esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Proposed Framework
#### 3.1.3 Local Feed-Forward Network (LFFN)  
Local feed-forward network (LFFN) [53] considers the locality in a feed-forward framework, which is essential for accurately capturing spatial dependencies in remote sensing data. The LFFN addresses the challenge of preserving local information by converting the sequence of tokens into a feature map and preserving location information by plac  
Figure 3: Multi-head convolutional attention (MHCA) module architecture.  
ing tokens at pixel locations. The architecture of the LFFN, shown in Figure 4, involves passing the mapped sequences through \(1\times 1\) pointwise convolutions and a \(3\times 3\) depthwise convolution. This structure allows the LFFN to effectively capture local features and neighboring information.  
The combination of pointwise convolutions and depthwise convolutions enables the LFFN to extract detailed local features. The pointwise convolutions help in reducing dimensionality while preserving essential information, and the depthwise convolutions focus on capturing spatial relationships within a small neighborhood.  
By mapping sequences to a feature map and applying convolutions, the LFFN preserves the spatial relationships between pixels. This preservation is critical for maintaining the integrity of local features, which are often lost in traditional feed-forward networks. This preservation of local information ensures that the subsequent attention layers receive a rich representation of spatial features. This feed-forward network helps the model to consider the locality and neighboring information in a feed-forward architecture. The output is reverted back to its sequence format to be used in the next attention layers.
## 3 Membrane Performance Measurements
### Uncertainty of Measurements
#### 3.6.1 Experimental Uncertainties  
The cup method measurements were performed based on ASTM E96, and the uncertainty analysis was conducted accordingly. The uncertainties were categorized as three types of errors: buoyancy, edge mask, and still air. A buoyancy correction was not performed in this experiment because such corrections are only required when the measured weight change is below 100 mg. Edge mask correction is used to prevent errors caused by the sideways movement of water vapor when a membrane with an area larger than the diameter of the device is used, as follows:  
\[\mathrm{Excess\ WVT}(\%)=\ \frac{400\delta}{\pi S}\ln\frac{2}{1+e^{-(2\pi b/ \delta)}} \tag{6}\]  
where \(\delta\) and \(b\) present specimen thickness and width of masked edge, respectively. \(S\) is a parameter of four times the test area divided by the perimeter. Calculations using the dimensions of our experimental device resulted in an error of 0.0883%. However, the edge mask part was further reduced using acrylic supports to minimize membrane deformation in the experimental device. In addition, due to the characteristics of the membrane used here, water vapor moves via the ion nanochannels, hence, the edge mask effect is expected to be even smaller. Thus, this error was considered negligible and was ignored in the error analysis.  
In this experiment, the still air error related to the effect of the distance between the membrane and absorbent is the greatest because the measurement was performed in stagnant state rather than in a gas flow. According to ASTM E96-14, due to a permeance of higher than 2 perms (\(\geq\)6.33 \(\times\) 10\({}^{-9}\) mol/m\({}^{2}\)s Pa, \(\geq\)20 GPU), correction is required for resistance due to still air. For the data measured in this experiment, the results were higher than 2 perms, hence the correction was performed by the correlation below.  
The diffusion coefficient as a function of the temperature and pressure of water vapor is given as follows [16]:  
\[D(T,p)=D(0,1)\left(\frac{p_{0}}{p}\right)\left(\frac{T}{T_{0}}\right)^{a} \tag{7}\]  
where \(D(0,1)\) is diffusivity at standard temperature and pressure (STP) condition of \(2.19\times 10^{-5}\) in this study. Subscript 0 denotes STP condition. From Equation (7), the resistance due to the still air is calculated by dividing \(D\) by the thickness (\(d\)) and temperature (\(T\)) of the air layer in the cell and the gas constant of the water vapor (\(R_{\text{H}_{2}\text{O}}\)):  
\[P_{\text{still air}}=\frac{D}{dR_{\text{H}_{2}\text{O}}T} \tag{8}\]  
The corrected water vapor permeance \(P_{\text{cor}}\) is calculated as follows [17].  
\[\frac{1}{P_{\text{cor}}}=\frac{1}{P_{\text{H}_{2}\text{O}}}-\frac{1}{P_{\text{ still air}}} \tag{9}\]  
These corrections resulted in errors of 15-35%. It was assumed that the experiment was conducted in a stationary state without fluid flow inside the cell, and the measurement condition was not atmospheric pressure.
# Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography
## 2 Materials and Methods
### Radiation Dosimetry  
Medical physics tests of the radiation dose report system in the CT console have been made, ensuring the accuracy of the reported radiation dose values. In this study, the radiation dose is evaluated per procedure, obtaining the dose-length product (DLP)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Philips** & **GE** \\ \hline Scan type & Helical & Helical \\ kVp * & 120 & 120 \\ mA * & 100 & 120 \\ Collimation (mm) & 40 (64 * 0.625) & 40 (64 * 0.625) \\ Rotation time (s) & 0.5 & 0.5 \\ Pitch & 1.375 & 1.375 \\ SFOV & Large & Large \\ Coverage & Diaphrag to ischiium & Diaphrag to ischiium \\ IRT & 40\% ASIR & 50\% ASIR \\ \hline \hline \end{tabular}  
* For standard size patient (70–90 kg); SFOV: scan field of view; IRT: iterative reconstruction technique; GE: General Electric.  
\end{table}
Table 1: Image acquisition parameters during CTC procedure.  
(in mGy.cm), and per slice by the volume computed tomography dose index, CTDI\({}_{\text{vol}}\) (in mGy). The total effective dose was calculated according to the International Commission on Radiological Protection (ICRP) by multiplying the DLP by the abdominal and pelvis conversion factor \(f\) to obtain a value in mSv.mGy\({}^{-1}\).cm\({}^{-1}\)[24], as follows:  
\[E\ (\text{mSv})=DLP(mGy.cm)\times f \tag{1}\]  
The cancer risk for the entire CTC procedure is calculated by multiplying the effective dose by the cancer risk factor (5.5 \(\times\) 10\({}^{-2}\)Sv\({}^{-1}\)) [24].
# Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems +
## 2 Related Work
### LSGO Tackling Approaches
#### 2.2.2 Variable Grouping Techniques  
CC approaches can be divided into three branches: static, random, and learning-based grouping techniques. Static grouping-based CC methods divide an optimization problem with \(N\) variables into the predefined number of subcomponents (\(m\)), where \(N=m\times s\), and \(s\) is the number of variables in each subcomponent. If subcomponents of an LSGO problem are known, variables are grouped in subcomponents according to their type: separable and non-separable.  
Random grouping CC methods assign variables in subcomponents at random after a certain number of FEV. The landscape of the optimization subproblem of each subcomponent becomes different after each random grouping. Random grouping techniques can help to avoid stagnation because a new set of variables leads to a new optimization problem. This type of methods shows good performance on some non-separable problems (with dimensionality up to one thousand). When the number of variables becomes more than 1000, the performance of random grouping techniques decreases [25].  
The last group of decomposition-based methods is the learning-based dynamic grouping. The idea behind these methods is to find an interaction between variables and to group these variables into subcomponents. Usually, the interaction of variables is detected by examining each variable with others. For example, the learning-based DG approach [26] requires 1001000 FEVs if the 1000-dimensional problem is fully-separable. It is equal to 33.3% of the fitness budget for each problem from the LSGO CEC'10 [10] and CEC'13 [27] benchmarks.  
Some of the popular grouping LSGO techniques are presented in Table 1. Each row in the table contains information about the authors, the year of the publication, the short name (the abbreviation) of the approach, the reference, and the type of the used grouping technique.
## 2 Materials and Methods
### Universal GNSS Channel Design Decisions
#### 2.1.5 Data & Pilot Components Merging  
Most new and modernized signals have two components combined in (counter-) phase or in phase quadrature, such as Galileo E1 B&C and GPS L5 I&Q, respectively. In order to deal with them, a  
Figure 5: Single-Component Products and Correlation (BPSK _vs._ MBOC Overhead).  
special design choice has to be made: either (1) each component is dealt with in a separate channel, whose correlation products are properly dealt with through a common (or distinct) discriminator; or (2) both components are integrated into one dual-component universal channel. Although more flexible, the first case would not allow for the HW reduction of the following shared resources (in the authors' opinion, when applied to the proposed architecture):  
1. Memory codes address and control logic.
2. Carrier and sub-carrier NCOs direct and derived clock signals.
3. Sub-Carrier generation of Prompt (P) and Differential (D) shared by both signal components as the data and pilot chipping rates are always equal in the publically disclosed signals.
4. Sine and cosine LUTs and carrier multipliers leading to the I and Q branches.
5. 27-stage LFSR L2 CL code generation implemented only once per dual component channel (_i.e._, implementing more than 32 universal channels would waste even more resources as there should not be more than 32 L2 CL codes being broadcast by the current GPS constellation).  
Thus, several architectures are possible, depending on the receiver performance _vs._ cost desired ratio. Dual-component channels allow maximizing the harvested signal power, whereas single-component architectures only allow one of the following:  
* Acquire and track the data component only, ignoring the pilot component available power.
* Acquire pilot component with a longer integration time for greater sensitivity and then transfer to data component tracking to extract the navigation message.
* Acquire and track both pilot and data components in independent channels.  
With the dual-component channel resources available, a faster sequential acquisition also becomes possible by splitting the search space into two sets of chip offsets:  
1. Dual-code delay search makes primary code acquisition two times faster and;
2. Once synchronized onto the primary code, a dual secondary chip estimation (_i.e._, either the secondary chip changes or not) allows for an integration time over twice the primary code period by using the best of these two integration outputs.  
In order to minimize power consumption in mobile devices, the pilot-related components may become idle during single-component signal tracking.
## 2 System Model and Problem Statement
### Secure Transmission Based on Adaptive Coding
#### 3.3.1 Adaptive Coding Based on the Equivalent Channel
\begin{split} P_{AB}^{\text{key}}&=\Pr[\text{Re}( \hat{h}_{BA})>0,\text{Re}(\hat{h}_{AB})<0]+\Pr[\text{Re}(\hat{h}_{BA})<0,\text{ Re}(\hat{h}_{AB})>0]\\ &=2\times\int_{-\infty}^{\infty}\Pr[\text{Re}(\hat{h}_{BA})>0, \text{Re}(\hat{h}_{AB})<0|\text{Re}(h_{AB})=u]f_{\text{Re}(h_{AB})}(u)du\\ &=\frac{1}{2}-\frac{1}{\sqrt{\pi}}\int_{0}^{\infty}\text{erf}^{2} \bigg{(}\sqrt{\frac{u^{2}\alpha P}{\sigma_{n}^{2}}}\bigg{)}\exp\Big{(}-u^{2} \Big{)}du.\end{split}  \tag{5}\]  
According to [17], The TBER of the BPSK signal transmitted in the Rayleigh fading channel can be calculated as  
\[P_{e}=\frac{1}{2}\bigg{(}1-\sqrt{\frac{\gamma_{d}}{1+\gamma_{d}}}\bigg{)}= \frac{1}{2}\bigg{(}1-\sqrt{\frac{P(1-\alpha)}{P(1-\alpha)+\sigma_{n}^{2}}} \bigg{)}. \tag{6}\]  
We take \(\gamma_{p}=\frac{\alpha P}{\sigma_{n}^{2}}\) and \(\gamma_{d}=\frac{P(1-\alpha)}{\sigma_{n}^{2}}\) to represent the SNRs of the pilot signals of Alice or Bob and the SNR of the received data signals, respectively. In step 2, to maximize the secure transmission rate, the problem can be expressed as  
\[ \begin{split}\max R&=\frac{k}{n}\\ s.t.\frac{t}{n}&\geq P_{et}\\ n-k&\geq 2t+1\end{split}  \tag{7}\]  
To solve this problem, we can compute that  
\[t\geq nP_{et} \tag{8}\]  
\[k\leq n-(2t+1) \tag{9}\]In this way, we can derive that \(k\leq n-2 nP_{et}-1\). Hence, the upper bound of the secure transmission rate is  
\[R^{LIB}=\frac{k^{LIB}}{n}=\frac{n-2 nP_{et}^{UB}-1}{n}. \tag{10}\]  
Furthermore, \(k^{LIB}=nR^{UB}=n-2 nP_{et}^{UB}-1\) can be calculated in step 3. By exploiting the relationship between \(n\) and \(k^{LIB}\), we can obtain the maximum value of the optimal parameter \(k\) by fixing \(n\). According to \(n\) and the maximum value of \(k\), we can use BCH code \((n,k,t)\) to encode source bits \(M_{A}\) to obtain \(X_{A}\) in step 4.
## 2 New Tomographic Techniques
### Ultrasonic Computerized Tomography (USCT)  
Ultrasonic tomography is a kind of an acoustic tomography. In this technique, we apply ultrasound whose frequency differs in accordance with the object for inspection. If the object is concrete structures, we apply ultrasound of the frequency about 50 KHz, while ultrasound of the frequency about 2 MHz is applied for the human body. Though we are able to study the same inverse problems in acoustic tomography, in this paper, let us study other problems than acoustic tomography, which are very special to ultrasonic tomography. For the propagation of the ultrasound, the following property is known.  
**Property 1**.: _Let \(\Omega\subset\mathbb{R}^{3}\) be a domain where the object locates, and \(C(x)\) be the propagation speed of the ultrasound at the point \(x\in\Omega\). For \(\alpha,\beta\in\partial\Omega\), we denote by \(\gamma_{\alpha,\beta}\) a route from \(\alpha\) to \(\beta\) contained in \(\Omega\). The primary wave of the ultrasound which travels from the point \(\alpha\in\partial\Omega\) to the point \(\beta\in\partial\Omega\) takes the route where the travel time is given by Equation (8)_  
\[\min_{\gamma_{\alpha,\beta}}\int_{\gamma_{\alpha,\beta}}1/C(x)d\gamma, \tag{8}\]  
_This route is called 'the fastest route'._  
For this property consult [9]. By virtue of this property, the mathematical problem for ultrasonic tomography is given as  
**Problem 1** (Problem to develop USCT).: _Let \(\Omega\subset\mathbb{R}^{3}\) be a domain and \(C(x)\) be the propagation speed of the ultrasonic wave at the point \(x\in\Omega\). For \(\alpha,\beta\in\partial\Omega\), we denote by \(\gamma_{\alpha,\beta}\) a route from \(\alpha\) to \(\beta\) contained in \(\Omega\). In this case, reconstruct \(C(x)\)\((x\in\Omega)\) out of the data as Equation (9)_  
\[\min_{\gamma_{\alpha,\beta}}\int_{\gamma_{\alpha,\beta}}1/C(x)d\gamma, \tag{9}\]for \(\forall\alpha,\beta\in\partial\Omega\)._  
This problem is also posed in [9]. With the mathematical solution to Problem 1 being very difficult and open, we can establish and solve practical problems related to Problem 1. In the paper [9], the authors posed and solved how to non-destructively inspect the concrete cover for reinforcement in reinforced concrete structures.  
If the object is small or close to homogeneous, then we can apply the same algorithm with the X-ray CT, that is, we assume that the ultrasound rectilinearly propergates and apply the same algorithm with the X-ray CT to obtain a rough sketch of the distribution of the reciprocal \(1/C(x)\) of the velocity. This idea is successful in medical imaging where the object is very small (cf. [10]). In the next section, we shall extend the this idea for further development of ultrasonic tomography.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 2 Problem Description  
The Multi-Satellite On-board Observation Scheduling Problem (MSOOSP) is a task-resource matching problem. The scheme is generated through autonomous communication and decision-making among various satellites. The plan needs to determine the execution satellite of the mission while meeting various constraints. The objective is to maximize the sum of the benefits obtained from performing the missions.  
For ease of reference, a summary of the notations used in the remainder of the paper is presented as follows. Table 1 shows the description list of abbreviations. Tables 2 and 3 show the description lists of mathematical indices and symbols.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Abbreviations** & \\ \hline EOS & the Earth Observation Satellite \\ ISL & the Inter-Satellite Link \\ MSOOSP & the Multi-Satellite On-board Observation Scheduling Problem \\ O-VTW & Observation-Visible Time Window between the satellite and mission \\ C-VTW & Communication-Visible Time Window between satellites \\ TPS & the Task Preemption Strategy \\ SCS & the Single-Chain Strategy \\ CNP & the Contract Network Protocol algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description list of abbreviations.
## 3 Results
### Frontside Plane-of-Array Irradiance (\(G_{POA,Front}\)) and Module Temperature (\(T_{MOD}\))
A notable difference in Figure 14 is the module temperature predicted by SAM, which is simply due to SAM's use of the NOCT versus Faiman model. The T\({}_{\text{MOD}}\) measurement comes from one 4-wire resistance temperature device (RTD) affixed to a single cell on the backsheet of a module within the array; no translation from back of module (backsheet) to cell temperature is made in Figure 14. The monofacial results of Figure 14 show that the simulations tend to underpredict the measured back of module temperature by 2-3 \({}^{\circ}\)C, but errors are often greater than 5 \({}^{\circ}\)C. Unfortunately, T\({}_{\text{MOD}}\) sensors were never installed on the bifacial arrays, and as such, only monofacial temperature measurements are available. Figure 13: Overlay of simulated and measured frontside global POA irradiance on fixed-tilt and HSAT structures. The data shown are from a clear sky day (23 March 2020) and are grouped by which solar radiation data were used as input to the simulations. All software studied here implement simplified steady-state thermal models that assume the PV array operates at a homogenous temperature, which can lead to discrepancies with measured values. Although healthy (i.e., non-damaged) modules are typically assumed to have homogenous temperatures within an array [68], cell temperatures are known to vary within a healthy module [69]. Therefore, the cell on which the RTD is placed may not be representative of the average cell temperature within the array, which is a potential source of error when comparing to modeled values. Appropriate values for device-specific thermal parameters, and the methods to acquire them from measurements, have been the subject of discussion and debate in the PV community [70]. Therefore, we performed a sensitivity analysis of the U\({}_{0}\) and U\({}_{1}\) thermal coefficients using the MoBiDiG VF tool. Our sensitivity found that the MBE of simulated monofacial DC power was minimized using U\({}_{0}\) and U\({}_{1}\) parameter values of 24 W\(\cdot\)m\({}^{-2}\).K\({}^{-1}\) and 1.6 W\(\cdot\)m\({}^{-3}\).K\({}^{-1}\).s, respectively. The sensitivity analysis showed that the best agreement between the modeled and measured module temperature would require U\({}_{0}\) and U\({}_{1}\) values near 20 W\(\cdot\)m\({}^{-2}\).K\({}^{-1}\) and 0 W\(\cdot\)m\({}^{-3}\).K\({}^{-1}\).s, respectively. Since these values are below the lower boundary of published values that we are aware of for open-air mounted PV systems [54; 71], it seems that the cell on which the RTD is placed is not representative of the average cell temperature within the array.
## 3 Applications
### Photocatalysis for Energy and Synthesis  
Titania is a promising candidate for photocatalytic applications related to sustainable schemes for energy and environment. For instance, titania is well-studied for the conversion of carbon dioxide into a range of useful fuels or commodities, such as methane, methanol,  
Figure 8: (**a**) TEM image of a graphene oxide flake covered by anatase titania nanocrystals that are highly homogeneous in size; (**b–d**) in silico investigation revealed how the nanocarbon’s oxygen-bearing functional groups (i.e., (**b**) epoxide, (**c**) hydroxyl, and (**d**) carboxylic acid) anchored titanium species, thus favoring nucleation over the growth of the nanocrystals. Adapted by permission from Springer Nature [101], copyright © 2020.  
ethylene, formaldehyde, or formic acid [188]. For hydrogenation reactions, such as those of carbon dioxide and monoxide, titania and titanates have also been used as active supports for Rh catalysts [189]. Additionally, iridium-based catalysts have benefited from research that developed titania-based active supports for the photocatalytic synthesis of benzimidazoles, whereby rutile surpassed anatase in terms of performance, thanks to an increase in the adsorption of the reagents and in the charge transfer from the support, both of which led to higher product yields [190].  
The photoelectrochemical generation of hydrogen from water has also enjoyed great prominence, where one-dimensional titania nanostructures demonstrated promising performance [191]. In silico tools are highly valuable to understand the nature of the rate-limiting steps and anticipate the better approaches to improve the efficiency of the process [192]. The further improvement of the photocatalytic performance of titania can be achieved through combination with other metal oxides (such as SnO\({}_{2}\)[193], ZnO [194], CuO [195]), gold [196; 197], as well as alkali metals and conducting polymers [198], carbon-doping [199], iron oxides to attain magnetic systems [200], gadolinium [201], various rare earths [202], and bismuth vanadate [203], amongst others. In particular, the use of 4D transition metals was shown to be effective through the production of impurity-originating intra-gap energy states, but also through the semiconductor-metal phase transition [204].  
A recent promising approach consists of single-atom doping. In the case of copper, light irradiation leads to electron transfer and the protonation of Cu/titania, and a local distortion around the copper atom stabilizes the deep-trap state on the copper d-orbital and its decoupling from free charges, thus yielding high photocatalytic hydrogen generation activity [205]. Further, the photocatalytic performance of Cu/titania can be improved by spin selection, which is achieved via optical intersite spin transfer or chiral semiconductor coating [205]. Both hydrogen adsorption and spin selection processes increase charge carrier lifetimes by an order of magnitude [205].