esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper deals with the electromagnetic compatibility (EMC) issues related to the direct and radiated emissions from a high-speed integrated circuits (ICs). These emissions are evaluated here by means of circuital and electromagnetic models. As for the conducted emission, an equivalent circuit model is derived to describe the IC and the effect of its loads (package, printed circuit board, decaps, etc.), based on the Integrated Circuit Emission Model template (ICEM). As for the radiated emission, an electromagnetic model is proposed, based on the superposition of the fields generated in the far field region by the loop currents flowing into the IC and the package pins. A custom experimental setup is designed for validating the models. Specifically, for the radiated emission measurement, a custom test board is designed and realized, able to highlight the contribution of the direct emission from the IC, usually hidden by the indirect emission coming from the printed circuit board. Measurements of the package currents and of the far-field emitted fields are carried out, providing a satisfactory agreement with the model predictions.  
electromagnetic compatibility; integrated circuits; EMI modeling; conducted emission; radiated emission +
Footnote †: journal: Applied Physics Letters
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 5 Ionospheric Space Weather Forecast  
Space weather centers have been set up in many countries to monitor and forecast space weather changes. Global uniform observations of F3/C and F7/C2 ionospheric RO profiles provide the best chance to observe 3D structures of electron density, which for the first time make the ionospheric weather monitoring, nowcast, and forecast possible. The NOAA/SWPC and CWB/SWOO have been assimilating the F3/C and F7/C2 RO ionosphere profiles for space weather monitoring, nowcast, and forecast. A brief introduction of these activities is provided here.
## 2 Mathematical Formulations
### Freezing Model
#### 2.2.2 Calculation of the Mass Transfer Rate  
Nucleation rate, a concept of the classical nucleation theory (CNT) which can reflect the icing capability of water, is introduced into the calculation of mass transfer rate during freezing in the present model. The parameter (\(J\)) provides the number of newly formed nuclei per cubic meter and per second, which is mathematically described as:  
\[J=\frac{k_{B}T}{h}\exp(-\frac{\Delta g}{k_{B}T})n_{L}\exp(-\frac{\Delta G^{*}} {k_{B}T}) \tag{14}\]  
where \(T\) represents the water temperature, \(h\) represents Planck constant which equals to \(6.626\times 10^{-34}\) J-s, \(k_{B}\) represents Boltzmann constant which equals to \(1.380\times 10^{-23}\) J-K\({}^{-1}\), \(\Delta g\) represents the activation energy of water molecules passing through water and ice interface which is around \(4.000\times 10^{-20}\) J, \(n_{L}\) represents the number of water molecule in a water volume which is about \(3.34\times 10^{28}\) m\({}^{-3}\). Furthermore, \(\Delta G^{*}\) reflects the variation of Gibbs function for the phase change between ice and water. Gibbs function can be described as [47; 48]:  
\[\Delta G^{*}=\frac{16\pi\gamma_{iw}^{3}T_{sat}^{2}}{3H_{Lv}^{2}\Delta T^{2}} \alpha_{ey} \tag{15}\]  
where \(\gamma_{iw}\) is the superficial free energy of the water-ice interface, \(H_{Lv}\) is latent heat per volume. Moreover, \(\alpha_{ey}\) is the shape coefficient of nucleation and can be written as:  
\[\alpha_{ey}=\frac{2-3 cos\theta+cos\theta^{3}}{4} \tag{16}\]  
where \(\theta\) is the contact angle ranging from \(0^{\circ}\) to \(180^{\circ}\). It is shown by Equation (14) to Equation (16) that the influence of the contact angle is considered in calculating the nucleation rate. As the other parameters are constant, the nucleation rate would gradually increase with the contact angle decreasing, which quantitatively reflects the change during nucleation from the uniform state to the inhomogeneous state. To reflect the effects of the contact angle on the variation of the mass transfer rate, a mathematical relationship between the relaxation time parameter \(\lambda\) in Equation (9) and the nucleation rate \(J\) is established, which is presented as:  
\[\lambda_{ls}=JV_{I} \tag{17}\]  
where \(V_{I}\) is the volume of water in the cell. Then, Equation (9) can be rewritten as:  
\[\dot{m}_{ls}=JV_{I}\alpha_{l}p_{l}\ \frac{T_{sat}-T_{l}}{T_{sat}}. \tag{18}\]Equation (17) is established on a simple idea that the nucleation rate and the volume of the water in which nucleation occurs would both influence the freezing rate. With the treatment shown by Equation (17), difference between homogeneous nucleation in the fluid domain (without solid surface) and heterogeneous nucleation on the solid wall can be reflected by the mass transfer rate obtained via Equation (18). Furthermore, the influence of the newly formed ice phase (treated as solid surface by specifying contact angle value for it) on the freezing process is also possible to be simulated using the newly proposed model, which is hard to be considered in previous models.
## 3 Results and Discussion
### Analysis of Electrical Results
#### 3.2.1 Comparison of Conductivities and Loss Tangent (Tg of \(\delta\))
In general, the Tg of \(\delta\) oscillates between 10\({}^{-1}\) and 10\({}^{-2}\) for almost all polymers except for the PA; the addition of GTR increases one order of magnitude. PVC has a variable behavior of up to an order of magnitude from the addition of GTR.
## 3 Results and Discussion
### Proximate Analysis  
The results of the proximate analysis of the plastic waste sample and its blends with lignite are presented in Figure 2 as a ternary diagram. In contrast to the lignite sample, the volatile content of the plastic waste sample was found to be much higher and approximately 92 wt.%, whilst ash content was substantially lower at about 5 wt.%. Ash content is important since ash is a secondary waste and requires further management. In addition, ash (depending on its quality) can cause severe problems, e.g., depositions on the surface of the burner or other equipment. Thus, low ash content is desired. For such reasons, ash content is a crucial factor in choosing the combustion technology. There are several combustion technologies for different fuel qualities. For instance, fuels with ash content lower than 10 wt.% (dry basis, d.b.) in large-scale installation can be burned in a moving grate combustor [36]. The results of plastic waste samples are in agreement with the literature [13]. The moisture content of plastic waste was significantly lower than the one of lignite. During the combustion process, as the moisture evaporates, the energy of  
Figure 1: Results of the energy content analysis: (**a**) Gross calorific value of the samples and \(\Delta\)GCV compared to lignite, (**b**) Dependent variable GCV\({}_{\text{experimental}}\) (GCV\({}_{\text{exp}}\), y) and independent variable wt.% percentage of PW into the blend with lignite (wt.% proportion of PW, x), (**c**) Scatterplot and linear regression model of dependent variable GCV\({}_{\text{experimental}}\) (GCV\({}_{\text{exp}}\), y) and independent variable GCV\({}_{\text{theoretical}}\) (GCV\({}_{\text{theor}}\), x).  
the fuel cannot typically be recovered. Moisture content is a crucial characteristic of the fuel in the combustion process. High moisture content leads to a low calorific value of fuel [21]. It has been reported [37] that during the lignite co-combustion with municipal solid wastes, in a fluidized bed combustor (FBC), as the moisture increases, the combustion temperature decreases. Overall, plastic waste is superior to lignite in terms of proximate analysis. As for the case of GCV, the blending of lignite with plastic waste leads to an improvement of lignite's characteristics (lower ash, moisture, and fixed carbon) and higher volatiles' content.  
Figure 3 presents the TG and DTG curves of lignite, PW samples, and their blends with lignite at the heating rate of 10 K/min. In lignite, three main stages can be recognized: dehydration of moisture, volatile release, and char combustion. In the plastic waste and the blended fuels, the second stage (release of volatiles) occurs at two sub-stages. As the content of PW increases in blends with lignite, the fuel ignites at lower temperatures and with higher reaction rates. In the TG graphs, it can be seen that in the PW sample, the combustion is completed earlier than lignite. In blends, as the PW proportion is increased, the combustion is completed at an earlier time. In other words, rapid ignition can be achieved by using mixed/composite fuel.
## 5 Research Challenges and Future Directions
### System Integration and Standardization  
The cost of designing and maintaining dual radio for the sensor node and the passive wake-up radio is a major challenge of this scheme. Instead of the current state of keeping detached components which is expensive, the components should be integrated together with the sensor platform intended for a specific application. Other aspects such as in-band or out of band, and addressing should also be applied on the same chip with main radio. Moreover, an integrated networking and system architecture is required for passive wake-up radios so that applications can be easily built upon it. The design of wake-up radios is yet to be standardized in terms of wake-up signal format, usable channels, frequency usage etc. An attempt is made in [125] for standardization in the 802.11 working group to have these design features standardized. By incorporating the passive WuR into the current electronic and hardware standards, it allows them to fully utilize RF energy harvesting, thus eliminating idle listening.
## 3 Modeling Parameters
### Flywheel rotor Simulation Parameters
Based on the Tsai-Wu failure criterion, a critical or limit velocity, \(\omega_{\text{limit}}\) was determined as 24,250 rpm. Note that failure is initially indicated at this velocity, for an \(SR\) lightly above unity (1.01); however, viscoelastic stress relaxation will improve the stress state, allowing for safe operation at this velocity over the long term. Case 2: This scenario is intended to more realistically simulate the application of an FESS in solar PV electric grids or EV charging support. For these applications, the FESS is assumed to experience a single charge/discharge cycle every day as the system charges during off-peak hours and discharges during peak hours. Peak electricity demands are typically observed in the mornings and evenings [35], which are also when PV systems have low productivity; therefore, a household would rely on the FESS during these times to operate appliances or support the charging of a vehicle. (It should be mentioned here that the considered FESS capacity is not sufficient to fully charge typical EVs on the market. Rather, energy storage is seen as a means to support EV fast charging and associated peak loads.) Minimum demands are observed in the middle of the day when a PV system is most productive, thereby recharging the system. For this study, each day is divided into three 8-h phases and assigned a different average velocity for each period. These are referred to as the maximum phase, intermediate phase, and minimum phase. It is recognized that charging or discharging the FESS may occur over a period of hours; therefore, the intermediate phase represents the average velocity during the charge-discharge periods. Attempting to simulate a real-world scenario, the rotor is assumed to operate below \(\omega_{\text{limit}}\); therefore, the velocity during the maximum phase, \(\omega_{\text{Pmax}}\), is set at 0.9 \(\omega_{\text{limit}}\). For the minimum phase, the angular velocity, \(\omega_{\text{Pmin}}\), is chosen to be 0.25 \(\omega_{\text{limit}}\), as discussed in [36]. Finally, the intermediate phase angular velocity, \(\omega_{\text{Pint}}\), is halfway between \(\omega_{\text{Pmax}}\) and \(\omega_{\text{Pmin}}\), i.e., 0.575 \(\omega_{\text{limit}}\). The rotor is simulated to rotate at each velocity, i.e., \(\omega_{\text{Pmax}}\), \(\omega_{\text{Pint}}\) and \(\omega_{\text{Pmin}}\), for 8 h each day, for 365 days per year.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 3 Methods
### RSVQ-Diffusion Model
#### 3.1.2 RSVQ-Diffusion Model Network Architecture  
In the diffusion image decoder of the VQ-Diffusion model, the original Transformer architecture, when processing images unfolded into one-dimensional sequences, relies solely on sequential positional encoding. This leads to the loss of spatial information, resulting in image distortions and overlaps of objects. To address this issue, the RSVQ-Diffusion model incorporates spatial positional encoding of two-dimensional images into the original positional encoding. This allows the model to consider the spatial location information of remote sensing images during the generation process, thereby producing remote sensing images with reasonable spatial distribution.  
Additionally, the original model's Transformer self-attention mechanism processes the entire input sequence as a whole, overlooking relationships within local sequences. To address this issue, the RSVQ-Diffusion model introduces a local awareness mechanism. This mechanism divides long sequences into multiple short sequences and calculates attention weights within these short sequences, followed by averaging. This approach encourages the Transformer to focus on local detail information in the images, thereby enhancing the richness of details in the generated images. By alternately using the local awareness mechanism and the original self-attention mechanism, the RSVQ-Diffusion model achieves synchronous learning of both local and global information in the diffusion image decoder. This results in the generation of remote sensing images that are rich in detail and have a reasonable spatial structure.  
This paper designs the TransLocalBlock module, which is interleaved with the TransformerBlock in the original model to introduce a local attention mechanism. This allows the RSVQ-Diffusion model to focus on information within short sequences during training, enhancing the learning of local features and improving computational efficiency and information capture capabilities when processing long sequences. In the RSVQ-Diffusion model, local attention primarily involves dividing the long sequence into multiple short sequences and employing a multi-head attention mechanism to focus on local information. The network structure of the RSVQ-Diffusion model is illustrated in Figure 2, where the TransLocalBlock module replaces the full attention mechanism in the original TransformerBlock with the proposed local perception mechanism (LMA).
## 4 Conclusions  
This study investigated the effect of berry thinning on Shine Muscat grape berries in terms of effects on sensory properties, increasing the TSS and reduced the TA of grape berries. The treatment increased the TSS and reduced the TA of grape berries. The treatment increased berry size and cluster weight but showed no effect on individual berry weight. TPC, PAC, and PTC tended to increase as the level of berry thinning increased. The compositional analysis of the volatile compounds confirmed that C6-compounds were the most abundant aroma compounds. Linalool, which provides a floral scent, was found to be the most abundant monoterpene. OAV analysis showed that (E)-2-hexen-1-ol, (E)-2-hexenal, 1-hexanal, (Z)-3-hexenal, (E)-\(\beta\)-damascenone, linalool, and (E)-linalool oxide were active odorants. The increasing trend of linalool concentration with increasing berry thinning was responsible for the high sensory flavor scores in the thinned berries. Moreover, linalool oxide detected only in BT30 and BT50 enhanced the Muscat flavor of thinned berries. It was concluded that berry thinning accelerated ripening and induced compositional changes in Shine Muscat grapes, resulting in improved sensory properties.  
Figure 5: Effects of berry thinning on the sensory properties of Shine Muscat grapes: BT0, BT30, and BT50 indicate the level of berry thinning in terms of percentage.  
**Author Contributions:** K.-O.C.: Study design, Methodology, Investigation, Writing--original draft/ review and editing; D.I.: Study conception and design, Methodology, Writing--original draft/review; S.J.P.: Study conception and design, Methodology; D.H.L.: Study conception and design, Methodology; S.J.K.: Study conception and design, Methodology; S.J.K.: Study conception and design, Methodology, Writing-original draft/review; Y.Y.H.: Methodology, Investigation, Writing--original draft/review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a grant from the Research Project for Agricultural Science and Technology Department (PJ01570701), Rural Development Administration, Republic of Korea.  
**Institutional Review Board Statement:** The study was approved by the Institutional Review Board (IRB) in May 2021 (P01-202105-13-004).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
One of the greatest advances in the field of radiation research is the evolution of the use of synchrotron X-rays in a variety of medical and biological applications, extending to imaging, diagnostics, and radiotherapy (RT). Typical photon energies in synchrotron beams dedicated to biomedical applications range from approximately 10 to 150 keV. Currently, many synchrotron facilities operate with an electron beam energy greater than or equal to 2.4 GeV and high electron-beam currents are achievable in the accelerator ring of third- and fourth-generation synchrotrons, which include the Australian Synchrotron (AS) in Melbourne, the National Synchrotron Light Source (NSLS) in Brookhaven, USA and the European Synchrotron Radiation Facility (ESRF) in Grenoble, France, [1; 2; 3]. A table summarizing third- and fourth-generation synchrotrons around the world housing beamlines capable of conducting radiobiological and clinical research is presented in Supplementary data (Section S1). Operation principles of synchrotrons are presented in Supplementary data (Section S2).  
The ESRF shares a physical site, called the European Photon and Neutron Science Campus, with the neutron source Institut Laue-Langevin and the European Molecular Biology Laboratory. The AS is a major research facility located in Clayton, a technology and innovation hub of southeast Melbourne. The AS belongs to the Australian Nuclear Science and Technology Organization (ANSTO), which is a statutory body of the Australian government, formed in 1987 to replace the Australian Atomic Energy Commission. Currently, AS and ESRF facilities are best configured for a wide range of biomedical research studies, including animals and potentially human cancer patients in the future.  
In this review, we discuss the properties of synchrotron X-ray beams that are essential for generation of novel radiation modalities and their applications for radiobiology and medicine. Synchrotron-based RT is, so far, preclinical, but has great potential to improve outcomes of cancer RT [4; 5; 6]. Studies on non-targeted effects of X-ray synchrotron RT are scarce. In recent years, our groups have conducted such studies at the Imaging and Medical Beamline (IMBL) of the AS and at the Biomedical Beamline (ID17) of the ESRF. Here, we summarize our findings and outline perspectives for future studies and implications in the biomedical arena.
## 3 Materials and Methods
### Development Environment and Tools
#### 3.3.2 Hardware Components Used
To support seamless multi-sensor integration and real-time data acquisition, the Monit4 Healthy system utilizes modular hardware assemblies designed for efficient sensor interconnection and reliable data transmission. Each Blackbox device incorporates standardized connectors (e.g., Qwic interface for I2C communication), allowing for rapid prototyping and scalability. Signal conditioning circuits, such as analog filters and amplifiers, are embedded within the hardware design to ensure high-fidelity signal acquisition and compatibility with microcontroller input ranges. Custom PCB layouts and compact enclosures, developed using parametric CAD tools, facilitate ergonomic deployment in clinical and remote settings. Wireless connectivity is achieved through integrated BLE and Wi-Fi modules, optimized for low-power consumption and robust communication, as detailed in Section 3.2.
## 1 Introduction
The computation of NIRv, as well as other VIs, such as the ratio vegetation index (RVI [42]), is possible with NIR-enabled PhenoCam by following the approach proposed by [PERSON] et al. [29] and [PERSON] et al. [34] for the computation of CamNDVI. However, up to now, we are not aware of studies evaluating the differences between PTDs derived from multiple PhenoCam-based VIs from PhenoCam (GCC, CamNDVI, CamNIRv, and CamRVI). Past studies related to derived PTDs from PhenoCam mainly focus on temperate/boreal forests and grassland (e.g., [32]), and only a few recent studies have focused on seasonally dry tree-grass ecosystems [43; 44]. Considering that tree-grass ecosystems are a widely distributed land cover type, which occupies 16-35% of the Earth's land surface [45; 46; 47], it is necessary to further investigate the methods to extract PTDs for these ecosystems. Moreover, the increasing number of sites with PhenoCam associated with EC flux measurements open interesting perspectives to evaluate: first, the consistency between PTDs derived from PhenoCam-based VIs and PTDs of ecosystem functioning (physiological phenology, i.e., [48]); second, the direct relationship between PhenoCam-based VIs and GPP. However, to our knowledge, only a few studies pay special attention to the differences between phenology of ecosystem structure and of ecosystem functioning and carbon fluxes [28; 32]. In this study, our main objective is to evaluate the potential of PhenoCam to monitor phenology of seasonally dry Mediterranean tree-grass ecosystems. Specifically, the objectives are (1) to characterize structural and physiological phenology of tree-grass ecosystems and their main climatic drivers using PhenoCam and GPP derived from EC measurements; (2) to compare the PTDs and growing season length (GSL) derived from different PhenoCam-based VIs, and to evaluate their performance in tracking the PTDs and GSL derived from GPP.