esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Sufficient posture regulation and stability are important factors for activities of daily living and physical activity [1]. In competitive sports, exceptional postural regulation and stability are necessary to perform at the highest performance levels [2,3] and are dependent on the specific movements and forces required by different sports. Postural regulation is defined as the ability to monitor body position and alignment in space, and involves multimodal interactions of the musculoskeletal and neural systems. Postural stability predominantly incorporates somatosensory information to control an individual's center of mass (CoM) in relationship to their base of support [4]. Furthermore, postural stability is a crucial determinant of functional movement reflecting interaction of different postural subsystems (visual, nigrostriatal, vestibular, somatosensory, and cerebellar).  
Postural regulation plays an important role in both team sports (e.g., basketball, handball) [2; 5; 6; 7], technical sports (e.g., diving, ski jumping, and gymnastics) [3; 8; 9; 10], different styles of riding [11], or the impact of therapeutic riding on gait and posture regulation [12]. For example, posture can be decisive for the starting position of high movements, such as in diving and gymnastic movements, as well as at the end of movements, such as in ski jumping and (again) gymnastics. In contrast, the posture required in team sports may play a smaller role because the level of posture regulation and stability is not as crucial for match performance compared to the technical evaluation in competitions determined by judges (e.g., basketball vs. diving).  
From a practical point of view, many coaches almost exclusively use subjective and semi-empirical impressions and considerations. Consequently, athletes may not be able to reach their maximum performance level based on these subjective assessments. An additional assessment of training and competition performances using an objective diagnostic tool would empirically support the coach and potentially confirm or otherwise correct his/her subjective evaluation. This would be beneficial during pre-season preparation, technical training, immediate competition preparation, and during the recovery or off-season phases [13].  
The aim of the study was to evaluate the postural control and stability in athletes from different sports using posturography under increasing levels of difficulty. We hypothesized that athletes in technical sports, such as gymnastics, ski jumping, and diving, would show a higher level of postural control and stability than athletes in team sports or recreational athletes.
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 2 Methods
### Classifier  
Several classifiers such as K-Nearest Neighbors (KNN), Back propagation neural network (BP-NN), Random Forest (RF), Naive Bayes and Support Vector Machines (SVM) are implemented and compared. The detail of these classifiers is presented below.
## 3 Numerical Results and Discussions
As deformation increases, damage accumulation and crack growth progress more rapidly, consequently resulting in a shorter fatigue life. On the other hand, lower levels of deformation correspond to a longer number of cycles to failure, indicating improved resistance to fatigue. Based on the X-directional deformation values depicted in Figure 15, it can be observed that CTS03 exhibits the lowest value, followed by CTS02, CTS05, and CTS04, respectively. This ordering of deformation values is inversely proportional to the fatigue life cycles, suggesting that components experiencing lower levels of deformation are likely to have longer fatigue life cycles observed for these specimens. Finally, ANSYS SMART crack growth has certain limitations, such as its ability to simulate fatigue crack growth only for materials that follow linear-elastic fracture mechanics. Additionally, its effective use requires significant expertise and experience, which may limit its accessibility to certain users. Figure 2:3 Fatigue life cycles for CTS05 under different stress ratios.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Position Aware Routing Protocols
#### 4.2.4. Ground Control System Based Routing (GCS)  
The authors of [46] specifically designed Ground Control System (GCS)-based routing for FANET to maximize the use of ground control systems. GCS routing is composed of two phases: network initialization phase and network maintenance phase. At the beginning, GCS centrally calculate the distance between the drone and its neighbors, and plan the running route for each drone in  
Figure 8: Modification of neighbor table in PP scheme.  
advance according to the distance value. Then the initialization phase is complete, and then enter the maintenance phase. When drones start performing tasks, the network structure changes dynamically due to high mobility, and GCS will update the routing table and send the routing table timely.  
**Advantages:** (1) GCS can use the position and motion information of the UAV to predict the cost of the link, so the UAV only needs to pay attention to whether the link will fail, which can be achieved by calculating the beacon message sent by the AP interface of the neighboring UAV. (2) GCS has the UAV's motion schedule, so it is capable of anticipating topological changes and then send notice to each UAV of new paths ahead of time. Therefore, network disconnect times can be reduced.  
**Disadvantages:** (1) Obstacles between nodes will cause link estimation failure. (2) In the routing update process, as the updated routing information can be received by the root node, but its child nodes have not been received, the formation of a transient loop can be observed.
###### Abstract  
The latest research on laser beam fusion cutting (LBFC) with static beam shaping have shown a limitation in the quality of cut parts for thick steel plates (> 6 mm) when using solid state lasers. The approach of dynamic beam oscillation has recently shown to be capable of overcoming this challenge, allowing to increase the cutting speed as well as improving cut edge quality beyond the state of the art. The present paper investigates the influence of longitudinal, linear beam oscillation in LBFC of 12 mm mild and stainless steel plates by analyzing different parameters as cutting speed, burr, surface roughness, heat affected zone (HAZ), and recast layer. Reasons for the observed process improvements compared to static beam shaping have been discussed. The adjustment of the energy deposition and interaction time of the laser beam with the material found to be most relevant for optimizing the LBFC process. In particular, for beam oscillation, a gradual energy deposition takes place and increases the interaction time. This reduces the heat input in terms of HAZ and recast layer by more than 50%, resulting in high cut edge quality and more than 70% faster cutting speed.  
laser beam fusion cutting; dynamic beam shaping; thick steel; heat conductivity; oscillation +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Author** & **Wavelength (nm)** & **Intensity (mW/cm\({}^{2}\)) or Power (mW)** & **Times of Exposition** & **Finding** \\ \hline [PERSON] et al. [40] & 632.8 & 50 mW & 10 and 1000 s & Morphological changes in the plants with higher, thicker, and larger ears in the case of corn. \\ \hline [PERSON] et al. [47] & 655 & 4.6 mW/cm\({}^{2}\) & 30, 60, 180, 300, and 600 s & Laser irradiation could be an alternative to control diseases in corn seeds. \\ \hline [PERSON] et al. [56] & 632.8 & 25 mW & 10 and 30 s & Irradiated seeds significantly increased crop yield in some variants. \\ \hline [PERSON] et al. [49] & 660 & 30 mW & 30, 60, 120, and 300 s & The experimental results indicated that there was bio-stimulation by the interaction of the laser light with the corn seed photoreceptors. \\ \hline [PERSON] and [PERSON] [57] & 650 & 27.4 mW & 20 s & No significant bio-stimulation effects were found for A and B genotypes, and a negative bio-stimulation effect was found for genotype C. \\ \hline \end{tabular}
\end{table} 
Table 3: Presenting the bio-effects of low-intensity laser light in maize seeds.
## 3 Jitter Modeling
### Jitter Components  
In this paper, the random jitter and deterministic jitter are generated by digital synthesis. As a result, the models of jitter components need to be created. They include models for sinusoidal jitter, rectangular jitter, duty cycle distortion jitter, and random jitter.
## 4 Relativistic Considerations  
As a final remark, the approximate nature of the Lagrangian formulation by [PERSON] [5] has not been left unnoticed. It could be attributed first to the non-relativistic description of mirror's motion which ultimately ignores higher-order interactions, and then to the relativistic nature of radiation friction force and the associated Doppler shift [66]. As a result, in a subsequent paper by [PERSON] and [PERSON] [67], it has been made clear that the non-relativistic optomechanical Hamiltonian is correct only to the first-order in \(\dot{q}\).  
The nature of the relativistic corrections can be quite different, as follows:  
* relativistic Doppler shift [66], which causes corrections in \(\dot{q}/c\),
* relativistic correction in radiation pressure term [67], the lowest-order of which being proportional to \(\dot{q}/c\),
* length contraction [68], due to the moving mirror boundary, resulting again in corrections as \(\dot{q}/c\).  
Not surprisingly, all these relativistic terms vanish in the limit of infinite light speed \(c\). These altogether could be taken into account in a fully relativistic formulation of the Lagrangian and equations of motions for the mirror and optical field [85], which has been recently carried out in an extensive research by [PERSON] and [PERSON] [68].  
As shown in Appendix D, the total relativistic correction terms added to the Hamiltonian takes the form  
\[\Delta\mathcal{H}=-\hbar\left(\hat{b}^{\dagger}-\hat{b}\right)^{2}\sum_{k}w_{ kj}\left(\hat{a}^{\dagger}_{k}+\hat{a}_{k}\right)\left(\hat{a}^{\dagger}_{j}+\hat{a}_{j} \right). \tag{65}\]  
For the single-mode cavity, \(w=\chi_{0}\pi\hbar d\Omega/4 mcl^{2}\), to be compared with \(\beta=\hbar\omega/m\Omega l^{2}\) in (48). Hence, the relativistic correction to the quadratic Hamiltonian \(\mathbb{H}_{4}\) is  
\[-\frac{w}{\beta}=-\frac{\chi_{0}\pi d\Omega^{2}}{4c\omega}. \tag{66}\]  
Again, it is seen that when \(\omega\gg\Omega\) is violated, the relativistic corrections might be quite significant. In any case, there is no relativistic correction to \(\mathbb{H}_{3}\).
## 3 Obstacle Avoidance Scenario
### Obstacle Detection Technologies
#### 3.3.1 Sonar Mapping  
The sonar (sound navigation and ranging) mapping system uses the acoustic wave reflection time from different angles to make an image or diagram of the surrounding environment. A sonar sensors represents the oldest obstacle detection technology, which was firstly used to measure the range of an underwater floor in the year 1912 [184]. Later on, this technology was broadly used in modern warfare to detect obstacles [185]. Eventually, it started operating in robotics. [PERSON] [186] used the sonar-based surrounding mapping for obstacle detection, where a sonar sensor detects the obstacle's range from different points of view, creating a 2D map. [PERSON] [187] used multiple sonar sensors for more accuracy from a different angle to create the 2D map in a ground robot. [PERSON] and [PERSON] [188] used a sonar array for targeting, localizing, classifying, and creating the 2D mapping. [PERSON] and [PERSON] [189] used the sonar sensor for localizing and classifying accurate 3D targets. Later, simultaneous localization and mapping or SLAM technology using sonar sensors were used for 3D imaging underwater by [PERSON] et al. [190]. [PERSON] and [PERSON] [191] used a biomimetics SLAM called BatSLAM, a mapping module using sonar to create a map for mobile robots. [PERSON] and [PERSON] [192] used a 3D sonar sensor and BatSLAM sonar module together to improve localization and mapping. Two  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
fully embedded real-time 3D imaging sonar architectures, also known as RTIS, were also demonstrated by [PERSON] et al. [193]. A series of sonar sensors were also used in a UAV by [PERSON] et al. [194] for low-altitude mapping where the sensors were setup at different angles. [PERSON], [PERSON], and [PERSON] [144], [PERSON], [PERSON], and [PERSON] [142], [PERSON] et al. [195], and [PERSON], et al. [196] used a round array setup of ultrasonic sensors in a small quadrotor UAV for obstacle mapping. Further studies on mapping obstacles using sonar sensors also have been conducted by various other researchers [197; 198; 199; 200].  
Sonar sensor mapping and obstacle detection sensors are mostly used in underwater vehicles because of the shortage of light in the deep sea. Because of the fact that the human body is mostly made of water, sonar sensors are also used in medical science to detect the internal body structures in humans [202; 203; 204; 205]. Besides that, sonar mapping is also used in ground robots and small quadcopters in some research to prove the concept of algorithms [142; 189; 191; 195; 196].
# DPS Clustering: New Results
## 3 Results: Iterative DPS
### Iterations of the DPS Algorithm  
Considering the algorithms \(\text{DPS}(P,\beta,r)\) and \(\text{DPS}_{c}(P,\beta,r)\) as their zero iterations: \(\text{DPS}^{0}(P,\beta,r)=\text{DPS}(P,\beta,r)\), \(\text{DPS}^{0}_{c}(P,\beta,r)=\text{DPS}_{c}(P,\beta,r)\), we define their \(i\)-th iterations as processes of building for space \(X\) its \(i\)-th iteration \(X(\beta,i)\) and then breaking it into \(r\)-connectivity components:  
\[\begin{array}{rcl}\text{DPS}^{i}(P,\beta,r)&\Leftrightarrow&X\Rightarrow X (\beta,i)\\ \text{DPS}^{i}_{c}(P,\beta,r)&\Leftrightarrow&X\Rightarrow C_{r}(X(\beta,i)) \end{array} \tag{11}\]  
Due to the disjunctive nature of the decomposition (10), the iteration index ind is correctly defined at the \(i\)-th iteration of \(X(\beta,i)\):  
\[\forall x\in X(\beta,i)\Rightarrow\text{ind}(x)=\left\{\begin{array}{l}0, \text{ if }x\in X(\beta)\\ \tilde{i}\in[1,i],\text{ if }x\in X_{\tilde{i}}(\beta)\end{array}\right..\]The index ind moves from points to connectivity components \(c=cX(\beta,i)\), thus becoming multivalued:  
\[\operatorname{ind}(c)=\{\operatorname{ind}(x):x\in c\}.\]  
In addition, this, in turn, makes it possible for any subset \(I\in[0,i]\) to define a conditional version of the \(\operatorname{DPS}^{I}\) algorithm:  
\[\begin{array}{l}\operatorname{DPS}^{I}(P,\beta,r)=\vee_{i\in I}X_{i}(\beta) \\ \operatorname{DPS}^{I}_{c}(P,\beta,r)=\left\{c\in C(X(\beta,i)):\operatorname{ ind}(c)\in I\right\}\end{array}. \tag{12}\]  
**Example 1**.: _Figure 4b-d show the sets \(X_{i}(\beta)\), \(i=0,1,2\) for the array \(X\) shown in Figure 4a, obtained as a result of the operation of the SDPS-algorithm at \(\beta=-0.09\) based on the fuzzy comparison \(n(t,s)=(s-t)/(s+t)\) of non-negative numbers \(s\) and \(t\)[1]._  
_Figure 5 shows both stages of the \(\operatorname{SDPS}^{1}\)-algorithm. Its result \(\operatorname{SDPS}^{1}_{e}(X)\) indicates that, in the general case, the second stage of \(\operatorname{DPS}\) cannot be considered final: many components of the \(r\)-connectivity in Figure 5b need further connection. This will be done by the third stage of \(\operatorname{DPS}\), the results of which on a given array \(X\) for a given \(\operatorname{SDPS}\)-algorithm will be shown in Example 4._
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 2 Supporting Technologies in the IoT-Edge-Cloud Continuum
### Subnetworks  
The increased number of wireless applications deployed at the network edge involving a limited number of network components, such as a sensor network in a manufacturing environment or vehicle-to-vehicle (V2V) communications, requires minimum latency with short-range transmission. To this end, the concept of subnetworks has been introduced, where a network component in the edge acts as a serving AP [54; 55]. However, the concept of subnetworks extends the provision of zero latency to the connected devices, as in cases where the connection with the core network is lost. In this case, as also mentioned in the autonomous driving application, the subnetwork should be in a position to operate autonomously for the provision of uninterrupted E2E connectivity. Sub-networks will be a key driving factor towards the 6G architectural concept due to their local topology in conjunction with the specialized performance attributes required, such as extreme latency or reliability. Moreover, the concept of subnetworks is crucial for the design of energy-efficient networks, where topology reconfiguration might take place in time-varying IoT sensor networks [56].  
In 6G terminology, subnetworks are also referred to as 'in-X' subnetworks, with the 'X' standing for the entity where the subnetwork is deployed, such as a production module in a smart manufacturing environment, a robot, a vehicle, a house, or even the human body in cases of wearable devices that can monitor various parameters [57]. A schematic diagram of such a network is shown in Figure 5, where data flows are categorized according to their latency requirements: low, such as in the cases of monitoring non-latency-critical key performance indicators (KPIs); medium, such as task offloading in edge servers; and high. The latter case includes, for example, control signals from the involved IoT devices in a smart manufacturing environment that necessitate immediate production termination in cases of malfunction. Therefore, the highly critical data flows are kept within the in-X subnetwork, as the tight latency requirement does not allow for external processing. For this reason, a local edge server can be in close proximity to the AP under consideration (in this case, an unmanned aerial vehicle--UAV).
## 3 Method
### Problem Formulation  
HD maps can provide a topology matrix to depict the adjacency of lane segments and real-time phases at signalized intersections. Therefore, the real-time connectivity among lane segments can be defined as a dynamic directed graph \(G^{t}=(V,E^{t},TM^{t})\), where \(V\) is  
Figure 2: The illustration of the cell space in conventional CTM at a signalized intersection.  
the set of lane segments, \(E^{t}\) is the set of directed edges for vehicle transmission between adjacent lane segments at time step \(t\), and \(TM^{t}\) is the corresponding adjacency matrix of \(E^{t}\):  
\[TM^{t}(\zeta,\xi)=\begin{cases}1,&\text{if }(\zeta,\xi)\in E^{t},\\ 0,&\text{otherwise},\end{cases}\text{for }i,j=1,2\ldots N_{ls} \tag{4}\]  
where \(v_{\zeta}\) denotes the lane segment \(\zeta\), and \(N_{ls}\) denotes the lane segment count. For lane segment \(\zeta\), HD maps also provide its direction vector \(\phi_{\zeta}\), which represents the direction of the centerline of lane segment \(\zeta\). The instantaneous state of a vehicle is simplified as \(x^{t}_{\zeta,i}=[v^{t}_{\zeta,i^{\prime}},\phi^{t}_{\zeta,i}]\), where \(v^{t}_{\zeta,i}\) and \(\phi^{t}_{\zeta,i}\) represent the spot speed and azimuth of vehicle \(i\) in lane segment \(\zeta\) at the time step \(t\). Based on this, a lane segment state at time step \(t\) is defined as a feature vector set \(X^{t}_{\zeta}=\{x^{t}_{\zeta,1},x^{t}_{\zeta,2},\ldots,x^{t}_{\zeta,N^{t}_{ \zeta}}\}\in\mathbb{R}^{N^{t}_{\zeta}\times 2}\), where \(N^{t}_{\zeta}\) denotes the internal vehicle count of lane segment \(\zeta\) at time step \(t\). Meanwhile, HD maps also supply vehicle-type attributes \(S^{t}_{\zeta}=\{s_{\zeta,1},s_{\zeta,2},\ldots,s_{\zeta,N^{t}_{\zeta}}\}\), where \(s_{\zeta,i}\) is a one-hot encoding corresponding to \(x^{t}_{\zeta,i}\). In this way, the state of road space at time step \(t\) is represented as \(X^{t}=\{X^{t}_{1},X^{t}_{2},\ldots,X^{t}_{N_{ls}}\}\) and \(S^{t}=\{S^{t}_{1},S^{t}_{2},\ldots,S^{t}_{N_{ls}}\}\). Moreover, HD maps provide the time attributes \(T^{t}\), which is a one-hot encoding to reflect the position of time step \(t\) in a week.  
The future state of road space is defined as a set of the number of vehicles in each lane segment, expressed as \(Y^{t}=\{N^{t}_{1},N^{t}_{2},\ldots,N^{t}_{N_{ls}}\}\). To this end, we take \(X=\{X^{1},X^{2},\ldots,X^{T_{H}}\}\) and \(S=\{S^{1},S^{2},\ldots,S^{T_{H}}\}\) as inputs, where \(T_{H}\) denotes the historical time steps. The predictive goal is \(Y=\{Y^{T_{H}+1},Y^{T_{H}+2},\ldots,Y^{T_{H}+T_{P}}\}\), where \(T_{P}\) denotes the predictive time steps. The temporal attributes \(T=\{T^{1},T^{2},\ldots,T^{T_{H}+T_{P}}\}\) and spatial attributes \(TM=\{TM^{1},TM^{2},\ldots,TM^{T_{H}+T_{P}}\}\) are also input into CeT to guide spatial-temporal feature extraction and prediction result generation.
## 4 Results and Analysis
### Algorithm Recognition Speed  
After building the Pruned-VGG16-Bi-LSTM model, we compared the time of recognition on the self-made dataset (0-9 English independent pronunciations), the result of which is shown in Figure 9.  
The times obtained from training were summed over the time of recognition (in milliseconds) for ten English pronunciations. In order to compare the performance im  
Figure 8: Loss function comparison chart.  
Figure 9: Time consumed in recognition.  
provement of the model, we recorded the recognition time for each test, and the total number of tests was 1000, which shows that as the pruning rate increased, the recognition speed increased and the recognition time decreased. The networks with pruning rates of 20% and 40% stabilized in the later stage of the test, and the network with the pruning rate of 60% had better recognition performance, and the speed stabilized after 500 tests and was better than the original network.  
For different pronunciations, the models differed in the recognition time of different digits, influenced by the number of iterations. In this paper, we compared the average recognition times of different network models (original network, pruned 20%, pruned 40%, and pruned 60%) for recognizing ten digit pronunciations, as shown in Figure 10. It can be seen that compared with the original model, the average recognition time after pruning is significantly reduced in all cases.