esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 1 Introduction
Support Vector Regression (SVR) is a variant of the SVM algorithm used to solve regression problems and is based on kernel functions. While SVM is mainly used for classification problems by finding the optimal hyperplane to maximize the margin between different classes, SVR is applied to handle regression problems by finding a function that makes data points as close to this function as possible while ensuring errors are within a small range. The goal of SVR is to find a function that minimizes the prediction error between given data points and unknown points, introducing an '\(\epsilon\)'-insensitive loss function to get as close as possible to all training samples within an '\(\epsilon\)' range. The advantage of SVR lies in effectively handling complex nonlinear relationships in data. In order to find a suitable algorithm and establish a more suitable training model based on the self-made resistance grain moisture meter, this paper uses an improved SSA-SVR algorithm to establish a grain moisture model according to the shortcomings of the SSA algorithm, so as to make the established model more accurate. The novelty and contribution of the work are as follows:  
(1) Based on the data obtained from moisture detection of long-grain rice using a self-made resistance-type moisture meter, an improved SSA-SVR method is employed to train the moisture model. The introduction of Circle chaotic mapping [8] can improve the diversity of the population. The sentinels cannot obtain the ideal effect when avoiding natural enemies due to their poor position. By adding a reverse learning mechanism [9], the sentinels can jump out of the local optimal solution by avoiding natural enemies and enhance the ability to search for the global optimal. The model curve trained by the improved SSA-SVR algorithm better fits the actual curve compared to models generated by other algorithms, resulting in more accurate moisture values. In grain drying sites, there are various interfering factors such as electromagnetic interference and temperature/humidity interference. Therefore, directly measuring the resistance value of long-grain rice on-site and deriving the corresponding moisture values through the model is preferred, as using capacitance or other methods to measure grain moisture values may be subject to significant influences. (2) The contribution lies in first completing the moisture testing of long-grain rice, then utilizing machine learning algorithms for learning, and finally selecting the improved SSA-SVR algorithm for model training [10, 11]. In order to compare the excellence of this model compared to models generated by other algorithms, three additional algorithms were chosen for comparison [12]. The improved SSA-SVR algorithm's RMSE, correlation coefficient, and determination coefficient are stronger than those of the other algorithms [13], demonstrating the accuracy, reliability, and applicability of this improved algorithm in model establishment. This provides a new approach for the more convenient and rapid detection of grain moisture.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 3 Devices for QKD Systems
### Technology Status Summary  
Photonic integration is a well-consolidated trend in classical optical networks, which leads to a tangible decrease of cost, footprint, and energy consumption. This trend is also expected to positively impact QKD systems. Single or entangled photon sources based on III-V materials integrated on silicon or silicon nitride are an example of devices made possible by recent integrated photonics advances. Regarding single-photon detectors, none of the current solutions achieve the desired figures for cost, footprint, and energy efficiency altogether: III-V SPADs are the most immediate solution, but WG-SPADs monolithically integrated in silicon are more suitable for mass production and can operate close to room temperature. Two dimensional materials, such as graphene, may lead to further advances, such as true single-photon emitters at room temperature. Regarding quantum subsystems, QRNG is by far the device with the highest maturity and is the most ready to be commercialized. It is also used in classical cryptography systems. Photonic chip-based implementations have high potential for mass production and generation of high bit rates. Regarding technology maturity, quantum repeaters are still at the proof of concept stage, and it is unclear at the moment what a realistic market-ready timeframe could be.  
So far, we have analyzed quantum systems from a technological viewpoint. Technology maturity is an important factor. However, it is not the only factor determining the large-scale deployment of commercial quantum systems. In the rest of this paper, we analyze in more detail application areas and market drivers.
# Accelerating FVM-Based Parallel Fluid Simulations with Better Grid Renumbering Methods
## 4 CQ: The Cell-Quotient Grid Renumbering Algorithm for Large-Scale Parallel Fluid Simulations
### An Extensible Grid Renumbering Framework for CFD Simulations  
The YHACT was designed initially for thermo-hydraulics analysis and implemented as a general-purpose CFD software based on an extensible architecture. As shown in Figure 4, the software adopts a classic hierarchical architecture. Benefitting from object-oriented technology and the modern features of the C++ language, YHACT is more modular and scalable. More details about the design philosophy of YHACT could be found in Ref. [24].  
In summary, the application layer includes the main program for solving application-specific problems such as incompressible flow, compressible flow, and multiphase flow. Algorithm-model layer, Spatio-temporal discretization layer, and linear system layer mainly accomplish the discretization of physical models, the assembly, and solution of sparse matrices. The renumbering module is crucial for integrating renumbering algorithms into numerical simulations. In a solving algorithm such as _Simple_, the renumbering interface should be called before the _discretize_. Due to the highly scalable software architecture, the higher-layer modules depend only on the interfaces that lower-layer modules could implement. Thus, new algorithms and models can be added continuously.  
We integrated the renumbering module and designed the corresponding interfaces from the software architecture perspective. Different numbering algorithms can be seamlessly integrated and tested. In addition, grid renumbering is one of the optimizing procedures before FVM-based simulations from the perspective of parallel computing. As presented in Figure 5, the renumbering algorithms will be executed in parallel after domain decomposing. Then, the FVM-based simulations are the same as the original algorithm.  
Figure 4: The extensible framework for integrating parallel grid renumbering techniques in YHACT. Different types of renumbering algorithms could be extended by implementing the interfaces of the renumbering module. The Application Layer, Algorithm-model Layer, Spatio-temporal Discretization Layer, and Linear System Layer are the original frameworks in YHACT. The renumbering module is a renumbered framework added in this paper.  
Due to the discrete nature of FVM, the solution of one target cell is only related to its neighbors. In other words, only the data of neighboring cells of different decomposed grids need to be communicated between processors. In the YHACT framework, the boundary cells of all decomposed grids are wrapped with a layer of dummy cells containing the necessary conditions and data for the solution of the boundary cells. As shown in Figure 5, after the decomposing phase, each process calls the renumbering interface for renumbering. In the parallel computation phase, the data of adjacent boundary surfaces between different processes are exchanged by calling MPI functions. Finally, the solution will be obtained by collaborative computing on multiple CPU nodes.
## 4 Discussion
### Future Directions
#### 4.5.2 Advanced Orchestration and Agenti Pipelines  
Moving forward, JAVIS aims to coordinate agentipelines that handle tasks in sequence: summarization, prediction, data extraction, and structured formatting. By connecting specialized LLMs and VLMs for distinct sub-tasks, JAVIS will support complex workflows tailored to various clinical or research scenarios. Future expansions of these pipelines include combining LLM-based extraction of key medical terms with rule-basedsystems to mitigate the risk of hallucinations, ensuring higher accuracy and reliability in hospital environments.
# Application of Computational Fluid Dynamics Analysis after Bimaxillary Orthognathic Surgery
## 3 Results
### Wall Shear Stress Analysis  
The distribution patterns and magnitudes of wall shear stresses formed in the airway are shown in Figure 5. Shear stress is strong at high-velocity gradients. At T0, the shear stress was generally low. Some areas of high stress at the level of the epiglottis are observed. The airway at T1 exhibited higher shear stress than the surroundings in the velopharynx. This is the same level at which velocity and pressure increase significantly. Shear stress is also high on both sides of the posterior wall of the oropharynx. This is similar to the position where the velocity change is large at T1 in the P2 and P3 cross sections shown in Figure 4. The airways at T2 generate high shear stress over a wide area. The area is concentrated in the upper and lower velopharynx, oropharynx, and epiglottis. As in T1, the positions where the velocity change is large and where the shear stress is high are similar. All these measurements are summarized in Table 2.  
Figure 4: Simulation results representing contoured pressure distribution of air flow in cases of pre-surgery, immediately after surgery, and post-surgery. P1, P2, and P3 indicate xz planes representing sectional information.
## 3 Smart Geo-Spatial Inclinometer Nodes Application Design  
The smart geospatial inclinometer nodes application designs (SGSINs) fill the gap of programmable resolution ADC, sampling rate, and communication for input- and output-tuning for NDT and NDE assessment procedures. In our research project, we designed and fabricated two SGBINs as:  
* Flat SGSINs with two accelerometers used as inclinometer sensors (F-SGSINs)
* Cylindrical SGSINs with \(2+N\) sensor support (C-SGSINs)  
CANopen was used as a communication interface and a common bus. The details of these nodes are given in their respective sections. The two nodes are exhibited in Figure 10.  
It is noteworthy to highlight that the SGBINs displayed in Figure 10 are enhanced with remotely programmable and configurable parameters using CANopen.
# Low-Profile Harmonic Transponder for IoT Applications
## 4 Experimental Results  
In this section, the transponder (RCS) without diode inclusion is measured in order to verify the designed and fabricated transponder antennas. Further, the link budget measurement is conducted to prove the diode integration and the corresponding matching.
## 4 Conclusions  
Compared with the Muscat Hamburg wines made by classical red and white-winemaking techniques, the results of this experiment demonstrated that the Muscat Hamburg wine made by carbonic maccation had significantly higher (\(p\)\(<\) 0.001) contents of esters (ethyl octanoate, ethyl hexanoate, ethyl decanoate, hexenyl acetate, isoamyl acetate and phenylethyl acetate) and terpen ((\(\pm\))-\(\beta\)-citronellol, linalool and limonene), which were the main aromatic compounds of the wine. Finally, the Muscat Hamburg wine prepared by carbonic maccation had a significantly better volatile profile and sensory evaluation scores relative to the wines prepared by the other two techniques. The results of this work will improve our understanding of the aromatic compounds, color and sensory properties of Muscat Hamburg wines. From this research, we can conclude that carbonic maccation is a better choice for making Muscat Hamburg wine and has great potential for broad future application. To date, research on wines prepared by carbonic maccation remains in the laboratory stage, and there are no wine produced by carbonic maceration in China. This is because Chinese wine consumers poorly understand the carbonic maceration technique, the quality of grapes are difficult to meet the large-scale production of carbonic maceration wines and the increased production cost also restricts the production of carbonic maceration wine. All carbonic macerated wines are imported from abroad. Therefore, we believe that the carbonic macerated Muscat Hamburg wine will meet the demands of wine consumers and the wine market in China.  
Data curation, Y.-S.Z. and Y.-T.G.; funding acquisition, D.M., W.-Q.G., G.D. and S.-Y.W.; investigation, Y.-S.Z., Y.-T.G. and G.D.; supervision, H.Z., S.-Y.W., W.-Q.G. and G.D.; Writing--Original Draft: G.D.; Writing--Review and Editing: D.M., B.-J.L., L.-W.W., C.B., M.-Y.W., H.Z., W.-Q.G. and G.D.  
This work was supported by the Tianjin Municipal Science and Technology Commission (16)CQNJC14600 and 18)CQNJC09900), the 13 th Five-Year innovation team \"Study on new processing technology and related mechanisms of agricultural products storage and processing\" (TD13-5087), and the National Natural Science Foundation of China (No. 31701172).  
The authors thank [PERSON] (Chinese Academy of Sciences, Beijing) for technical support with the GC-MS analysis. The authors are especially grateful to the expert tasters from Tianjin Dynasty Winery for their assistance with the sensory evaluation. We also thank [PERSON] from Lincoln University (New Zealand) for the critical review of this paper.  
The authors declare no conflict of interest. And the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Simulation Results and Discussion
### Plug-and-Play Operation with the Proposed Control Scheme  
In this case study, the effectiveness of the proposed secondary control scheme under plug-and-play operation is verified. DG4 is plugged out and plugged back at \(t\) = 1 s and \(t\) = 4 s, respectively. The other simulation process is similar to the case study under load change. The simulation results are demonstrated in Figure 10.  
Figure 10 shows the performance of the proposed secondary control scheme when the DG4 occurs plug-and-play operation. After DG4 is plugged out at \(t\) = 1 s, its output reactive power drops to 0. Considering that DG4 will be plugged back at \(t\) = 4 s, primary control is still activated for DG4 to make its voltage and frequency to meet the requirements of the plug-in operation. A decentralized frequency secondary control scheme (see Appendix A) is also applied at \(t\) = 1 s, which ensures all DGs operate at the same frequency when DG4 is plugged back at \(t\) = 4 s. While being plugged out, DG4 sends a message to its neighbors and all communication links connected to DG4 are deactivated. Nevertheless, the remaining communication links still contain a connected graph, and the proposed secondary control scheme is able to readjust the reactive power sharing among the remaining DGs while regulating the average voltage to the nominal value. When DG4 is plugged back, it can activate the related communication links and try to find its nearest neighbors by the aforementioned graph discovery algorithm. The simulation results show that our method can properly share the reactive power and achieve the average voltage recovery when DG4 is plugged back. Therefore, the proposed secondary control scheme can restore voltage and realize reactive load power sharing in the case of plug-and-play operation and communication topology changes.  
Figure 9: Performance of the traditional control with periodic communication mechanism: (**a**) DG output voltages; (**b**) evolution of the estimated average value of voltage \(V^{AVE}\); (**c**) DG output reactive power; (**d**) evolution of the estimated average value of reactive power \(Q^{AVE}\).
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model  
In this section, we thoroughly explain the proposed mathematical-based model for the link duration estimation process for the DSDV routing protocol. As mentioned above, the mobility nature of a mobile node causes several performance challenges for the routing protocol, especially during the process of estimating the path duration. The two most important factors that play a crucial rule in any mobility scenario are link and path durations. The link duration is the amount of time that determines how long the established link between two adjacent nodes will be active. This time period is affected by mobility speed model and direction, as well as the distance between the node and its neighbors. During the link activation period, neighboring nodes can exchange packets using direct links. In the case that some information is needed for a non-adjacent node, the path duration mainly depends on the duration of the links connecting all nodes along the path from the source to the destination. In this case, the optimal path duration is the one that contains the shortest link duration.
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 5 Methodology
### Preprocessing  
The importance of _preprocessing_ was highlighted in [93] along with the proposal of a general-purpose methodology for preprocessing in data science. In the current research work, sensor data consist of all numerical measurements, and the preprocessing step includes only a few steps from the general methodology. The very first step is to re-label the sensor names to support direct conceptualization and knowledge production. The actual names of the sensors are anonymized due to confidentiality issues. All variables represent sensor measurements captured as numerics but with different scales and units. Normalization is applied across sensors to bring them to a uniform scale.  
The sensor data addressed in this work come from multi-jet fusion 3D printers in a smart factory setting that feeds data continuously to the cloud environment, which reduces the probability of missing-data instances to be negligibly small.  
In all the experiments performed throughout the research, no missing values are observed yet. This eliminates the need to perform any missing value imputation in the current state of the research. Thus, the missing data treatment, which is based on multivariate interpolation techniques, is delayed to further steps of the research. As a matter of fact, the missing data issue will become important in the context of processed log files, which is out of the scope of this paper.  
The proposed approach relies on using raw log files instead of processed ones since the authors pretend that the proposal works well in real time when introduced in an industrial manufacturing process and the method must be able to work with raw and unprocessed data. Thus, no feature extraction steps are considered to deal with transformed data. The hierarchical clustering-based methodologies are also useful since they are robust to the presence of outliers and there is no need for prior outlier detection.  
Following the approach described in [94] and the main goals of this research, clustering methods are most suitable to find patterns in sensor data. Clustering is one of the widely used unsupervised distance-based learning methods that aims at deciphering the hidden patterns in the data. Some key measures of distance were discussed in [95]. [PERSON] et al. [96] presented a good overview of clustering algorithms and their applicability in the real world. In this work, since no previous hypotheses are available about the number of real existing patterns, hierarchical clustering is used as a starting point.
## 2 Materials and Methods
### Analyser Performance Assessment  
The concentrations obtained from the wet-chemistry method were firstly converted to ppm\({}_{v}\) before further analysis. The duplicates that had large within-trial differences were detected and discarded according to a two-sided Grubb's test at 95% confidence level [13]. The mean of each remaining duplicate was taken to represent the measured concentration of the corresponding trial. The simultaneously acquired measurements from each analyser were averaged over the same period.  
The inter-device agreement was quantified by pair-wise Pearson's correlation coefficients \(r\) and root-mean-square error (RMSE). The RMSE were also calculated for each analyser with respect to the mean of all analysers. The equivalence between the analysers and the wet-chemistry method was assessed based on three criteria according to EN 14793: (1) \(r<0.97\), (2) orthogonal regression intercept \(|\beta_{0}|\leq\sigma_{rm}\), and (3) orthogonal regression slope \(|\beta_{1}-1|\leq\frac{\beta_{0}}{\mu}\), where \(\sigma_{rm}\) and \(\mu\) are, respectively, the documented standard deviation of the wet-chemistry method and the grand mean of claimed measurement range. To get an indication of margin for accuracy improvement, median absolute deviations (MAD) of the absolute and relative residuals were estimated from the calibration of the analysers using the wet-chemistry measurement data.