esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
In summary, in this paper, we have presented a method of analysis for human body 3D scan sequences that allows for the generation of skeletal animations, along with body shape deformation animations. The algorithm consists of three stages: pose tracking, shape mapping, and template mesh morphing. We performed quality tests on the obtained morphed meshes for two hundred frames from two sequences of the Dynamic FAUST dataset. We compared our method to the SMPL method by performing a similar test. The resulting reproduction quality of our method was demonstrated by the almost twice-smaller reconstruction error, compared to that of the state-of-the-art SMPL method. Moreover, an important advantage of our method is the possibility to use a template with any mesh topology and resolution, thanks to which one can generate an efficient preview animation with less detail. Furthermore, the skeleton used can also be customized.  
The directions of future improvement are the transition areas between subsequent segments, where artifacts caused by the loss of data in shape map calculation (only points from a given segment and a small surrounding area participate in the shape map calculation) and vertex squeezing after parametric mapping of the shape map may appear. Furthermore, with small resolutions of the shape map, modest faults appear on the surface of the reconstructed mesh that are caused by value averaging in shape map cells.  
In the future, we would like to achieve even better reconstruction quality by replacing averaging and mipmapping in the shape maps with polynomial approximation or another mathematical model. This may result in a smoother surface of the reconstructed mesh and better handling of areas without measurement points. The second field for development in the future is the template mesh morphing algorithm. Our goal will be to achieve a smoother output mesh though the application of a more advanced method for fitting the template mesh to the shape map. Last but not least, we would like to pursue more tests on robustness of the pose tracking algorithm with lower frequency capture.  
Conceptualization, M.N.; methodology, M.N.; software, M.N.; validation, M.N.; writing--original draft preparation, M.N. and R.S.; writing--review and editing, M.N. and R.S.; visualization, M.N.; supervision, R.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We would like to thank [PERSON], [PERSON], [PERSON], [PERSON], and Max Planck Institute for Perceiving Systems for making their scanning dataset named Dynamic FAUST freely available to other researchers.  
R.S. declares no conflict of interest. M.N. is a contractor at Platige Image.  
Source code will not be available; data will not be available.  
Figure 18: (**a**) Vertex squeezing in collar-bone area; (**b**) Reconstruction artifacts on neck as a result of lacking shape map support points.
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 2 Materials and Methods
### Software Analysis  
From a hardware perspective, this system is relatively simple, consisting of a microcontroller, sensors, and battery. Further details regarding the system's operation are presented in the following chapter.  
Using the previously mentioned hardware, an automated system has been developed to achieve the desired movement monitoring. The system consists of three devices (microcontroller-sensor-battery) attached to a human arm, where each device functions as a slave unit, transmitting data to the master device. An additional ESP32 microcontroller serves as the master device, constituting the fourth microcontroller in the system.  
The primary focus of this paper revolves around the monitoring data, which consists of output values from the MPU6050 accelerometer. This sensor is capable of measuring both acceleration and gyroscopic movement.  
In the configuration setup, a scale range of \(\pm\)2 [g] (1 [g] = 9.8 [m/s\({}^{2}\)]) is used, meaning that an acceleration force of 1 gravity unit corresponds to an output value of 16,384 for each axis (with a raw value range of \(-\)32,768 to 32,767 due to the use of a 16-bit signed integer). Notably, the Z-axis requires special consideration, as it will register a default value of 1 gravity unit due to the effect of gravity, even when the sensor is stationary.  
The raw values outputted from the MPU6050 are normalized by dividing them by 16,384, the sensitivity value for the \(\pm\)2 [g] configuration. This normalization reduces the working interval, making it easier to calculate and transmit data to the feedback interface.  
Figure 3: Added prototypes for arm example.  
Since the system relies solely on accelerometer data, it monitors movements based on acceleration. These acceleration values provide information on how the movement is being executed over time, including motion patterns, velocity changes, and the force exerted during the movement.
## 2 Methodology  
### Standard Local Projection Algorithm  
Consider a series of \(N\) scalar measurements \(s(1)\), \(s(2)\),..., \(s(N)\)\(\in\)\(R\) from a chaotic system. By choosing the appropriate embedding dimension m and the delay time \(\tau\), it can be reconstructed into the \(m\)-dimension phase space, and each phase point in the reconstructed phase space can be expressed as:  
\[\mathbf{X}_{n}=(s_{n},s_{n-\tau},\cdots,s_{n-(m-1)\tau}). \tag{1}\]  
According to the embedding theorem [24], when the fractional dimension of the system attractor is \(m\), it needs to meet the conditions \(m\geq 2d+1\). The reconstructed phase space and the original dynamic system are differential homeomorphisms, namely they have the same dynamic characteristics. In the \(m\)-dimensional phase space, the attractor that reflects the dynamics of the system is usually confined to a low-dimensional subspace \(m_{0}\) (\(d<m_{0}<m\)). In the case of the absence of noise interference, there is zero subspace (\(m-m_{0}\)) in the phase space. When noise is present, the noise is randomly distributed in the phase space, so the component in the null subspace (\(m-m_{0}\)) is generated by the noise. In the neighborhoods of each phase point, the eigenvalue decomposition of the covariance matrix composed of neighborhood points is performed. The subspace formed by eigenvectors corresponding to \(m_{0}\) largest eigenvalues is considered as the signal subspace, and the subspace formed by the eigenvectors corresponding to the other (\(m-m_{0}\)) smaller eigenvalues is the noise subspace. The main content of the local projection noise reduction is to identify the noise subspace, and then subtract the projection of the phase point on the subspace, and the basic steps of the algorithm are as follows:  
1. Choosing the appropriate parameters \(m\) and \(\tau\) to reconstruct the noisy time series into the \(m\)-dimensional phase space.
2. Determine the neighborhood \(U_{n}\) of each phase point. There are two methods to determine the neighborhood: the fixed neighborhood number and the fixed neighborhood radius. In this paper, the former is used.
3. Calculate the centroid of each neighborhood point.
4. The covariance matrix \(C\) of each phase point is calculated, and the eigenvalue decomposition is performed, and the eigenvalues corresponding to (\(m-m_{0}\)) smaller eigenvalues \(a_{q}\) (\(q=1,2,\)..., \(m-m_{0}\)) are obtained.
5. Subtract the projection of the phase point on the noise subspace:\[\mathbf{X}_{n}^{\prime}=\mathbf{X}_{n}-\mathbf{R}^{-1}\sum_{q=1}^{m-m_{0}}a_{q} \cdot[\mathbf{a}_{q}\cdot\mathbf{R}(\mathbf{X}_{n}-\overline{\mathbf{X}}_{n})], \tag{2}\]  
where \(\mathbf{R}\) is a diagonal weight matrix and is used to suppress the distortion of the elements at the beginning and end of the phase, in addition to keeping the elements stable in the middle. In this paper, \(\mathbf{R}_{11}=\mathbf{R}_{mm}=103\), and \(\mathbf{R}_{ii}=1\). \(\overline{\mathbf{X}}_{n}\) is the centroid of the neighborhood, and \(\mathbf{X}_{n}^{\prime}\) is the signal after noise reduction.  
Return to step (2) until all data have been processed.
## 5 Results  
In the optimization process for the 3-dof robotic manipulator, the first joint connecting to the ground is used as the master motion and controlled by the time domain PID controller, and the other two joints are viewed as he slave motions and controlled by the PDC-PID controller. There are nine control gains that need to be optimized for the control of the 3-dof robotic manipulator for contour tracking. The optimized control gains for linear and nonlinear contours under three different fitness functions can be seen in Tables 6 and 7, respectively. All optimization procedures are successful, producing gains inside the specified bounds and providing valid solutions for each case. Figure 2 shows the best values of each fitness function per iteration for every optimization algorithm in the linear contour. One can see that, in all cases, GA converged faster (locally minima) than DE and PSO, something that was expected mostly due to literature. DE and PSO took a similar number of iterations to converge with convergence values of PSO however being closer to GA than DE. Also, Figure 3 shows that, for the nonlinear case, GA converged once again faster than the other two algorithms which featured similar convergence trends. However, this time DE led to lower fitness values than PSO for all three fitness functions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization** & **Fitness** & & & \\
**Algorithm** & **Function** & & & \\ \hline \multirow{4}{*}{**DE**} & ISE & \(diag\{100,4885,155\}\) & \(diag\{100,10,000,385\}\) & \(diag\{100,10,000,9783\}\) \\  & IAE & \(diag\{9866,5774,100\}\) & \(diag\{8543,10,000,198\}\) & \(diag\{10,000,100,100\}\) \\  & MSMAE & \(diag\{5336,100,490\}\) & \(diag\{5949,7771,321\}\) & \(diag\{5573,2451,8693\}\) \\ \hline \multirow{4}{*}{**GA**} & ISE & \(diag\{7196,7776,72\}\) & \(diag\{6183,1372,1223\}\) & \(diag\{5924,193,9774\}\) \\  & IAE & \(diag\{1612,7300,1761\}\) & \(diag\{4847,866,551\}\) & \(diag\{7788,820,8292\}\) \\  & MSMAE & \(diag\{6583,3196,1130\}\) & \(diag\{7635,592,2771\}\) & \(diag\{6752,166,6211\}\) \\ \hline \multirow{4}{*}{**PSO**} & ISE & \(diag\{839,4098,2657\}\) & \(diag\{3276,666,291\}\) & \(diag\{9997,260,4773\}\) \\  & IAE & \(diag\{4143,9572,976\}\) & \(diag\{5060,9324,217\}\) & \(diag\{2710,4056,9083\}\) \\ \cline{1-1}  & MSMAE & \(diag\{4210,6421,5722\}\) & \(diag\{8451,1026,6392\}\) & \(diag\{6605,10,1567\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Resulting gains for linear contour. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Method** & **Optimization Parameter** & **Value/Method** \\ \hline \multirow{2}{*}{Differential Evolution} & \(CR\) & 0.7 \\  & \(F\) & 0.8 \\ \hline \multirow{4}{*}{Genetic Algorithm} & Selection & Stochastic Universal Sampling * \\  & R & 1 \\  & Mutation & Gaussian * \\ \hline \multirow{2}{*}{Particle Swarm Optimization} & \(\alpha_{1}\) & 0.5 \\  & \(\alpha_{2}\) & 1.0 \\ \hline \hline \end{tabular}  
* More information on Stochastic Universal Sampling and Gaussian Mutation can be found in [17].  
\end{table} 
Table 5: Optimization methods parameters.
## 2 Problem Formulation  
The surge motion dynamics of AUVs can be described by the following formula as  
\[\left\{\begin{array}{l}\dot{x}=u+c,\\ (m-X_{a})\dot{u}=X_{u|u}|u|+(1-C_{n})\tau+\Delta f(x,u)\end{array}\right. \tag{1}\]  
where \(x\) represents the surge displacement state of AUV, \(u\) is the surge forward velocity of AUV relative to ocean current, \(c\) is the velocity of ocean current, \(m\) and \(X_{\dot{u}}\) are the mass of the AUV itself and the added mass, \(X_{u|u|}\) is the nonlinear surge drag coefficient, \(\tau\) is the thrust force and \(\Delta f(x,u)\) is the unmodeled dynamics. In (1), \(m\), \(X_{\dot{u}}\), \(X_{u|u|}\) and \(C_{n}\) are unknown constants, and \(m>X_{\dot{u}}\) holds. The reference trajectory is \(x_{d}\), which satisfies Assumption 1.  
The control objective of this work is to develop a controller such that \(x\) can track the given reference trajectory \(x_{d}\), with \(\ e=x-x_{d}\) being constrained during the operation of the AUV system.  
\(x_{d}\) is continuously differentiable, i.e., \(x_{d}\) and \(\frac{dx_{d}}{dt}\) are continuous with respect to time \(t\).  
\(c\) is time-varying but bounded, i.e., \(\frac{dc}{dt}\
eq 0\) and \(|\frac{dc}{dt}|<d_{c}\), with \(d_{c}\) being an unknown positive constant.  
**Lemma 1** ([32]): _For \(h\in R\) and \(\beta>0\),_  
\[0\leq|h|-\frac{h^{2}}{\sqrt{h^{2}+\beta^{2}}}<\beta. \tag{2}\]  
**Lemma 2** ([33]): _Consider a linear and nonlinear combined differentiator as follows:_  
\[\begin{cases}\dot{\eta}_{1}=\eta_{2}\\ \varepsilon^{2}\dot{\eta}_{2}=-a_{0}(\eta_{1}-v(t))-a_{1}|\eta_{1}-v(t)|^{a_{ 1}}\text{sign}(\eta_{1}-v(t))-b_{0}\varepsilon\eta_{2}-b_{1}|\varepsilon\eta_ {2}|^{a_{2}}\text{sign}(\varepsilon\eta_{2}),\end{cases}\]  
_where \(0<a_{2}<1\), \(\frac{a_{2}}{2-a_{2}}<a_{1}<1\), \(a_{0}>0,b_{0}>0,b_{1}>0\) and \(\varepsilon>0\) are design parameters. For the continuous second-order differentiable signal \(v(t)\), there exits \(\zeta>\frac{2}{a_{2}}\) and \(\Gamma>0\), which satisfy_  
\[\eta_{1}-v(t)=o(\varepsilon^{a_{2}\zeta}),\eta_{2}-\dot{v}(t)=o(\varepsilon^{ a_{2}\zeta-1}),\forall t>\varepsilon\Gamma. \tag{3}\]  
**Remark 2**: _According to Assumption 2, the velocity of an ocean current is bounded, and the acceleration of an ocean current is also bounded, which is consistent with the actual situation._  
For simplicity, in this paper, the arguments of functions are often omitted while no confusion arises.
## 3 Hybrid Visual SLAM with Deep Keypoints, Depths, Poses, and Line Features
### Integrating Line Features and Deep Keypoints in the Front End
#### 3.2.2 Self-Supervised Learning of Deep Keypoints
The matched descriptor loss, \(\mathcal{L}_{d1}\), is defined in Equation (4), involving \(N\) pairs of aligned keypoint locations \((\mathbf{x}_{i},\mathbf{\hat{x}}_{i})\) with indices \(i\) ranging from 0 to \(N\). The unmatched descriptor loss, \(\mathcal{L}_{d2}\), is outlined in Equation (5) and considers \(Z\) pairs of descriptors near the matched ones, with indices \(j\) ranging from 0 to \(Z\), representing unmatched keypoint pairs \((\mathbf{x}_{i},\mathbf{x}^{\prime}_{j})\). The overall descriptor loss, \(\mathcal{L}_{d}\), described in Equation (6), incorporates a margin boundary \(b\) and is designed to minimize the feature discrepancy among corresponding descriptors while maximizing it among non-corresponding ones. The total training loss, \(\mathcal{L}_{\text{kd}}\), integrates the detector and descriptor losses, with a balancing factor \(\mu\) that controls the influence of each component. \[\mathcal{L}_{d1}(\mathbf{x}_{i})=\|\mathbf{d}_{i}(\mathbf{x}_{i})-\mathbf{d} _{w1}(\mathbf{\hat{x}}_{i})\|^{2} \tag{4}\]  
\[\mathcal{L}_{d2}(\mathbf{x}_{i})=\frac{1}{Z}\sum_{j=1}^{Z}(\|\mathbf{d}_{I}( \mathbf{x}_{i})-\mathbf{d}_{w1}(\mathbf{x}^{\prime}_{j})\|^{2}) \tag{5}\]  
\[\mathcal{L}_{d}=\frac{1}{N}\sum_{i=1}^{N}\max(0,b+\mathcal{L}_{d1}-\mathcal{L} _{d2}) \tag{6}\]  
\[\mathcal{L}_{\text{kd}}=(\mathcal{L}_{k}+\mathcal{L}_{wk})+\mu\mathcal{L}_{d} \tag{7}\]
## 3 Proposed Protocol Architecture
### Protocol Architecture
#### 3.1.2 SMAG  
The SMAG acts like a sink node in a traditional sensor network. In the proposed IP-WSN based on SPMIPv6 it acts like an access gateway router with the main function of detecting sensor node movement and initiating mobility-related signaling with the sensor node's border router on behalf of the sensor node. It can move with its member sensor node as a SMAG domain within or outside a IP-WSN domain similar to the body sensor network of a patient. It consists of different functional modules such as routing, neighbor discovery, sensor information table, adaptation module and interfacing modules to the sensor node and border router. The routing module performs the efficient data transmission among individual sensor nodes and facilitates the end to end communication. The neighbor discovery module performs neighbor discovery and duplicate address detection functionality. The adaptation module performs the task of transmitting IPv6 packets over IEEE 802.15.4 link as mentioned in the 6 LoWPAN  
Figure 1: Global IP-WSN Architecture.  
adaptation layer. The sensor information table provides the up to date information about the sensor nodes to the border router. It works with the close association of the binding cache entry of the border router, and the two interfacing modules communicate with the border router and the sensor nodes. SMAG domain consists of FFD and RFD nodes. We also consider the SMAG domain as a personal area network (PAN) when it is regarded as a patient's body area network in our global IP-WSN-based patient care unit described in Section 3.3.
## 3 Results and Discussion  
### Bioreactor Performance  
#### 3.1.1 COD Removal  
To evaluate the performance of the AWFR system, the seasonal COD removal efficiencies were investigated at different HRTs for 364 days. During the whole operation period, the influent CODconcentration ranged from approximately 157 to 469 mg/L (Figure 2). At the beginning of the start-up period (days 1-30), the AWFR was operated in the temperatures ranged from 8 \({}^{\circ}\)C to 17 \({}^{\circ}\)C, with an HRT of three days. The COD removal efficiency fluctuated in the range of 24.6-51.8%. Based on the COD removal efficiency in the start-up period, we concluded that microbial community adapted to the operation conditions and the AWFR was successfully initiated. The COD removal efficiency increased gradually in spring (days 31-90), thus resulting in an average COD removal efficiency of 61 \(\pm\) 7.9% (\(n\) = 60) at an HRT of two days, with average temperature variations of 21 \(\pm\) 3.0 \({}^{\circ}\)C (\(n\) = 60). COD removal efficiency can improve with temperature. Thus, the HRT was adjusted to one day when the average temperature increased to 31 \(\pm\) 3.7 \({}^{\circ}\)C (\(n\) = 90) in summer (days 91-180). Despite shortening the HRT, following temperature increase, the average effluent COD concentration decreased to 82.5 mg/L, and the average COD removal efficiency increased to 76 \(\pm\) 7.2% (\(n\) = 90). The change in COD removal efficiency might be due to higher activities of microbial communities in summer. In order to keep COD removal efficiency above 50%, the HRT was adjusted from two days (autumn) to three days (winter). Even though the OLR decreased from 135 \(\pm\) 32.9 mgCOD/L/day (\(n\) = 90) in autumn to 82 \(\pm\) 12.9 mgCOD/L/day (\(n\) = 94) in winter, the COD removal efficiency still decreased from 57 \(\pm\) 9.4% (autumn, average temperature 25 \(\pm\) 5.2 \({}^{\circ}\)C, \(n\) = 90) to 52 \(\pm\) 5.9% (winter, average temperature 10 \(\pm\) 2.2 \({}^{\circ}\)C, \(n\) = 94). These findings indicated that the temperature played an important role in the AWFR system operation. Low temperature might increase the wastewater viscosity, slow down the rate of biological reaction, and decrease COD removal efficiency. Compared to the previous study with elastic fiber carriers in two-stage anaerobic filter for domestic wastewater treatment [12], the COD removal efficiency in this study was better, which may be because the wool felt carrier enhanced the agglomeration of the microbial community in the AWFR system. Overall, the performance of the AWFR system was relatively stable and the COD removal efficiency was higher in summer than in winter. To achieve better performance of the AWFR system at the temperature ranging from 8 \({}^{\circ}\)C to 35 \({}^{\circ}\)C, an HRT of three days is recommended.
## 2 Existing Studies
### Spatial Topological Analysis  
The spatial topology analysis relies on the geometric information of the MEP model. IFC can describe spatial topology objects, such as ducts and pipelines, as well as abstract concepts, such as  
Figure 2: The foundations of automatically generating MEP logical relationships. MEP: Mechanical, Electrical, and Plumbing.  
spaces, organizations, relations, and processes [11]. For BIM applications in the O and M phase an extension of IFC that includes FM related information and repair information of MEP assets [12; 13; 14]. has been established. IFC-based BIMs can store various types of geometric data. Conventionally, the 3D object is identified as either a surface model or a solid model in the BIM. The solid model can further be divided into swept solid and CSG (constructive solid geometry) representations. Apart from geometric property of objects, 3D models need spatial relations (or topological relations) of components to facilitate complicated analysis and decision-making, e.g., building object classification [15]. Existing BIMs can be directly used to estimate spatial relations between 3D objects for spatial queries or further analysis. Some algorithms [16] have been proposed to automatically suggest topological relationships of building components from 3D CAD (Computer Aided Design) models. Generally, existing analytical BIM studies focus on information extracted from completed 3D models [17]. Particularly, based on the representation of 3D objects, the following topological relations were computed: adjacency, separation, containment, intersection, and connectivity. However, they are still limited in the cases of simple building configurations. Topological [18] and directional [19] relations can also be extracted from an IFC-represented BIM. Other proposed method can estimate topological relations for spatial queries based on boundary representations (B-rep) of BIMs [20]. B-rep is the most common surface-based representation, in which a shape is described by a set of surface elements (its surface boundary) along with connectivity information to describe the topological relationships between the elements. Previous works mainly focus on the architecture elements, such as the space, wall, and so on. However, most MEP components are represented in line structures such as air ducts and pipelines, which means topological analysis can be optimized with its specific swept solid representation. Different from the aforementioned work, this paper explores methods to complete the spatial relations (especially connections in MEP system) relying on both B-rep and swept solid represented models to improve the efficiency and accuracy of topological information generation.
## 3 Optimization Problem
### QoS Constraints  
To enhance user fairness, it is assumed that the weak user in each cell has a QoS requirement, which implies that a minimum rate \(R_{min}\) needs to be guaranteed corresponding to the optimization problem considered, which can be expressed as follows [22]:  
\[\text{log}_{2}(1+\delta_{n})\geq R_{min} \tag{10}\]  
where \(\delta_{n}\) is the SINR for \(n\)th user and \(R_{min}\) is the minimum transmission rate required in the system [23]. This constraint can be simplified in many ways, suppose we have \(R_{m\to k}\) which is the rate of user \(k\) to detect the signal of user \(m\), where \(1\leq k\leq m\) and \(R_{m}=R_{min}\). When user \(k\) is not able to detect the message of user \(m\) with rate \(R_{min}\), this can be indicated as \(R_{m\to k}<R_{min}\)[23]. The complement of this event can be formulated as follows:  
\[\frac{|h_{k}|^{2}P_{t}\alpha_{m}}{|h_{k}|^{2}P_{t}\sum_{i=1}^{m-1}\alpha_{i}+ \sigma^{2}}>(2^{R_{min}}-1) \tag{11}\]  
where \(\alpha_{i}\) is the power factor for \(i\)th user in the system. By dividing both the numerator and denominator of left-hand side of Equation (11) by the noise power \(\sigma^{2}\), Equation (11) can be reformulated as:  
\[|h_{k}|^{2}\rho\Bigg{(}\alpha_{m}-(2^{R_{min}}-1)\sum_{i=1}^{m-1}\alpha_{i} \Bigg{)}>(2^{R_{min}}-1) \tag{12}\]where \(\rho\) represent the signal power to noise power ratio. Equation (12), also declares that in order to satisfy the minimum transmission rate and avert that \(m\)th user being in outage, the following condition must be achieved:  
\[\alpha_{m}>\left(2^{R_{min}}-1\right)\sum\limits_{i=1}^{m-1}\alpha_{i} \tag{13}\]  
where \(\alpha_{m}\) is the power assigned for \(m\)th user in the system.