esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
### Model  
The model used throughout the experiments is a NACA 0015 profile, with a chord length of \(150\,\mathrm{mm}\) and a span of \(450\,\mathrm{mm}\). This span is designed to be exactly that of the wind tunnel test section's width for optimal fixing at prescribed angles of attack. It incorporates a recess \(0.8\,\mathrm{mm}\) deep around the leading edge, stretching from \(x/c=0.09\) on the suction side to \(x/c=0.11\) on the pressure side. Its length along the span of the model is \(360\,\mathrm{mm}\). This recess allowed for flush actuator integration in order to minimise surface discontinuities during the plasma experiments. Twenty-one pressure taps were incorporated into the model at the locations shown in Figure 1. Only sixteen of these could be used as four of the taps were located within the recessed area that was covered during the experiments on the model by the actuator upper electrode, while another pressure tap just upstream of the trailing edge of the airfoil could not be used due to the data acquisition system. The model has a span corresponding to the test-section width, which enables the model to be fixed at the correct angles of attack using two different sets of test-section windows with fixed cut-outs at 16\({}^{\circ}\) and 24\({}^{\circ}\). The indentation for holding within the cut out slots are shown at the top left of Figure 1. The accuracy of the setting of the angles of attack was assured by the precise manufacturing and can be considered to be less than \(\pm 0.05^{\circ}\). Another set of wind tunnel side windows incorporated a circular inside disc for varying the angle of attack, in which the inserts could be fixed; this is not optimal for high fidelity PIV measurements but provide a first quicker approach for changing the angle of attack for pressure tap measurements. In this case, the angle of attack is measured by means of a Bevel Box remote angle sensor with a resolution of 0.1\({}^{\circ}\).  
The airfoil model is made of three parts, two parts containing the side-wall inserts and a third centrally mounted part that contains the pressure tap tubing (see top left of Figure 1). Two Scanivalve DSA3217 pressure sensors were used to measure the static pressure from the seventeen operational pressure taps along the model chord. The pressure was sampled at a frequency of 20 Hz and averaged over a sample of 100 measurements.
## 7 Research Gaps and Future Opportunities
### Protection against Computational Attacks
#### 7.2.2 Lack of Business Process Integration  
Contemporary blockchain solutions in supply chain enforce protection of the information as per implementation of the smart contract, which mainly consists of rules for transactions and their compliance. However, the contracts only implement rules that are related to the transactions, and they have no information about the actual business process level agreements for the involved stakeholders. Therefore, such solutions may not detect threats that cause or impact on the involved stakeholders business, which is mostly the case in modern attacks as the goal of attackers is to damage the actual business assets or resources. Hence, due to the lack of integration of actual business process agreements of the stakeholders, such solutions cannot protect the business resources. This also hinders transparency of the supply chain business, because it is hard to identify which asset/resource of the involved stakeholders has been compromised.
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 4 Experimental Results
### Experimental Measurement on Spinning Motion of PMSM Rotor at the Center Point  
When the rotor was rotating around the z axis at the center point, i.e., the tilting angle \(\theta\) was \(0^{\circ}\), as shown in Figure 17. The spinning angle of the rotor shaft can be calculated from the images which were taken by the high-speed camera.  
Figure 18a shows the images taken by the high-speed camera and the coordinate values of the three OFPs in the ICF. After calculating the tilting angle\(\theta\) and yaw angle\(\_\varphi\), we can get the position of rotor output shaft tip, which is shown in Figure 18b.  
Figure 16: Experimental results of rotor tilting motion: (**a**) Experimental and analytical results of \(\theta\); (**b**) difference between experimental and analytical results of \(\theta\); (**c**) experimental and analytical results of \(\varphi\); (**d**) difference between experimental and analytical of \(\varphi\); (**e**) experimental and analytical results of \(\omega\); (**f**) difference between experimental and analytical of \(\omega\).  
Figure 17: Spinning motion of PMSM rotor at center point.  
Figure 19a shows the tilting angle_\(\theta\) when the PMSM rotor made a spinning motion at the center point, however the maximum tilting angle was about 2.8\({}^{\circ}\). This may be caused by the position direct (PD) control algorithm and the time delay of position detection applied in this PMSM. The maximum difference between the experimental and analytical results was about 0.25\({}^{\circ}\), as shown in Figure 19b. Figure 19c shows the yaw angle_\(\varphi\), it varied from 90\({}^{\circ}\) to 210\({}^{\circ}\), the maximum difference between the experimental and analytical values was about 0.3\({}^{\circ}\), as shown in Figure 19d. Figure 19e shows the spinning angle_\(\omega\). It can be seen that the PMSM rotor rotated clockwise at about 5.5 turns in 10 s, and the maximum difference between the experimental and analytical values was about 0.3\({}^{\circ}\), as shown in Figure 19f.  
Figure 19: _Cont._  
Figure 18: Experimental measurement on the spinning motion of PMSM rotor at center point: (a) Images captured by high-speed camera and coordinate values of optical feature points; (b) Position of rotor shaft tip.
## 2 Materials and Methods
### Manipulator Links  
The links of the manipulator were the main focus of this study. There were four different types of links used in this study. Each type of manipulator link is defined by the different parameters listed in Table 3. Each parameter is an integer and is passed to the simulator at initiation. Figure 3 shows the comparison of all link types.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **PR 70** & **PR 90** & **PW 70 (axis1, axis2)** \\ \hline Maximal torque & 46 Nm & 145 Nm & 24 Nm, 4 Nm \\ Weight & 1.7 kg & 3.6 kg & 1.8 kg \\ Maximal current & 8 A & 12 A & 8 A, 6.5 A \\ Flange size & 70 mm & 90 mm & 70 mm \\ Type ID (in the program) & 0 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Joint module physical parameters.  
Figure 1: Example of a manipulator with links, joints, and an end-effector.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range (Limits for Optimization)** \\ \hline ID & \(\{0,1,2\}\) \\ or\({}_{\text{a}}\) & \(\{0,\pi/2,\pi,3\pi/2\}\) \\ or\({}_{\text{b}}\) & \(\{0,\pi/2,\pi,3\pi/2\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Joint module parameters used for optimization.  
Figure 2: Schunk joint modules: **(a)** PR 70; **(b)** PR 90; **(c)** PW 70. The simulated counterparts: **(d)** PR 70; **(e)** PR 90; **(f)** PW 70.  
The linear link type is a straight link of variable length. In this case, there is also only one parameter describing the link module. However, instead of selecting a link with a predefined length, the link is generated according to the length parameter.  
The rounded link type is defined by three parameters and has a semicircular shape. This type of link module allows for bends and twists in the links; as such, the joint axes do not have to be orthogonal and unconventional kinematic structures can be achieved.  
The Hermite link type is built using a Hermite spline [25]. This allows for even more control over the shape of the link than the other types and should be better suited to avoid self-collisions. The drawback of this type of link is that it is described by 7 parameters, and thus makes the problem computationally demanding for optimization algorithms.  
Flange sizes for linear, rounded, and Hermite links are not prepared in advance, as in the case of basic links. Instead, the thickness of the generated link object in the simulation is adjusted accordingly based on the flange size.  
These four types of manipulator links extend the commonly used straight links to include rounded and curved links. They also highlight the differences between predefined straight links and straight links with variable length.
# Spacing Allocation Method for Vehicular Platoon: A Cooperative Game Theory Approach
## 3 Flock's Model for Platoon
\begin{array}{c}J_{i}\\ =J_{ai}+J_{vi}+J_{ci}\\ =d+\omega_{v}\Big{(}\dot{X}_{i}-\frac{\dot{X}_{i-1}+\dot{X}_{i+1}}{2}\Big{)}+ \omega_{c}\Big{(}X_{i}-\frac{X_{i-1}+X_{i+1}}{2}\Big{)}\\ =-\frac{\omega_{v}}{2}X_{i-1}-\frac{\omega_{v}}{2}\dot{X}_{i-1}+\omega_{c}X_{ i}+\omega_{v}\dot{X}_{i}-\frac{\omega_{v}}{2}\dot{X}_{i+1}-\frac{\omega_{v}}{2}X_{i+1}+d \end{array}  \tag{8}\]  
Considering \(\dot{J}_{i}\) as the desired spacing between vehicle \(i-1\) and vehicle \(i\), the desired position \(X_{desi}\) of the vehicle \(i\) can be calculated by Equation (9). \[X_{desi}=X_{i-1}-J_{i} \tag{9}\]  
The principles described above reflect the purposes of consistency control for platoon. When avoiding-collision principle is met, the safety of the vehicles in platoon can be guaranteed. Velocity-matching and center-gathering principles express the information flow in platoon that the action of the neighbors' action will affect the spacing decision of the host vehicle. The traditional information flow is used for acceleration decision-making to achieve the desired spacing and improve the response speed and error convergence accuracy; in contrast, the spacing decided by proposed method reflects a movement trend, and additional motion control is required to realize it.
## 2 Related Works  
With the development of real-time applications, resource requests generated by terminal devices tend to be independent and the service requests from MUs exhibit more dynamic characteristics. Specifically, as noted in [8; 9], within MU groups, the popularity of services follows a Zipf distribution, implying that a single server-based service requires maintaining a large cache database to improve hit rates, leading to significantly increase system redundancy. Simultaneously, computation offloading services impose considerable pressure on the transmission bandwidth of the wireless uplink from end to edge, while the computational capabilities of terminal MU devices are limited and consume a considerable amount of energy [10]. Therefore, coordinating energy consumption and service efficiency to maximize the system's offloading benefits is a worthy issue to explore. Given the current state of limited infrastructure capacity and the limited computational capabilities of user devices in edge networks, the effective use of network resources to improve resource utilization and system performance has become an urgent challenge for network service providers.  
To address this situation, researchers primarily employ two methods: firstly, by designing fine-grained and efficient computation offloading algorithms, and secondly, by considering the heterogeneity of edge devices and their geographical distribution characteristics, leveraging collaborative services between edges to enable on-demand resource mobility and fully utilize existing computing devices to ensure service quality. The following two subsections will provide detailed explanations of these approaches.
## 2 Simulation Setup  
The configuration of this setup is shown in Figure 1: when a linearly polarized laser irradiates a 90\({}^{\circ}\) cone target along the angular bisector of the cone, the laser is polarized along the \(+y\) direction (\(p\)-polarized) and propagates along the \(+x\) direction. The open-source, high-performance and multi-purpose particle-in-cell (PIC) code for plasma simulation Smilei [23] was used to carry out the 2D-PIC simulations.  
The laser field set on the left boundary is \(a=a_{0}\exp(-(t-t_{0})^{2}/\tau^{2})\exp(-y^{2}/w^{2})\)\(\sin(\omega_{0}t+\phi)\), where \(a_{0}=eE_{0}/m_{e}\omega_{0}c=30\) is the normalized amplitude of laser field, \(2\tau=3T_{0}\) is the pulse duration of the laser and \(w=w_{0}\sqrt{(1+(x_{s}/f)^{2})}\). \(w_{0}=5\lambda_{0}\) is the focal spot radius, \(x_{s}=8\lambda_{0}\), and \(f=\pi w_{0}^{2}/\lambda_{0}\) is the Rayleigh length, \(\lambda_{0}=800\) nm. \(a_{0}=30\) corresponds to the laser peak intensity of I \(\approx 2\times 10^{21}\) W/cm\({}^{2}\), which is approximately two orders of magnitude lower than the maximum laser intensity currently available [24]. The grid size of the simulation box is 16\(\lambda_{0}\times 24\lambda_{0}\) with 4096 \(\times\) 6144 cells. The numbers of macroparticles of electrons and ions for each cell are both 16. The cone target's longitudinal  
Figure 1: (Color online). The schematic diagram of an ultra-intense laser pulse irradiating a carbon cone target.  
and lateral length are set as \(12\lambda_{0}\) and \(24\lambda_{0}\), respectively, and the angular bisector of the cone target is positioned at \(y=12\lambda_{0}\).  
The thickness of the cone target is \(d=0.3\lambda_{0}\) and the density is \(n_{e}=40n_{c}\), where \(n_{c}=m_{e}e_{0}\omega_{0}^{2}/e^{2}\approx 1.72\times 10^{21}\) cm\({}^{-3}\) is the critical density of plasma when the laser wavelength \(\lambda_{0}=800\) nm. The preplasma with a scaling length \(L=0.12\lambda_{0}\) is also considered in the cone's inner side, whose density increases exponentially from \(n_{c}\) to \(n_{e}=40n_{c}\), which corresponds to the similarity parameter [25]\(S\equiv\frac{n_{e}}{40n_{c}}\approx 1.33\). It ensures that the target is opaque to the laser and also allows the laser to oscillate on the target surface, such that this setup is in favor of HHG.
# Object Detection-Based Video Compression
## 3 Proposed Methods
### Experimental Results
* The object detection algorithm, YOLOv3, sometimes fails to detect objects that should be detected; fast motion and rapid zooming in and out of the camera are the main issues that lower the detection accuracy. When the size and shape of objects change quickly, the object detection algorithm has difficulty detecting objects properly. These issues result in a false-alarm area, where the object should be accurately detected but is not. High quantization error occurs in the false-alarm area due to the high QP value. For this reason, high inter prediction errors occur due to the low quality of the false alarm areas to be used as the reference picture. Therefore, the PSNR and BD-rate performance in the RA configuration that uses inter and intra predictions are worse than those in the AI configuration that use only intra prediction. In particular, the average Y BD-rates decreased to 4.10%, 3.54%, and 3.74%, respectively, in the BasketballDrill, BasketballDrive, and BQMall sequences, due to their fast object movements. * In Table 5, the BD-rates of the DaylightRoad2 and BQMall sequences in the AI configuration show improvement despite the poor PSNR quality in the background areas. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & **Random Access (RA)** & & **All Intra (AI)** & \\ \hline \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Sequence**} & **Average Y** & **Average Cb** & **Average Cr** & **Average Y** & **Average Cb** & **Average Cr** \\  & & **BD-Rate** & **BD-Rate** & **BD-Rate** & **BD-Rate** & **BD-Rate** & **BD-Rate** \\ \hline \multirow{3}{*}{Class A1 4K} & Tango2 & 2.84\% & 3.14\% & 3.51\% & 0.73\% & 5.47\% & 3.44\% \\  & FoodMarket4 & 3.72\% & 4.92\% & 5.63\% & 0.83\% & 1.60\% & 1.50\% \\ \cline{1-1}  & Campfire & 1.27\% & 1.21\% & 1.38\% & 0.69\% & 0.19\% & 1.10\% \\ \hline \multirow{2}{*}{Class A2 4K} & CatRobot1 & 1.49\% & 1.67\% & 1.32\% & 1.01\% & 2.32\% & 1.19\% \\ \cline{1-1}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental result of the BD-rate increments for class (A1 to C) sequences.
## 2 Proposed Network Architecture
The number of taps are optimized for the system presented in Figure 1a for the minimum least square error (LSE) and fastest convergence time. In order to avoid the use of training sequence and complexity of the overall transmission, blind adaptive equalization is employed using constant modulus algorithm (CMA). As nonlinearities present in the system are multiplicative in nature for long haul transmission distances, a Kalman filter approach is used instead of matched filter for the detection of received bits. It uses a likelihood thresholding ratio (LTR) approach for the detection of bit levels, and updates threshold after each iteration. Table 1 summarizes the list of different parameters and their values used in the considered model. The three sub-sections show the values used at the transmitter side, optical propagation channel, and the receiver. For the simulation work, most of the parameters are kept constant to ensure a fair comparison between the conventional and the proposed system, while some important set of values is used as a range for the factors affecting the performance of the system during the transmission, such as nonlinear refractive index, nonlinear effective area, WDM spacing, linear and nonlinear dispersion, and dispersion slope. The optimized performance is achieved 1553 nm reference wavelength, \(2.6\times 10^{-17}\) refractive index, and 90 \(\upmu\)m\({}^{2}\) nonlinear effective area of the fiber for long haul optical communication system. For a realistic approach towards the practical system design and analysis, the set of values shown in Table 1 have been taken from real time field trials and experimental work in this domain. The optical and RF components are modeled with commercially available devices [9; 15] and nonlinearities are included using the measured set of values [16; 17].
# A Comprehensive Review on Music Transcription
## 3 Transcription Techniques: Insights and Advances
### Solo Vocal Note Transcription  
The note-level solo vocal transcription focuses specifically on capturing the sung or spoken notes within a solo vocal performance in a song [52; 22; 53]. The work in [22] introduces VOCANO for singing note transcription (SVT) using multi-task semi-supervised learning techniques. The work transcribes the vocal notes of singing from polyphonic audio using virtual adversarial training (VAT). Similarly, the probabilistic transcription approach is carried out in [52] to solve the problems of pitch fluctuations in singing voice transcription. It employs a hierarchical Hidden Markov Model (HMM) to manage note transitions and pitch variations, achieving promising results when assessed on a monophonic sung melody dataset. A huge number of labelled data is necessary to train the supervised deep learning model. Keeping this in mind, the work in [53] introduces a novel contrastive learning-based data cleansing model after identifying the incorrect labels in vocal note event annotations. The transcription process for solo vocals is more complex than in instrumental music due to the inherent variability and expressiveness of the human voice [54]. Note-level transcription for vocals involves accurately determining the pitches, timings, and durations of each sung note [18; 55]. To correctly identify vocal notes, the work in [18] uses Viterbi decoding as a post-processing technique after following pitch estimation in monophonic audio. To overcome the limited number of labeled training data, the work in [55] constructs a dataset of solo vocals that includes the annotations in frame-level f0, notes, lyrics, and track-level meta-data. To accurately transcribe the notes of solo vocals, the machine learning-based models must consider factors like vibrato, ornamentation, and changes in articulation that contribute to the unique character of the vocal performance. This type of solo vocal note transcription is essential for applications like lyrics synchronization, vocal synthesis, and cover song generation.
## 4 Problem Definition and Optimization Approaches
### Deep Q-Learning Approach  
To use Q-learning [21] for our problem, we should model our problem into a Markov decision process (MDP) [22]. An MDP has a finite or infinite set of states and actions where we take an action to go from one state to another. The new state after each action depends on the previous state and the action taken. However, new states are not deterministic and each action can result in many different states, though with different probabilities.  
In a Q-learning algorithm, given an MDP, whenever we take an action to move from one state to another, we receive a reward based on how good that action was. The purpose of Q-learning is to choose an action from a set of possible actions at each state to maximize the sum of all rewards earned during the execution of the algorithm. Q-learning in its vanilla form uses a table to approximate the total reward that can be earned for every possible combination of states and actions if we use the Q-learning algorithm with this table thereafter. This approach, however, does not work if the state space is too large or continuous, as the length of the table will become extremely large or infinite. Modeling our problem with an MDP will result in an infinite number of states; hence the vanilla form of Q-learning cannot be employed. To overcome this issue, we can use a deep Q-learning model instead of keeping a table of records. In the following subsections, we explain  
this approximation and not calculating the Q-value for every connected edge is to avoid favoring the offloading of a task compared to local execution. To understand the reason for this behaviors, let us assume that the Q-value has the probability of \(\alpha\) to incorrectly give a higher value for the action of transmission instead of local execution. If an edge is connected to \(n\) other edge devices and we calculate the Q-value of all \(n\) edge devices, the probability of receiving at least one incorrectly higher value to transmission will be  
\[P(a>0)=1-(1-\alpha)^{n} \tag{31}\]  
where \(a\) is the number of connected edges for which the Q-value is incorrectly higher for transmission. This value is clearly higher than \(\alpha\), which proves our notion that calculating multiple Q-values will favor transmission of a task.