esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 3 Possible Solutions to Reduce the Electric Field
### The Novelty of the Ampacity Calculation in the New, DLR-Based Expert System  
The novelty of the new DLR-based expert system is twofold; on the one hand, it treats the electric field as a new input parameter, which was not previously discussed by any DLR calculation algorithm, and on the other hand, it further developed the international DLR models. Both the CIGRE (International Council on Large Electric Systems) and IEEE (Institute of Electrical and Electronics Engineers) DLR models are empirical formulas that discuss the external and internal factors influencing the conductor's thermal state separately. None of these models previously calculated the cooling effect of precipitation, which, in addition to the average precipitation intensities in Europe (5-10 mm/h), can be expected to provide 5-8% additional load capacity compared to the basic physical models. The new expert system contains this cooling effect in Equations (6) and (7) to exploit the real ampacity limits of the OHLs [5; 6; 35].  
Another innovation of the existing system is the way the conductor temperature is determined. In standard cases, the line monitoring sensor provides this parameter for the ampacity calculation; however, even if the device fails for any reason, an accurate, real-time conductor temperature is required. For this purpose, the BME (Budapest University of Technology and Economics) research group has developed a new neural network-based temperature calculation subsystem to ensure the conductor temperature parallel to the sensor measurements. This new subsystem's accuracy is within the transmission line sensor's measurement accuracy (\(\pm\)1.0 \({}^{\circ}\)C) based on simulations and international experience. In this way, the sensor temperature availability is ensured from several sides, which further increases the system's reliability [5; 9; 35].
# Investigation of Self-Driven Profiler with Buoyancy Adjusting System towards Ocean Thermal Energy
## 2 Design of the Self-Driven Buoyancy Regulation System  
A new type of fluid control system that relies on OTE for buoyancy control has been developed. Compared with the conventional buoyancy regulation system that is used in profilers or Argo buoys, the new system has great energy-saving advantages, because it uses OTE for buoyancy driving. The principle of the buoyancy regulation system is shown in Figure 1.  
The system is mainly composed of PCMs (e.g., n-hexadecane, whose phase change temperature is 18 \({}^{\circ}\)C), PCM containers, accumulators, pressure sensors, solenoid valves, hydraulic pumps, internal cylinders, displacement sensors for internal cylinder piston, external cylinder, displacement sensor for external cylinder piston. The PCM containers are  
Figure 1: Principle diagram of the buoyancy adjustment system.  
fully exposed to the surrounding water. When PCM melts, the heat transfers from seawater to the container wall, and then from the container wall to the PCM. The heat transfer path is the opposite when solidified. When the temperature of seawater is higher than the phase change temperature of the PCM, the PCM melts and expands, and the oil enters the accumulator to compress the nitrogen bladder and causes the pressure to rise. The solenoid valve 2 opens when the accumulator pressure rises to the set pressure, and the external cylinder discharges the fluid into the internal cylinder under the action of the outer spring, so the volume of the outer oil sac decreases and the profiler starts to sink. When the profiler sinks to the low-temperature zone, the PCMs solidifies and shrinks, and the fluid flows to the PCM containers from the internal cylinder. When the piston of the internal cylinder reaches a certain position, solenoid valve 1 opens and high-pressure oil in the accumulator flows into the external cylinder, and the profiler starts to float. The displacement sensors make the oil discharge and the absorption process can be precisely controlled. In this system, the fluid from the internal cylinder to the inner oil sac is driven by the internal cylinder spring and the oil from the external cylinder to the internal cylinder is driven by the external cylinder spring. The elastic potential energy of these two springs comes from OTE, because the accumulator compresses the outer spring first and then the inner spring. Driven by OTE, this equipment can greatly reduce electrical power consumption. The hydraulic pump can also transfer hydraulic oil from the inner cylinder to the outer cylinder, but it does not work when the profiler is working normally. The hydraulic pump has two functions: (1) Emergency floating: When the profiler fails, the hydraulic pump pumps hydraulic oil from the inner cylinder into the outer cylinder; (2) Depth control: When the profiler needs to be suspended at a fixed depth, the hydraulic pump fine-tunes the oil volume of the outer cylinder to make the profiler achieve neutral buoyancy to achieve fixed-depth suspension.
## 3 Experimental Results
Nevertheless, the slope efficiency in the experiments was almost constant, as shown in Figure 2. This shows that the mode-matching efficiency was also almost constant. This means that while the effect of thermal lenses on the laser and pump modes could be high, the variation in thermal lenses with varying pump power is considered to have a low effect on the variation of the mode-matching efficiency and therefore to have a low effect on the variation of the slope efficiency as well. Namely, in our experiments, while the thermal lens effects on the laser and pump modes might be high, the thermal lens effect on the mode-matching efficiency is considered to be almost constant. One possible reason for this is that by adjusting the pump focusing position, the arm angles and the distances between the gain medium and the curvature mirrors, we were able to align the laser beam focus to the center of the thermal lens in the gain medium. This may allow us to suppress some variation in the thermal lensing effect on beam propagation and made it relatively easy to align the cavity for higher laser efficiency.
# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 1 Introduction
[14] used the visual DenseNet architecture with 34 layers for two classifications (Normal /Abnormal), with lead-II ECG data measured in a hospital. This structure achieved an overall accuracy of 98.89% and an F1 score of 99.09%. Their results showed that a single-lead ECG, rather than the 12-lead ECGs measured in a hospital, was sufficient to distinguish between the normal and abnormal classes. In contrast to the methods mentioned thus far, some researchers have used short-term Fourier and wavelet transforms to convert ECG data into two-dimensional (frequency, time) data and used them as input for a deep neural network. [PERSON] et al. [15] used the transformation \"spectrogram\" from a one-dimensional (1D) ECG signal from the MIT-BIH dataset and the European ST-T dataset to make 2D images. They also used a 161-layer DenseNet, pre-trained on millions of images, to extract abstract information and then applied a SVM for four-class classification (Normal Sinus/Atrial Fibrillation and Flutter/Ventricular Fibrillation/ST Segment Change). Their model's accuracy and F1 score were 97.23 and 97.35%, respectively. [PERSON] et al. [16] used two-dimensional spectrograms through short-time Fourier transform and data augmentation to classify data from the MIT-BIH arrhythmia dataset as: normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. Their model consisted of four convolutional layers and four pooling layers; the accuracy and F1 score were 99.11 and 98.0%, respectively. [PERSON] et al. [17] constructed an ECG-based heartbeat-classification model that consisted of preprocessing (filtering and segmentation), feature extraction (Morlet wavelet transform and short-term Fourier transform),and a densely connected network. Their model's F1 score was 83.4%, using the following classification: Normal Sinus/Atrial Fibrillation/Sinus Tachycardia/Sinus Bradycardia/Ventricular Bigeminy/Ventricular Trigeminy/Ventricular Tachycardia/Paroxysmal Supraventricular Tachycardia(PSVT)/Noise/Ventricular Ectopic Beats(VEB).
# Net Ecosystem Exchange of CO\({}_{2}\) in Deciduous Pine Forest of Lower Western Himalaya, India
## 4 Discussion
### Seasonal Variability of Ecosystem CO\({}_{2}\) Fluxes
The carbon source/sink of the pine forest across the seasons was illustrated in Table 2. The understory was cleared before the experimentation started in November 2010 and overstory pine was in winter dormancy due to which the system acted as a source of carbon (\(384\pm 0.9\) g C m\({}^{-2}\) month\({}^{-1}\)) and persisted until the advent of spring (March). During the growing season (May-July), the pine forest was a sink of carbon (-\(315\pm 5.4\) g C m\({}^{-2}\) month\({}^{-1}\)), but having a larger carbon sink in the post-monsoon season (-\(508\pm 23\) g C m\({}^{-2}\) month\({}^{-1}\)). A notable decline in NEE was observed (\(208\pm 8.6\) g C m\({}^{-2}\) month\({}^{-1}\)) during rainy and overcast days in the monsoon season (e.g., July3d-Aug3d). The year 2010-2011 received about 31% higher precipitation than the long-term average (i.e., 70 years). This decline in NEE was mostly because of the decline in canopy photosynthesis, while soil and foliage respiration remained enhanced and even it was at its peak. These findings were also consistent with [PERSON] et al. [39] that suggested clouds can significantly affect NEE in a temperate pine mixed forest and in a subtropicalevergreen broad-leaved forest in China. [PERSON] et al. [33] while working in this region in a young, mixed-deciduous broad-leaved forest at 300 m of elevation (mean rainfall of 1500 mm), they reported that the nine year old mixed forest plantation (comprising of _Holoptelea integrifolia_, _Dalbergia sissoo_, _Acacia catechu_, and _Albizia procera_) remained as a carbon sink in July-August during 2013. Thus, the number of rainy days and monsoon dry-wet spells may have a marked influence on NEE. In particular, at the seasonal time-scale, NPP exceeded \(R\)s almost during the growing season (April-October), and consequently NEE has exhibited a net carbon uptake. During the winter and spring seasons, NEE was consistently positive (or the carbon source) due to the physiological dormancy stage of pine forest. These results were consistent with the physiological understanding of ecosystems, and with other studies in the region in broadleaf young deciduous vegetation types [33] and forests worldwide [40]. Further, presence of lantana (an invasive understory) during winter 2011 had helped to drop down the magnitude of the carbon source. Nevertheless, at the annual time-scale, the system remained as a carbon sink (\(-99\pm 9.9\) g C m\({}^{-2}\) year\({}^{-1}\)). The magnitudes of the carbon sink were within the range of other studies that reported across the variety of ecosystems worldwide [41]. However, our estimated annual carbon sink was lower than the _Pinus_ forests over Europe and North America that ranged from \(-224\) g C m\({}^{-2}\) year\({}^{-1}\) to \(-277\) g C m\({}^{-2}\) year\({}^{-1}\)[42; 43].
# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 3 Modeling of the System
### Mechanical System  
The mechanical system consists of various mechanical parts, and the most critical component is the moving block as illustrated in Figure 1. It simply applies a continuous force to the piezoelectric blocks by user shaking. This results in energy harvesting by the piezoelectric blocks. As mentioned in the previous section, two piezoelectric blocks are used for energy harvesting. One is located at the bottom of the moving block while the other is placed on the moving block. It is worth noting that it is unnecessary to define the dimension of the piezoelectric blocks as it does not directly affect the performance of the system. Then, the force induced (\(F\)) can be given simply by mass of the moving block (\(m\)) and shaking acceleration (\(a\)) as  
\[F=m\ a. \tag{1}\]  
where it is important to note that the system is independent from the forces of gravity because the moving block becomes motionless while shaking the system. In this case, user's hand balances the force, and hence, only the shaking acceleration holds.  
Figure 2: A simple diagram of the electronic circuit of the system.  
Furthermore, the term \(m\) can also be calculated by using the outer-inner radius of the block (\(r_{1}\), \(r_{2}\)), the height of the block (\(h_{1}\)), as well as the material density of the block (\(\rho\)) as follows  
\[m=\rho\ \Big{(}\pi r_{1}^{2}h_{1}\ -\ \pi\ r_{2}^{2}h_{2}\Big{)} \tag{2}\]  
where, as shown Figure 3, \(h_{2}\) is the height of the lower block which can be expressed as  
\[h_{2}=h_{1}-(r_{1}-r_{2}). \tag{3}\]  
After substituting Equation (3) in Equation (2), the term \(m\) can be calculated by  
\[m=\rho\ \pi\ \Big{[}h_{1}(r_{1}^{2}-r_{2}^{2})+r_{2}^{2}\ (r_{1}\ -r_{2})\big{]}. \tag{4}\]  
The range of acceleration can be estimated experimentally. To this end, a mobile accelerometer application may work well. For this purpose, a water bottle (1.5 kg) and a smartphone (0.18 kg) in combination were shaken up and down while the application recorded the acceleration continuously. The mean value and standard deviation of the acceleration were calculated in order to estimate the approximate range of the acceleration. Base radius of the transparent drinking glass is also important for the UV radiation dosage. It should be slightly smaller than the inner radius of the moving block. Overall, considering the typical size of similar water storage systems (for example, a canteen), Table 1 shows some typical values and the ranges of the mechanical system parameters.  
Figure 3: The dimensions of the lower and upper blocks.
## 5 Context Aware CL Optimization on MAC and NET Layers
### Context Aware DAAM  
The original DAAM is a multi-path reactive routing protocol based on the AODV routing protocol. In DAAM, multiple node-disjoint paths can be discovered by a single route discovery procedure. In addition, DAAM modifies the original AODV routing algorithm by adding the delay information for each available path. The original AODV allows the nodes, after receiving a route reply (RREP) packet, to update their routing table entities according to the following rule:  
\[\begin{array}{l}\textbf{if ((seq\_num^{d}{}_{a}<seq\_num^{d}{}_{b})\textbf{ or ((seq\_num^{d}{}_{a}==seq\_num^{d}{}_{b})\textbf{ and (hop
## 3 Results
### Overview of Selected Studies
The remaining 27181 images were classified as 18510 UM images, 8671 nevus images, and 1192 healthy eye images with a clinical diagnosis. Diagnosis was made using Optos images, autofluorescence, OCT scans, and US. The RETFound DL model, which was pretrained on more than 900.000 fundus images for various conditions, was used. The model was first fine-tuned for binary classification (UM vs. nevi) and then for tertiary classification (UM, nevi, and healthy eyes). The data were split into training (70%), test (20%), and validation (10%) sets. The images were resized to 224 \(\times\) 224 pixels and converted to grayscale. The RETFound model used two stages: self-supervised pre-training and refinement for disease classification. The training process was performed on an NVIDIA GTX 4090 GPU with a batch size of 16 and a learning rate of 0.0005. The effectiveness of the model was evaluated through metrics such as accuracy, sensitivity, specificity, and F1 score. For binary classification, the model achieved an accuracy of 0.83, a specificity of 0.87, a sensitivity of 0.79, a F1 score of 0.84, and an AUCROC (area under the receiver operating characteristic curve) of 0.90, demonstrating a strong differentiation between UM and nevi. In multi classification, the model achieved a mean accuracy of 0.82, a mean sensitivity of 0.73, a mean specificity of 0.85, a mean F1 score of 0.72 and AUCROC of 0.92. The multi-classification model showed a similar success rate for identifying UM and nevi as the binary classification, but the model often misclassified healthy eyes and frequently confused nevi with healthy eyes. The study concludes that RETFound is a promising tool for automated differentiation of UM and nevi, but further validation with external datasets is needed to refine its accuracy, particularly in differentiating small nevi from healthy eyes [32].
## 2 Modeling and Controller Design
### Controller Design of MMC-HVDC System  
Modular multilevel converter is the central unit of the high voltage high power HVDC transmission network. Figure 3 presents an MMC equivalent circuit. Apart from outer loop and inner current loop, it requires submodule balancing control and arm circulating current.
# Using a Cable-Driven Parallel Robot with Applications in 3D Concrete Printing
## 2 Mechanical Structure
### Kinematics Problems  
The kinematics problems of CDPRs describes the relation of the variable joints (length of the cables) and the pose of the moving platform. Figure 2 illustrates the kinematic structure of the eight cables parallel robot, where \(\text{A}_{\text{i}}\) and \(\text{B}_{\text{i}}\) (\(\text{i}=1,\,\ldots,\text{n}\)) are the two attaching points of the ith cable to the moving platform and the base, respectively; \(\mathbf{a}_{\text{i}}\) represents the position vector of Ai in the base or B-frame; and \(\mathbf{b}_{\text{i}}\) represents the position vector of \(\text{B}_{\text{i}}\) in the moving platform or E-frame. In this Figure, variable vectors \(\mathbf{l}_{\text{i}}\) denote the length of the cable; \(\mathbf{r}_{\text{i}}\) corresponds to the position vectors; and \(\mathbf{R}_{\text{i}}\) corresponds to the rotation matrix that denotes the orientation of the mobile platform. The combination of the orientation matrix \(\mathbf{R}\) and the position vector \(\mathbf{r}\) correspond to the E-frame of the moving platform on the base frame. The inverse kinematics of the cable robot can be written as  
\[a_{i}-r-\mathbf{R}b_{i}=l_{i} \tag{1}\]otation matrix R from the E frame to B frame can be obtained as follows:  
\[\mathbf{R}=\mathbf{R}_{z}(\gamma)\mathbf{R}_{y}(\beta)\mathbf{R}_{x}(\alpha)= \left[\begin{array}{ccc}c\gamma c\zeta\beta&c\gamma s\beta s\alpha-s\gamma c \alpha&c\gamma s\beta c\alpha+s\gamma s\alpha\\ s\gamma c\beta&s\gamma s\beta s\alpha+c\gamma c\alpha&s\gamma s\beta c\alpha-c \gamma s\alpha\\ -s\beta&c\beta s\alpha&c\beta c\alpha\end{array}\right] \tag{2}\]  
In Equation (2), \"s\" denotes the sine, while \"c\" denotes the cosine of its indexed argument. Moreover, \(\alpha\), \(\beta\), and \(\gamma\) stand for the angles of rotation of the \(x\), \(y\), and \(z\) axes. Multiplying the two sides of Equation (1) with their transpose leads to the following:  
\[l_{i}^{T}l_{i}=\left(a_{i}-r-\mathbf{R}b_{i}\right)^{T}\left(a_{i}-r-\mathbf{R }b_{i}\right) \tag{3}\]  
So, the inverse kinematic for each cable of the CDPR is  
\[|l_{i}|^{2}=|a_{i}|^{2}+|r|^{2}+|\mathbf{R}b_{i}|^{2}-2a_{i}(r+\mathbf{R}b_{i}) \tag{4}\]
## 2 Phonons and Plasmons in Nanoshells
In Equations (3) and (4), \(\varepsilon_{\omega,i}(i=1,2,3)\) is the optical dielectric constant of the \(i_{\text{th}}\) medium, and \(\omega_{\Omega,i}\) and \(\omega_{\Omega,i}\) (\(i\) = 1, 2) are the frequencies of the \(LO\) and \(TO\) phonons in the \(i\)th medium, respectively. The bulk plasmon frequency in the \(i\)th medium is \(\omega_{Pl,i}=e\sqrt{N_{e}/\left(m_{ej}\varepsilon_{\omega,i}\varepsilon_{0} \right)}\), where \(e\) is the absolute value of the electron charge, \(\varepsilon_{0}\) is the vacuum dielectric permittivity, \(N_{e}\) is the electron concentration, and \(m_{e,i}\) is the electron band mass in the \(i\)th medium. We use, here, the Drude model of plasmons for InAs and GaAs, aiming at a general understanding of phonon-plasmon absorption spectra in semiconductor nanoshells. The multiband k \(\times\) p method would be appropriate to obtain more precise predictions [54]. The dispersion equation for the hybrid interface phonon-plasmon eigenfrequencies is obtained from the secular equation for the set of algebraic equations (1):  
\[b_{11}b_{22}-b_{12}^{2}=0. \tag{5}\]  
The phonon-plasmon energies with quantum number \(l\) = 1 as a function of the electron concentration in the doped InAs are shown in Figure 1b,c for the d-InAs/u-GaAs/Hexane and the u-GaAs/d-InAs/Hexane nanoshells, respectively. Solid lines show the energies of the hybrid phonon-plasmon modes, calculated using Equation (5). Dashed and dash-dotted lines represent the energies of the non-interacting interface phonon and interface plasmon modes. These energies are obtained from Equation (5) by substituting \(\omega_{Pl,i}=0\) into Equation (3) for the interface phonons and using \(\varepsilon_{InAs}(\omega)=\varepsilon_{\infty,InAs}(1-\omega_{Pl,InAs}^{2}/ \omega^{2})\) and \(\varepsilon_{GaAs}(\omega)=\varepsilon_{\infty,GaAs}\) for the interface plasmons. Here,\(\varepsilon_{\infty,InAs}\) and \(\varepsilon_{\infty,GaAs}\) are the optical dielectric constants of InAs and GaAs, respectively. The interface plasmon and phonon modes are weakly coupled at both low electron concentrations (\(N_{e}\) < 10\({}^{18}\) cm\({}^{-3}\)) and at high electron concentrations (\(N_{e}\) > 5 \(\times\) 10\({}^{18}\) cm\({}^{-3}\) for the d-InAs/u-GaAs/Hexane nanoshell and \(N_{e}\) > 15 \(\times\) 10\({}^{18}\) cm\({}^{-3}\) for the u-GaAs/d-InAs/Hexane nanoshell), and the energies of the interface polarization modes approach the limiting values for the non-interacting interface phonon and plasmon modes. The interface phonons and plasmons strongly interact and hybridize at intermediate electron concentrations, where these modes have comparable energies.
## 3 Results and Discussion
The highest final temperature was determined in the G sample at 584 \({}^{\circ}\)C. The maximum degradation temperature is the lowest value in the control sample at 329 \({}^{\circ}\)C and the highest value in the G sample at 347 \({}^{\circ}\)C. From 140 \({}^{\circ}\)C to 476 \({}^{\circ}\)C, and 584 \({}^{\circ}\)C, hemicellulose, the remaining extractives, lignin, and cellulose were decomposed [22; 79]. The residue weight varied depending on the method of impregnation. The rate of RW at 600 \({}^{\circ}\)C in the samples was 16.2% in the control sample (A), between 18.3 and 22.3% in the samples impregnated with the immersion method, and between 19.8 and 24.9% in the samples impregnated with the vacuum method. The TGA study results showed that as the concentration of calcium hydroxide increased, the heat resistance of the fibers gradually increased. Additionally, the amount of residue detected in the vacuum method is slightly higher than in the immersion method. These values are relatively low when compared with the literature results [80; 81; 82; 83; 84]. \(\bullet\) SEM analysis  
Observation under SEM at high magnifications showed the samples of impregnated and unimpregnated black poplar (Figures 4 and 5). SEM analysis of impregnated wood material revealed the presence of impregnation substances concentrated along the wood lumen cell and transition edges. Additionally, nanoparticles were observed to form clusters within certain regions of the trachea [85]. It can be seen that the amount of impregnation filling the lumen cell is related to the change in concentration.