esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks
## 2 Related Work  
Machine learning (ML) can be used to improve the performance and efficiency of the handover process in wireless networks by providing more accurate and efficient handover decisions. ML-based handover algorithms can be trained to analyze data on the network conditions and the requirements of the mobile device to make informed decisions about when and how to perform a handover. For instance, ML algorithms can be trained to predict the quality of the new connection before committing to the handover, which can help to minimize the interruption of the call and increase the chances of a successful handover. Many researchers have studied handover technique in new generation wireless networks [14; 15; 16; 17; 18; 19; 20], ranging from traditional techniques such as multi-attribute decision making (MADM) [21] to deep learning techniques such as the SINR change of a UE in the handover problem in 5G networks [22]. In [22; 23; 24; 25; 26], the authors combined their efforts to address two issues and proposed a learning-based load balancing handover for multi-user mobile mmWave networks where they characterized the user association as a non-convex optimization problem, and then they attempted to approximate the optimization solution of the problem by using the deep deterministic policy gradient (DDPG) method. Traditional cell-to-cell handovers rely on measurements of target cell radio strength, which necessitates frequent measurement gaps. Therefore, a handover solution based on machine learning was proposed in [27; 28; 29]; also, prediction-based handover strategies are recommended to reduce the number of measurement windows as in [30], an ultrafast and effective XGBoost-based predictive handover technique for next-generation mobile communications was presented. Another fascinating study about conditional handover was proposed in [31]. In this study, the author presents a novel prediction-based CHO (PCHO) scheme that employs deep-learning technology to address the drawbacks of CHO and make better informed preparatory decisions. All of the above-mentioned handover strategies are related to terrestrial networks (TNs); however, in NTNs, the network architecture and infrastructure are dissimilar from those of conventional terrestrial networks, which might make handover more challenging. Moreover, an in-depth analysis of the current status of onboard artificial intelligence and machine learning (AI/ML) processing is provided in [32], along with a list of important factors to take into account while implementing it in communication satellites. Also, it offers a helpful framework for comparing and assessing the feasibility of various AI chipsets for onboard AI/ML processing. However, our proposed architecture is for a regenerative onboard satellite payload; therefore, the machine-learning model is being trained offline. On-device ML can be utilized to execute inference with models directly on a device.
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 3 Inter-Satellite Links Pointing and Tracking in-Orbit Test Method
### Characterization of Virtual Target Satellite Orbit Based on Double Vectors  
In order to solve the orbital parameters of the virtual target satellite, a characterization method is provided using the position and velocity at a particular moment.  
For circular orbits, based on the orbital altitude and angular velocity, the norm of flight velocity of the virtual target satellite is given as follows:  
\[|\mathbf{v}_{A}(t_{1})|=\frac{2\pi}{T_{o}}(R_{e}+h_{o}) \tag{20}\]  
The direction of the velocity vector \(\mathbf{v}_{A}(t_{1})\) of the virtual target satellite at time \(t_{1}\) in the J2000 is obtained using the cross product of position vectors \(\mathbf{r}_{A}(t_{1})\) and \(\mathbf{r}_{B}(t_{1})\). The angular momentum vector \(\mathbf{r}_{n}\) is denoted as:  
\[\mathbf{r}_{n}=\frac{\mathbf{r}_{A}(t_{1})\times\mathbf{r}_{B}(t_{2})}{|\mathbf{r}_{A}(t_{1}) \times\mathbf{r}_{B}(t_{2})|} \tag{21}\]  
According to triangulation theory, the position vectors of the ground stations \(\mathbf{r}_{A}(t)\) and \(\mathbf{r}_{B}(t)\) should be non-collinear. The velocity vector of the virtual target satellite at time \(t_{1}\) is obtained as  
\[\mathbf{v}_{A}(t_{1})=\frac{\mathbf{r}_{n}\times\mathbf{r}_{A}(t_{1})}{|\mathbf{r}_{n}\times \mathbf{r}_{A}(t_{1})|}\cdot\frac{2\pi}{T_{o}}(R_{e}+h_{o}) \tag{22}\]  
Using the satellite position vector and the velocity vector at the time \(t_{1}\), the orbital parameters of the virtual target satellite orbits can be obtained.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Style Transfer
#### 2.2.2 Implementation Details  
This paper conducted experiments based on CycleGAN. The generator used the ResNet [33] architecture, the discriminator used PatchGAN [31], and the cycle loss function was the L1 loss function. The model training platform was the same as Section 2.1. The initial learning rate was 0.0002, the Adam optimizer with parameter values of 1 = 0.9 and 2 = 0.999 was used, the batch size was set to 4, and the training epoch was 250.
# Link Prediction with Hypergraphs via Network Embedding
## 2 Materials and Methods
### Datasets  
In this paper, the real library loan records of seven universities in Shanghai, which were Shanghai University of Electric Power (SUEP), Shanghai Ocean University(SHOU), Shanghai University of Finance and Economics(SUFE), University of Shanghai for Science and Technology(USST), Shanghai International Studies University(SISU), Shanghai Normal University(SHNU) and Tongji University(TU), were used to validate the performance of our approach. The datasets were collected from Huiyuan sharing [43]. We organized the data from 2017 to 2018 and took two columns of data, ISBN and PATRON_ID, as the different types of nodes to construct the hypergraphs. PATRON_ID represented \(Node1\) and ISBN denoted the hyperlinks in Figure 1. The structural properties of the hypergraphs are analyzed in the Table 1. As shown in Table 1, \(n\) denotes the number of nodes, \(m_{0}\) refers to the total number of links between nodes, \(\langle k\rangle\) means the average degree of nodes, \(m\) is the number of hyperlinks, \(\langle d_{H}(v)\rangle\) refers to the average hyperdegree of a node, \(\langle\delta(e)\rangle\) means the average degree of hyperlink, and \(\langle\delta_{H}(e)\rangle\) is the average hyperdegree of a hyperlink.
## 1 Introduction
These include: exfoliation-adsorption, in situ intercalative polymerization, melt intercalation, and template synthesis [47]. The incorporation of layered nanoparticles at the interphase of fiber reinforced composites is also considered a promising strategy to increase the composite's toughness [48; 49; 50; 51]. Encouraging results have been obtained for fiber reinforced polymer/clay nanocomposite [48; 51; 52]. The ability of nanoclay to increase the fracture toughness is attributed to a combination of mechanisms which include: deflection and pinning of the crack propagation, prevention of the crack formation, and stop crack propagation due to mechanical interlocking and void bridging [53]. The ability of nanoparticles to improve the mechanical properties of fiber reinforced composites has also been supported by computational studies [49; 50; 53; 54], that highlighted that the nanoplatelets localized in the fiber/matrix interfacial layer are more effective at crack deflection than nanoplatelets dispersed throughout the matrix [49; 54]. However, the possibility to modify the fiber-matrix interface of cementitious based composites with coating containing nanoclay particles has been poorly investigated. Previous studies have shown that the nanoclay particles can promote the formation of hydration products at the fiber matrix interface [29; 30]. Nonetheless, to the authors' best knowledge, the contribution of nanoclay particles to the fiber-matrix interfacial bond has never been characterized. In this work, sodium montmorillonite (MMT) nanoparticles were added to a styrene-butadiene (SB) coating and applied to AR-glass fibers produced by a lab scale spinning device without surface treatment during fiber manufacturing. Energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD) were employed to investigate the dispersion of the MMT nanoparticles in the polymeric matrix. Single fiber tensile tests were conducted on unsized fibers, as well as sized and coated fibers. The effects on the fiber-matrix interaction were investigated by means of micro-mechanical single fiber pull-out (SFPO) tests. The pull-out behavior of fibers modified with different coatings, with and  
Figure 1: (**a**) Structure of a silicate layered coating on the surface of glass fibers and its barrier properties according to [PERSON] et al. [46]; (**b**) nanoclay particles dispersed in the coating promoting the formation of hydration products at the fiber-matrix interface. without addition of nanoclay, was compared with that of fibers treated with only organosilane coupling agent. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) analyses were conducted to correlate the mechanical results obtained with the morphological and chemical properties of the fibers surface. Growth of crystals on the fibers surface was investigated with a digital microscope and employed as qualitative method to measure the effect of nucleation on the formation of hydration products.
# A Broadband Analog Predistortion Linearizer Based on GaAs MMIC for Ka-Band TWTAs
## 2 Analog Predistortion MMIC Topology
### Nonlinear Frequency Adjustment Module Design for Broadband Application  
#### 3.2.1 Nonlinear Frequency Adjustment Module Design Principle  
The gain compression of a Ka-band TWTA is about 7 dB at the operating band of 25.1-27.5 GHz, and decreases as the frequency is increased, exhibiting a difference for about 1.5 dB in the operating band. The phase compression of a TWTA is about 40\({}^{\circ}\) and increases with frequency, having a difference of about 20\({}^{\circ}\) in the operating band. It can be seen that the gain compression difference is comparatively smaller than the phase compression difference. To obtain a better nonlinearity of TWTA through cascaded APD MMICs, the phase expansion of the APD MMIC in the operating band should increase more with frequency.  
The NFAM is used to realize the desired phase expansion difference. A capacitance in parallel to the gain branch can be used as an NFAM, at the position shown in Figure 1. The gain and phase change in the signal passing through the NFAM can be expressed  
Figure 5: Simulated results of the independent tuning of **(a)** gain conversion and **(b)** phase conversion.  
as \(G_{\rm N}\) and \(\varphi_{\rm N}\), as expressed in Equations (14) and (15), where \(Z_{0}\) is the characteristic impedance of the microstrip in the gain branch in parallel with \(\rm C_{j}\). \(G_{\rm N}\) and \(\varphi_{\rm N}\) are both affected by the capacitance value \(\rm C_{j}\). Through reasonable design, a small gain expansion difference and a large phase expansion difference can be realized in the APD MMIC within the operating band.  
\[G_{\rm N} =\frac{2}{\sqrt{4+\left(\omega\rm C_{j}Z_{0}\right)^{2}}} \tag{14}\] \[\Phi_{\rm N} =-\arctan(\frac{\omega\rm C_{j}Z_{0}}{2}) \tag{15}\]
## 2 Materials and Methods
### Analytical Methods  
Dry depositions of aerosol particles and contribution of their soluble components to wet depositions can significantly influence amount of nutrients in samples, thus in calculated estimates. To assess contribution of dry deposition of nitrogen, phosphorus and silicon, permanently open and wet-only samplers have been used in parallel since 2015 in Sevastopol and since 2016 in Katsiveli. Unlike the bulk samples, collected in open sampler, wet-only samplers are closed with a lid when there is no precipitation. Therefore, in principle, there is no effect of dry deposition on the collected sample [19]. A polyethylene bucket was used to collect rain samples.  
Collected samples were photometrically analyzed in the laboratory in Marine Hydrophysical Institute RAS (Sevastopol) shortly after sampling or were frozen after sampling for further analyses. Ammonium was determined using a modified Sadgi-Solorzano method [20], which is based on determining the indophenolic dye forming in an alkaline medium from phenol, ammonia, and hypochlorite. The method uses nitroprusside as a catalyst for the reaction. The sensitivity of this method is 0.05 uM. To determine the nitrate-nitrite concentration, we used the method of reducing nitrates to nitrites with copper-bonded cadmium on a flowing system of AutoAnalyzer. Minimum detectable nitrates are 0.36 uM with the method error is \(\pm\)0.20 uM [21]. The concentration of silicon was determined based on the formation of a blue silicomolybdenum complex, during procedure a correction for the salinity was applied. The determining the inorganic phosphorus concentration was carried out by the method based on the formation of a blue phosphomorpholybdenum complex.  
Hydrometeorological data that are independent characteristics of the state of environment have been also recorded during the work. These data are used for interpretation the dynamics of the nutrients content in the atmosphere.  
Statistical data processing has been performed by descriptive statistics methods, correlation (correlation coefficient and correlation ratio) and regression (paired and multiple regression) analyses using the Statistica program (StatSoft, Inc., Tulsa, OK, USA) and \"Data analysis\" add-on in Excel. The significance of correlation coefficients, correlation relations, and determination coefficients was checked by the Student's criterion. The Kolmogorov-Smirnov criterion was used to assess the normality of empirical distributions. The critical value of the significance level is assumed to be 0.05.  
To exclude errors, the rule of 3 sigma (3\(\sigma\)) was applied, which allows to exclude data whose deviation from the arithmetic mean exceeds the tripled standard deviation. As a result, three values of the total concentration of inorganic forms of nitrogen obtained  
Figure 1: Monitoring sites: layout of sampling stations (**a**), station in Katsiveli (**b**), station inSevastopol (**c**).  
during the analyses of precipitation samples in Sevastopol were discarded and five values for Katsiveli results.
## 5 Towards Enhanced Use of High Resolution EO Data in Earth System Modelling
### An International Surface Working Group  
The International Surface Working Group (ISWG) has been active since 2016 to gather requirements specific to surface observations, and to enhance both our understanding and ability to monitor the components of the Earth system. Land, vegetation, snow, ice, coastal waters and open waters are included. An international science working group focused on land surface would integrate the other active scientific groups, gathering requirements that are specific to land and surface-water monitoring. A coordinated effort to bring concerns and advances from the scientific community could bring a clear focus by reviewing what technology has reached a maturity level to be directly used in land surface monitoring and modeling applications and feed into weather and climate. Usage of readily available observing capacity that has not been exploited (such as several geostationary platforms) could be fostered by dedicated workshops and a gap analysis for observing needs from this community could provide a link to future missions. The recognition of ISWG with its focus on existing international scientific coordination can be critical to support operational applications in forecasting and monitoringof weather and climate. In particular, the satellite based estimation of soil moisture, snow, land surface temperature and surface water body extents are particularly poorly considered for continuity in current satellite missions (with few exceptions). Unfortunately, when satellite data is available, users' uptake is often confined to local applications not encompassing global weather and environmental forecasting or climate monitoring, which would naturally scale the number of users to a broader community. The primary task of ISWG should be to bring forward a continuity plan using existing systems and ones which would fill application gaps in future. ISWG would provide the community a point of focus for applications of EO data that would be particularly relevant to bridge gaps between monitoring and mapping of terrestrial surfaces that have been historically developed in different communities.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 5 Results
### Stationary Heart Standard Phantom  
Finally, the RiB method was tested on an actual measurement with notable impedance differences. One of the most obvious scenarios for this kind of test is cardiac imaging. In this case, the same before mentioned setup has been used, resulting on images in Figure 7. Comparing DAS, see Figure 7a, to RiB, see Figure 7b, one may observe how speckle effect has been considerably mitigated. RiB speckle reduction is especially evident on cavities where DAS reflects reverberations--convex interfaces with respect to the array.  
Conversely, in Figure 7c,d NLmeans filtered image versions are depicted, using the same parameters as in Section 5.3. For these parameters setup and the resolution used to generate the images, search window is approximately 1.6 mm. This, and filter memory used to model convolutive noise, see (12) and (13), explain aberrations on DAS filtered image, Figure 7c, hindering heart cavity segmentation. RiB definition facilitates correct segmentation, delivering good tissue interface discrimination capabilities, a desirable feature for this kind of medical imaging applications.  
Figure 7: Heart phantom results: (**a**) DAS, (**b**) RiB(10%), (**c**) NLmeans-DAS and (**d**) NLmeans-RiB (10%)
## 2 Related Work
To the best of our knowledge, existing literature on MAC protocols in UASNs has mainly concentrated on enhancing network efficiency and optimizing energy usage; no prior study was specifically aimed at optimizing energy harvesting through channel access strategies. Our method combines multi-armed bandits with cooperative, independent Q-Learning and proposes a decentralized and robust solution that adapts to dynamic environments with minimal communication overhead.
# Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques
## 3 Problem Identification  
* The random nature of the solar array generates fluctuations in electric power. These fluctuations harm the stability and power quality of electric power systems.
* Due to the high penetration level of solar energy in distribution systems, the utility (grid) is of high concern, because it provides a threat to the entire system in terms of THD, voltage regulation, and stability.
* To develop an innovative and integrated approach using optimization techniques that enhance the power quality of solar power systems.
* In all conditions, the main purpose of PV with an additional inverter topology is to develop energy savings, along with excellent service. The challenging tasks for this type of system design are:
* An appropriate multilevel inverter topology.
* A control strategy for the PV generator.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focused on Placement Related Problems in WSNs
#### 3.4.2 Coverage Efficiency  
Optimal placement of sensor network has been considered in the context of various preferences namely, coverage efficiency, network life time, energy conservation and minimum node density. In [120] a multi-objective evolutionary algorithm has been proposed for solving optimal sensor placement problem. The proposed approach has been used to maximize coverage, maximize desired connectivity level and minimize energy cost. Optimal sensor node placement in the field of interest has been addressed in [121], by utilizing a biologically inspired multi-objective optimization algorithm. The proposed algorithm searched optimal placement of sensor network to maximize the coverage and connectivity with minimum energy consumption. Simultaneous optimization of coverage efficiency and network life time has been addressed in [122] through optimal deployment of sensor nodes.
# A Taxonomic Survey of Physics-Informed Machine Learning
## 3 Discussion of Relevant Applications
### Physics-Informed Neural Operators  
Neural operators are included in our discussion of physics-informed machine learning for their obvious potential for physics-model-driven learning machines--specifically, whether or not methods discussed previously distinctly have implemented drivers of physics-informed machine learning. Regardless, advancements in neural operators have already led to the use of physics-informed neural operators (PINOs). [PERSON] et al. [35] employed Fourier neural operator layers, alongside physics-informed learning with observational biases. Learning bias is introduced via physics-informed residuals computed by automatic differentiation via autograd, function-wise differentiation, or Fourier continuation. The physics-informed neural operator is applied to a wide range of examples including Burgers, Darcy, and Kolmogorov flow problems. [PERSON] et al. [36] provide the framework for physics-informed DeepONets, applying this to a Burgers transport problem and a 2-dimensional eikonal equation, among other PDE models. A physics-model informed learning bias is introduced by augmenting loss calculations by merging latent representations of solutions. Toward its general applicability, the DeepONet does not specify the architecture of its constituent branch and trunk networks, affording the use of many learning architectures.  
Regularization mechanisms can also force functional learning toward desired partial differential equation formulation. [PERSON] et al. [37] have given a variational physics-informed DeepONet that can be applied to quasibrittle materials modeling. Two well-studied fracture models are used to benchmark variation. By extension of the underlying models, learning bias is introduced into the functional learning framework in the loss calculations. Additionally, [PERSON] et al. [38] propose an extreme learning machine (X-TFC) approach for learning functionals, employing physics-model-informed learning bias for several optimal control problems by initial and boundary constraints in the extreme learning machine algorithm with constrained expressions.  
Physics-informed neural networks have become widely applied in research areas where physics-model- and physics-data-driven information is available. Consequently, an all-encompassing discussion of applications is difficult. Instead, the remaining focus of the discussion will be on work which has advanced the theory of machine learning via physics-informed neural networks or discussions on the limitations facing physics-informed machine learning.