esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrails and Their Dependence on Meteorological Situations
## 2 Materials and Methods
### Aerological Measurements  
Aerological measurements in the Czech Republic are carried out at two stations in Prague-Libus and Prostejov. In the case of the Czech Meteorological Office (CHMI) in Prague, probes carried by weather balloons are launched regularly at 05:30, 11:15, and 23:15, and occasionally at 17:15 UTC. Currently, CHMI uses Vaisala RS41 SG probes and the Vaisala MW41 ground-based evaluation system. The RS41 SG sends a frequency-modulated (GFSK) signal in the 400 MHz band with a bit rate of 4800 bit/s and a message rate of once per second. The message contains the measured temperature and relative humidity (the probe does not have a pressure sensor; this is calculated by the ground station), GPS position, and other additional information. Wind direction and wind speed data are determined based on the movement of the probe using the GPS system. The data from the ongoing measurement can be obtained either by contract with the CHMI in the form of a transmission of a user-defined format (containing corrected data of measured elements and data of calculated elements without GPS position, but transmitted with a delay of several minutes behind the measurement) or by receiving and decoding the signal of the active probe (containing uncorrected measured data and GPS position in the ECEF coordinate system), e.g., by means of a software-defined radio [19; 20].
## 2 Problem Description and Identification Model
Define the parameter vector \(\boldsymbol{\theta}_{\mathrm{v}}\) and the information vector \(\boldsymbol{\phi}_{\mathrm{v}}(k)\) as:  
\[\boldsymbol{\phi}_{\mathrm{v}} :=[e_{1},e_{2},\cdots,e_{n_{e}}]\in\mathbb{R}^{n_{e}},\] \[\boldsymbol{\phi}_{\mathrm{v}}(k) :=[v(k-1),v(k-2),\cdots,v(k-n_{e})]^{\mathrm{ T}}\in\mathbb{R}^{n_{e}}.\]  
From Equation (3), we have:  
\[e(k)=\boldsymbol{\phi}_{\mathrm{v}}^{\mathrm{ T}}(k)\boldsymbol{\phi}_{ \mathrm{v}}+v(k). \tag{16}\]  
Thus, we obtain the noise identification model in Equation (16). The following derives the bias compensation-based identification algorithm based on the system model in Equation (15) and the noise model in Equation (16).
## 5 Simulation Analysis
### Influence of Cost Allocation Ratio  
Take \(\{K=0.3,K=0.4,K=0.4\}\), the strategy evolution process and results are shown in Figure 2.  
Figure 2a-d shows that as the cost allocation ratio increases, the stable state of the system evolution does not change significantly. Under the static reward and static punishment mechanisms, dynamic reward and dynamic punishment mechanisms, the most obvious change of the system evolution characteristic is the oscillation amplitude, and the oscillation amplitude of the system evolution gradually decreases as the cost allocation ratio increases. The most obvious change in the system evolution characteristic under the mechanism, dynamic reward and dynamic punishment mechanism, is the convergence speed, and the speed of evolution convergence gradually becomes slower as the cost allocation ratio increases.  
Figure 1: (**a**) Evolution path diagram under static reward and static penalty mechanism, (**b**) Evolution path diagram under dynamic reward and static punishment mechanism, (**c**) Evolution path diagram under static reward and dynamic punishment mechanism, (**d**) Evolution path diagram under dynamic reward and dynamic punishment mechanism.  
Figure 2: _Cont._Figure 2: **(a)** Influence of cost sharing ratio on system evolution under static reward and static penalty system. **(b)** Influence of cost sharing ratio on system evolution under dynamic reward and static penalty system. **(c)** Influence of cost sharing ratio on system evolution under static reward and dynamic penalty system. **(d)** Influence of cost sharing ratio on system evolution under dynamic reward and dynamic penalty system.  
It is clear that there is no direct and obvious linear relationship between the cost allocation ratio and the system evolution's stability. As a result, an appropriate cost allocation ratio should be chosen in the synergistic practice of shared manufacturing quality. The strategy of pursuing high-cost allocation blindly cannot ensure the effectiveness of quality coordination and will also become a cost burden for demanders.
# A High-Thrust Screw-Type Piezoelectric Ultrasonic Motor with Three-Wavelength Exciting Mode
## 2 Structure and Operating Principle
### Operating Principle of Screw-Type Ultrasonic Motor  
The operating principle of the screw-type piezoelectric ultrasonic motor is shown in Figure 2. The proposed motor is drive based on the three-wavelength exciting mode. The three-wavelength exciting mode of the proposed ultrasonic motor can be achieved by the first and third bending vibration modes along the axial and circumferential directions, and the three-wavelength travelling wave can be synthesized by the three-wavelength exciting mode. In order to excite the motor to produce the three-wavelength travelling wave and the elliptic trajectory for driving rotation of the rotor on the surface points of the stator, proper combination of the piezoelectric plates is needed. The twelve rectangular piezoelectric plates are polarized in the direction of thickness and the \(d_{31}\) working mode is adopted by the twelve rectangular piezoelectric plates, respectively. The twelve rectangular piezoelectric plates are divided into two groups. Six rectangular piezoelectric plates, from the A\({}_{1}\) to A\({}_{6}\), constitute exciting group A. Exciting group B includes the remaining six rectangular piezoelectric plates, B\({}_{1}\)to B\({}_{6}\). Simultaneously, the metal elastomer with the internal threads is connected with the ground lead. The standing wave vibrations S\({}_{\rm A}\) and S\({}_{\rm B}\) with three crests and troughs can be generated by the exciting signals \(U_{0}\)sin\(\omega t\) and \(U_{0}\)sin(\(\omega t+\phi\)), respectively. The frequency of the exciting groups A and B is supposed to be coincident with resonant frequency of the stator. During the working process of the motor, the three-wavelength exciting mode can drive the motor by means of the three contact points between the stator and the screw output shaft touched at the same time. The rotary motion can be converted into linear motion by using the screw output shaft, and the torque is converted into linear thrust. Above all, the linear thrust can be magnified by the three-wavelength travelling wave, because the effective contact area between the stator and the screw output shaft is increased. Specifically, when a small linear thrust is applied to the shaft to generate a torque, and the torque will be converted into a large thrust. When the phase shift between the exciting groups A and B is 90\({}^{\circ}\), a three-wavelength travelling wave can be synthesized by standing wave vibrations \(S_{\rm A}\) and \(S_{\rm B}\). Thus, the screw output shaft can be directly rotated to realize forward output motion. When the phase shift between exciting groups A and B is 270\({}^{\circ}\), the reversed output motion can be obtained. Here, the \"arrow\" represents the polarized direction of the rectangular piezoelectric plates.  
Figure 2: The three-wavelength exciting mode of the proposed piezoelectric ultrasonic motor.
## 3 Results and Discussion
### Characteristics of N and P Pollution
#### 3.4.1 Nh\({}_{3}\)-N  
In Figure 5a, most of the average NH\({}_{3}\)-N concentrations in the overlying water of the six rivers exceeded the level V limit value of surface water in the national standard GB3838-2002. The water qualities of Jingshui River, Chuan Lake, West Lake, and Xinkai River were above the level V standard, and their mean overshoot multiples were 4.389, 1.713, 3.220, and 2.144, respectively. The water quality of Yitong River was level V, and that of Yongchun River was level III. In Changchun City, the areas with serious NH\({}_{3}\)-N pollution were as follows: Kuancheng District, where Jingshui River and Chuan Lake were located, and Lvyuan District, where West Lake and Xinkai River were located. There are industrial parks around Jingshui River, whose industrial wastewater and domestic sewage are discharged directly into the river all year round. In addition, the residual chemical fertilizers and pesticides from farmland also enter the river along with the surface runoff, which are the main reasons for its environmental destruction. Because of the urban expansion, the environment of Chuan Lake has been damaged. The area around Chuan Lake has become a garbage dump for industrial sewage discharge and construction waste landfill in the west of Changchun City during the urbanization. West Lake is a small reservoir formed by impounding the tributaries of Xinkai River. Both West Lake and Xinkai River carry the domestic sewage of Changchun City, which is also the source of their water environmental pollution.  
NH\({}_{3}\)-N, a nutrient in water, can lead to eutrophication, and is also the main oxygen-consuming pollutant in water. It is toxic to fish and some aquatic organisms, and seriously endangers the health of infants and children [16]. The pollution of NH\({}_{3}\)-N in water is often ignored, but [PERSON] et al. [17] have proved that NH\({}_{3}\)-N is the most important factor impacting the water quality of Yellow River. Therefore, the NH\({}_{3}\)-N quality of Jingshui River, Chuan Lake, West Lake, and Xinkai River could not be neglected during the comprehensive water treatment.
## 3 Results
### Perceptual Measures: Effort, Strain, and Dysphonia  
The relationship between RFF values and perceptual measures of vocal effort, strain, and dysphonia have varied widely across studies (\(n=7\)). [PERSON] et al. [7] reported poor relationships between listener-perception of vocal effort and offset cycle 10 and onset cycle 1 (\(R^{2}=0.21\) and \(R^{2}=0.26\), respectively) in individuals with typical voices while self-modulating their vocal effort. The relationship strength increased to moderate for offset cycle 10 (\(R^{2}=0.46\)) and onset cycle 1 (\(R^{2}=0.56\)) when analyzed within speaker. Conversely, relationships were significant between offset cycle 10 and listener-perceived ratings of vocal effort when analyzed by group across three other studies in those with typical and disordered voices, but no relationships were found for onset cycle 1 [4,8,27].  
[PERSON] et al. [47] found that RFF onset cycle 1 values were significantly correlated with overall dysphonia severity in a large group (\(n=111\)) of patients with MTD. However, [PERSON] and colleagues [27] also investigated subjects with VH and reported weak relationships between onset cycle 1 and dysphonia ratings. [PERSON] et al. [2] took a slightly different approach in a group of subjects with adductor laryngeal dystonia by examining a combination of several acoustic measures, finding that offset cycle 10, changes between specific RFF cycles (e.g., change from onset cycle 1-6) and additional spectral characteristics were significantly related to dysphonia in their model.  
A study by [PERSON] et al. [9] manually manipulated cycles in acoustic waveforms to understand how raising and lowering RFF would affect auditory-perceptual ratings of vocal strain. Eight adults without voice disorders produced VCV utterances in their typical voices and then again with maximal vocal effort. When intervocalic cycle values were artificially reduced in the typical voice samples, listeners perceived the voices as having greater strain; conversely, when RFF values were artificially raised in voicing samples collected during effortful speech, they were perceived as having less strain. This manipulation paradigm is perhaps some of the most convincing evidence of how cycle changes within a speaker can contribute to changes in perceived vocal characteristics. The directionality of the changes (i.e., lower RFF increased perceived strain while higher RFF reduced perceived strain) was consistent with the existing theoretical model of RFF not only being an indicator of laryngeal tension, but one that may be perceived by listeners through strained speech.
## 2 Versatile Video Coding (VVC)
### Picture Reconstruction  
For VVC, a similar but extended CTU concept was inherited from its predecessor, HEVC. In the new standard, the quadtree scheme had been replaced earlier by a quadtree plus binary tree [25]. Therefore, a vertical or horizontal directional partition of a binary tree was added to extend the quadtree plus binary tree. Finally, QTMT partitioning was adapted to the VVC standard. In the QTMT scheme, the blocks were divided into smaller square sub-blocks for the QT structure and into smaller rectangular binary and ternary sub-blocks for the multi type tree structure. Moreover, a block can be divided into the horizontal or vertical direction using binary and ternary splitting. Further, in the ternary split, a block was divided into three sub-blocks with a central sub-block 2 times larger than the outer blocks. The VVC supported partition schemes are displayed in Figure 2. In addition, VVC allowed a maximum CTU size of 128x128 pixels [26]. These features helped VVC achieve a more flexible and efficient partitioning structure compared to the HEVC standard.
## 3 Implementation of RUL Prediction Based on TCNMABG
### Model Verification and Comparison
#### 3.3.4 Model Training  
Before starting model training, it is necessary to determine the RUL labels of the training set and test set data. According to the segmented function definition in Section 2.3.3, add RUL labels to the feature data of each bearing. Due to the significant differences in the full life cycle of different bearings, this paper normalizes the remaining useful life of each bearing in the dataset. The health status is one, and the complete failure is zero. The RUL tag of Bearing1_1 is shown in Figure 11.  
The parameters of the TCNMABG model are set as follows. The training batch size is 64, the initial learning rate of the model is 0.001, the number of training rounds epoch is 100, the optimizer is \"Adam\", the number of BiGRU layers is 1, the dimensionality of the hidden layer is 64, the output dimension of each layer of the TCN module is [4], the  
Figure 11: Bearing1_1 Remaining Useful Life RUL label.  
Figure 10: FPT recognition is based on the KFCM clustering method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Total Experiment Time/(\(\times\)7.5 s)** & **FPT/(\(\times\)7.5 s)** \\ \hline Bearing1\_1 & 984 & 628 \\ Bearing1\_2 & 1288 & 510 \\ Bearing1\_3 & 1264 & 875 \\ Bearing1\_5 & 416 & 318 \\ \hline \hline \end{tabular}
\end{table}
Table 3: XJTU-SY dataset condition 1 bearing FPT recognition results.  
dropout size is 0.1, the time step of the input data is 30, and the linear output layer is 1 neuron. The output dimension of each layer of the module is [4], the dropout size is 0.1, the time step of the input data is 30, and the linear output layer is 1 neuron.  
The hardware environment configuration of all experiments is set as follows. Intel (Santa Clara, CA, USA) Core (TM) i9-13900 HX processor, 16 GB RAM, RTX4060 graphics card, Python version 3.8.16, torch version 1.13.1, CUDA version 11.7, MATLAB R2021b, and the Windows 11 operating system.
## 2 Pose Estimation Using Single Graphical Marker  
The fundamental approach to pose estimation using a single graphical marker involves capturing the marker with a camera and subsequently determining the relative pose between the camera and marker coordinate systems. Using the estimates, mobile robots mounted with cameras can determine their own relative pose in relation to the graphical marker. In our investigation, we employed ArUco markers as the primary graphical markers due to their widespread adoption in robotics applications.  
The mathematical framework for pose estimation begins with the processing of acquired ArUco marker images, which yields two essential components: the position vector \(\overrightarrow{t}_{cm}\) and the rotation vector \((\overrightarrow{r}_{cm})\) of the ArUco marker coordinate system relative to the camera coordinate system. These vectors are formally expressed as follows [29]:  
\[\overrightarrow{t}_{cm}=\begin{bmatrix}t_{cm,x}\\ t_{cm,y}\\ t_{cm,z}\end{bmatrix} \tag{1}\]  
\[\overrightarrow{r}_{cm}=\begin{bmatrix}r_{cm,x}\\ r_{cm,y}\\ r_{cm,z}\end{bmatrix} \tag{2}\]The position vector in Equation (1) represents the three-dimensional spatial displacement of the ArUco marker coordinate system origin with respect to the camera coordinate system. The rotation vector in Equation (2) encapsulates both the axis of rotation and the magnitude of rotation required to align the two coordinate systems.  
To facilitate practical implementation, the rotation angle (\(\alpha_{cm}\)) and the unit vector of the rotation axis (\(\mathbf{u}_{cm}\)) are derived from the rotation vector through the following transformations:  
\[\alpha_{cm}=\left|\overrightarrow{r}_{cm}\right| \tag{3}\]  
\[\mathbf{u}_{cm}=\left[\begin{matrix}u_{cm,x}\\ u_{cm,y}\\ u_{cm,z}\end{matrix}\right]=\overrightarrow{r}_{cm}\left/\left|\overrightarrow{ r}_{cm}\right|\right. \tag{4}\]  
The rotational transformation between coordinate systems is expressed through the rotation matrix (\(R_{cm}\)), computed using Rodrigues' rotation formula:  
\[R_{cm}=cos(\alpha_{cm})I_{3\times 3}+(1-cos\alpha_{cm})\mathbf{u}_{cm}\mathbf{u}_{cm} ^{T}+sin(\alpha_{cm})\mathbf{u}_{cm}\times \tag{5}\]  
In Equation (3), \(I_{3\times 3}\) and \(\mathbf{u}_{cm}\times\) denote the three-dimensional identity matrix and the skew-symmetric matrix derived from the unit vector of the rotation axis.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 4 Method Design
### Criterion of Classifying Road Structures  
In this subsection, we define a criterion to classify three typical road structures (junction, straight road, and curved road):  
In previous works [25; 26], the broadcasting in junction scenarios is completed through two-hop relay-node selection, and its message propagation direction is multidirectional. In curved road scenarios, there will be both normal and reverse relay-node selection for broadcasting, and the message propagation is bidirectional. To achieve full coverage of RoI, the priority of judgment for the criteria of road structures is junction, curved road, and straight road.  
**Definition 3**.: _Junction scenario is a scenario in that there exists a junction in the communication range of the sender in the message propagation direction._It is widely accepted that the criterion for judging whether a road structure is a straight road or not is whether the line-of-sight condition exists. We define a curving rate to facilitate the definitions of the curved road and the straight road, as follows.  
**Definition 4**.: _The curving rate \(\beta\) is expressed as_  
\[\beta=\frac{l}{R} \tag{1}\]  
_where \(l\) is the length of the road within the communication range of the sender in the message propagation direction, and \(R\) is the communication radius._  
Based on the definition of curving rate, we give the definitions of curved road and straight road.  
**Definition 5**.: _Curved road scenario is a scenario that \(\beta>\beta_{\varepsilon}\) when no junction exists in the communication range of the sender in the message propagation direction, where \(\beta_{\varepsilon}\) is a threshold._  
**Definition 6**.: _Straight road scenario is a scenario that \(\beta\leq\beta_{\varepsilon}\) when no junction in the communication range of the sender in the message propagation direction._  
We discuss the value of threshold \(\beta_{\varepsilon}\) based on whether obstacles on the roadside affect the line-of-sight propagation. When obstacles on the roadside affect the line-of-sight propagation of the message, the road will have at least one roundabout. For this circumstance, the road length must be more than twice the road width \(w\) beyond the communication radius, that is,  
\[\beta_{\varepsilon}=\frac{l_{\varepsilon}}{R} \tag{2}\]  
where \((l_{\varepsilon}>R+2*w)\).
## Materials and Methods
### Experimental Setup  
All experiments were performed on a laptop computer featuring an Intel Core i7-4720 HQ 2.60 GHz CPU, 8 GB RAM memory and a 3 GB NVIDIA GeForce RTX 1080 GPU. The algorithms were implemented in MatLab (2020a, MathWorks, Natick, MA, USA).  
Regarding the evaluation of the method, five-fold cross-validation was used: from 183 cross-sectional images of coronary images of 33 patients, 6 groups were randomly selected. Each group consisted of images of 5 different patients, apart from one group that consisted of images of 3 patients. To assess the efficiency of the proposed pipeline, 5 rounds of tests were applied. In each run, one of the aforementioned groups was used as the validation set, one as the test set, and the remaining images were used as the training set. Training, validation, and test sets combined consisted of 196,580 A-lines (11,333 calcified, 39,956 fibrous, 17,724 mixed plaque, 40,183 lipid plaque, and 87,384 normal tissue).
# How Does C-V2X Help Autonomous Driving to Avoid Accidents?
## 2 Solution for C-V2X VRUCW
### C-V2X VRUCW Deployment
#### 2.2.3 System Message Flow  
According to Figure 6, IP camera sends video stream to AI Server. The image size 2592 \(\times\) 1944 and image extraction speed 10 frames per second are used in our solution. The AI server equipped with the target classification algorithm is utilized to determine the pedestrian's category results, which include person, car, motorcycle, person with umbrella, baby carriage and person at night. If pedestrian passes this area under camera coverage, AI server will detect this object and transfer object recognition, position information and movement prediction analysis results to RSU. RSU encapsulates these messages into broadcast packages and broadcasts these information to all the OBUs within the coverage area of RSU. Meanwhile, autonomous driving vehicle's control unit sends GNSS and CAN messages to OBU by 50 Hz or 100 Hz frequency. OBU is responsible for combining all the information to determine if a collision is approaching. We use below Algorithm 1 for collision warning trigger threshold after detecting an object. \(S\) is the distance between host vehicle and pedestrian. \(V\) is the speed of host vehicle. When all the trigger points are met for alert threshold, a VRU warning message will be sent out from OBU to autonomous vehicle control unit. Autonomous vehicle own control policy for slowing down or stopping is out scope of our solution.  
```
1:if \(S>50\) m then
2: the target pedestrian is keeping a safe distance with the host vehicle, no collision warning is sent.
3:elseif\(V<10\) km/h then
4: the host vehicle speed is not fast so that pedestrian can walk away from danger situation, no collision warning is sent.
5:elseif\(S\leqslant 50\) m and \(V\geqslant 10\) km/h then
6: the target pedestrian is keeping a danger situation with the host vehicle, collision warning is sent.
7:endif
```  
**Algorithm 1** Threshold for VRUCW Trigger in OBU  
Threshold parameter values for \(S\) and \(V\) can be changed flexibly according to requirement.  
Figure 6: System Message Flow for C-V2X VRUCW.
## 4 Results and Analysis
### Implementation Details  
We used a high-end configuration system to implement the proposed model. All the implementations were performed using the Linux I7 operating system, 16 GB of DDR4 RAM, a 1 TB SSD, and a 32 GB GPU. We utilized the Anaconda navigator with the Spyder environment and also experimented on Keras. All the models and algorithms were programmed on Python. The hyperparameters and their values utilized to build the model are shown in Table 5.