esa-sceva/satcom-chunk-collection huggingface dataset:
# Cornering Stiffness Prediction Based on Geometric Method
## 2 Method Description
### Cornering Stiffness
#### 2.2.1 Magic Formula  
The load dependence for the cornering stiffness, under pure side slip conditions, is given below. The quantities \(p_{1}\) and \(p_{2}\) are model parameters.  
\[K_{y}=p_{1}\cdot\sin(2\arctan(\frac{F_{z}}{p_{2}})). \tag{26}\]  
According to Formulas (25) and (26), we have:  
\[K_{yi}=\frac{p_{1}}{2}\cdot\sin\left(2\arctan\left(\frac{F_{zi}}{\frac{p_{2} }{2}}\right)\right),i=1,2, \tag{27}\]  
where \(K_{yi}\) is the cornering stiffness of the half-tire. \(F_{zi}\) is the corresponding load.
## 2 Modeling of Mounting System
### Control Using the NLMS Algorithm  
To isolate the source vibration of the target path in the previous section, the amplitude and phase of the actuator were calculated. However, it is evident that the vibration was greatly increased for the untargeted path. In addition, the calculation method cannot be applied to a signal with a real multi-frequency. Therefore, both the source and the receiver cannot control vibrations satisfactorily. Therefore, to compare with the computational isolation method, the simulation was carried out for the source and receiver vibration isolation using the NLMS algorithm. The simulation was based on the schematic representation shown in Figure 8 and was used as an input to the actuator using the signal from the source.  
Figure 7: Result of the vibration of the receiver related to each active path; the blue and red lines represent the vibration of the receiver before and after applying the control, respectively.  
The inputs of the simulations were provided under the same conditions for comparison, and the signals entering the actuator were applied by appropriate adjustments to the signals from the NLMS algorithm. The simulation results of the source and receiver are shown in Figures 9 and 10, respectively. Table 3 presents the values for each output.  
Figure 8: Schematic representation of the simulation using the NLMS (Normalized Least Mean Squares) algorithm.  
Figure 9: Result of the vibration of the source related to each active path using the NLMS algorithm; the blue and red lines represent the vibration of the source before and after applying the control, respectively.  
were compared with the those of the control using NLMS, the vibrations of \(a_{st1,g1}\) and \(a_{st2,g1}\) tended to greatly increase in the calculation method, but the result of using the NLMS algorithm tended to decrease the vibration in \(a_{st2,g1}\). Moreover, the result of the calculation of vibration for \(a_{st1,g1}\) showed a tendency to increase by 450.47%, which is about 5.5 times, whereas for the result using the NLMS algorithm, the vibration tended to increase by 105.62%, i.e., it doubled. Therefore, it can be seen that the use of the NLMS algorithm in the calculation method is more efficient for the whole system than the use of the actuator input.
## 3 Proposed Lightweight Deep Learning Approach
### Characteristic Analysis  
As mentioned in Section 2, in the time domain, the acquired percussion signal may appear as an impact spike, but it can also become indistinguishable, probably due to signal attenuation over long distances. Additionally, as the experiment was conducted in a construction environment, various noise sources in the surrounding area contributed to interference. These sources included the operation of tunnel-boring machines and fans, manual work activities, and the distant running of metro trains. Figure 9 illustrates an example of the acquired data, including time and frequency domain representations of the intrusion signal. Figure 9a shows a time domain plot of the intrusion signal, where the spike in the waveform may represent an impact from an external source. However, these spikes are difficult to identify due to signal attenuation or noise interference. Figure 9b shows the time-frequency image of this intrusion signal after STFT [36]. In this spectrogram, we can observe the intensity and frequency distribution of the signal over time, and the five knocks are presented as distinguishable high-frequency energy, which is useful for identifying the characteristic frequency pattern associated with the intrusion; thus, we use the STFT feature as an input to the neural network.  
Figure 8: Some of the signals in the training and test datasets. (**a**) Normal samples in the training set; (**b**) abnormal samples in the training set; (**c**) normal samples in the test set; and (**d**) abnormal samples in the test set.
# Influence of Contacts and Applied Voltage on a Structure of a Single GaN Nanowire
## 3 Results
### Operando Studies of GaN NWs  
To study properties of the GaN NWs with the diameter of 350 nm, we have applied voltage bias to their ends and followed the evolution of the 1010 GaN Bragg diffraction pattern. The Bragg peak intensity distribution as a function of applied voltage is shown in Figure 6. It is clearly visible in the figure that the distance between the Bragg peaks increases with the voltage causing an additional redistribution of the coherently scattered radiation around the Bragg peaks. As was already discussed above, this deformation of the Bragg peak is attributed to the GaN NW bending due to its expansion caused by the piezo effect under the applied voltage. The scheme of the bent NW and its intensity distribution in reciprocal space is shown in Figure 7a. The gap between two peaks increased with the applied voltage till the maximum elongation at two volts (see Figure 6d). After this critical value, the applied voltage bias was increased to five volts, and the NW was destroyed. Therefore, we assumed that the breakdown voltage of the system \"GaN NW + Au contacts\" was between two and five volts. The SEM images of the NWs after the breakdown voltage was applied are presented in the Supplementary Materials.  
Figure 6: Evolution of intensity distribution around 1010 GaN Bragg reflection of a contacted GaN NW with the diameter of 350 nm. The values of the applied voltage bias: 0.1 V (**a**), 0.5 V (**b**), 1 V (**c**), 2 V (**d**). To enhance the diffraction pattern details, the intensity distributions are represented by two different iso-surfaces. The length of coordinate arrows corresponds to 0.1 nm\({}^{-1}\). The intensity is normalized to the maximum and the iso-surface values are \(10^{-3.4}\) and \(10^{-4}\).  
Investigation of the applied voltage to the GaN NWs with a diameter of 200 nm was not successful due to the unstable contacts between the NWs and the metallic contacts. The 200 nm GaN NW positioned on the top of the second type of Au contacts was lost after the first applied voltage bias of 0.5 volts. In this situation, one can only conclude that this type of NW fixation is much weaker than the variant with the Au contacts on the top of the NW.
## 5 Computational Simulation Results and Discussion
### The Conditions for Isolated Solution  
A study was conducted to examine the effect of the dimensionless SFD constant and unbalance on the response with an isolated solution. The results identified the conditions and parameters that cause the presence of nonsynchronous response.  
In Figure 18, the coloured region indicates the conditions for the presence of an isolated solution in the frequency response curve. The contours are plotted for a range of the nondimensional SFD parameter from 0.01 to 1.5, and unbalance from 0.05 to 0.95. The coloured region in Figure 18 has an upper (denoted with the symbol u) and a lower (denoted with the symbol l) boundary. The upper boundary is primarily determined by the  
Figure 16: The basins of attraction for the nondimensional speed of 9.0 and the initial conditions, based on a quasiperiodic response, with 10,000 (**a**) and 90,000 (**b**) time samples.  
Figure 17: Detail of the structure of basins of attraction for D1 (**a**) and D2 (**b**) regions.  
value of unbalance and represents the point at which the resonance peak and the isolated solution merge. The lower boundary is defined by the maximum permitted value of the dimensionless speed, which is 20 in this case.  
The maximum permitted value of the nondimensional rotational speed was established to ensure that the rotor does not suffer mechanical damage during operation. It affects the size of the region where the isolated solution can originate. When a higher maximum speed is allowed, the area in which the isolated solution can originate will be larger.  
It was found that the external damping not only affects the resonance peak, causing it to shift and decrease in amplitude, but also shifts the origin of the isolated solution to higher speeds for a given unbalance, as demonstrated by the upper boundary in Figure 18a,b. Furthermore, when the dimensionless SFD constant is less than 1.0, the range of unbalance values for which an isolated solution can exist is larger. However, when the dimensionless SFD constant is greater than 1.0, the range of unbalance values is reduced into a narrow region.  
This region defines a specific range of combinations of the dimensionless SFD constants and unbalance values for the presence of the isolated solution in the frequency response. The conducted numerical research shows that the nonsynchronous solution can arise only in the frequency response region with the isolated solution. Operating the rotor above the lower boundary region in Figure 18 poses a risk as it can result in nonsynchronous oscillations or synchronous motion with the circular centred orbit with a large radius.
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Aerospace Systems
(2020) applied recurrent neural networks (RNNs) to structural health monitoring data, including strain and vibration signals. The model effectively detected small cracks and signs of corrosion, enabling timely maintenance [69]. Hydraulic systems are critical because they control landing gear, brakes, and flight surfaces. [PERSON] et al. (2020) applied decision tree algorithms to detect hydraulic system faults, such as pressure loss and leakage. Their method provided interpretable results and facilitated in-flight decision-making [99]. Moreover, integrating sensor data enhances understanding of aerospace system health. [PERSON] et al. (2022) explored transfer learning to improve fault detection across various aircraft platforms. By combining data from temperature, pressure, and vibration sensors, their model effectively recognized complex failure patterns and generalized across differing conditions [100]. In prognostics, machine learning is used to estimate the remaining useful life (RUL) of aerospace components. [PERSON] et al. (2021) employed long short-term memory (LSTM) networks to predict RUL from sequential sensor data, allowing airlines to plan maintenance more effectively and reduce operational costs [101]. Fault detection and diagnosis (FDD) is particularly challenging for spacecraft due to extreme environmental conditions and limited maintenance opportunities. [PERSON] et al. (2022) developed a hybrid model combining convolutional neural networks (CNNs) and RNNs to diagnose faults in spacecraft electrical systems. Their model effectively identified common issues such as wiring faults and power irregularities [48]. Predictive maintenance is another essential application. [PERSON] et al. (2021) employed deep neural networks (DNNs) to analyze engine performance data, vibration patterns, and temperature variations. Their model successfully predicted maintenance needs, helping airlines minimize unexpected downtime [3]. Given the harsh and dynamic environments in which aerospace vehicles operate, real-time performance monitoring is crucial. [PERSON] et al. (2020) applied multilayer perceptron (MLP) networks to monitor flight parameters such as speed, altitude, and attitude. Their model effectively identified anomalies during complex flight scenarios [102]. Additionally, multi-task learning (MTL) is gaining traction in this field due to its ability to handle multiple FDD objectives simultaneously.
## 1 Introduction  
Human pose estimation is one of the challenging tasks of computer vision, due to its wide range of use cases. It aims to determine the position of a person in an image frame or video by detecting the pixel location of different body parts/joints [1, 2]. Human pose estimation is usually performed using image observations in either 2D or 3D [3, 4] by obtaining the pose of the detected person's joints and selected body points. In the literature, there are several approaches proposed from the traditional use of morphological operators to complex human pose estimation using convolutional neural networks and deep learning [5, 6, 7, 8]. However, these methods involve challenges--inaccuracy in determining a point's location, finding correlation between variables, and high computational load--since they deal with sensor capability and high-computing devices [9, 10].  
The processes of human pose estimation have progressed significantly due to the existence of deep learning and many publicly available datasets. The first of a few applications of human pose estimation are seen in the fields of animation and human monitoring [11, 12].  
These systems have been expanded to video surveillance, assistance systems used for daily living, and driver systems [13; 14].  
Within a human pose estimation framework, pose classification emerges in recognizing the specific pose that a person is in from a predefined set of poses. This can be used to recognize certain actions or movements, such as yoga poses or dance moves, and can also be used in security or surveillance systems to detect suspicious behavior.  
However, this task brings challenges such as joints that are occluded, multi-person detection in one frame, spatial difference because of clothing, lighting, and backgrounds, and investigation of complex positions. However, the cost of 3D camera sensors has been decreasing over the last few years; there is an emergence of machine learning and deep learning methods. This might bring new and innovative approaches to the challenges it is currently facing.  
Over the last two years, the COVID-19 pandemic has greatly disrupted many traditional and conventional working routines. This includes switching from the traditional and typical office-based setup to a new working-from-home (WFH) setting on short notice. It has been suggested by many studies that this new setup will have considerable negative impacts on employees' overall well-being and productivity [15]. Nevertheless, many studies also show positive impacts of WFH settings [16; 17; 18; 19]. Given this, human posture recognition systems could be used to assess sitting postures and might lessen the negative impacts of the work-from-home setup.  
This study aims to  
1. Investigate the related studies pertaining to human sitting posture recognition;
2. Develop a custom dataset for sitting posture recognition;
3. Develop a model to recognize proper and improper sitting posture using a rule-based approach; and
4. Evaluate the effects of other ergonomic and demographic elements on sitting posture.
# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 5 Conclusions  
The characteristics of SAR data that are able to identify objects, based on their physical form, are an advantage in identifying burn scar areas. The characteristics of burnt land in the form of open land will have a low backscatter value, compared to unburnt forest areas, which have a coarser texture, so that they have a higher backscatter. In addition, another advantage, when compared to the optical imagery is that it is able to provide visual objects without being constrained by clouds or weather and smoke that usually covers objects, especially when forest fires occur.  
The identification of the burnt areas using SAR backscatter differences data, in the conditions before and after a fire, is able to produce s good accuracy. This can be seen from the results of the accuracy test of the three classifiers used in this research, showing an overall accuracy of over 88% with a processing time reduced from 12 h to 1 h. Based on the results, we select the support vector machine method as the best to be applied to support the forest fire mitigation system.  
[PERSON] is the principal researcher, [PERSON] provided with field data and machine learning. [PERSON] also contributed to the machine learning and paper writing. [PERSON], [PERSON], and [PERSON] contributed to the GIS mapping and analysis of the data. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Universitas Indonesia Research Group Grant under Grant Number NKB-651/UN2.RST/HKP05.00/2021.  
Not applicable.  
The current authors dedicate this article to the late [PERSON] who developed the research from the beginning and formulated this article up until the time this article being submitted.  
The authors declare no conflict of interest.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 5 Sensors Driving Underwater SLAM
### Vision-Based SLAM
#### 5.1.1 Monocular Vision Systems  
Monocular camera systems offer a lightweight and cost-effective solution for underwater SLAM, though they face inherent scale ambiguity challenges. Recent advancements have significantly improved their reliability through hybrid visual-inertial approaches. For instance, Ou et al. [51] demonstrated that combining active monocular vision with inertial measurements can reduce scale estimation errors. Integrating camera data with acoustic or inertial measurements (from IMUs or DVLs) helps compensate for the limitations of relying solely on visual input. This fusion of sensor data can offset the impact of bad visibility or changing conditions, leading to more robust and adaptable SLAM solutions. Jung et al. [52], for example, enhanced an AUV's SLAM performance by supplementing camera feeds with artificial landmarks and additional navigation sensors, resulting in a more stable and reliable SLAM framework. These systems are particularly effective in shallow water inspections where their simplicity and low power consumption provide op  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Type** & **Advantages** & **Disadvantages** & **Use Cases** \\ \hline \multirow{3}{*}{Vision} & High-resolution imagery, rich texture/color information, suitable for object recognition & High computational load, limited recognition accuracy in turbid water, short effective range, sensitive to lighting conditions & Archaeological site documentation, coral reef monitoring, pipeline inspection, visual odometry in clear waters \\ \hline \multirow{3}{*}{LiDAR} & Millimeter-level depth accuracy, works in complete darkness, unaffected by turbidity & High power consumption, limited by absorption, sensitive to scattering particles & High-precision 3D mapping, ship hull inspection, underwater structure measurement \\ \hline \multirow{3}{*}{Sonar} & Long range, works in zero visibility and in the presence of particles & Low angular resolution, multipath interference, slow refresh rate & Deep-sea exploration, mine detection, large-area seabed mapping, navigation in featureless terrain \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of sensors for underwater SLAM.  
erational advantages. However, their performance degrades in feature-poor environments or under extreme lighting variations, necessitating careful system design and frequent loop closures to maintain accuracy.
## 3 Results
### The Effective Flood Control Capacity  
The effective flood control capacity (EFC) can be calculated, as follows:  
\[V_{e}=S\times\Delta h=S\times(H_{max}-h_{i}) \tag{4}\]  
where \(V_{e}\) (m\({}^{3}\)) is the effective flood control capacity, S (m\({}^{2}\)) is the surface area of each lake, \(H_{max}\) (m) is the maximum water level in each flood wave, and \(h_{i}\) (m) is the incipient rising water level.  
Table 3 shows the EFC of each part of Dongting Lake (i.e., WDL, SDL, and EDL) for the 1998 type flood before and after the application of the IFCS. For the natural condition, the total EFC of Dongting Lake is about 1.44 billion m\({}^{3}\), 3.35 billion m\({}^{3}\), and 1.45 billion m\({}^{3}\) in the first, second, and third flood wave, respectively. The largest EFC occurred in the second flood wave due to the biggest variation range of the water level (\(\Delta h\)). The IFCS has a dramatic effect on the \(\Delta h\) in EDL. The \(\Delta h\) significantly increases in EDL, which is about nine times, 4.7 times, and 2.8 times higher than that in the natural condition for the first, second, and third flood wave, respectively. Therefore, the EFC has significantly increased in EDL, i.e., 6.37 billion m\({}^{3}\), 7.28 billion m\({}^{3}\), and 2.16 billion m\({}^{3}\) in the first, second, and third flood wave, respectively. For the WDL and SDL, the \(\Delta h\) decreases a little in the first and the second flood wave and it is almost the same in the third flood wave. Hence, the EFC decreases a little or it is nearly the same as that in the natural condition. However, due to the EDL contributing the most EFC in Dongting Lake (see Table 3), the total EFC of Dongting Lake significantly increases, which is about 4.7 times, 2.8 times, and two times higher than that under the natural condition during the first, second, and third flood wave, respectively, after the application of the IFCS.
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
###### Abstract  
Hand gesture recognition is an essential Human-Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper wide deployment. This paper proposes the novel system TaGesture, which employs an inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand-tracking-smoothing algorithm with an Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.  
hand gesture recognition; inaudible acoustic sensing; training-free sensing; device-free sensing Article