esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 1 Introduction
This is the primary objective of this work. In this work we develop a Neural Network based quality control filter for CYGNSS Level 2 winds which can effectively identify and remove outliers. We also consider the performance of the CYGNSS retrieved winds before and after this filter is applied to assess its efficacy. The reminder of the paper is structured as follows. Section 2 describes the datasets used. Section 3 explains the details of the proposed quality control filter. In Section 4 the performance of the proposed algorithm is analyzed and the CYGNSS level 2 data performance is assessed before and after the filter. Section 5 discusses the trade-offs in performance and conclusions of this study.
## 3 Orbit Determination Method for GEO Satellite Based on RDSS Data
### Program Design  
The RDSS data are obtained from seven RDSS calibration stations deployed in Beijing, Sichuan, Hainan, Southeast China, and Northeast China, and two in Xinjiang. The SLR station was deployed in Beijing. The arc length for orbit determination is three days, and the data sampling rate is 60 s. The estimated parameters include the GEO initial orbit parameters of GEO satellites, solar radiation pressure parameters, and RDSS equipment time delay parameters. The RDSS equipment time delay is the sum of outstation and in-station link equipment time delays \(\tau_{all}\). The normal equations of SLR data and the RDSS four-way ranging data are formed. Based on these, the pseudorange/pseudophase data and the RDSS four-way ranging data can be processed independently, thus flexibly processing different types of data.  
Figure 2: Flow chart of calibrating RDSS equipment time delay based on SLR data.  
As for the SLR data preprocessing, first, the phase centres of the antennas of tracking stations are corrected; second, the phase centres of the antennas that transmit satellite navigation signals are attributed to the centre of mass of the satellite; third, the Marini model is used to deduct the tropospheric error; and finally, the errors of general relativity and the tidal effect are corrected. In the RDSS data preprocessing, the phase centres of the satellite and station antenna are corrected; then, the Black model is used to correct the errors of tropospheric delay and the 14-parameter Klobuchar model is used to correct the ionospheric delay; finally, the error terms of the Earth rotation effect and general relativity are corrected.  
The orbits for BDS-2 5 GEO satellites, BDS-3 3 GEO satellites are determined. Table 1 shows the strategies for the combined POD methods by RDSS and SLR data.
## 3 Results and Discussion
### Nanoparticle Tracking Analysis (NTA)  
NTA was utilized to precisely determine the size and distribution of the AgNPs, their colloidal stability, and their zeta potential (Table 3). These results indicated that the AgNPs synthesized from the methanolic leaf and stem extracts had a mean diameter of 111.3 \(\pm\) 4.4 nm and 110.7 \(\pm\) 4.2 nm, which contradicts the HRTEM results. As stated previously, various sizes of the synthesized AgNPs supports were visualized using HRTEM and measured using image1.53 e analysis. However, the sizes and zeta potential of the AgNPs, as well as their complexes, were evaluated using NTA. The differences in results may be due to the fact that, during HRTEM analysis, AgNPs solutions of the various extracts were pipetted onto copper grids, dried with the aid of a UV lamp and thereafter visualized. Nonetheless, NTA involves the preparation of solutions in an aqueous suspension for analysis, yielding a hydrodynamic diameter more akin to what is expected in a biological system. It has been reported that sizes obtained from NTA are expectedly slightly larger than that observed under TEM [86, 87]. The zeta potential of a solution is regarded as the electrostatic value of the nanoparticle, and this then correlates to the surface charge of nanoparticles [88].  
Zeta potential has been commonly used by the pharmaceutical industry to evaluate their formulations for stability. A desirable zeta potential value of less than \(-\)30 mV or greater 30 mV is usually considered to have adequate repulsive force in order to achieve improved physical colloidal stability [89]. A zeta potential within this range is regarded as stable, owing to the substantial mobility within the solution, and greater levels of electrostatic repulsion, thereby reducing NP aggregation [90]. Conversely, a minute zeta potential value could lead to the aggregation or flocculation of NPs due to the influence of van der Waals attractive forces, resulting in the physical instability of the NPs [91, 92, 93]. The zeta potential of a sample is influenced by two key factors: the pH and the conductivity of the solution in which the NPs are dispersed [94]. AgNPs synthesized from the leaf and stem methanolic extracts exhibited a high zeta potential of \(-\)33.2 \(\pm\) 0.1 and \(-\)28.2 \(\pm\) 0.0, respectively (Table 3). This indicates good long-term colloidal stability and potential suitability for in vivo application. The lowest zeta potential was observed in the AgNPs synthesized from the fresh leaf extracts (\(-\)8.8 mV).
# Energy and Distance-Aware Hopping Sensor
## 1 Introduction
However, the possibility of sensing hole appearance becomes higher when the specific path is repeatedly accessed. Reference [16] suggests a disjointed relocation path when the sensing hole is generated. Thus, it enhances the mobility-capable sensor ratio through the network coverage. Reference [17] takes into consideration the category of sensing field like rock or mud. The shortest path scheme is not taken seriously in this manner. Past researches include the effectiveness of the shortest path method, the multipath method, and the quality of the path method to recover or conceal the sensing hole. However, in these past researches, every cluster header is forced to acknowledge all the network information to route the path for the recovery. Actually, the overhead to maintain all the information of the network is quite burdensome and induces a lot of control message transaction. Figure 1: Examples for robots based on hopping: (**a**) MSU [11]; (**b**) DARPA [12; 13]. Our proposed protocol does not need to obtain the network managing information from the adjacent cluster header and/or whole network for the current cluster header to recover a sensing hole. The current cluster header is enough to recover the sensing hole by requesting sensor nodes to adjacent cluster header. Cluster headers may direct sensor nodes in their cluster range to the neighbor sensing hole. When the cluster header selects sensor nodes to be moved, the remaining energy of the sensor nodes and the distance from the sensing hole are considered. In previous researches, almost all results were obtained using the relocation path through an unrealistic sensor network topology which is hard to be known. However, it is very important that this study is the first simulation using OMNeT++ which reflects actual environments, as far as we know. The structure of our paper is as follows. Section 2 demonstrates the detailed operation of proposed relocation protocol. The simulation results and performance comparisons are described in Section 3. Section 4 concludes this paper.
## Materials and Methods
### Determination of Energy Consumption
#### 3.5.3 Specific Energy Consumption of CD, VMD, and CPD-VMFD  
Specific energy consumption is calculated to determine the energy consumption per unit mass of fresh weight and energy consumption per unit mass of water. The specific energy consumption expressed in kJ/g fresh weight of sample can be useful in prediction of energy expenditure for drying of specified plant material. On the other hand, specific energy consumption expressed in kJ/g water contained in sample enables comparison of energy consumption for different plant materials with different moisture content.  
Specific energy consumptions of CD, \(E_{C_{m}}\left(\text{kJ g}^{-1}\;fw\right)\), and \(E_{C_{W}}\left(\text{kJ g}^{-1}\;\text{water}\right)\) were calculated based on Equations (8) and (9) [53], and the specific energy consumptions of VMD, \(E_{VM_{m}}\left(\text{kJ g}^{-1}\;fw\right)\) and \(E_{VM_{W}}\left(\text{kJ g}^{-1}\;\text{water}\right)\), Equations (10) and (11) [53], were calculated as (1) ratio of energy consumption to the initial sample mass, \(m\) (grams); and (2) ratio of energy consumption to the mass of water \(W\) (grams) removed from the sample.  
\[E_{C_{m}} =\frac{E_{C}}{m} \tag{8}\] \[E_{C_{W}} =\frac{E_{C}}{W}\] (9) \[E_{VM_{m}} =\frac{E_{VM}}{m}\] (10) \[E_{VM_{W}} =\frac{E_{VM}}{W} \tag{11}\]  
Specific energy consumptions for CPD-VMFD, \(E_{C-VM_{m}}\left(\text{kJ g}^{-1}\;fw\right)\) and \(E_{C-VM_{W}}\left(\text{kJ g}^{-1}\;\text{water}\right)\), are as shown in Equations (12) and (13), respectively [53]. Both equations are the ratios of the sum of energy \(E_{C}\) and \(E_{VM}\) to the mass of initial sample, \(m\) (grams) and total mass of water (grams) from CPD, \(W_{C}\) and VMFD, \(W_{VM}\) removed from the sample, respectively.  
\[E_{C-VM_{m}} =\ \frac{E_{C}+E_{VM}}{m} \tag{12}\] \[E_{C-VM_{W}} =\ \frac{E_{C}+E_{VM}}{W_{C}+W_{VM}} \tag{13}\]
### Data Validation Using OWL  
With regard to data validation, it could be argued that OWL is not necessarily the most suitable tool; it was never intended as such and moreover is based on the open-world assumption (OWA) that essentially restricts the inferences that can be made on a given data set (even when the data set is known to be complete). The OWA assumes additional information is always potentially available but not yet disclosed. The CR common data set in contrast corresponds to a closed-world system in which all the information regarding a data set is known (at least as far as the validation rules are concerned).  
Whereas the OWA does present challenges, they are generally not insurmountable although they do have certain implications on the design of an ontology used for inferencing purposes. The advantages of OWL (particularly with reference to its positon in the semantic web stack) more than outweigh the disadvantages and provide a convenient framework to facilitate the devolution of the centralised cleaning process to the local CR level. One of the ways of overcoming OWA-related issues is by making use of the pattern of general concept inclusions [23], whereby a complex class is subclassed from an atomic class rather than the more usual contrary mechanism. The pattern is followed extensively in the axioms described in Section 2.4. An ontology designed along these lines marks a de parture from the majority of axioms in OWL ontologies that have atomic classes subclassed from complex classes [23] with the consequence that axioms of standard ontologies cannot easily be reutilised for inferencing purposes. Within the Open Biological and Biomedical Ontologies (OBO) Foundry [24], the NCIthesaurus (NCIt) has a number of terms relevant to the MPT ontology, but the ontology is structured in such a way that the inferences needed for the multiple-primary-tumour rules cannot be made. In addition, the standard ontologies tend to be quite large to cater for all the wide variety of needs (NCIt has over 2 million axioms) and would impose performance constraints on automatic reasoning.  
In contrast to OWL, the RDF data shapes languages SHACL and ShEx (Shape Expressions) do allow the closed world assumption (CWA, in which something not stated to be true is considered false) and are particularly suited to data validation. SHACL has been used with good effect to validate EHRs [18] and ShEx provides a useful tool for a preprocessing step to trap syntactic errors, range errors, and the more basic inter-variable semantic checks within the common CR data sets. However, both ShEx and SHACL suffer limitations when dealing with the more complex types of inter-variable checks that are better suited to OWL.
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 1 Introduction
The algorithms included RCNN, SPPNet, FasterRCNN, MaskRCNN, FPN, YOLO, SSD, RetinaNet, Squeeze Det, and CornerNet; these algorithms were compared and analyzed based on accuracy, speed, and performance for important applications including pedestrian detection, crowd detection, medical imaging, and face detection. Moreover, we also previously implemented a real-time weapon detector model based on YOLOv4 trained with a custom dataset. The model has a decent accuracy but lacks performance [7]. Therefore, we continued our research to further improve the model in terms of mAP and performance for both cloud and edge computing devices. In addition, the existing solutions are not highly accurate, lack real-time performance, and often utilize cloud computing in which resources are abundant. Cloud implementations have issues like performance bottlenecks caused by network bandwidth, latency and privacy breaches, etc. Hence, there is a need to improve the existing solutions by utilizing the state-of-the-art object detection algorithm deployed on edge computing devices to rectify the mentioned shortcomings of cloud computing. The notable limitations of the previous works are the usage of an older deep learning model that has lesser accuracy compared to the state-of-the-art models, very few and outdated preprocessing steps for dataset preparation, and no performance analysis on any embedded device. Furthermore, to the best of our knowledge, there is no previous work done to deploy and compare the performance of the weapon detection model on edge computing devices in real time. The main contributions of this research work are as follows:  
1. Improved the accuracy and performance of the existing weapon detection model by utilizing state-of-the-art algorithm and preprocessing techniques;
2. Improved number of frames per second FPS for real-time deployment;
3. Compared and analyzed the performance of the different deep learning models on different computing devices;
4. Utilized the TensorRT for network optimization that resulted in improved latency, throughput, power efficiency, and lower memory consumption. The rest of the paper is organized as follows. Section 2 contains the related work and the literature review of different approaches that were being used to achieve the desired outcomes. Section 3 elaborates on the methodology of the research problem and what steps should be taken to successfully carry out this research. Section 4 contains the results of the research and a discussion on it that provides the reader with deep insight into the research findings. Finally, Section 5 concludes the paper and provides possible future directions in this area.
# Passive Intermodulation at Contacts of Rough Conductors
## 2 Electrical Nonlinearities of Conductor Joints
### The Effect of Surface Roughness  
Surface roughness significantly affects the performance of electrical contacts in high frequency applications [49; 50; 51; 52; 54]. Asperity random heights and patterns of the contact surfaces are never the same, even when the materials and fabrication process used are identical. Therefore, the statistical models have been used for simulating the contacts of rough surfaces. These techniques were applied to the characterisation of losses in printed circuit boards [48]. Surface roughness also proved to be an important factor influencing the performance of MEMS devices, and the contact phenomena in MEMS with rough conductors have been extensively studied in [50; 51; 52; 53; 60; 63; 64].  
Gaussian distribution of contact asperity heights with the standard deviation up to 20% from an average value was examined in [65], taking into account the effect of an insulator thickness on the contact resistance of rough surfaces. Using the Brinkman-Dynes-Rowell model [66], it has been shown that a single thickness model reasonably estimates the surface conductance when the standard deviation of asperity heights remains within 5% of the average value, see Figure 4. However, as roughness increases, the average thickness of the insulator layer becomes smaller than its actual value used earlier for approximating  
Figure 3: Current density as a function of applied gap voltage \(V_{g}\), for two gold (Au) electrodes (W = 5.1 eV) separated by a vacuum gap (\(\epsilon_{r}\) = 1) of width D = 1 nm, at T = 300 K. CL curve is for the classical Child–Langmuir law. \(J_{nat}\), SCM curve is calculated with the self-consistent model (SCM) described in [26]. Curves “Simmons” and “Simmons (V–0)” are calculated with Simmons’ formulas [23] for general \(V_{g}\) and \(V_{g}\)–0, respectively.  
the surface conductance. Moreover, at a larger standard deviation of thickness, the mean thickness decreases that makes a single-thickness approximation less accurate. Roughness also \"flattens\" the conductance as if the barrier were thinner. This effect is illustrated by the conductance curves for ideal single-thickness barriers (i.e., \(\sigma\equiv 0\)) presented for comparison (solid lines) in Figure 4[65].  
To relate a mean thickness to its actual value, inferred from the conductance measurements, the rules of thumb were suggested in [55]. However, the accuracy and uniqueness of such fitting the normalised conductance values in a range of asperity heights remain a major problem, as was earlier pointed out in [67].
## 4 Sensitivity Analysis and Discussions
### The Impact Analysis of Injection Flow Rate  
Under varying flow rates, the outlet brine concentration patterns tend to be consistent, initially surging, and then gradually stabilizing. Higher injection flow rates yield lower brine concentrations with less fluctuation, as shown in Figure 23. The cavern volume exponentially increases with higher flow rates, leading to greater cavern heights and volumes, as shown in Figure 24. Cavern expansion enlarges the contact area between the salt rock wall and brine, thus enhancing the leaching rate.  
Figure 25 illustrates that the final shape of the cavern formed under various injection flow rates remains largely consistent. Higher flow rates result in taller dissolution and reflux caverns, with uniform increases in the cavern contour height corresponding to uniform increases in the flow rate. The contour of the cavern shaped by cor  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Borehole Length** & **Step Time (Days)** & **Flow Rate (m3/h)** & **Concentration** & **Step Distance (m)** \\  & **(m)** & **m** & **(g/L)** & **(g/L)** & **(m)** \\ \hline A & 250 & 50 & 100, 150, 200, 250, & 0 & 50 \\ B & 250 & 50 & 200 & 0, 50, 100, 150, 200 & 50 \\ C & 250 & 50 & 200 & 0 & 50, 60, 70, 80, 90, \\  & & & & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The technological parameters of the three groups of simulations.  
Figure 23: Outlet brine concentration evolution with leaching time under different flow rates.  
Figure 24: Cavern volume evolution with leaching time under different flow rates.  
with an increase from 100 to 300 m\({}^{3}\)/h in flow rate raising the cavern contour height by about 3 m, suggesting that high flow rates are not advisable at the first leaching stage. Higher injection flow rates result in taller dissolution caverns and the quicker expansion of the previously formed caverns, as shown in Figure 26. In the later leaching stages, these caverns continue to dissolve and expand upwards. Increasing the injection flow rate enhances forced convection, intensifies fluid flow in the dissolution cavern, speeds up convective diffusion, and promotes continuous cavern expansion. Thus, increasing the injection flow rate and increasing the dissolution time in the subsequent stages is necessary to make the cavern smoother.
## 2 Principle and Model  
A schematic diagram of the optical setup is shown in Figure 1. The radially polarized beam with a certain angle to the optical axis first passes through a confocal scanning system composed of a 2D galvanometer system, a scan lens, and a tube lens, then transmits through a binary phase pupil filter (BPPF), and is finally subsequently focused by a high NA lens. A radially polarized beam can be generated by modulating a linearly polarized laser beam with a polarization converter. The structure of the BPPF is shown in Figure 1c. A phase difference of \(\pi\) is created between the bright yellow and dark gray regions of the BPPF. The three-dimensional view of the 2D galvanometer system is shown in Figure 1b. The radially polarized beam can be deflected by the galvanometer mirrors in the x- and y-directions. For convenience, a box labeled liquid crystal is used to indicate a voltage-controlledliquid crystal that can produce small phase differences. The axis center of the x-axis galvanometer is at the same level as the focus of the scan lens (_f1_), tube lens (_f2_), and the high NA lens (_f3_). Because any lateral motion would distort the linearity of the scan, the galvanometer mirror must be placed at image planes conjugate to the entrance pupil. When passing through _f1_,_f2_, and _f3_, the laser beams are parallel, but at different angles to the optical axis. Although the scanning optic is afocal, it acts as a focal system for the motion of the beam. At the field diaphragm, the angle between the laser beam and the optical axis is called the oblique incidence angle (\(\theta\)).  
In the process of the interaction between light and substance, the high NA lens tightly focuses the light field to achieve spatial light field modulation. The optical needle produced by tight focusing of the radially polarized incident beam is formed by the coherent superposition of various spatial spectral components in the focused space. According to the principle of interference, a BPPF is used to further modulate the light field to reduce the focal spot size or extend the DOF. According to the principle of confocal scanning, the angle of incident light can be controlled by a 2D galvanometer system. Although oblique incidence attenuates the DOF, the DOF can be elongated by compensating for the additional phase difference caused by oblique incidence. Next, its optical propagation characteristics will be further analyzed.
## 4 Model Training and Experimental Results
### Experiment Based on sEMG Signals from 8-Channel Wireless Armband
This variability may stem from several factors, such as inconsistent muscle strength exerted by experimenters during gesture performance or discrepancies in movement standards. Despite these variations, the gesture recognition model maintains a high accuracy and stability, underscoring the InRes-ACNet model's robust capability in gesture recognition tasks, particularly in natural grasp, cylindrical grasp, and tip pinch. For comparative purposes, the ResNet50 model was also employed in gesture prediction tasks. Utilizing the same ten experimenters as in the previous InRes-ACNet model tests, the gesture prediction results are summarized in Table 4. The InResNet50 model achieves an average recognition accuracy of 85.63%, while the InRes-ACNet model attains an average recognition accuracy of 88.37%, highlighting the superior performance of the InRes-ACNet model in gesture prediction tasks.