esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This illustrates the need for research that standardises and validates which 3 DP technologies and 3 DPLM types are most feasible and cost-effective for different 3 DPLM clinical applications [17, 39]. Ultimately, the selection of 3D printing methods and materials depends on the 3 DPLM's clinical application. SLA 3D printing methods use photopolymers to create transparent or opaque resin models with high detail, which may assist in surgical planning, while SLS technology often uses nylon to produce models with high elasticity, strength, and heat resistance for surgical training. Despite the aforementioned limitations of FFF 3D printing, this method is popular as it is suitable to use with a range of materials with different properties and produces minimal waste. ABS and PLA produce tough, rigid models and are some of the most affordable materials used in FFF 3D printing. [PERSON] et al. [30] reported statistically significant improvements in patient education supported by PLA FFF 3 DPLMs. The low cost of production in this context is particularly encouraging for further research. Alternatively, FFF can be used to create more flexible 3D models from elastomers. [PERSON] et al. [38] demonstrated several silicone elastomer materials, and [PERSON] et al. [39] demonstrated agarose, or a 1:1 mixture of polyvinyl alcohol and  
Figure 5: Baseline CT image (**left**) vs. segmented image (**middle**) where green = tumour, yellow = liver parenchyma, pink = arterial vasculature, blue = venous vasculature, vs. final STL 3D rendering (**right**) demonstrating corresponding anatomical structures. Reproduced with permission under the open access from [PERSON] et al. [17]. phytagel, as potential 3 DPLM materials that mimic biological liver tissue. They point toward an exciting future where patient-specific, tissue-life-like 3 DPLMs can be used to preoperatively rehearse and perfect complex patient LCRs [14; 22; 36; 38; 39].
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 4 Discussions
### Study Contributions to the Sustainable Development Goals (SDGs)  
The government has made efforts to reduce gas flaring, although little progress has been achieved; however, there is still more to be done. The results of this study have strong and direct implications and contributions to the Sustainable Development Goals (SDGs), specifically related to Affordable Clean Energy (SDG 7), climate change (SDG 13), Responsible Consumption and Production, and Industry, Innovation, and Infrastructure (SDG 9). The integration of lean-green management principles and blue hydrogen production supports SDG 7 by promoting access to cleaner alternatives, such as low-carbon blue hydrogen, and contributes to Nigeria's transition plan towards a more sustainable and secure energy system. The exploration of blue hydrogen as a feasible and economically viable alternative leverages Nigeria's abundant natural gas resources to diversify the country's energy mix while reducing environmental harm. In line with SDG 13, the study addresses climate action by targeting the reduction in greenhouse gas emissions through operational efficiency improvements and proposing an economically viable technology for gas utilization. Furthermore, the integration of lean and green principles advances SDG 12 by enhancing process efficiency, minimizing waste, and encouraging the adoption of sustainable practices within Nigeria's petroleum industry. Lastly, the study encourages investment in cleaner technologies and sustainable infrastructure, aligning with SDG 9.
## 3 Materials and Methods
### Hybrid Discrete-Continuous Vortex Search Algorithm (DCVSA) Encoding  
The case study proposed in this article implemented a DCVSA version, where the structure of Equation (15) sets a solution space.  
\[C_{i}^{t}=\Big{[}n,2,...,k\mid P_{\min}^{pv}P_{\max}^{pv},...,q\Big{]} \tag{15}\]  
where \(i\) corresponds to the position of the potential solution and \(t\) to the current iteration. Furthermore, \(k\) represents a number of a random node, \(n\) the number of the last node in the system, and \(q\) a random number between \(P_{\min}^{pv}\) and \(P_{\max}^{pv}\).  
The candidate solutions of the solution space are divided into two parts. The first part represents the discrete variables of the problem since they are the nodes that can install the PV systems, while the second part depicts the continuous variables since they are the generation capacity that PV systems may have.  
According to the above, and considering the restrictions of the MINLP model, the DCVSA algorithm will work as follows:  
* In its slave stage, the algorithm will solve the power flow from the SA method to identify the energy losses described in Equation (16). Figure 1 presents the solution diagram for the power flow problem using the SA method. \[E_{loss}=real\ \Bigg{\{}\sum_{i\in N}\sum_{j\in N}\sum_{hcH}\mathbb{V}_{ih} \Big{(}\mathbb{V}_{ij}\mathbb{V}_{jh}\Big{)}^{*}\Bigg{\}}\Delta_{h}\] (16)
* In its master stage, with hybrid coding, DCVSA will explore and exploit the solution space to identify the optimal location and dimensioning to reduce energy losses. Algorithm 1 represents the computational logic to implement the DCVSA based on the structure proposed in [7].
## 3 Experimental Work
### Material Characterization Based on the SPHB Data
#### 3.3.3 Equation of State  
This equation is used to relate the concrete density with the pressure, assuming that the concrete is compact without any gap. The formula is as follows:  
\[{\rm P}={\rm K}_{1}\overline{\mu}+{\rm K}_{2}\overline{\mu}^{2}+{\rm K}_{3} \overline{\mu}^{3} \tag{8}\]  
where \(\overline{\mu}\)\(=(\mu-\mu_{\rm lock})/(1+\mu_{\rm lock})\) is the modified volumetric strain, where \(\mu_{\rm lock}=\frac{\rho_{\rm grain}}{\rho_{0}}-1\). \(\rho_{\rm grain}\) is the density of the compact concrete without a gap, while \(\rho_{0}\) is the initial density. \(\mu_{\rm lock}\) is the locking volumetric strain. \({\rm K}_{1},~{}{\rm K}_{2},~{}{\rm K}_{3}\) are the model parameters to be determined.  
All of the model parameters have been determined using a multi-variable regression based on the measured data from both quasi-static and Split Hopkinson Bar tests. At the end of the fitting process of Equations (5)-(8) for the unknown values of the model parameters to match the recorded experimental data, the values given in Table 4 were obtained. The parameters in Table 4 represent the material character of the ultra-high-strength concrete of 135 MPa strength investigated in this study.  
The authors think that reliable material model parameters can be determined if the material characterization is performed considering data from all of the tests. This approach is based on the authors' unpublished previous experience with material characterization.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 5 Diamond-Based Electric Biosensors  
The possibility of functionalising the surface of the diamond films with a specific detector layer opens the door to the detection of larger and specific molecules such as DNA or even viruses. The operating principle of these biosensors is based on the occurrence of intermolecular interactions between the target molecules and the receptor layer, which bring them improved selectivity. The use of more than one type of receptor molecule on the surface allows for the production of sensors capable of detecting multiple analytes. In addition, when molecules of the analyte are recognized by their counterparts at the receptor layer, the supramolecular interaction results in a change in the electrical properties of the surface of the biosensor that allows for other interrogation techniques in addition to cyclic voltammetry, such as impedance measurements. By specifically probing the changes in the impedance of the surface, these measurements may provide a more accurate tool to detect the presence of the molecules of the analyte.
## 3 Results
### Single Cell Stimulation  
Finally, to show that we are able to stimulate a single neuron with our setup, we conducted a preliminary stimulation experiment. For comparison, we first stimulated cells using wide-field stimulation with 488 nm LED light. We recorded the electrical response of all the electrodes with a temporal resolution of 25 kHz. Stimulation consisted of 10 pulses of 500 ms length, followed by a pause of 5 s, during which no illumination was present. Figure 8a shows a crop of the raster plot of this experiment, i.e., the recorded action potentials of all electrodes as a function of time. The 500 ms stimulation intervals are marked by the 4 blue vertical line pairs. The network under investigation exhibited a strong spontaneous, i.e., unstimulated, activity. The average activity that was recorded by the electrodes during stimulation is shown in Figure 8b as a heat map. Electrode 64 recorded the highest activity with 34 action potentials/s, other electrodes registering high activity are electrode 82 and 32. We then manually set our setup to stimulate a neuron close to electrode 32. The temporal parameters of the experiment were the same as with full-field stimulation. The corresponding raster plot and activity heat map are shown in Figure 9a,b, respectively. Electrode 32 recorded activity that was highly correlated with the stimulation pulses. The mean activity amounted to 17 spikes/s, which is a marked increase in activity as compared to 6.8 spikes/s during full-field stimulation. Apparently, no other electrode recorded activity correlated to the stimulation pulses.  
Figure 8: (**a**) Section of the raster plot for full-field stimulation. Red dots show the action potentials recorded by all electrodes. The blue vertical lines indicate the time windows of 500 ms full field optical stimulation. A high spontaneous, i.e., unstimulated activity is present in the network, stimulation leads to increased activity on some electrodes. (**b**) Full field stimulation activity heat map showing the mean per-electrode activity in spikes per second during optical stimulation.  
Figure 9: (**a**) Raster plot single-cell stimulation. (**b**) Single-cell stimulation activity heat map.  
To ascertain that only a single neuron was stimulated in the single-cell stimulation experiment, we performed spike sorting using Wave_Clus [41; 42]. This revealed two spike clusters for electrode 32 during full field stimulation, which are depicted in Figure 10a,b. As expected, spike sorting of the signal of electrode 32 for the single cell stimulation revealed only one cluster in Figure 10c, demonstrating the ability to stimulate a single neuron using our holographic illumination.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements
#### 3.2.5 Effect of Diacetyl Concentrations in Alcholic and Aqueous Solutions  
The fabricated SnO\({}_{2}\) sensor was investigated by varying the diacetyl concentration both in aqueous and alcoholic solutions in a CO\({}_{2}\) environment (Figure 5). The response curves for different diacetyl concentrations (0.05-3.2 mg/L) are reported in Figure 5a,b. It can be seen that the response of the SnO\({}_{2}\) sensor increases by increasing diacetyl concentration with an exponential trend. The response of the sensor (Y) can be fitted as a  
Figure 4: (**a**) Sensor response in N\({}_{2}\) and CO\({}_{2}\) to alcoholic (5% ethanol) solution, aqueous diacetyl solution (0.4 mg/L) and alcoholic (5% ethanol) diacetyl solution (0.4 mg/L). Error bars are calculated on three runs; (**b**) response and recovery times of sensor exposed at different concentration of diacetyl in aqueous and alcoholic solutions in CO\({}_{2}\) and N\({}_{2}\) ambient. Error bars are calculated on three runs.  
logarithmic function of Y = 1.14 + 0.07 logX with X representing the diacetyl concentration in mg/L and with a regression coefficient R\({}^{2}\) of 0.97, as shown in the inset of Figure 5a. An exponential trend of the response depending on the diacetyl concentration in alcoholic solution is instead reported in Figure 5b. The sensor response in the alcoholic solution results higher than diacetyl in a water solution, following a logarithm profile. This is due to the high sensitivity of SnO\({}_{2}\) to ethanol as reported in previous works [37,38]. Moreover, the presence of ethanol markedly enhanced the gas sensing performance toward diacetyl in the alcoholic solution.  
By fitting the sensor response with a logarithmic function Y = 1.28 + 0.13 logX (R\({}^{2}\) = 0.99) it is evident how the slope of the curve is greater than the response of diacetyl in water solution. From the fit it is possible to extract the lower detection limit (LDL), the minimum concentration of gas detectable by gas sensor [45,46].  
For SnO\({}_{2}\) sensor LDL can be estimated by the extrapolation of the linear fit of the response curve down to the minimum response R\({}_{\text{min}}\)/R\({}_{0}\) where R\({}_{\text{min}}\) = R\({}_{0}\) - 3\(\sigma\), and \(\sigma\) is the standard deviation of the baseline resistance before analyte exposure. For diacetyl in an aqueous solution LDL results 0.1 mg/L, while for measurements performed on diacetyl in an alcoholic solution LDL is less than 0.01 mg/L. In turn, ethanol presence increases the sensitivity of the sensor, proving the capability of the SnO\({}_{2}\) sensor to detect diacetyl in alcoholic solution in a CO\({}_{2}\) atmosphere.
## 2 Position and Heading Estimation Approach Based on Cross-Modal Image Matching
### Fine Alignment Based on End-to-End Trainable Matching Network
#### 2.2.1 Loss Function  
Suppose the ground truth homography transformation between a real-time image and a referenced image is \(H_{gt}\), and the homography transformation estimated between the subareas of this training pair is \(\hat{P_{k}}\), then the loss function is calculated by the following:  
\[\text{loss}=\sum_{k=1}^{4}\sum_{i=1}^{N}\left\|H_{gt}(x_{i},y_{i})-\left(\hat{ P_{k}}\big{(}x_{i}-t_{x},y_{i}-t_{y}\big{)}+\big{(}t_{x},t_{y}\big{)}\right) \right\|^{2} \tag{10}\]  
where \(N\) is the number of grid points, and \(\big{(}t_{x},t_{y}\big{)}\) is the translation between the subarea and the real-time image, as shown in Figure 5. The loss function is based on the transformed grid loss proposed in [24], which minimizes the discrepancy between the estimated transformation and the ground truth transformation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Image Size** & **Computation Amount** & **Accelerating Ratio** \\ \hline \(m=128,n=32\) & \(\begin{array}{c}O(n^{2}(m-n+1)^{2}=9,634,816\\ O(2m^{2}logm)=229,376\end{array}\) & 42.0 \\ \hline \(m=256,n=64\) & \(\begin{array}{c}O(n^{2}(m-n+1)^{2}=152,571,904\\ O(2m^{2}logm)=1,048,576\end{array}\)
###### Abstract  
The integration of augmented reality (AR) in education is promising since it enhances teaching and offers more engaging and appealing learning experiences. Teachers can have a catalytic role towards the adoption of AR in education; therefore, their perspectives with regard to AR in teaching and learning are very important. The current study explores teachers' views on the integration of AR in education through an open-ended questionnaire that has been answered by 93 educators worldwide. A set of digital skills that can support student-centered pedagogies in an appropriate infrastructure are the main requirement for effective teaching with AR. Among the perceived benefits and opportunities are interactive teaching and learning, increased interest and engagement, better understanding of complex concepts. As barriers, participants reported the lack of AR educational applications, the cost of buying and maintaining AR equipment and resources, the lack of teachers' and students' digital skills, classroom management issues, and security and ethical issues. Moreover, survey participants highlighted the need for raising teachers' awareness for the added value of AR in education and the need for teachers' continuous professional development. Implications and future research recommendations on the integration of AR in education are discussed.  
augmented reality; augmented reality in education; augmented reality in teaching; teachers' perceptions; teachers' skills; teachers' training +
Footnote †: journal: _Future Internet_  
out that there is limited research on teachers' perspectives with regard to AR in school teaching and learning.  
The current study is aiming to increase the understanding of the teachers' perceived opportunities and challenges regarding the integration of AR in education as well as to capture teachers' needs and their recommendations to effectively integrate AR into their professional practice. Thus, the study will try to shed light to the following research questions (RQ):  
RQ1: What do teachers think about the level of their AR competences?  
RQ2: What do teachers think are the important digital skills for integrating AR in education?  
RQ3: How easy do teachers find AR educational resources?  
RQ4: What do teachers think are the safety, security, and ethical issues that are associated with using AR in education?  
RQ5: What pedagogical and teaching methods would teachers use for integrating AR in their teaching?  
RQ6: What do teachers think are the benefits and opportunities that AR can offer to education?  
RQ7: What do teachers think are the obstacles and challenges that may prevent AR to be integrated in education?  
RQ8: What recommendations do teachers propose to overcome these obstacles?  
The following Section 2 presents previous studies on teachers' views with regard to the integration of ARinE. Section 3 describes the research methodology followed by the current study and Section 4 presents the results. Finally, Section 5 presents the main conclusions of this study and Section 6 suggests future research directions.
## 9 Application for Other Activation Functions  
Sigmoid activation is one of the most popular and well known, but many other activations are used in practice. The simplest of them (ReLU, LeakyReLU, and PReLU) can be implemented in the proposed RCE by \"maximum\" and \"MAC\" operations and do not require a complex design. Swish activation [18] is based on the sigmoid and requires minor  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Implementation** & **Total Size, LE** & **PE Size, LE** & **Max Delay, ns** & **Average Absolute Error** & **Max Absolute Error** \\ \hline Centralized & 312 & 312 & 14 & \(4\times 10^{-3}\) & \(1\times 10^{-2}\) \\ Distributed & 13,175 & 296 & 18.5 & \(4\times 10^{-3}\) & \(1\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Implementation** & **E\({}_{\text{max}}\)** & **E\({}_{\text{avg}}\)** & **Input Format** & **Output Format** & **LUT** & **DSP** & **Delay, ns** \\ \hline Hajduk (McLaurin) & \(1.192\times 10^{-7}\) & \(1.453\times 10^{-8}\) & 32b FP & 32b FP & 1916 & 4 & 940.8 \\ Hajduk (Pade) & \(1.192\times 10^{-7}\) & \(3.268\times 10^{-9}\) & 32b FP & 32b FP & 2624 & 8 & 494.4 \\ [PERSON] et al. & \(1\times 10^{-2}\) & N/A & 32b FP & 32b FP & 363 & 2 & N/A \\ [PERSON] et al. & \(4.77\times 10^{-5}\) & N/A & 32b FXP & 32b FXP & 1388 & 22 & 1590–2130 \\ [PERSON] et al. & \(1.85\times 10^{-2}\) & \(5.87\times 10^{-3}\) & 16b FXP & 16b FXP & N/A & N/A & N/A \\ [PERSON] et al. & \(1.25\times 10^{-2}\) & \(4.2\times 10^{-3}\) & 16b FXP & 12b FXP & 140 & 0 & 9.856 \\ PLAN & \(1.89\times 10^{-2}\) & \(5.87\times 10^{-3}\) & 16b FXP & 16b FXP & 235 & 0 & 30 \\ NRA & \(5.72\times 10^{-4}\) & \(8.6\times 10^{-5}\) & 16b FXP & 16b FXP & 351 & 6 & 85 \\ \hline Centralized & \(1\times 10^{-2}\) & \(4\times 10^{-3}\) & 16b FXP & 16b FXP & 312 & 0 & 14 \\ Distributed & \(1\times 10^{-2}\) & \(4\times 10^{-3}\) & 16b FXP & 16b FXP & 13175 & 0 & 18.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the developed models and alternative research.  
modifications to the proposed design. P-Swish activation [19] is a combination of Swish and ReLU functions, so it can be implemented with \"minimum\" and \"gate\" operations.  
As mentioned above, the distributed implementation of the sigmoid activation can be effectively reused to perform approximations of another functions. Thus, the proposed RCE is able to support a wide variety of activations. The approximation of the exponential function makes it possible to implement ELU [20] and Softmax [21] activations. The piecewise linear approximations of Tanh and Softplus activations can be introduced according to the given design. To improve the accuracy of these approximations, intervals of small or variable length can be used. To support variable-length intervals, the key-shift operation must be used several times with different shift values.
# Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter
## 3 Principle of Defect Inspection Algorithm
### Gaussian Filtering  
Enhancing image quality is necessary because the images from the image acquisition module contain noise. Row-by-row Gaussian filtering is applied to filter image noises according to the speed and performance of image-enhancing processes. The 1D digital Gaussian filter can be expressed as Equation (1). In noise filtering, a model is commonly assumed as the convolution of two signals (Equation (2)).  
\[G(x) = \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{x^{2}}{2\sigma^{2}}} \tag{1}\] \[M(x) = S(x)\otimes Z^{\prime}(x) \tag{2}\]  
where \(\sigma\) represents the standard deviation of the Gaussian filter, \(x\) is the pixel index, \(M(x)\) is the observed signal, \(S(x)\) is the original signal, \(Z^{\prime}(x)\) is the identical distribution Gaussian noise with zero mean, and \(\sigma^{2}\) is the variance.  
The partition Gaussian process is applied, considering the effect of the nonuniform background illumination of the battery edge and the difference between the edge background and the internal surface defects of the battery. Gaussian filtering is processed row-by-row in the inner area \(W_{2}\) and column-by-column in the edge area \(W_{1}\). Figure 5 shows the results of partition Gaussian method and traditional Gaussian methods.  
Figure 5: Results of Gaussian filtering. (**a**) Result of partition Gaussian processing; (**b**) Partial magnification result of internal horizontal filtering of image while edge longitudinal filtering; (**c**) Result of traditional Gaussian processing without partition processing; (**d**) Partial magnification result of image global lateral filtering.
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 3 Core Dimensions, Core Losses, and Winding Losses  
The core dimensions, core losses, winding losses, and dispersion inductances of each MFT are calculated in this section. This inductance is a crucial parameter for power flow control in Dual-Active-Bridge (DAB) DC-DC converter.  
Afterward, in Section 5, the experimental results from both laboratory prototypes are compared. This comparison provides MFT designers with useful, cutting-edge information to better select the most suitable core material, and obtain new, improved DC-DC converters with higher power density, lower cost, and smaller size.