esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
## 1 Introduction
In [19], the authors proposed a machine learning surrogate model to predict the outcome of the acceptance criteria in a clinically driven population of aortas. The authors demonstrated that the sampling strategy and the verification of the generated cases impacted the efficiency of the process and the quality of the resulting cohort. A centerline-based statistical shape model (SSM) was presented by [20] to generate a data-driven population of aortas; 154 real patient geometries were used to train an SSM that used a principal component analysis (PCA) to capture the morphological features in order to create a large virtual population (about 3000 patients). Within this context, this work proposes an automatic procedure to generate a data-driven virtual cohort of AIS patients' anatomies. The objective is the creation of a tool which enables the generation of large cohorts of virtual populations of cerebrovascular anatomies suitable for high-fidelity thrombectomy modeling. This will facilitate the development of in silico trial approaches, reducing the issues related to the availability of high-quality images of stroke patients and to privacy concerns when sharing patient data between hospitals and modelers. Moreover, the availability of large populations for in silico modeling is required in general when running in silico stroke trials, and in particular to train accurate and fast surrogate models like the one used in [8]. To this aim, first, (i) an SSM approach is used to generate cerebrovascular anatomies described by their centerlines and associated diameters, starting from a cohort of 88 real AIS patients. Then, (ii) an automatic 3D reconstruction of the vessels from the centerlines and associated diameters is implemented and (iii) validated.
## 1 Introduction
However, noble gases and high voltages are typically required for plasma generation in the case of NEAPPS (as well as in the case of NEAPPJ), and a substantial amount of electrical energy is required to sustain the discharge, when only a limited volume (typically several milliliters) of the medium can be treated at one time [32; 33; 34; 35; 36; 37; 38; 39]. Low energy efficiency, the requirement of noble gases, and the limited volume of liquid that can be treated make a plasma-activated medium (PAM) expensive, and its use in practical agriculture applications impossible [18]. Considering the cheapest price of ambient air compared to other gases, and the high concentration of nitrogen and oxygen required for RONS production, the most effective way to reduce the cost of plasma treatment is the use of ambient-air high-density plasmas for liquid treatments [40; 41; 42; 43; 44; 45; 46; 47; 48; 49]. Energy-effective treatment could be achieved by employing the plasma treatment of electrospray liquid; however, the reported flowrate for this system is below 1 L/min, which could result in problems with scaling PAM production [16]. A possible way to scale up production and reduce the cost of PAM is use of plasmas generated in ambient air to treat a continuous flow of liquid [19; 40]. Several works have reported on treating water flow; however, plasma sources used in these studies either had a considerably low RONS production rate or required the use of noble gases, which could limit the wide application of the produced plasma-activated water (PAW) [40; 50; 51; 52; 53; 54]. In the case of using liquids treated with plasmas, a PAM could be stored after treatment to reduce temperature and be utilized after storage considering the presence of long-living RONS [14; 55]. Additionally, PAM temperature could be reduced by employing liquid flow [19]. For the reason stated above, thermal and \"warm\" plasmas could be used for medium irradiation following PAM cooling, and use in biomedical applications [19; 56; 57]. There are many works on the generation of arc and glow discharges, and other types of plasmas inside or on the surface of various liquids; however, there is a limited number of works reporting on treatments of organic liquids for applications in agriculture, and there are no works about the scalable treatment of organic-compound solutions for hydroponic-plant-growth enhancement [3; 6; 18; 58; 59; 60]. In this work, we applied a recently reported scalable ambient-air low-current arc-treatment system for treating flowing solutions of organic compounds following the use of the treated medium in hydroponic plant growth [19]. The reported discharge burns in glow mode rather than in arc considering the discharge characteristics; therefore, in the present work, the authors use the term \"glow discharge\" instead of \"low-current arc\", used in the previous work [19]. The use of a scalable plasma-treatment system in ambient air reported in the present work is a step towards the practical use of PAM in hydroponic plant growth and other applications in agriculture and medicine.
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 3 Results
### Performance
In this way, next-generation networks are deployed more efficiently and faster, giving the ground to MNOs to compete through innovation at the service level, focusing better on end-user needs and very often resulting in price reduction. In parallel, entry costs for new players are much lower, since there is no passive infrastructure to build. This increases competition, giving the chance to newcomers to enter the market. Adopting such strategies, network expansion in unserved and underserved areas, where there is less revenue potential, is now possible. Infrastructure deployment is less costly, providing the chance to MNOs to differentiate in providing better quality of service and faster speeds. This is of critical importance, as Greece is in the middle of 5G expansion and in the beginning of fixed broadband deployment.
# A Regression-Based Technique for Capacity Estimation of Lithium-Ion Batteries
## 2 Reasons for Lithium-Ion Batteries Ageing  
Capacity decrease and power fade of lithium-ion batteries are complicated phenomena that originated for various reasons. Processes influence the calendar life of the battery during storage, such as impedance rise and self-discharge. Whereas ageing processes occur during use, such as lithium metal plating, mechanical degradation affects cycle life. It should be considered that ageing processes with cycle life and calendar life originated from changes of:  
* Interface of electrolyte and electrodes;
* Active material;
* Composite electrode.  
A straightforward model of electrodes is shown in Figure 1. Battery power results from ion transition between electrodes. The power will decay if this transition is disrupted or damaged. Side reactions include structural decay and are the main cause of disruption of the ion current [22; 23].  
This phenomenon degrades lithium-ion batteries over time and changes their parameters. It is well established that SEI growth and lithium plating are essential ageing mechanisms in modern lithium batteries. Therefore, it is necessary to estimate ageing parameters and adjust some of them to manage battery packs. These estimations algorithms are part of the BMS unit.  
One of the approaches to evaluate lithium-ion battery ageing is to model its behavior by electrical elements, known as equivalent circuit model. This is a prevalent method due to a tradeoff between accuracy in open-circuit voltage estimation and computational load [24; 25; 26; 27]. The equivalent circuit method is shown in Figure 2, and our approach to estimating SOC is to calculate the value of equivalent-series resistance and total capacity. Both side reaction and structural decay will reduce the lithium-ion storage capability in one or both electrodes, reducing total capacity, known as capacity fade. The cell's series resistance will increase with ageing and result in power fade. Some ageing mechanisms, such as structural decay and SEI growth, could happen even in the rest time of the battery.
## 4 Discussion
Even though proprioception is altered in OA patients, studies have shown how with TKR, patients improved their proprioception, balance, and kinesthesia compared to AO ones treated conservatively, but they never return to normal, pre-arthritic levels [34,35]. Other studies found no difference in proprioception before and after a TKR, and one work even showed a worsening condition. There is instead some evidence that, to obtain the best results, patients should start balance and proprioception exercises immediately after TKR [1]. After a TKR, a reacquisition ofthe compensatory role of the knee in balance control and the skill happens to perform an appropriate muscular activation sequence [36]. Given the statistically not significant difference in \(\Delta\) pre-post, for both limbs, the obtained performance results in this study could be influenced by different factors not taken into account in this study. Therefore, further assessments to a longer follow-up are recommended to highlight any improvement or worsening trends over time. Although the statistical correlations between the mean DPPS values and pre- and post-operative pain were not statistically significant, the role of osteoarthritis and post-surgical painful symptomatology in the proprioception of the joints and how it can affect the postural control has placed points of discussion in the scientific community. [PERSON] et al. [37] described how patients with asymptomatic ankle OA and healthy control subjects reported similar results in balancing performance, unlike those with symptomatic OA, suggesting that from a clinical point of view, postural control deficits may be predominantly affected by pain. Similar aspects have been confirmed by other authors [38; 39; 40], placing pain at the center of postural deficits. These aspects are consistent with the work of [PERSON] et al., who state that rehabilitative exercise ultimately improves endurance and muscle strength but not proprioceptive balancing and coordination [41]. In this regard, the reduction in the mean values to the proprioceptive stabilometric evaluation by DPPS, taking into account vestibular, visual, and mechanoreceptor feedback (relatively little influenced by muscle strength, joint ROM, and resistance), can be considered, in a completely speculative way, confirmation of how the resections of the cartilaginous surfaces, without affecting the ligamentous apparatuses, could influence the collection of postural joint information. This study has some limitations. The first limitation is the reduced number of patients included in the case studies who have completed all the pre- and post-operative trials and which, in some cases, has probably not allowed for an adequate statistical analysis. The second limit is represented by the short-term follow-up that does not allow for the estimation of a possible tendency of improvement or worsening in the time of the proprioceptive characteristics in the patients subjected to TAR. A third limitation can be traced back to the use of a single prosthetic model with the anterior approach.
# A Robust Control Algorithm of a Descent Vehicle Angular Motion in the Earth's Atmosphere
## 8 Numerical Example
The statistics contains mathematical expectations (ME) and mean square deviations (MSE) for stabilization accuracy: absolute \(\delta^{tm+}_{\text{max}}\) (maximum deviations from programmed values after PP in 30 s) and average \(\delta^{all}_{avt}\) (average deviations from programmed values for the entire control time). The consumptions are given in the units of the highest ME total flow rate \(\bar{Q}^{BT0}_{alg0}\). The upper index is the number of bank turns, the lower one is the number of the algorithm (0--standard, 1--new). Further, according to criterion (43) with fuel limitation  
\[Q_{\text{max}}=\bar{Q}^{BT0}_{alg0}-2\sigma\Big{(}Q^{BT0}_{alg0}\Big{)},\]  
where \(Q^{BT0}_{alg0}\) - fuel consumption for the existing algorithm (its ME and MSE are used) under the characteristic IC (44), the \"optimal\" values of the parameters of the controller matrix (41) were determined  
\[s^{*}_{x}=0.3,\quad s^{*}_{y}=1.4,\quad s^{*}_{z}=0.9,\quad m^{*}_{x}=0.2. \tag{45}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{4}{c}{**Stabilization Accuracy**} & \multirow{2}{*}{
\begin{tabular}{c} **Consumption** \\ **(Units \(\bar{Q}^{BT0}_{alg0}\))** \\ \end{tabular} } \\ \cline{3-6}  & & \multicolumn{2}{c}{**By Angles (\({}^{*}\))**} & \multicolumn{2}{c}{**By Velocities (\({}^{*}\)/s)**} & \multicolumn{2}{c}{**(Units \(\bar{Q}^{BT0}_{alg0}\))**} \\ \cline{3-6}  & & \(\delta^{tm+}_{\text{max}}\) & \(\delta^{all}_{avt}\) & \(\delta^{tm+}_{\text{max}}\) & \(\delta^{all}_{avt}\) & **In Channel** & **Total** \\ \hline \multirow{2}{*}{\(x_{y}\)} & ME & 2.489 & 1.347 & 1.250 & 0.487 & 0.289 & \\ \cline{2-6}  & MSE & 0.147 & 0.100 & 0.104 & _0.008_ & _0.046_ & \\ \hline \multirow{2}{*}{\(y_{0}\)} & ME & 0.931 & 1.000 & 2.922 & 1.760 & 0.231 & **1.000** \\ \cline{2-6}  & MSE & _0.018_ & _0.006_ & _0.030_ & _0.007_ & _0.009_ & **0.163** \\ \hline \multirow{2}{*}{\(z_{y}\)} & ME & 1.805 & 1.360 & 6.661 & 2.518 & 0.495 & \\ \cline{2-6}  & MSE & 0.197 & _0.053_ & _0.756_ & 0.172 & _0.158_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of lander stabilization processes without roll overturns according to the standard algorithm. Figure 3: Typical process of stabilization of the lander without roll overturns according to the standard algorithm. MATLAB simulates 64 samples (Table 2) of 100 processes of the lander stabilization without roll overturns using a new robust algorithm with controller matrix (41) and parameters (45). The worst statistical characteristics of the processes for samples are presented in Table 5, and the graphs of the corresponding process under typical IC (44) are shown in Figure 4.  
As an additional example of using the proposed robust algorithm, a similar simulation of the spacecraft motion with increased moments of inertia and proportionally increased thrust of control motors was carried out (Tables 6 and 7). Figure 4: Typical process of stabilization of the lander without roll overturns according to the new algorithm.
## 2 Materials and Methods
### System Design
#### 2.1.3 Feedback Logic  
The automatic feedback system is optimized under the condition that all beamline devices on the optical path are optically aligned and the EPU gap and PGM angle are selected. Figure 3 shows the change in the four blade-edge voltage signals along with the position changes of M2, where the incident X-ray energy is 920 eV and the Slit 2 size is 50 \(\upmu\)m \(\times\) 50 \(\upmu\)m. \(\mathrm{S_{L}}\), \(\mathrm{S_{R}}\), \(\mathrm{S_{U}}\) and \(\mathrm{S_{D}}\) represent the left, right, up, and down blade-edge signals. The change in the horizontal signals is an order of magnitude larger than the change in the vertical signals, indicating that the deviation in the light spot in the horizontal direction is much larger than that in the vertical direction. Therefore, only horizontal deviation is considered in the feedback system, and the horizontal blade-edge signals are used to assess the position of the X-ray light flux in the downstream optical path of Slit 2.  
Figure 4 illustrates that the gold mesh current signal reaches its optimal value along with the change of the position of M2. When the M2 position parameter is smaller, the right blade-edge signal is larger than the left, indicating that the X-ray beam at Slit 2 is deviated to the right direction. The difference between the left and right blade-edge signal decreases as the M2 position increases. At a specific M2 position value, the left and right blade-edge signals become the same, and the gold mesh signal almost reaches its maximum value. Therefore, the first logic feedback criterion can be defined as follows:  
\[S_{RL}=\frac{S_{R}-S_{L}}{S_{R}+S_{L}}, \tag{1}\]  
If \(S_{RL}\) is positive, it needs to increase the M2 position parameter and reduce the signal difference between the left and right blade edges. Conversely, if \(S_{RL}\) is negative, it needs to reduce the M2 position parameter and reduce the signal difference between the left and right blade edges.  
Figure 3: The measurement results of the Slit 2 blade-edge signals with M2 position changes at 920 eV. \(\mathrm{S_{L}}\), \(\mathrm{S_{R}}\), \(\mathrm{S_{U}}\) and \(\mathrm{S_{D}}\) represent the left, right, up and down blade-edge signals, respectively. The horizontal axis represents the M2 position, with a minimum moving step size of 200 nm. The left coordinate system is for \(\mathrm{S_{L}}\) and \(\mathrm{S_{R}}\), while the right coordinate system is for \(\mathrm{S_{U}}\) and \(\mathrm{S_{D}}\). The horizontal blades are illuminated over a larger range than the vertical blades, resulting in the horizontal blade signals that is nearly 10 times stronger than the vertical blade signals.
## 5 Simulation and Experimental Results
### The QO with Amplitude of Output Current  
To demonstrate the theoretical study, the behavior of the proposed electronically controllable OTA-C QO with amplitude of output current has been verified by simulations and experimental measurements. The QO, as shown in Figure 3, was designed for the f\({}_{0}\) = 72.3 kHz, with g\({}_{\text{m1}}\) = 1.18 mS, g\({}_{\text{m2}}\) = g\({}_{\text{m3}}\) = g\({}_{\text{m4}}\) = 1 mS, and C\({}_{\text{1}}\) = C\({}_{\text{2}}\) = 2.2 nF. As mentioned earlier, g\({}_{\text{m1}}\) was designed to be greater than the theoretical value to guarantee start-up of oscillation. Figure 23 shows the simulation quadrature output voltage waveforms. It can be seen from the simulation results that the FO is approximately equal to 71 kHz, close to the theoretical value, and the error rate is \(-\)1.8%. Figure 24 represents the experimental quadrature outputs V\({}_{\text{o1}}\) and V\({}_{\text{o3}}\). According to the experimental results, the measured FO is equal to 73.78 kHz, which is close to the theoretical value, and the error rate is 2%. Figure 25 shows the simulated and experimental results of the FO by varying the values of g\({}_{\text{m3}}\). Figure 26 shows how to calculate the phase noise using the Agilent phase noise measurement solution. The phase noise in the proposed electronically controllable OTA-C QO is lower than \(-\)74.93 dBc/Hz (at 1 kHz offset). To show the usability of the electronically controllable OTA-C QO in Figure 3, the applications of AM and ASK generators are confirmed in Figures 27-32, where Ig\({}_{\text{B4}}\) is sine wave, triangle wave, and pulse signals respectively, with a frequency of 7.4 kHz. These simulation and experimental measurements are close to the theoretical prediction, confirming the feasibility of the proposed electronically controllable OTA-C QO.  
Figure 27: The simulation result of the AM signal generator using sine wave input signal.  
Figure 26: Measured result of the phase noise.  
Figure 28: The simulation result of the AM signal generator using triangle wave input signal.  
Figure 30: The measurement result of the AM signal generator using sine wave input signal.  
Figure 29: The simulation result of the ASK signal generator using pulse wave input signal.
## 5 Case Study
### Effectiveness Analysis of the Collaborative Iterative Strategy  
This section aims to verify whether the collaborative iterative strategy effectively improves the NSGA-II-MOEA/D hybrid algorithm and to what extent. To ensure fairness, both algorithms share identical parameters except for the application of the collaborative iterative strategy. Each algorithm runs 50 times, and the best result is selected. Figure 10 presents the Pareto front comparison between the NSGA-II-MOEA/D-CIS algorithm with the collaborative iterative strategy and the NSGA-II-MOEA/D algorithm without it. As shown in Figure 10, the Pareto front of NSGA-II-MOEA/D-CIS is broader and exhibits a more evenly distributed solution space. In contrast, the Pareto front of NSGA-II-MOEA/D is more concentrated, either in the region of low production cost with high delayed delivery time or in the region of high production cost with low delayed delivery time. Additionally, NSGA-II-MOEA/D-CIS obtained 12 non-dominated solutions, whereas NSGA-II-MOEA/D obtained only 10. These findings demonstrate that the collaborative iterative strategy effectively expands the solution space and enhances solution diversity.  
Figure 10: Comparison of Pareto fronts with and without the collaborative iterative strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Upper**} & \multirow{2}{*}{**Lower**} & \multicolumn{4}{c}{**Dimension**} \\ \cline{3-6}  & & **Algorithm** & **Solution** & **Computational** & **Rank** \\  & & **Performance** & **Quality** & **Efficiency** & \\ \hline MOEA/D & MOEA/D & 6 & 6 & **1** & 4 (tie) \\ NSGA-II & NSGA-II & 2 & 2 & 6 & 3 \\ MOEA/D & NSGA-II & 5 & 3 & 5 & 4 (tie) \\ MOPSO & MOEA/D & 3 & 4 & 2 & 2 \\ SPEA2 & MOEA/D & 4 & 5 & 4 & 4 (tie) \\
**NSGA-2** & **MOEA/D** & **1** & **1** & 3 & **1** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comprehensive comparison results of different algorithm combinations.  
Moreover, the proposed collaborative iterative strategy is essentially designed to accelerate the convergence process of the algorithm and obtain higher-quality solutions. Therefore, it is necessary to compare the HV indicator (evaluating the uniformity of the solution set) and the IGD indicator (evaluating the proximity of the solution set) for both algorithms. Figure 11 presents the comparison of the HV and IGD variation curves with and without the collaborative iterative strategy. It can be observed that the NSGA-II-MOEA/D-CIS algorithm reaches convergence at approximately 100 generations, whereas the NSGA-II-MOEA/D algorithm takes around 120 generations to complete convergence, with lower final convergence values than NSGA-II-MOEA/D-CIS. Thus, it can be demonstrated that the collaborative iterative strategy effectively improves the uniformity and proximity of the solution set.