esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Procedure for Improving Sustainability of Transformation Houses
### Reduction of Copper Losses
#### 3.1.1 Reduction of Copper Losses Caused by Reactive Currents  
Losses caused by fundamental frequency reactive currents (\(P_{cr+}\)) are usually the main copper losses of transformers in distribution networks and on which the managers of these electricity networks have the greatest savings. These losses can be calculated from the fundamental-frequency positive-sequence reactive powers \(Q_{+}\) and line-to-neutral voltages, as follows:  
\[P_{cr+}=\frac{1}{3}\,r_{cc}\cdot\left(\frac{Q_{+}}{V_{+}}\right)^{2} \tag{5}\]  
measured in the secondary of the transformer, and the short-circuit resistance of the transformers, and is obtained by:  
\[r_{cc}=P_{cN}\cdot\left(\frac{V_{20}}{S_{N}}\right)^{2} \tag{6}\]  
where \(V_{20}\) is the RMS value of the secondary line-to-line no-load voltages, \(P_{cN}\) is the rated copper power losses, determined according to IEC 60076-1 [37], and \(S_{N}\) is the transformer rated power.  
Losses due to the reactive currents can be greatly reduced by using relatively inexpensive devices such as capacitor banks. In highly distorted electrical networks, capacitor banks can be replaced by harmonic filters, both active and passive, which are capable of jointly reducing load losses caused by reactive and distortion currents.  
However, the use of non-variable compensation devices, both capacitor banks and passive filters, can be the cause of overcompensation, which usually increases the value of these losses in distribution transformers even above the existing values without reactive compensation devices. This case will be dealt with in more detail in the application case, in Section 4.
# Recent Developments of Solar Cells from PbS Colloidal Quantum Dots
###### Abstract  
PbS (lead sulfide) colloidal quantum dots consist of crystallites with diameters in the nanometer range with organic molecules on their surfaces, partly with additional metal complexes as ligands. These surface molecules are responsible for solubility and prevent aggregation, but the interface between semiconductor quantum dots and ligands also influences the electronic structure. PbS quantum dots are especially interesting for optoelectronic applications and spectroscopic techniques, including photoluminescence, photodiodes and solar cells. Here we concentrate on the latter, giving an overview of the optical properties of solar cells prepared with PbS colloidal quantum dots, produced by different methods and combined with diverse other materials, to reach high efficiencies and fill factors.  
colloidal PbS quantum dots; quantum dot solar cells; semiconductor; heterojunction; ligand; open-circuit voltage; short-circuit current; power conversion efficiency; fill factor +
Footnote †: journal: Applied Physics Letters
## 4 Traffic Filtering-Based Techniques
### Enhanced Port Locking (EPL)
#### 4.4.3 Advantages and Disadvantages  
As an enhanced version of the PL approach, EPL improves traffic performance in HSR networks compared with the PL approach. The EPL approach prunes unicast traffic not only for unused DANH rings, but also for unused QuadBox rings. Therefore, it provides better unicast traffic reduction than the PL approach. Additionally, like the PL approach, the EPL approach does not use control messages for pruning unicast traffic; thus there is no additional control overhead in the networks.  
However, the EPL approach has some disadvantages. First, the EPL approach does not remove duplicated and circulated unicast frames from active QuadBox rings. Additionally, it is difficult to determine optimized value for the parameter \(\psi\) that depends on the topology and size of HSR networks. Finally, since QuadBoxes learn MAC addresses of destination nodes that do not belong to their corresponding rings, the size of PL and EPL locking tables is large and directly proportional to the size of the network, resulting in high memory requirements.
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 3 Temperature Dependence in Data from the Saint Petersburg Observatory  
This section presents the most characteristic examples of the correlation between the G record and variometer sensor temperature, hereafter referred to as temperature dependencies, which one may observe in unadjusted 1-min data from the SPG observatory for various months of 2021. Figure 1 shows the observed variometer baseline values and the sensor temperature values at the time of the absolute measurements. Note that sharp temperature fluctuations in January-May and November-December 2021 led to jumps in the baseline values.
###### Abstract  
This study aims to clarify the relationship between surface hardness and the peak interfacial frictional coefficient under different particle sizes. The relationship between particle size and the peak interfacial frictional coefficient with different surface hardness values has not been addressed in previous literature. A pile-soil interfacial shear test was conducted using a multifunctional interfacial shear instrument was developed in the laboratory. The influences of surface hardness and particle size on the shear characteristics of pile-soil interfaces and the peak interface friction coefficient were studied. The test results revealed that the interfacial shear stress-shear displacement curves showed typical softening behavior when the surface of the model pile was smooth. The difference in curve characteristics for different particle sizes gradually cleared as the surface hardness decreased. The initial shear stiffness of the interface increased with increasing surface hardness and decreased with increasing particle size. The peak interfacial friction coefficient decreased with increasing surface hardness for different particle sizes. Furthermore, the peak interface friction coefficient increased with increasing particle size when the surface was hard. However, when the surface was soft, the peak interfacial friction coefficient decreased with increasing particle size. The linear relationship between the peak interfacial friction coefficient and particle size for different surface hardness values was established.  
surface hardness; particle size; shear characteristics of pile-soil interface; peak interfacial friction coefficient; pile-soil interfacial shear test +
Footnote †: journal: Applied Sciences
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 2 Problem Formulation
### Multipath Effect Analysis for BOC Signals
Specifically, the synthesized correlation function for subcarrier discrimination is calculated as:  
\[R_{sc}=R_{u}+R_{l}^{*}=A\bigg{(}R(\Delta\tau_{d})\cos(\Delta\theta_{d})+\sum_{m =1}^{M}\alpha_{m}R(\Delta\tau_{d}-\tau_{m})\cos(\Delta\theta_{d}+\theta_{m}) \exp(j\phi_{m})\bigg{)}\exp(j\Delta\phi_{d}) \tag{10}\]  
Equation (10) demonstrates that multipath signals lead to a phase rotation of the complex correlation function for subcarrier discrimination. The phase rotation of \(R_{sc}\) is the subcarrier multipath error. When there are many multipath signals, it is difficult to derive an analytical formula for the subcarrier multipath error. Assuming that there is only one multipath signal, the subcarrier multipath error of the DBT method can be calculated as:  
\[\Delta\phi_{ME}=\arctan\bigg{(}\frac{\alpha_{m}R(\Delta\tau_{d}-\tau_{m})\cos (\Delta\theta_{d}+\theta_{m})\sin(\phi_{m})}{R(\Delta\tau_{d})\cos(\Delta \theta_{d})+\alpha_{m}R(\Delta\tau_{d}-\tau_{m})\cos(\Delta\theta_{d}+\theta_{ m})\cos(\phi_{m})}\bigg{)} \tag{11}\]  
The synthesized correlation function for carrier discrimination is calculated as:  
\[R_{c}=R_{u}+R_{l}=A\bigg{(}R(\Delta\tau_{d})\cos(\Delta\phi_{d})+\sum_{m=1}^{M }\alpha_{m}R(\Delta\tau_{d}-\tau_{m})\cos(\Delta\phi_{d}+\phi_{m})\exp(j\theta _{m})\bigg{)}\exp(j\Delta\theta_{d}) \tag{12}\]  
Equation (12) demonstrates that multipath signals lead to a phase rotation of the complex correlation function for carrier discrimination. The phase rotation of \(R_{c}\) is the  
Figure 2: Normalized correlation functions for each single sideband of the BOC(15, 2.5) signal, where E, P, and L denote early, prompt, and late correlators, respectively: **(a)** in-phase correlation functions; **(b)** quadrature-phase correlation functions. carrier multipath error. Assuming that there is only one multipath signal, the carrier multipath error of the DBT method can be calculated as:  
\[\Delta\theta_{ME}=\arctan\left(\frac{\alpha_{m}R(\Delta\tau_{d}-\tau_{m})\cos( \Delta\phi_{d}+\phi_{m})\sin(\theta_{m})}{R(\Delta\tau_{d})\cos(\Delta\phi_{d} )+\alpha_{m}R(\Delta\tau_{d}-\tau_{m})\cos(\Delta\phi_{d}+\phi_{m})\cos(\theta _{m})}\right) \tag{13}\]  
It is worth noting that the estimated carrier phase \(\Delta\theta_{d}\) and subcarrier phase \(\Delta\phi_{d}\) in Equations (11) and (13) contain multipath errors, indicating that the carrier and subcarrier multipath errors affect each other. If multipath mitigation techniques are only used in the SPLL, the subcarrier discrimination output still has deviations due to the indirect effect of carrier multipath errors. Similarly, if multipath mitigation techniques are only used in the PLL, the carrier discrimination output still has deviations due to the indirect effect of subcarrier multipath errors. Therefore, it is required that the multipath mitigation method for BOC signals based on the DBT loop can mitigate both the carrier and subcarrier multipath errors.
## 4 Experiments and Results  
### The Overall Process of Extracting Shade Houses  
Figure 10 illustrates the main technical process for shade house extraction, comprising three key steps: data preparation, algorithm validation, and result prediction.  
1. In the data preparation phase, we manually annotated the downloaded high-resolution remote sensing images through visual interpretation, storing the annotation results as binary grayscale images. Subsequently, the images were cropped, and the dataset was divided as described in Section 3.1.3.
2. In the algorithm validation phase, the mean Intersection over Union (mIoU, %) was used as the evaluation metric to assess the accuracy of each model. The best accuracy weight file for each model was selected through the validation set and then evaluated on the test set. The specific details of the model training settings are described in Section 4.3.
3. In the result prediction phase, we divided the original images into several image patches, input the trained weight files into the ShadeNet, and obtained the predicted masks. The mask images of all image patches were then stitched together using geographic coordinates, ultimately producing the shade house extraction result for the designated area of the original image.
# Energy Harvesting over Rician Fading Channel:
## 3 System Performance
### Asymptotic Analysis
#### 3.4.1 Outage Probability  
As the \(P/N_{0}\) approaches infinity, it is obvious to see that the SNR in Equation (9) is asymptotically equal to  
\[\gamma_{1}^{\infty}=\frac{X_{2}}{aX_{2}+aX_{1}}=\frac{X_{2}}{a(X_{1}+X_{2})}, \tag{20}\]and, similarly,  
\[\gamma_{2}^{\infty}=\frac{X_{1}}{a(X_{1}+X_{2})}, \tag{21}\]  
where \(a=\kappa^{2}+\kappa_{r}^{2}(1+\kappa^{2})\).  
By using the similar approach as in the exact analysis of outage probability, we can get the results as stated in the following theorem.  
**Theorem 3** (Outage probability--Asymptotic form).: _The asymptotic outage probability and achievable throughput for node \(S_{1}\) of the proposed half-duplex bidirectional wireless sensor network using time-switching energy harvesting strategy over a Rician fading channel can be provided as_  
\[P_{out\_1}^{\infty}=1-\zeta_{1}e^{-2K}\sum_{l=0}^{\infty}\sum_{k=0}^{\infty} \sum_{m=0}^{l}\frac{K^{l+k}\xi_{1}^{k}\xi_{2}^{m}}{l!m!(k!)^{2}}\left(a\tilde{ \gamma}_{th}\right)^{m}\frac{\Gamma(m+k+1)}{(\zeta_{2}a\tilde{\gamma}_{th}+ \zeta_{1})^{m+k+1}} \tag{22}\]  
_and_  
\[\tau_{1}\triangleq(1-P_{out\_1}^{\infty})\frac{R}{2}=\frac{\zeta_{1}Re^{-2K}} {2}\sum_{l=0}^{\infty}\sum_{k=0}^{\infty}\sum_{m=0}^{l}\frac{K^{l+k}\xi_{1}^{ k}\xi_{2}^{m}}{l!m!(k!)^{2}}\left(a\tilde{\gamma}_{th}\right)^{m}\frac{ \Gamma(m+k+1)}{(\zeta_{2}a\tilde{\gamma}_{th}+\zeta_{1})^{m+k+1}}, \tag{23}\]  
_where \(a=\kappa^{2}+\kappa_{r}^{2}(1+\kappa^{2}),b=\frac{(1+\kappa_{r}^{2})}{P/\sigma _{r}^{2}},c=\frac{(1+\kappa^{2})}{\
u P/\sigma_{r}^{2}},\zeta_{i}=\frac{K+1}{ \lambda_{i}},\tilde{\gamma}_{th}=\frac{\gamma_{th}}{1-\sigma_{th}}\), and \(\Gamma(\cdot)\) is the complete Gamma function, which is defined by \(\Gamma(z)\triangleq\int_{0}^{\infty}x^{z-1}e^{-x}dx\)._  
Proof of Theorem 3.: See Appendix C.
## 3 Results
### Environmental Effects on ET, GPP, and WUE  
Net radiation (Rn) significantly affected GPP and ET across stand age with \(R^{2}\) = 0.74 and \(R^{2}\) = 0.82, respectively (Figure 7). A high cluster Rn (150 Wm\({}^{-2}\) to 250 Wm\({}^{-2}\)) affected ET more (\(R^{2}\) = 0.43) than the low (\(R^{2}\) = 0.31) or medium (\(R^{2}\) = 0.19) cluster Rn. However, Rn did not significantly affect WUE (\(p\) > 0.05). The overall effect of air temperature on GPP and ET over time was strong (\(R^{2}\) = 0.71 and \(R^{2}\) = 0.72, respectively). However, GPP and ET were more sensitive to low temperatures, ranging from 0 to 12 \({}^{\circ}\)C, than the higher air temperatures up to 28 \({}^{\circ}\)C. Meanwhile, WUE was not affected by air temperature at any time (Figure 7). Overall, the groundwater depth did not significantly affect GPP, ET, or WUE (Figure 7).  
Figure 6: Panel **(a)** is the relationship between evapotranspiration (ET) and gross primary productivity (GPP) across all years in YP and MP with season as a factor. Panel **(b)** is the ET-GPP relationships across the entire period with site as a factor. Each circle indicates the monthly ET and GPP colors according to month (**panel a**) and site (**panel b**). The color line indicates the regression line.  
Figure 7: Relationship of clustered air temperature and net radiation (low, medium, high) and water table depth (shallow, medium, deep) on (**a–c**) evapotranspiration (ET), (**d–f**) gross primary productivity (GPP), and (**g–i**) water use efficiency (WUE) across all years at YP and MP combined. Each circle indicates the monthly ET, GPP, and WUE colors according to the cluster for each variable. The color line indicates the regression line for each cluster variable.
# Path Planning for Yarn Changing Robots Based on NRBO and Dynamic Obstacle Avoidance Strategy
## 3 NRBO-DWA Fusion Algorithm
### NRBO-DWA Fusion Algorithm  
#### 3.3.1 Local Path Planning with DWA Algorithm  
The Dynamic Window Approach (DWA) is a widely adopted local path planning method frequently utilized in mobile robotics. This technique relies on velocity sampling, where the robot's linear and angular velocities are sampled at a given moment and combined with its current position and orientation to forecast possible motion paths within a predefined short sampling period \(\Delta t\)[18]. These trajectories are evaluated based on a predefined evaluation function to identify the optimal motion path. Since the sampling period \(\Delta t\) is relatively short, it is reasonably assumed that the robot executes uniform linear motion during this period. A key advantage of the DWA is its simplicity, which enables real-time decision-making, making it highly effective for mobile robot path planning in dynamic environments.  
\[\left\{\begin{array}{l}y(t+\Delta t)=y(t)+\
u(t)\Delta t\cos(\Delta\theta) \\ z(t+\Delta t)=z(t)+\
u(t)\Delta t\sin(\Delta\theta)\\ \theta(t+\Delta t)=\theta(t)+\omega(t)\Delta t\end{array}\right. \tag{35}\]  
The coordinates \(y(t)\) and \(z(t)\) represent the robot's positions along the Y-axis and Z-axis at time \(t\), respectively, while \(\theta(t)\), \(\
u(t)\) and \(\omega(t)\) represent the robot's orientation angle, linear velocity, and angular velocity at time \(t\)[19]. Here, \(\Delta\theta\) denotes the difference in orientation angle.  
\[F(\
u,w)=\alpha h(\
u,w)+\beta g(\
u,w)+\gamma\
u(\
u,w) \tag{36}\]  
The evaluation function of the DWA is outlined as follows: \(h(\
u,w)\) is the orientation evaluation function which assesses the deviation between the robot's movement direction and the target point [20]. \(g(\
u,w)\) is the obstacle distance evaluation function, measuring the minimum distance between the predicted trajectory and obstacles to gauge collision risk. \(v(\
u,w)\) represents the robot's speed evaluation function, reflecting the robot's speed during task execution. The weights \(\alpha\), \(\beta\), and \(\gamma\) correspond to these three evaluation functions and collectively define the robot's overall performance during path planning.
## 3 Results and Discussion
### Subclustering  
Each of the initial eight clusters was further divided into two subclusters. We refer to the first and second subcluster for each cluster by the corresponding cluster number and a letter, \"a\" for the first subcluster and \"b\" for the second subcluster. For example, Subclusters 8a and 8b are the subclusters corresponding to Cluster 8 (colored brown in maps above). For simplicity, we will refer to the model which subclusters each of the original eight clusters into two subclusters as the 16-subcluster model. A CONUS map of the subclusters is given in Figure 5 (see Figure A9 for individual CONUS subcluster maps). The first color for each subcluster, corresponding to all \"a\" subclusters, is the same as the initial cluster color. The second color for each subcluster, corresponding to all \"b\" subclusters, is a lighter shade of each initial cluster color.  
The 8-cluster and 16-subcluster models are overall similar in identifying influential environmental factors underlying the model cluster assignments (see Tables 1 and 2). Rankings in Table 2 were calculated in a similar manner to those in Table 1. Some of the largest observable differences between the 8-cluster and 16-subcluster models are the further distinction of Clusters 3, 7, and 8. In the 16-subcluster model, predominant wetland areas within Cluster 3 are more clearly separated from the rest of the coastal plains. Subclustering also helps differentiate between two densities of urban activity (Subclusters 7a and 7b). In the 8-cluster model, the deserts of the western United States are represented well by  
Figure 5: CONUS subcluster assignments after clustering each cluster from the 8-cluster model into two subclusters.  
Cluster 8. In the 16-subcluster model however, Cluster 8 subclusters distinguish between the cold (Subcluster 8b) and hot (Subcluster 8a) deserts of the western United States.
## 5 Simulation and Results of Cardiovascular Disease Prediction
### Validation of Dense Belief Network for Cardiovascular Disease Prediction
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Models** & **Accuracy** & **F1 Score** & **Recall** & **Precision** & **Execution Time (s)** \\ \hline DBN & 0.857 & 0.859 & 0.845 & 0.875 & 1687 \\ \hline DenseNet & 0.861 & 0.856 & 0.828 & 0.885 & 1468 \\ \hline \multirow{5}{*}{DB-Net} & **0.918**, 0.921, & **0.918**, 0.921, & **0.918**, 0.921, & **0.919**, 0.921, & **2548**, 2604, 2604, \\  & 0.926, 0.928, & 0.926, 0.928, & 0.926, 0.928, & 0.926, 0.929, & 2564, 2532, 2531, \\ \cline{1-1}  & 0.924, 0.921, & 0.924, 0.921, & 0.924, 0.921, & 0.925, 0.922, & 2527, 2524, 2527, \\ \cline{1-1}  & 0.924, 0.923, & 0.924, 0.923, & 0.924, 0.923, & 0.921, 0.922, & 2542, 2522, **1883** \\ \cline{1-1}  & 0.921, 0.923, & 0.921, 0.923, & 0.921, 0.923, & 0.923, 0.924, & \\ \cline{1-1}  & 0.921, **0.916** & 0.921, **0.910** & 0.921, **0.932** & 0.923, **0.893** & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of 10-FCV DB-Net (30 epochs) for cardiovascular disease prediction. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & **Accuracy** & **F1 Score** & **Recall** & **Precision** & **Execution Time (s)** \\ \hline DBN & 0.853 & 0.849 & 0.841 & 0.829 & 1654 \\ \hline DenseNet & 0.864 & 0.861 & 0.865 & 0.881 & 1161 \\ \hline \multirow{5}{*}{DB-Net} & **0.920**, 0.920, & **0.920**, 0.920, & **0.920**, 0.920, & **0.920**, 0.920, & **1586**, 1524, 1584, \\  & 0.926, 0.928, & 0.926, 0.928, & 0.926, 0.928, & 0.921, 0.928, & 1524, 1523, 1524, \\ \cline{1-1}  & 0.921, 0.922, & 0.923, & 0.922, 0.924, & 0.925, 0.921, & 1526, 1527, 1527, \\ \cline{1-1}  & 0.924, 0.923, & 0.921, 0.924, & 0.922, 0.922, & 0.920, 0.922, & 1524, 1523, **1524** \\ \cline{1-1}  & 0.923, 0.924, & 0.923, & 0.925, 0.921, & 0.922, 0.924, & \\ \cline{1-1}  & 0.922, **0.914** & 0.921, **0.909** & 0.921, **0.929** & 0.923, **0.890** & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of 10-FCV DB-Net (20 epochs) for cardiovascular disease prediction. Figure 6: ROC-AUC curve of DB-Net for cardiovascular disease prediction. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Models** & **Accuracy** & **F1 Score** & **Recall** & **Precision** & **Execution Time (s)** \\ \hline DBN & 0.853 & 0.849 & 0.841 & 0.829 & 1654 \\ \hline DenseNet & 0.864 & 0.861 & 0.865 & 0.881 & 1161 \\ \hline \multirow{5}{*}{DB-Net} & **0.920**, 0.920, & **0.920**, 0.920, & **0.920**, 0.920, & **0.920**, 0.920, & **1586**, 1524, 1584, \\  & 0.926, 0.928, & 0.926, 0.928, & 0.926, 0.928, & 0.921, 0.928, & 1524, 1523, 1524, \\ \cline{1-1}  & 0.921, 0.922, & 0.923, & 0.924, 0.922, & 0.925, 0.921, & 1526, 1527, 1527, \\ \cline{1-1}  & 0.924, 0.923, & 0.921, 0.924, & 0.922, 0.922, & 0.920, & 1524, 1523, **1524** \\ \cline{1-1}  & 0.923, 0.924, & 0.923, 0.922, & 0.925, 0.921, & 0.922, 0.924, & \\ \cline{1-1}  & 0.922, **0.914** & 0.921, **0.909** & 0.921, **0.929** & 0.923, **0.890** & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of 10-FCV DB-Net (20 epochs) for cardiovascular disease prediction.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.3.2 Light-Sensitive Hydrogels  
The most common light sources used to trigger photo-sensitive hydrogel response are ultraviolet (UV), visible light and near-infrared light [39]. The presence of a photoreactive moiety, normally a photochromic chromophore, within the hydrogel polymeric network generates a response to the optical signal [40]. There are three mechanisms by which hydrogels can respond to light: (1) photo-sensitive molecules within the hydrogel convert light energy to heat, which increases the temperature of the material. When the hydrogel reaches a phase transition temperature, it becomes responsive; (2) following exposure to light, photosensitive molecules cause ionization inside the hydrogel, resulting in the generation of ions and a light response; (3) the introduction of chromophoric molecules to the polymer can cause changes in their dipole moment and their overall structure, following light exposure [40]. Light-sensitive hydrogels have shown applications for the release of small molecules for tissue engineering and wound healing applications [41], alongside drug delivery to tissues [42].