esa-sceva/satcom-chunk-collection huggingface dataset:
Improvements of conventional electroporation protocols by addition of Au NPs has been already observed experimentally in vitro by showing increased mammalian cells transfection [21; 22] and efficiency in anticancer therapy [22]. Moreover, two recent studies aimed to obtain a better comprehension of the phenomenon [23; 24] by the numerical assessment of the local enhancement of the electric field due to Au NPs using different conventional electroporation protocols. Regarding the use of Au NPs in contactless permeabilization by time-varying magnetic fields, a first in vitro attempt was described in [18], assuming that the Au NPs could act as nanoelectrodes, locally enhancing the electric field amplitude to the levels known to allow permeabilizing the cell membrane in conventional electroporation protocols. The results described in [18] showed that the combined use of Au NPs and PEMF enhanced the permeabilization of both CHO cells and Gram-negative bacteria, with no effect on survival. However, apart from these experimental findings, no numerical assessment and model of the nanoparticles placed in time-varying electromagnetic fields was described in the literature. This study focused on filling this research gap: using a computational approach, the numerical assessment of the electric field enhancement by using Au NPs placed in time-varying electromagnetic fields was carried out. The experimental set-up described in [18] was modelled, and the influence of the presence of Au NPs on the transmembrane potential were investigated. To account for the uncertainty about the positions, the geometry, and the aggregation of Au NPs around the cell membrane, different conditions including spherical NPs, cubic NPs, and aggregated of NPs were simulated. Using a spherical cell model and a pore dynamics model, the influence of the position and shape of the Au NPs, as well as the distance from the HI-PEMF source on the pore opening mechanism was assessed. Such an assessment, quantifying amplitude and localization of the enhancement of the electric field due to the presence of the Au NPs, could help in giving a better comprehension about the mechanisms, still not completely understood, underlying cell membrane permeabilization by time-varying magnetic fields. This contribution could potentially enable the use of a contact-less cell permeabilization technique offering important improvements compared to the existing protocols based on conventional electroporation.
## 4 Critical Data-Based Incremental Cooperative Communication (CD-ICC)  
### Proposed Protocol Description  
In this paper, we propose an emergency-based cooperative communication for WBAN, named Critical Data-based Incremental Cooperative Communication. The proposed protocol works in a cooperative fashion when considering critical data. The CD-ICC has two events which can be summarised as follows:  
1. The first event is the Critical Data Event (denoted as X) which is occurs when data gathered by the sensor is critical and must be transmitted to the destination efficiently. In such cases, critical data is delivered to the destination utilising ICC.
2. The second event is the Normal Data Event (denoted as Y) which occurs when data gathered by the sensor isn't critical and it can be transmitted directly to the destination.
## 4 Distribution Problems  
Distribution problems have many different formulations, first, we consider the classical [PERSON]'s _Transportation/Transshipment Problem_ with \(m\) suppliers (sources, warehouses) and \(n\) customers (consumers), where we assume the transportation of a single type of material (goods) with an objective to minimize the total cost of transporting the material [68].  
Assume the following notation:  
\(a_{i},\;i=1,\,\ldots,\,m\)... capacity (stocks) of suppliers,  
\(b_{j},\;j=1,\,\ldots,\,n\)... customer requirements,  
\(c_{ij},\;i=1,\,\ldots,\,m,\;j=1,\,\ldots,\,n\)... the matrix of rates for the transport of a unit quantity between the \(i\)th supplier and the \(j\)th customer,  
\(x_{ij},\;i=1,\,\ldots,\,m,\;j=1,\,\ldots,\,n\)...the sought quantity transported between the \(i\)th supplier and the \(j\)th customer.  
If total stocks are equal to total requirements, this means:  
\[\sum_{i=1}^{m}a_{i}=\sum_{j=1}^{n}b_{j}, \tag{18}\]  
we are talking about a _balanced distribution problem_, where all stocks are exhausted and all demands are met, and the following mathematical model corresponds to this:  
\[z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\rightarrow\min \tag{19}\]  
subject to  
\[\sum_{j=1}^{n}x_{ij}=a_{i},\ i=1,\,\ldots,\,m \tag{20}\]  
\[\sum_{i=1}^{m}x_{ij}=b_{j},\ j=1,\,\ldots,\,n \tag{21}\]  
\[x_{ij}\geq 0,\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n \tag{22}\]  
Equation (20) corresponds to the stock drawdown, and Equation (21) expresses the fulfillment of requirements.  
Obviously, the assignment problem is a special case of the balanced transportation problem, where:  
\[m=n\]  
\[a_{i}=1,\ i=1,\,\ldots,\,m\]  
\[b_{j}=1,\ j=1,\,\ldots,\,n\]  
\[x_{ij}\in\{0,\,1\},\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n\]  
However, the balanced case is rare in practice, usually, total stocks exceed total requirements, i.e.,  
\[\sum_{i=1}^{m}a_{i}>\sum_{j=1}^{n}b_{j} \tag{23}\]  
In this case, all requirements can be met, but not every stock will be used up. The model then changes as follows:  
\[z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\rightarrow\min \tag{24}\]  
subject to  
\[\sum_{j=1}^{n}x_{ij}\leq a_{i},\ i=1,\,\ldots,\,m \tag{25}\]  
\[\sum_{i=1}^{m}x_{ij}=b_{j},\ j=1,\,\ldots,\,n \tag{26}\]  
\[x_{ij}\geq 0,\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n \tag{27}\]  
In the case of material shortages, the opposite situation may occur, where the total stock is insufficient for the total requirements, i.e.,  
\[\sum_{i=1}^{m}a_{i}<\sum_{j=1}^{n}b_{j} \tag{28}\]This means that stocks are used up but not all requirements can be met. The model must then be modified as follows:  
\[z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\rightarrow\min \tag{29}\]  
subject to  
\[\sum_{j=1}^{n}x_{ij}=a_{i},\ i=1,\,\ldots,\,m \tag{30}\]  
\[\sum_{i=1}^{m}x_{ij}\leq b_{j},\ j=1,\,\ldots,\,n \tag{31}\]  
\[x_{ij}\geq 0,\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n \tag{32}\]
# Exploring the Use of Landmarks to Aid Pedestrian Wayfinding
## 5 Discussion
### Implications of the Research
#### 5.1.3 Cognitive Engagement in Navigation  
The research highlights the cognitive processes involved in wayfinding, particularly the differences between using landmark-based navigation and other navigation aids. This study suggests that engaging with landmarks requires users to actively participate in their navigation, fostering a better understanding of their surroundings. This contrasts with the passive reliance on automated directions, which can diminish spatial knowledge and awareness.
## 2 Methodologies
### Evaluation Measurements  
In the first two experiments, the confusion matrix and the accuracy measure are used to present the classification results for ease of visualization. This measurement works the best when the number of classes is few. It presents the correct classified results along the diagonal entries in a tabular format. The incorrectly identified results are off the diagonal, and the class to which they were wrongly classified can easily be identified.  
The definition of accuracy measure is as follows: [10]  
\[Accuracy=\frac{All\ Wafer\ Images\ in\ the\ Same\ Class\ Correctly\ Identified \tag{3}\]  
This measure defines how well a classifier performs for a certain class. The overall accuracy is just the average of all the accuracy measures for all the classes.  
In the last experiment, we selected additional measures, include precision, recall, and the F-measure. Precision is the measure of how close the predicted results are to the actual value. Recall is the measure of the classifier's ability to predict all the data of interest. The F-measure is a trade-off between these two, and its value is high when the predicted results and the actual results are close to each other. The definition of precision, recall and F-measure is [10]:  
\[Precision=\frac{Wafer\ Images\ in\ the\ This\ Class\ Truly\ Identified \tag{4}\]  
\[Recall=\frac{Wafer\ Images\ in\ the\ This\ Class\ Truly\ Identified \tag{5}\]  
\[F-Measure=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{6}\]The AUC (area under the roc curve) measure is the area under the ROC curve [10]. The ROC curve is a plot of Recall vs. the False Positive Rate, which is:  
\[\textit{False Positive Rate}=\frac{\textit{Wafer Images in the This Class Falsely Identified}}{\textit{All Wafer Images Not In This Class}}, \tag{7}\]  
That shows the degree of separability within the same class. The higher the AUC value implies that the classifier is better at predicting each label class or distinguishing between different defect classes. These are all measurements used in previous papers [7; 8; 9; 10] as measurements for the effectiveness of their classifier.
## 3 Results
### Initial Conditions  
\(N\) UAVs are supposed to fly under free boundary conditions. Each UAV is steered by (1). The initial position of each UAV is randomly generated in a cubic area with size \(L\), and its initial speed is randomly generated from the interval \((0,0.5)\). Besides, its initial heading angle and flight-path angle are randomly generated from the interval \((-\frac{\pi}{2},\frac{\pi}{2})\).  
To keep the continuity and comparability of simulation results under different group sizes, these UAV systems' initial density \(\rho=\frac{N}{L^{3}}\) should be a constant. That is to say, the scale of group size \(N\) directly proportional to the cube of the area size \(L\). In the following simulations, the initial density \(\rho=0.1\), and each experiment's simulation time \(T=100\) s.  
The specific potential function in the control protocol (11) is given as [14]  
\[V(\|p_{ij}(t)\|)=\frac{b}{\|p_{ij}(t)\|^{2}}-a\ln(D^{2}-\|p_{ij}(t)\|^{2}) \tag{21}\]  
where the first part is the repulsive potential item and the second part is the attractive potential item. Here, \(a\) is an attractive coefficient, and \(b\) is a repulsive coefficient. There exists a unique distance \(\|d^{*}\|=\sqrt{\frac{\sqrt{P^{2}+4 abD^{2}}-b}{2a}}\), at which the attraction and repulsion balance. Without loss of generality, the parameters are chosen as \(a=1\), \(b=1\), and thus \(\|d^{*}\|=\sqrt{\frac{\sqrt{1+4D^{2}}-1}{2}}\doteq\sqrt{D}\).  
One of the most important initial conditions is that the initial communication network must be connected. To satisfy this condition, three steps should be carried out. First, the initial positions of these UAVs are generated randomly. Second, the connectivity of the communication network based on the above initial positions is checked. If the network is connected, then these UAVs implement the simulations under control protocol (11); otherwise, a new set of initial positions is randomly generated for these UAVs. Third, the above process is repeated until a connected initial communication network is found out.
## 4 Experiments and Results
### Results of Image Quality Enhancement
#### 4.2.1 Measurement Method
In the case of images containing damaged objects with complex structures, the experimental results verified that the identification performance was further improved. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**Image Detection Result**} \\ \cline{2-5}  & **Image** & \multicolumn{1}{c}{**LR**} & \multicolumn{1}{c}{**SRCNN**} & \multicolumn{1}{c}{**Fast-SRGAN**} \\ \hline  & & & & & \\
002.jpg & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Efflorescence object identification result. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{6}{c}{**Image Detection Result [Accuracy]**} \\ \cline{2-9}  & \multicolumn{4}{c}{**LR**} & \multicolumn{4}{c}{**SRCNN**} \\ \cline{2-9}  & **DIC** & **DIA** & **
## Materials and Methods
### Model-Based Interleaved Biplane Fluoroscopy Image Tracking
#### 2.4.1 Two-Dimensional/2D Template Registration
Increments in in-plane rotation \(\Delta\mathbf{R}_{j}^{2D}\) and translation \(\Delta\mathbf{v}_{j}^{2D}\) of the template (Figure 2C) between preceding and current frames (\(l_{k-1}\to l_{k}\)) were approximated by halving the motion increments from preceding to succeeding frames (\(l_{k-1}\to l_{k+1}\)) and were expressed in \(G_{XYZ}\) following the equations:  
\[\Delta\mathbf{v}_{j}^{2D}=\mathbf{R}_{G_{XYZ}}^{F_{xyz}^{j}}\left[\frac{1}{2} (t_{x_{j}}^{k+1}-t_{x_{j}}^{k-1}),\frac{1}{2}(t_{y_{j}}^{k+1}-t_{y_{j}}^{k-1}), 0\right]^{T}, \tag{3}\]  
\[\Delta\mathbf{R}_{j}^{2D}=\mathbf{R}_{G_{XYZ}}^{F_{xyz}^{j}}\mathbf{R}(\phi), \tag{4}\]  
\[\phi=\frac{1}{2}\Big{(}t_{x_{j}}^{k+1}-t_{z_{j}}^{k-1}\Big{)}, \tag{5}\]  
where \(\mathbf{R}_{G_{XYZ}}^{F_{xyz}^{j}}\) describes the rotation between coordinate systems \(G_{XYZ}\) and \(F_{xyz}^{j}\). The above procedure was also carried out between \(t^{k-2}\) and \(t^{k}\), which approximated the in-plane rotation and translation increments from the other X-ray view. Given \(\Delta\mathbf{R}_{j}^{2D}\) and \(\Delta\mathbf{V}_{j}^{2D}\) from the two X-ray views and the registered pose in the preceding frame \(\mathbf{T}\Big{(}\mathbf{R}^{k-1},\mathbf{v}^{k-1}\Big{)}\), \(\mathbf{R}_{trg}^{k}\) and \(\mathbf{v}_{trg}^{k}\) were estimated by:  
\[\mathbf{R}_{trg}^{k}=\left(\prod_{j}\Delta\mathbf{R}_{j}^{2D}\right)\mathbf{ R}^{k-1}, \tag{6}\]  
\[\mathbf{v}_{trg}^{k}=\mathbf{v}^{k-1}+\Delta\mathbf{v}^{3D}, \tag{7}\]  
where the 3D incremental translation \(\Delta\mathbf{v}^{3D}\) is determined by minimizing the differences between its 2D projections and \(\Delta\mathbf{v}_{j}^{2D}\) on the two X-ray views and is defined as:  
\[\Delta\mathbf{V}^{3D}=\operatorname*{argmin}_{\Delta\mathbf{v}^{3D}}\sum_{j} \left|\left(\left(\left(z_{j}\times\Delta\mathbf{v}^{3D}\right)\times z_{j} \right)\cdot\frac{\left|\mathbf{v}_{j}^{k-1}-s_{j}\right|}{\left|\mathbf{v}^{ k-1}-s_{j}\right|}\right)-\Delta\mathbf{v}_{j}^{2D}\right|^{2}, \tag{8}\]  
where \(s_{j}\) is the position of the X-ray source of the \(j\)th X-ray unit, \(\mathbf{v}_{j}^{k-1}\) is the position of the perspective projection of \(\mathbf{v}^{k-1}\) onto the \(j\)th image plane, and \(z_{j}\) is the unit normal to the \(j\)th image plane (Figure 2C). The Levenberg-Marquardt nonlinear least-squares method was used to solve for (8) [48].
## 2 Acquisition Frequency and Reasonable Acquisition Frequency
This is specifically observed through the Fourier transform of the response time-history curve, which allows for the determination of the frequency at which the signal amplitude gain reaches \(-3\) dB, thus establishing the \"cutoff frequency\" of the signal. Furthermore, the conventional approach for selecting the cutoff frequency may result in inaccuracies during signal decomposition. A proposed method is introduced to enhance the accuracy of selecting the cutoff frequency for signal decomposition. This method utilizes a power spectrum-based approach to determine the appropriate acquisition frequency for the structure. The distinction lies in the fact that the reasonable value primarily reconstructs the quasi-static response signal of the acquired signal based on reliability theory [25; 26], thereby approximating the peak point.
## 3 Results  
### Moisture Dynamics and Volumetric Behaviour Under WD Cycles  
In the first and second wetting cycles, untreated samples reached saturation moisture contents of 26% and 27%, respectively, accompanied by swelling strains of approximately 14%. In subsequent cycles, the saturated moisture contents and swelling magnitudes diminished, stabilising within 20-25% and 8 to 10%, respectively. Drying was carried out to a target matric suction of \(-1500\) kPa, resulting in 12 \(\pm\) 1% residual moisture contents. The associated shrinkage strains varied between \(-2\)% and \(-7\)%. This pronounced volumetric response underscores the high susceptibility of the untreated expansive soil to structural degradation under cyclic hydric loading (Figure 2).  
In contrast, cement-treated samples exhibited markedly different behaviour. During the initial wetting phase, these samples attained a saturation moisture content of 34% and exhibited swelling strains of 22%, significantly exceeding those of their untreated counterparts (Figures 2 and 3). Following drying to \(-1500\) kPa, the samples retained a residual expansion of approximately 15%, even though the moisture content reverted to the initial optimum moisture content (OMC). Over the 15 wetting-drying (WD) cycles, the swelling strain under saturated conditions progressively increased, reaching up to 30%. However, the post-drying residual swelling stabilised at approximately 15%. Notably, the saturation moisture content increased with each cycle, eventually peaking at 43% (Figure 2).  
The volumetric response of guar gum-treated samples was similar to that of the untreated soils. Across the WD cycles, swelling strains ranged between 10% and 13%, comparable to the untreated soil (\(\sim\)12%). Drying-induced shrinkage strains varied from \(-1\) to \(-6\)%, mirroring the untreated response in magnitude. Despite the similarity in strain amplitudes, the guar gum treatment appreciably altered the water-infiltration behaviour. Guar gum-treated samples required six days to reach full saturation, in contrast to four days for the untreated counterparts. The saturation moisture contents in the first two cycles were 23% and 21%, respectively, lower than those of the untreated samples. In later cycles, saturation levels remained within the 20-25% range, similar to the untreated samples (Figure 2). Although the overall swelling-shrinkage amplitude remained unchanged, visual inspection revealed that guar gum-treated samples exhibited enhanced structural integrity. Desiccation cracking was severely reduced, with finer and more superficial cracks compared to the extensive cracking observed in untreated samples by cycle 10. The guar gum matrix likely functioned as a biopolymeric binder, preserving the columnar structure and limiting crack propagation [26; 32].
## 3 Results and Discussion of RSM Experiments
### Central Composite Response Surface Design
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multicolumn{4}{c}{**Factors**} & \multirow{2}{*}{**Stacking Angle/(\({}^{\circ}\))**} \\ \cline{2-2} \cline{4-5}  & **C\({}^{\prime}\)** & **E\({}^{\prime}\)** & **G\({}^{\prime}\)** \\ \hline
1 & 0 & 0 & 0 & 42.30 \\
2 & \(-1\) & \(-1\) & 1 & 40.00 \\
3 & 0 & 0 & 0 & 42.35 \\
4 & \(-1\) & 1 & \(-1\) & 43.80 \\
5 & 1 & \(-1\) & 1 & 43.01 \\
6 & 0 & 0 & 0 & 42.01 \\
7 & 0 & 0 & 0 & 41.54 \\
8 & 0 & \(-1.68179\) & 0 & 40.04 \\
9 & 1.68179 & 0 & 0 & 42.79 \\
10 & 0 & 1.68179 & 0 & 43.56 \\
11 & 1 & 1 & \(-1\) & 43.00 \\
12 & 0 & 0 & 0 & 42.01 \\
13 & \(-1\) & \(-1\) & \(-1\) & 39.58 \\
14 & 0 & 0 & 0 & 42.22 \\
15 & 0 & 0 & 1.68179 & 40.66 \\
16 & \(-1.68179\) & 0 & 0 & 41.34 \\
17 & 0 & 0 & 0 & 42.20 \\
18 & 0 & 0 & \(-1.68179\) & 40.79 \\
19 & 1 & \(-1\) & \(-1\) & 39.92 \\
20 & 1 & 1 & 1 & 43.02 \\
21 & 0 & 0 & 0 & 42.02 \\
22 & \(-1\) & 1 & 1 & 40.95 \\
23 & 0 & 0 & 0 & 42.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: CCD scheme and results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum** & **Degrees** & **Mean** & **F-Value** & \(p\)**-Value** & \\
**of Variance** & **of Squares** & **of Freedom** & **Square** & **F-Value** & \\ \hline Model & 31.12 & 9 & 3.46 & 64.26 & \textless{}0.0001 & significant \\ C & 3.65 & 1 & 3.65 & 67.80 & \textless{}0.0001 & \\ E & 14.72 & 1 & 14.72 & 273.62 & \textless{}0.0001 & \\ G & 0.0156 & 1 & 0.0156 & 0.2897 & 0.5995 & \\ CE & 0.5408 & 1 & 0.5408 & 10.05 & 0.0074 & \\ CG & 3.84 & 1 & 3.84 & 71.30 & \textless{}0.0001 & \\ EG & 5.02 & 1 & 5.02 & 93.38 & \textless{}0.0001 & \\ C2 & 0.0075 & 1 & 0.0075 & 0.1397 & 0.7146 & \\ E2 & 0.0822 & 11 & 0.0822 & 1.53 & 0.2382 & \\ G2 & 3.25 & 1 & 3.25 & 60.33 & \textless{}0.0001 & \\ Residuals & 0.6995 & 13 & 0.0538 & & \\ Misfit & 0.2139 & 5 & 0.0428 & 0.7048 & 0.6360 & Not significant \\ Error & 0.4856 & 8 & 0.0607 & & \\ Total & 31.82 & 22 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: CCD quadratic regression analysis of variance. The regression formula is  
\[\theta=42.10+0.5169C+1.04E+0.0338G-0.26 CE+0.6925 CG-0.7925 EG+0.0218C^{2}-0.0719E^{2 }-0.452G^{2} \tag{13}\]
## 3 Results and Discussion
### Adsorbent Characterization
#### 3.2.4 Raman Analysis of ADAV and TTAV300  
Raman spectra of ADAV and TTAV300 are shown in Figure 6. The spectrum of ADAV did not show any peaks, probably because carbon exists as an organic matter. On the other hand, the spectrum of TTAV300 had a clear D- band, which appeared at 1351 cm\({}^{-1}\), and a clear G- band at 1585 cm\({}^{-1}\). These two peaks were distinctive in the spectra of all carbonaceous materials, and their arrangement was the same as graphene oxide [45].  
Figure 5: FT-IR spectra of ADAV and TTAV300.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Adsorbent** & **N (\%)** & **C (\%)** & **H (\%)** \\ \hline
**ADAV** & 0.96 & 35.79 & 5.34 \\
**TTAV300** & 1.10 & 52.73 & 4.22 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Carbon hydrocarbon nitrogen (CHN) analysis of the prepared adsorbents.  
The properties of the carbon lattice, including defects and sizes, were displayed by the D- band; however, it did not display the chemical conformation of the carbon materials. The wavelength of the laser beam mainly affected the position of the D- band. On the other hand, the G- band identified the stretching of C-C for the \(sp^{2}\) system [46]. Obtained data confirmed the formation of activated carbon in the case of TTAV300, and this was confirmed by the presence of the two bands (G- and D-).