esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Optimization Design of Spaceborne Microwave Probe Antenna
## 2 Introduction to the Antenna Structure of the On-Board Microwave Sounder  
The scanning drive mechanism of the spaceborne microwave-sounding antenna consists of two planar reflective surfaces driven by a permanent magnet servomotor. It is shown in Figure 1.  
Figure 1: Schematic diagram of the planar reflective surface and the permanent magnet servomotor.  
There are many factors affecting the disturbance torque of a mechanism, including motor cogging torque, friction torque, and the moment of inertia. The cogging torque and friction moment are usually small and can be minimized by designs such as motor and bearing lubrication. The disturbance moments are mainly due to mass imbalance and variable speed modes. As shown in Figure 1, the two reflecting surfaces are mounted at an angle and have a resulting dynamic imbalance, which causes the perturbation moments. Since the scanning drive mode of the rotating mechanism is variable-speed scanning, as shown in Figure 2, the acceleration and deceleration processes occur within a short time, which generates the perturbation moment.  
Figure 3 shows the design of the original reinforcing bar structure of the planar antenna. The rotational inertia is 1.95 \(\times\) 10\({}^{-3}\) Kg\(\cdot\)m\({}^{2}\) and the mass of the reinforcing bar is 210.5 g. The perturbation moment generated at uniform speed is 0.060 Nm, and the perturbation moment when the speed is varied with the maximal acceleration of 96.94 rad/s\({}^{2}\) is 0.1890 Nm. It can be seen in Figure 3 that the reinforcing bar of the original structure is mainly distributed at the edge, and the scope of the reinforcing bar is much wider along the planar antenna around the circumference. This form of reinforcement layout leads to excessive rotational inertia of the planar antenna. To reduce the structural disturbance moment caused by the variable speed mode, it is necessary to reduce the rotational moment of inertia of the structure and optimize the layout and size of the reinforcement bars of the planar antenna.  
Figure 3: Planar antenna original reinforcement structure design drawing.  
Figure 2: Scanning driver operating mode.
## 1 Introduction
### Definitions
#### 1.2.2 CPS and CPPS
It can be described as a system able to autonomously exchange information, controlling and actuating independently on its own components, smart machines, storage systems, and production facilities. CPPS can accelerate improvements not only in the industrial processes but also in product engineering, supply management, material usage, and life cycle management [12].
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### System-Level Performance Impact
#### 6.1.2 Message Signal Bandwidth and Slew Rate Limitations  
A. Differentiator Frequency Response Limitations  
The performance of the differentiator varies significantly depending on the characteristics of the input signal. To ensure effective operation, the signal bandwidth should be restricted to less than fs/10 (100 Hz for a 1 kHz sampling rate), allowing noise levels to remain within acceptable limits. When signals exceed this bandwidth, they undergo severe noise amplification, which can overpower the desired signal content. Slew rate limitations become apparent for high-frequency or rapidly changing signals. The maximum recommended input signal slew rate is 100 V/s to prevent differentiator saturation and maintain linear operation. Signals with higher slew rates experience nonlinear distortion and increased noise sensitivity.
# A Methodology to Evaluate Standards and Platforms within Cyber Threat Intelligence
## 5 Platforms Evaluation Results
### Platforms Selected for Analysis  
Given the presented results, the platforms selected for further analysis and evaluation are **MISP, CIF, CRITs, OpenCTI and Anomali STAXX**. Following, a succinct presentation of the platforms is provided.  
* **Malware Information Sharing Platform**: MISP is an open source TI platform that allows the sharing, storage and correlation of indicator of compromise (IOCs) [59]. The tool provides a database of indicators, including technical and general information about cyber threats, which are stored in a structured format and with a flexible data model. The stored data is automatically correlated to describe the relationships between attributes and indicators.
* **Collaborative Research into Threats**: CRITs is an open source web-based tool that integrates a repository of malware and cyber threats with other software capable of offering mechanisms for analyzing and correlating the information [60]. This initiative was developed by MITRE with the main objective of assisting the cybersecurity community in the process of analyzing and sharing data about threats [61].
* **OpenCTI**: It is an open source framework with the main objective of aggregating, in a comprehensive way, general and technical information from the CTI context [62]. It assists organizations in the process of managing their content about cyber threats, allowing the structuring, storage, organization and graphic visualization of this information.
* **Collective Intelligence Framework**: CIF is a system focused on speed, performance and integration used in threat information management [63]. It assists users in formatting, normalizing, processing, storing, sharing and building threat data sets. The system extracts information about cyber threats from a wide range of sources and creates a sequential and chronological grouping about a specific threat [64].
* **Anomali STAXX**: It is a tool that provides bidirectional sharing between sources of threat intelligence that use the STIX and TAXII standards [65], facilitating access to information about cyber threats. The platform provides an intuitive dashboard to present data obtained from different sources.
## 3 Result and Discussion
### Breakdown of the Leakage Vortex Core
#### 3.3.2 Vortex Structure  
To study the vortex structure, a plane normal to the rotating axis of the vortex is considered on the suction surface at 0.85 \(C_{\mathrm{x}}\). Figure 15a shows the projection of the main velocity streamline and the location of the tip-leakage vortex on this plane for the outlet Mach number of 0.79. Moreover, Figure 15b illustrates the jet-like leakage flow from tip gap.  
Considering the transverse pressure in the blade passage, the leakage flow rolls up and flows towards the blade midspan. The velocity contour indicates that the leakage vortex is composed of the high-shear flow. As the vortex develops downstream, the shear flow induced by the leakage flow is continuously rolled into the outer layer of the leakage vortex. Meanwhile, the upstream shear layer gradually rolls up into the inner side and forms the low-velocity vortex core. Therefore, the leakage vortex can be considered as a swirling vortex. The main structures of the tip-leakage vortex can be divided into two parts, including the vortex core with low-velocity fluid and the shear flow wrapped around the low-velocity core.  
Based on the swirling vortex theory, the movement of the fluid in the vortex core region can be approximated as the rotation motion of a rigid body [36; 37], which can be described by the Rankine vortex [38].  
Figure 14: Distribution of the stagnation position of leakage vortex breakdown along the outlet Mach number.  
Figure 16 shows the relative vorticity contour on the 95% span. The relative vorticity is normalized by the physical angular velocity of the rotor, which is expressed as \(\omega_{\rm n}=\omega/2\Omega\). In the subsonic flow without shock wave as shown in Figure 16a, the vorticity in the vortex core gradually approaches to nearly zero. The supersonic flow in Figure 16b shows the boundary before and after the shock wave. It is observed that before the shock wave, the high vorticity value of the leakage vortex appears at the core of the leakage vortex and the surrounding region. However, a low vorticity region suddenly appears at the core of the leakage vortex after the shock wave and the high vorticity region forms at the edge of the leakage vortex. The velocity field in Figure 11g indicates that the leakage vortex breaks up rapidly after passing the shock wave. Therefore, it can be inferred that the shock wave interaction has a direct impact on the instability of the leakage vortex.
# Real-Time UAV Trash Monitoring System
## 5 Conclusions  
In this study, we developed a marine trash detection and real-time monitoring system. The experiments show that the proposed system can successfully detect coastal trash, perform data analysis, and transmit trash information to a remote site. The onboard computer, video streaming server, internet communications, Mongo database, and Kafka connector were well integrated. We expect to contribute to global environmental protection with the proposed UAV trash monitoring system. The UAV trash monitoring system is designed for UAV swarm. In the future, multiple UAVs can be constructed across the coastline of target areas for trash monitoring. However, one issue was not included in this study, which is obstacle avoidance. In our next project, obstacle avoidance control will  
Figure 17: Trash map: the heatmap of trash pollution areas. (**a**) A heatmap of trash pollution area (north of Taiwan). (**b**) A zoom in heatmap of trash pollution area (Badouzi fishing port).  
be applied to the UAV monitoring system. For the commercialization, benchmarks of the proposed UAV trash monitoring system should be considered, such as the evaluation of real-world trash detection results, the reliability of the IoT architecture, and the cost of the hardware devices.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]-[PERSON] and [PERSON]; software, [PERSON]-[PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON]-H.L.; writing--original draft preparation, [PERSON]-H.L.; writing--review and editing, [PERSON]-[PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology (Taiwan), grant number MOST 110-2221-E-019-075-MY2. The APC was funded by National Taiwan Ocean University and Ministry of Science and Technology.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Imaging Channel Processing Results  
The proposed modified BP algorithm was used to perform two-step azimuth phase compensation on the three sets of experimental data using the Doppler phase extracted from the reference channel. The echo energy of the target between different pulses was adjusted to be consistent, and coherent integration could be achieved. Figure 17 shows the imaging results before and after ionospheric scintillation error compensation. The red dotted lines indicate the location of the selected strong scattering areas, which were used for subsequent comparative analysis of the azimuth profile. Here, we performed error separation on the total Doppler phase, and we only compensated or did not compensate the residual error after polynomial fitting to separately observe the impact of ionospheric scintillation. In fact, azimuth focusing can be achieved by directly using the total reference channel Doppler phase for compensation according to the process in Figure 9.  
Figure 17: Imaging results before and after ionospheric scintillation error compensation: (**a1**) imaging results before phase error compensation (morning); (**a2**) imaging results before phase error compensation (middle); (**a3**) imaging results before phase error compensation (evening); (**b1**) imaging results after phase error compensation (morning); (**b2**) imaging results after phase error compensation (midday); (**b3**) imaging results after phase error compensation (evening).  
To reduce the contrast ratio of the imaging results, the SAR images were taken as decibel values and normalized with the noise energy as the reference. Without ionospheric scintillation error compensation, the imaging results were obviously defocused in the azimuth direction. In addition, when different satellites (PRN4, 30 and 18) were used as illuminators, the strong scattering areas of the imaging results were roughly the same, but there were still differences. For example, the amplitude at the position 300 m eastward in Figure 17(b1) was significantly stronger than Figure 17(b2). This was because there were certain differences in the altitude angles of the three satellites at the experiment time, and the geometric configurations could not be exactly the same, which ultimately led to differences in strong scattering details.
## 3 Results
### Divergent Response of sAET/sGPP/sWUE to Drought across Different Climate and Biomes  
For more insights about the responses of sAET, sGPP, and sWUE to DS, the ecosystem's responses were highlighted under different climate groups and land cover types (Figure 10). The sWUE of bare soil and shrub-covered areas presented the highest positive response to multiyear DS, where the 5 drought years increased the WUE from D5 to D1 events. On the other hand, the sGPP of croplands, grassland, and tree-covered areas has clear and high negative values in the DS group, where D1 has the highest negative values and was associated with relatively stable and slight positive responses for sWUE among the five drought years.  
DS exhibited divergent impacts across WUE of climate patterns. The WUE of both BWh and BWk arid recorded the highest positive response to DS years from D1 to D5 where the drought events increased the WUE from D5 to D1. The sGPP for the same climatic patterns had a stationary negative variance amongst the DS group. While the sWUE of temperate and cold climate patterns (Cs and Ds) showed the lowest positive response against the DS events, the response amongst the group was stable and insignificant. It was coupled with significant negative variance between the DS group and the sGPP of the same climate patterns.  
Figure 11 illustrates ecosystem WUE response patterns to extreme drought (D1). In 44.3% of the study area, positive sWUE values coincide with negative sGPP and sAET, suggesting a greater decrease in AET compared to GPP. This WUE pattern is primarily influenced by physical processes like evaporation and water availability. The second dominant pattern, covering 29% of the area, exhibits negative sWUE values alongside negative sGPP and sAET, indicating a more pronounced reduction in GPP compared to AET. This WUE pattern is largely controlled by biological activities such as carbon assimilation. he third dominant pattern, accounting for 21.4% of the area, showcases positive sWUE values alongside positive sGPP and negative sAET. This suggests fluctuations in WUE influenced by both biological and physical processes, with both GPP and AET co-regulating the variability.  
Figure 9: Cumulative probability density (CPD) of sGPP/WUE values during the D1 drought year (brown line for sGPP and pink line for sWUE) and the last rank of the D5 drought year (green line for sGPP and violet line for sWUE).  
Figure 10: Mean and standard deviation (Std.) of sAET, sGPP, and sWUE values (mean value is denoted by a column, the lower and higher bound of vertical line indicate the Std. of anomalies values), which were calculated during the first 5 worst drought years (D1, D2, D3, D4, and D5) ranked by its intensity from the highest to lowest severity, for each land cover type and climate pattern separately. The header panels represent spatial frequency of DS values across land cover and climate patterns.
## 3 Results and Discussions  
### The Transition from ZND to Cellular Detonations without Artificial Perturbations  
When a one-dimensional ZND detonation is placed in a two-dimensional domain as an initial condition, the planar structure can only sustain itself for a short distance. Then small-scale distortion appears on the front because of the detonation instability and numerical truncation error. The latter one actually triggers the distortion, but its development is controlled by the detonation instability. The distortion causes a transverse gradient of the reactivity and hence causes the formation of weak transverse waves. As the weak perturbations amplified itself at a rate controlled by the instability factor, a balance will eventually be obtained with the presence of transverse waves with a constant spacing. Thus a fully-developed cellular detonation can always be formed, as shown in Figure 5. In the case with \(k_{\mathrm{R}}=2\) s\({}^{-1}\), distortion first appears at a distance of 200 \(\Delta_{\mathrm{I}}\), and the final equilibrium is reached at a distance of about 700 \(\Delta_{\mathrm{I}}\). When \(k_{\mathrm{R}}=3\) s\({}^{-1}\), these two values change to 100 \(\Delta_{\mathrm{I}}\) and 500 \(\Delta_{\mathrm{I}}\). When \(k_{\mathrm{R}}=4\) s\({}^{-1}\), these two values change to 80 \(\Delta_{\mathrm{I}}\) and 400 \(\Delta_{\mathrm{I}}\). It suggests that, with the increase of \(k_{\mathrm{R}}\), or equivalently the increase of instability factor \(f\), these two distances are dramatically reduced. The increase of instability renders the chemical reactions to be more sensitive to the perturbations of temperature and hence accelerates the amplifying rate of the distortion and transverse waves. Figure 6 shows five pressure history curves at different height along the channel. It is easy to locate where the distortion occurs, how the perturbations amplify, and when the final equilibrium state is reached. The fluctuations of the cellular detonation front can also be observed. It is found that cases with large instability factors show large fluctuation scattering. The cell length variation is also plotted in Figure 6d. The cell sizes of three fully-developed detonation waves with different \(k_{\mathrm{R}}\) only show small variation. It can be explained that the cell size usually has a linear relation with the induction length that does not change since \(k_{\mathrm{I}}\) is set as a constant number.
## 1 Introduction
This study aims to address this problem by developing a species- and site-specific model that could simulate the post-release behavior, dispersal, and mating performance of released sterile males in a heterogeneous and mosaic landscape, and thus serve as a decision support system (DSS) to guide the planning of local SIT interventions. The DSS was developed based on the existing PESTonFARM model [24; 25], validated on fruit producer farms [26; 27]. The model is species-specific and simulates behavior and fate of large cohorts of medfly females in a heterogeneous landscape and their response to different IPM scenarios. The model works with a spatial resolution of 10 \(\times\) 10 m and daily temporal resolution. The individual development of each \"virtual\" female fly is stochastically simulated from the egg up to adult stage, and then its daily events such as oviposition and fruit infestation, local dispersal and shifts among fruits of different phenology, and finally mortality caused by natural factors or IPM treatments. Males are not simulated because they do not damage the fruit, but their presence in a 1:1 ratio and numbers sufficient to fertilize all mature females are assumed. Simulated IPM scenarios may consist of up to several different IPM treatments applied in different farm zones according to user-defined schedules. The model generates results characterizing effects of each IPM treatment, medfly seasonal densities and mortality profiles (eggs, larvae, pupae,and adults), fruit infestation at harvest and net costs and benefits of IPM [27]. The presented DSS, SIT-adapted PESTonFARM model, was developed by designing additional software modules emulating the specific mechanism of SIT treatment, the actions of sterile males released into the field and their interactions with wild females. The aim of this article is to present the main features of the developed DSS modules emulating SIT and to demonstrate its ability to capture and quantify the impact of relatively small changes in site topography on medfly development and on the effectiveness of different approaches to local SIT interventions. The overall performance of the model was verified by relating the results of simulated SIT application scenarios to general knowledge on SIT. Empirical validation of the developed DSS is ongoing within the framework of Horizon project REACT (grant agreement No 101059523), where a series of site-specific SIT scenarios were prepared for several'real' fruit-growing locations in the Naoussa Valley, Greece. These scenarios will be implemented and evaluated during the 2025 and 2026 seasons and the results will be published separately.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 2 AIoT in PV Systems: Background
### Internet of Things
In PV systems, fog computing is used to reduce the latency involved in data processing, allowing real-time analysis of energy generation and consumption data from solar panels and other PV components. By processing data closer to the source, such as at solar inverters or energy meters, fog computing minimizes the need to send large amounts of data to the cloud, improving both the speed and efficiency of monitoring and control processes. This localized computing also helps ensure that critical decisions, such as system adjustments and fault detection, can be made with minimal delay, enhancing the overall performance and reliability of PV systems. Table 3 presents a comparison of edge, cloud, and fog computing in PV Systems.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## Appendix B  
Taking the derivative of \(Obj\big{(}\theta;\ \mathbf{\mathcal{S}}^{*},\Delta\mathbf{\mathcal{S}}^{\prime},\mathbf{D}^{ \prime}\big{)}\) with respect to \(\theta\), we have  
\[\frac{\partial}{\partial\theta}\sum_{i=1}^{\pi}(a_{i}+b_{i}\cos( \theta)+c_{i}\sin(\theta))^{2}\] \[= \sum_{i=1}^{n}2(c_{i}\cos(\theta)-b_{i}\sin(\theta))(a_{i}+b_{i} \cos(\theta)+c_{i}\sin(\theta))\] \[= \sum_{i=1}^{n}\left(-a_{i}b_{i}\sin(\theta)+a_{i}c_{i}\cos(\theta )+b_{i}c_{i}\cos^{2}(\theta)-b_{i}c_{i}\sin^{2}(\theta)+(c_{i}^{2}-b^{2})\sin( \theta)\cos(\theta)\right)\] \[= 2\Big{(}-a_{n}\sin(\theta)+\beta_{n}\cos(\theta)+2\gamma_{\pi} \cos^{2}(\theta)+\delta_{n}\sin(\theta)\cos(\theta)-\gamma_{\pi}\Big{)}, \tag{15}\]where  
\[\alpha_{n} =\sum_{i=1}^{n}a_{i}b_{i}, \beta_{n} =\sum_{i=1}^{n}a_{i}c_{i},\] \[\gamma_{n} =\sum_{i=1}^{n}b_{i}c_{i}, \delta_{n} =\sum_{i=1}^{n}(c_{i}^{2}-b_{i}^{2}).\]  
If we let \(\sin(\theta)=\lambda\in[-1,1]\) and \(\cos(\theta)=\pm\sqrt{1-\lambda^{2}}\). Then setting (A7) to 0 leads to:  
\[\gamma_{n}\pm\beta_{n}\sqrt{1-\lambda^{2}}-2\gamma_{n}\lambda^{2}\pm\delta_{n }\lambda\sqrt{1-\lambda^{2}}-\alpha_{n}\lambda=0.\] (A8)  
Rearranging (A8) and squaring both sides, we have:  
\[\left(\pm(\beta_{n}+\delta_{n}\lambda)\sqrt{1-\lambda^{2}}\right) ^{2}=\left(-\gamma_{n}+2\gamma_{n}\lambda^{2}+\alpha_{n}\lambda\right)^{2}\] \[\implies A_{n}\lambda^{4}+B_{n}\lambda^{3}+C_{n}\lambda^{2}+D_{n} \lambda+E_{n}=0,\] (A9)  
where  
\[A_{n} =4\gamma_{n}^{2}+\delta_{n}^{2}, B_{n} =2(2\alpha_{n}\gamma_{n}+\beta_{n}\delta_{n}), C_{n} =\alpha_{n}^{2}+\beta_{n}^{2}-4\gamma_{n}^{2}-\delta_{n}^{2},\] \[D_{n} =2(-\alpha_{n}\gamma_{n}-\beta_{n}\delta_{n}), E_{n} =-\beta_{n}^{2}+\gamma_{n}^{2}.\]  
Equation (A9) is a quartic equation [43] and has at most 4 real roots. Suppose that we have \(1\leq m\leq 4\) solution(s) from (A9) satisfying \(\lambda_{j}\in[-1,1]\), \(j=1,\ldots,m\), then the corresponding collection of angles is  
\[\boldsymbol{\theta}_{\lambda}=\big{\{}\arcsin(\lambda_{1}),g(\pi-\arcsin( \lambda_{1})),\ldots,\arcsin(\lambda_{m}),g(\pi-\arcsin(\lambda_{m}))\big{\}} \in[-\pi,\pi).\]