esa-sceva/satcom-chunk-collection huggingface dataset:
# Color-Space-Based Visual-MIMO for V2X Communication +
## 3 Results and Discussion
Such robustness of the visual-MIMO to any misalignment between the transmitter and receiver is important, especially when applying V2X communication, so, the choice of multiplexing and/or diversity depends largely on the distance between the source and destination node.
# Role of Kir4.1 Channel in Auditory Function: Impact on Endocochlear Potential and Hearing Loss
## 4 Discussion
### Kir4.1 Channel
#### 4.3.2 Embryonal and Postnatal Development
In contrast, in guinea pigs, KCNJ10 channel expression began to appear in intermediate striated cells at embryonic day 50 (E50) [37; 38]. The low expression of KCNJ10 has been observed to begin at E30, increases at E45, and remains unchanged until adulthood. In particular, at E40, the KCNJ10 channel protein was expressed in spiral ganglion satellite cells of the basal turn, and at E45, it exhibited a gradient of expression from the base to the apex of the cochlear spiral. In the myelin sheath of the cochlear nerve, the KCNJ10 protein was abundant between E40 and E45, but it slowly disappeared with age. The altered development of marginal cells, leading to impaired KCNJ10 expression, was confirmed in adult mice with C-Raf\(-\)/ \(-\) genotypes: hearing loss appears to be correlated with a marked downregulation of KNCJ10, despite the presence of normal stria vascularis morphology and the absence of impairment of the sodium-potassium pump and KCNQ1 [39]. Further evidence was demonstrated in mice treated with the intermediate cell-specific tyrosinase promoter and under the control of the diphtheia toxin [40]. In their adulthood, these mice experienced the consequences of losing their endocchlear potential and profound deafness. Figure 3: The expression and activation of Kir4.1 genes in embryonic and postnatal cochlear development are depicted in diagrams. The embryonic development of humans is documented from week 8 (W8) to week 18 (18); the arrows show the time at which the expression of the RNA of different proteins was detected in the cochlear tissue [7]. In rodent models, it has been documented that Kir4.1 expression (pink color) occurs postnatally and correlates with the endocchlear potential generation (green) but not with the potassium ion concentration (blue) [28]. Until 3 weeks of age, Kir4 expression was not observed in striated vascular melanocytes in those mice, but then a minimal level of Kir4.1 expression was observed. Furthermore, in all striated vascular cells, excess melanin granules were shown, indicating that striatal homeostasis had been disrupted [40]. Remarkably, regarding the KCNJ10 expression in the root cells of the outer sulcus, during cochlear development, it looks comparable to the expression patterns of pendrin in the outer sulcus of the adult mouse cochlea. In adultness, the pendrin colocalizes with the KCNJ5 expression in the outer sulcus of the mouse cochlea, showing the contribution of this potassium channel to pH buffering of the endolymph [41]. The correlation with the embryonic expression of KNCJ10 provides a conspicuous link between the two proteins and provides a new point of view on the etiology of sensorineural hearing loss in Pendred syndrome [7].
## 2 Materials and Methods
### Data Processing and Statistical Analysis
#### 2.5.6 Path Analysis Based on Structural Equation Model (SEM)  
To determine how drought stress, in synergy with micrometeorological factors, affects the relationship of GPP-SIF, we used structural equation modeling (SEM) to examine the direct or indirect effects of the meteorological factors on the R\({}^{2}\) and K of the GPP-SIF relationship. The goodness-of-fit index (GFI), comparative fit index (CFI), and root mean squared error of approximation (RMSEA) were employed as fit indices to evaluate the model fitness. The SEM and path analysis were performed using the R 4.3.3 package \"lavaan\".  
5.7 Construction of GPP Estimation Model Based on SIF Considering the Key Drought Synergistic Effect Factor  
To mitigate the adverse effects of drought stress on the SIF-based GPP estimation accuracy, we introduced key drought synergistic effect factors into the GPP-SIF relationship. In our results (Section 3.5), the key factor VPD was incorporated into the GPP-SIF model. We separately attempted to use SIF, VPD, and SIF \(\times\) VPD as independent variables to estimate GPP. When SIF and VPD were used as independent variables, considering the influence of multicollinearity, we performed transformations on SIF and VPD through logarithmic, square, and square root methods, etc. The model \"\(\text{GPP}=\text{c}+\text{a}\times\ln(\text{SIF})+\text{b}\times\text{VPD}^{2}\)\" demonstrated the best performance (details in the Supplementary Materials), effectively compensating for the negative impact of drought stress on GPP estimation using SIF.
# The Performance of LiF:Mg-Ti for Proton Dosimetry within the Framework of the MoVe IT Project
## 2 Materials and Methods  
### Proton Beam Trento Facility  
The Trento Proton Therapy Center (PTC) is equipped with two medical treatment rooms and an experimental area that contains a research beam line dedicated to scientific applications, including medical physics, detector testing, and radiobiology. Consistentwith an institutional agreement with Trentino Healthcare Agency (Azienda Provinciale per i Servizi Sanitari, Italy), research activities are managed and supervised by INFN-TIFPA, who organize the Program Advisory Committee responsible for picking the access requests. After the beam production and acceleration by the cyclotron, an energy selection system reduces the cyclotron extraction energy to the desired value, and the beam can be directed either to the treatment gantries or to the experimental room. A fixed pencil beam line, in the energy range between 70 and 228 MeV, is available in the \"physics\" line of the research room. Energies below 70 MeV can be obtained using dedicated in-air degraders acting as pre-absorbers just ahead of the target. The passive scattering system implemented in the facility is based on the dual ring, which at the target position provided an irradiation field of 6 cm diameter with a dose uniformity >90%. An in-depth description of the experimental rooms at Trento PTC facility used for beam production and transport is available in [23; 24].
# A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning
## 4 Experimental Validation and Results
### Data Processing  
To reduce the overfitting issue, the cross-validation verification method is proposed in this study. First, we collected 270 thermal contrast curves from the simulation data (5 thermal contrast curves extracted from each defect averagely). Then, we shuffled all of the data and spilt 80% of the data to train with the GRU model, while 20% were utilized to validate the performance of the GRU.  
All obtained training data (the thermal contrast evaluation curves) were normalized and truncated as a fixed length of duration. Simultaneously, the input data were normalized by subtracting from the mean value of the thermal curves (\(\mu_{T_{c}}\)) and dividing by the standard deviation of thermal contrast (\(\sigma_{T_{c}}\)) using Equation (10).  
\[T_{c,n}^{\dagger}(t)=\frac{\hat{T}_{c}(t)-\mu_{T_{c}}}{\sigma_{T_{c}}} \tag{10}\]
# Evolution of Permeability and Sensitivity Analysis of Gas-Bearing Coal under Cyclic Dynamic Loading
## 5 Sensitivity Analysis of Permeability to Cyclic Loading
### Fitting of the Permeability Variation Curve  
Figure 12 illustrates the variation in coal rock permeability with respect to the frequency of cyclic dynamic loading. Under the condition of a fixed static load phase, the permeability of the coal rock increases in an exponential manner with the rise in the cyclic dynamic load. Initially, as the frequency of cyclic dynamic loading increases, the energy imparted by the load rises slowly, resulting in minimal damage and fewer fractures within the coal mass. Consequently, the rate of increase in permeability is gradual. However, as the frequency of cyclic dynamic loading continues to rise, the rate of energy increase accelerates, leading to a corresponding rapid increase in permeability.  
Figure 11: Maximum permeability curve of coal under different cyclic dynamic loads.  
Figure 12: Variation law of coal rock permeability with cyclic dynamic load frequency: (**a**) 0.3 \(\sigma_{c}\); (**b**) 0.45 \(\sigma_{c}\); and (**c**) 0.6 \(\sigma_{c}\).  
Fitting the curve in Figure 12 reveals that, when the adsorptive gas and static load phase are held constant, the permeability of the coal rock increases exponentially with the frequency \(v\) of cyclic dynamic loading:  
\[\mathbf{k}=\mathbf{a_{0}}\mathbf{e}^{b_{0}\mathbf{v}}\]  
where \(k\) represents the permeability of the coal body, and \(a_{0}\) and \(b_{0}\) are both fitting constants. \(a_{0}\) signifies the magnitude of permeability, while \(b_{0}\) indicates the rate of change in permeability. Table 2 presents the fitting results of the permeability of the coal body for various gasses across different phases of static loading, along with the \(R^{2}\) values, illustrating that the exponential function yields a commendable fit.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## Appendix A  
In this section, we describe the details of the smallest-circle problem introduced in Section 4 of this paper. The algorithms are summarized in Algorithms A1 and A2.  
As mentioned in this paper, the smallest-circle problem is the smallest circle that contains all points given a set of multiple points. The smallest-circle problem has been shown in the literature [36] to be computable in O(n) time but, in this study, we use the following algorithm for simplicity.  
The algorithm can be divided into the following two steps:  
1. Find the convex hull of the point cloud;
2. Find the minimum inclusive circle from the elements of the convex hull.  
A convex hull is the smallest convex polygon that encloses a set of points. Since the smallest circle can be derived from two or three of the components of the convex hull, we first find the convex hull. There are many different algorithms for finding the convex hull. However, we believe that the convex hull of the results obtained by using those methods is identical and has no effect on the proposed method in this paper. Each algorithm is shown below.  
```
0: point cloud \((x_{k},y_{k})\)
0: Convex hull of a point cloud \((Q_{n})\)
1. Let \(Q_{1}\) be the point with the minimum y-coordinate value.
2. Let the angle with the x-axis be \(\theta\) concerning the line segment from \(Q_{1}\) to the other point group, and let \(Q_{2}\) be the point where \(\theta\) is minimized.
3. While \(Q_{n+1}=Q_{n}\)
4. Let \(Q_{n}\) be the most recently set point.
5. For the line segment between \(Q_{n}\) and the rest of the points, the angle between the line segments \(Q_{n}\) and \(Q_{n+1}\) is \(\theta\), and let \(Q_{n+1}\) be the point where \(\theta\) is minimum.
6. End
```  
**Algorithm A1** Find the convex hull method  
```
0: point cloud \((x_{k},y_{k})\), Convex hull of a point cloud \((Q_{n})\)
0: Information about the smallest \(-\) circle \((\text{X},\text{Y},\text{ R})\)
1. While Repeat until the set circle encompasses all the point clouds.
2. Select any three points in the vertex set \((Q_{n})\) of the convex method.
3. If the triangle formed by connecting the three points is an acute triangle.
4. Let circumcenter be the center center (X,Y) and the distance from it to the farthest point be the radius (\(R\)).
5. Else
6. Let the midpoint of the obtuse pair of sides be the center (X,Y) and the distance to be the farthest point be the radius (\(R\)).
7. End
8. End
```  
**Algorithm A2** Find the smallest-circle method
Several studies have been presented in this research area with different perspectives, mainly based on four types of antennas: monopole [9; 10; 11], dipole [12], helical [13], and microstrip patch. The application of patch antennas for 5G is in full development for CubeSats [14; 15; 16], mobile communications [17; 18; 19; 20], and the use of new frequency bands for this technology [21; 22; 23]. This type of antenna stands out for its practicality for application in nanosatellites through the design of antenna elements or arrays focused on the operating frequencies of the ultra-high frequency (UHF), L, S, X, Ku, and K bands. In [24], the design and analysis of microstrip antenna arrays for meteorological nanosatellites for UHF uplink were demonstrated. Later, UHF and S-band antenna arrays for data retransmission were presented in [25]. The work in [26] presented the design and compatibility analysis of a solar panel-integrated UHF antenna. On the other hand, the research in [27] focused on the design and characterization of an antenna that operates in the S-band (2.4 GHz). This antenna sends all the ADS-B (Automatic Dependent Surveillance-Broadcast) data collected to the ground to automatically and periodically transmit air traffic information for each aircraft, including identity number, position, speed, and destination during all phases of flight to avoid collisions. Moreover, this antenna showed return loss values of \(-18.5\) dB, a bandwidth of 163 MHz, and a gain of 6.08 dB. Concerning the X band, a design for a compact antenna for isoflux radiation with circular polarization operating in the X band (8-8.4 GHz) was presented in [28]. Moreover, an array of 25 elements operating in the central frequency of 8.2 GHz with a realized gain of \(-10\) dB was proposed in [29]. In [30], the design of a 4 \(\times\) 4 element microstrip-phased antenna array for a satellite application of the Internet of Things (IoT) was developed, operating on the center frequency of 8.21 GHz. Additionally, for the K band, the design of a patch antenna operating at 22.5-23.5 GHz with high gain for low Earth orbit interconnection between nanosatellites, forming an array of 4 \(\times\) 4 elements with a gain of 21.8 dBi, was presented in [31].  
The main contribution of this work is the design of an antenna array based on microstrip technology with a low profile for high-gain wireless links at low frequencies of 5G in CubeSat communications. To this end, the design presents the use of miniaturized antenna elements that allow for its implementation in the limited structure of a nanosatellite.
# Coupling Denoising to Detection for SAR Imagery
## 3 Results
### Qualitative Evaluation  
Figure 8 shows paired examples of real SAR images and corresponding synthetically denoised SAR images where the denoised SAR images are the intermediate results in our model. After the denoising stage, the general speckle noises are drastically reduced; however, there inevitably exists a trade-off between the noise level and image clarity. Especially, a lot of buoys that usually look like actual ships are located in the first example of Figure 8 and in the denoised SAR image, brightness of the buoys relatively gets faded and the visual difference with the surrounding ships becomes clear. In addition, scattering waves around target objects which are one of factors hindering accurate localization is blurred after the denoising. The denoising within our network confirms such positive effectiveness.  
Some image triples of groundtruth, baseline detection, and our detection visualizations are presented in Figure 9. We train the baseline detection model with non-preprocessed and raw noisy SAR images. For a fair comparison, both the baseline and our detection model equally adopt Faster RCNN with ResNet-101-FPN [43; 44] backbone architecture. The detection results show that our model could localize overall objects accurately with higher confidence scores and detects with a small number of false alarms compared to with the baseline detection model in the given patch images. Although the progress made by our detection models are inspiring, our detectors still have a room further improvement due to the few remaining false alarms and missing targets.
## 4 Kinematic Model
### Results of Kinematic Model  
Given the fault displacement, trishear zone, and final tipline position, the author manually searched for the best trishear model that fitted the seismic interpretation results. The results of the trishear kinematic model are shown in Figure 6a and Table 1. The P/S ratio in the model is 2.5. The results are also compared to the seismic interpretation (Figure 6b), indicating that the kinematic model fits the seismic interpretation well. In other words, the deformation of the host rock over the mafic sil can be explained by the mechanism of trishear fault-propagation folding.  
However, the kinematic results show that the fault displacement at the disconformity between the Middle and Upper Devonian strata is about 58 m, which is slightly larger than the seismic interpretation (52 m). The dip angle of the fault in the trishear model is vertical, whereas the seismic interpretation suggests an inward dip value of 88\({}^{\circ}\) (Table 1). This result may be due to artificial errors in seismic interpretation and seismic data processing. In addition, the kinematic model also determines where the fault tip line was when the trishear fold began to form.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Seismic Interpretation** & **Kinematic Model** \\ \hline Trishear apical angle & 40\({}^{\circ}\) & 40\({}^{\circ}\)1  \\ Displacement & 52 m & 58 m \\ Dip angle & 88\({}^{\circ}\) & 90\({}^{\circ}\) \\ P/S ratio & / & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and kinematic parameters of the peripheral fault from the seismic interpretation and kinematic model.  
Figure 5: (**a**) Structural pattern of the peripheral fault derived from seismic interpretation after time–depth conversion. Solid lines with color are the boundaries of the structural units and black concordant dashed lines are internal bedding planes. T, P\({}_{3}\), P\({}_{2}\), P\({}_{1}\), D\({}_{3}\)-C, S-D\({}_{1-2}\) are strata symbols of structural units (see text or Figure 3 for details). (**b**) Initial trishear kinematic model of the peripheral fault based on seismic interpretation. Pre-growth, growth, and post-growth are defined based on the relative sequence of deposition and uplifting (see text or Figure 3 for details).
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model
### External Noise
#### 3.1.2 Snow Attenuation Model  
Connectivity in WSNs experiences a decrease in reliability and performance as a result of signal attenuation and depolarization induced by air particles. Similar to rainfall, snowfall may have a substantial impact on the transmission of radio frequency (RF) communications. It is a widely used approach to consider snow as equivalent to rainfall and calculate the attenuation of signal transmission using a rain model. According to [24], the wet snow attenuation \(\gamma_{\text{snow}}\) (dB/km) is significantly influenced by the frequency of propagation. The variable \(R_{5}\) (mm/h) is defined as the liquid water content, and the snow attenuation is determined as follows:  
\[\gamma_{\text{snow}}=0.00349\times\frac{R_{s}^{1.6}}{\lambda^{4}}+0.00224 \times\frac{R_{s}}{\lambda}, \tag{6}\]  
where \(\lambda\) (cm) is the wavelength of the propagating signal carrier frequency \(f\) (Hz).
## 2 Principe of Proposed End-to-End Optical Fiber System with the Adaboost Algorithm Receiver Design  
### The Design of End-to-End Optical Fiber System with the Adaboost Algorithm Receiver  
Figure 1 shows a schematic diagram of the entire end-to-end optical communication system. The characteristic of fiber dispersion is that it will lead to the ISI effect from the front and back symbols to the current symbol. We consider optimization of the end-to-end design by replacing ANN with the Adaboost algorithm on the receiver to reduce the computation complexity, at the cost of sacrificing BER performance of the whole optical fiber system (detailed analysis in Section 3). The ANN at the transmitter encodes the message \(m\in\{1,\ldots,m\}\). Binary Random Sequence generated by the Python Numpy Library [28] is a bit stream consisting of only a unipolar signal (0,1). Therefore, a few bits from this bit stream represent the message m and are encoded into a one-hot vector. Thus, the message m is expressed as a one-hot vector (containing \"1\" at position m and zero at other positions), and then processed by ANN to obtain n samples to form a waveform. The obtained waveform is upsampled for each sequence point by \(N\) times, and then transmitted to a low-pass filter (LPF) and the fiber channel.  
As shown in Figure 1, the waveform through the fiber channel is processed by the receiver. ANN and the Adaboost algorithm can also decode the generated waveform into the one-hot vector as in the transmitter before.  
In ANN (Figure 1a), the ANN structure of the receiver is reversed with the ANN structure of transmitter. In the Adaboost algorithm (Figure 1b), it is composed of several Decision Trees, and its weight is updated by iterative training. Due to different training methods, ANN can be trained together, and the Adaboost algorithm cannot be trained together. Therefore, we need to train the Adaboost algorithm after training ANN in order to obtain the ANN transmitter. This is detailed description in Section 3.1.
## 6 General Discussion  
This study has attempted an estimate of the exergy storage (and stored exergy to work) potential of major bedded halite deposits of the UK onshore and offshore East Irish Sea areas. Storage would be using salt caverns constructed in the massively bedded halites and storage estimates are based on three thermodynamic models for the temperature and pressure variations within CAES caverns developed by ref [38]. Clearly, a number of significant assumptions and generalisations have been necessary when assessing entire sedimentary basins. However, current salt cavern hosted gas storage facilities prove that the UK halite beds studied are capable of hosting large, stable caverns for high-pressure gas storage. 'Static' theoretical storage volume is enough to meet the UK electricity demand of 300 TWh, although this is unrealistic. Various filters applied to the cavern storage data together with cycle numbers based upon gas storage operational parameters provide more realistic dynamic exergy storage and stored exergy to work estimates of at least 36 MWh, illustrating that salt caverns onshore and in the EIS could deliver significant EES and grid-scale support. Estimates for future UK electrical energy storage capacity needs for a net-zero system in 2050 range from about 1 TWh in total [50] to the latest National Grid Future Energy Scenario (NGFES) estimates of about 200 GWh [51]. In both cases, the majority of capacity requirement will be for large-scale, long-duration energy storage, with CAES in all three NGFES net-zero scenarios contributing about 20-40 GWh. Currently, PHS accounts for the majority of the UK energy storage capacity, which has 2.8 GW power capacity and 27.6 GWh storage capacity. In 2019, the total energy discharged by PHS in the UK was 1.7 TWh, which is only about 1/70 of the total gas power generation. Due to the potential site-specific negative environmental and ecological impacts of PHS and the limited availability of favourable sites, further expansion of PHS capacity in the UK will be difficult. Lithium-ion battery storage and hydrogen are two promising technologies that may fulfil this required capacity. Lithium-ion batteries have attracted attention and undergone significant development in the last 5 years. However, the cost structure (high CAPEX of energy in $/kWh) renders it suitable only for mainly daily cycling applications, instead of energy storage operations at timescales greater than 10 h, even with a significantly reduced cost in future (e.g., $150/kWh) [52].