esa-sceva/satcom-chunk-collection huggingface dataset:
# Bacon, a Lightweight Deep Reinforcement Learning Benchmark Library for Flow Control
## 3 Environments
### Mixing  
#### 3.3.1 Physics  
We consider the resolution of the 2D Navier-Stokes equations coupled to a passive scalar convection-diffusion equation in a cavity of length \(L\) and height \(H\) with moving boundary conditions on all sides. The resulting system is driven by the following set of equations:  
\[\
abla\cdot\mathbf{u} =0, \tag{15}\] \[\partial_{t}\mathbf{u}+(\mathbf{u}\cdot\
abla)\,\mathbf{u} =-\
abla p+\frac{1}{\text{Re}}\
abla^{2}\mathbf{u},\] \[\partial_{t}c+(\mathbf{u}\cdot\
abla)\,c =\frac{1}{\text{Pe}}\
abla^{2}c,\]  
where \(\mathbf{u}\) and \(p\) are, respectively, the non-dimensional velocity and pressure of the fluid, and \(c\) is the concentration of a passive species. The dynamics of system (15) are controlled by two adimensional numbers. First, the Reynolds number Re, which represents the ratio between inertial and viscous forces as follows:  
\[\text{Re}=\frac{UL}{\
u},\]  
where \(U\) and \(L\) are, respectively, the reference velocity and length values, and \(\
u\) is the kinematic viscosity of the fluid. Second, the Peclet number Pe, which represents the ratio between the advective and diffusive transport rates as follows:  
\[\text{Pe}=\frac{UL}{D},\]  
where \(D\) is the diffusion coefficient of the considered species. In essence, a system with a high Pe value presents a negligible diffusion, and scalar quantities move primarily due to fluid convection. System (15) is completed by the following initial and boundary conditions:  
\[\mathbf{u}(x,y,0)=0\text{ and }c(x,y,0)=c_{0}\mathbb{1}_{x_{\min}\leq x \leq x_{\max}} \tag{16}\] \[\mathbf{u}(x=0,y,t)=(0,v_{l})\text{ and }\mathbf{u}(x=L,y,t)=(0,v_{r}),\] \[\mathbf{u}(x,y=0,t)=(u_{b},0)\text{ and }\mathbf{u}(x,y=H,t)=(u_{t},0),\] \[\partial_{y}c(x,y=0,t)=0\text{ and }\partial_{y}c(x,y=H,t)=0,\] \[\partial_{x}c(x=0,y,t)=0\text{ and }\partial_{x}c(x=L,y,t)=0,\]  
where \(\mathbb{1}\) is the indicator function, and \(v_{l}\), \(v_{r}\), \(u_{b}\), and \(u_{t}\) are user-defined values. In essence, the boundary conditions (16) correspond to a multiple-lid-driven cavity, where tangential velocity can be imposed independently on all sides with an initial patch of concentration in the center of the domain. Snapshots of the evolution of the system in time with \((v_{l},v_{r},u_{b},u_{t})=(0,0,1.0,-1.0)\) are presented in Figure 9.  
Figure 9: **Evolution of the concentration in time** with constant boundary conditions \((v_{l},v_{r},u_{b},u_{t})=(0,0,1.0,-1.0)\). The non-dimensional concentration ranges from 0 (deep blue) to 1 (deep red).
# Resilient Scheduling as a Response to Uncertainty in Construction Projects
[37] developed an ontology-based risk management framework to improve the overall effectiveness of risk management practices for a construction contract. The study of [PERSON] et al. [38] coupled ontology and semantic web technology in a BIM environment to manage construction risk knowledge. Apart from enhancing general risk management procedures, other practical applications of ontology in the construction domain include knowledge sharing [39; 40; 41], information extraction [42; 43; 44], and performance analysis [45; 46; 47; 48]. So far, however, there has been little discussion about modeling a comprehensive knowledge base by considering general sources of uncertainty in construction projects. Therefore, the first aim of this research was to structure uncertainty sources related to complex construction projects in a faceted taxonomy, as a basis for the analysis and uncertainty management in construction projects from the early planning stages. Comprehensive identification and characterization of uncertainties in the construction domain is a first step towards increasing the probability of reaching project goals during the execution phase. Considering the nature of complex construction projects, which are financially extremely demanding undertakings, appropriate cash procurement is of vital importance. The major source of financing for construction projects is the establishment of the bank overdraft [49; 50]. If the cash deficit occurs during the project realization period, contractors will encounter difficulties related to the implementation of the project activities in accordance with a baseline plan [51]. Therefore, the development of a schedule where the cash flows will be suitable for the established bank overdraft is an important subject, since large construction projects require extensive investments and rarely depend solely on the savings of the contractor [49, 50]. The main objective of this research is to propose a comprehensive mathematical model for resilient scheduling as a trade-off between project robustness, project duration, and contractor's profit. Previous studies on resilient project scheduling [28, 29, 30] have not dealt with the finance flow modeling, so the mathematical problem presented in this study aims to contribute to the growing area of research on resilient baseline scheduling by introducing the additional objective function for profit maximization at the end of the project. The present research contributes to a more practical setting in the context of resilient construction scheduling by including the financial aspect in the existing problem. In this way, we assess the issue of financial feasibility in resilient scheduling for construction projects.
## 4 Case Study: AQM Implementation
### Modeling and Execution of the CNS  
The agent-based CNS has been modeled using the AnyLogic multi-method modeling tool [40]. State chart is used to model agent activities where the runtime verification and quality assessment are implemented at strategic transitions [51]. To activate an account, a user needs to choose a username. Agents can choose whether to use a username that contains personal identity information, such as their first name or company name, or use an anonymous name. Whether the username is real or anonymous, by default, other agents can see the location of active agents. Although an agent can choose to become invisible so that its presence or online status is unknown to other agents, this choice is a constraint that limits agent functionality to report and share information with other agents. These choices and preferences are an example of conditions that determine whether the confidentiality of agent identity and location is violated.
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 3 Modeling the Entire Flux-Linkage Characteristics
### Electromagnetic Torque Characteristics  
The electromagnetic torque of a phase in SRM can be obtained using:  
\[T=\frac{\partial W_{c}(\theta,i)}{\partial\theta}\mid_{i=const}=\frac{ \partial\int_{0}^{i}\psi(\theta,i)di}{\partial\theta}\mid_{i=const} \tag{14}\]  
where \(T\) is the phase torque and \(W_{c}\) is the co-energy.  
It should be noted that \(i\) and \(\theta\) are decoupled in the proposed method, which makes the differential more convenient. Substituting (9) into (14) yields:  
\[\begin{split}& T=\frac{\partial}{\partial\theta}\int_{0}^{i}[ \lambda_{0}(i)+\lambda_{1}(i)cos(N_{,}\theta)+\lambda_{2}(i)cos(2N_{,}\theta) +\lambda_{3}(i)cos(3N_{,}\theta)+\lambda_{4}(i)cos(4N_{,}\theta)]di\mid_{i=const }\\ &=-N_{,}sin(N_{,}\theta)\int_{0}^{i}\lambda_{1}(i)di-2N_{,}sin(2 N_{,}\theta)\int_{0}^{i}\lambda_{2}(i)di-3N_{,}sin(3N_{,}\theta)\int_{0}^{i} \lambda_{3}(i)di-4N_{,}sin(4N_{,}\theta)\int_{0}^{i}\lambda_{4}(i)di\end{split} \tag{15}\]  
Combining Equations (12) and (15), the torque characteristic can be calculated.
## 3 Results
### The Dynamics of uC2C Levels after TKR
#### 3.4.1 Koos  
We demonstrated significant improvements in all KOOS subscales one year after TKR in comparison with pre-TKR (\(p<0.0001\), Wilcoxon paired test; Figure 1). However, none of the scores of any subscale rose to the level of 85 (healthy). The limitations on the use of knee joints were more pronounced in more demanding activities such as squatting, kneeling, running, jumping, and twisting, as expressed by the function in sport and recreation subscale (Table 2).  
To evaluate the substantial clinical benefit (SCB) change in KOOS, defined as changing by more than \(\pm 20\) units, we divided patients into SCB subgroups: worsening, no change, or improvement (Table 3). We demonstrated that clinically significant worsening of KOOS was very rare (only one patient in our study). Approximately two-thirds of patients reported clinically significant improvements in the pain and the function in daily living subscales (70% vs. 30% and 65% vs. 35%, respectively; Table 3). However, improvement in the KOOS sp/rect subscale was in the minority compared to the no change subgroup (36% vs. 64%).  
We demonstrated that the baseline (preoperative) uC2C level was statistically significantly higher in the improvement subgroup of KOOSsymp compared to the no change subgroup of the same subscale (Table 4; \(p=0.01\), Wilcoxon test). We found no statistically significant differences in uC2C levels in the SCB subgroups of other KOOS subscales.  
Figure 5: Line plot of uC2C dynamics in (**A**) the whole group, (**B**) males, and (**C**) females, grouped by postoperative uC2C changes (decrease or increase means a \(>20\%\) decrease (green) or increase (red) in the 12-month post-TKR uC2C/Crea value compared to the preoperative one; mean and SEM shown).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Clinically Important** & **Substantial Worsening, KOOS Change * & **No Change (\%)** & **Substantial Improvement (\%)** & **Total **\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\  
Using GLM models, we demonstrated that preoperative uC2C levels predicted clinically significant improvement in KOOSsymp in the whole group (OR = 2.79; CI 95% 1.19-6.53; Table 11), as well as in females (OR = 8.56; CI 95% 1.42-51.59). The predictive value of uC2C was missing in males. Moreover, we found no associations between uC2C and other KOOS subscales. We demonstrated that adjusting the model of KOOSsymp for well-known confounders (e.g., age, sex, and BMI) removed the statistical significance of the model for the whole group; however, it was still significant for females (adjusted OR = 9.43; CI 95% 1.19-74.73).
## 4 Performance Assessment of Single Point Positioning with Smartphone Code Pseudoranges  
This section is devoted to the performance assessment of the positioning based on smartphone code observations. We analyze an accuracy level that may be reached with employed smartphones; we evaluate a potential benefit from the \(C/N_{0}\)-dependent weighting scheme and from the application of the signals that are transmitted on the second frequency band (L5/E5).
# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Synthesis of the Selected Works
#### 3.8.12 Endovascular Procedures  
Endovascular surgery has been the focus of many recent studies due to its advantages like small incisions, less blood loss, quicker recovery over traditional surgeries. In this area, [PERSON] et al. developed a VF-based training simulation for heart arteries navigation [107]. The simulation was developed using Chai3d, where a Novint Falcon haptic device is used to navigate the tip of a catheter inside the heart arteries. The catheter is simulated as a navigating sphere, and the arteries were constructed using cylindrical and corner elements. To provide guidance, the VF is based on a midpoint-generated trajectory, using force-field guidance and forbidden-region constraints with visual cues. Twelve participants tested the environment. Results state that their performance improved. They improved their task accuracy, and short task completion times were achieved in path-following procedures.  
[PERSON] et al. designed and implemented an endovascular simulator system in Reference [108]. The authors used a Geomagic Touch haptic device to develop catheter navigation training. [PERSON] et al. used Unity to develop the simulation, and they provide visual assistance during training via a notification module. Moreover, the authors added a telecommunication system to provide teleoperating or teletraining features. [PERSON] et al. tested their training simulator internally; therefore, no expert validation or user perception results were reported.
# Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight
## 6 Optimization Results and Analysis
### Combined Optimization Results for WGP and WKP
For pitch aerodynamic power (\(P_{x,\text{total}}\)) during forward or backward stroke (Figure 11b), the added-mass power (\(P_{x,\text{add}}\)) dominates the other three terms, the subdominant term belongs to the rotational damping power (\(P_{x,\text{rd}}\)), and the third one is rotational circulation power (\(P_{x,\text{rot}}\)), while the translational circulation power (\(P_{x,\text{trans}}\)) makes minimum contribution to pitch power consumption. The pitch inertial power (\(P_{x,\text{inert}}\)) is reverse to \(P_{x,\text{add}}\) but with nearly equivalent amplitude, their mutual subtraction leads the positive peak of total pitch mechanical power output (\(P_{x,\text{total},\text{posi}}\)) near to zero during the whole stroke (Figure 11b), which is not observed in the results from sole WGP or WKP optimization. Moreover, the main time interval of pitch reversal occupied by pitch power consumption is wider than that for sole WKP optimization, which results from the fact that the pitch regulating parameter of \(C_{\psi}\) is smaller than the latter one, thus the pitch reversal completes slowly. For flapping aerodynamic power (\(P_{Z,\text{total}}\)) during the forward or backward stroke (Figure 11c), the translational circulation power (\(P_{Z,\text{trans}}\)) dominates the other three terms, the subdominant term belongs to added-mass power (\(P_{Z,\text{add}}\)), and the third one is rotational circulation power (\(P_{Z,\text{rot}}\)), while the rotational damping power (\(P_{Z,\text{rd}}\)) has minimum contribution to flapping power consumption. All of them may be synergistically generated from lower frequency, larger flapping amplitude, shorter pitch time scale (\(C_{\psi}\)) and larger WGP except non-dimensional location of the pitch axis in comparison to the situation of sole WKP optimization (Table 3, Figures 9a and 11a). The flapping inertial power (\(P_{Z,\text{inert}}\)) is likewise positive during the translation acceleration and negative during the translation deceleration but with relatively lower amplitude than that for sole WKP optimization. Their synergistic effect results in two positive peaks of total flapping mechanical power output occurring before the middle of half stroke and at the middle of the accelerating pitch interval, respectively. The reason why these positive peaks occurred at these locations is very similar to that analyzed for sole WKP optimization. The positive value of the total flapping mechanical power output (\(P_{Z,\text{total},\text{posi}}\)) nearly spans the whole stroke except at the short interval of starting of next half stroke where \(P_{Z,\text{rot}}\) and \(P_{Z,\text{rd}}\) are negative and might be stored in elastic joint or dissipated.
## 3 1-vs.-1 WVR Air Combat Engagement Modelling
### Action Space of 1-vs.-1 WVR Air Combat  
In most previous studies [3; 4; 8], UCAV's action space was discrete, and basic fighter maneuvering (BFM) was used as an air combat maneuvering action space, which includes nine actions: left climb, climb, right climb, horizontal left turn, horizontal forward flight, horizontal right turn, dive left, dive, dive right. Although these nine actions cover all common maneuvering actions of a UCAV, the parameters for these maneuvering actions are fixed, and the maneuvering parameters cannot be changed quantitatively, and cannot be combined.  
In this paper, a continuous action space is used to control the UCAVs. As air combat is carried out in the same horizontal plane, there are two continuous control variables which control UCAV, namely the thrust and the roll rate. The pitch angular velocity and yaw angular velocity are not considered. Therefore, the action space can be indicated by:  
\[(u_{t},u_{\dot{\psi}}) \tag{10}\]  
where, \(u_{t}\) is the thrust of a UCAV and \(u_{\dot{\psi}}\) is the roll rate of a UCAV. They have upper and lower limits, and their upper and lower limits will vary depending on the performance of the UCAV. Together, they will affect the performance of a UCAV's acceleration and turning maneuvers.
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 4 CW Single-Frequency Phosphate Fiber Lasers and Amplifiers
### Yb-Doped Single-Frequency Phosphate Fiber Lasers
[76] reported also a MOPA laser with over 1 W of single-frequency emission, obtained by amplifying a seed laser with a core-pumped one stage Yb\({}^{3+}\)-doped phosphate fiber amplifier of 4 cm of length. On the other hand, with Yb\({}^{3+}\)-doped phosphate fibers, emission at longer wavelengths was also investigated. Low noise single-frequency single polarization fiber laser emitting at 1083 nm was reported from a 1.8 cm-long heavily (18.3 wt %) Yb\({}^{3+}\)-doped phosphate fiber. 100 mW of output power was achieved, with a slope efficiency of almost 30% and a laser linewidth lower than 2 kHz [70].
## 3 Background
### Types of Graph Neural Network
#### 3.3.2 GraphSAGE  
GraphSAGE [34] stands for graph sample and aggregate. It is a GNN model for large graphs, and was introduced for the first time in 2017. Unlike other models such as GCN and GAT that aggregate information from all neighboring nodes of a node, GraphSAGE pre-specifies the number of neighboring nodes at each node that is aggregated to update its embedding information. This aggregator method of GraphSAGE helps the model overcome the limitations of traditional GNN models when processing large graph data.  
Based on the above idea, the message-passing mechanism in GraphSAGE includes two processes: neighborhood sampling and aggregation. The sampling operation is denoted by:  
\[N_{s}(u)=SAMPLE(N(u),s), \tag{2}\]  
where \(N_{s}(u)\) represents the set of neighborhood samples of node \(u\), with \(s\) denoting the number of nodes selected from the total number of neighboring nodes \(N(u)\) of node \(u\). By choosing \(s\) neighbors for each node, GraphSAGE helps the model significantly reduce the size of the computational graph and memory requirements, thereby reducing the space and time complexity of the algorithm. After specifying the number of neighbors for each node, GraphSAGE utilizes aggregation functions to synthesize information from them:  
\[\mathbf{h}^{(l)}_{N_{s}(u)}=AGG(\{\mathbf{h}^{(l-1)}_{v},\forall v.s.\in N_{s }(u)\}), \tag{3}\]  
where \(\mathbf{h}^{(l)}_{N_{s}(u)}\) represents the information aggregated from the selected neighbors and \(AGG\) is the aggregation function. In GraphSAGE, many types of aggregation functions can be applied, including sum, mean-pooling, max-pooling, LSTM, and so on.  
The aggregated data from that neighborhood are utilized to calculate and update the embedding for each node, akin to other GNN models:  
\[\mathbf{h}^{(l)}_{u}=\sigma([\mathbf{h}^{(l-1)}_{(u)},\mathbf{h}^{(l-1)}_{N_{ s}(u)}].\mathbf{W}^{(l)}), \tag{4}\]  
where \(\mathbf{h}^{(l)}_{u}\) is the embedding of node \(u\) at layer \(l^{th}\), calculated from embedding \(\mathbf{h}^{(l-1)}_{(u)}\) of node \(u\) in the layer \((l-1)^{th}\) and the information aggregated from its neighbors \(\mathbf{h}^{(l-1)}_{N_{s}(u)}\). \(\mathbf{W}\) is the weight matrix used at layer \(l^{th}\) of the model.  
In general, GraphSAGE can solve the limitations of GCN and GAT models when it works effectively with large graphs and fast training. However, through experiments, we can see that GraphSAGE does not significantly improve accuracy compared to other models.
## 2 Materials and Methods
### Reflectance Measurements and Vegetation Indices  
#### 2.2.1 Spectral Reflectance Sensors (SRS)  
Spectral data was collected through the use of commercial automated SRS-PRI sensors with an Em50 datalogger (METER Group. Inc., Pullman, WA, USA). PRI sensors contain photodiodes with interference filters at selected peak wavelengths 532 \(\pm\) 2 and 570 \(\pm\) 2 nm with 10 nm full width half maximum (FWHM) bandwidths. Photodiode and filter construction was based on prototypes  
Figure 1: Location of the Peace River Environmental Monitoring Super Site (red star). Red triangle shows the location of the flux tower at the edge of an old growth deciduous boreal forest. The tower contains eddy covariance system, meteorological sensors, and proximal remote sensing sensors. A Wireless Sensor Network (WSN) composing of 36 nodes is located east of the eddy covariance tower, within the flux footprint.  
reviewed by [PERSON] et al. [43]. Downward looking PRI pairs contain field stops, restricting the field of view (FOV) at 36\({}^{\circ}\). Upward looking pairs contain Teflon cosine diffusers allowing near 180\({}^{\circ}\) FOV. They provide continuous irradiance measurements used to normalize radiance spectral responses measured through the downward looking sensor pair. Upwelling and downwelling PRI sensors were placed on a 25 m tower and positioned 10\({}^{\circ}\) off-nadir at an approximate distance of 10-12 m from the top of the canopy, representing a footprint of approximately 50 m\({}^{2}\) towards the eddy covariance area of influence.  
Each sensor produced a radiance and irradiance output, for each waveband (532 nm and 570 nm) at 1-minute intervals throughout the 2015 season. Data points were filtered to remove precipitation events which caused significant signal noise attributed to water droplets accumulating on the flat Teflon cosine diffusers of the hemispherical sensor pair. Uncorrected reflectance at each waveband was expressed as the ratio of measured radiance (\(r\)) to irradiance (\(i\)) (Equations (1) and (2)) and used to calculate uncorrected PRI (Equation (3)):  
\[\rho_{532}=\frac{r_{532}}{i_{532}} \tag{1}\]  
\[\rho_{570}=\frac{r_{570}}{i_{570}} \tag{2}\]  
\[\text{PRI}=\frac{\rho_{532}-\rho_{570}}{\rho_{532}+\rho_{570}} \tag{3}\]  
where \(\rho\) represents reflectance at a specific wavelength. Calculated PRI values were averaged over 30 min to match eddy covariance binned measurements. PRI values were expressed as scaled PRI (sPRI) using the formula [49; 50]:  
\[\text{sPRI}=\frac{(1+\text{PRI})}{2} \tag{4}\]  
which transformed PRI values into a 0-1 range commonly used in remote sensing vegetation indices. Before and after field deployment, the sensors underwent a series of cross-calibration and sensor response validation experiments. Details of these experiments are outlined in Sections 2.2.2 and 2.2.3 below.