esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Figure 7: Percentage increase in the antibacterial effect of antibiotics with AgCl NPs against the tested strains. Synergism between antibiotics and nanoparticles was evident when using erythromycinycin in combination with Ag/AgCl NPs synthesized by _Raoultella planticola_ (activity was determined against _S. aureus_). The bio-reduced silver chloride nanoparticles were active in inhibiting the growth of _S. aureus_, _S. pyogenes_, _Salmonella_ and _B. amyloliquefaciens_. [PERSON] and collaborators have reported the development of core-shell silver and silver chloride nanostructures with applications in nanomedicine. The obtained nanostructures have a good wound healing capacity and excellent therapeutic efficacy against methicillin-resistant _Staphylococcus aureus_ infections in in vivo mice models [44]. The efficiency of many favorable technologies that use in vitro and in vivo assays has been proven by research conducted in the domain of antibacterial bio-coatings. The bacterial-derived coating has been shown to interfere with bacterial adhesion and with the initial phases of the biofilm formation in in vivo assays [45]. A critical overview of the effect of the potent antibacterial properties of silver-based coatings and nanomaterials on the inflammatory response has been published by [PERSON] and co-workers. They stated that the immunological response depends on many factors, including the size, shape, surface charge, and surface chemistry of the nanomaterial [46]. Therefore, further studies should be performed with the purpose of exploring the effect of silver on the immune system.
## 3 Thin Film Characterizations  
A series of measurements and characterization tests were performed on the fabricated micro-Si thin films in order to analyze their material and electrical properties. Raman spectroscopy and X-ray diffraction (XRD) technique were used to study the crystallization rate of the microcrystalline thin films. Raman peak shift was measured for the wave numbers from 400 to 600 cm\({}^{-1}\) with a laser wavelength of 532 nm by a LabRam HR spectrometer, made by HORIBA. The crystal orientation and grain size of the crystals formed in the micro-Si layer were studied by XRD spectroscopy. Then, XRD spectra was measured by a Rigaku SmartLab system equipped with a Bruker D8 diffractometer using CuK\(\alpha\) radiation (\(\lambda\alpha\)1 = 1.5406 A), covering 2-\(\theta\) angles from 10\({}^{\circ}\) to 90\({}^{\circ}\). We studied the surface morphology of the thin film samples on a glass substrate using an Olympus BH3-WHP6 optical microscope. A Merlin Field Emission Secondary Electron Microscopy (FE-SEM) system made by ZEISS was used to study the morphology and observe the microcrystal layer formation for the samples made on the C-Si substrate. Furthermore, we used Energy Dispersive Spectroscopy (EDS) measurements to determine the elemental and chemical properties of the micro-Si films. The EDS system was made by \"OXFORD INSTRUMENTS\", equipped with an X-MaxN Si drift detector with a detection area of 50 mm\({}^{2}\). The electrical properties of the fabricated samples were studied by Hall Effect, External Quantum efficiency (EQE) and J-V curve measurements. Study of the carrier concentration and mobility was conducted by Hall Effect measurements. We used an HMS 3000 system made by ECOPIA for this purpose. EQE measurement of the fabricated samples on C-Si wafers was performed using a QEX10 system made by \"PV Measurement Inc.\" for a wavelength range of 300 to 1100 nm. Finally, the J-V characteristics of the fabricated p-n junction were measured using a Keithley-2400 source meter in dark and illuminated conditions. The illumination condition was adjusted in accordance with the AM1.5G irradiance standard by means of an XES-70S1 solar simulator.  
Figure 3: Profile of the thermal treatments applied in the third step of the CuIC thin film fabrication process.
## 3 Research Methods  
### Research Design, Subjects, and Materials  
In this study, two sets of experimental interface prototypes were designed on the basis of the two VR objectives, namely freeform creativity and goal-directed training. \"Freeform Creativity\" primarily allows users to express their creativity and explore freely; in this study, participants were asked to engage in freeform drawing. \"Goal-Directed Training\" allows participants to experience VR dumbbell challenge exercises, characterized by purpose and guidance. Participants underwent both experimental groups in sequence. The detailed experimental steps are described as follows:  
1. At the beginning, the instructor initiated the VR equipment and introduced the experiment's precautions and tasks and participants filled out the consent form. This step took about 5 min. Subsequently, the instructor asked participants to put on the VR equipment, addressed any wearing issues, and ensured that participants correctly donned the VR equipment. Under the instructor's guidance, participants began to familiarize themselves with the VR operations, gradually adapting to the VR equipment. The instructor answered questions and resolved issues during this step, which took about 10 min;
2. Participants in the \"Freeform Creativity\" group used the Gravity Sketch program to freely create for 10 minutes, using a virtual brush to draw anything they preferred. The instructor guided participants in simulating a brush with the controller, using buttons to draw lines, change colors, and depict desired plants or animals;
3. In the \"Goal-Directed Training\" experimental group, participants operated a dumbbell challenge exercise program designed with Unity. Participants were required to complete three specified tasks within 10 min. These tasks included the following: (a) picking up the controller (virtual dumbbell), (b) selecting a level, and (c) completing at least one exercise cycle within the allotted time. The exercise cycle comprised five levels, each involving simple dumbbell stretching exercises performed in five different postures;
4. After completing the above steps, participants filled out a questionnaire and participated in a semi-structured interview. The experiments were conducted on the SteamVR platform operated on Microsoft Windows 10 by using HTC Vive VR headsets, controllers, base stations, and a VR Ready laptop operated by the instructor to start the VR device and monitor the experiment. Table 1 lists the required tasks, interface operations, on-site experimental setup, and hardware configurations of the two VR experiment groups.
## 4 Experiments and Results
### Results  
Table 3 presents the obtained error rate performances of different state-of-the-art network models in the category of three related techniques, comparing with the proposed PrimeNet learning algorithm. We run the PrimeNet algorithm for several repetitions and recorded the top-1 and top-5 results with their _mean (\(\mu\))_ and _standard deviation (\(\sigma\))_. For the MNIST digits dataset, PrimeNet has obtained a record top-1 performance surpassing all the results from considered baselines. MNIST is assumed to be too easy, but it is our first choice to test our algorithm because, if an algorithm fails on MNIST, it is likely to fail on other tests. To further evaluate the performance of the proposed algorithm, we tested it on the Street View House Number (SVHN) dataset. With SVHN, the PrimeNet has obtained an acceptable performance close to the considered state-of-the-art method. We also evaluated our algorithm on the CIFAR-10 dataset for more generalized observations. This is to note that the state-of-the-art results are obtained without using any data preconditioning, and the input batches are shuffled while loading datasets.  
The observations are: (1) The proposed PrimeNet structure acts as a helper network for obtaining the prime visual indicators from the input and is most suitable for early convolution stages. PrimeNet is helpful to reduce the size of large deep networks with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Capsule (Shallow Network)** & \multicolumn{2}{c}{**PrimeNet (Classifer)**} \\ \hline
**Layer** & **Parameter** & **Layer** & **Parameter** \\ \hline Conv2D & \(1\times 1,32\) & MaxPool & \(3\times 3\) \\ Conv2D & \(3\times 3,5\times 5,7\times 7,64\) & Conv2D & \(1\times 1,64\) \\ BN & NA & BN & NA \\ LReLU & NA & MaxPool & \(2\times 2\) \\ Flatten & NA & Conv2D & \(3\times 3,128\) \\ Dense & \(28\times 28\times 1,32\times 32\times 3\) & GAP & NA \\  & & Flatten & NA \\  & & Dense & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Capsule and classifier model configuration information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Input Size** & **No. of Classes** & **Train Size** & **Test Size** \\ \hline MNIST & \(28\times 28\times 1\) & 10 & 60,000 & 10,000 \\ CIFAR-10 & \(32\times 32\times 3\) & 10 & 50,000 & 10,000 \\ SVHN & \(32\times 32\times 3\) & 10 & 73,257 & 26,032 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the datasets used for the experiments.  
lesser weight adjustment operations. (2) The PrimeNet is an independent adaptive deep neural structure with its own backpropagation free gradient update technique. PrimeNet infers that the complex deep neural networks can comprise a backwards-locking free mechanism to reduce computational complexities further. (3) The experiments also suggest that the standard convolutional layers can be easily replaced or combined with PrimeNet.
## 3 Methodology
### Target-Oriented Processing Module
Given the corresponding image pixel's position, the time delay at every moment in the azimuth direction is calculated as  
\[t_{0p}(\eta)=\frac{2\|P(xy)-P_{\textit{rec}}(\eta)\|_{2}}{c} \tag{15}\]  
where \(P_{\textit{rec}}(\eta)\) is the EPC position of the system at azimuth time \(\eta\); \(P(x,y)\) is the pixel's position \((x,y)\) in the imaging plane. 2. Calculate the position of response in the aperture buffer. Given the time delay calculated before, the corresponding position in the aperture buffer is calculated as  
\[ID(\eta)=round\left(\frac{t_{0p}(\eta)-T_{delay}+\frac{1}{2}T_{r}}{T_{r}}\right) \tag{16}\]  
where \(T_{delay}\) is the time delay of the receiving window and \(round(\cdot)\) is the rounding operator. Figure 9: Streaming BP processing flow. 3. Compensate the phase term. Given the time delay calculated before, the corresponding compensated response is calculated as  
\[temp=s_{r}(t,\eta,i)\text{exp}\big{\{}j2\pi f_{0}t_{0p}(\eta)\big{\}} \tag{17}\]
4. Coherent accumulation. Given the phase compensated response, the image pixel's value is coherently accumulated with the new response as  
\[I(x,y)\,\leftarrow\,I(x,y)+temp \tag{18}\]  
The above calculations are repeated until the necessary resolution in the azimuth direction is achieved. The streaming BP computation complexity is determined by the number of pixels in the 2D image--the size of the target, in other words--and the length of aperture to be accumulated. For a single round of the above calculation steps, one operation of multiplication and one operation of addition are conducted. At the end of this subsection, we conclude it by summarizing the computation time resource consumption of the proposed target-oriented processing module, as presented in Table 2. The comparison with the current processing module is also included. In Table 2, \(N_{a}\) is the number of azimuth sampling points for the whole scene. \(N_{x}\) and \(N_{y}\) are the number of image pixels in the range and azimuth direction, respectively. \(\beta\) is the target-oriented ratio of necessary azimuth resolution vs. full azimuth resolution that is no more than 1; \(N_{rl}\) is the number of sampling points in the range direction. \(N_{rl}^{\prime}\) is the number after interpolation. Similarly, with the help of the sparsity of the scene, the small size of targets, and the GPU device, this processing module reduces the computation time consumption a lot, according to Table 2. In addition, considering the need for target detection, the required resolution may be less than the maximum available resolution. Thus, azimuth accumulation time is saved.
# Monitoring of Reinforced Concrete Corrosion: Active and Passive Bars Exposed to Climate
## 3 Results
### Corrosion Parameters of the Beam without Chlorides  
As anticipated, the uncontaminated beam did not experience any corrosion throughout the monitoring period. The corrosion rate remained below 0.1 \(\upmu\)A/cm\({}^{2}\) (refer to Figure 6). Additionally, the corrosion potential was consistently maintained above \(-575\) mV\({}_{\text{MnO2}}\). The resistivity, especially in the summer, increased significantly by several orders of magnitude, while in the spring and autumn, it decreased to values close to 100 k\(\Omega\) cm. A correlation between the corrosion rate and resistivity existed, with an evident decrease in the corrosion rate as the resistivity increased. Meanwhile, Figure 5 shows that the surface temperature of the beam can reach over 50 \({}^{\circ}\)C during the summer season, while the ambient temperature is 40 \({}^{\circ}\)C at that time.
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 3 A Hybrid State Estimator Based on SCADA and PMU Measurements
### State Variables and Measurement Equations in Robust State Estimation
\begin{array}{ll}\frac{v^{r}_{h_{\phi}}}{v^{r}_{slack\phi}}=1\ \frac{v^{r}_{h_{\phi}}}{v^{r}_{slack\phi}}=0\ \frac{v^{r}_{h_{\phi}}}{v^{r}_{slack\phi}}=0\ \frac{v^{x}_{h_{\phi}}}{v^{r}_{slack\phi}}=1\\ \frac{v^{r}_{h_{\phi}}}{i^{r}_{l\phi}}=-r_{l_{l\phi\phi}}\ \frac{v^{r}_{h_{\phi}}}{i^{r}_{l \phi\phi}}=x_{l\phi\phi}\ \frac{v^{r}_{h_{\phi}}}{i^{r}_{l\phi\phi}}=-x_{l\phi\phi}\ \frac{v^{r}_{h_{\phi}}}{i^{r}_{l \phi\phi}}=-r_{l_{l\phi\phi}}\end{array} \right. \tag{5}\]  
From Equations (2)-(5), it can be seen that the bus voltage measurements will affect the real and imaginary parts of the root bus. As a result, the root bus voltage will not be a fixed reference bus, but an estimated value that is closer to the actual situation based on the actual measurements. When the measurements in polar coordinates obtained from the PMUs are converted into the rectangular coordinates, and the power measurements are converted into the equivalent current measurements, the measurement error transmission is involved. The covariance of equivalent measurements [35] can be obtained through measurement correlation, and the covariance matrix of the branch current measurements in rectangular coordinate can be expressed as Equation (6):  
\[\sum{i_{hk_{\phi}}}=\left[{ \begin{array}{*{20}{c}}{\partial_{hk_{\phi}}^{r}} &{\partial_{hk_{\phi}}^{r}}\\ {\partial_{hk_{\phi}}^{r}}&{\partial_{hk_{\phi}}^{r}}\\ {\partial_{hk_{\phi}}^{r}}&{\partial_{hk_{\phi}}^{r}}\\ \end{array} }\right]\times\left[{ \begin{array}{*{20}{c}}{\sigma_{hh_{\phi}}^{2 }}&{\partial_{hk_{\phi}}^{r}}\\ {\sigma_{hh_{\phi}}^{r}}&{\partial_{hk_{\phi}}^{r}}\\ {\sigma_{hh_{\phi}}^{r}}&{\partial_{hk_{\phi}}^{r}}\\ \end{array} }\right]^{T} \tag{6}\]  
where, \(i_{hk_{\phi}}^{r}\) and \(i_{hk_{\phi}}^{r}\) are the real and imaginary parts of the branch \(h-k\) after the conversion, respectively. \(I_{hk_{\phi}}\) and \(\delta_{hk_{\phi}}\) are the current amplitude and phase angle of branch \(h-k\) obtained by PMUs, respectively. \(\sigma_{hk_{\phi}}^{2}\) and \(\sigma_{hh_{\phi}}^{2}\) are the current covariance of the amplitude and phase angle, respectively. Both the conversion covariance of the PMU voltage measurements and the power measurements can be obtained by Equation (6), and the weights of the measurements can be expressed as the reciprocal of the covariance. The correction of state variables can be obtained using Equation (7):  
\[\mathbf{H^{T}WH\Delta x}^{(k)}=\mathbf{H^{T}W}\Big{(}\mathbf{z}-h\Big{(}\mathbf{ x}^{(k-1)}\Big{)}\Big{)} \tag{7}\]  
where, \(\mathbf{H}=\partial\mathbf{h}/\partial\mathbf{x}\) is the Jacobian matrix, \(\mathbf{W}\) is the measurement weight matrix considering the delay of SCADA data transmission [36], \(\mathbf{x}^{k-1}\) are the state variables obtained by the \((k-1)\)th iteration, \(h(\mathbf{x})\) is the measurement function, and \(\mathbf{z}\) is the real-time measurement data of SCADA and PMU or pseudo-measurements.
## 1 Introduction
### Room Criteria in Active Classrooms  
Since the 2000s, an increasing number of standards in classroom acoustics set the requirements of reverberation time (RT) in an occupied state. In occupied conditions, the reverberation time may be expressed in its classical and predictive form with the Sabine formula:  
\[\mathrm{RT_{k}}=0.16\frac{V}{A_{\mathrm{hall,k}}+A_{\mathrm{people,k}}+A_{ \mathrm{air,k}}}\quad\mathrm{(s)} \tag{1}\]  
where  
* the subscript k corresponds to the k-th octave band, usually in the range 125-4000 Hz;
* \(V\) is the volume of the hall, in m\({}^{3}\);
* \(A_{\mathrm{hall,k}}=\sum\alpha_{i}S_{i}\) is the equivalent absorption area of the hall in furnished and unoccupied state, in m\({}^{2}\) Sabine (\(\alpha_{i}\) is the absorption coefficient of the i-th surface, \(S_{i}\) is the area of the i-th surface, in m\({}^{2}\));
* \(A_{\mathrm{people,k}}=d\mathrm{NA_{1 pers,k}}\) is the equivalent absorption area due to the occupancy, in m\({}^{2}\) Sabine (where \(d\) is the percentage of occupancy considered, N is the maximum seating capacity, \(A_{\mathrm{1 pers,k}}\) is the equivalent absorption area of a single person, in m\({}^{2}\) Sabine);
* \(A_{\mathrm{air,k}}=4m_{\mathrm{k}}V\) is the equivalent absorption area of the air. \(m_{\mathrm{k}}\) depends on the temperature and the relative humidity according to ISO 9613-1 [29].  
The speech transmission index (STI) quantifies the deterioration of a speech signal through a transmission channel, i.e., the room. Its range spans from 0 (signal fully degraded) to 1 (signal fully preserved). It is derived from the modulation transfer function \(m(f_{\mathrm{m}})\), expressed in a simplified form as follows:  
\[m_{\mathrm{k}}(f_{\mathrm{m}})=\frac{1}{\sqrt{1+\left(\frac{2\pi f_{\mathrm{ m}}\mathrm{RT_{k}}}{13.8}\right)^{2}}}\frac{1}{1+10^{(-\mathrm{SNR_{k}}/10)}} \tag{2}\]  
where  
* \(f_{\mathrm{m}}\) is the modulation frequency, in Hz, and the subscript k corresponds to the k-th octave band (from 125 Hz to 8 Hz);
* \(\mathrm{RT_{k}}\) is the reverberation time, in s;
* \(\mathrm{SNR_{k}}\) is the signal-to-noise ratio, in dB.  
Therefore, two factors affect the intelligibility: the reverberation time \(\mathrm{RT_{k}}\) of the room and the signal-to-noise ratio (\(\mathrm{SNR_{k}}\)) at the student's position. The latter is the difference between the sound pressure level received at the listener position (SL) and the background noise, which, in the case of the occupied state, is mostly the student activity (SA) due to higher sound pressure levels compared to the HVAC systems. The speech signal is deteriorated by high reverberation time values and by low SNR values at the receiver location. Equation (1) shows that the sound absorption due to the students \(A_{\mathrm{people,k}}\) decreases the reverberation time, increasing the communication between teacher and student. On the other hand, Equation (2) shows that the SA, when the activity is not useful communication but becomes background noise, increases the overall background noise, leading to lower SNR values and thus worse teaching conditions.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 5 Determination of Local Stress and Strain
### Neuber Equation Correction
#### 5.2.1 Cyclic \(\sigma-\varepsilon\) Curve Correction  
[PERSON]'s law was applied to the elastic strain component, whereas the plastic deformation theory was used for the plastic component:  
\[\sigma_{a}{}^{\prime}=\sigma_{a}/\left(1-\mu_{1}+\mu_{1}^{2}\right)^{\frac{1}{2}} \tag{10}\]  
\[\varepsilon_{a}{}^{\prime}=\varepsilon_{a}\Big{(}1-\mu_{1}^{2}\Big{)}/\left(1 -\mu_{1}+\mu_{1}^{2}\right)^{\frac{1}{2}} \tag{11}\]  
\[\mu_{1}=\frac{\mu+E\varepsilon_{pa}/(2\sigma_{a})}{1+E\varepsilon_{pa}/\sigma_ {a}} \tag{12}\]  
where: \(\sigma^{\prime}{}_{a}\)--the maximum main stress under plane strain condition, \(\varepsilon^{\prime}{}_{a}\)--the maximum main strain under plane strain condition, \(\mu_{1}\)--generalized Poisson's ratio, \(\mu_{1}=\frac{\mu+E\varepsilon_{pa}/(2\sigma_{a})}{1+E\varepsilon_{pa}/\sigma_ {a}}\), \(\mu\)--Poisson's ratio, \(\varepsilon_{pa}\)--plastic strain amplitude of cyclic \(\sigma-\varepsilon\) curve under uniaxial stress condition, \(E_{a}\)--elastic modulus of cyclic stress-strain curve.  
The equation is achieved by \(\sigma^{\prime}{}_{a}\)--\(\varepsilon^{\prime}{}_{a}\) fitting as follows:  
\[\varepsilon^{\prime}{}_{a}=\sigma^{\prime}{}_{a}\Big{(}1-\mu^{2}\Big{)}/E+ \left(\sigma^{\prime}{}_{a}/K^{\prime}\right)^{\frac{1}{n^{\prime}}} \tag{13}\]  
where \(K^{\prime\prime}\) is corrections of \(K^{\prime}\), \(n^{\prime\prime}\) is corrections of \(n^{\prime}\).  
The parameter data found in Table 4 were put into Equation (13), and then the revised cyclic \(\sigma-\varepsilon\) curve equation was obtained via Equation (14):  
\[\varepsilon^{\prime}_{a}=\frac{\sigma^{\prime}_{a}(1-\mu^{2})}{E_{a}}+(\frac{ \sigma}{590.716})^{1.014} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strain Levels** & \(\varepsilon^{\prime}{}_{a}\) & \(\sigma^{\prime}{}_{a}\)**/MPa** & \(\mu_{1}\) & \(K^{\prime\prime}\) & \(n^{\prime\prime}\) \\ \hline
0.6 & 0.682 & 0.14 & 0.427 & & \\
0.5 & 0.594 & 0.097 & 0.419 & & \\
0.4 & 0.473 & 0.083 & 0.417 & & \\
0.3 & 0.348 & 0.076 & 0.423 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correction parameters of cyclic \(\sigma-\varepsilon\) curve.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strain Levels** & **0.6** & **0.5** & **0.4** & **0.3** \\ \hline \(K_{t}\) & 1.973 & 1.947 & 2.046 & 1.938 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Theoretical stress concentration factor of notch.