esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulation and Model
### The Circuit Model  
The equivalent circuit model of the whole superconducting synchronous generator is shown in Figure 2. For the stator windings, since the three-phase stator windings are symmetrical, only the variables related to the phase-A winding are explained.  
The circuit equation of the phase-A is:  
\[U_{A}=R_{LA}i_{A}+L_{LA}\frac{di_{A}}{dt} \tag{11}\]  
where \(U_{A}\), \(L_{LA}\), \(R_{LA}\), \(i_{A}\) are the output voltage, the load inductance, the load resistance and the current of the phase-A winding, respectively.  
For the HTS winding, it can be seen as a series of turns (right part of the Figure 2), and the circuit equations are [24]:  
\[\begin{array}{c}U_{A}=\sum\limits_{k=1}^{N}\left(e_{k}-i_{k}\frac{R_{max}R_ {max}}{R_{max}+R_{max}}\right)\\ e_{k}=\frac{\int E_{x}dS}{S_{k}}L\\ i_{k}=i_{su,k}+i_{cu,k}\\ R_{su,k}=E_{0}\frac{i_{su}\kappa^{-1}}{L_{(B_{su,k},T_{su,k})}}^{\mu}k\\ R_{cu,k}=\frac{\rho_{cu}(T_{su,k})l_{k}}{S_{cu,k}}\\ R_{su,k}i_{su,k}=R_{cu,k}i_{cu,k}\end{array} \tag{12}\]  
where the subscript \(k\) represents the \(k\)-th turn of the HTS coil. \(N\) is the total number of turns of the coil. \(e_{k}\) is the generated electromotive force. \(E_{z}\) is the electric field strength in the coil domain. \(S_{k}\) is the cross-sectional area of the conductor. \(L\) is the core length. \(i_{k}\) is the total current. \(i\), \(R\) and \(T\) represent the current, the resistance and the temperature, respectively. The subscript \(su\) and \(cu\) represent the superconducting layer and copper layer, respectively. \(B_{su,k}\) is the magnetic flux density in the superconducting layer. \(l_{k}\) is the tape length. \(\rho_{cu}\) is the copper layer resistivity which is from [25]. \(S_{cu,k}\) is the cross-section area of the copper layer.
# Pockels Effect at the Interface between Water and Ti Electrode
## 3 Experimental Results
### Calculation of Electroreflectance Spectra in Aqueous Solution  
Next, the oxide film thickness was fixed at \(d_{2}=14\) nm, and the \(\Delta R/R\) spectra were calculated using the method in (B,C), assuming that an EDL of water with a refractive index changed from bulk was generated between the oxide film and bulk water. As shown in Figure 12a, the experimental result was reproduced when it is assumed that with a positive voltage application of +2 V, an EDL of water (\(\Delta n\times d_{1}=0.0255\) nm) was generated, and the photon energy dependence of the complex refractive index in the TiO\({}_{2}\) layer was red-shifted by 1.5 meV. The assumption of an energy shift in the complex refractive index of TiO\({}_{2}\) (SCL in TiO\({}_{2}\)) in contact with the EDL of water follows the method used in the analysis of the Pockels effect of water at the ITO electrode interface, which assumed a shift in the ITO band edge due to the band population effect [4; 12; 20; 31]. On the other hand, assuming that only the refractive index change of the water EDL (Figure 12b) or only the red-shift of the complex refractive index of the TiO\({}_{2}\) layer (Figure 12c) occurred, the \(\Delta R/R\) spectral features could not be reproduced even qualitatively. The complex refractive index change in the TiO\({}_{2}\) film assumed in the calculations of Figure 12 is shown in Figure 13.
## 4 Discussion
### Instrumental Effects  
A most important result of this research is the identification of narrow absorption signatures in the small nucleic acid samples, siRNA, 50-bp DNA, and 1-kbp DNA. The associated linewidths (FWHM) are surprisingly narrow (10-32 GHz) and represent quality factors in the range of \(\sim\)20-100. These linewidths are much narrower than have been reported in the literature for aqueous nucleic acids, to the best of our knowledge, and this  
Figure 7: Center frequency of three prominent signatures vs. sample type. Note that the horizontal axis is not proportionate.  
begs the question: why? Based on our longstanding experience in the THz spectroscopy field, we attribute the observation of such narrow signatures in part to spectral resolution and dynamic range. Most of the reports on nucleic acid signatures have been carried out either by FTIR, THz TDS, Raman sideband spectroscopy, or, more recently, optical Kerr-effect spectroscopy. With all these techniques, there is a spectral resolution limit determined by delay line travel (FTIR and TDS) or pulsed laser sideband noise (Raman and Kerr effects). Without heroic efforts, these generally render a spectral resolution of roughly 0.5 cm\({}^{-1}\) (15 GHz) or higher. By contrast, frequency-domain spectroscopy, as utilized in this work, routinely provides a spectral resolution of 1.0 GHz or better (0.5 GHz in our case). So, in the longstanding reports of THz nucleic acid absorptive signatures going back roughly 40 years, there is a possibility that the experiments have been spectroscopically under-resolved.  
The second issue is dynamic range, which is related to instrumental SNR in the presence of the sample and all noise effects, random and systematic. Remarkably, many of the THz spectroscopy results do not report this metric, nor do they report an instrumental noise floor spectrum. So, even if a narrow signature were present and the instrument was configured to have suitable spectral resolution, the poor SNR would not allow a positive result.
## 3 Methodology
### Gateway Optimization
#### 3.5.2 Description of EPSO Implemented  
The optimization model implemented in the simulations is the Evolutionary Particle Swarm Optimization (EPSO), which is based on evolutionary simulations [27] and Particle Swarm Optimization algorithms [28], thus resulting in a self-adaptive evolutionary algorithm, in which a particle motion operator replaces the recombination. The new operator proves to be more effective than the recombination in generating solutions that approach the optimal value. Furthermore, its self-adapting feature makes EPSO independent of the external definition weights and parameters in [29].  
EPSO's operating scheme follows five steps for a given iteration: replication, mutation, reproduction, evaluation, and selection. EPSO's motion rule is for a particle \(X_{i}\), a new particle \(X_{i}^{new}\) is given by [30]:  
\[X_{i}^{new}=X_{i}+V_{i}^{new}, \tag{11}\]  
and their respective speeds are  
\[V_{i}^{new}=w_{i0}^{*}V_{i}+w_{i1}^{*}(b_{i}-X_{i})+w_{i2}^{*}(b_{g}^{*}-X_{i}). \tag{12}\]  
The motion rule retains its terms of inertia, memory, and cooperation, as in the standard PSO algorithm. However, in EPSO, the weights change as (13):  
\[w_{ik}^{*}=w_{ik}+\tau N(0,1), \tag{13}\]  
where \(N(0,1)\) represents a random variable with a Gaussian distribution, mean 0, and variance 1. The global best \(b_{g}\) is randomly perturbed, as follows:  
\[b_{g}^{*}=b_{g}+\tau^{\prime}N(0,1). \tag{14}\]  
\(\tau\) and \(\tau^{\prime}\) are fixed learning parameters or are treated as strategic parameters and, therefore, subject to mutation.  
Table 3 consolidates the strategic parameters defined for EPSO, which are added in (13) and (14).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Population size & 100 \\ Mutation rate & 0.4 \\ Communication probability & 0.7 \\ Max. number of fitness evaluations & 10,000 \\ Max. number of generations & 100,000 \\ Max. number of generations with the same global best & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strategic parameters of EPSO.
## 3 Results and Discussion  
### Physicochemical and Chemical Characterization of Juices  
Physicochemical parameters of juices were determined, in terms of pH, titratable acidity, soluble solid content, colorimetric parameters, and viscosity. Measuring pH--giving relevant information on the juice acidity--is important to characterize the product flavor and to evaluate the subsequent technological processes. Statistical analysis of juice pH, a significant interaction of the two factors--cultivar and treatment--was performed. Indeed, considering the starting condition, in NT juices, PIN showed the statistically lowest pH, as already reported [22]; after the stabilization treatments, RD and PIN juice pH remained constant, while GD juice pH decreased, becoming statistically comparable with PIN pH (Table 1). Effectively, only for GD, statistically analysis evidenced significant differences among treatments, with thermal-treated juices with a pH value statistically lower than those NT, and the HPP ones with an intermediate behavior (Table 1). It has been already reported that, in juices and pulp obtained from other fruits, HPP treatment increased the ionic dissociation constant of water and weakened acids [23], which determine the increase of H\({}^{+}\) ions [24]; however, for other fruit juices, the HPP treatment is not as incisive, as reported for lithchi-based mixed fruit beverage [25] and from PIN and RED juices. The thermal treatment does not have an unambiguous effect on fruit juices either, as several reports have indicated that thermal pasteurization is irrelevant on pH juice [26; 27; 28]. Results reported in this study, regarding the effect of thermal treatment on RD juice, are confirmed by [PERSON] et al. (2019) [28], in the same apple cultivar. The lowering effect of thermal and/or HPP treatment on apple juice pH value could be genotype dependent, as reported by [PERSON] et al. (2017) [12]. Together with the pH, the titratable acidity value contributes to the definition of juice flavor; the statistical analysis evidenced how \"Cultivar\" was the factor that mainly influenced this parameter. Specifically, it seems that RD juice has a titratable acidity significantly lower than GD and PIN (2.8 g/L malic acid vs. 4.8 g/L malic acid for both) (Table 1). TTA values recorded in this study are in line with those reported in previous
### Limitation of Existing Studies
Since the CCDP in POS 7, 8, 9, and 12B is 0 for the unit in shutdown, the site CCDPs when the unit is in these POSs correspond to those obtained from the five units at-power. When one unit is in a certain POS, such as POS 4B, the site CCDP is up to four times higher than when all six units are at power. However, the time fractions of POSs with a higher CCDP are low, while those of POSs with a lower CCDP are high. This case illustrates how assuming only the POSs with a higher CCDP for the entire O/H period can lead to conservative results. Since existing studies have focused on selecting a number of representative combinations, in many cases using the term \"site operating state (SOS)\", they have limitations in reducing this conservatism. It is difficult to find published research that has attempted \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Organization** & **Number of Reactor Units** &
\begin{tabular}{c} **Description of Modeling** \\ **POS Combinations** \\ \end{tabular} \\ \hline \hline \multirow{4}{*}{[PERSON] [12]} & \multirow{4}{*}{NUBIKI Nuclear Safety Research Institute} & \multirow{4}{*}{4} & A total of 115 viable combinations for four reactors and four spent fuel pools were defined through a detailed evaluation, but only one combination of two units at FP was considered in the small-scale pilot study. \\ \cline{1-1} \cline{3-4}  & & & To demonstrate a simulation model for estimating the fraction of time for each combination, 15 LPSD POSs for each unit were classified into three groups. \\ \hline \hline \end{tabular} Note: The number of units and the description of the modeling of POS combinations for each reference are based on the case study included in the reference.  
\end{table} 
Table 1: _Cont._  
Figure 1: Example of POS-dependent site CCDP profile (one unit in shutdown, five units at power). to model as many combinations as possible to solve this problem. Therefore, there is a strong need to develop a pragmatic approach that can reduce the conservatism caused by selecting a small number of POS combinations.
# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 4 Methodology
### USB Loss: Uncertainty-Based Sample-Balanced Loss
```
0: data \(x\), label base model \(g(\cdot)\), dropout probability \(p\),  number of iterations \(T\)
0:\(Loss_{usb}\)
1: % uncertainty quantification
2:for\(t=1\) to \(T\)do
3:\(\hat{y}_{c,\;s}^{t}\leftarrow\text{Dropout}\;(g(x),\;p)\)
4:endfor
5: \(\hat{y}_{c,\;s}\leftarrow\frac{1}{T}\sum_{i}^{T}1\;\hat{y}_{c,\;s}^{t}\)where \(t=\text{trial}\), \(c=\text{class}\), \(s=\text{batch}\)
6: \(variance_{\hat{y}_{c,\;s}}\gets E\left(\left[\hat{y}_{c,\;s}^{t}-\hat{y}_ {c,\;s}\right)^{2}\right]\) # class-wise variance per sample
7: \(uncertainty_{\hat{y}_{c}}\leftarrow\frac{1}{c}\sum_{c=1}^{C}variance_{\hat{y}_{c, \;s}}\) # sample-wise uncertainty
8: % calculate USB loss
9: \(\hat{y}_{c,\;s}\leftarrow\text{Dropout}\;(g(x),\;p)\)
10: \(Loss_{usb}\leftarrow-uncertainty_{\hat{y}_{s}}*\sum_{c=1}^{c}y*\log\left(\frac{e ^{\hat{y}_{c,\;s}}}{\sum_{c=1}^{c}e^{\hat{y}_{c,\;s}}}\right)\)
11:return\(Loss_{usb}\)
```  
**Algorithm 1** Uncertainty-Based Sample-Balanced Loss (Hard Sample)  
Learning is performed by applying this quantified uncertainty to the existing loss function, such as cross-entropy, and varying the weight of the loss for each sample. Therefore, the proposed loss function is called Uncertainty-Based Sample Balanced Loss. When the hard sample mode is selected as the learning strategy, uncertainty is applied, like in line 10, and when the easy sample mode is selected, confidence that is inversely proportional to uncertainty is applied.
## 4 Review of the Routing Protocols
### Link Quality-Based Routing Protocols Comparison  
The link quality-based routing protocols are robust and select the next hop based on link quality information such as energy, hops, processing and memory. Additionally, low-power radios are very sensitive to noise, interference and multipath distortions. For the low-power WBASNs, link quality is considered a crucial metric for the selection of the next hop [152; 153; 154]. There are many link-aware routing protocols of WBASNs that can be adopted with IoTs [155; 156; 157; 158; 159; 160; 161]. These protocols mostly use IEEE 802.15.4 or IEEE 802.15.6 as MAC and PHY layer standards. Various methods are used to compute the link quality for these routing protocols [162]; however, RSSI and link quality indicator (LQI) are considered strong
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 2 Preliminaries
### Task Queue  
The task queue is the set of tasks to be executed every day. The parameters of the task model include the code of the working frequency band, the code of the task's start time, and the delay time of the task, which can be expressed in triples:  
\[Task\ =\ <\ FreqBand,StartTime,DelayTime> \tag{1}\]  
The task queue can be described as:  
\[TaskQueue=\left\{Task_{1},Task_{2},\ldots,Task_{j},\ldots Task_{p}\right\} \tag{2}\]  
where, \(p\) is the number of tasks, \(Task_{j}\) is the \(j\)-th of tasks, \(Task_{j}\).\(FreqBand\) is the frequency-band code of the working frequency of the task, \(Task_{j}\).\(StartTime\) is the code of the start time of the task, and \(Task_{j}\).\(DelayTime\) is the delay time of the task.
# Gamification and Immersive Experiences: A Gamified Approach for Promoting Active Aging
## 5 Discussion
Despite these promising outcomes, the study's short duration and small sample size limit the generalizability of the findings. The pilot study's single 60 min session offered only a snapshot of user engagement, leaving the long-term effects of Twist on cognitive health, social well-being, and digital literacy unexplored. Previous research has highlighted the importance of longitudinal studies in assessing the sustained impact of cognitive interventions [16; 35]. Additionally, the issue of limited far-transfer effects--where improvements achieved in platform tasks fail to generalize to real-world cognitive challenges--remains a concern [7; 36]. This highlights the need for future iterations of Twist to incorporate real-world tasks that bridge the gap between platform-based cognitive exercises and everyday life. Accessibility emerged as another challenge in the study. While Twist was designed with an intuitive interface, integrating advanced tools such as virtual reality glasses posed initial barriers for older users, consistent with findings from [PERSON] (2023) [18] and [PERSON] et al. (2020) [28]. Older adults often require additional time and training to adapt to these technologies, which can initially impact engagement and satisfaction. Ensuring that immersive features remain accessible to users with varying levels of technological familiarity is essential for the platform's inclusivity and effectiveness. Future research should focus on expanding the participant pool to include a more diverse demographic, encompassing different genders, cognitive abilities, and physical health profiles. Such diversity will allow for a more comprehensive evaluation of the platform's accessibility and effectiveness across various user groups [37]. Moreover, longitudinal studies will provide critical insights into the platform's ability to support sustained cognitive stimulation, improve digital literacy, and foster social connections over time. By incorporating control groups and evaluating both qualitative and quantitative outcomes, future studies can better assess the unique benefits of Twist compared to traditional cognitive training methods [14; 32]. To enhance the transferability of cognitive gains, the platform should integrate tasks simulating everyday scenarios, such as managing schedules, navigating environments, or solving practical problems. This approach aligns with recommendations from [PERSON] et al. (2016) [36] and [PERSON] et al. (2020) [7] on designing interventions that promote real-world applicability. Additionally, accessibility improvements, such as simplified interfaces and adaptive difficulty levels, will ensure that Twist accommodates users with varying cognitive and physical capabilities [18; 30]. We think that the Twist platform can offer significant potential as an innovative tool for active aging. By addressing its current limitations and building on the insights gained from this pilot study, future iterations can contribute meaningfully to older adults' cognitive and social well-being.
## 3 Results and Discussion
### Efficiency Calculation  
To easily compare the present photocatalytic outcomes with previous findings in the literature survey, the apparent quantum efficiency (AQE) and the light-to-chemical energy efficiency (\(\eta\)) are determined from the best photocatalytic data obtained according to the following Equations (4) and (5):  
\[\text{AQE }(\%)= \frac{2\cdot\text{r}_{\text{H}_{2}}}{\text{moles of incident photons}/\text{time}}\cdot 100 \tag{4}\]  
\[\eta= \frac{\text{r}_{\text{H}_{2}}\ (-\Delta\text{H}_{\text{comb}}^{0})}{ \text{I}\cdot\text{S}} \tag{5}\]  
where:  
\(\text{r}_{\text{H}_{2}}\) (mol\(\cdot\)s\({}^{-1}\)) is the hydrogen generation rate;  
\(\Delta\text{H}_{\text{comb}}^{0}\) is the standard change in enthalpy for the combustion of hydrogen and oxygen (\(-282.0\cdot 103\) \(\cdot\) mol\({}^{-1}\));  
I (W \(\cdot\) cm\({}^{-2}\)) is the light source's specific irradiance;  
S (cm\({}^{2}\)) is the illuminated area.  
In Figure 9 the most significant outcomes are reported. When a high-pressure Hg lamp is used, along with a (1% wt.) Cu\({}_{2}\)O/TiO\({}_{2}\) composite photocatalyst prepared through the ball milling method (200 rpm, 1 min), at a system temperature of 80 \({}^{\circ}\)C, maximum AQE values of 3.17% and 6.54% are calculated under visible light radiation in presence of methanol and glycerol, respectively. Under identical circumstances, the maximum light-to-chemical energy efficiencies values of 1.16% and 2.36% are obtained for methanol and glycerol, respectively.  
Additionally, the efficiency values obtained in the visible light spectrum (AQE = 1.56%, \(\eta\) = 0.56) are only slightly lower than those collected under identical circumstances (T = 35 \({}^{\circ}\)C, methanol as a scavenger) by using UVA + Visible light radiation (AQE = 2.31%, \(\eta\) = 0.83). In fact, as reported in the figure, despite the huge discrepancy in hydrogen generation in the presence and absence of UVA radiation, the different light intensities induce comparable activities in terms of quantum yield and light-to-chemical energy efficiency.  
Figure 9: Mean hydrogen generation rate (\(\blacksquare\)), AQE (\(\blacksquare\)), and \(\eta\) (\(\blacksquare\)) obtained under visible light irradiation (\(\lambda>400\) nm) and UV-A + Visible light conditions.
## 4 Selected Methods and Techniques in Landscape Assessment
### DTM and DSM  
A digital terrain model (DTM) approximates a part or the whole of the continuous terrain surface by a set of discrete points with unique height values over 2D points. Heights are, in approximation, vertical distances between terrain points and some reference surface (e.g., mean sea level, geoid, and ellipsoid) or geodetic datum. Mostly arranged in terms of regular grids, the 2D points are typically given as geodetic coordinates (latitude and longitude) or planar coordinates (North and East values). DTMs usually assign a single unique height value to each 2D point, so they cannot describe vertical terrain features (e.g., cliffs). DTMs are, therefore, \"2.5D\" rather than truly 3D models of the terrain [48]. The DTM is commonly used to determine the height of any point on a terrain, create axonometric drawings of the terrain surface, calculate the volume and balance the earth mass analysis of mutual visibility of points, determine local terrain slopes and their analysis of local slopes and their azimuths, analyze flood plains and river basins, and create contour lines and crosses [49] and 2D and 3D surface representation (visualization) [31; 32; 33; 50; 51].  
Digital surface models (DSMs) are also widely used. They are a combination of data about the heights of the terrain relief, along with information on the situational and height locations of the objects on the surface of the analyzed area. These objects may be natural, e.g., aforestation formations, shrubs, and lakes, or related to the activity of people, e.g., buildings, roads, and artificial water reservoirs.