esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Material Preparation  
Considering that optical instruments almost always feature rotationally symmetric lenses, the mechanical components are typically also rotationally symmetric. Therefore, winding or braiding technology may seem like a suitable choice. Parts that deviate from a cylindrical shape present challenges for winding, primarily due to fibre slippage on contoured surfaces [61] or the bridging of concave surfaces [62]. Another viable option is one of the manual laying technologies (wet laying, prepreg, infusion). These technologies offer greater flexibility in terms of manufactured shapes, but they are more labor-intensive compared to winding [63].  
Despite the aforementioned limitations, winding technology was selected for the technological samples, primarily due to the availability of a wide range of commercially wound cylindrical tubes. The technological sample (Figure 1) consists of a wound quasi-isotropic CFRP tube made of [90\({}^{\circ}\)/0\({}^{\circ}\)/\(\pm\)30\({}^{\circ}\)] layers of T700 12k fibres with a length of 90 mm, an inner diameter of 50 mm, and a wall thickness of 1.5 mm. The tube was wound using a multi-axis winding machine and cured at room temperature with subsequent post-curing in an oven. The tube was supplied by a third party along with a technical datasheet, which indicated the following mechanical properties: Axial Young's Modulus 66.6 MPa, Bending Stiffness 4.4 \(\times\) 10\({}^{9}\) N-mm\({}^{2}\), and coefficient of thermal expansion in axial direction 1.2 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) No further specifications are available since the material was acquired from a third party. This tube was chosen based on matching dimensions with the requirements of the reference device and, as there were no increased stiffness requirements in this particular case, the quasi-isotropic pan-fibre composition is sufficient. The CFRP tube is bonded to anodized EN_AW-6061-T651 aluminium alloy sleeves, which represent the interface for mounting optical assemblies, such as objective and eyepiece of the Keplerian telescope. A 1 mm wide gluing gap allows for compensation of manufacturing inaccuracies of the carbon components. The precise alignment of the two aluminium alloy sleeves, in accordance with the specified tolerances defined by a total runout of the inner diameters of sleeves of 0.05 mm, is ensured by precise bonding or turning after bonding.  
An assembly fixture was designed for bonding the technological sample with the required precision (Figure 2).  
Figure 1: Geometry of a technological sample.  
Figure 2: An assembly fixture.  
For the proposed technology to be a relevant alternative to CNC machining, it must achieve comparable accuracies. The total runout of the inner diameters of both sleeves was identified as a critical parameter, with a maximum allowable value set at 0.05 mm (Figure 1). These requirements must be met both post-production and following a series of mechanical and climatic tests, as detailed in the subsequent chapter.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 4 Initial Attempts
### Doppler Frequency Based Rotational Localization  
We introduced a rotating table in the experiment to rotate the antenna at the edge of the rotating table in order to reduce the cost of system deployment. Based on the Angle of Arrival (AOA) method, the transmission direction and relative incident angle of the signal between the antenna and tags are calculated using the Doppler frequency and parameters of the rotating table. As shown in Figure 3, the antenna rotates counterclockwise at an angular velocity \(\omega\) at the edge of the rotating table O with a radius r, and the transmission signal collects the tag information at point T. It is easy to know that the linear velocity of the antenna rotation is \(\omega\times r\) from the geometric nature of the circle. When the antenna rotates from point A to point C, the relative velocity between the antenna and the tag is positive, and it reaches maximum at B (straight line BT is tangent to the circle O); when the antenna is rotated from point C to point A, the relative velocity is negative, and it is minimized at D (the line DT is tangent to the circle O). The relative velocity is 0 between two points A and C. After one rotation of the antenna, two points B and D are determined according to the change of Doppler frequency data, and two points of A and C correct the results. Finally, the tag can be located by solving the triangle BDT.  
This method can reduce the cost of deployment to a large extent. However, test results are not ideal. The main reason is that the Doppler frequency is unstable. The error is very large. If the radius of the rotating table is small, it will cause a larger error. If the error of Doppler frequency can be processed well, it can be used for localization.
# An Ensemble Model for Multi-Level Speech Emotion Recognition
## 3 Proposed Method
### Expert 1: Double-Channel Model Based on CNN
#### 3.2.1 Analysis and Preprocessing of Speech Signals  
Analysis and preprocessing of speech signals are two important methods. The analysis of time and frequency domains has its own characteristics, but it also has certain limitations. Time domain analysis does not have an intuitive understanding of signal frequency characteristics. Frequency domain characteristics do not reflect the relationship of speech over time.  
A spectrogram combines the characteristics of the spectrum and the time domain waveform to dynamically display changes in the speech spectrum over time. Therefore, more information is included in the spectrogram than in the simple time or frequency domain. The vertical axis of the spectrogram represents frequency, the horizontal axis represents time, and the depth of the color represents the energy of the signal of the arbitrary specific frequency component at the corresponding time.  
Although, as a whole, the characterization and essential characteristic parameters of speech signals change over time, this seems to be a nonstationary process. However, in this research, it is found that the characteristics of speech signals in a short time can be regarded as remaining unchanged, that is, they have a smoothness. Therefore, when they are preprocessed, short-time stationary technology is generally adopted. Short-term analysis is carried out to eliminate the redundant part of the signal, and it is split into units of segments to analyze its characteristic parameters. Therefore, before acquiring the spectrogram, the existing speech in the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus is first segmented in the time domain and combined with the characteristics of the corpus speech, the speech is segmented into units of 1 s, and then the spectrogram is stretched or compressed in the time domain to achieve data expansion and generate many spectral maps under various amplitudes. At the same time, based on the rotational invariance of the CNN model, the time and frequency domain information of the spectrogram are flipped from multiple angles, in order to add a new set of basic data after transformation. The goal of this design method is to extract time-frequency features and highlight the expression of local features.  
Through the above transformation, 2 sets of homologous spectrograms are obtained. To feed them into the CNN model, the spectrograms need to be adjusted to a uniform size.
# Influence of Impeller Speed Patterns on Hemodynamic Characteristics and Hemolysis of the Blood Pump
## 2 Materials and Methods
### Speed Modulation of the Blood Pump Impeller  
Considering the influence of impeller speed waveform on AOP fluctuation, an impeller with three speed patterns was designed in two cardiac cycles to satisfy the normal blood pressure (80-120 mm Hg) and conform to the normal pulse range of human blood pressure as far as possible, as shown in Figure 8.  
According to the characteristics of the H-Q curve and avoidance of reflux phenomenon, the impeller speed of the blood pump working in parallel with the native heart was set to 2600 rpm in a constant speed pattern. The two other speed patterns were sinusoidal and pulsatile patterns. The impeller speed of the constant speed pattern was the reference speed of the sinusoidal speed pattern. The pulsatile  
Figure 8: Amplitude and frequency characteristics of the three kinds of speed patterns of a blood pump impeller. The central speed was the same as that of the constant speed pattern, which was also the reference speed of the sinusoidal speed pattern.  
Figure 7: Characteristics of left ventricular pressure (LVP) and aortic pressure (AOP) in healthy and heart failure conditions. Solid and dotted lines represent the characteristics of the blood pressure output of normal individuals and patients with heart failure, respectively. The aortic valves had normal opening and closing function at all times.  
speed pattern waveform was determined using the human aorta waveform, which consisted of the sinusoidal and constant parts, as shown in Equation (5). The reference speed \(\omega_{1}\) of the sinusoidal part was 2400 rpm, and the amplitude \(A_{1}\) was 400 rpm. The period depended on the cardiac cycle in which the heart rate was \(b\). Additionally, \(60/b\) represented the cardiac cycle. In this study, the heart rate was 75 bpm for analysis; thus the cardiac cycle time was 0.8 s. The pulsatile speed pattern waveform increased from 2400 rpm at low speed to 2800 rpm, and then decelerated to 2400 rpm. It maintained a constant speed 2400 rpm at 0.4 to 0.8 s, which corresponded to the diastole process of the native heart.  
\[\omega(t)=\begin{cases}\omega_{1}+A_{1}\sin(2\pi bt/60)&0\leq t<30/b\\ \omega_{1}&30/b\leq t<60/b\end{cases}. \tag{5}\]  
The sinusoidal speed pattern fluctuated sinusoidally relative to the central speed \(\omega_{2}\), as shown in Equation (6), which was the same as the constant speed pattern. The amplitude \(A_{2}\) of the speed waveform was 200 rpm, and the period between sinusoidal and pulsatile speed patterns was the same.  
\[\omega(t)=\omega_{2}+A_{2}\sin(2\pi bt/60). \tag{6}\]  
The design basis of the pump speed waveform was to ensure that the peak phase of the speed waveform was consistent with the peak phase of the LVP, so that the maximum pulse pressure was obtained when the cardiac output blood pressure and the peak value of the pump output pressure were superimposed on the aorta.
## 2 Literature Review  
### On Analysis Based on SCPC Data  
As has been discussed above, the SCPC has been carried out so as to let government officials, researchers, and the public to obtain an easier approach to consumer payments behavior, and to obtain a better understanding of the usage situation of credit cards. Until now, various research efforts has been made by employing the data in this survey. By statistical analysis, [PERSON] et al. [5] have pointed out that the average consumer held 5.1 of the nine common payment instruments and used 4.2 of them during a typical month in the U.S. of the year 2008. [PERSON] et al. [6] have tried to discover whether U.S. merchants are using their recently granted freedom to offer price discounts and other incentives to steer customers to pay with methods that are less costly to merchants, and have found that only a very small fraction of transactions received a cash or debit card discount, and even fewer were subjected to a credit card surcharge. [PERSON] et al. [7] developed and estimated a structural model of adoption and use of payment instruments by U.S. consumers. After that, they utilized a cross-section from the Survey of Consumer Payment Choice. Finally, they evaluated substitution and income effects.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{6}{c}{**GCP-Transformed**} & \multicolumn{6}{c}{**ICP-Transformed**} \\ \cline{2-9}  & **Original** & **MLS** & **Shift** & **Shift** & **Shift** & **Original** & **MLS ICP** \\  & **RMSD** & **RMSD** & **d\(\mathbf{X_{MLS}}\)** & **d\(\mathbf{Y_{MLS}}\)** & **d\(\mathbf{Z_{MLS}}\)** & **RMSD** & **RMSD** \\ \hline
1 & 0.021 & 0.018 & 0.006 & \(-\)0.033 & \(-\)0.017 & 0.013 & 0.005 \\
2 & 0.016 & 0.014 & & \(-\)0.021 & \(-\)0.004 & 0.010 & 0.006 \\
3 & 0.017 & 0.011 & 0.009 & \(-\)0.002 & 0.010 & 0.015 & 0.006 \\
4 & 0.024 & 0.021 & & 0.011 & 0.022 & 0.012 & 0.005 \\
5 & 0.028 & 0.026 & \(-\)0.007 & 0.025 & 0.031 & 0.012 & 0.005 \\
6 & 0.031 & 0.027 & & 0.027 & 0.026 & 0.013 & 0.005 \\
7 & 0.038 & 0.028 & \(-\)0.015 & 0.035 & 0.019 & 0.024 & 0.010 \\
8 & 0.018 & 0.013 & & 0.023 & 0.002 & 0.009 & 0.005 \\
9 & 0.017 & 0.014 & \(-\)0.013 & 0.005 & \(-\)0.007 & 0.010 & 0.005 \\ Mean & 0.022 & 0.017 & - & - & - & 0.012 & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the evaluation of individual profiles in GeoSLAM 2 data compared to the Leica P40 reference cloud in individual directions (RMSDs in meters). Figure 13: Shifts of GeoSLAM \(1-\)MLS data from the reference cloud in millimeters (Table 4): (**a**) shifts of d\(\mathbf{X_{MLS}}\) and d\(\mathbf{Y_{MLS}}\), (**b**) shift d\(\mathbf{Z_{MLS}}\). Tables 3 and 4 show the gradual changes in the errors of the GeoSLAM clouds compared to the reference. The deviations were calculated for individual profiles (bridge pillars, supports, and central parts of the bridge between the pillars). For pillars and supports, shifts were calculated in all three axes (longitudinal shift dX, transverse shift dY, and elevation shift dZ), while in the profiles not directly supported by pillars, only the transverse error dY and elevation error dZ were calculated. The results are visualized in Figures 13 and 14. We can see that in both passes, the maximum deviation from the reference cloud in any individual direction was 0.035 m. Mean errors for both clouds were below 0.015 m and, when applying ICP, as little as below 0.01 m.
## 2 Architecture
### M2M Gateway Resource Control and Management
#### 2.2.3 Data Traffic Congestion and Scheduling  
With the continuously increasing number and types of sensors in M2M gateway, the data volumes of sensors in M2M gateways are increasing rapidly, whereas the processing speed and capacity of the middleware of M2M gateways are limited to a certain degree. In addition, due to different sensor types, the data access cycles, data volumes, and requirements for reading and writing are also different. Therefore, it is necessary to define a suitable scheduling algorithm for accessing different sensors, which can increase the efficiency of the communication between an M2M gateway and sensors and save the communication and processing resources of the M2M gateway.  
The buffer queue combined with polling to implement the data scheduling, which ensures the real-time performance of the data and service quality. First, for the data cache problem, a data buffer queue is configured for each device to store the data that cannot be processed for the time being. Second, for the data scheduling problem, priority configuration is performed on the buffer queues based on the user-defined parameters in the service quality configuration file, in addition, data scheduling is performed based on the priority levels. The priority configuration of data buffer queues is not simply to assign high, medium, and low priority levels, instead, a hierarchical configuration method is used, and each data buffer queue is expressed by priority level and priority weight attributes. If the priority levels are the same, then the priority weights are compared. The queue with a higher priority weight has the higher priority and will be processed first. The priority weight is obtained from the mapping function of the maximum allowed processing time. For the same priority level, the mapping functions are the same. For different priority levels, the mapping functions are also different.
# Networked Roadside Perception Units for Autonomous Driving
## 3 Design and Implementation of the RSPU System
### Implementation of AutoC2X  
Figure 3 provides an overview of AutoC2X's architecture. C++ language was used to develop AutoC2X through the extension of OpenC2X (standalone v. 1.5) and Autoware (v. 1.11.1). The source code can be accessed at [[https://github.com/esakilab/AutoC2X-AW](https://github.com/esakilab/AutoC2X-AW)]([https://github.com/esakilab/AutoC2X-AW](https://github.com/esakilab/AutoC2X-AW)).  
All functions of AutoC2X are explained here. Certain features such as planning, decision, and localization are not available in the RSPU setting. In the figure, these boxes denote the functions. Arrows from right to left (\(\rightarrow\)) and left to right (\(\leftarrow\)) signify the receiver side sequence and sender-side, respectively. After initiating a system, Autoware reads the 3D maps and begins receiving the sensor data. The system localizes the ego-vehicle and then gets the neighboring objects detected. Coordinates of the ego-vehicle, as well as detected objects, then get transformed from local to the global coordinate system before being sent across to the OpenC2X router using TCP/IP over Ethernet. OpenC2X gets the ego-vehicle's coordinates encoded in CAM and the coordinates of neighboring objects in the CAM-encoded collective perception message (\(CPM_{CAM}\)) form, before transmitting them over DSRC with the BTP/GN header. Simultaneously, the LDM stores the data.  
Upon receiving CAMs and \(CPM_{CAM}\)s, the OpenC2X router extracts the information relating to the neighboring objects from the messages before storing it in the LDM. OpenC2X then transmits the objects' coordinates to the Autoware. Finally, the object-related data extracted from the V2X communication gets visualized in RViz following the coordinate transformation. Data with regard to planning and decision function is not fed back to Autoware. The realization of this feedback forms an important component of our future work.
# Influence of the Microwave Heating Time on the Self-Healing Properties of Asphal Mixtures
## 3 Results and Discussion
### Influence of Fibres and Heating Time on the Asphalt Self-Healing Properties
On the one hand, the worse fitting to the Weibull distribution function of \(CRR\) data corresponding to heating times of 10 s and 20 s is related with the high scatter and randomness of healing levels of asphalt mixtures heated during short times. On the other hand, in the case of asphalt samples heated during 30 s and 40 s, they have enough time to increase their temperature and distribute it. Based on these results, it is necessary to define an optimum heating time of asphalt mixtures via microwave radiation. Figure 8: (a) Maximum flexural force versus the air void content of asphalt mixtures with different fibres content; (b) Weibull probability-probability plot for all Cycle Reduction Ratio data. In this context, Figure 9 presents the healing levels of asphalt mixtures with 4% of fibres, at 7 different heating times (from 10 s to 100 s). In this Figure, the heating times of 60 s, 80 s and 100 s have been obtained from a previous work of [PERSON] and [PERSON] [17]. As it can be seen in Figure 9 there are 2 possible optimum times, 40 s and 100 s, since they allow asphalt mixtures to obtain the highest healing levels. However, heating time of 100 s was discarded against 40 s due to the fact that heating asphalt mixtures by microwave during 100 s melted the bitumen, thus damaging the composite material. In addition, the effect of heating the samples during 100 s can be observed in Figure 9, as the reduction on the healing levels after each healing cycle. Therefore, it can be stated that using a 700 W microwave with a working frequency of 2.45 GHz, the optimum heating time is 40 s.
# Brief Review of Recent Developments in Fiber Lasers
## 3 Rare Earth-Doped Fiber Lasers
### RE-Doped Radiation-Balanced Fiber Lasers  
In 1999, [PERSON] [28] proposed a radiation-balanced (athermal) RE-doped laser which operates without internal heating. In this laser, all photons generated in the laser cycle are annihilated in the cooling cycle, that is, the heat generated from the stimulated emission is offset by cooling from anti-Stokes emission (Figure 2b). The idea of optical cooling using anti-Stokes fluorescence was first proposed by [PERSON] in 1929 [29]. It is significant that in order to keep the radiation balance at each point in the laser medium, the pump intensity must be distributed properly along the length of the laser fiber. Any deviation from this distribution will result in heating or cooling in some parts of the laser medium. In radiation-balanced lasers and amplifiers, the power of the amplified signal increases only linearly with the length of the active medium. The theory of radiation-balanced (athermal) RE-doped fiber amplifiers was developed in [30]. To overcome the problem of linear signal power growth, fiber lasers and amplifiers with a cooler made from RE ions and integrated into the body of the device have been proposed and theoretically investigated [18; 31; 32]. The theory of mJ-level pulse amplifiers in Yb-doped double-clad optical fibers was developed in ref. [33].  
In 2021, the first radiation-balanced silica fiber amplifier was demonstrated. An Yb\({}^{3+}\)-doped silica fiber served as an active medium [34]. The core diameter of the silica fiber was 21 \(\upmu\)m. Its numerical aperture was 0.13. The Yb\({}^{3+}\) concentration was 2.52 wt.%. The fiber was co-doped with 2.00 wt.% Al to reduce concentration quenching. The wavelength of the pump was 1040 nm and the signal wavelength was 1064 nm. The mean fluorescence wavelength of Yb\({}^{3+}\) was 1003.9 nm and the radiative and quenching lifetimes were 765 \(\upmu\)s and 38 ms, respectively. The Yb\({}^{3+}\) critical quenching concentration was 21.0 wt.%. The absorptive loss of the fiber was 18 dB/km. The temperature of the fiber decreased to \(\sim\)130 mK below room temperature. Different schemes of athermal lasers and amplifiers are overviewed in ref. [35]. The latest advances in radiation-balanced fiber lasers and amplifiers are considered in ref. [36].
## 1 Introduction
The primary challenge facing computer vision approaches in driver behavior monitoring and road traffic analysis lies in their high implementation costs, which stem from the necessity of vision sensors like cameras and LiDAR, as well as powerful computing systems capable of handling heavy-duty computer vision tasks such as Convolutional Neural Networks (CNNs). This study addresses this challenge by introducing a cost-effective system that leverages in-vehicle data from smartphones and OBD sensors to accurately predict driving styles and traffic conditions. It achieves this by employing Artificial Neural Networks with architectures designed to maintain high accuracy even under conditions of imbalanced classes while also being conducive to onboard system implementation. These networks are used to forecast driving styles as aggressive or normal and to detect road traffic conditions classified as high, medium, or low traffic. The subsequent sections of this paper are structured as follows: Section 2 reviews recent literature addressing similar challenges; Section 3 outlines the research methodology by explaining the prediction model system, dataset, and the ANN algorithms; Section 4 discusses the training techniques implemented to improve detection performance; Section 5 presents the results of the detection process and compares the deep learning model against SVM as a reference algorithm; and Section 6 concludes and summarizes the research findings and proposes avenues for future research.