esa-sceva/satcom-chunk-collection huggingface dataset:
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Pre-Processing Step 1: Lidar-IMU Hand-Eye Calibration
* The rotational axes for these multiple transformation pairs should not be parallel. After completing the hand-eye calibration, each point of the frame \(\mathbf{f}^{\text{L}}_{\text{k}}\) can be transformed by Equation (9) and be represented in the coordinate system \(\{B\}\).
# Multi-Beam Focusing and Deflecting Characteristics of Liquid Crystal Optical Phased Array
## 1 Introduction  
A liquid crystal optical phased array (LCOPA) is used as a new type of programmable optical device. Unlike the traditional mechanical beam control style, it has many advantages such as small size, light weight, low power consumption, and no mechanical inertia [1, 2]. An LCOPA shows unique potential for several application fields, such as satellite laser communication [3], LiDAR [4], and electro-optical countermeasures [5]. Multi-beam fan-out is one of the more novel applications, which also has important prospects and practical value, especially in some fields, which include multi-target indication of laser weapons [6], satellite laser communication [7], and optical wireless communication [8]. In LiDAR [9], multi-beam fan-out enables simultaneous scanning in multiple directions, which significantly improves the speed of data acquisition and spatial resolution. This provides more accurate 3D images and real-time maps in autonomous driving [10], environmental monitoring [11], and geographic information systems (GISs) [12].  
However, commercially used liquid crystal optical phased array devices generally adopt reflective structures and operate by computer desktop software [5, 13]. The way that this device is used will have some drawbacks.  
Firstly, for the use of these reflective devices, the light beam undergoes a reflection, which requires the optical path system to have additional optical components to ensure theproper transmission and guidance of the light beam. This will add the complexity of the optical path system and cause inconvenience in constructing the optical path. Secondly, these commercial LCOPA devices are controlled by computer desktop software. This requires buying and placing a computer closely to the device to connect, which is costly and may affect the placement of the experimental setup. Before using this device, it is also necessary to install specialized desktop software on the computer, which may reduce the convenience of use. Lastly, although the multi-beam fan-out function of LCOPA devices is exciting, it may be inadequate in some applications. For instance, multi-beam fan-out together with focusing function may bring some new benefits.  
To solve these problems, we developed a new programmable transmissive liquid crystal optical phased array device. This device is transmissive and is controlled by a fully embedded system. It is manipulated through a touch screen instead of a host computer to realize human-computer interaction. Compared with the current commercial computer-controlled devices, it is more portable, easier to operate, and lower cost. Meanwhile, based on the GS algorithm and the method of lens phase superposition, we experimentally demonstrated the composite function of multi-beam fan-out with focusing.
# Enhancement of Radio Frequency Plasma Plume by Pulsed Plasma Bullet at Atmospheric Pressure
## 4 Conclusions  
In summary, the atmospheric cascade plasma plume was developed experimentally, in which the pulsed discharge jet in terms of a travelling plasma bullet was manipulated to interact with RF discharge to enhance the RF discharge plume in terms of the length and intensity of the plasma plume. Through the spatio-temporal evolution of the discharge at the nozzle of the discharge tube, it was demonstrated that the length of the RF discharge plume is elongated from 2 mm to 6 mm and the intensity of the RF plasma plume is enhanced by four times in terms of image intensity and optical emission line intensity, which can be maintained for tens of microseconds. The cascade plasma plume was employed to treat the surfaces of carbon fibers, in which the C-O single bond and carboxyl bond on the surface of the carbon fiber were significantly improved.  
Q.H.: Conceptualization, Methodology, Formal analysis, Investigation, Writing--original draft. Y.G.: Supervision, Investigation, Writing--review and editing. Y.Z.: Investigation, Writing--review. J.Z.: Investigation. J.S.: Supervision, Conceptualization, Investigation, Writing--review and editing. All authors have read and agreed to the published version of the manuscript. This work was funded by the Natural Science Foundation of China (Grant Nos. 11875104 and 12175036). The authors declare no conflict of interest.
## 1 Introduction
The results using the MMT in this paper have several unique features compared to previous studies in the following aspects. First, resonant transmittance patterns are studied in detail as a function of the permittivity of a real metal, when the metal is lossy. Then, periodic transmission peaks and attenuations are examined, as the thickness of the plate increases. We also observe changes in guide wavelengths and resonant transmittances, as the imaginary part of the permittivity of the metal decreases, which cannot be obtained by other numerical techniques, such as the FDTD method or finite element method. Finally, orthogonal relations are thoroughly studied for lossless and lossy metallic plates. These resonant transmissions or extraordinary transmissions can be applied to practical devices like band-selective spatial filters [17; 18] and chemical sensors [19; 20]. In addition, controllable dual transmissions have attracted significant attention to research societies, because these phenomena can be practically applicable to research areas such as switching, sensing, polarimetry microscopy, hyperspectral imaging, and security encryption [21; 22; 23; 24; 25; 26]. In this paper, a dispersion equation applied into the MMT formulation is explained first. Two transmission quantities are subsequently introduced, before the transmittances for lossless metals are obtained. Transmission characteristics for the lossy metal case are then computed, and the results are compared with the lossless case. The modal orthogonal relation, which is an important prerequisite of the MMT, is finally discussed when considering both lossy and lossless metals.
# CEPP: Perceiving the Emotional State of the User Based on Body Posture
## 2 Related Work  
In this section, we briefly explain the background information and related work that are used in the proposed algorithm.  
In terms of recognizing the emotion in an automatic fashion, [PERSON] et al. proposed four layers to recognize emotions from dance movements [10]. The authors relied on the body movement to recognize the emotion instead of focusing on the overall body posture as a whole. Additionally, [PERSON] et al. proposed a method which utilized a Bayesian classifier to recognize emotion based on body gestures and speech [11]. In this literature, the authors collected multimodal data and then trained the data with a Bayesian classifier to recognize the emotion.  
Much of the related research [12] has focused on recognizing emotion based on the user's body movements, gestures, and speech, with authors utilizing a Bayesian classifier or support vector machine (SVM) to train the data. Instead of using a single image of the user, these approaches require extra images to analyze the user's body movement. Furthermore, the machine learning algorithms consume extensive computational resources. We differentiate our algorithm from them by providing a fast emotional condition recognition algorithm (less than 1 second). Moreover, we focus on recognizing the user's entire body posture and provide novel methods for extracting a feature and analyzing the similarity distance.  
Another related study was performed by [PERSON] et al. [13]. In this paper, the authors recognized body emotion from pressure sensors and accelerometers. The main idea of this work is the proposal of a scheme which can recognize emotional state while the user is sitting down, which is in contrast to our study.  
One of the areas of application of identifying the emotional state of the user based on body posture is in clinical research. [PERSON] et al. investigated on the problem of recognizing the emotional state of clinically depressed patients based on the body language [14]. Thus, the applications of recognizing emotion based on body posture can be utilized in clinical research to better understand patients.  
Lastly, dynamic time warping (DTW) is an algorithm for measuring the similarity between two temporal time sequences. It is widely used in the area of speech and signature recognition, and in some partial shape matching [15]. However, only few works applied DTW to classify user emotional state. One related work was conducted by [PERSON] et al. [16]. The authors analyzed the speed of user's body movement and amplitude to specify emotional condition. Although this work may look very similar to our proposed algorithm, it is different in terms of feature extraction. The authors measured the quantity of motion based on the user's movement in terms of velocity and acceleration of hand movement. In our proposed algorithm, we utilize the user's overall body posture as a feature. Besides, the computation of similarity distance is an approach different to that used in traditional DTW.
## 4 Results and Discussion
### Results for Electrical Resistance Tomography Experiments  
Figure 11a shows the voltage injections from the 16 electrodes (V1-V16) in the demineralized water, tap water, and industrial food grade saturated sucrose solution. The system was auto-calibrated for each solution. The voltage in millivolts injected was auto-determined by Roc-GUI software according to the conductivity of the solution under consideration. Figure 11b shows the current detected by each of the sensors in a single frame of record. The sensors closer to the source had higher currents, whereas the sensor away from the source had lower currents detected. The difference in the currents detected by the adjacent sink electrodes was higher than the sink electrodes located at the opposite periphery. A U-shaped pattern was visible due to the distance of the electrodes. The maximum absolute difference  
Figure 10: Parameters of Model 3 for various working conditions, where: coefficients in numerator (**a**) A\({}_{1}\), (**b**) A\({}_{0}\) and denominator (**c**) B\({}_{1}\), (**d**) B\({}_{0}\), and B\({}_{2}\) = 1.  
in currents measured using Equation (17) for tap water was 1.747 mA and the industrial food grade saturated sucrose solution was around 1.808 mA whereas for demineralized water, it was 0.191 mA.  
\[\text{Absolute difference}=\overline{\text{max}(\text{F}_{\text{i}}\in\text{v} )}-\overline{\text{min}(\text{F}_{\text{i}}\in\text{v})}\text{,} \tag{17}\]  
where F\({}_{\text{i}}\) is the dataset of all currents (i1-i15) for sensor excited with voltage V.  
Figure 12a displays the 10 mm ABS phantom reconstructed using the Bayesian method in sucrose solution. Figure 12b shows the qualitatively detected density distribution of the white sucrose crystals in the calibrated solution of industrial food grade saturated sucrose. Cross-sectional tomographic images acquired from the real-time measurements of the solid particle dispersion visualize non-conducting regions in the black colour. Since there is no mixing in the reactor, the particles are not uniformly distributed, and the agglomeration can be observed towards the left region. The images are mapped after normalizing the changes in the conductivity \(\Delta\sigma_{\text{Normalized}}\) from 0 to 1. Similar results were obtained using ERT techniques in gas hold up applications within stirred vessel [61].  
Figure 11: (**a**) Voltage injections at the sensor for a frame, (**b**) Current detections through the sensors.
## 3 Experimental Results
### Comparison of Location Results in Different Scenarios
#### 3.1.4 Headspace Occluded Scene
The FZR mode resets the covariance matrix of the sub-filter, so the error of velocity and attitude angle does not change much. The velocity error is less than 0.3 m/s and the attitude angle error is less than 1.2\({}^{\circ}\) in the FZR mode. Figure 19: Velocity error of different mode federated filtering. Figure 18: Position error of different mode federated filtering. In this subsection, the positioning results under different federated filtering modes are compared, and it is found in the experiment that FZR mode and FNR mode have less influence on the sub-filter covariance matrix than FFR mode, and the positioning accuracy is better. In Section 3, by comparing positioning results in different scenarios, positioning results in different information factors, and positioning results in different federated filtering modes, it is found that higher information factors should be set for sensors with higher accuracy according to different application scenarios. If the error state vector modeling is adopted, the FNR and FZR mode positioning results of federated filtering are better.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 4 Test Data Processing of the Vibration Signal
### Multi-Domain Feature Selection  
The selected characteristic parameters of time domain and calculation methods are shown in the Table 2. In the time domain characteristics, the root mean square reflects the irregular continuity of vibration and the energy of signal. The peak-peak value can be used to represent the impact vibration caused by the wear of the the inner edge. The spike degree of vibration signal waveform about piston pump is expressed by peak value. The variance indicates the energy and intensity of vibration signal. The degree to which vibration signals deviates from normal distribution is reflected by skewness and kurtosis.The waveform index is a sensitive and stable parameter, which can well represent the slight damage of slippers with different wear degrees. With the aggravation of wear, the impulsion index and tolerance index increase obviously [43; 44; 45].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Status of** & & & & \\
**the Slippers** & & & & \\ \hline IMF\({}_{1}\) & 0.944 & 0.891 & 0.593 & 0.505 \\ IMF\({}_{2}\) & 0.762 & 0.787 & 0.904 & 0.994 \\ IMF\({}_{3}\) & 0.423 & 0.186 & 0.415 &  
In order to obtain degradation information from the frequency domain of vibration signals, we select \(F_{10}\): average frequency, \(F_{11}\): frequency variance, \(F_{12}\): gravity frequency, \(F_{13}\): frequency standard deviation, and statistical indexes about frequency domain in reference [46] are \(F_{14}\), \(F_{15}\), \(F_{16}\) in the table. The average frequency reflects the vibration energy in the frequency domain. The frequency variance, \(F_{14}\) and \(F_{15}\), indicate the dispersion or concentration of the spectrum. The position change of the main frequency band is obtained according to gravity frequency, frequency standard deviation, and \(F_{16}\). The numerical explanation is shown in Table 3.  
\(\mathrm{s}(i)\) is the spectrum value of effective IMF, \(i=1,2,3,...,N\), \(N\) is the total number of spectral lines, and \(f_{i}\) is the frequency value of the \(i\)-th spectral line.  
These characteristic values listed in Table 2 and Table 3 represent the degradation trend of the piston pump from different perspectives, which in time domain and frequency domain vary with the different degradation states. In order to further extract the piston pump characteristic information of different degradation state, the permutation entropy [47], approximate entropy [48], information entropy [49], and energy entropy [50] of effective IMFs are calculated, respectively, forming the feature information set based on IMF frequency band. The four entropy features of each degradation state are shown in Figure 7.  
It can be seen from Figure 7 that different degradation states have a good hierarchy. With the degree of wear increasing, the vibration signal over time series becomes relatively chaotic and complex, but its entropy values are more stable.
## 1 Introduction  
The demand for small-sized electronics that contain lithium-ion batteries (LIBs), such as smartphones and mobile batteries (such as power banks), has increased in recent years. As newer electronic products replace older ones, many obsolete electronic products are disposed of as e-waste. In Japan, the government has enacted legislation promoting the recycling of small-sized electronics [1]. The Ministry of the Environment and the Ministry of Economy, Trade, and Industry jointly developed guidelines for collecting small-sized electronics, including target items, collection methods, and personal data protection measures [2]. Unfortunately, improper disposal of used small-sized electronics containing LIBs has led to many cases of fires in waste disposal facilities; these reasons include failure to remove LIBs, misclassification of waste, and so on. Such issues negatively impact waste management systems, causing delays in disposal, serious injuries, or property damage.  
The Ministry of the Environment previously investigated fires caused by rechargeable batteries [3]. The findings revealed that waste disposal facilities are the most common sites of fire incidents, as shown in Figure 0(a). Fires caused by LIBs typically result from collisions during transportation, dismantling, and crushing operations employed in recycling.  
According to analyses of the failure modes, methods, and effects of LIBs, the mechanical mode is considered one of the most dangerous, which causes stress by denting or crushing to be transferred to the electrodes and possibly results in a short circuit [4,5].  
The results of the investigation showed that [3] the mobile battery (power bank) was identified as the most frequent cause of fire incidents, as shown in Figure 1b. The high-capacity LIBs in mobile batteries make them particularly prone to fire hazards. Furthermore, it is challenging to remove LIBs from these items, which are not designed for the LIB units to be exchanged. Despite repeated instructions from local governments to consumers to remove batteries from small-sized electronics before disposal, these fire accidents keep on happening as a result of the presence of small-sized electronics containing LIBs because users may not know how to remove LIBs from the products or may not have the appropriate tools. Therefore, there is an urgent need to develop a method for detecting and separating fire-causing items that contain LIBs from other small-sized electronics.
# Multi-Object Deep-Field Digital Holographic Imaging Based on Inverse Cross-Correlation
## 4 Experiment
### Large-Depth-of-Field Reconstruction in an Overlapping Case  
In real applications, overlapping between measured objects is frequent, and this is the key problem in multiplanar reproduction. The experimental results based on this algorithm are shown in Figure 9.  
Figure 9a shows the hologram obtained via CCD, and Figure 9b shows the inverse cross-correlation number matrix \(Cc\) under \(z_{3}=12.0\) cm and \(z_{4}=18.0\) cm. Since T and U overlapped with each other in the four measured objects at this time, three different sizes for the rectangular boxes were chosen to be reconstructed in turn, in which S, M, and L were 30, 50, and 80 pixels, respectively. In addition, Figure 9c shows the schematic diagram of the M \(\times\) M rectangular box. The color bar values indicate the normalized amplitude.  
Figure 10 shows the reconstruction results, where Figure 10a,b are divided into the phase distribution under a single plane versus that which corresponds to this algorithm. The color bars indicate the phase values. In this case, each plane can be reproduced even if the measured objects overlap. The reproduced overlap can still be interfered in different planes, but its value is low and negligible, as shown by the red box. The \(S_{FT}\) of the single-depth operator was \(0.58\times 10^{8}\) (calculated using Equation (4)). The \(S_{FT}\) of the proposed algorithm was \(1.20\times 10^{8}\). Under the overlap of the measured object, this algorithm can still accurately reproduce the depth of field of different planes, and it can calculate the relative positions between different planes.  
Figure 9: (**a**) Hologram. (**b**) Inverse cross-correlation coefficient matrix. (**c**) Schematic diagram of the rectangular box of M \(\times\) M pixels.
## 2 Detailed Procedure for Fault Diagnosis
### Classifier Learning  
After identifying the suitable features, in order to classify the sensor data patterned according to the state of each signal, we need the classification process with the suitable classifier. The classifier was applied to distinguish the features of sensor data represented in two-dimensional spaces. The classification process recognizes the given data into a suitable pattern for the features based on the criteria obtained by learning the sensor data of the existing fault and normal operating state. Note that the dotted line of the right side in Figure 4 indicates the classifier. In this study, Naive Bayesian classifiers were used. The Naive Bayesian classifiers assign the most likely class to a given example described by its feature vector, expressed in Equations (6) and (7). They determine the point where the error  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Index** & **Description** \\ \hline Root mean square & \(RMS=\sqrt{\frac{1}{h}\frac{n-1}{i=0}x_{i}^{2}}\) \\ Standard deviation & \(\sigma=\sqrt{\frac{1}{h}\frac{n-1}{i=0}(x_{i}-\mu)^{2}}\) \\ Kurtosis value & \(k=\frac{\left[\int(X-u)^{q}p(x)dx\right]}{\sigma^{q}}\) \\
3 rd central moment & \(E([X-u]^{3})=\int(X-u)^{3}p(x)dx\) \\
5 th central moment & \(E([X-u]^{5})=\int(X-u)^{5}p(x)dx\) \\ Crest factor & \(CF=\frac{\text{peak}}{RMS}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature index used for the diagnosis of the MAB equipment.  
Figure 4: Example of dimensions reduced features into two-dimensional space by principal component analysis (PCA).  
is minimized by appropriately moving the decision boundary and finding the minimum error where the probability density functions of two data classes are idealized. Therefore, the features can be simplified by assuming that features are in an independent class for fault diagnostics [21].  
\[P(x|y)=\frac{P(x\cap y)}{P(y)}=\frac{P(y|x)P(x)}{P(y)} \tag{6}\]  
\[\hat{y}=\underset{k\in\{1,\cdots k\}}{argmax}p(C_{k})\prod_{i=1}^{n}p(x_{i}|C_ {k}) \tag{7}\]
## 4 Discussion
### RQ1: Progression Through Acceptance Phases  
Below we discuss important aspects of our participants' contexts that influenced their progression through the acceptance phases. While prior work includes many models of technology acceptance (e.g., Technology Acceptance Model (TAM; [41]), Extended TAM (ETAM; [42; 44; 74]), Unified Theory of Acceptance and Use of Technology (UTAUT; [45]), Theory of Reasoned Action (TRA; [36]), Theory of Planned Behavior (TPB; [37; 38]), Social Cognitive Theory (SCT; [39])), they tend to examine acceptance over the short term. In this paper, we contribute to the literature by indicating the main factors of longitudinal technology acceptance that emerged over seven months using a bottom-up approach. These factors did not fit neatly into any model of technology acceptance--although some had similarities to factors from various models, which we discuss below. Our factors were as follows: solving problems with technology, life situations, and personality/mindset. We recommend that future researchers consider these factors in longitudinal technology acceptance models.