esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Formation of Copper Oxides  
In the experiment, a copper-to-copper contact surface was primarily used. Copper has the property of being able to oxidize with oxygen at high temperatures. The main mechanism for initiating a glowing connection is as follows.  
If the copper oxide bridge has already been established at the contact point and the bridge itself has been fully insulated, then an external heat source must be applied onto the contact area. In the normal usage of electrical appliances, the plug blade is in contact with the electrical socket, producing an electrical discharge. This type of discharge occurs when the electrical device has been switched on. Then, the contact point of the plug causes a current to flow into the appliance and initiate a spark. The electrical discharge near the oxide area is usually energetic enough to raise the temperature of the contact area to the point where copper is able to oxidize with oxygen in the air and turn into copper oxide. Common oxides formed from copper-based contacts are copper(I) oxide, Cu\({}_{2}\)O, and CuO. Both of these oxides exhibit electrical insulating properties at room temperature. Cu\({}_{2}\)O is considered to be a semiconductor material for temperatures between 200 \({}^{\circ}\)C and 500 \({}^{\circ}\)C [24; 25; 26]. As the temperature rises, the resistance decreases. The conductivity of the material also depends on the purity of the oxides themselves and whether there are enough electron holes to conduct effectively at certain temperatures. Additionally, according to the research mentioned earlier, copper oxide exhibits conductive properties at temperatures up to 1035 \({}^{\circ}\)C. CuO is also regarded as a semiconductor material. To make CuO conduct current, it must be doped to activate its carrier. In our experiments, both types of oxide material were produced in their natural states. Therefore, CuO is treated as an insulator in this research. To create CuO, Cu\({}_{2}\)O is first made by oxidizing 4 Cu + O\({}_{2}\). CuO is then produced by oxidizing 2 Cu + O\({}_{2}\) at temperatures between 300 \({}^{\circ}\)C and 800 \({}^{\circ}\)C. Cu\({}_{2}\)O does not require high temperatures at the copper contact surface to form. Regular uncoated copper surfaces will naturally form into Cu\({}_{2}\)O. At temperatures higher than 600 \({}^{\circ}\)C, CuO can react with certain gasses in the atmosphere, such as hydrogen, carbon monoxide, and carbon, creating a reduction reaction and converting CuO back into Cu. However, at these temperatures, Cu that was reduced from CuO can once again be oxidized back into Cu\({}_{2}\)O  
Figure 14: The same brass contact surface after it was subjected to an electrical arc of 4 A, viewed under an electron microscope at 100\(\times\) magnification. This brass sample was not treated with HCl due to zinc being reactive to the acid which may damage the surface.  
and CuO. This process continues in a cycle until the temperature is no longer high enough to continue the oxidizing process.
# Implementing a Circadian Adaptation Schedule after Eastward Flight in Young Male Athletes
###### Abstract  
This study examined the effectiveness of a circadian adaptation schedule in male cricketers after an 8.5 h eastward time zone change. Ten participants (aged 18.7 \(\pm\) 0.9 y) were randomly assigned to a control group or an intervention group. Participants in the intervention group followed a light exposure schedule in which they were instructed to seek light in the three hours preceding, and avoid light in the three hours following their estimated core body temperature minimum. The rate of adaptation was assessed using the nightly excretion rate of urinary 6-sulphatoxylinematonin (aMT6s). General linear mixed models were conducted to assess the effect of condition (i.e., control and light intervention) on nocturnal secretion of aMT6s. Significant main effects of day (_F(7, 35) = 10.4, p \(<\) 0.001_) were reflected by an increase in nocturnal melatonin excretion (i.e., all participants gradually adapted to the destination time zone). Subjective jet lag decreased by day (_F(7, 54) = 22.9, p \(<\) 0.001_), bedtime was delayed by day (_F(7, 54) = 3.1, p = 0.007_) and get up time was earlier by day (_F(7, 35) = 5.4, p \(<\) 0.001_). On average, it took 7 days for all participants to return to baseline levels following transmeridian travel. Similarly, it took 7 days for subjective jet lag to alleviate. In the initial 4 days of the protocol, the intervention group registered higher levels of nocturnal urinary melatonin, however, there was no significant differences in the rate of adaptation between the groups. It is possible that participants did not adhere to the intervention or that they followed the intervention but it was ineffective.  
jet lag; zeitgebers; circadian rhythms; transmeridian travel; melatonin +
Footnote †: journal: Applied Sciences
## 3 General Findings
### Studies Distribution in Chronological Order
#### 3.1.1 Research Methods  
Various research methods and approaches have been used to adopt the BIS in the literature such as conceptual, quantitative, qualitative, and mixed methods. It is noticed by the findings of the current review that quantitative research approach based on survey has been adopted frequently by most of the researchers (52.20%) for the BIS adoption. In this research approach, web and paper-based questionnaires were used for data collection from targeted respondents with authoritative positions such as IT executives, managers/owners and BI professionals [45; 49; 53]. Whereas only 21.15% of total identified studies have adopted a qualitative research approach based on case study or Delphi study in which data were collected with in-depth interviews of individuals (case study), or group of experts (Delphi study [63; 81]. Only a total of 6.30% of studies have utilized mixed methods (quantitative & qualitative) for data collection. The results also showed that the case study method was used by six studies in which data collection has been done by conducting in-depth interviews. Four studies have used observations with interviews, whereas, only one study has used the Delphi method. The Delphi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Years Categories**} & \multirow{2}{*}{**Individual** **Contect**} & \multirow{2}{*}{**Technology** **Context**} & \multirow{2}{*}{**Organization** **Context**} & \multirow{2}{*}{**Environment** **Context**} & \multirow{2}{*}{**Miscellaneous**} \\ \hline
2011 & [36] & & [37; 38] & & \\
2012 & [39; 40] & & [841] & & [42; 43; 44] \\
2013 & [45; 46] & & [47] & [45] & [48] \\
2014 & [6; 49; 50; 5  
method and in-depth interviews are helpful to know the perception of targeted respondents about the BIS [65,81] and 19.40% studies were conceptual. The summary of mentioned scenarios is depicted in Figure 3.
## 1 Introduction
### Gait Analysis  
Gait is a complex motor skill that involves the nervous, musculoskeletal, and cardiorespiratory systems to produce mobility [23]. Gait is significantly affected by alcohol intoxication, making it a viable non-invasive biomeasure that can be utilized for intoxication detection. Alcohol impairs gait by disrupting muscle coordination, making it difficult to maintain balance and control movement [24]. Intoxicated gait exhibits increased trunk sway, altered kinetics, kinematics, and changes in velocity, balance, cadence, and stride attributes [25, 26, 27, 28, 29]. Alcohol can affect the cerebellum, giving rise to atax gait--a condition characterized by a staggering walk [30]. It also increases step width and angle of foot rotation, further indicative of an atax gait [31]. When taken excessively, alcohol can cause alcoholic neuropathy--a type of nerve damage--impairing sensation and motor control [32]. Researchers have investigated gait as a modality by which alcohol intoxication can be detected.  
Gait analysis has previously been found to be useful in the detection of many diseases and types of impairment, leading to attempts to employ it in the field of alcohol consumptiondetection. Smartphone-based gait analysis has been used to assess gait [33; 34] using powerful accelerometer and gyroscope sensors that collect gait data passively. The smartphone-based approach is appealing to use because smartphones' are ubiquitous (owned by 98% of Americans [35]), affordable, and have powerful processors that can run machine learning programs, and their sensors are low cost. This work utilizes accelerometer smartphone sensor due to its reliability, measurement accuracy, and robustness over several days [36] as well as its ability to adapt to changes in walking speed and surface conditions [37; 38].
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 6 Numerical Results
### Reconstructing a Shape from its 3D Zernike Moments: Example of the Sphere  
The reconstruction of a shape function \(\rho(\mathbf{x})\) can be carried out starting from Equation (33). For the sphere with radius \(r_{0}\), the triple summation in this equation converges to a step function equal to 1 for \(r<r_{0}\) and 0 for \(r>r_{0}\). We assessed the quality of this reconstruction for the discrete sphere of radius \(r_{0}=0.75\) represented with a triangular mesh with 1,310,720 facets. We computed \(D(r)\) at a set of points \(\mathbf{x}\) whose spherical coordinates are \((r,0,0)\). In Figure 8, we show \(D_{r}\) for \(N=20\), 50 and 200. Note that all reconstructions have a value near 0.5 for \(r=r_{0}\). This observation indicates that general shape reconstruction consists of computing a field over the unit sphere with the field value at a point \(\mathbf{x}\) being the summation in Equation (33) for \(n=0\) up to a maximum preset value \(N\), and then taking the isosurface at level 0.5 of this field to define the surface of the reconstructed object.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 1 Introduction
The most promising outcomes were attained by employing a multitask pipeline that simultaneously addressed both anatomical intersection detection and segmentation. In previous work, [PERSON] et al. [16] used a VGG16 [17] architecture to automatically segment atria from LAX four- and two-chamber Cine-MRI images. They attained respective  
Figure 1: An example of a visible anomaly (**right**) compared to normal case (**left**) in LAX 2-chamber view, at the level of the apex (and not visible in SAX). The anomaly is the result of a localized aneurysm in the apical region. The condition is concomitant to hypertrophic cardiomyopathy and exhibits a thickened myocardium wall. The presented images are part of a Cine-MRI sequence. Dice scores of \(0.93\pm 0.05\) on the LAX two-chamber left atrium and \(0.95\pm 0.02\) and \(0.96\pm 0.02\) for the LAX four-chamber left atrium and right atrium, respectively. It is worth mentioning that they achieved inference times of approximately 0.2 s for the ED and ES LAX images and 1.4 s when the entire sequence was included. Other notable works involve the integration of Statistical Models of Deformation (SMOD) with UNet-based segmentation, as proposed by [PERSON] et al. (2018). Their study focused on hypertrophic cardiomyopathy and normal examinations, incorporating LAX two- and four-chamber views, with the right atrium being the sole missing class. [PERSON] et al. (2019) used SPADE-GAN image generation in conjunction with VAE-based label deformation by interpolation over LAX four-chamber images to perform data augmentation prior to segmentation. The augmentation generated by the GAN improved their results compared to the use of more traditional morphological alterations (Kalalil  
the impact of hierarchical data augmentation on segmentation improvement, with an additional focus on extrapolating the Left Ventricular Ejection Fraction (LVEF). The remainder of this paper is structured as follows. In Section 2, we provide details on the datasets, our hierarchical data-augmentation technique and the network architecture used for our comparison. In Section 3, the scores with and without data augmentation are presented as well as the clinical metric estimation over the LVEF. In Section 4, the segmentation results are analyzed along the data-augmentation influence, and finally, in Section 6, we present our conclusions.
## 2 System and Channel Models
### Statistical Models
#### 2.2.2 Turbulence Models  
In order to analyze the impact of turbulence-induced fading, we present statistical models describing atmospheric fading. For weakly turbulent conditions, one considers a LN fading model to characterize the fading of the atmosphere. Its probability distribution function is as follows [23]:  
\[f_{h_{a,\text{LN}}}(h_{a})=\frac{1}{2h_{a}\sqrt{2\pi\delta_{X}^{2}}}\exp(- \frac{\left(\ln h_{a}+2\delta_{X}^{2}\right)^{2}}{8\delta_{X}^{2}}) \tag{4}\]  
where \(\delta_{X}^{2}\) is the log-amplitude variance approximately equal to \(\delta_{R}^{2}/4\), where \(\delta_{R}^{2}\) is the widely used Rytov variance in the turbulence models which is expressed as \(\delta_{R}^{2}=1.23C_{n}^{2}k^{7/6}z^{11/6}\). Additionally, \(C_{n}^{2}\) represents the refractive index structure parameter of the atmosphere, \(z\) denotes the path length, and \(v=2\pi/\lambda\) is the THz wave number, where \(\lambda\) is the wavelength. For moderate to strong turbulence, the GG model is used widely, and its elaborate PDF is given as [16]  
\[f_{h_{a,\text{GG}}}(h_{a})=\frac{2(\alpha_{\text{GG}}\beta_{\text{GG}})^{\frac {\alpha_{\text{GG}}+\beta_{\text{GG}}}{2}}}{\Gamma(\alpha_{\text{GG}})\Gamma (\beta_{\text{GG}})}(h_{a})^{\frac{\alpha_{\text{GG}}+\beta_{\text{GG}}}{2} -1}\times K_{\alpha_{\text{GG}}-\beta_{\text{GG}}}(2\sqrt{\alpha_{\text{GG} }\beta_{\text{GG}}h_{a}}) \tag{5}\]  
where \(\Gamma(\cdot)\) represents the Gamma function, and \(K_{\alpha-\beta}(\cdot)\) denotes the modified Bessel function of the second kind with an order of \(\alpha_{\text{GG}}-\beta_{\text{GG}}\). In the context of plane wave propagation, which is a suitable approximation for long-distance transmissions using reflector antennas, the effective parameters \(\alpha_{\text{GG}}\) and \(\beta_{\text{GG}}\), associated with small and large eddies in a turbulent medium, are defined as follows [24]:  
\[\alpha_{\text{GG}} =\left[\exp\left(\frac{0.49\sigma_{R}^{2}}{\left(1+0.65d^{2}+1.11 \sigma_{R}^{12/5}\right)^{7/6}}\right)-1\right]^{-1} \tag{6}\] \[\beta_{\text{GG}} =\left[\exp\left(\frac{0.51\sigma_{R}^{2}(1+0.69\sigma_{R}^{12/5 })^{-5/6}}{\left(1+0.9d^{2}+0.62d^{2}\sigma_{R}^{12/5}\right)}\right)-1\right] ^{-1} \tag{7}\]where the parameter \(d\) is defined as \(d=\sqrt{kD^{2}/4z}\), in which \(z\) is the path distance, \(k\) is the wavenumber, and \(D\) is the receiver aperture diameter.
## 5 Data Processing and Artificial Intelligence Methods  
The data-processing methods of experimental rumen boluses used for research tasks is the application of statistical methods used widely in scientific work. These statistical methods are not worth discussing in this study due to space constraints.  
However, the use of complex, long-life sensors requires new solutions in the field of data processing and data analysis and presents unusual problems in this field.  
We have the following requirements regarding the data-processing methods of the sensors to be developed:  
* Animal data should be extracted from the primarily measured sensory signal series.
* Often, the measurement is not taken with the optimal sensor according to the measured characteristic, which makes the evaluation difficult.
* It has to learn the individual characteristics of each animal.
* Deviations from the individual characteristics of the animals are detected.
* Based on the detected deviations, they can start a special measurement program, and send a notification or alert.
* They can set up a holistic model, based on the differences from the individual's base level.
* Experience should be continuously incorporated into the system, so the number of erroneous evaluations may decrease over time.  
Several experts agree that the above goals can only be solved using AI algorithms [3; 4; 62].
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 3 Signal Design Scheme
### Modulation Type  
The modulation type determines the chip waveform and spectrum shape of the signal. This section focuses on the design of modulation type for L1 and L5 frequency band signals, focusing on the following two requirements: high-precision measurement and high data rate.
## 4 Analysis and Discussion of Results
### Test No. 4: Electric Current with Payload Variation  
Test no. 4 consisted of verifying the electric current behavior with the load variation (weight) in the effector. Four different positions were selected, and the arm trajectory was always considered starting from the origin position P (0,0,0), being (0,0,0) corresponding to the angle [\({}^{\circ}\)] of the three joints. The four positions are: 1--P (\(-\)50,0,0), only variation in the axis of joint 1; 2--P (0,50,0), only variation in the axis of joint 2; 3--P (0,0,50), only variation in the axis of joint 3; and 4--P (\(-\)50,50,50), variation in the three axes. Figure 17 represents the result of current as a function of weight obtained during test no. 4.  
From the analysis shown in Figure 17, it can be concluded that:  
* For a weight less than 100 g, the electric current is low, which means the measured current is influenced by the common mode gain of the amplifier used in the current measurement circuit.
* It is possible to distinguish the increase in current from the increase in weight (P (0,50,0) and P (0,0,50)), when it is only the movement in only 1 axis of each joint. It  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Position** & **Accuracy [mm]** & **Repeatability [mm]** & **Accuracy [\({}^{\circ}\)]** & **Repeatability [\({}^{\circ}\)]** \\ \hline P5 & \begin{tabular}{c} APp = 0.090 \\ APx = 0.041 \\ APy = 0.074 \\ APz = \(-\)0.031 \\ \end{tabular} & RP = 0.2811 & APo = 0.0191 & RPo = \(\pm\)0.0459 \\ \hline P4 & \begin{tabular}{c} APp = 0.100 \\ APx = 0.051 \\ APy = \(-\)0.047 \\ APz = \(-\)0.072 \\ \end{tabular} & RP = 0.3423 & APo = 0.0173 & RPo = \(\pm\)0.0579 \\ \hline P3 & \begin{tabular}{c} APp = 0.091 \\ APx = \(-\)0.047 \\ APy = \(-\)0.014 \\ APz = \(-\)0.076 \\ \end{tabular} & RP = 0.254 & APo = 0.0217 & RPo = \(\pm\)0.0213 \\ \hline P2 & \
\begin{tabular}{c} APp = 0.170 \\ APx = 0.022 \\ APy = 0.026 \\ APz = 0.166 \\ \end{tabular} & RP = 0.3835 & APo = 0.0344 & RPo = \(\pm\)0.0524 \\ \hline P1 &
\begin{tabular}{c} APp = 0.131 \\ APx = \(-\)0.013 \\ APy = \(-\)0.051 \\ APz = 0.119 \\ \end{tabular} & RP = 0.2836 & APo = 0.0316 & RPo = \(\pm\)0.0364 \\ \hline \end{tabular}
\end{table}
Table 6: Calculated precision and repeatability.  
Figure 17: Current result as a function of weight obtained during test no. 4.  
was not possible to verify the same for P (-50,0,0), because the current variation was reduced, being influenced by the differential gain.
* The increase in current with the weight increase during the movement from P (0,0,0) to P (-50,50,50) is not significant because it is a movement in more than one joint; the force exerted depends on the angles of the other joints that are varied. This makes the graph P (0,50,0) different from P (0,50,0) [62] and P (0,0,50) different from P (0,0,50) [63].
# RRM Prediction of Erythrocyte Band3 Protein as Alternative Receptor for SARS-CoV-2 Virus
## 2 Methods and Materials
### Materials-Protein Sequences Analysed by RRM  
The following protein sequences from UniProt Database have been analyzed using the RRM:  
Nine S1 spike proteins:  
>active fragment s1 sp|PODTC2|SPIKE_SARS2 Spike glycoprotein OS=Severe acute respiratory syndrome coronavirus 2 OX=2697049 GN=S PE=1 SV=1  
>active fragment s1 sp|P15423|SPIKE_CVH22 Spike glycoprotein OS=Human coronavirus 229E OX=11137 GN=S PE=1 SV=1  
>active fragment s1 sp|P36334|SPIKE_CVHOC Spike glycoprotein OS=Human coronavirus OC43 OX=31631 GN=S PE=3 SV=1  
>active fragment s1 sp|Q6Q152|SPIKE_CVHNNL Spike glycoprotein OS=Human coronavirus NL63 OX=277944 GN=S PE=1 SV=1  
>active fragment s1 sp|P59594|SPIKE_CVHSA Spike glycoprotein OS=Human SARS coronavirus OX=694009 GN=S PE=1 SV=1  
>active fragment s1 sp|K9N5Q8|SPIKE_CVEMC Spike glycoprotein OS=Middle East respiratory syndrome-related coronavirus (isolate United Kingdom/H123990006/2012) OX=1263720 GN=S PE=1 SV=1  
>active fragment s1 sp|Q02 ME7|SPIKE_CVHN5 Spike glycoprotein OS=Human coronavirus HKU1 (isolate N5) OX=443241 GN=S PE=1 SV=1  
>active fragment s1 sp|Q5 MQD|SPIKE_CVHN1 Spike glycoprotein OS=Human coronavirus HKU1 (isolate N1) OX=443239 GN=S PE=1 SV=1  
>active fragment s1 sp|Q14 EB0|SPIKE_CVHN2 Spike glycoprotein OS=Human coronavirus HKU1 (isolate N2) OX=443240 GN=S PE=3 SV=1  
Ten ACE2 receptors:  
>sp|Q5 EGZ1|ACE2_RAT Angiotensin-converting enzyme 2 OS=Rattus norvegicus OX=10116 GN=Ace2 PE=1 SV=1  
>sp|Q56 NL1|ACE2_PAGLA Angiotensin-converting enzyme 2 OS=Paguma larva OX=9675 GN=ACE2 PE=1 SV=1>splQ9 BYF1|ACE2_HUMAN Angiotensin-converting enzyme 2 OS=Homo sapiens OX=9606 GN=ACE2 PE=1 SV=2  
>splQ8R0I0|ACE2_MOUSE Angiotensin-converting enzyme 2 OS=Mus musculus OX=10090 GN=Ace2 PE=1 SV=1  
>splQ59 RR0|ACE2_CANAL Cell wall transcription factor ACE2 OS=Candida albicans (strain SC5314 / ATCC MYA-2876) OX=237561 GN=ACE2 PE=4 SV=2  
>splP21192|ACE2_YEAST Metallothioneein expression activator OS=Saccharomyces cerevisiae (strain ATCC 204508 / S288c) OX=559292 GN=ACE2 PE=1 SV=1  
>spQ56H28|ACE2_FELCA Angiotensin-converting enzyme 2 OS=Felis catus OX=9685 GN=ACE2 PE=2 SV=1  
>spQ58 DD0|ACE2_BOVIN Angiotensin-converting enzyme 2 OS=Bos taurus OX=9913 GN=ACE2 PE=2 SV=1  
>spQ58 RFN1|ACE2_PONAB Angiotensin-converting enzyme 2 OS=Pongo abelii OX=9601 GN=ACE2 PE=2 SV=1  
>spQ6 FJQ9|ACE2_CANGAL Cell wall transcription factor ACE2 OS=Candida glabrata (strain ATCC 2001 / CBS 138 / JCM 3761 / NBRC 0622 / NRRL Y-65) OX=284593 GN=ACE2 PE=3 SV=1  
Five RBC Band3 proteins:  
>spP02730|B3 AT_HUMAN Band3 anion transport protein OS=Homo sapiens GN=SLC4A1 PE=1 SV=3  
>spP04919|B3 AT_MOUSE Band3 anion transport protein OS=Mus musculus GN=Slc4a1 PE=1 SV=1  
>spP23562|B3 AT_RAT Band3 anion transport protein OS=Rattus norvegicus GN=Slc4a1 PE=2 SV=3  
SV=2  
>spP15575|B3 AT_CHICK Band3 anion transport protein OS=Gallus gallus GN=SLC4A1 PE=2 SV=1
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Link Quality Prediction Model Development  
#### 2.3.1 Link Quality Estimator Selection  
In the link layer metrics, packet reception rate (PRR) is one of the key indicators for assessing the quality of network links and can directly reflect the current status of the link. However, it is less flexible due to the long time required to obtain the PRR, which makes it impossible to estimate the link quality quickly [7]. The physical layer metric, the received signal strength indicator (RSSI), cannot distinguish between links with moderate and poor quality but is capable of quickly and accurately identifying high-quality links [31]. In order to quickly obtain the communication radius of nodes with good link quality, this study uses RSSI as a link quality estimator.  
Signal attenuation occurs during wireless signal propagation in the channel due to increasing transmission distance and the presence of obstacles in practical scenarios. This signal attenuation affects the received power at the receiver, so the total path loss power PLtotal can be expressed as the sum of the transmitted power Ptx, transmitter antenna gain Gtx, and receiver antenna gain Gtx, less the received power Prx, as shown in Equation (1).  
\[PL_{total}=(P_{tx}+G_{tx}+G_{rx})-P_{rx} \tag{1}\]  
When the transmit power \(P_{tx}\) is 0 dB, the total path loss power \(PL_{total}\) is defined as the negative of the received signal strength indicator (RSSI), as shown in Equation (2).  
\[PL_{total}=-RSSI \tag{2}\]