drug monitoring purposes using a novel method. In [78], a novel off-the-shelf OPM magnetometer, used for magnetorelaxation investigation, that can characterize various nanoparticle categories is presented. In [79; 80], two magnetometers for three-axis magnetic field measurements are proposed. The first one is a single-beam OPM and the second one is an improved version of the first, in terms of sensitivity. Specifically, it is about a novel pump-probe OPM, with a single fiber-coupled structure. In [81], an OPM is presented for measurements in the range of the magnetic field of the earth. This system presents low-frequency noise levels and low white noise floor, while it can be used in applications like geophysical exploration, biomagnetism, archeology, etc. In [82], an OPM that can be used for applications like nanoparticle characterization and contains a feedback-controlled spin ensemble of cesium atoms in spin-polarized vapor is proposed. In [83], an OPM is used for MRX measurements based on a 2D nanoparticle imaging setup. Finally, in [84], an OPM and a SQUID magnetometer are utilized and compared for the detection of cancer cells using the magnetorelxometry method.
# Improving the Solving of Optimization Problems:
## 3 Quantum-Compliant Problem Formulations
### Writing an Optimization Problem as QUBO  
Most quantum solvers support QUBO/Ising formulations, while only a few handle PUBO or PCBO. Due to the intuitive use of unipolar variables and straightforward conversion, PCBO, PUBO, and QUBO are preferred over Ising. This work focuses on reducing problems to the more widely supported QUBO formulation.  
The first step involves identifying the problem variables, i.e., the elements to optimize. Two situations can occur:  
* The optimization subjects can be easily described through binary variables since the target is to optimize decisional elements;
* The optimization subjects can assume several continuous or discrete values.  
In the first case, a suitable data structure is needed to reference variables in the cost function. While some cases are straightforward, more complex scenarios, such as associating one set of elements with another, may require a binary variable grid. Columns and rows represent different objects (e.g., data points and classes), with a value of 1 indicating an association. Constraints are typically added to prevent multiple associations.  
The second case is more complex, as continuous variables must be converted into binary form. This typically begins with discretization, which reduces solution accuracy. Therefore, precision must balance variable count and representation accuracy. Several encoding mechanisms can be considered for describing the discretized variables.  
In both cases, converting the function to a polynomial form may be required. This can be challenging and may reduce accuracy, impacting reliability.  
Afterwards, constraints have to be included in the formulation, either directly (PCBO) or via penalty functions. Creating a penalty function may frequently require the addition of auxiliary variables. Moreover, an appropriate penalty weight has to be chosen to correctly penalize the configurations violating the constraints. This task is crucial since a low weight may neglect constraints, while a high weight can flatten the objective function, hindering solution evaluation. Some techniques can be applied to effectively estimate its value.  
At this point, if the solver cannot handle the PUBO formulation, a polynomial reduction step, introducing further auxiliary variables, has to be applied to obtain the QUBO formulation. Finally, the QUBO formulation can be moved to Ising if needed by using the relation of Equation (5).  
The stages below summarize the process of transforming an optimization problem into a form compatible with a quantum solver:  
* Define the variables, objective functions, and eventual constraints.
* Choose the solver.
* Convert the problem into a solver-compliant format (e.g., QUBO) by executing the following steps:  
1. Describe variables as binary.
2. Compose the cost function.
3. Write constraints as penalties.
4. Estimate penalty weights to integrate constraints into the cost function.
5. Perform polynomial reduction if necessary.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 2 Materials and Methods
### Rotation Issues for the CAI
To evaluate the impact of rotation on the CAI output, the interferometer phase has to be calculated by combining Equations (1), (3) and (5). This resulting output would correspond to the contribution of only one atom. To take into account all the detected atoms, this one-atom probability has to be averaged over the position/velocity distribution of the detected atomic cloud. In our calculations, we assumed Gaussian distributions. For the sake of simplicity and to give a first idea of the rotation impact on the CAI contrast, we give in the following a simplified expression of the atomic contrast considering constant, on the timescale of the interferometer phase, the angular velocity and the angular acceleration. The rotation was considered around the \(Z\) axis (cf. Figure 8), corresponding to a \(\Psi\) rotation for the EA proof-mass (cf. Figure 3), and the acceleration measurement along the \(X\) axis. This leads to:  
\[C=C_{0}\cdot\exp\left[-2\cdot k_{eff}^{2}T^{4}\cdot\left(\sigma_{y}^{2}\cdot \dot{\Omega}_{z}^{2}+\sigma_{x}^{2}\cdot\Omega_{z}^{4}+4\cdot\sigma_{xy}^{2} \cdot\Omega_{z}^{2}\right)\right] \tag{6}\]  
where \(C_{0}\) refers to the CAI contrast without rotation. \(\sigma_{x}\) and \(\sigma_{xy}\) correspond to the position and velocity dispersion of the atomic cloud, respectively, along the \(X\) axis and the \(Y\) axis.