esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Cellular Basis of Laser-driven Radiation Therapy
### Ion Acceleration Program at BNL ATF and UCLA  
By expanding the spectral range of available laser drivers to the mid-IR region, the 9-10 \(\upmu\)m carbon dioxide (CO\({}_{2}\)) lasers offer new opportunities to explore ion acceleration. Several advantages of the long-wavelength radiation, _i.e._, stronger ponderomotive electron heating and reduced critical plasma density, facilitate access to the Shock Wave Acceleration (SWA) regime from gas jets. Attractive features of this mechanism are: monoenergetic ion energy spectra, near linear ion energy scaling with the laser intensity, and targets of pure composition that are better suited for high repetition rate operation. These features have been experimentally demonstrated with picosecond CO\({}_{2}\) lasers operated at UCLA's Neptune laboratory [47] where high-brightness pure proton beams of 3-20 MeV energy with 4-10 percent energy spread have been generated with supersonic hydrogen jets and at the Accelerator Test Facility (ATF) at the Brookhaven National Laboratory (BNL) [48].  
The BNL's ATF is a DOE user facility open for international collaborations. The SWA experiment at ATF has been conducted in close partnership with Imperial College (London) and Stony Brook State University (New York). Further progress of this collaborative effort at ATF towards ion beams with kinetic energies suitable for medical applications relies on the current CO\({}_{2}\) laser upgrade to the 50-100 TW peak power range. Preliminary work suggests that protons with energies approaching 100 MeV in a quasi-monoenergetic peak may be reached if \(a_{0}\) (normalized vector potential) values greater than 6 can be achieved [119]. This next-generation ultra-fast (pulse duration near 10 cycles) CO\({}_{2}\) laser will integrate several innovative design features such as pulse stretching and compression that has not been previously implemented with molecular gas lasers, isotopic amplifiers, and nonlinear self-chirping and filtering.  
When this system is completed, one of the long term campaigns will be aimed at proton (and other ion) acceleration by exploring collisionless shock techniques. The experimental work will be supported by PIC code simulations. This is to be a joint undertaking led by UCLA and BNL. The upgrade underway at ATF is a three-year plan that includes dedicated experimental space for applying laser-produced ion beams to user experiments with biological samples and living tissue. The Stony Brook University Department of Clinical Oncology will join the effort during this user stage.
## 1 Introduction
Other retrieval methods attempt to separate the SIF component in one or more spectral bands, either by estimating and removing specific non-SIF components, or by removing the influence of non-SIF components as layers of systematic variation, with methods such as principle component analysis (PCA) or singular vector decomposition (SVD). Contemporary spectral fitting methods typically employ a combination of these approaches to consider the SIF signal amalgamated across many spectral bands and features [19; 28; 29; 30], and as a result have shown considerable success in mitigating noise in well-established instruments. Figure 1: A sample of upwelling radiance of vegetation observed from fluorescence imaging of red and far-red light yield (FIREFLY) (black line), accompanied by an example SIF function to scale (green line), and the same solar induced fluorescence (SIF) function multiplied by a factor of 100 (green dashed line) to show the position of the peaks. The FIREFLY activity was a technology demonstration that flew as part of the G-LiHT system for two years of data acquisitions. NASA Goddard's G-LiHT airborne imager provides a unique combination of lidar and optical remotely sensed data concerning ecosystem structure and function,  
Figure 2: Flow-chart of potential influences on SIF signal. Influences in orange boxes are actively investigated in this study. Green components represent the SIF portion of the signal, and gray components represent the non-SIF portion of the signal. collected simultaneously. It integrates commercial off-the-shelf lidar, hyperspectral, and thermal sensors in a compact, and portable system. The coincident data from the G-LiHT instrument package may address many challenges currently facing SIF retrievals. For example, lidar can provide observations of vegetation canopy structure, noted as essential for providing the link to leaf-level traits [4], and provide characterization of its influence on SIF [10], which has been determined to result in strong directionality of SIF [15; 31]. The addition of a high resolution (\(\sim\)3 cm) Phase One optical camera can provide estimates of vegetation cover and type within FIREFLY footprints, providing important context to SIF retrievals [32; 33]. This study aimed to provide the following: robust characterizations of the attributes of the FIREFLY sensor system relevant to SIF retrieval; assessments of various SIF retrieval methods in the context of these sensor system attributes; modelling tools, including the ability to simulate high resolution upwelling radiance spectra, to tailor design of future SIF products to specific sensor system attributes; and recommendations for further measurements and deployment strategies to optimize SIF retrieval.
# OpenCare5G: O-RAN in Private Network for Digital Health Applications
## 3 Proposed Solution
### 5G Core Network (5 GCN)
#### 3.5.1 Core Architecture Based on Services and Reference Point  
The Core Architecture is SBA and PR based on the 5 GC through the 5G base stations shown in the architecture of Figures 2 and 3 to perform the baseband functions using the vRAN in the project with the interfaces that carry out the interactions with the NFs.  
Figure 10 illustrates the 5 GCN SBA Architecture and Reference Point (PR) with the E2E CP protocol stack and its main interfaces.  
In the project, new open interfaces were defined in the SBA and PR core architecture at points \"F\" and \"E1\" within the O-RAN node interface between the PHY and RU sublayers with the eCPRI protocol. One node ends in the CP in \"C\" of Figures 10 and 11 of the gNB-CU, called gNB-CU-CP. The other node that end the user plane \"U\" of the gNB-CU and is termed gNB-CU-UP. The default open interface between these nodes is specified as \"E1\", shown in Figures 10 and 11.  
According to Figures 2, 3, 10 and 11, in OpenCare5G the gNB (5G NR Node) is divided into a Distributed Unit (gNB-DU), a Centralized Unit Control Plane (gNB-CU-CP) and a Centralized Unit Data Plane (gNB-CU-DP). Therefore, there is a separation of the control plane (gNB-CU-CP) and the user plane (gNB-CU-UP) that form CUPS in gNB-CU, as per 3 GPP [51].
# An Analysis of the Process of Applying Technical Lubricating Coatings
## 2 Materials and Methods  
### Experimental Set-Up and Materials  
The experimental set-up, which was used to apply the materials being tested to the surface, included: time-pressure valve 781S-SS (manufacturer Nordson EFD); nozzles (Nordson EFD 7856F-46 SS 0.046\" and Nordson EFD 7856-46 SS 0.046\"); Nordson EFD Valve Mate 7140 regulator; pressure regulator; a material reservoir in the form of an applicator from which material is introduced into the system; and 3-axis robot. A simplified diagram of the measurement station is shown in Figure 1[34]. To record the spray obtained and also the angle of atomization, which was necessary for further computer image analysis, the following were used: Canon EOS-100D camera; and USB Dino Lite AM3113T digital microscope.  
Figure 1: Scheme of the measuring station [34].  
Two plastic lubricants were used to create the coatings. These were PFPE fluorine lubricants, which had a number of advantages. They were resistant to loads and aggressive environmental conditions and had good thermal conductivity. These lubricants are used in precision mechanisms, at low temperatures. They are widely used in the electronic, electromechanical, chemical, and automotive industries. They are particularly used in the production of various types of seals, fittings, connectors, valves, and micro-mechanisms. The characteristics of the lubricants used are shown in Table 2[35].  
The Nordson EFD 781S series valve is part of a group of time-pressure valves. It is used for spraying small volumes of low-pressure lubricants. It creates an even coating of material with low to medium viscosity. Using this valve, the applied material path on the entire surface has the same thickness, without clogging, dripping, or drying of the material in the device. The spray itself occurs without material splash or mist. It is used for spraying substances such as lubricants, inks, and oils. Depending on the nozzle used, despite the same diameter of the outlet holes of the valve, different material/lubricant path can be obtained: a fan-flat spray for Nordson EFD 7856F-46 SS 0.046\" (A) or a full cone spray for Nordson EFD 7856-46 SS 0.046\" (B) (Figure 2)[34]. Using the 781S-SS series valve, the spray cycle rate can exceed 400 per minute. The maximum material pressure that can be obtained is 20.7 (bar), or 300 (psi), while the maximum operating temperature is 102 \({}^{\circ}\)C[35].
## 4 Discussion
In applying ML models, it is essential to carry out the sensitivity analysis to evaluate the importance of the input parameters used for training and validating the models. Thus, in the present study, a sensitivity analysis was carried out for estimating the degree of importance of each input variable on the prediction of IRI. The sensitivity analysis was calculated using the PSOANFIS model, as its prediction capability has been proved as the best. For this purpose, quantile values at 21 points (from 0 to 1 with a resolution of 0.05) of each input have been extracted and served as a new dataset for the calculation of IRI through the PSOANFIS model. In order to explore the sensitivity, each input was varied from its smallest (quantile 0) to biggest (quantile 1) values while all other variables remained at their median (quantile 0.5). Therefore, the normalized change in IRI output results could be used to quantify the degree of importance of input variables. Such calculation could be expressed by the following equation for computing the normalized change \(\phi\) in IRI output results:  
\[\varphi_{i}^{j}=\frac{IRI_{i}^{j}-IRI_{quantile-0.5}^{all-inputs}}{IRI_{quantile- 0.5}^{all-inputs}} \tag{12}\]  
where \(IRI_{quantile-0.5}^{all-inputs}\) was the configuration \"zero\" when all inputs remained at their medians, \(IRI_{i}^{j}\) is the IRI output results using \(j\)th input at its \(i\)th quantile level. In this work, quantile level varies from 0 to 1 with a resolution of 0.05 (\(i=1,\,\ldots,21\)). Finally, the degree of importance of each input was calculated based on the following equation:  
\[\Phi^{i}=\sum_{i=1}^{21}\varphi_{i}^{j} \tag{13}\]  
Indeed, such variation in statistical behavior of inputs allows efficiently computing their influence in the prediction of IRI. Results of sensitivity analysis are scaled into the range of [0, 100%] and plotted in Figure 9 in the descending order. Obviously, all inputs exhibited impact on the prediction of IRI through the PSOANFIS model. However, the most influenced variables were maximum depth of rut, summed cracks and average depth of rut, which exposed 40, 34 and 17% of degree of importance, respectively. The other two inputs such as analysis area and road length exhibited less than 9% of degree of importance. Such obtained information was relevant and could help engineers save time and cost in applications and practices. The results of this sensitivity analysis might be helpful in selection of suitable input parameters for better performance of ML models for prediction of IRI and other properties of road surface.
# Comparison of Countermovement and Preferred-Style Jump Biomechanics in Male Basketball Players
## 3 Results  
### COM Parameters  
The statistical analysis showed that the peak GRF were greater during the Pref than the CMJ (Table 1). The statistical analysis also showed that the ground contact times during the Pref were shorter than the movement times during the CMJ (Table 1). In addition, peak COM velocities and jump height were greater during the Pref than during the CMJ (Table 1). Lastly, COM displacements during the lowering phase of the Pref were smaller than in the CMJ (Table 1).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **CMJ** & **Pref** & \(p\)**-Value** & **Effect Size** \\ \hline Peak GRF (N-kg\({}^{-1}\)) & & & & \\ Total & 2.53 \(\pm\) 0.10 & 3.77 \(\pm\) 0.28 & **0.001** & 2.31 \\ Lead Leg & & 1.82 \(\pm\) 0.14 & & \\ Trail Leg & & 2.19 \(\pm\) 0.23 & & \\ \hline Movement/Contact time (s) & & & & \\ Total & 0.72 \(\pm\) 0.05 & 0.40 \(\pm\) 0.04 & **0.001** & 1.18 \\ Lead Leg & & 0.39 \(\pm\) 0.04 & & \\ Trail Leg & & 0.23 \(\pm\) 0.03 & & \\ \hline Peak pelvis velocity (m-s\({}^{-1}\)) & & & & \\ \hline Pelvis displacement (m) & & & & \\ \hline Peak jump height (m) & & & & \\ \hline \hline \end{tabular} Note: significant \(p\)-values are shown in **bold**. GRF—vertical ground reaction force.  
\end{table}
Table 1: Mean \(\pm\) SD whole-body biomechanical parameters during the countermovement (CMJ) and preferred-style (Pref) jumps.  
Figure 3: Ensemble average of the hip (**left column**), knee (**middle column**), and ankle (**right column**) joint angles (**first row**), joint angular velocities (**second row**), net joint moments (**third row**), and net joint powers (**fourth row**) during the countermovement jump (CMJ) and preferred-style jump (Pref)).
## Materials and Methods
### Biotransformation
#### 2.1.3 Procedure for Preparative Biotransformation  
Preparative biotransformations were executed in the same way as the screening. Fresh, healthy, and undamaged vegetables and fruits were ground after washing. For each of the 10 300-mL Erlenmeyer flasks, 20 mL of the ground catalyst in 50 mL of phosphate buffer was added and 20 mL of the substrate dissolved in 200 mL of acetone was included in each of the samples. After 48 h, the transformation mixture was extracted with 50 mL of chloroform. The chloroform layer was then carefully collected and dried using anhydrous MgSO\({}_{4}\). The resulting solution was filtered and concentrated in a vacuum evaporator.  
The structures of the products obtained were determined on the basis of the physicochemical data:  
**(1R, 4R)-(+)-dihydrocarvone (2) (Figures S1-S3) \({}^{\bf 1}\)H NMR** (CDCl\({}_{3}\)): \(\delta\) 1.04 (d, \(J_{\rm HH}\) = 6 Hz, 3H, 7-CH\({}_{3}\)), 1.38 (qd, \(J_{\rm HH}\) = 3.5 and 13 Hz, 1H, 6a-CH\({}_{2}\)), 1.63 (qd, \(J_{\rm HH}\) = 4 and 11 Hz, 1H, 5a-CH\({}_{2}\)), 1.75 (s, 3H, 9-CH\({}_{3}\)), 1.94 (m, 1H, 5b-CH\({}_{2}\)), 2.13 (m, 1H, 6b-CH\({}_{2}\)), 2.27 (m, 1H, 3a-CH\({}_{2}\)), 2.33 (m, 1H, 4-CH), 2.35 (m, 1H, 1-CH), 2.45 (m, 1H, 3b-CH\({}_{2}\)), 4.74 (brs, 1H one proton of 10->C=CH\({}_{2}\)), 4.76 (brs, 1H, one proton of =CH\({}_{2}\)) \({}^{\bf 13C}\) NMR (CDCl\({}_{3}\)): 14.35 (C-7), 20.45 (C-9), 30.78 (C-5), 34.92 (C-6), 44.75 (C-4), 46.88 (C-1), 47.03 (C-3), 109.62 (C-10), 147.63 (C-8), 212.64 (C-2).  
**(1R,2S,4R)-neodihydrocarveol (5) (Figures S4-S6) \({}^{\bf 1}\)H NMR** (CDCl\({}_{3}\)): 0.98 (d, \(J_{\rm HH}\) = 6.8 Hz, 3H, 7-CH\({}_{3}\)), 1.23 (dddd, \(J_{\rm HH}\) = 12.8, 12.4, 4.5 and 3.2 Hz,1H, 5a-CH\({}_{2}\)), 1.42 (m, 1H, 3a-CH\({}_{2}\)), 1.44 (m, 2H, 6-CH\({}_{2}\)), 1.53 (m, 1H, 1-CH), 1.74 (t, \(J_{\rm HH}\) = 1.0 Hz, 3H, 9-CH\({}_{3}\)), 1.77 (dddd, \(J_{\rm HH}\) = 12.8, 3.5, 3.3 and 3.2 Hz, 1H, 5b-CH\({}_{2}\)), 1.93 (dddd, \(J_{\rm HH}\) = 13.6, 3.3 and 3.2 Hz, 1H, 3b-CH\({}_{2}\)), 2.28 (tt, \(J_{\rm HH}\) = 12.4 and 3.2 Hz, 1H, 4-CH), 3.91 (m, 1H, 2-CH), 4.71 (brs, 2H, 10->C=CH\({}_{2}\)) \({}^{\bf 13C}\) NMR** (CDCl\({}_{3}\)): 18.32 (C-9), 20.97 (C-7), 28.14 (C-6), 31.40 (C-5), 36.07 (C-1), 37.82 (C-4), 38.67 (C-3), 71.01 (C-2), 108.4 (C-10), 150.3 (C-8).  
**(1R,2R,4R)-dihydrocarveol (4) (Figure S7) \({}^{\bf 1}\)H NMR** (CDCl\({}_{3}\)): 1.05 (d, \(J_{\rm HH}\) = 6.8 Hz, 3H, 7-CH\({}_{3}\)), 1.24-1.62 (m, 7H, 1-CH, 3-CH\({}_{2}\), 5-CH\({}_{2}\), 6-CH\({}_{2}\)) 1.74 (brs, 3H, 9-CH\({}_{3}\)), 2.15 (m, 1H, 4-CH), 3.20 (dddd, \(J_{\rm HH}\) = 10.0, 10.0 and 4.0 Hz, 1H, 2-CH), 4.72 (brs, 2H, 10->C=CH\({}_{2}\)).
## 4 Experimental Setup and Results
### Selection of Parameters in the Sliding Window  
The first analysis regarding the sliding window used to calculate the average requested bandwidth of residential users, compared the response time of the dynamic bandwidth algorithm considering different window sizes. The optimal performance of the algorithm implies an optimum choice of some parameters, such as the size of the sliding window, which contains the last samples of the requested bandwidth of users. Then, if the number of samples is too large, the sliding window may contain quite old values of the requested bandwidth which can make the algorithm not react fast enough when changes in traffic conditions appear. In contrast, if the size of the sliding window is very short, the real-time estimation of the mean requested bandwidth cannot be reliable as the number of samples is not enough. Then, Figure 6a-d show the time that the algorithm takes to react when the associated bandwidth of one network subscriber (ONT) is being modified according to its real-time demands, considering different window sizes (5, 10, 20, and 30 samples). All the graphs are represented with Wireshark that can capture real- time network data [31]. As it can be observed in Figure 6c, the window of 20 samples has a response of approximately 16 s, while higher windows sizes such as 30 samples (Figure 6d) add a delay of approximately 20 s. In contrast, if low window sizes are considered, five and 10 samples (Figure 6a,b), the response time is lower, 8 and 12 s, respectively.  
However, a too small window can be more sensitive to possible transmission peaks and fluctuations, as it can be seen in Figure 7. As a consequence, to test our dynamic service reconfiguration algorithm, a 10-samples window is chosen since the delay and the fluctuations are low and its performance is quite similar to high window sizes (20/30 samples).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flexible Internet Plans** & **Basic Bandwidth** & **Excess Bandwidth** \\ \hline Basic Flexible Plan & \(\leq\)100 Mbps & +20\% Mbps \\ Premium Flexible Plan & \(>\)100 Mbps & +30\% Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Flexible Internet plans offered by Internet service providers (ISPs)/network operators.
# Aerodynamic Performance Assessment of Distributed Electric Propulsion after the Wing Trailing Edge
## 3 Wind Tunnel Testing
### Experimental Results  
Figure 3 shows the distribution of \(Cl\) and \(Cd\) with different rotor locations, where \(D_{rp}\) is the horizontal position, \(Xr\) is the vertical offset, and 'Baseline' is the isolated wing without interference from any rotor.  
It can be seen that the lift coefficient and drag coefficient increase linearly with the AoA in horizontal positions. The propeller has no significant effect on the lift of the wing when the AoA is less than 16\({}^{\circ}\). It also can be seen that the drag coefficient of DEP with the pusher structure almost coincides with the baseline. However, the lift coefficient curve begins to show a decreasing trend when the AoA is higher than 16\({}^{\circ}\). Specially, the lift coefficient of the DEP configuration is significantly higher than the baseline, which indicates that the lift is greatly improved. Also, the drag coefficient is also significantly increased in this case. Considering the effect of the horizontal position, as shown in Figure 3a,c, it can be seen that the lift and drag coefficients increased with the decreased horizontal distance. Additionally, the vertical offset seems have a greater impact on the aerodynamic performance of the wing.  
Figure 3: Distribution of lift coefficient (Cl) and drag coefficient (Cd). (**a**) Cl in various horizontal positions; (**b**) Cl in various vertical offsets; (**c**) Cd in various horizontal positions; (**d**) Cd in various vertical offsets.  
As a result, both the lift coefficient and drag coefficient of the wing gradually increase with the effect of the propeller. For the configuration with \(Xr=1\), \(D_{rp}=0.1c\), it is interesting to note that the lift coefficient obtained a maximum at \(30^{\circ}\) with an increasing tendency.  
To determine the final performance of the DEP, Figure 4 presents the distribution of the L/D with different DEP configurations.  
The lift is much improved at a higher AoA for the pusher configuration of the DEP, with a consistent tendency for a lower AoA. The effect of the horizontal position is more obvious for the performance of the wing, where the outflow is deformed by the propellers. Although L/D variation is within the experimental uncertainty limits with no significant difference inferred, delay in stall and increase in lift at a higher AoA are observed when the pusher props are placed above the wing chord line. Thus, it is important to study the flow field with a higher AoA.
## 3 Results and Discussions
### General Flow Patterns in OHP
#### 3.2.3. Annular Flow  
In the OHPs, when the continuous vapor flows through the center of the tubes with a liquid film formed around the inner tube wall, the annular flow comes out, as indexed by 'A' in Figure 5. This flow pattern is usually transformed from the slug flow, and usually appears in the 'upheaders' with a large volume fraction of vapor produced by the drastic boiling in the evaporator under high heating load. In the 'upheaders', the high-speed vapor induced by drastic boiling always breaks the liquid bridges between the vapor slugs and triggers the transition from slug flow to the annular flow.  
Figure 5: Flow patterns occurring in the OHPs with different diameters. Flow patterns indexes: B: bubbly flow; S: slug flow; A: annular flow.  
Moreover, as shown by Figure 5, smaller inner diameter induces greater effect of surface tension in the OHP and thus generates larger capillary instability, which leads to more irregular waves on the liquid film of the annular flow in the OHP with \(D_{\text{i}}=1\) mm. These irregular waves easily cause the break of the continuous vapor core in the annular flow via the formation of the liquid bridges, which can induce the transition from the annular flow to the slug flow. Therefore, the stability of annular flow deteriorates with the decreasing inner diameter of the OHPs.