esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correlation with Sex  
Table 10 and Figure 5 report the correlation between the bone thickness and sex of the patients. The only statistically significant dependency was found at the I2 and I3 points, where bone thickness was greater in male patients.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
\begin{tabular}{c} **Bone Thickness** \\ **(mm)** \\ \end{tabular} & **Sex** & **N** & **Mean** & **SD** & **Median** & **Min** & **Max** & **Q1** & **Q3** & \(p\) \\ \hline \multirow{2}{*}{M1} & Female & 50 & 2.96 & 2.77 & 3.29 & 0.00 & 9.53 & 0.00 & 4.86 & \(p\) = 0.078 \\  & Male & 50 & 2.03 & 2.25 & 1.73 & 0.00 & 7.86 & 0.00 & 3.60 & \\ \hline \multirow{2}{*}{M2} & Female & 50 & 2.65 & 2.16 & 2.71 & 0.00 & 7.59 & 0.15 & 4.00 & \(p\) = 0.438 \\  & Male & 50 & 2.42 & 2.68 & 1.48 & 0.00 & 8.21 & 0.00 & 4.03 & \\ \hline \multirow{2}{*}{M3} & Female & 50 & 2.32 & 1.96 & 1.96 & 0.00 & 7.20 & 0.79 & 3.78 & \(p\) = 0.794 \\  & Male & 50 & 2.52 & 2.36 & 2.42 & 0.00 & 8.15 & 0.00 & 4.06 & \\ \hline \multirow{2}{*}{D1} & Female & 50 & 3.54 & 2.64 & 3.50 & 0.00 & 9.87 & 1.40 & 5.05 & \(p\) = 0.616 \\  & Male & 50 & 3.88 & 2.90 & 3.74 & 0.00 & 11.18 & 1.97 & 5.36 & \\ \hline \multirow{2}{*}{D2} & Female & 50 & 3.00 & 2.12 & 3.04 & 0.00 & 8.04 & 1.62 & 4.20 & \(p\) = 0.565 \\  & Male & 50 & 3.22 & 2.57 & 3.42 & 0.00 & 9.79 & 0.66 & 5.03 & \\ \hline \multirow{2}{*}{D3} & Female & 50 & 2.44 & 1.83 & 2.14 & 0.00 & 6.51 & 1.06 & 3.50 & \(p\) = 0.691 \\  & Male & 50 & 2.74 & 2.31 & 2.82 & 0.00 & 8.64 & 0.78 & 4.06 & \\ \hline \multirow{2}{*}{I1} & Female & 50 & 5.53 & 2.40 & 5.61 & 0.00 & 10.10 & 4.18 & 7.52 & \(p\) = 0.11 \\  & Male & 50 & 6.52 & 2.80 & 6.50 & 0.00 & 12.80 & 4.95 & 8.02 & \\ \hline \multirow{2}{*}{I2} & Female & 50 & 4.14 & 2.09 & 3.94 & 0.00 & 8.01 & 2.51 & 5.91 & \(p\) = 0.012 \\  & Male & 50 & 5.34 & 2.10 & 5.17 & 1.05 & 10.60 & 3.98 & 6.69 & \\ \hline \multirow{2}{*}{I3} & Female & 50 & 2.86 & 1.74 & 2.84 & 0.00 & 7.21 & 1.26 & 3.99 & \(p\) = 0.003 \\  & Male & 50 & 4.06 & 1.94 & 3.86 & 0.48 & 8.22 & 2.58 & 5.28 & \\ \hline \hline \end{tabular} \(p\)—Mann-Whitney test, SD—standard deviation, Q1—lower quartile, Q3—upper quartile. * statistically significant (\(p\) < 0.05).  
\end{table}
Table 10: Correlation between the bone thickness and sex of the patients.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Age** \\ \cline{2-2}  & **Spearman’s Correlation Coefficient** \\ \hline M1 (mm) & r = \(-\)0.37, \(p\) < 0.001 * \\ M2 (mm) & r = \(-\)0.381, \(p\) < 0.001 * \\ M3 (mm) & r = \(-\)0.424, \(p\) < 0.001 * \\ D1 (mm) & r = \(-\)0.378, \(p\) < 0.001 * \\ D2 (mm) & r = \(-\)0.406, \(p\) < 0.001 * \\ D3 (mm) & r = \(-\)0.42, \(p\) < 0.001 * \\ I1 (mm) & r = \(-\)0.147, \(p\) = 0.144 \\ I2 (mm) & r = \(-\)0.178, \(p\) = 0.077 \\ I3 (mm) & r = \(-\)0.107, \(p\) = 0.289 \\ \hline \hline \end{tabular}  
* statistically significant (\(p\) < 0.05).  
\end{table}
Table 9: Spearman’s correlation coefficient test for comparison of parameters measured according to the age of the patients.
# Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey
## 3 Materials and Methods
### Expectation Maximization Clustering  
It extends the K-Means paradigm in a different way. While the K-Means algorithm assigns each data point to a cluster, each object in the Expectation-Maximization (EM) model is assigned to each cluster according to a weight representing the probability of membership. In other words, there is no definite limit between clusters and new centers are calculated in terms of weighted measures [50]. EM clusters data points using a finite mixture density model, i.e., normal distribution, of \(k\) probability distributions, where each distribution represents a cluster.  
As in the K-Means clustering, the process starts with selecting cluster centroids randomly. The procedure continues with two steps to refine the parameters (i.e., clusters) iteratively based on statistical modeling: Expectation (E) step and Maximization (M) step [51]. In Step E, a function to determine the probability of cluster membership of an instance is generated using the present estimate for the attributes using Equation (1) where \(p\big{(}o_{i}|C_{j}\big{)}\) follows the normal distribution and \(i=\{1,\ldots,n\}\), \(j=\{1,\ldots,k\}\).  
\[P\big{(}o_{i}\in C_{j}\big{)}=p\big{(}C_{j}|o_{i}\big{)}=\ \big{(}p\big{(}C_{j} \big{)}p\big{(}o_{i}|C_{j}\big{)}\big{)}/p(o_{i}), \tag{1}\]  
Step M is applied as in Equation (2) for re-estimating the model parameters by discovering the attributes which maximizes the expected log-likelihood found in Step E. The iterative process continues until obtaining the optimal value.  
\[m_{j}=\ (1/n)\sum\
olimits_{i=1}^{n}\!\frac{o_{i}P\big{(}o_{i}\in C_{j} \big{)}}{\sum\
olimits_{t}P(o_{i}\in C_{t})}, \tag{2}\]
# Sustainable Additives for the Production of Hydrogen via Sodium Borohydride Hydrolysis
## 3 Results  
### Influence of the Additives on the Yield of Hydrogen  
Among the critical process parameters, the hydrogen yield is intimately related to the overall performance efficiency of NaBH4 hydrolysis. Figure 2 illustrates the mean values of hydrogen yield in the presence of each of the tested additives CMC, PAM, SDS, and BCD. The results are compared with those obtained in the conventional process in the absence of additives. The deviation of the results is also presented for each case. It is important to remark that the yields were calculated considering the real and theoretical pressure of the system. In all the cases, the additives were added to the aqueous solution in a concentration of 1 wt%.  
Based on the results, PAM can be considered a promising additive for hydrogen production given that it enables hydrogen yields close to 100% with standard deviations below 1.5%. PAM is a water-soluble polymer that functions as a stabilizing agent and rheological modifier. When added to a NaBH4-water mixture, PAM can reduce the formation of gas bubbles, which can hinder the release of hydrogen gas. Moreover, PAM is relatively inexpensive and easily available, thus facilitating large-scale industrial applications.  
In contrast, the additive BCD led to the lowest yield of 57%, which is far from the desirable values for NaBH4 hydrolysis. Furthermore, hydrolysis tests in the presence of BCD showed lower values compared to those achieved with the conventional process (control). This is because BCD has a lower ability to stabilize the reaction mixture and prevent the formation of gas bubbles as compared to PAM. In addition, BCD can reduce the availability of hydroxide ions (OH\({}^{-}\)) in the solution, which slows down the rate of the hydrolysis reaction. Intermediate values of around 85% and 76% were obtained with the additives CMC and SDS, respectively, with standard deviations in the range of 5-7%. It is worth noting that the traditional method without additives showed a significantly wider variability. In this case, although the average yield was 80%, the standard deviation was about 10%. In the case of CMC, this additive promotes the yield of H2 by facilitating the reaction between NaBH4 and water and improves the efficiency of the H2 production process. Thus, the presence of PAM and CMC is clearly beneficial for NaBH4 hydrolysis given that their yields outperformed the control test. In addition, the use of all additives showed narrower variability, contributing to the stabilization performance of the process.
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
## 3 The Proposed Gate Driver for Buck Converter  
Figure 2 illustrates the proposed integrated buck converter architecture using the high-voltage GaN process, which mainly consists of three parts, namely the power switch, gate driver, and buffer stage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **E-Mode** & **D-Mode** \\ \hline Threshold voltage (Vt) @ Id = 1 mA/mm) & 0.9–1.5 & \(-\)10—7 \\ gm\_max (mS/mm) & 67–88 & - \\ Ron (\(\Omega\)-mm) @ Vds = 6 V & 14–19 & 13–15 \\ Ids\_max (mA/mm) & 277–295 & 300–360 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance summary of p-GaN/AlGaN/GaN HEMT.  
Figure 1: High-voltage GaN device structure (**a**), the SEM cross-sectional view of the device (**b**), the E-mode GaN profile (**c**), the D-mode GaN profile (**d**), masking layout of the E-mode GaN device (**e**), and masking layout of the D-mode GaN device (**f**).
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 4 Model Application and Results
### M-Test for the Selection of Training Data Length  
After the selection of optimal input combinations through GT, we proceeded to determine the suitable data length for the model development, which was neither underfitted nor over-fitted. In general, the literature suggested that a data length ranging from 70% to 90% to train a model is appropriate for the construction of a smooth model. However, in order to find the optimal training data length, we used the M-test for model development. The M-test shows the relationship between datapoints and Gamma statistic (\(\Gamma\)) values, which provide direction for the selection of data length with a nearly uniform function convergence. The results of the M-test for the Pirna and Magdeburg-Strombrucke gauging stations are displayed in Figures 4 and 5. The M test identified a uniform convergence of the gamma statistic to a value of 0.000105 and 0.00104 at around 7000 (approximately 73% of total data and 7330 (approximately 75% of total data) datapoints to train a smooth model development at the Pina and Magdeburg-Strombrucke gauging stations, respectively.
## 1 Introduction
At the same time, it is important not only to monitor the environmental parameters, but to make their corrections at the right moment and ensure uninterrupted power supply. In order to avoid power fluctuations in power circuits or microbreaks, backup power systems such as UPS or high-capacity (1 F and more) capacitor batteries are often used [19; 20; 21; 22]. Information related to environmental parameters, recorded by sensors (humidity, temperature, etc.) and converted into a digital or analog signal, respectively, can be sent to the central system in various ways: IoT technology, RF local radio receiver-transmitter network, RS 485, RS 232, Ethernet network, etc. Programmable microcontrollers are widely used to ensure the process of growing mushrooms, because they are characterized by high reliability, low cost, low risk of failure, and, as stated in the literature, they are characterized by good resistance to environmental effects [23; 24; 25]. Control systems based on microcontrollers are characterized by small geometric dimensions, which is important for saving space, and microcontroller systems can be powered by relatively low-power (10 W and more) power supplies. The microcontrollers of the PIC series are also convenient that they have a sufficient number of logical inputs and outputs, allowing both to collect information sent by various sensors and to control various devices (for example, turning water pumps ON/OFF at the right time, etc.). While using the microcontrollers, it is possible to reliably ensure the limits of fluctuations in environmental parameters and track their deviations from the norm (e.g., ambient temperature, etc.). Cloud technology or a database such as MySQL can be used to store the operating modes of the devices and the information received from the sensors. The control system can be connected to the data cloud using IoT technology, while the database can be connected to the control system using a local area network such as Ethernet, etc. The main problem of the sensor systemsdiscussed in the literature is that most of the attention is paid to the sensors themselves, but little attention is paid to the universality of systems and stability of data transmission of individual parts [1; 26; 27; 28]. The automatic environmental parameter control systems presented in most literature sources are not universal and are only suitable for certain types of mushrooms. The aim of this work is to create a universal automatic system for maintaining environmental parameters (using temperature, humidity and CO2 level sensors and a hybrid data transmission network) with an application that ensures the cultivation process of various types mushrooms (as well as other plants).
## 2 Materials and Methods
### Crack Model
#### 2.4.2 Crack Model Validation  
Using the above experimental procedure, the crack propagation of prestressed concrete sleepers under static capacity test is simulated using the Pre-Mesh Crack of SMART method. Material properties of sleeper model are presented in Table 2. The stress intensity factor used in the sleeper model is assumed as 9.42 MPa\(\cdot\)mm\({}^{0.5}\)[37]. Section 2.2 indicates that the initial crack needs to be specified before simulation. Previous research has determined the initial cracking load of the FE sleeper model was 42.16 kN [2]. The applied loading is controlled by displacement in order to match the static capacity test results.  
In the experiment, two major cracks were observed at the top fibre of the prestressed concrete sleeper at midspan. Therefore, cracks are created in the small notches on the FE sleeper model according to actual experimental crack positions as shown in Figure 4. The notches are only used for determining crack positions, and the size of notches can be neglected. The notches have no influence on the structural performance of the prestressed concrete sleeper. Crack fronts are specified at the small notches using the node-based named selection. The limit of this simulation is that the crack propagation is symmetrical. Table 3 presents two experimental crack length results, in which the crack length-1 results are selected to compare with the numerical results. Figure 5 presents the simulation of crack propagation in the sleeper model at midspan. A comparison of the crack propagation in the numerical and experimental results is illustrated in Figure 6.  
Figure 6 presents the comparison of the numerical and experimental results on crack propagation. In the simulation, the trend of crack propagation for numerical results is the closest to experimental results. The numerical model has a good correlation with the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load** & **Crack Length-1** & **Crack Length-2** \\
**(kN)** & **(mm)** & **(mm)** \\ \hline
45 & 0 & 0 \\
55 & 18 & 43 \\
65 & 21 & 48 \\
75 & 27 & 51 \\
80 & 36 & 60 \\
90 & 46 & 82 \\
100 & 91 & 84 \\
115 & 100 & 110 \\
120 & 103 & 116 \\
125 & 106 & 120 \\
130 & 110 & 123 \\
135 & 112 & 127 \\
140 & 116 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of load-crack length response.  
Figure 3: Crack propagation of the prestressed sleeper at midspan.  
initial crack point and ultimate crack point, with the difference being only 6.31 and 4.34% respectively. The crack length of the sleeper model propagates steadily. In the experimental results, crack growth is not uniform between 65 kN and 100 kN. When loading increases from 90 kN to 100 kN, the crack length suddenly increases by 45 mm. This phenomenon was probably caused by the brittleness of concrete. In the simulation, results of crack propagation increase uniformly. In general, the performance of the numerical crack model is validated.
## 4 Discussion
### Study Limitation  
Our study assessed the placental expression of three genes related to the VD pathway-- VDR, CYP27B1 and CYP2R1--which could elucidate the significance of disturbances of VD metabolism in preeclampsia women. The presented analysis covers a relatively small sample size of material; however, the patients were carefully enrolled into the study based on the presence of severe preeclampsia, and subsequently divided into early- and late-onset preeclampsia. Moreover, in the performed analysis, the concentration of vitamin D in women with preeclampsia was not determined.  
However, on the other hand, the most pivotal results, such as placental expression levels, were correlated with important baseline clinical and laboratory data, which provided important results such as the inverse correlation of placental VDR protein level with systolic and diastolic blood pressure, as well as the positive correlation with gestational age and infant birth weight in the whole studied group.  
Unfortunately, we did not have the opportunity to compare mRNA and protein expression in placentas between the EOPE group and a gestational-age-appropriate group of women.  
Furthermore, it should be remarked that some additional factors influence VD metabolism, such as other polymorphism variants of candidate genes of VD metabolism, life style, and another environmental agents.
## 4 Parametric Analysis and Parametric-Sensitivity Analysis
### Influence of Shear Modulus (\(G_{1}\))  
The shear modulus, \(G_{1}\), is increased by 20%, 40% and 60% and decreased by 20%, 40% and 60%, while other parameters are kept constant. Figure 4 shows the settlements at the centre of the rectangle with time for different \(G_{1}\). The analysis of viscoelastic solutions in the previous section shows that \(G_{1}\) influences the instantaneous and ultimate settlements, and Figure 4 shows that the larger \(G_{1}\) is, the smaller the instantaneous and ultimate settlements. Moreover, in the creep deceleration stage of \(0<t<+\infty\), the settlement value and the change amplitude of settlements also decrease as \(G_{1}\) increases, and the change amplitude of settlements is also related to the time, t. When \(G_{1}\) increases from 24 to 96 MP, the instantaneous settlements are 0.0360, 0.0262, 0.0212, 0.0181, 0.0160, 0.0144 and 0.0131 m, while the amplitudes of the change in settlements are 54.14%, 27.62%, 17.13%, 11.6%, 8.84% and 7.18%, respectively. In addition, the ultimate settlements are 0.0475, 0.0379, 0.0331, 0.0302, 0.0282 m, 0.0268 and 0.0257 m, with corresponding amplitudes of the change in settlements of 31.79%, 15.89%, 9.60%, 6.62%, 4.64% and 3.64%, respectively.  
Figure 3: Creep curves under different \(\alpha\): (**A**) \(0\leq\) t \(\leq\) 2000 d, (**B**) \(0\leq\) t \(\leq\) 80 d.
## 3 Event Cameras
### DVS Pixel  
The DVS pixel circuit transforms light intensity values into events through the following five steps. First, the photoreceptor converts received photons into a photocurrent (\(i_{ph}\)). Then, the analog circuitry transduces the photocurrent into a voltage (\(V_{out}\)), which is logarithmically related to the incident light intensity (using a trans-impedance logarithmic converter, TLC). Second, a source follower (SF) buffer isolates the photoreceptor signal from the rest of the circuit. Third, a switched-capacitor differential amplifier (DA) amplifies changes (\(\Delta V\)) between the current signal and the memorized value, corresponding to the intensity sampled after the last event was reported. Up to this point, all the signals involved are analog. Section 4 involves comparing the amplified signal with ON and OFF thresholds. When either threshold is reached, this triggers the generation of either an ON or an OFF event, which is then transmitted externally from the array. When two or more pixels send digital data values to the shared bus simultaneously, an arbiter protocol named the address event representation (AER) protocol interferes to avoid data collision. The AER protocol queues the digital data, which await their turn to be sent by the shared bus. Due to the waiting time, the timestamp corresponds to the moment of data transmission to the output bus and not to the time of event detection (latency effect). When using this arbitration mechanism, the arbiter ensures that a row or column, once serviced, is guaranteed not to be served again until all other rows and columns with registered requests have been completed (non-greeding) [24]. In the last part, once the event is sent out to the shared bus, the AER protocol sends an acknowledgment digital signal to reset the value stored in the DA, memorizing a new intensity value and consequently allowing the pixel to spike another event [12]. The scheme of the DVS pixel can be seen in Figure 2.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 1 Introduction
### The Electromagnetic Spectrum  
Electromagnetic waves are produced whenever electric charges are accelerated. The oscillation of charged particles like electrons in an antenna generates electromagnetic waves with a frequency matching the oscillation of the charge. The varying electric fields induce changing magnetic fields, and vice versa, creating wave propagation. The oscillating electric and magnetic fields propagate through space. X-rays can be produced when high-energy electrons collide with a target material like tungsten in an X-ray tube, causing the electrons to rapidly decelerate and release energy in the form of a continuum of bremsstrahlung (an X-ray photon) radiation. In addition, monoenergetic characteristic X-rays are emitted, superimposed on the continuum of bremsstrahlung radiation by target atoms as orbital electrons change orbits due to energy absorbed from the collision electrons.  
The basic regions of the electromagnetic spectrum are listed in Table 1. At low frequencies, microwaves have wavelengths from 1 mm to 1 m and are easy to shield. Gamma rays are the most energetic form of electromagnetic radiation. X-rays and gamma rays have the shortest wavelengths and highest frequencies and energies and are the most challenging types of waves to shield [8,9]. The size of nanotubes and nanoparticles relative to the wavelength of the radiation affects the shielding performance of the material. Single-walled carbon nanotubes (SWCNTs) typically have diameters between 0.5 and 3.0 nanometers, with lengths usually ranging from 1 to 100 \(\upmu\)m [10,11]. Multi-wall carbon nanotubes (MWCNTs) typically have diameters in the 3 nm to 30 nm range. Nanoparticles have diameters ranging from typically 10 nm to 1000 nm. Radio waves and microwaves have wavelengths larger than the size of nanotubes and nanoparticles. Thus, radio and microwaves can be easily coupled to bulk nanostructured materials and shielded. On the other hand, X-rays and gamma rays have wavelengths smaller than the size of nanotubes and nanoparticles. Thus, X-rays and gamma rays are difficult to shield based on the size or shape of nanostructured materials [12]. Shielding X-rays and gamma rays is related to the atomic cross-sections of atoms in a material. The larger the atomic cross-section, the more likely the radiation is to interact with the atoms in the shielding material, making it more effective at blocking the radiation [12].