esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 4 The Study
### Method
#### 4.1.2 Design  
A between-subject design experiment was performed. We considered two independent variables that are respectively: the display setting, which can be \"Shared\" or \"Non-Shared\", and the presence of unexpected events, \"No Event\" and \"Event\".  
The dependent variables express the operator's performance. They are the situation awareness (SA), the communication among the team members (CT) and the number of drones back home (DBH).  
SA is measured through the situation awareness global assessment technique (SAGAT).  
SAGAT is a tool that measures situation awareness in terms of three levels: _perception_, _comprehension_, and _projection_. For each level of situation awareness, SAGAT poses a number of questions whereby only one of the possible answers is correct.  
SAGAT scores provide a detailed collection of information that can directly be compared to reality. Hence, SAGAT is a more objective method that is not dependent on a subjective evaluation because there is one right answer that is defined from the outset.  
The SAGAT questionnaire is filled out while the simulation is at rest. In this way, participants can only report what they really perceived and remembered from the last seen situation. The questions refer to objects or events from the just presented situation [19].  
The communication among the team members was assessed during the activity as it is also related to TSA [9]. The team members can exchange information to develop TSA. To test this idea, the team's communication was analyzed in [9]. In particular, the frequency with which team members asked questions (NQ) about the status of the mission was measured. The authors observed that the questions provided a good indication of the extent to which team members tried to improve their SA through communication with the team.  
During the mission, the lack of situational awareness can cause the drones to be lost. Then, the number of drones returning home (DBH) is an additional parameter that measures the operator's performance.
## 2 Proposed Scheme and Block Diagram  
Figure 1 shows the overall block diagram of the proposed scheme, which consists of three main stages: the preprocessing stage, signal approximation and estimation stage, and fault identification stage.  
At first, in the preprocessing stage, the vibration signals are resampled, and next, the RMS features are extracted from the resampled signals. The second stage is used to model and estimate the resampled RMS signals. Based on the difference between estimated signals and original ones, the signals can be classified into different conditions.  
Thus, to estimate the resampled RMS signal, a modern control-based observation method is suggested. To build a modern control-based observer, first, the resampled RMS signal in normal condition needs to be modeled. So, the first step in the second stage is signal modeling. The combination of AR, external uncertainties inputs (XU), and MBR, which is denoted as AMRXU in the paper, is suggested for normal resampled RMS signal modeling. More specifically, the AR technique is used for normal signal modeling. To increase the robustness and accuracy, the AR technique is combined with the XU technique to implement the ARXU method for modeling the resampled RMS signal in the healthy state. This signal is nonlinear and nonstationary. Subsequently, to increase the accuracy of signal modeling with the property of nonlinearity and nonstationary, the resampled RMS signal in the normal condition is modeled with a combination of the ARXU and the MBR, which is denoted as AMRXU.  
After modeling the resampled RMS signal in the normal condition, the indirect self-tuning observer, which is a combination of the modern control-based observer and AI-based approach, has been designed. Thus, first, the PI observer is implemented when the signal is modeled by the AMRXU technique (henceforth called AMRXU-PI). After that, to improve  
Figure 1: The proposed scheme for fault diagnosis of bearing based on three stages: preprocessing, signal approximation and estimation, and fault classification.  
the accuracy, the AMRXU-PI observer is combined with integral term (I), which is called the AMRXU-PMI observer. Moreover, to increase the robustness in uncertain conditions (e.g., variant motor torque loads and variant bearing crack sizes) the AMRXU-PMI is combined with a VS-Lyapunov scheme, which is denoted as the AMRXU-VSPMI method. Next, the indirect self-tuning observer is designed by using the combination of the AMRXU-VSPMI with the self-tuning network-fuzzy system (SNFS) technique, which is denoted as AMRXU-AVSPMI. After estimating the resampled RMS signals using the proposed AMRXU-AVSPMI method, in the third stage, the machine learning technique is used for fault classification. To do this, first, the residual signals are computed using the difference between the original and estimated signal. Next, the support vector machine (SVM) is used for classification such that the residual signals will be classified into four main groups: normal (NORM), ROLF, INRF, and OURF.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 6 Conclusions  
In this work, we aimed to develop an intuitive hand gesture system for the control of drone flights, accurately recognized using a 2D RGB camera. Motivated by the need for natural and efficient drone manipulation, we introduced a novel set of hand gesture vocabularies encompassing four key movement factors: roll, yaw, pitch, and throttle. Each factor had three values (increase, decrease, neutral), enabling complex flight motions with at least 81 combinations. To implement these, we categorized gestures into motion-based and posture-based types. Motion-based gestures relate to hand pose alignment with distance and rotation, suitable for pitch and yaw movements, utilizing the pinhole camera model and hand joint lines for measurement. Posture-based gestures, with ten designs, control the throttle, roll, stop, and neutral states. A lightweight MLP model classifies these gestures effectively. Our HGR algorithm demonstrated high accuracy in gesture classification. We tested the system's usability by integrating the HGR model with a drone flight simulator, comparing gesture control to keyboard control. The participants found gesture control similarly effective overall, although throttle control initially posed challenges due to occlusion issues with the 2D RGB camera. In future work, we will address these challenges by exploring solutions involving head-mounted RGB sensors to improve the throttle gesture recognition and overall control efficiency.  
Conceptualization, G.Y. and D.H.K.; methodology, G.Y.; software, G.Y.; validation, G.Y.; formal analysis, G.Y. and D.H.K.; investigation, G.Y. and D.H.K.; resources, D.H.K.; data curation, G.Y.; writing--review and editing, G.Y. and D.H.K.; visualization, G.Y.; supervision, D.H.K.; project administration, D.H.K. and H.K.; funding acquisition, D.H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korean Research Institute for Defense Technology Planning and Advancement (KRIT)--grant funded by the Defense Acquisition Program Administration (DAPA) (KRIT-CT-21-027).  
Ethical review and approval were waived for this study due to the use of hand gesture data collection, which does not require approval from an ethical committee.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
Author [PERSON] was employed by the company Hanwha Systems Co., Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A High-Sensitivity Magnetic Field Sensor Based on PDMS Flexible Resonator
## 4 Conclusions  
PDMS is an elastic polymeric silicon compound with high light transmittance and a small Young's modulus. Traditional sensing methods detect a single resonant wavelength  
Figure 10: The spectral shift (**a**) and the fitting curve (**b**) of sandwich structures in the magnetic field range of 0.01 mT to 0.08 mT. Grey part: spectral shift due to external magnetic field action.  
shift under external stimuli, while the small Young's modulus of PDMS becomes a natural material property advantage of this sensing method, which undoubtedly makes PDMS flexible resonators become high-quality platforms for sensor devices. In this paper, based on the sandwich structure of PDMS flexible resonators, we investigate the mechanism of magnetic field sensitivity of the PDMS flexible resonator sandwich structure and analyze the effect of the change in the refractive index and radius on the sensor device. In order to optimize the sensor sensitivity, the impacts of three aspects on the sensor sensitivity are simulated and analyzed when the external magnetic field is applied to the sandwich structure: different coupling positions of PDMS flexible resonators and different radii and PDMS mixing ratios. The sensitivity trends are obtained, and the physical interpretation of the sensitivity trends is analyzed. By optimizing these three aspects, the magnetic field sensitivity is finally calculated as 19.02 nm/mT. Based on the existing experimental conditions and the PDMS flexible resonator preparation technique, the measured magnetic field sensitivity is 4.23 nm/mT. To maintain the long-term stability of the sensor, we keep the humidity of the experimental environment stable. This sensor paves the way for the development of polymer-resonator-based magnetic field sensors.  
[PERSON] conceived the idea, revised the paper and wrote the paper; [PERSON] designed the model, [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] provided scientific support and conceptual advice. All authors discussed the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Joint Funds of the National Natural Science Foundation of China (No. U21A20141), the Natural National Science Foundation of China (Nos. 52005457 and 62004179), the Innovative Research Group Project of National Natural Science Foundation of China (No. 51821003), Shanxi Key Laboratory of Advanced Semiconductor Optoelectronic Devices and Integrated Systems (No. 2022 SZKF01).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Performance Test
### Switching Between 10 Combinations  
By using the same testing method as in Section 5.1, the 0 th, 24 th, 48 th, 52 nd, 64 th, 115 th, 139 th, and 162 nd frames of switching between 10 combinations are obtained, as shown in Figure 21a-h, corresponding to 0 s, 0.05 s, 0.1 s, 0.108 s, 0.133 s, 0.246 s, 0.29 s, 0.338 s, respectively. Compare the velocity curve in Figure 12b as a theoretical reference, corresponding to eight states: stationary, accelerating, reaching maximum angular velocity, uniform angular velocity state 1, uniform angular velocity state 2, preparing to decelerate, decelerating, and already stopped. In the reference theoretical velocity curve, the time between 52 nd and 64 th is 0.05 s; during this period, the upper gears rotate 75\({}^{\circ}\). The time between 64 th and 115 th is 0.106 s; during this period, the upper gears rotate 319\({}^{\circ}\). The upper gear has rotated two revolutions during the entire process. The results of different frame images indicate that the switching process conforms to the theoretical speed time curve, verifying that the switching time of adjacent combinations is less than 0.34 s, which is consistent with the results of the dynamic analysis.  
Figure 20: Different frame images of switching between adjacent combinations: (**a**) the 0 th frame; (**b**) the 8 th frame; (**c**) the 16 th frame; (**d**) the 24 th frame; (**e**) the 32 nd frame; (**f**) the 40 th frame; (**g**) the 47 th frame.
## 1 Introduction
largely suppresses the arbitrariness, the initial step of the protocol still requires an operator to select the initial point of the ducts. In addition, this method accurately defines the centerlines of the ducts but ignores the SCs local diameter as a geometric parameter that can help to improve the characterization of the other parameters. In this study, we were interested in defining in a group of normal subjects the planar orientation of the three SCs with the use of 3D high-resolution MRI images. We present an automated method that allows computing the SC duct morphological characteristics without subjectivity or operator variability. Our approach is based on an algorithm that computes the geometric characteristics of the ducts (3D orientation, diameter, and non-planarity) through a skeletonization process based on a fictitious repulsive force. In addition, the algorithm tests the influence of retaining only a fraction of the ducts (mimicking the potential inter-operator variability).
## 3 Results
### Normal Multi-function vs. Super-Multi-Junction; Practical Conditions  
The design of the super-multi-junction cells by the worst-case atmospheric conditions can be done assuming both aerosol density and water precipitation.  
The achievement in Section 3.1 implies that we can apply the model to the practical conditions by validated energy generation model of the multi-junction solar cell affected by the spectrum variation, considering complexed conditions listed in Table 1 and using the calculation flow in Figure 3. However, we need local data for both climate (solar irradiance) and atmospheric parameters. The model depends on local conditions and is not applied globally.  
Another crucial point is that the distribution of atmospheric parameters, especially aerosol density, was worst for the general performance on multi-junction solar cells with more than three junctions, even though the airmass level (20\({}^{\circ}\) of latitude) is low. The worst-case distribution of the aerosol density is close to North India [57; 58; 59; 60], and this region is known as one of the worst areas for energy generation in multi-junction solar cells in the field experience [104; 105]. This is another reason that we need to develop an annual performance model based on realistic atmospheric conditions with a probability the realistic variations.
## 1 Introduction
### Paper Organization  
The paper is organized as follows: Section 2.1 illustrates a review of various conventional classification schemes, and Section 3 portrays the system model. Section 4 explains the routing scenario using hybrid optimization, and Section 5 elaborates on the developed framework. Results are explained in Section 6 and the conclusion is described in Section 7.
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy (FT-IR)  
The FT-IR spectra of the thermally treated powders are shown in Figure 2 and the corresponding vibration bands are presented comparatively in Table 1. In Figure 2 the spectrum in the case of the LTA sample is shown. The vibration band at 806 cm\({}^{-1}\) is attributed to the stretching vibrations of the Ti-O-Ti bridges, which are characteristic of the TiO\({}_{6}\) octahedra in the titanate structures and 776 cm\({}^{-1}\) is assigned to the Ti-O stretching vibrations, specifically related to the TiO\({}_{6}\) octahedra framework within La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)[64]. The vibration bands at 620 cm\({}^{-1}\) and 547 cm\({}^{-1}\) represent La-O stretching and Ti-O vibrations within the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) lattice, which contribute to the stability of the titanate framework. The vibration bands at 491 cm\({}^{-1}\) and 464 cm\({}^{-1}\) are assigned to La-O stretching vibrations and Ti-O bending modes, which are fundamental vibrations in La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\). The presence of these bands confirms the formation of a well-crystallized La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) phase. The vibration bands between 800 and 400 cm\({}^{-1}\) are assigned to metal-oxygen (M-O) stretching vibrations, suggesting the formation of La-O and Ti-O networks [65].  
Figure 2: FT-IR spectra of the (**a**) LTA and (**b**) LTA-Pt.  
Comparing the FT-IR spectra in Figure 2a,b for the Pt-modified sample, no changes are observed due to the incorporation of the noble metal into the perovskite structure.
# An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network
###### Abstract  
In vehicle navigation scenarios, the RTK positioning results of smartphones are prone to jumps due to the interference of complex urban environments, and the heading angle of dead reckoning (DR) is prone to divergence. In order to obtain more stable and high-precision smartphone positioning results, this paper proposes an RTK/DR positioning method combined with the OpenStreetMap road network. The OpenStreetMap road network data are used to correct the heading angle during the linear motion phase to improve heading angle accuracy. In order to reduce the impact of RTK results jumping on subsequent DR, it is possible to set up a measurement update switch, which combines the RTK covariance matrix, vehicle motion state, and RTK heading angle change information to determine whether to perform a measurement update. The research uses two smartphones to carry out four vehicle positioning tests. The eight sets of test results show that the heading angle correction method based on the OpenStreetMap road network can effectively control the accumulation of heading angle errors and allow DR trajectory to be more consistent with the benchmark. Compared with RTK, the forward accuracy of RTK/DR positioning method is almost unchanged, even though the direction accuracy and lateral positioning accuracy are significantly improved. The RTK/DR horizontal positioning accuracy of both smartphones is approximately 1.3 m, which is better rather than the RTK results. The proposed RTK/DR positioning method can obtain more reliable orientation and position information than RTK.  
dead reckoning; real time kinematic; OpenStreetMap; vehicle navigation +
Footnote †: journal: _remote sensing_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network. _Remote Sens._**2023**, _15_, 398. [[https://doi.org/10.3390/s15020398](https://doi.org/10.3390/s15020398)]([https://doi.org/10.3390/s15020398](https://doi.org/10.3390/s15020398))
## 2 Methodology
### Fuzzy Logic Model Development  
The fuzzy logic model was developed to analyze the relationship between project complexity coefficients and risk probabilities. The model is based on a Sugeno-type Fuzzy Inference System (FIS), which allows for computational efficiency and is well suited for real-world applications where crisp outputs (such as probability values) are needed. This model provides a flexible and adaptive approach to risk assessment by incorporating both quantitative data and expert knowledge. The input variables for the model are the three project complexity coefficients: K1 Environment coefficient, K2 Contractual coefficient, and K3 Design coefficient. These coefficients represent the key factors affecting the probability of risk occurrence. Gaussian functions were used to describe the input variables, while the seven output variables were modeled using linear functions.  
The models operate on a set of fuzzy rules derived from the training dataset of 25 projects. These fuzzy rules are generated using the Wang and Mendel approach [41; 42]. Each rule represents an if-then relationship between the input coefficients and the probability of occurrence for seven defined risk groups, and the rule base is designed to capture the relationships between input variables and risk group probabilities. These risk groups include RG1 Design Risks, RG2 External Risks, RG3 Resource Risks, RG4 Employer Risks, RG5 Contractor Risks, RG6 Engineer Risks, and RG7 Project Risks. By accommodating uncertainty and variability in complex projects, the fuzzy logic approach enables a more detailed and adaptive risk evaluation.  
The model's output variables represent the probabilities of occurrence for each of the seven risk groups, which are further categorized into major and secondary risks. These probabilities were modeled using linear functions. These outputs provide a structured basis for calculating the overall risk probabilities and identifying high-priority risk areas.  
The model was validated by comparing its outputs with empirical data from the testing dataset, ensuring accuracy and reliability in real-world applications. By incorporating expert-defined rules and data from real projects, the fuzzy logic model offers an advanced framework for dynamic risk assessment in road infrastructure projects.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 1 Introduction
Among them, the shortest path routing is adopted to transmit I-level packets, which can guarantee that the important communication packets are transmitted to the destination as soon as possible. Additionally, the global dynamic routing is adopted to transmit II-level packets to avoid the hub nodes being overloaded. Therefore, the delivery path depends on the communication services level. The minimum number of nodes between the source and destination is obtained under the shortest path routing. The cost function is set by considering the queue length and delivery capacity information of nodes in the path, and the path with the minimal cost is selected to transmit the packets under the global dynamic routing.