esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Proximal Tools  
The proximity operator [63] is a key tool of proximal splitting techniques. Let \(\mathbf{x}\in\mathbb{R}^{N}\) be an input vector. For any \(\gamma>0\), the primary operator of \(f\) over \(\mathbb{R}^{N}\) is defined by  
\[\text{prox}_{\gamma f}(\mathbf{x}):=\arg\min_{\mathbf{y}\in\mathbb{R}^{N}}f( \mathbf{y})+\frac{1}{2\gamma}\|\mathbf{x}-\mathbf{y}\|^{2}. \tag{5}\]  
For a given nonempty closed convex set \(\mathcal{C}\), the indicator function of \(\mathcal{C}\) is defined by  
\[\iota_{\mathcal{C}}(\mathbf{x}):=\left\{\begin{array}{ll}0,&\text{if }\mathbf{x} \in\mathcal{C},\\ +\infty,&\text{otherwise}.\end{array}\right. \tag{6}\]  
The proximity operator of \(\iota_{\mathcal{C}}\) is expressed as  
\[\text{prox}_{\gamma\iota_{\mathcal{C}}}(\mathbf{x}):=\arg\min_{\mathbf{y}\in \mathbb{R}^{N}}\iota_{\mathcal{C}}(\mathbf{x})+\frac{1}{2\gamma}\|\mathbf{x}- \mathbf{y}\|_{2}^{2}. \tag{7}\]  
The solution of \(\text{prox}_{\gamma\iota_{\mathcal{C}}}\) should be in the set \(\mathcal{C}\) and minimize \(\|\mathbf{x}-\mathbf{y}\|_{2}^{2}\). Thus, for any index \(\gamma>0\), this proximity operator is equivalent to the metric projection onto \(\mathcal{C}\), i.e., \(\mathcal{P}_{\mathcal{C}}(\mathbf{x})=\text{prox}_{\gamma\iota_{\mathcal{C}}}( \mathbf{x})\).  
Let \(\mathbf{l}\) and \(\mathbf{u}\in\mathbb{R}^{N}\) be the lower and upper bounds, respectively. The box constraint forces each element of \(\mathbf{x}\) into the dynamic range \([l_{i},u_{i}]\) for \(i=1,\ldots,N\), and its closed convex set is defined as  
\[\mathcal{C}_{[\mathbf{l},\mathbf{u}]}:=\Big{\{}\mathbf{x}\in\mathbb{R}^{N}\ \ |\ \ l_{i}\leq x_{i}\leq u_{i}\ \ (i=1,\ldots,N)\Big{\}}. \tag{8}\]  
The computation of the metric projection onto \(\mathcal{C}_{[\mathbf{l},\mathbf{u}]}\) for \(i=1,\ldots,N\) is given by  
\[\Big{[}\mathcal{P}_{\mathcal{C}_{\mathbf{l},\mathbf{u}}}(\mathbf{x})\Big{]}_ {i}=\left\{\begin{array}{ll}l_{i},&\text{if }x_{i}<l_{i},\\ u_{i},&\text{if }x_{i}>u_{i},\\ x_{i},&\text{if }l_{i}\leq x_{i}\leq u_{i}.\end{array}\right. \tag{9}\]  
The \(\ell_{2}\) ball constraint forces the Euclidean distance between the vector \(\mathbf{x}\) and the centered vector \(\mathbf{v}\) to be less than the radius \(\epsilon\), and its closed convex set is defined as  
\[\mathcal{B}_{\mathbf{v},\epsilon}:=\Big{\{}\mathbf{x}\in\mathbb{R}^{N}\ \ |\ \ \| \mathbf{x}-\mathbf{v}\|_{2}\leq\epsilon\Big{\}}. \tag{10}\]  
The computation of the metric projection onto \(\mathcal{B}_{\mathbf{v},\epsilon}\) is given by  
\[\mathcal{P}_{\mathcal{B}_{\mathbf{v},\epsilon}}(\mathbf{x})=\left\{\begin{array} []{ll}\mathbf{x},&\text{if }\|\mathbf{x}-\mathbf{v}\|_{2}\leq\epsilon,\\ \mathbf{v}+\epsilon\frac{\mathbf{x}-\mathbf{v}}{\|\mathbf{x}-\mathbf{v}\|_{2} },&\text{otherwise}\.\end{array}\right. \tag{11}\]
## 3 Results and Discussion
### Impact of Precipitation Distribution on Vegetation Phenology and Forage Production  
Annual total precipitation may not be helpful to characterize differences in vegetation phenology and forage production, as distribution of precipitation during growing seasons is a key factor that affects vegetation phenology and productivity [(29)]. To illustrate differences in vegetation phenology and forage production with different distribution patterns of precipitation, we compared the dynamics of EVI in three years with lower annual total precipitation (2011, 2012, and 2014) for the pasture situated at the uppermost landscape position (P14). All three years received roughly two-third of the annual total precipitation. The spring was cooler and drier in 2011 and 2014, but warmer and wetter in 2012. Total precipitation for January-May was 162, 306, and 154 mm in 2011, 2012, and 2014, respectively. Average \(\rm T_{a}\) for January-May was 10, 13, and 9 \({}^{\circ}\)C in 2011, 2012, and 2014, respectively. As a result, prairie vegetation greened-up early (around mid-March) in 2012, but later (after mid-April) in 2011 and 2014 (Figure 3). The EVI peaked before mid-May 2012, while it peaked in early June in 2011 and early July in 2014. Peak EVI approximated 0.46, 0.52, and 0.72 in 2011, 2012, and 2014, respectively. Total precipitation received in June-July of 2011, 2012, and 2014 was 68, 58, and 260 mm, respectively. Due to severe dry and warm conditions, the EVI declined sharply after mid-June in 2011 and 2012. Due to higher precipitation in summer 2014, the EVI remained substantially higher throughout the growing season. The results illustrate the importance of timely distribution of precipitation during the growing season to productivity.
## 7 Value of the Work and Findings  
In the planning and scheduling of hybrid or distributed energy systems, the effective integration between predictive analytics and prescriptive analytics plays a vital role in enhancing operational stability and optimizing resource allocation. Predictive analytics focuses on quantifying and forecasting the output fluctuations of renewable energy sources such as wind and PV, while prescriptive analytics formulates optimal response strategies and capacity configuration schemes based on these forecasts. This paper proposes a method for configuring the capacities of pumped storage units, wind power, and PV generation based on the adjustable capacity of cascade small hydropower stations. Through spectral analysis, the power output fluctuations of renewable sources are decomposed and characterized in the frequency domain. By setting the fluctuation rate threshold to no more than 10%, the expected output power of renewable sources that meets the volatility constraint is derived. This spectral analysis process can be regarded as a form of predictive analytics, serving to identify and quantify the fluctuation characteristics of renewable power outputs.  
Building upon this foundation, the expected output power of renewable energy sources is further integrated with the regulation capability of cascade small hydropower to construct an optimized scheduling model for the cascade hydropower-wind-PV-pumped storage complementary power generation system. This model is used to verify the effectiveness of capacity configuration under multiple scenarios, representing the core task of prescriptive analytics. This logical chain from prediction to decision-making significantly enhances the scientific rigor and adaptability of capacity planning.  
This paper achieves a methodological integration from fluctuation analysis to capacity configuration optimization, reflecting the combined value of predictive and prescriptive analytics in the planning of renewable energy systems. The proposed approach carries important theoretical and practical significance for improving the operational stability and resource utilization efficiency of multi-energy complementary systems.  
Similarly, the wind ramp event-based forecasting model proposed in Reference [1] also starts from the fluctuation characteristics of power output and provides predictive support for optimization models within prescriptive analytics. If integrated with capacity configuration strategies in future work, this model could further enhance the system's robustness and proactive response capability against uncertainty disturbances.
## 3 Main Technical Research
### Research on Point Cloud Registration and Thickening Method Combining ORB-SLAM3 and NDT Technology
#### 3.1.2 Optimization and Densification of Point Cloud Registration  
In order to enhance the accuracy of point cloud registration and achieve real-time densification, this study first employs a spatial filtering algorithm to eliminate vehicular interference from the point cloud, retaining the fundamental point cloud of the tunnel and tunnel wall to improve matching precision. Following this, the ORB-SLAM3 technique is integrated, combining vehicular camera information with point cloud data for joint analysis. When leveraging the coupling relationship between the point cloud and the image, distinctive features in the visual images are extracted, and the results of ORB-SLAM3 computation are utilized to constrain the outcomes of point cloud registration.  
Figure 4: Comparison of ICP and NDT Methods.  
Figure 3: ICP and NDT registration results. (**a**) ICP registration result. (**b**) NDT registration result.  
Lastly, using a real-time mapping computation method, an adjustable mapping frame threshold N (information on the number of data frames contained in real-time mapping) is designed. The first frame of mapping data, Map1, is obtained, and subsequent frame data are cumulatively transformed into Map1 via the interframe transformation matrix, generating a new mapping (Map2). If the number of frames in Map2 is less than the predetermined threshold N, the computation of the transformation matrix for the next frame continues. This process is repeated, integrating each frame of data into the current mapping dataset via cumulative matrix transformation until the frame count reaches the threshold N, thus achieving real-time densification of the point cloud. The degree of interframe registration error is defined by the score size: a smaller score corresponds to a smaller registration error and higher matching precision, and vice versa.  
In summary, the NDT point cloud registration algorithm, with the support of grid division technology and its independence from corresponding point feature computation and matching, is advantageous. It is applicable for registering large, dense point cloud maps and non-rigid point cloud data. For high-speed rail tunnels characterized by low overlap rates and minimal plane structure, the point cloud registration and densification method integrating ORB-SLAM3 and NDT techniques can effectively acquire dense, real-time point cloud data.
# A QUBO Model for the Traveling Salesman Problem with Time Windows
## 4 A QUBO Formulation for the TSPTW
\begin{split} A_{p_{1}}^{2}&=\;\left(x_{0,1}^{1}c_{ 0,1}+x_{0,2}^{1}c_{0,2}+x_{0,3}^{1}c_{0,3}+x_{0,4}^{1}c_{0,4}\right)^{2}\\ &=(x_{0,1}^{1})^{2}c_{0,1}^{2}+(x_{0,2}^{1})^{2}c_{0,2}^{2}+(x_{0,3}^{1})^{2}c_{0,3}^{2}+(x_{0,4}^{1})^{2}c_{0,4}^{2}+2x_{0,1}^{1}c_{0,1}x_{0,2 }^{1}c_{0,2}+2x_{0,1}^{1}c_{0,1}x_{0,3}^{1}c_{0,3}\\ &+2x_{0,1}^{1}c_{0,1}x_{0,4}^{1}c_{0,4}+2x_{0,2}^{1}c_{0,2}x_{0,3 }^{1}c_{0,3}+2x_{0,2}^{1}c_{0,2}x_{0,4}^{2}c_{0,4}+2x_{0,3}^{1}c_{0,3}x_{0,4}^ {1}c_{0,4}\\ &=x_{0,1}^{1}c_{0,1}^{2}+x_{0,2}^{1}c_{0,2}^{2}+x_{0,3}^{1}c_{0, 3}^{2}+x_{0,4}^{1}c_{0,4}^{2}+2x_{0,1}^{1}c_{0,1}x_{0,2}^{1}c_{0,2}+2x_{0,1}^ {1}c_{0,1}x_{0,3}^{1}c_{0,3}\\ &+2x_{0,1}^{1}c_{0,1}x_{0,4}^{1}c_{0,4}+2x_{0,2}^{1}c_{0,2}x_{0,3 }^{1}c_{0,3}+2x_{0,2}^{1}c_{0,2}x_{0,4}^{1}c_{0,4}+2x_{0,3}^{1}c_{0,3}x_{0,4}^ {1}c_{0,4}\end{split}  \tag{31}\]  
Similarly, we see that:  
\[\left(\sum_{k=1}^{2}kt_{k,1}\right)^{2}=(1t_{1,1}+2t_{2,1})^{2}=t_{1,1}^{2}+4t _{2,1}^{2}+4t_{1,1}t_{2,1}=t_{1,1}+4t_{2,1}+4t_{1,1}t_{2,1} \tag{32}\]  
\[ \begin{split} W_{1}&=x_{0,1}^{1}c_{0,1}^{2}+x_{0,2}^{1}c_{0,2 }^{2}+x_{0,3}^{1}c_{0,3}^{2}+x_{0,4}^{1}c_{0,4}^{2}+2x_{0,1}^{1}c_{0,1}x_{0,2}^ {1}c_{0,2}+2x_{0,1}^{1}c_{0,1}x_{0,3}^{1}c_{0,3}\\ &+2x_{0,1}^{1}c_{0,1}x_{0,4}^{1}c_{0,4}+2x_{0,2}^{1}c_{0,2}x_{0,3 }^{1}c_{0,3}+2x_{0,2}^{1}c_{0,2}x_{0,4}^{1}c_{0,4}+2x_{0,3}^{1}c_{0,3}x_{0,4}^{ 1}c_{0,4}\\ &+t_{1,1}+4t_{2,1}+4t_{1,1}t_{2,1}\\ &+x_{0,1}^{1}l_{1}^{2}+x_{0,2}^{1}l_{2}^{2}+x_{0,3}^{1}l_{3}^{2}+x _{0,4}^{1}l_{4}^{2}+2x_{0,1}^{1}l_{1}x_{0,2}^{1}l_{2}+2x_{0,1}^{1}l_{1}x_{0,3}^ {1}l_{3}\\ &+2x_{0,1}^{1}l_{1}x_{0,4}^{1}l_{4}+2x_{0,2}^{1}l_{2}x_{0,3}^{1}l_ {3}+2x_{0,2}^{1}l_{2}x_{0,4}^{1}l_{4}+2x_{0,3}^{1}l_{3}x_{0,4}^{1}l_{4}\\ &-2x_{0,1}^{1}c_{0,1}t_{1,1}-2x_{0,1}^{1}c_{0,1}t_{2,1}-2x_{0,2}^ {1}c_{0,2}t_{1,1}-2x_{0,2}^{1}c_{0,2}t_{2,1}\\ &-2x_{0,3}^{1}c_{0,3}t_{1,1}-2x_{0,3}^{1}c_{0,3}t_{2,1}-2x_{0,4}^ {1}c_{0,4}t_{1,1}-2x_{0,4}^{1}c_{0,4}t_{2,1}\\ &-2x_{0,1}^{1}c_{0,1}t_{1}-2x_{0,2}^{1}c_{0,2}t_{2}-2x_{0,3}^{1} c_{0,3}l_{3}-2x_{0,4}^{1}c_{0,4}t_{4}\\ &-2x_{0,1}^{1}c_{0,1}x_{0,2}^{1}l_{2}-2x_{0,1}^{1}c_{0,1}x_{0,3}^ {1}l_{3}-2x_{0,1}^{1}c_{0,1}x_{0,4}^{1}l_{4}\\ &-2x_{0,2}^{1}c_{0,2}x_{0,1}^{1}l_{1}-2x_{0,2}^{1}c_{0,2}x_{0,3}^ {1}l_{3}-2x_{0,2}^{1}c_{0,2}x_{0,4}^{1}l_{4}\\ &-2x_{0,3}^{1}c_{0,3}x_{0,1}^{1}l_{1}-2x_{0,3}^{1}c_{0,3}x_{0,2}^ {1}l_{2}-2x_{0,3}^{1}c_{0,3}x_{0,4}^{1}l_{4}\\ &-2x_{0,4}^{1}c_{0,4}x_{0,1}^{1}l_{1}-2x_{0,4}^{1}c_{0,4}x_{0,2}^ {1}l_{2}-2x_{0,4}^{1}c_{0,4}x_{0,3}^{1}l_{3}\\ &-2t_{1,1}x_{0,1}^{1}l_{1}-2t_{1,1}x_{0,2}^{1}l_{2}-2t_{1,1}x_{0, 3}^{1}l_{3}-2t_{1,1}x_{0,4}^{1}l_{4}\\ &-4t_{2,1}x_{0,1}^{1}l_{1}-4t_{2,1}x_{0,2}^{1}l_{2}-4t_{2,1}x_{0, 3}^{1}l_{3}-4t_{2,1}x_{0,4}^{1}l_{4}\end{split}  \tag{37}\]  
Similar calculations, albeit too lengthy to include here, confirm that all time window constraints have similar patterns, that is they constitute legitimate expressions within the QUBO framework.
# An Optimization Strategy to Position CHIEF Points in Boundary-Element Acoustic Problems
## 2 Materials and Methods
### The CHIEF Method  
It is known that the application of the described BEM technique for solving external acoustic problems (infinite media) may produce a non-unique response [4]. The resonance modes observed within the domain are on the imaginary axis and, theoretically, would never be excited by the external incident field [21]. However, it has been observed that the internal resonance modes do not vanish completely, in such a way that numerical error occurs when the vibration frequencies of the infinite domain \(\Omega^{+}\) approach the natural frequencies of the internal body \(\Omega^{-}\).  
By analyzing the boundary eigenvalue problem, if the resonance modes inside the domain are within the analyzed frequency range, it can be noticed that spurious or fictitious eigenfrequencies arise. In case of instability, the results for high frequencies begin to oscillate with exponentially increasing amplitude [21]. In order to solve this problem and to \"filter\" such eigenfrequencies, the combined Helmholtz integral equation formulation (CHIEF) technique can be used, which is the focus of this paper.  
The purpose of the CHIEF formulation is to minimize the error by enforcing additional constrains. The technique consists of adding \(NPC\) collocation points outside the domain of analysis (the so-called CHIEF points), and through these new points adding new equations to the algebraic system. If the BEM collocation points are on the body boundary, the idea is that the CHIEF points are outside the body of analysis, in the internal domain itself. Thus, each additional equation has the form:  
\[\int_{\Gamma}q^{*}(\mathbf{x},\mathbf{\xi})p(\mathbf{\xi})d\Gamma(\mathbf{\xi})-\int_{\Gamma}p^ {*}(\mathbf{x},\mathbf{\xi})q(\mathbf{\xi})d\Gamma(\mathbf{\xi})=0, \tag{13}\]  
which corresponds to enforcing null \(p\) values at specific points in \(\Omega^{-}\). Thus, by defining \(M=NE+NPC\), the system defined by Equation (10), in matrix form, would result in:  
\[\left[H\right]_{Mx\ NE}\cdot\left\{p\right\}_{NE}=\left[G\right]_{Mx\ NE}\cdot \left\{q\right\}_{NE}, \tag{14}\]  
The final non-square system of equations can then be solved using a least-squares technique.
## 4 Numerical Results
### Irregular Wave
For the mooring system with three fairleads, its performance exhibited little sensitivity to the included angle between the two mooring lines in the same group. In C4, the first peak values for T0 and T3 were 0.004\({}^{\circ}\) and 0.0022\({}^{\circ}\), respectively; that is, the developed mooring system with three fairleads could reduce wind turbine yaw movement by 47.5%. Figure 14: Frequency\(-\)amplitude curve and first peak amplitude in C5: (**a**) and (**b**) surge; (**c**) and (**d**) pitch; (**e**) and (**f**) yaw. Generally speaking, when under irregular waves, the developed mooring systems could effectively reduce wind turbine surge movement by 56.08%, reduce pitch movement by 25%, and yaw movement by 47.5%. Meanwhile, the mooring system's performance exhibited little sensitivity to the included angle between the two mooring lines in the same group and the number of fairleads. The mooring system with three fairleads and 40\({}^{\circ}\) included angle is recommended.
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 4 Experiments
### Evaluation Metrics  
The Dice score and Hausdorff distance are generally employed as measures of brain tumor segmentation. The Dice score measures the similarity between two samples, the range of values is 0-1, the best value is 1 and the worst value is 0. The definition of Dice is as follows.  
\[Dice=\frac{2|Q\cap G|}{|Q|+|G|} \tag{7}\]  
where \(Q\) represents the prediction mask and \(G\) represents the ground truth.  
The Hausdorff distance is a metric that evaluates the maximum segmentation boundary distance between \(Q\) and \(G\), the definition is as follows.  
\[HD=\max\biggl{\{}\max_{q\in Q}\min_{g\in G}d(q,g),\max_{g\in G}\min_{q\in Q}d(q,g)\biggr{\}} \tag{8}\]
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 3 Management of Acute Asthma Exacerbation
### Intravenous Aminophylline  
Methylxanthines can cause bronchial smooth muscle relaxation through multiple mechanisms and are one of the most commonly prescribed drugs for asthma worldwide [39]. The effect of methylxanthines is due to a combination of inhibition of phosphodieserase enzymes, translocation of calcium, adenosine receptor blockade, and modulation of GABA receptors, resulting in the accumulation of cyclic adenosine monophosphate and subsequent bronchodilation [39; 40; 41; 42]. There is a reasonably large body of literature supporting the use of oral theophylline in difficult asthma, but evidence for the use of intravenous aminophylline (a more soluble form of methylxanthine) as an adjunct in acute asthma exacerbation has remained controversial, and indeed its use has declined in recent decades [43]. However, intravenous aminophylline has a narrow therapeutic index and should be used with caution, as it requires monitoring of plasma concentrations to avoid toxicity.  
While the guidelines currently do not recommend the routine use of intravenous aminophylline, it continues to be used in patients with severe asthma as an adjunct to provide additional bronchodilation [44; 45]. RCTs in adults investigating the use of IV aminophylline in life-threatening or near-fatal asthma are space, and most studies were performed on small sample sizes. Currently, BTS recommends the use of oral theophylline for use in asthma only under specialist guidance [10]. Conversely, the guidelines state that intravenous aminophylline is unlikely to result in any additional bronchodilation compared with standard care with inhaled bronchodilators and steroids, and the guidelines remind clinicians of the increase in side effects such as vomiting and arrhythmias [10]. A meta-analysis involving seven trials and 380 children concluded that although there appeared to be an improvement in lung function as measured by PEF and FEV\({}_{1}\), they found that there was no apparent reduction in symptoms, number of nebulised treatments, or length of hospital stay [46]. A meta-analysis in 2012 of 11 studies involving 350 patients found no consistent evidence favouring either intravenous \(\beta\)2 agonists or intravenous aminophylline in the treatment of acute asthma. It went further to state that the heterogeneity of the trials included, alongside the increased risk of adverse events, should be considered given the lack of evidence surrounding the efficacy of these agents [47]. Although intravenous aminophylline is sometimes used as a salvage therapy, clinical trials or a large data registry are required to assess its effectiveness as an adjunctive treatment for patients with severe asthma in the ICU.
## 3 Results
### Identification of the \"Points of Interest\"  
Figure 6a shows the proximity to the EZ of the points of interest identified through the extreme value distribution-based algorithm applied to the various investigated layers of the TF image as well as through the support vector machine's classifier used on all the layers. We can observe that the individual layers are able to identify the EZ within median proximity of 8-12 mm (Layer 1: 8.17, Layer 2: 10.36, Layer 3: 9.50, Layer 4: 9.40, Layer 5: 9.57, Layer 6: 11.27, Layer 7: 9.19, Layer 8: 9.26, Layer 9: 8.85, Layer 10: 10.79, Layer 11: 9.66, Layer 12: 11.63, and Layer 13: 11.73).  
When we combine features from all layers and use a support vector machine's classifier-based classifier, we observe that the points of interest are very proximal to the EZ with a median distance of 7 mm, which is significantly shorter compared to using any individual layer threshold (support vector machines classifier vs. Layer 1, 3, 4, 5, and 12: \(p<0.001\); support vector machines classifier vs. layer rest of the layers: \(p<0.05\)).  
Figure 6b shows the distribution of points of interest across proximity to the EZ. About 35% of the points are within the EZ (indicated by the blue line), and about 60% of them are inside a 10 mm radius (indicated by the barplot).  
We tested the effect of the type of pathology on the localizing error and found that patients with focal cortical dysplasia (\(n=8\)) have a lower localization error as compared to the other groups, which included unknown etiology, tumor, and polymicrogyria (6 mm vs. 12 mm; \(p=0.018\)). We also tested the effect of age at surgery on localization error and found no significant correlation (\(p>0.05\)).
## 4 Discussion
Joint reaction forces are of interest in cycling, since they can describe force applied at the patellofemoral cartilage which can influence the incidence of overuse injuries. Despite the limitations of the presented model, it plays an important role in clinical settings due to its simplicity and demonstrated reliability. A more recent study made by [PERSON] and [PERSON] (2013) focused on tibiofemoral compressive and shear force and patellofemoral compressive force. However, only a 2D pedal dynamometer was used, which limited the interpretation of knee loading only to these compressive and shear forces [22]. For better understanding of the knee loading, varus/valgus torque needs to be considered [7]. In recent years, musculoskeletal models using the OpenSim modeling tool have been used in biomechanical research [23]. Such models can give meaningful information about a wide range of variables which can be computed. Furthermore, these models have big potential in certain pathologies such as osteoarthritis and in biomechanical research since they can be used to better understand how muscles act during movement and their impact on forces and moments across all joints. However, many assumptions have been made in the development of a musculoskeletal model, such as limited experimental evidence, size, age, etc. [24; 25]. The model presented in this paper can therefore provide important information for applied scientists, clinicians, and practitioners in relation to: (1) Prevention and occurrence of overuse injury in cycling and (2) use of stationary cycling in knee rehabilitation. From a practical point of view, the model was described in detail, which makes it easy for the reader to implement it and use in further studies or practical settings. Its practical value is in its simple design and quick setup which can be used on daily bases in, e.g., clinical work. The 3D model evaluated knee joint loads during seated cycling. Such a model may be needed for adjusting bike position to an individual with the intention to optimize knee joint loads, which can be key to preventing overuse injuries. Furthermore, knee loads can be minimized using different pedaling techniques. In rehabilitation purposes, such a model can be used to prevent a patient from producing high load in a certain direction. The model was developed to describe 3D loads using a simple and straightforward marker setup, which is the key for daily use. The duration of pedaling was also one of the limitations of this study that potentially resulted in higher inter-subject variability.