esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 6 Experimental Conclusions  
This article designed a frequency synthesizer with low phase noise and low spurious signals in the L-band using the HMC830 chip. The SPI protocol communicates between the MCU and the PLL. The voltage conversion chip provides the voltage to the PLL chip. The phase noise of the test output signal can reach \(-105.59\) dBc@1 KHz. The proximity spur suppression is up to 70 dB. The loop lock time is as short as 94 us. This frequency synthesizer provides a high-quality output signal, which can be employed as the local oscillation signal source of the RF receiving front end, fully meeting the wireless communication requirements. Due to the importance of the loop filter in the performance of the frequency synthesizer, different filter types with different loop bandwidths can affect the output signal quality. Thus, a passive fourth-order RLC loop filter is proposed to improve output signal quality. This loop filter is compared with the passive fourth-order RC loop filter. By analyzing the transfer functions of the two filters and the zero-pole distribution, observing the simulation and experimental results, and analyzing the output signal parameters, such as phase noise, capture time, and spur suppression, it can be concluded that the output signal quality of the frequency synthesizer of the passive fourth-order RLC filter is better than that of the passive fourth-order RC loop filter.  
Conceptualization, X.Z., Q.D., Y.M. and J.L.; methodology, X.Z. and Q.D.; software, X.Z.; validation, X.Z., C.L., H.Z. and Y.L.; investigation, X.Z., Q.D. and C.L.; writing--original draft preparation, X.Z. and Q.D.; writing--review and editing, X.Z., Q.D., C.L., H.Z., Y.M., Y.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. 42074042 and no. 42104032) and the Youth Cross Team Scientific Research Project of the Chinese Academy of Sciences (grant no. JCTD-2021-10).  
Figure 11: Spur suppression: (**a–c**) passive fourth-order RC filter and (**d–f**) passive fourth-order RLC filter.  
Data Availability Statement: [[https://www.scidb.cn/en/s/fqEVze](https://www.scidb.cn/en/s/fqEVze)]([https://www.scidb.cn/en/s/fqEVze](https://www.scidb.cn/en/s/fqEVze)).
## 4 Research Results
### Regression Analysis  
In the multiple regression analysis, as shown in Table 4, we can find that the overall F-value for the model is 35.020, and that the p-value is smaller than 0.5, showing that the overall explained variance of the regression model is statistically significant. The regression coefficients of the four predictors in the standardized regression equation (attitude, subjective norms, perceived behavioral control, and sports commitment) are all positive, showing a positive influence on continuance intention, with subjective norms and perceived behavioral control having a greater influence on continuance intention. The significance t-test values of the regression coefficients for the four independent variables are 2.009 (\(p<0.05\)), 5.092 (\(p<0.05\)), 4.255 (\(p<0.05\)) and 2.067 (\(p<0.05\)), and the variance inflation factors (VIF) are 0.201, 0.206, 0.345 and 0.313, which are all smaller than 10, and therefore there is no multicollinearity among independent variables.
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 5 Simulations and Results  
The proposed IOSCKF algorithm is applied in CV target tracking and spacecraft orbit tracking, respectively, in this section.
## 4 Results and Discussion
### Evaluation of Communication Range and Channel Attenuation Modeling
Furthermore, it is also evident that, in the case when the SNR values are lower than \(-\)5 dB and the RSSI values are in the range from \(-\)110 dBm to \(-\)120 dBm, data delivery still occurs, while in the case when the RSSI values are lower than \(-\)120 dBm, the result is a complete packet loss.
## 3 Problem Description & Mathematical Modeling  
### Problem Description  
The flexible job-shop scheduling problem with heat treatment process studied in this paper is an extension of traditional flexible job-shop scheduling problem. This problem involves n jobs to be processed, each job \(J_{j}\) (\(j=1,\,2,\,\ldots,\,n\)) has different delivery dates \(D_{j}\), the quantity and size are \(N_{j}\) and \(S_{j}\) respectively, each workpiece has machining process and a heat treatment process, and heat treatment process can be an intermediate processing process. With m-1-machining equipment and 1 heat treatment equipment of limited capacity,each machining equipment can only process one workpiece simultaneously, and each heat treatment equipment can process multiple workpieces at the same time on the premise of unexceeding its capacity. Each job is known to be able to combine with other jobs of heat treatment simultaneously. The difficulty of this research problem lies in rational arrangement of job processing sequence and equipment selection under the premise of limited resource scheduling, combined batch processing in heat treatment part, as well as minimization of maximum makespan of workpiece and utilization rate of heat treatment equipment volume, while meeting job delivery date.  
In the problem of this article, the following assumptions need consideration:  
1. The process is not performed by batches in the machining part;
2. Total volume of each batch of job is less than maximum volume of heat treatment equipment;
3. All jobs can start processing at time 0;
4. All equipment can be processed at time 0;
5. The processing date system of all equipment is consistent;
6. The circulation time between adjacent processes is not considered;
7. The processing time of each equipment in machining process is unchanged;
8. After the previous process of heat treatment process is completed, uniformly move for heat treatment processing;
9. Once heat treatment starts, it cannot be interrupted, and other jobs cannot be added before the treatment is completed;
10. All workpieces in the same job must complete heat treatment, before uniformly move for the next process.
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 4 Discussion
Generally, the most serious power outages occurred immediately after the disaster. It is difficult to detect short-term light outages if the lost power is recovered a few days after the disaster because of heavy cloud coverage. There are also some uncertainties when using the DNB data to assess the impact of natural disasters. Firstly, water brought by storms and floods can lead to changes in surface reflectance [82], which causes the surface to reflect less light. Secondly, the DNB data do not apply atmospheric corrections. The atmosphere, especially aerosol, has a scattering effect on NTL and can blur the artificial light [69]. Such limitations need to be fully assessed and considered when using NTL data.
## 3 Samples and Methods
### Methods
#### 3.2.2 Stable Carbon Isotope Analysis of Natural Gas  
After natural gas was enriched using a drainage and gas collection method in supersaturated brine, a 50 \(\upmu\)L gas sample was extracted using a gas-tight syringe (Hamilton Company, Reno, NV, USA) and injected into the instrument. The acquisition program was then initiated. Hydrocarbon components were oxidized to CO\({}_{2}\) in the combustion unit and subsequently carried by helium gas into the isotope ratio mass spectrometer (IRMS) for carbon isotope analysis [35]. The instrument used was a MAT 253 stable isotope ratio mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), equipped with an HP-PLOT Q capillary column (30 m \(\times\) 0.53 mm \(\times\) 40 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA). Helium (\(\geq\)99.999%, Air Liquide, Shanghai, China) was used as the carrier gas, with a column flow rate of 1.3 mL/min. To ensure analytical accuracy, one standard gas was analyzed after every 12 samples to monitor instrument performance.
## 3 Mathematical Modeling
### Grid Generation and Independence  
The 3D geometry shown in Figure 2 was built using commercial CAD program. Due to the complicated structure of the shell side, the tetrahedral grid was elected to mesh computational domain, as shown in Figure 3. A careful check for grid independence was conducted to obtain accurate simulation results as shown in Figure 4. It is found that the relative deviation of pressure drop \(\Delta P_{\text{o}}\) and heat transfer coefficient per unit pressure drop \(h_{\text{o}}\cdot\Delta P_{\text{o}}{}^{-1}\) between 8.6 \(\times\)\(10^{6}\) and 1.1 \(\times\)\(10^{7}\) grid cells are both less than 1.0%. Considering time cost, solution accuracy and memory space, the grid system of 8.6 \(\times\)\(10^{6}\) cells was taken for the whole numerical simulation.  
Figure 4: Results of different grid systems (base fluid, \(Re_{0}\) = 480).  
Figure 3: Meshes of numerical models.  
Figure 2: 3D geometry of tested heat exchanger.
## 4 System Model and Assumptions  
The following features are assumed to form the system model:  
1. A two-dimensional square network. The area size of the network is denoted as \(S\) in \(m^{2}\).
2. A collection of unknown nodes, UNs, are distributed randomly around the network. The number of UNs is denoted as \(N\).
3. Initially, all UNs are not location-aware.
4. The deployed nodes are stationary, thus, no change to their location after deployment.
5. Each sensor node has a stable communication range of \(R_{Tx}\) in \(m\).
6. A mobile anchor, MA, can determine its own position at any point of the network area. It is able to travel freely around the entire network in straight directions. The number of MAs is denoted as \(M\).
7. For simplicity, no obstacles in the deployment area are considered.
8. The movement distance of the MA is limited by the value of the maximum distance to travel, \(d_{max}\), where the MA's movement cannot exceed that value.
9. The MA stops frequently to provide nodes with information containing its current position and continue moving. Each stopping point is called a localization point.
10. Each MA and UN can contact each other only if their locations are within the communication range \(R_{Tx}\).  
Figure 1: Fuzzy-logic system structure.  
Once a UN receives any three different locations information, it will be able to estimate its own location using the applied localization algorithm.
* Each node that succeeds in estimating its location, will be converted from a UN to a reference node, RN. Each RN can share its location with the other nodes, helping them to estimate their own locations.
## 3 Cluster Formation
### Optimal Clustering
#### 3.1.2 Balanced Energy Expenditure
To find the data correlation of the nodes, the BS explores the data relevance of each node in the first round and divides the network area into a number of data-related areas. Therefore, clustering happens within each area, through dividing the regions into several belt sectors based on the distance of nodes to the BS and the predefined angle threshold. The algorithm forms fan-shaped clusters with high proportions of data correlation.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Needed Transmission Slots  
In cases of effective aggregation, fewer data packets are generated. The higher aggregation factor requires lower transmission slots to convey the data. EE-FDAS has achieved the lowest transmission slot usage from simple to highly complex environments. The lessened transmission slots reduce the chances of collisions, retransmissions, and interference during data transmission. Therefore, lower data loss takes place as a result of the efficient data transmission procedure by the higher aggregation factor. As demonstrated in Figure 3(b), the X-axis represents the number of attributes, while the Y-axis depicts the needed trans  
Figure 4: The impact of the Number of Attributes on Average SL is shown in (**a**) whereas Needed Transmission Slots are displayed in (**b**).  
mission slots for data transmission. In the context of EE-FDAS, only 230 transmission slots are used. The FDAS, DICA, DICA\(\_\)EXTENSION, and EHDA require 450, 630, 600, and 250 transmission slots, respectively, when the attribute count is 6.
## 2 Methods
### Comparative Study
#### 2.3.1 Part Shape and Material  
In this work, three conventional designs (i.e., cantilever beam, bridge-shaped structure, and L-shaped beam) are adopted for the comparative study. As seen in Figure 2, the initial dimension of 100 mm \(\times\) 50 mm \(\times\) 30 mm is selected for a rectangular profile to be formed into cantilever and bridge shapes. In the figure, the red area indicates the exclusion region that is not considered during optimization, where the purple area indicates the topology or design region, and the yellow arrow indicates the addition of response constraints. The initial design domain of the L-shaped beam has a height and width of 100 mm each. The leg height of 40 mm is defined with a form gap of 60 mm and a thickness of 30 mm. As shown in Table 1, Ti-6 Al-4V was selected as the material because it is one of the widely used materials for metal AM in industry. Ti-6 Al-4V has a high strength-to-weight ratio and a high modulus of elasticity, which ensures that the designed structure will be stiff and have less deflection. The material property is based on the nonlinear material data available in the ANSYS Workbench Additive Manufacturing Material Library.  
Meshing is then performed in the initial stage of structural analysis. A multi-zone meshing method is used. Multi-zone meshing is a meshing technique used in computational modeling and simulation, particularly for complex geometries. It involves dividing the computational domain into separate regions or zones and then creating a mesh for each of these zones independently. It is used because it is advantageous when dealing with models that have areas with different stress levels or different material properties. This method allows the creation of a mesh that is specifically tailored to the behavior of each region, thus increasing the accuracy of the analysis results [26]. For the cantilever beam, a vertical load of 100 N is applied to the lower right area of the opposite free end (Figure 3a). For the bridge-shaped structure, both lateral ends are provided with a fixed support, and a uniformly distributed load of 100 N is applied to the top (Figure 3b). In the L-shaped beam, the upper part of the larger section is fixed while the right bottom edge of the lower end  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Properties** & **Values** \\ \hline Density & 4400 kg/m\({}^{3}\). \\ Melting point & 1675 \({}^{\circ}\)C \\ Young’s modulus & 114 GPa \\ Tensile strength & 895 MPa \\ Yield strength & 828 MPa \\ Poisson’s ratio & 0.37 \\ Thermal expansion coefficient & 8.9 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C \\ Specific heat & 0.0005 J/kg\({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material Properties.  
Figure 2: Initial design domain for topology optimization: (**a**) Cantilever beam; (**b**) Bridge-shaped structure; and (**c**) L-shaped beam.  
is assigned the load of 100 N (Figure 3c). Next, the solver option is used to perform the structural analysis in ANSYS.
## 2 In-Network Computing: What and Why
### Representative Use Cases
#### 2.2.1 XR and Holographic Communications  
XR and holographic communications represent the frontier of immersive technologies, offering unparalleled experiences in fields ranging from entertainment to gaming, telemedicine, and Industry 4.0 [37; 38]. These applications aim to create seamless, real-time interactions that merge the physical and digital worlds, requiring ultra-low latency, high bandwidth, and precise synchronization.  
Holographic communications, in particular, have been identified as a critical use case for 6G networks due to their potential to revolutionize digital interactions [39; 40]. They push connectivity demands to the extreme by transmitting massive data volumes to render three-dimensional, life-like representations in real time. For instance, end-to-end latency must be less than 1 ms to ensure smooth and immersive experiences, as delays beyond this threshold disrupt the perception of real-time interactivity. Additionally, data rates of several terabits per second are required to transmit the volumetric data necessary for high-quality three-dimensional rendering [41]. Handling these requirements necessitates not only high-capacity links but also efficient data processing across distributed nodes. Additionally, the dynamic nature of holographic applications, involving multiple users and devices, introduces challenges related to maintaining seamless and synchronized delivery, especially in scenarios with rapidly changing network conditions.  
Traditional cloud-based architectures struggle to meet these stringent requirements due to inherent delays in processing and data transmission over long distances. Similarly, while edge computing reduces latency by bringing computational resources closer to the user, it remains limited by its reliance on fixed infrastructure, constrained resource availability, and potential bottlenecks during peak demand or in large-scale, multi-user scenarios.  
By leveraging the processing power of distributed network elements, INC reduces reliance on cloud and edge servers, minimizes end-to-end latency, and ensures efficient use of bandwidth. It can enable real-time operations such as motion tracking, rendering, and object detection to be performed closer to the user, ensuring smooth and immersive experiences. Coupled with distributed AI capabilities and communication technologies envisioned for 6G [42], including cognitive networks, sub-terahertz and Visible Light Communication (VLC), and Intelligent Reflecting Surfaces (IRSs), INC can enable the synchronized delivery of high-quality, multi-perspective holographic streams to multiple users simultaneously. Network nodes can dynamically optimize their resources, prioritize tasks, and pre-emptively manage computational workloads. For example, predictive algorithms can estimate user movements in XR or adjust holographic stream quality based on device and network conditions. Moreover, by distributing computational tasks, such as rendering and encoding, across network nodes, INC also ensures efficient and fair resource utilization.
## 4 Discussion  
Current literature on VR-BCI systems reveals notable gaps, particularly regarding their generalizability across diverse populations. Studies often focus on a narrow demographic--usually younger, tech-savvy individuals--while overlooking variations in age, cognitive ability, cultural background, and neurological conditions that could influence BCI performance and usability. Additionally, there is limited exploration of adaptive techniques in VR-BCI systems that could enhance personalized user experiences, such as tailoring interfaces to individual brain patterns or adjusting VR environments in real-time based on user fatigue or cognitive load. Research on the long-term effects of VR-BCI use is also sparse, especially regarding how prolonged exposure might impact mental health, cognitive function, or adaptation to real-world interactions. Ethical frameworks and guidelines for data handling, privacy, and user consent are underdeveloped, leaving critical questions unanswered in this rapidly advancing field. Lastly, interoperability between different VR-BCI systems and standardization of protocols remain underexplored, making it challenging to integrate findings across studies and develop universally applicable insights.
# An Overview of Bioactive Flavonoids from _Citrus_ Fruits
## 1 Introduction  
_Citrus_ genus is one of the world's leading fruit crops cultivated for food processing as well as fresh juice production. The genus _Citrus_ belongs to the family Rutaceae, containing several species such as orange types, sweet and sour oranges, lemon, tangerines (mandarins), and tangors. Each species or hybrid cross has one or more varieties. Besides being a rich source of vitamins A, C, and E, mineral elements, and dietary fibers, _Citrus_ fruits are a great source of secondary metabolites, such as polyphenols and terpenoids [1]. Flavonoids and phenolic acids are the main classes of phenolic compounds found in _Citrus_ fruits [2]. In general, the fruit skin contains a higher concentration of antioxidant substances than the other parts of the fruit [3]. _Citrus_ flavonoid contents and profiles vary significantly from one species to another [4]. _Citrus_ peel, which represents between 50% and 65% of the total weight of the fruits, is a rich source of bioactive compounds, including natural antioxidants such as flavonoids [5]. Several studies showed that _Citrus_ flavonoids are found to have anti-inflammatory, anti-cancer, anti-bacterial, anti-aging, and cardiovascular protection activities [6; 7].  
Our objective here is to provide an overview of the structure, class, and origin of the diverse classes of _Citrus_ flavonoids. In addition, we attempt to summarize data from the scientific literature and present values on the flavonoids in some _Citrus_ species and their health-promoting properties.
## 5 Experiment
### Performance Evaluation
#### 5.4.4 Training Time  
MST-VAE not only improved the detection performance but also achieved less training time compared to the InterFusion method. Since MST-VAE does not use GRU layers in its architecture, the model can benefit from parallel computation and accelerate the training process. Table 4 compares training time per epoch on the five datasets between MST-VAE and Interfusion. The results demonstrate that MST-VAE can reduce training time by an average of 7.25 times.
## 3 The Implementation of the Novel ARPD-RTK/INS Algorithm
### Determine the Integer Solution and the Correction Step  
As we discussed above, the ultimate ambiguity solution can be calculated by:  
\[\
abla\Delta\tilde{\mathbf{N}}_{rb}=R\left(\sum P(\mathbf{x}_{k}|\mathbf{z}_{k})\cdot \
abla\tilde{\mathbf{\Delta N}}_{rb}\right) \tag{24}\]  
Using carrier-phase measurements with suboptimal integer ambiguities may attain more accurate positioning results than the float AR solutions or pseudorange measurements. The following correction step is identical to the second types of tightly coupled RTK/INS integration described in Section 2.3. The implementation of the ARPD-RTK/INS algorithm is depicted in Figure 6.  
Figure 6: The implementation of the ARPD-RTK/INS algorithm.