esa-sceva/satcom-chunk-collection huggingface dataset:
# Is Running Technique Important to Mitigate Hamstring Injuries in Football Players?
## 1 Introduction
Efficient running biomechanics are essential not only for to improving injury prevention but also for enhancing acceleration capabilities and sprint velocity, two key components of professional football performance [12; 32; 33]. The concept of front-side mechanics emphasizes the importance of maintaining specific postures or kinematics, particularly of the lower limbs, in the front of the body's center of mass during running [17]. This approach minimizes excessive elliptical motion behind the center of mass, optimizing running efficiency and reducing the risk of injury [12; 33; 34]. Efficient running with front-side mechanics involves key movements such as a high knee and forward thigh position during the swing phase and dorsiflexion of the ankle promoting a reduced braking forces, improve propulsion and overall running performance [35; 36]. However, the current understanding of the relationship between running technique and the susceptibility to hamstring injuries in football players is limited. Studies by [PERSON] et al. [12] and [PERSON] et al. [33] have concluded that targeted running biomechanics interventions can reduce the risk of hamstring injuries, but their methodologies differ significantly. [PERSON] et al. [12] implemented a comprehensive six-week multimodal program combining sprint technique, strength and conditioning, and physical therapy, whereas [PERSON] et al. [33] employed a single session sprint training protocol focused solely on sprint mechanics. In contrast, our study adopts an intervention protocol with the same six-week duration, but exclusively targets running technique training. This focused approach emphasizes the importance of analyzing lower limb and trunk kinematics during sprinting to provide new and valuable insights into hamstring injury prevention strategies. The current study aims to evaluate the effects of a front-side-oriented training program on running mechanics in football players and its implications for mitigating biomechanical risk factors associated with hamstring injuries as well as enhancing sprint performance. The hypothesis is that improving the player's running technique will decrease pelvic anteversion during the touchdown and toe-off instants, leading to a reduction in the hamstring tension and ground contact time while increasing stride length. The study focuses on identifying measurable changes in running mechanics following the intervention, with a particular emphasis on mitigating factors associated with hamstring strain injuries.
## 3 Results and Discussion
### Answering RQ1 and RQ2
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Quality Category**} & \\ \cline{2-9}
**Ref.** & **Year** & **AR as a** & **AR** & **AR-Based** & **Application** & **Technology** & **Sample** & **Results** \\  & **Central** & **Actual-** & **Metrology** & **In-Line Quality** & **Control** & & & & \\ \hline
[194] & 2020 & x & & & Car body & HMD, & Pre-test: 20 participants & The MR-based interface was weighted with non-MR experience & 80.25/100 for the usability test \\  & & & quality control & Model-based tracking & Test: 7 experts from Alitate & \(\Rightarrow\) high potential of industrial adoption but still needs improvement & \\ \hline
[210] & 2020 & x & x & Shop floor & Monitor, & N/A & 36\% reduction of execution time besides \\  & & & procedures & Markerless tracking & N/A & reducing the risk of human errors \\ \hline
[235] & 2020 & x & & & Aritation system & Hybrid tracking (markers tracking, & N/A & The Edge-based tracking algorithms are developed and tested, which shows potential in a new way of tracking for the AR system \\  & & & & single-based tracking & & & \\ \hline
[8] & 2019 & x & x & Polised surfaces & HMD, & & & \\  & & & quality assessment & Model-based tracking & & N/A & The metrology data is successfully shown on the real parts \\ \hline
[195] & 2019 & x & & Design variations & HMD, & 20 participants (8 factory workers, 12 engineers) & Provides medium-to-high levels of usability \\ \hline
[196] & 2019 & & x & & Car body & HMD, & & \\  & & & quality control & Marker-based tracking & & & \\ \hline
[197] & 2019 & & & & KORT Water & & & \\  & & & Condition & Markerless tracking & N/A & A prototype was shown for proof-of-concept \\  & & & & Monitoring & & & \\ \hline
[198] & 2019 & x & & & Packaging &
\begin{tabular}{c} Spatial display, \\ Marker-based tracking \\ \end{tabular} & 4 operators & & Preventing data less, reducing costs & Less error-prone \\  & & & & & Marker-based tracking & Potential functionality using data analytics for decision making \\ \hline
[204] & 2019 & & x & \begin{tabular}{c} Bottleneck \\ identification \\ \end{tabular} & \
\begin{tabular}{c} HHD, HMD, \\ Markerless tracking \\ \end{tabular} & 20 participants &
\begin{tabular}{c} Performance in bottlenecks detection with AR \\ App outperformed the traditional \\ lean observers \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cnt._
## 5 PAPR-Aware Transmission Strategies
### Influence of Wireless Channel on PAPR  
In this subsection, the channel influence on the PAPR of the received signal without pre-equalization is discussed. Once we understand this influence, an optimal pre-equalizer that determines the weights of each tone in a multisine signal, as to optimize the normalized PCE at the receiver, can be designed. The PAPR is calculated considering the block fading channel where the channel coefficients stay the same during the considered time block.  
Figure 5: (**a**) Measurement set-up and (**b**) measured DC output power \(P_{DC}\) with increasing PAPR of the signal at different power levels; the dotted lines of the same color plot the corresponding \(P_{DC}\) using CW signal excitation.  
When considering a constant amplitude and no equalization, the \(PAPR_{r}\) of (7) becomes  
\[PAPR_{r}=\frac{Max\left(A\left(t\right)\right)^{2}}{\left\|\mathbf{h}\right\|^{2 }\frac{a^{2}}{2}}\leq\frac{2\left\|\mathbf{h}\right\|_{1}^{2}}{\left\|\mathbf{h }\right\|^{2}}, \tag{16}\]  
where \(A(t)\) can be written as  
\[A(t) =a\sqrt{\sum_{n=1}^{N_{t}}\left|h_{n}\right|^{2}+\sum_{i\
eq j}2 \left|h_{i}\right|\left|h_{j}\right|\cos\left(\varphi_{i}\left(t\right)-\varphi _{j}\left(t\right)\right)},\] \[\leq a\left\|\mathbf{h}\right\|_{1}. \tag{17}\]  
The maximum PAPR is only reached when  
\[\varphi_{i}\left(t\right)-\varphi_{j}\left(t\right) =2\pi\left(i-j\right)\Delta ft+\angle h_{i}-\angle h_{j},\] \[=0,\text{ for all }i\
eq j, \tag{18}\]  
which means that all tones have the same phase at the receiver.  
In addition to a phase constraint, there is an amplitude constraint. Given the fact that \(\left(N_{t}\left\|\mathbf{h}\right\|^{2}-\left\|\mathbf{h}\right\|_{1}^{2} \right)=\sum_{i\
eq j}\left(\left|h_{i}\right|-\left|h_{j}\right|\right)^{2}\geq 0\), we know that \(N_{t}\left\|\mathbf{h}\right\|^{2}\geq\left\|\mathbf{h}\right\|_{1}^{2}\) or \(N_{t}\geq\frac{\left\|\mathbf{h}\right\|_{1}^{2}}{\left\|\mathbf{h}\right\|^{2}}\). Then the PAPR, which is the ratio \(\frac{2\left\|\mathbf{h}\right\|_{1}^{2}}{\left\|\mathbf{h}\right\|^{2}}\) is always smaller than \(2N_{t}\) and reaches the maximum when \(\left|h_{i}\right|=\left|h_{j}\right|\). Considering a frequency flat channel where each tone has the same channel coefficient \(h\), PAPR is maximum and equal to \(2N_{t}\), because the same channel implies \(\left|h_{i}\right|=\left|h_{j}\right|\) and \(\angle h_{i}=\angle h_{j},\ i\
eq j\).
## 3 Results and Discussion
### The Effects of Load Resistances and Spacing Ratios on Energy Conversion
#### 3.3.2 Effect of Spacing Ratio on Energy Conversion  
(a) For UTP, \(U_{r}\leq 9.125\), the prism performed better compared to STP in terms of active power and efficiency for \(R_{L}=21\)\(\Omega\), shown in Figure 18. As \(U_{r}\geq 9.125\), the active power and efficiency of the STP were higher than those of UTP. At the end of the galloping branch at \(U_{r}=12.25\), \(P_{harm}=11.36\) W, \(L/D=12\), corresponding, \(\eta_{harm}=3.43\%\). The active power and efficiency of STP (\(P_{harm}=12.52\) W, \(\eta_{harm}=3.78\%\)) were a little higher at \(U_{r}=12.25\), as shown in Figure 18.  
For UTP, \(3\leq L/D\leq 6\), SG occurred; as the velocity increased, the active power gradually increased to \(14.14\) W (\(L/D=6\)) at \(U_{r}=12.25\), corresponding to \(\eta_{harm}=4.26\%\); the values were lower than those of STP (\(P_{harm}=16.33\) W, \(\eta_{harm}=4.92\%\)) for \(R_{L}=13\)\(\Omega\). For \(9\leq L/D\leq 15\), HG occurred, and UTP performed worse compared to STP in terms of active power and efficiency, shown in Figure 19.  
(b) For DTP, slight suppression was observed for \(R_{L}=21\)\(\Omega\); due to the disruption of the flow between two T-section prisms, the active power and efficiency of DTP were lower than those of UTP and STP. The overall performance of DTP was better at \(L/D=6\); the active power and efficiency reached \(11.2\) W and \(3.38\%\), respectively, at \(U_{r}=12.25\), as shown in Figure 20.  
Figure 19: Energy conversion of UTP at various spacing ratios for \(R_{L}=13\)\(\Omega\): (**a**) Active power; (**b**) Energy conversion efficiency.  
Figure 18: Energy conversion of UTP at various spacing ratios for \(R_{L}=21\)\(\Omega\): (**a**) Active power; (**b**) Energy conversion efficiency.  
For DTP, \(3\leq L/D\leq 6\) and \(L/D=15\), the active power and efficiency begun to increase sharply as the velocity increased for \(U_{r}\geq 9.125\). The active power climbed to 13.79 W (\(L/D=6\)), 11.56 W (\(L/D=3\)), and 8.87 W (\(L/D=15\)) at \(U_{r}=12.25\), corresponding to \(\eta_{harn}=4.15\%\), \(\eta_{harn}=3.48\%\), \(\eta_{harn}=2.67\%\); these values are lower than those of the STP (\(P_{harn}=16.33\) W, \(\eta_{harn}=4.92\%\)) for \(R_{L}=13\Omega\). For \(9\leq L/D\leq 12\), in the VIV lower branch, the DTP performed better compared to the STP in terms of active power and efficiency as shown in Figure 21.
# Service for Deploying Digital Twins of QKD Networks
## 2 Background
### Bits and Qubits  
Unlike classical communication, where information is presented in the form of binary units called bits, which can be either in state 0 or 1, quantum information relies on quantum bits or _qubits_.  
Qubits, as well as every quantum object, exhibit characteristics that have no equivalent in the classical world. The state of a qubit can be a combination (superposition) of the two classical states of a bit, 0 and 1. Moreover, the mere observation (measurement) of the state of a qubit disturbs it irreparably. Therefore, if a qubit is in a superposition state and is measured, it will _collapse_ in one of the _measurement basis_ states, with probabilities that depend on the original state. After the measurement, the initial state of the qubit is destroyed. Note that, if the initial state of the qubit corresponds to one of the measurement basis states, the result of the measurement can only be that state. The standard basis for qubits is the one whose states are the classical states of the bits, 0 and 1, but there is an infinite number of measurement bases that can be chosen depending on the situation.  
Another relevant characteristic of qubits is that, unlike classical bits, it is not possible to copy an unknown qubit state while keeping the original intact, as the no-cloning theorem states [18].  
Last but not least, qubits also experience the phenomena of entanglement. Entanglement is a unique quantum feature that implies the existence of global states of composite systems which cannot be written as a product of the states of individual subsystems [19]. This means that two entangled qubits cannot be treated as individual entities: whatever happens to one of them will inevitably have repercussions on the other. For example, if one of the qubits of the entangled pair is measured, it will break the entanglement and the state of the other will collapse as well, no matter how far apart they are, or how much time has passed since the start of the entanglement. There are ways to test the entanglement between qubits. For example, a CHSH test can be performed [20].
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### Image Preprocessing  
#### 2.1.1 Image Interpolation  
Bilinear interpolation is a commonly used image interpolation method for calculating new pixel values between discrete pixel values [27]. This method is based on the principle of linear interpolation and performs calculations in both the horizontal and the vertical directions. The focus of the bilinear interpolation algorithm is to design a gradient template for determining the precise position of each pixel in the image, followed by polynomial interpolation to calculate new pixel coordinates. For the image \(I\) to be interpolated, the size and pixel position of the target image must be determined. For each target pixel, the four closest original image pixels are found. As shown in Figure 2, these are the pixels in the top left, top right, bottom left, and bottom right. The coordinates of these four pixels are assumed to be \((x_{1},y_{1})\), \((x_{2},y_{1})\), \((x_{1},y_{2})\), and \((x_{2},y_{2})\), respectively, where \(x_{1}\) and \(x_{2}\) are the nearest neighbors in the horizontal direction, and \(y_{1}\) and \(y_{2}\) are the nearest neighbors in the vertical direction. The specific process is as follows:  
First, a linear interpolation in the horizontal direction is performed. Next, the distance ratio of the target pixel to the nearest neighboring pixels in the horizontal direction is calculated. Then, based on the pixel values of \(Q_{11}=(x_{1},\,y_{1})\) and \(Q_{21}=(x_{2},\,y_{1})\), the pixel value of the temporary pixel \(R_{1}=(x,\,y_{1})\) is calculated. Finally, based on the pixel values of \(Q_{12}=(x_{1},\,y_{2})\) and \(Q_{22}=(x_{2},\,y_{2})\), the pixel value of the temporary pixel \(R_{2}=(x,\,y_{2})\) is calculated. \(R_{2}\) represents the position of the target pixel relative to the nearest neighboring pixels in the horizontal direction. The following formula  
\[f(R_{1})=\frac{x_{2}-x}{x_{2}-x_{1}}f(Q_{11})+\frac{x-x_{1}}{x_{2}-x_{1}}f(Q_{ 21}) \tag{1}\]  
Figure 1: Algorithm flowchart.  
similarly, is used to calculate \(R_{2}=(x,y_{2})\).  
Then, linear interpolation is performed in the vertical direction, calculating the distance ratio of the target pixel to the nearest neighboring pixels in that direction. According to the pixel values of \(R_{1}=(x,y_{1})\) and \(R_{2}=(x,y_{2})\), the final result \(P=(x,y)\) pixel value is calculated as:  
\[f(P)=\frac{y_{2}-y}{y_{2}-y_{1}}f(R_{1})+\frac{y-y_{1}}{y_{2}-y_{1}}f(R_{2}) \tag{2}\]  
The bilinear interpolation method considers the weights of the nearest four pixels, which better preserves the image's smoothness and details.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 6 Experimental Evaluations
### Automated Causal Analysis for Anomaly Event  
The interpretability of machine learning refers to the degree to which humans can understand the relevant reasons for decision-making. The higher the interpretability of machine learning models, the easier it is for people to understand why certain decisions or predictions are made. In the interpretability research of machine learning models, Shapley Additive exPlans (SHAP ([[https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)]([https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)), (accessed on 13 October 2024))) can explain both global and local aspects and provides many excellent visualization methods that are increasingly favored by scholars [21]. The basic idea is to quantify the marginal contribution of each feature added to the model, take its mean, and record it as the contribution value of that feature to the model prediction.  
Figure 9a is a global explanatory chart of the model presented in this paper. From the figure, it can be seen that the top parameters contributing to the output results of the model include radio altitude (_RALTC_), decline rate (_IVV_), ILS glide slope (_GLIDE_DVC_), difference between air speed and approach speed target (_IAS - VAPP_), air speed (_IAS_), and so on. The result proves that all other parameters with high contribution are monitoring parameters for abnormal events, which is consistent with the facts and shows that the model is reliable.  
In this paper, we equate the contribution of features to the local prediction of the model with the contribution of feature values to causing the abnormal event. Specifically, Figure 9b is a SHAP interpretation chart of the abnormal event with the _Large Decline Rate_ (2000 ft\(\sim\)1000 ft). As expected, the top ranked features are related to the monitoring parameters _IVV_ and the monitoring point radio altitude _RALTC_ of the _Large-Decline-Rate_ (2000 ft\(\sim\)1000 ft) event. So, we removed these two features and their combinations from the SHAP interpretation chart, and the results are shown.  
It can be seen that the features that contribute more to the abnormal event include flight path angle (_FPA_), low altitude speed ratio of engine 1 (_N11_), fuel mass flow rate of engine 1 (_FF1_), etc. These parameters belong to pilot operation parameters or flight state parameters, which are human factors. Based on this, we can preliminarily determine that the abnormal event was caused by human factors.  
Figure 8: Results of sensitivity analysis.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 3 Research Motivation  
### The Sparsity Issue  
In CPS, due to the large number of services and limited invocation experiences, the number of services that are commonly invoked by users is usually quite small. The real-world service invocation scenario is shown in Figure 1.  
In Figure 1, there are three users and six services in CPS, and here we take the response time as the QoS property to explain. User 1 and user 2 both invoke service 2 and service 3, and user 2 and user 3 both invoke service 4 and service 5. User 1 and user 3 do not have any service both invoked. If we adopt the PCC to compute the similarity of users, and similarity result of user 1 and user 2 is close to 1, and the similarity result of user 2 and user 3 is also close to 1. It can be inferred that user 1 and user 2 are similar to each other, and user 2 and user 3 are also similar. But note that, since user 1 and user 3 do not share any invocation experience, the similarity of user 1 and user 3, which is directly computed according to the QoS records, is 0. However, due to the fact that user 1 and user 2 are similar, and user 2 and user 3 are also similar, it can be inferred that user 1 and user 3 are also similar. In this paper, we all the similarity that is computed directly from the known QoS values the direct similarity. In many cases, it is not enough to reflect the real similarity only using direct similarity.  
In this paper, our idea is to employ the transitive relation computation to acquire the real similarity. We use graph as the basic data structure to model the transitive relation. In the graph, a user is a node, and if the direct similarity of two users is not 0, there will be an edge connecting the two nodes in the graph. We employ the random walk model to find the potential similarity among nodes without direct connection. In detail, we use the converged probability after the random walk process to represent the similarity of two nodes, which is regarded as the real similarity. Besides, since the network location is a key factor that influences the QoS value, we integrate the network location into the state transition process of random walk model. Further, we propose two network location-based random walk models, specific to the user side and service side, respectively.
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 2 Background and Related Work
### Cloud Computational Imperatives  
Cloud computing, as defined by NIST, is a model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. These resources can be rapidly provisioned and released with minimal management effort or service-provider interaction [17].  
Main characteristics of cloud computing:  
1. On-demand self-service: CSCs can manage, provision, and scale computing power and storage without direct interaction with CSPs [18].
2. Broad network access: Cloud services are accessible via networks, typically through a cloud carrier such as an Internet Service Provider (ISP) or directly via a CSP using specialized internet protocols which constitute the data transmission gap.
3. Resource pooling: CSPs combine storage and computing resources in multi-tenant models, shared among different consumer groups. CSCs have little control over physical resources and processing/resting locations.
4. Rapid elasticity: Cloud services can be rapidly deployed and scaled to match consumer demands, with automatic scaling of resources up/down using the pay-as-you-use model.
5. Measured services: Consumption of cloud resources is optimized, measured, and charged by the CSP's control system.
6. Five essential factors in cloud computing: (i) consumer, (ii) provider, (iii) carrier, (iv) auditor, and (v) broker. The primary stakeholder, the cloud consumer (CSC), engages with a CSP through service agreements, selecting services from the CSP's catalog, and managing payments accordingly.
7. Cloud Monitoring by Major Providers: Companies like Amazon, Microsoft, and Google employ cloud monitoring to track the effectiveness and utilization of their services (e.g., Amazon uses CloudWatch to monitor various components of its infrastructure, enabling quick issue identification and resolution).
8. Three Delivery Provisioning Models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). These models cater to different consumer needs and preferences. See Tables 1 and 2.