esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Derivations of the Solutions
### Considering Variable Permeability Coefficient  
The equations that relate the permeability coefficient to the matric suction proposed by [PERSON] and [PERSON] [40] were used to consider the variation in the permeability coefficient during the consolidation of unsaturated soils.  
When \((u_{\text{a}}-u_{\text{w}})\leq(u_{\text{a}}-u_{\text{w}})_{\text{b}}\), \(k_{\text{w}}=k_{\text{s}}\), \(k_{\text{a}}=0\);  
\[\text{When }(u_{\text{a}}-u_{\text{w}})>(u_{\text{a}}-u_{\text{w}})_{\text{b}},\]  
\[k_{\text{a}}=k_{\text{d}}\Bigg{\{}1-\bigg{[}\frac{(u_{\text{a}}-u_{\text{w}}) _{\text{b}}}{(u_{\text{a}}-u_{\text{w}})}\bigg{]}^{\lambda}\Bigg{\}}^{2}\Bigg{\{} 1-\bigg{[}\frac{(u_{\text{a}}-u_{\text{w}})_{\text{b}}}{(u_{\text{a}}-u_{\text{ w}})}\bigg{]}^{2+\lambda}\Bigg{\}} \tag{16a}\] \[k_{\text{w}}=k_{\text{s}}\bigg{[}\frac{(u_{\text{a}}-u_{\text{w}})_{\text{b}}}{(u_ {\text{a}}-u_{\text{w}})}\bigg{]}^{\mu}. \tag{16b}\]  
Here, \(k_{\text{d}}\) represents the permeability coefficient of the air phase in the dry state, \(k_{\text{s}}\) represents the permeability coefficient of the water phase in the saturated state, \((u_{\text{a}}-u_{\text{w}})_{\text{b}}\) represents an air entry value, and \(\lambda\) and \(\mu\) are empirical constants.  
In this paper, it is supposed that the permeability coefficients of the water and air phases remain unchanged if the suction change is small enough \(((\Delta u_{\text{a}}-\Delta u_{\text{w}})/(u_{\text{a}}-u_{\text{w}})<10^{-8})\). On the other hand, the permeability coefficient will be recalculated cyclically until the consolidation is completed if \((\Delta u_{\text{a}}-\Delta u_{\text{w}})/(u_{\text{a}}-u_{\text{w}})>10^{-8}\). This method is simple, and the alteration of the permeability coefficient follows the actual situation. Figure 3 shows this general process.  
Figure 3: Schematic diagram of semi-analytical solution implementation process considering changes in permeability coefficient.  
Moreover, the impact of the permeability coefficient variation on the consolidation is analyzed according to three scenarios, that is, where both \(k_{\text{a}}\) and \(k_{\text{w}}\) change, only \(k_{\text{w}}\) changes, and \(k_{\text{a}}\) and \(k_{\text{w}}\) are both constant.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 2 Materials and Methods
### Signal Model  
A multi-channel data model with static sources and diffuse noise can be written as follows:  
\[y_{i}(t)=h_{i,j}(t)*s_{j}(t)+v_{i}(t)\text{, for }i=1,2,\ldots,L_{\
u},\ j=1,2, \ldots,Q, \tag{1}\]  
where \(L\) denotes the number of microphones, and \(Q\) denotes the number of source signals. The notation '\(*\)' indicates convolution, and \(t\) indexes a time sample. \(y_{i}(t)\) denotes the signal at microphone \(i\), and \(s_{j}(t)\) denotes the \(j_{th}\) source signal. \(h_{i,j}(t)\) defines the Room Impulse Response (RIR), which models the aspect of sound propagation from source to receiver. An array of \(L\) microphones was utilized throughout this work. In the Short Time Fourier Transform (STFT) domain, if the environment can be assumed anechoic, the vector of received signal, \(\mathbf{y}(n,k)=\left[Y(n,k,1),\ldots,Y(n,k,L)\right]^{\mathrm{T}}\), at time frame \(n\) and frequency bin \(k\) is given by Equation (2):  
\[\mathbf{y}(n,k)=\mathbf{x}_{d}(n,k)+\mathbf{v}_{d}(n,k)+\mathbf{v}(n,k). \tag{2}\]  
The noise is divided into two components: diffuse noise, denoted by \(\mathbf{v}_{d}(n,k)\), and spatially uncorrelated microphone self-noise, denoted by \(\mathbf{v}(n,k)\).
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 4 OBFNs with Phase Shifter Architectures
### Modulator-Induced Phase Shifter Architectures  
For optical beamforming, realizing the functions of modulation and the phase shift by the same modulator may largely reduce system complexity, especially for OBFN receivers. OBFNs with this kind of modulator can be called modulator-induced phase shifter architectures. A phase modulator is one kind of such modulators that can be adopted to tune the phase difference of two coherent optical carriers [94]. The phase modulator, manufactured by LiNbO\({}_{3}\), has two different electro-optic coefficients for the TE mode and TM mode; namely, the former is one-third of the latter, inducing a phase difference (\(\Delta\phi_{i}\)) between the TM mode and TE mode in one channel as (\(2\pi V_{i}\))/(\(3V_{\pi}\)), where \(V_{i}\) is the applied voltage in this channel, and \(V_{\pi}\) is the half-wave voltage of the phase modulator [94]. Thus, the phase of the RF signal at each antenna is induced by a phase difference of two coherent optical carriers in the orthogonal polarization direction. The phase difference of adjacent channels in an antenna array can be given by Equation (16), where \(\Delta V\) is the difference of the applied voltages on the adjacent phase modulators. Additionally, modulation and the phase shift can be realized by a dual-drive MZM, as plotted in Figure 18[100]. The phase shift in a channel can be induced by the bias voltage of the modulator [101]. The phase difference between adjacent channels can be expressed by Equation (17), where \(\Delta V_{\mathrm{DC}}\) is the difference of bias voltages added on the dual-drive MZMs in adjacent channels. In addition, DMZM is also modulated by the LO signal which has a small frequency difference from the RF signal and down-converts the RF signals into IF signals at the PD. With these phase shifter array architectures, a simplified OBFN without or with less discrete phase shifters can be built for PAAs operating with a given bandwidth. Although fibers are used to connect discrete components, this OBFN architecture has the advantage of immunity to the influence of the environment, due to the fact that the two coherent optical signals employed pass through the same path.  
\[\Delta\phi=(2\pi\Delta V)/(3V_{\pi}) \tag{16}\]  
\[\Delta\phi=(\pi\Delta V_{\mathrm{DC}})/(3V_{\pi}) \tag{17}\]
## 1 Introduction
Recently, several green synthesis methods of titanium dioxide production have been explored, including the use of tea polyphenols (green tea extract) or _Psidium guajava_ (guava extract) [20; 21]. Previously, our research group developed novel single-step anodization processes to produce magnesium-doped carbonated hydroxyapatite and tricalcium phosphate coatings on four titanium implant alloys in a citric-acid-based electrolyte [22]. Additionally, we investigated the use of natural citrus fruit sources, including calcium-fortified orange juice and juiced navel oranges, as the major components of the anodization electrolytes [23; 24]. In one study, we used galvanostatic anodization waveforms in calcium-fortified orange juice and juiced navel orange-based electrolytes to produce calcium titanate-containing oxide coatings with surface Ca/P ratios in the range of those shown for human bone [23]. In the other study, we explored further additions of calcium phosphate to the calcium-fortified orange juice-based electrolyte and utilized pulsed galvanostatic waveforms to produce carbonated bone-like hydroxyapatite-containing surfaces with promising surface topography and chemistry to improve the clinical outcomes of future implants [24]. Navel oranges are from the species _Citrus sinensis_ and are globally cultivated and recognized for having antioxidant, anti-inflammatory, and antimutagenic properties [25]. The extracts of navel oranges are commonly used as key ingredients in functional foods as they contain bioactive compounds such as flavonoids, carotenoids, limonin, phenolic compounds, and amino acids [25]. Polyphenolic compounds such as flavonoids have been shown to significantly promote osteogenic differentiation of mesenchymal stem cells, enhance alkaline phosphatase activity, increase mineral deposition, and upregulate osteogenic transcription factors such as Runx2 and BMP2 [26]. An adsorption and coprecipitation methodology was shown to immobilize the flavonoid kaempfer on titanium dioxide implant surfaces and showed significant enhancement of osteogenic differentiation in rat bone marrow stromal cells (rBMSCs) [27]. Promotion of new bone formation through increased alkaline phosphatase activity, calcium deposition, and upregulation of osteogenic markers including Runx2, osteoclast, and osteepontin was also shown in the kaempfer study [27]. Therefore, the addition of flavonoids on implant surfaces has previously shown promising potential for modulating early-stage cell responses at the bone-implant interface. In the present study, we explored additions of calcium phosphate or magnesium phosphate to juiced naval orange-based anodization electrolytes to produce coatings containing bone-like carbonated hydroxyapatite. To our knowledge, these novel green electrolyte anodization processes have never been explored before.
## 5 Future Directions
### Federated Learning  
Implementing federated learning approaches to train AI models across decentralized industrial environments addresses the critical need to enhance AI capabilities without compromising data privacy [75]. Federated learning allows AI models to be trained collaboratively on data distributed across multiple locations, such as different industrial sites, without requiring the data to be centralized. This method ensures that sensitive information remains local, significantly reducing the risk of data breaches and privacy violations. By leveraging federated learning, industries can benefit from the collective intelligence and improved accuracy of AI models while maintaining stringent data privacy and security standards [76].
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 6 Performance Evaluation
### Metrics  
We evaluated the performance of our solution in terms of accuracy, precision, recall, and F1-score. Accuracy is defined as \(acc=\frac{TP+TN}{TP+TN+FP+TN}\), where \(TP\) (True Positive) indicates how many samples of \(X_{J}\) are correctly classified as jammed, \(FP\) (False Positive) indicates how many samples of \(X_{NJ}\) have been incorrectly classified as jammed signals, \(FN\) (False Negative) indicates how many samples in \(X_{J}\) are wrongly classified as non-jammed, and finally \(TN\) (True Negative) indicates how many samples of \(X_{NJ}\) are correctly classified as non-jammed. We also used the _True Positive Rate_ (TPR) and _True Negative Rate_ (TNR) as evaluation metrics to measure robustness against the number of false alarms (_False Positive_ (FP)) and number of no alarms (_False Negatives_ (FNs)) using the \(TPR=\frac{TP}{TP+FP}\), \(TNR=\frac{TN}{TN+FN}\). The precision is the ratio of the number of \(TP\) to the total number of \(TP\) and \(FP\), defined as \(\frac{TP}{TP+TP}\). Recall measures the fitness of the model to detect positive labels, defined as \(\frac{TP}{TP+TN}\). Finally, the F1-score is the harmonic mean of the recall and precision, defined as \(\frac{2\times Precision\times Recall}{Precision+Recall}\).  
In the remainder of this work, we will consider the previously defined metrics to compare the performance of our solution while varying different parameters, such as the receiver knowledge (either Scenario 1 or 2), the distance between \(\mathcal{A}\) and \(\mathcal{E}\), and the RJP parameter. In particular, while the scenario choice affects the data and the associated labels considered for training the model, i.e., jammed and non-jammed for Scenario 1 and only non-jammed for Scenario 2, the testing phase is always characterized by two balanced datasets characterized by jammed and non-jammed samples.
## 5 Conclusions  
1. The horizontal azimuth of the Sun's rays during the day when using vertical stationary solar PV-modules with double-sided solar panels with \"East-West\" orientation reaches its minimum at noon. In the morning and evening, such solar modules are located perpendicular to the flow of the Sun's rays, with a deviation in this flow in summer and winter by \(\pm\)23.45\({}^{\circ}\). At the same time, the cosine of the horizontal azimuth of the Sun's rays during the day reaches its maximum level at noon, which is from 0.917 to 1. In the morning and evening, the cosine of the horizontal azimuth of the Sun's rays has a value of zero. At the same time, the vertical azimuth of the fall of the Sun's rays remains constant throughout the day, and can vary on different days of the year from 66.55 to 113.45\({}^{\circ}\). Accordingly, the cosine of the vertical azimuth of the Sun's rays during the day remains unchanged, and can vary on different days of the year within the range of \(\pm\)0.398.  
2. The cosine of the Sun's rays falling on solar panels with an \"East-West\" orientation on vertical stationary solar PV-modules during the day reaches its minimum at noon. In the morning and evening, the cosine of the incidence of the Sun's rays has a maximum value within the range of 0.917 to 1. The weighted average daily installation efficiency of the vertical stationary solar PV-modules with double-sided panels with an \"East-West\" orientation is the same as that of PV-modules with a \"South\" orientation, and can vary on different days of the year from 45.87 to 50%. However, these installation options have a \"mirror\" value of the cosines of the Sun's rays falling on the surface of the panels of vertical stationary solar PV-modules, and can have values from 0.917 to 1 on different days of the year.  
Conceptualization, G.G., N.T., S.K. and E.B.; methodology, G.G., E.B., N.T. and A.J.; validation, E.S., V.N. and A.H.; formal analysis, G.G., N.T., A.H. and E.B.; data curation, E.B. and A.J.; writing--original draft preparation, E.B., G.G. and N.T.; writing--review and editing, S.K. and E.S.; visualization, N.T.; project administration N.T. and S.K.; Supervision, E.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflicts of interest.
## 3 Analysis and Comparison with Fundamental Bandwidth Limit
However, these antennas fabricated with air foam are soft and vulnerable to heat, so their durability is relatively poor. On the other hand, the proposed antenna with 3D printed quasi-air substrates achieved a broad bandwidth while demonstrating high durability. Figure 7 shows the normalized electric field distributions of the antenna with and without the metal cavity at 3 GHz. As can be seen in Figure 7a, leakage electric fields that formed on the sides and back of the antenna are suppressed by the metal cavity structure. In contrast, without the metal cavity structure, a strong leakage electric field is observed around the antenna structure, as shown in Figure 7b. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research** & **Substrate** & **Bandwidth** & **Antenna Height** & **Durability** \\ \hline \multirow{2}{*}{[5]} & Air foam & \multirow{2}{*}{21\%} & \multirow{2}{*}{0.085\(\lambda\)} & Poor \\  & (\(e_{r}=1.07\)) & & & \\ \multirow{2}{*}{[13]} & Air foam & \multirow{2}{*}{20\%} & \multirow{2}{*}{0.085\(\lambda\)} & Poor \\  & (\(e_{r}=1.05\)) & & & \\ \multirow{2}{*}{[11]} & Air foam & \multirow{2}{*}{41\%} & \multirow{2}{*}{0.094\(\lambda\)} & Poor \\  & (\(e_{r}\approx 1\)) & & & \\ \multirow{2}{*}{Proposed} & Quasi-air & \multirow{2}{*}{21\%} & \multirow{2}{*}{0.13\(\lambda\)} & Good \\  & (\(e_{r}=1.8\)) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with other stacked coupled-fed antennas. Figure 6: Fundamental bandwidth limit of the patch antenna according to the antenna efficiency.
# Numerical Simulation of Fluid-Structure Coupling for a Multi-Blade Vertical-Axis Wind Turbine
## 5 Conclusions  
The object studied in this paper is the vertical-axis wind turbine. Considering the different number of wind turbine blades, combined with the aerodynamic theory, the aerodynamic performance of wind turbines is analyzed by computational fluid dynamics (CFD) technology. The FSI method is used to study the pressure exerted on the blade surfaces of three-, four-, five-, and six-blade vertical-axis wind turbines under flow conditions. The results show that:  
1. For four kinds of wind turbines with different blade numbers, the maximum pressure in the overall structure all appears in the variable angle positions of the blades in the windward region under the same wind speed. This is primarily due to the rapid airflow changes, complex turbulence, and significant influence of the wind field on the blades at these positions, making them prone to bending or damage;
2. The equivalent pressure on the surface of four-blade and six-blade wind turbines is significantly higher than that of five-blade and three-blade wind turbines under the same external wind field environment. The equivalent pressure distribution curves at different measurement points on the three blades exhibit a gentle trend. The four-blade distribution curve shows minor fluctuations in localized areas but has an overall gentle trend. The pressure distribution curve of the five blades decreases slowly at first and then rises slowly with the increase of the measurement point value. The pressure distribution curve of the six blades has a drastic change, rapidly decreasing as the measurement points increase, reaching the lowest point, and then quickly rising;
3. The maximum deformation of the blades primarily occurs at the blade tips and edges. Additionally, among the four-wind turbine configurations with different blade numbers, the six-blade configuration has the highest maximum deformation, while the four-blade configuration has the lowest maximum deformation.  
The numerical results obtained in the present study could provide theoretical guidance for designing and optimizing vertical-axis wind turbines.  
All authors contributed to this work by collaboration. Conceptualization, M.Z.; data curation, X.Z.; formal analysis, X.Z.; funding acquisition, M.Z.; investigation, X.Z.; methodology, M.Z.; writing--original draft preparation, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the projects of the Science and Technology Development Fund (2013 GY2-03) and NSF (2013 JK1018) of the Educational Department of Shaanxi Province.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors are grateful for the financial support provided by research funds from the Educational Department of Shaanxi Province.  
The authors declare no conflict of interest.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 4 Evaluation Framework
### Metrics
#### 4.2.2 Metrics Related to Efficiency  
To evaluate the efficiency of a semantic image segmentation model is vital to use metrics that define the processing time of the models and their computational and memory burden.  
* _Frames per second:_ A standard metric for evaluating the time needed for a deep-learning model to process a series of image frames of a video is \"Frames per second\". Especially in real-time semantic segmentation applications, such as autonomous driving, it is crucial to know the exact number of frames a model can process below the time of a second. It is a very popular metric, and it can be really helpful for comparing different segmentation methods and architectures.
* _Inference time:_ is another standard metric for evaluating the speed of semantic segmentation. It is the inverse of FPS (Frame Rate), and it measures the execution time for a frame.
* _Memory usage:_ it is also a significant parameter to be taken into consideration when comparing deep-learning models in terms of speed and efficiency. Memory usage can be measured in different ways. Some researchers use the number of parameters of the network. Another way is to define the memory size to represent the network and lastly, a metric used frequently is to measure the number of floating-point operations (FLOPs) required for the execution.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantic-Guided Color Histogram (SCH) Loss  
Color histograms are an important tool for describing the color statistics of an image. They provide global features of the color distribution and are commonly used to measure the color similarity between two images. If the color histograms of two images are similar, it indicates that their color distributions are also consistent. In image enhancement tasks, color histograms are often used to constrain the color restoration process. However, traditional color histograms primarily capture global color information and overlook color feature differences between instances, which may lead to local color biases and detail loss. To address this issue, this study proposes the semantic-guided color histogram (SCH) loss. The core idea is to leverage semantic information to guide the calculation of color histograms, allowing the color distribution to be adjusted for each instance, thus preserving local color information more accurately during the enhancement process. The overall structure of the SCH loss is shown in Figure 4.  
Figure 3: Transposed attention mechanism. x in circle means matrix multiplication operation, + in circle means element add operation.
# Estimating Degradation Costs for Non-Cyclic Usage of Lithium-Ion Batteries
## 2 Materials and Methods
### Degradation
#### 2.2.1 Degradation of NMC Cells  
The [PERSON] et al. [19] study is based on UR18650E commercial NMC cells. It needs a function fitted for \(OCV\), and simulation of \(SoC\) and \(V\). It also requires ambient temperature and current profiles.  
The calendar capacity loss depends on voltage, cell temperature, and time. Furthermore, cyclic capacity loss depends on an average voltage, \(DoD\), \(Q\), and the total Ah-throughput, but not on charge/discharge current rate (\(CR\)).  
Equations (12)-(14) formulate, respectively, the remaining cell capacity for NMC technology at a given point in time normalized with \(C_{BOL}\) (\(C^{NMC}\)), calendar capacity loss of NMC technology from the beginning of life normalized with \(C_{BOL}\) (\(C^{NMC}_{loss,cal}\)), and cyclic capacity loss of NMC technology from the beginning of life normalized with \(C_{BOL}\) (\(C^{NMC}_{loss,cyc}\)).  
\[C^{NMC}\ =\ 1-C^{NMC}_{loss,cal}-C^{NMC}_{loss,cyc}, \tag{12}\]  
\[C^{NMC}_{loss,cal}\ =\ \alpha_{cap}\cdot t^{0.75}, \tag{13}\]  
\[C^{NMC}_{loss,cyc}\ =\ \beta_{cap}\cdot\sqrt{Q}, \tag{14}\]  
The expressions for \(\alpha_{cap}\) and \(\beta_{cap}\) depend on \(V\), \(T\), \(DoD\), and quadratic average voltage (\(V_{qa}\)), as shown in Equations (15) and (16).  
\[\alpha_{cap}(T,V)\ =\ (7.543V-23.75)\cdot 10^{6}e^{\frac{-6695}{7}}, \tag{15}\]\[\beta_{cap}\big{(}V_{qa},DoD\big{)}\ =\ 7.348\cdot 10^{-3}.(V_{qa}-3.667)^{2}+7.6 \cdot 10^{-4}+4.081\cdot 10^{-3}.DoD, \tag{16}\]  
For \(V_{qa}\), we apply the value of \(OCV\) at the mean \(SoC\) of the cycle.