esa-sceva/satcom-chunk-collection huggingface dataset:
# Stable and Low-Spurs Optoelectronic Oscillators: A Review
## 1 Introduction  
Oscillators, as energy conversion devices, can transform a continuous DC signal to a periodic analog signal [1; 2], which is the core component of modern electronic systems, such as communication systems [3], radar [4], and electronic warfare [5]. Along with providing a clock signal for high-speed digital systems [6; 7], oscillators can also be used as local oscillators for up-and-down frequency conversion [8; 9], and a reference source in synchronous systems [10; 11]. As the microwave electronic systems develop rapidly [8; 9], these systems vigorously demand higher levels of oscillator performances, including frequency range, stability, and phase noise.  
In general, the energy storage characteristics of resonators determine the quality of the signals produced by oscillators [12]. In order to generate high-quality microwave signals, energy storage elements with a high Q-factor and low losses are essential. The energy storage of contemporary microwave oscillators, including dielectric oscillators [13] and crystal oscillators [14; 15], are mostly based on electronic and acoustic resonators. When these resonators operate at frequencies above GHz, the energy storage characteristics will drop sharply, and then the phase noise of generated high-frequency microwave signals will deteriorate. Owing to microwave photonic technology [16], optoelectronic oscillators (OEOs), utilizing low-loss optical fiber for energy storage, were originally proposed by [PERSON] and [PERSON] in 1996 [17; 18]. Moreover, the OEO is deemed as one of the most promising and powerful methods [19] in producing low-phase noise microwave signal from hundreds of MHz to 100 GHz.  
We have reviewed the literature of optoelectronic oscillators of the past two decades involving several well-known and recognized academic societies, including but not limited to IEEE, OSA, and SPIE. We will briefly introduce the fundamental structure and operation principle of optoelectronic oscillators and review current research on the enhancement of OEO performance, including spurious mode suppression and long-term frequency stability, which have not been summarized in detail before. We sincerely hope that this review will help the broad masses of researchers to understand and improve OEO more effectively.  
This paper is organized as follows: Section two demonstrates the fundamental setup and the principle of an optoelectronic oscillator and briefly summaries the research status; section three analyzes the improvement of OEOs, including spurious suppression and frequency stabilization; Section 4 presents the conclusions and looks into the future.
## Results and Discussion
### Hot Deformation Behavior during Compression  
In Figure 3, the experimental stress vs. strain compression curves are shown. The flow stress increases with decreasing deformation temperature and rising strain rate. The stress-strain curve's shape indicates that the process of dynamic recrystallization (DRX) proceeds during deformation at all temperatures and all strain rates. The recrystallization proceeds due to austenite low stack fault energy. The stack fault energy of the ferrite phase is larger than in the austenite phase, and as a result, the dynamic recovery process (DRV) is preferable. The dislocations move intensively, forming the boundaries of cells and subgrains during the DRV.  
The microstructure of 13 CrMoNbV steel after hot compression at temperatures of 1100, 1150, 1200, and 1250 \({}^{\circ}\)C with strain rates of 0.1, 1, and 10 s\({}^{-1}\) is shown in Figure 4. As one can see, the deformation at the strain rate of 10 s\({}^{-1}\) stretches the equiaxed ferrite grains into fibers. The fibers are divided into separate grains/subgrains, the sizes of which are increased with increases in the deformation temperature. Deformation at a lower strain rate leads to the formation of the equiaxed ferrite and martensite (former austenite) grains/subgrains, the sizes of which are slightly influenced by the deformation temperature.  
Figure 3: True stress curves at temperatures in the range of 1100 to 1275 \({}^{\circ}\)C and strain rates of (**a**) 0.1 s\({}^{-1}\), (**b**) 1 s\({}^{-1}\) and (**c**) 10 s\({}^{-1}\).
## 3 Results
### Analysis Part
#### 3.2.4 Antibacterial Susceptibility Test Results  
The disk diffusion method is distinguished by its simplicity, low cost, ability to test an excessive number of microorganisms and ease of result interpretation [43]. This method is especially suitable for fastidious bacterial pathogens [43] and serves as the indicator of the antibacterial activity of samples. Hence, in order to evaluate the antimicrobial effect of DFMACs, this method was used. According to CLI (Clinical and Laboratory Standards Institute), diameters of inhibition zones for sensitive antimicrobials (S) are accepted as greater or equal to 20 mm, for intermediate antimicrobials (I) as between 15 and 19 mm and for resistant antimicrobials (R) as less or equal to 14 mm [44]. Intermediate antimicrobials inhibit the selected bacteria, and their inhibition zone can easily be widened by an increase in the concentration of antibacterials [44]. In Figure 11, DMSO was a blank control group for gram-positive and gram-negative bacteria. All maleic anhydride copolymers, pure antibiotics and DFMACs were tested against _S. aureus_ gram-positive and _E. coli_ gram-negative bacteria.  
Since DMSO was a blank control group, it showed only the radius of the wells (6 mm). Maleic anhydride copolymers exhibited very little inhibition zones against the selected bacteria. All the DFMACs inhibited gram-positive and gram-negative bacteria, but they were more effective on gram-positive bacteria than gram-negative bacteria.  
Cefalexin monohydrate is a hydrophobic compound, so it was retained by gram-negative and more effective on gram-positive bacteria whereas ampicillin trihydrate is  
Figure 11: Inhibition zones of samples against gram-positive and gram-negative bacteria.  
moderately hydrophilic and less effective on gram-positive bacteria than cefalexin monohydrate. This affected the antibacterial activity of DFMACs on gram-positive bacteria. All of the DFMACs introduced in this study inhibited the growth of gram-negative (_E. coli_) and gram-positive (_S. aureus_) bacteria like pure antibiotics. The inhibition zones of DFMACs can be enhanced [44] by increasing the drug to polymer ratio (Table S5). The aim of the antibacterial study was to show that the polymers gain antibacterial properties after drug functionalization. This analysis proved the efficacy of the DFMACs on certain bacterial groups, alongside the enhancement of pharmacological properties of free antibiotics by DFMACs.
## 2 Characteristics of the Prosumer Installation  
### Description of the Installation  
The generic structure of the discussed prosumer installation connected to the low-voltage distribution network is presented in Figure 1. It consists of loads, a PV system, a stationary energy storage system, and an electric vehicle.  
According to the binding regulations, the prosumer uses an energy source for their own purposes. Therefore, the rated power of a PV system should not be greater than the power contracted with the DSO. The prosumer is billed for electricity according to the 2-zone tariff, in which the higher energy prices are from 6 am to 1 pm and from 3 pm to 10 pm.  
The ESS is used to reduce the costs of purchasing energy from the network for the prosumer. It performs a function of a power and energy buffer for the time periods of high energy when load demand is higher than local generation, and its operating charging and discharging schedule is defined according to the energy tariff. If the energy price is high, the ESS is discharged with constant power and thus decreases the energy drawn from the grid. In the rest of the time, the ESS is charged, and the charging power is dependent on the duration of the low-price tariff and the rated power capacity of the inverter of the stationary storage.  
The EV exploited by the prosumer is equipped with a bi-directional drive inverter, which is used for driving and, additionally, together with the EV battery, it can be used as an energy storage system during parking. In the second case, in general, the EV drive inverter is expected to charge and discharge EV batteries according to the assumed strategy to support the energy management in the prosumer installation. However, the availability of EV used for ancillary services strongly depends on the user's behavior. It will be described in the next Section.
# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
(**Middle**): forest wildfire smoke [17]. (**Right**): vehicle smoke on road (ours). vehicle matching module can easily remove these false positives, such as non-road and other non-vehicle regions. As found in practice, the detected regions of static images from a smoke detector are difficult for human beings to further check if they are cropped, while we can easily distinguish them by watching the spatial-temporal extent of each region. In the last step, we considered the spatial-temporal extent of a smoke region, and developed a short-term spatial-temporal network ((ST)\({}^{2}\)Net) to classify the spatial-temporal extent for refinement. Specifically, we explored two kinds of (ST)\({}^{2}\)Net based on ResNet18: one is called (ST)\({}^{2}\)Net-prefix, which replaces the early 2D convolutional layers of ResNet18 with 3D ones, and the other is called (ST)\({}^{2}\)Net-suffix, which performs 3D convolution in the last feature maps of ResNet18 over several frames. We extensively evaluated our method and other recent advanced methods on four frame-wise metrics including detection rate, false alarm rate, precision, and F1 score. Almost all previous works only provide one or two of them for performance evaluation, which is not fair for a comprehensive comparison. We conducted experiments on our LaSSoV and LaSSoV-video since there is no public smoky vehicle data, and demonstrated that our framework is significantly superior to those hand-crafted feature-based methods and recent advanced methods. In the remainder of this paper, we briefly review the related works on smoke detection and smoky vehicle detection in Section 2. We then introduce our datasets and compare them to existing datasets in Section 3. We further describe our methods in Section 4 and present experimental results in Section 5. Finally, we present a conclusion and discuss future work in Section 6.
## 4 Discussion
The overall run times for adapting the models to the full loading regime were approximately 24 h for the isotropic model, 40 h for the orthotropic model, 25 h for the hybrid isotropic model and 38 h for the hybrid orthotropic model. The continuum and hybrid isotropic models reported similar runtimes to the structural model. The main factor in slowing down the adaptation of the orthotropic and hybrid orthotropic models was post-processing. Adapting a model with a much finer mesh to the same loading regime would result in a longer runtime. Thus, although the scale poses a limitation, the models are considered to present a reasonable balance between accuracy and computational efficiency. The computational models presented in this study were developed with the purpose of simulating bone adaptation in the pelvic construct using different modelling approaches and material models and comparing the effect that different modelling approaches have on bone adaptation. A distinct advantage of using predictive modelling is the potential of gaining insight into the role of different activities and corresponding muscle loading patterns on driving bone adaptation without requiring information from CT data. Considering that the CT derived model is isotropic, it is unexpected that those models which include directionality either through the use of structural elements or material directionality all compare more favourably with the CT derived model than the predicted isotropic continuum model. A potential explanation for this is that the conversion going from HU values in the CT images to the [PERSON]'s modulus values in the FE model picks up the dominant rather than the mean [PERSON]'s modulus. To assess this, a combined model was generated in which the material directionalities and ratios between the [PERSON]'s moduli found for the converged adapted orthotropic continuum model were imposed on the CT derived model, while setting the dominant [PERSON]'s modulus value to that given by the CT scan, resulting in an overall average stiffness of 2403 MPa. If the conversion process finds the dominant [PERSON]'s modulus value, then this combination of the predicted continuum orthotropic and CT derived model would be expected to have a similar response to the unaltered CT derived model for the single leg stance load case. The slopes of the lines of best fit for the hybrid model compared to the CT derived model are 0.96 and 0.99 for the maximum and minimum principal strains, respectively, indicating that the conversion from HU to [PERSON]'s modulus does find the dominant rather than the mean [PERSON]'s modulus.
## Composition of the CLNVD and Solution of Its Amplification Coefficient
### The CLNVD's Displacement Amplification Coefficient and Energy Dissipation Coefficient  
In Figure 1a, the structural damping is Rayleigh damping. The mass proportional damping of the \(i\)-th floor is \(C_{mi}\), and the stiffness proportional damping is \(C_{ki}\). The constitutive relationship of NVD formed by VD and NSD in parallel is \(F=\text{csgn}(i_{d})\left|i_{d}\right|^{\alpha}+k_{N}u_{d}\), where \(c\), \(\alpha\), and \(k_{N}\) are damping coefficients, damping index, and negative stiffness, respectively.\(u_{d}\) and \(i_{d}\) are VD's deformation and velocity, respectively. Under earthquake action, the maximum displacement and velocity between the \(i\)-th floor of the structure are \(U_{i}\) and \(U_{i}\), respectively; VD's maximum displacement on the \(i\)-th floor is \(u_{do}\). When \(U_{i}\) is kept constant and \(c=0\) and \(k_{N}=0\), the VD's displacement is \(u_{ao}\). We can define it as follows:  
\[\gamma_{i}=u_{ao}/U_{i},\ \eta_{di}=u_{do}/u_{ao} \tag{2}\]  
\(\gamma_{i}\) is referred to as the geometric amplification coefficient in this paper. \(\eta_{di}\) represents the effect of the equivalent stiffness of the intermediate column-lever on the VD's displacement. In this paper, it is called effective displacement factor.  
The displacement amplification coefficient \(f_{di}\) of CLNVD is defined as the ratio of VD displacement \(u_{do}\) on the \(i\)-th story to the inter-story displacement \(U_{i}\), which is illustrated in Equation (3). The energy dissipation coefficient \(f_{Ei}\) of CLNVD is defined as the ratio of the maximum energy dissipation of VD on the \(i\)-th floor in a single cycle to the largest energy dissipation of the structural stiffness proportional damping in a single cycle, which is shown in Equation (4).  
\[f_{di}=\frac{u_{do}}{U_{i}}=\gamma_{i}\eta_{di} \tag{3}\]  
\[f_{Ei}=\frac{\lambda cu_{do}u_{do}^{\alpha}}{\pi\mathbb{C}_{ki}U_{i}\dot{U}_{i }}=\frac{\lambda cf_{di}^{1+\alpha}\dot{U}_{i}^{\alpha-1}}{\pi\mathbb{C}_{ki}} \tag{4}\]  
where the value of \(\lambda\) is referenced in the Chinese Code [50].  
The amplification ability of CLNVD is related to coefficient \(\gamma_{i}\) and coefficient \(\eta_{di}\): \(f_{di}\) describes the displacement amplification capability of the CLNVD, and \(f_{Ei}\) describes the energy consumption capacity of the CLNVD. Therefore, \(f_{di}\) and \(f_{Ei}\) can be applied to describe the comprehensive performance of CLNVD.
## 5 Case Studies
### Expansion to Three Distribution Networks of the Republic of Korea  
To expand the applicability of the proposed approach, the validity of the proposed approach for applying an ODB, as presented in this study, is verified by using three distribution networks of the Republic of Korea, areas A, B, and C. A comparative analysis is conducted on the TP performance across KEPCO's three distribution networks. The time taken to receive input data from the RDB, the RDF triple store, and the GDB through the data agent and middleware, and to generate a payload in the application is measured. Table 7 shows the number of nodes for each component of the distribution system managed by each area of the Republic of Korea. As shown in Table 7, the total number of nodes managed by areas A, B, and C is 874,774, 524,748, and 220,450, respectively, with area A overseeing the highest number of nodes.  
Table 8 represents the TP speeds when applying the RDB and the ODB to their respective target systems. As shown in Table 8, the exploration speed of the RDB compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Terminal** & **Connectivity Node** & **Conducting Equipment** & **Total Objects** \\ \hline A & 441,284 & 164,222 & 269,268 & 874,774 \\ B & 265,981 & 95,544 & 163,223 & 524,748 \\ C & 112,205 & 39,849 & 68,396 & 220,450 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Number of network nodes for three areas in the Republic of Korea.  
Figure 19: Topology processing time comparison: (**a**) RDF triple store and RDB; (**b**) GDB and RDB.  
Figure 20: Topology processing time of the RDF triple store and GDB.  
to the ODB increases rapidly as the number of nodes in the target system increases. This demonstrates the advantage of employing the ODB in larger systems. The exploration time for the RDB ranges from a maximum of 2283 [ms] to a minimum of 82 [ms]. It is evident that the performance of TP in the ODB improves in comparison to the RDB as the number of nodes managed by the areas increases. Area A finds that the TP speed of the RDF triple store is approximately 20,210 times faster than that of the RDB, while the GDB is approximately 78,749 times faster. Similarly, for area B, querying the RDF triple store is approximately 9846 times faster than the RDB, while the GDB is approximately 39,383 times faster. For area C, the exploration speed of the RDF triple store is approximately 3452 times faster than that of the RDB, while the GDB is approximately 11,834 times faster. As the number of nodes managed by the DMS increases, the difference in TP speed between RDB and ODB applications becomes more pronounced.
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools
### Summary and Implications  
This comprehensive analysis underscores that no single existing simulation platform provides a fully integrated solution spanning all functional modules required for GEO satellite communication systems. The strengths and limitations identified across individual tools highlight the necessity of a modular, integrated simulation framework capable of leveraging specialized  
Figure 2: Co-simulation framework and inter-tool interfaces.  
capabilities across platforms. Although a full-scale simulation-based validation is beyond the scope of this manuscript, the proposed framework builds upon well-established integration practices using widely adopted tool chains (e.g., STK-MATLAB-NS-3), which have been successfully applied in mission planning, system evaluation, and scheduling analysis in both academia and industry. Our structured evaluation (Sections 4.1-4.5) confirms the technical feasibility of such cross-platform interoperability, while the system requirements and scenarios detailed in Sections 2 and 3 demonstrate the framework's practical relevance and scalability. These insights collectively motivate the hybrid co-simulation strategy proposed in this study and lay the groundwork for a fully implemented prototype in future work.
## 1 Introduction
### Compliant Joints and Their Applications  
A CJ is a mechanical joint encountered in compliant mechanisms (CM) that allows motion due to the elastic displacement of their flexible parts, known as flexure hinges (FHs). In other words, compliant joints are the key components in compliant mechanisms; they act analogously to the kinematic pairs in rigid-body mechanisms [1]. Thanks to the use of different types of CJs, many applications have appeared, including in specific areas of mechanical engineering such as robotics [2; 3; 4; 5] and micro-electromechanical systems (MEMSs) [6; 7; 8; 9]. In robotics, the use of CJs has grown significantly, reflected in the design of complex mechanisms and robots. That was the case for [PERSON] and [PERSON], whose main objective was to improve the performance of a compliant Sarrus linkage by means of topological linkage modification [10]. Another example of this growth was presented by [PERSON] et al.; their work was an implementation of a compliant spine for a flapping wing in unmanned aerial vehicles, with the goals of weight reduction and the relief of stress on the component [11]. Additionally, other works where the main objective was the development of optimal robotic members are also reported in the literature. For example, a three-degrees-of-freedom robotic arm with compliant fingers [12], the design of a compliant finger for a 3D endoskeleton with soft matter [13], the development of an engineering method for quantifying the CJ compliance of robotic hands [14], and the design of soft monolithic robotic fingers for prosthetic hands [5]. In the field of MEMSs, CJs have been widely used and tested, resulting in progress for medical devices and precision engineering applications. In medicine, MEMSs have been used for surgical implementations [15], such as in intraocular surgeries [16] and biomedical executions [17]. In the case of precision engineering applications, MEMSs have been explored for use as flow sensors [8] and for consumer product applications where IC-based technology tends to fail [9].  
The range of motion that a CJ can achieve depends on the stiffness of its flexures, which in turn depend on the external geometry, dimensions, and base material used. In order to widen the range of applications for CJs, researchers have explored different approaches to tailoring their stiffness, as recently reviewed by [PERSON] et al. [1]. With the aim of reducing stiffness (increasing motion range) in CJs, [PERSON] and [PERSON] [18] explored the use of lattice structures. In [18], the implementation of mechanical metamaterials as lattice flexures instead of blade flexures was studied. This study concluded that lattice flexures could achieve reductions in bending stiffness in the range of 60 to 80% compared to fully solid blade flexures of the same size. The lattice-compliant joints studied in [18] were additively manufactured.