esa-sceva/satcom-chunk-collection huggingface dataset:
# Control of an Omnidirectional UAV for Transportation and Manipulation Tasks
## 3 Modeling
### Kinematics  
To properly derive the dynamic model of the system, the position and velocities of the center of mass (CoM) of the ODQuad components, i.e., platform, mobile frame, and propeller's frame, have to be computed. To this aim, let us define three new reference frames: \(\mathbf{\Sigma}_{C_{0}}\), \(\mathbf{\Sigma}_{C_{1}}\) and \(\mathbf{\Sigma}_{C_{2}}\), attached, respectively, to the CoM of the platform (\(C_{0}\)), mobile frame (\(C_{1}\)) and propeller's frame (\(C_{2}\)). The linear and angular velocities of \(\mathbf{\Sigma}_{C_{*}}\), \((*=0,1,2)\), can be expressed in terms of the generalized coordinates as  
\[\left\{\begin{array}{rcl}\dot{\mathbf{p}}_{C_{0}}&=&J_{P_{0}}(q)\dot{q},\\ \omega_{C_{0}}&=&J_{O_{0}}(q)\dot{q},\\ \dot{\mathbf{p}}_{C_{1}}&=&J_{P_{1}}(q)\dot{q},\\ \omega_{C_{1}}&=&J_{O_{1}}(q)\dot{q},\\ \dot{\mathbf{p}}_{C_{2}}&=&J_{P_{2}}(q)\dot{q},\\ \omega_{C_{2}}&=&J_{O_{2}}(q)\dot{q},\end{array}\right. \tag{3}\]where the Jacobian matrices \(\mathbf{J}_{P_{*}}\in\mathbb{R}^{3\times 8}\) and \(\mathbf{J}_{O_{*}}\in\mathbb{R}^{3\times 8}\), \((*=0,1,2)\) are given by [17]  
\[\begin{array}{rcl}\mathbf{J}_{P_{0}}&=&\big{[}\mathbf{I}_{3}\ -\mathbf{S}(\mathbf{r}_{0,C_{0}}) \mathbf{T}(\mathbf{\phi}_{0})\ \ \mathbf{0}_{3}\ \ \mathbf{0}_{3}\big{]},\\ \mathbf{J}_{O_{0}}&=&\big{[}\mathbf{O}_{3}\ \ \mathbf{T}(\mathbf{\phi}_{0})\ \ \mathbf{0}_{3}\ \ \mathbf{0}_{3}\big{]},\\ \mathbf{J}_{P_{1}}&=&\big{[}\mathbf{I}_{3}\ -\mathbf{S}(\mathbf{r}_{1,C_{1}})\mathbf{T}(\mathbf{\phi}_{0}) \ -\mathbf{S}(\mathbf{r}_{1,C_{1}})\mathbf{x}_{0}\ \ \mathbf{0}_{3}\big{]},\\ \mathbf{J}_{O_{1}}&=&\big{[}\mathbf{O}_{3}\ \ \mathbf{T}(\mathbf{\phi}_{0})\ \ \mathbf{x}_{0}\ \ \mathbf{0}_{3}\big{]},\\ \mathbf{J}_{P_{2}}&=&\big{[}\mathbf{I}_{3}\ -\mathbf{S}(\mathbf{r}_{2,C_{2}})\mathbf{T}(\mathbf{\phi}_ {0})\ -\mathbf{S}(\mathbf{r}_{2,C_{2}})\mathbf{x}_{0}\ -\mathbf{S}(\mathbf{r}_{2,C_{2}})\mathbf{x}_{1}\big{]},\\ \mathbf{J}_{O_{2}}&=&\big{[}\mathbf{O}_{3}\ \ \mathbf{T}(\mathbf{\phi}_{0})\ \ \mathbf{x}_{0}\ \ \mathbf{x}_{1}\big{]}, \end{array} \tag{4}\]  
the vector \(\mathbf{r}_{*,C_{*}}\) denotes the position of \(C_{*}\) with respect to \(O_{*}\), \((*=0,1,2)\), expressed in the inertial frame; \(\mathbf{T}(\mathbf{\phi}_{0})\) is the matrix relating the derivative of the roll-pitch-yaw angles, \(\dot{\mathbf{\phi}}_{0}\), to the angular velocity, \(\mathbf{\omega}_{0}\)[34]; \(\mathbf{I}_{3}\) and \(\mathbf{O}_{3}\) are the \((3\times 3)\) identity and null matrices, respectively; \(\mathbf{0}_{3}\) is the \((3\times 1)\) null vector; while \(\mathbf{x}_{*}\) is the first column of \(\mathbf{R}_{*}\ (*=0,1)\), which is the rotation matrix expressing the orientation of the frame \(\Sigma_{*}\) with respect to the inertial frame.
# The Mobility Analysis of a 3-CCR Parallel Manipulator with Three Screw-Type Terminal Constraints
## 3 Terminal Constraint and DOFs Determination Based on Principal Screws Analysis
### Analytical Expression of the Terminal Constraint
\begin{array}{l}\mathbf{r}_{i1}=d_{i1}\mathbf{P}_{i1}\mathbf{,}\ \mathbf{r}_{i2}=\mathbf{r}_{i1}+l_{i}\mathbf{L }_{i}\ \mathbf{,}\ \mathbf{r}_{i3}=\mathbf{r}_{i1}+l_{i}\mathbf{L}_{i}+d_{i2}\mathbf{P}_{i2}\ \mathbf{,}\ \mathbf{R}_{i2}=\mathbf{R}_{i3}\times\mathbf{R}_{i1}/|\mathbf{R}_{i3} \times\mathbf{R}_{i1}|\ \text{,}\\ \mathbf{P}_{i1}\cdot\mathbf{P}_{i2}=0\ \mathbf{,}\ \mathbf{P}_{i2}\cdot\mathbf{R}_{i3}=0\ \mathbf{,}\ \mathbf{L}_{i}=\mathbf{P}_{i1}\times\mathbf{P}_{i2}\end{array}  \tag{6}\]  
Substituting Equation (6) into Equation (5), it leads to the following:  
\[\mathbf{s}_{i0}^{r}=d_{i1}\mathbf{P}_{i1}\times\mathbf{L}_{i}-\frac{d_{i2}\mathbf{P}_{i1} \cdot\mathbf{R}_{i3}}{\mathbf{L}_{i}\cdot\mathbf{R}_{i3}}\mathbf{L}_{i} \tag{7}\]  
From Equations (4) and (7), it is known that the physical meaning of \(\mathbf{\xi}_{i}^{r}\) is that the terminal constraint in the \(i\)-th limbs is a screw-type constraint passing through the intersection point of the link \(l_{i}\) and the joint \(P_{i1}\), along the direction of \(l_{i}\), with a pitch \(-d_{i2}\mathbf{P}_{i1}\cdot\mathbf{R}_{i3}/\mathbf{L}_{i}\cdot\mathbf{R}_{i3}\). The terminal constraints are illustrated by the green lines in Figure 1.  
Equations (4) and (7) demonstrate the analytical relationships between the screw-type terminal constraint and the joint vectors in each limb, laying the foundation for establishing the principal screws of the terminal constraint screw system. According to Equation (7), it can be seen that the expression for the screw-type constraints of this 3-CCR mechanism is very simple. This makes the 3-CCR mechanism suitable as an example for studying the relationship between independent screw-type constraints and DOFs.
## 4 Conclusions  
This paper proposes a musical conducting recognition system that can classify three basic musical conducting gestures and recognize these gestures up to 150 BPM. We used the Kinect to capture the depth map of a musical conductor and formed a skeleton model with palm data points. The time-varying palm data points were formed as a sequence of our input. The value of the data points was relative to the musical conductor's height, so the system was adaptive to the height of most users. Our proposed method is based on a DTW algorithm, which is a dynamic programming method for sequential data analysis. Combined with the Kinect depth camera and the Kinect SDK, the system can capture and track the movement of users' palms and classify the gestures. Before classification, the continuous gestures are segmented into separate ones. Additionally, the BPM of each gesture is calculated to help users practice the gestures. Once the BPM of the gesture is determined, the gesture can be identified and classified. We collected the musical gestures of three musical professionals and combined their gestures to create a template for comparison in our DTW algorithm.  
In the experiment, we used three different musical gestures of five users at five different speeds and seven different angles for evaluation. We collected all the results and achieved a total accuracy of 89.17%. Our system can recognize three different musical gestures at five different speeds and seven different angles, with the fastest speed being 200 BPM and the largest angle being 90\({}^{\circ}\), with an overall frame rate of 30 frames per second (FPS).  
In this study, we recognized the three most common gestures in musical conducting, which are the \"duple\", \"triple\", and \"quadratic\". For future work, we recommend that more types of musical gestures should be added for recognition. Also, the current tolerance angle between the musical conductor and the camera is 90% from left or right and 45% from upper or lower, which can be extended. Finally, in this experiment, the recognition often failed when a gesture was halted abruptly, which means that the robustness of the system to a sudden unexpected change in the behavior of musical conductors can be improved.  
The authors contribute equally to this work.  
The authors thank the Ministry of Science and Technology, Taiwan (R.O.C.), for supporting this work in part (MOST-107-2221-E-011-113-MY2).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Prototypes' Effectiveness in Reducing a Phone's Magnetic Field
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{3}{*}{**Cover**} & **Magnetic Field (\(\mu\)T)—Without** & **Magnetic Field (\(\mu\)T)—Installed** & \multirow{3}{*}{**RD2\% of Phone**} \\  & **Use of Prototype 2 (X2)** & & **with Prototype 2 (Y2)** & \\ \cline{2-5}
**Size** & **Mean** & **Standard** & **Mean** & **Standard** & **(\%)** \\ \hline
5 mm & & & 1.31 & 0.24 & 57\% \\
10 mm & & & 0.29 & 0.03 & 91\% \\
15 mm & & & 0.17 & 0.01 & 94\% \\
20 mm & & & 0.08 & 0.01 & 97\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of phone radiation reduced (RD2) after using Prototype 2 while Wi-Fi, data, GPS, Bluetooth, and Hotspot are on. the phone is 10 mm, considering the factor of user-friendliness with less weight and smaller size, Prototype 3 with a 10 mm cover is optimal and preferable. Regarding Prototype 3's bottom part, if the goal is to prevent at least 90% of phone radiation while the phone is on standby in the user's trousers pocket, considering the factor of user-friendliness with less weight and smaller size, the 10 mm thick Prototype 3 case is optimal and preferable. However, if the users are super concerned about the prevention of phone radiation and the goal is to prevent as much radiation as possible, the 20 mm thick Prototype 3 case is preferable.
## 2 Theoretical Premises
The parameters \(Z_{x}\) and \(Z_{y}\) depend on the installation angles of the central \(\alpha\) and lower \(\beta\) links of the rear-mounted mechanism of the MDD power module (Figure 3). In further investigations, we will discuss three options for setting this mechanism (Table 1). In addition, we will agree to consider the angle to be positive if it is located in the first quadrant of the coordinates, and negative if it is located in the fourth quadrant. With this in mind, angle \(\alpha\), displayed in Figure 3, is positive, and angle \(\beta\) is negative. The negative values of angle \(\alpha\) are not considered since, with such an installation of the central link, the rear-mounted mechanism PM becomes inoperative. From the analysis of the expressions in Table 1, it follows that, in addition to the angles \(\alpha\) and \(\beta\), parameters \(Z_{x}\) and \(Z_{y}\) depend on the height of the connection of the lower links of the power module to the frame of the TM \(Z_{x}\) and \(h_{n}\), and the height of the connecting mechanism of this module, \(h_{s}\). Their values can be determined from the following expressions (see Figure 3):  
\[h_{n}=S+r_{n}\cdot\sin\beta \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Angle** & **Calculation Formula** \\ \hline
1. \(\alpha>0\); \(\beta\leq 0\). & \(Z_{x}=\frac{h_{s}}{\tan{a}+\tan{|\beta|}}\); & \(Z_{y}=h_{n}+Z_{x}\cdot\tan{|\beta|}\). \\ \hline
2. \(\alpha>0\); \(\beta\geq 0\); \(\alpha>\beta\). & \(Z_{x}=\frac{h_{s}}{\tan{a}-\tan{\beta}}\); & \(Z_{y}=h_{n}-Z_{x}\cdot\tan{\beta}\). \\ \hline
3. \(\alpha>0\); \(\beta\geq 0\); \(\alpha<\beta\). & \(Z_{x}=\frac{h\[h_{s}=D-r_{n}\cdot\sin\beta+\tan\alpha\cdot(r_{n}\cdot\cos\beta-H) \tag{3}\]  
where \(r_{n}\) is the length of the lower links of the power module hitch, m.  
It should also be recalled that, in Equation (3), the term \(\left[\tan\alpha\cdot(r_{n}\cdot\cos\beta-H)\right]\) is equal to \(r_{v}\cdot\cos\alpha\), where \(r_{v}\) is the length of the central link of the power module hitch, m.  
To assess the level of the vertical load of the front wheels of the tractor, the controllability index \(\lambda_{\rm o}\) was used in the following form [10]:  
\[\lambda_{\rm o}=\frac{q\cdot L\cdot(G_{t}+G_{b}-Y)\cdot\sin\gamma\cdot\sin\varphi} {G_{t}\cdot\{q\cdot L[f_{t}\cdot f\cdot(\sin\varphi-\sin\delta)]-V^{2}\cdot \sin(\varphi+\delta)\cdot\cos\delta\}} \tag{4}\]  
where \(g=9.81\) m\(\cdot\)s\({}^{-2}\); \(\gamma\) is the angle of inclination of the MDD's moving surface with a semitrailer (grad. ); \(\varphi\), \(\delta\) is the angle of turning and the slip angle of the front-driven wheels of the tractor, respectively; \(f_{t}\) is the coefficient of the friction of rubber (the tire) along the soil; and \(V\) is the speed of the MDD with a semitrailer (m\(\cdot\)s\({}^{-1}\)).
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 2 Methods
### OISL-OSU Service Slicing
#### 2.2.3 IP over OISL-OSU  
For OSU-carried packet traffic, the fundamental application requirement is MAC transparent transmission. The maximum bit rate of a packet only needs to consider the nominal Ethernet MAC bit rate, which is the maximum assured bandwidth that the packet can deliver. An OSU-carried packet is suitable for carrying Ethernet traffic without a defined bit rate. The bit rate of the packet fluctuates with traffic changes, ranging from 4 to \(C\) times the PB benchmark bit rate (\(R_{PB}\)). \(C\times R_{PB}\) represents the maximum bit rate of the packet, where \(C\) refers to the maximum guaranteed bandwidth offered by the packet connection for Ethernet traffic. The maximum OSU bit rate (\(R_{\text{OSU}}\)) and the maximum payload bit rate of the OSU (\(R_{\text{OSU}\_\text{PLD}}\)) for typical Ethernet services are listed in Table 3[24].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ethernet Service Rate** & \(C\) **Value** & \(R_{\text{OSU}\_\text{max}}\) (Kbps)** & \(R_{\text{OSU}\_\text{PLD}-\text{max}}\) (Kbps) \\ \hline
10 Mbps & 4 & 10,400 & 10,020.833 \\
100 Mbps & 40 & 104,000 & 100,208.333 \\
1 Gbps & 400 & 1,040,000 & 1,002,083.333 \\
10 Gbps & 3996 & 10,381,800 & 10,003,296.875 \\ \hline \hline \end{tabular} \(R_{\text{OSU}}=C\times R_{\text{PB}}\), \(R_{\text{PB}}=2.6\) Mbps. \(R_{\text{OSU}\_\text{PLD}}=C\times R_{\text{PB}}\times\frac{185}{22}\), \(R_{\text{PB}}=2.6\) Mbps.  
\end{table}
Table 3: OSU bit rate and payload bit rate for typical Ethernet service rates.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Drawbacks of cross-layer approach  
* Substantial software changes are required to the existing protocol structure [84].
## 5 Conclusions  
1. The modeled ozone changes between 2019 and 2020 are generally consistent with the observed ozone changes using procedures that are used to make future-year ozone projections, which lends confidence in our future-year ozone projections;
2. Accounting for changes in background ozone in the free troposphere due to COVID-19 effects could substantially change our findings;
3. Although meteorology played the major role in the increases in ozone between 2019 and 2020, the reduction in NOx emissions due to the response of the COVID pandemic also caused ozone increases in Los Angeles County and into western San Bernardino County, with more widespread ozone decreases further east;
4. Ozone formation in parts of the SoCAB is still VOC-sensitive, and the locations where NOx reductions cause ozone increases occur in areas with some of the highest population density in the SoCAB;
5. The evaluation of VOC/NOx emission control strategies to attain the ozone NAAQS needs to examine ozone levels in the intervening years between the current and the attainment year to better understand whether ozone may be getting worse or cause more population exposure to high ozone concentrations rather than focusing solely on ozone levels in the attainment year.
## 6 Results and Discussion
### Performance Evaluation Using the Feature Selection/Reduction Methods
The feature selection/reduction methods, the discretize filter, and the combination of these methods significantly decreased the model building time from 213.18 s at the initial reading to a low reading at 8.7 s while slightly compromising the detection accuracy. The initial detection accuracy was 99.87% compared with the lowest detection accuracy at 99.46%, which was achieved by the wrapper method, and the highest detection accuracy was 99.85%, which was achieved by the discretize filter classifier. These results suggest using the NBTree classifier for a high level of detection accuracy with a reasonable model building time. Figures 11 and 12 present the performance of the NBTree classifier in terms of accuracy and model building time when used with different feature selection/reduction techniques. Figure 10: BayesNet model building time performance. Figure 9: BayesNet detection accuracy performance. From the above results, the authors confirm that feature selection/reduction methods affect the detection accuracy and model building time of classifiers when run on the full NSL-KDD training dataset. Most likely, the model building time of the tested classifiers was clearly further reduced by the use of the discretize filter, the wrapper method, the filter method, the wrapper method in combination with the discretize filter, and the filter method in combination with the discretize filter. [PERSON] and [PERSON] [4] also reported this finding when they tested different classifiers. Researchers can further improve or slightly compromise detection accuracy with the discretize filter, the wrapper method in combination with the discretize filter, and the filter method in combination with the discretize filter (see Tables 6-11). [PERSON] and [PERSON] [4] obtained similar results but with different tested classifiers. The best classifier with lesser model building time and high detection accuracy was the REPTree classifier using the filter method in combination with the discretize filter (see Table 4). Its model building time was 0.6 s, and its detection accuracy was 99.73%. This implies that REPTree outperforms the J48, random forest, bagging, and PART classifiers that [PERSON] and [PERSON] [4] reported. The results of this study support [PERSON] and [PERSON]'s [4] assumption that feature selection and reduction methods help a number of classifiers perform well. Figure 4:0 NBTree detection accuracy performance. Figure 4:1 NBTree model building time performance.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme
### Context Acquisition and Utilization  
Three different types of context information, namely connection-dependency (connection-dependent or connection-independent), communication type (unicast or broadcast), and packet payload size, are considered in the proposed scheme. More context information can be included with a simple extension. Figure 6 shows the MAC broadcast frame reception ratio for various packet payload sizes, where the data are achieved by conducting a real-world VANET experiment with IEEE 802.11 b/g/n devices. It is easy to observe the significant effect of packet size on the packet reception probability.  
VANET applications can be classified into three different categories, specifically end-to-end connection-dependent unicast, end-to-end connection-independent broadcast, and end-to-end connection-independent unicast applications. Different applications have different requirements. Specifically, for broadcast applications, since there is no retransmissions at the MAC layer, a more reliable link between two communication nodes is required. For unicast applications, the requirement on the link quality could be looser than broadcast as frame retransmissions are possible to improve the reliability.  
In the proposed scheme, three different sizes of hello (probe) packets are used to estimate the frame reception probability for each size. The hello message size is randomly selected from [56; 512; 1024] (bytes) (this can be easily extended to support various payload sizes). This selection lasts for 100 s (This is a system parameter which could be tuned according to the network environment. If the node density is not sufficiently high, this value should be set to a smaller one, which is better to calculate the reception ratios for different sizes of hello packets in a short period. Please note that the value we use here (100 s) is acceptable for most scenarios according to our simulations, and the dynamic tuning of this parameter is beyond the scope of this paper). If the selected payload size is not sufficient to transmit all the data required, the node uses multiple packets to transmit. If the selected size is larger than the size required, the node uses zero padding. Link quality information is maintained for  
Figure 5: An example for \(Q\)-value update.  
Figure 6: MAC broadcast frame reception ratio for various packet payload sizes.  
each possible hello payload size ([56; 512; 1024]) (bytes). Upon reception of a hello message, the link quality information is updated for the corresponding payload size. For unicast applications, the hello reception ratio (\(CCF\) in Equation (5)) is calculated by considering three retransmissions at the MAC layer. By using different sizes of hello packets, we can utilize the context information to improve the efficiency of packet forwarding process (see Figure 7).
## 5 Reviewed Papers on Empirical-Based Path Loss Models  
Numerous studies have sought optimal path loss models for signal attenuation prediction in 5G communication scenarios [60], especially in mid-band and high-band frequencies. A case in point is the work of [61], which devised a model for indoor environments at 3.5 GHz. Using two omnidirectional antennas in building measurements, this research highlighted the utility of path loss parameters for describing coverage and large-scale fading in propagation channels. However, the development of representative models calls for extensive measurement campaigns across comparable buildings. [PERSON] et al. [62] introduced a path loss prediction model for 5G mm-wave applications using Grey modeling. They trained this model with 28 GHz path loss data and compared it to four existing empirical models, revealing its strong performance, particularly in high-band frequencies [63]. Additionally, a comparison between the single-frequency FI model and the single-frequency CI models indicated their similar performance across all frequencies under line-of-sight scenarios [64; 65; 66]. However, it is important to note that the measurement did not account for material properties, which may have contributed to the symmetrical behavior of the data due to potential reflection and diffraction effects. The associated investigation for these empirical path loss models spanning mid-band and high-band frequencies are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 4 Results and Discussion  
This section presents the statistical approaches utilized to evaluate and validate the performance and assumptions of the proposed models for predicting and classifying water quality indices. The key methodologies include assumption and diagnostic tests, such as the Breusch-Pagan and ShapiroWilk tests, to assess homoscedasticity and normality of residuals, respectively. Additionally, feature importance analysis identifies the most influential water quality parameters affecting model accuracy. Principal component analysis (PCA) reduces dimensionality and reveals patterns in the dataset, while learning curve analysis examines the impact of training data size on model generalization. These analyses provide insights into model reliability, feature contributions, and areas for optimization.