esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study FEM Buried Pipe under Haul Road
### Analysis of Results
#### 4.4. Trends--Parametric Analysis  
The objective of this section is to show the trends produced by the changing of the analysed variables and to identify the variables with more impact in the pipe structural behaviour.  
The analysis started from a base case identified as Case 1 in Figure 28. This base case presents the condition of minimum stress on the pipe, which implies a higher backfill height (H = 3 m), a lateral fill with a higher modulus of elasticity (E = 60,000 kPa), a higher bed height (1/6 OD) and a tighter trench width (OD + 0.6 m).  
Figure 27: Stresses on a 36\({}^{\prime\prime}\) STD A53-B pipe under a CAT 979 F truck load for 36\({}^{\prime\prime}\) + 0.6 m trench width; Installed: (**a**) at a height of 1 m; (**b**) at a height of 2 m; (**c**) at a height of 3 m (Stress S in kPa).  
Figure 26: Deflections of 42\({}^{\prime\prime}\) STD A53-B pipe buried at 1 m under a CAT 979 F truck load for 42\({}^{\prime\prime}\) + 0.6 m trench width with (**a**) lateral soil elasticity modulus E1 = 30,000 kPa and (**b**) lateral soil elasticity modulus E2 = 60,000 kPa.  
Figure 28: Increase in pipe stress at crown due to change in variables (**a**) pipe 36\({}^{\prime\prime}\) STD A53 Gr. B; (**b**) pipe 42\({}^{\prime\prime}\) STD A53 Gr. B; (**c**) pipe 48\({}^{\prime\prime}\) STD A53 Gr. B.  
The trend graphics for the three analysed pipes (36\({}^{\prime\prime}\), 42\({}^{\prime\prime}\), 48\({}^{\prime\prime}\)/STD A53-Gr.B) are shown in the Figure 28.  
Figure 28a shows that when the backfill height is reduced from 3 to 2 m, the stresses increase around 19%, whereas when reduced to 1 m, the stresses increase by about 40%. This variable has the greatest influence on the results.  
Likewise, by reducing the elasticity modulus of the lateral fill (EM) from 60,000 kPa to 30,000 kPa, the stresses increase around 13%. This variable has the second greatest influence on the results.  
On the other hand, by decreasing the pipe bedding height (C) from 1/6 OD to 1/12 of OD, the stresses only increase around 1.8%, which means that this variable does not affect significantly the results. As mentioned in Section 4.3, the pipe bedding height is important during the installation process, preventing the pipe from deflecting due to the pipe's own weight.  
When analysing the increase in the trench width (A), it is observed that the impact is almost negligible. If anything, it highlights that the trend for the three pipe diameters is different. For the 36\({}^{\prime\prime}\) pipe, the value of the stresses decreases slightly, while for the 42\({}^{\prime\prime}\) and 48\({}^{\prime\prime}\) it increases.
# Current Context and Research Trends in Linear DC-DC Converters
## 4 Dominant Characteristics of LDOs
### Higher Efficiency  
The efficiency of an LDO is highly dependent on the dropout voltage. There are ultra-low-dropout regulators which achieve a high level of efficiency. Table 2 shows some commercially available highly efficient ultra-low-dropout regulators. The LDOs used for this comparison are selected based on those specifications suitable for portable device power supplies.
## 3 Network Model
### Network Variables  
In this paper, we consider four different variables that affect the network system.  
1. Rate: This is the number of bits of a packet that are transmitted per second. The rate of transmission of data for \(k\)th SU on the \(i\)th channel shall be denoted as \(R_{k}^{i}\). For ease of notation, we shall drop the subscript and superscript where we refer to the rate of a single arbitrary SU on any channel and instead use \(R\).
2. Power: The power of the \(k\)th SU on the \(i\)th channel shall be denoted by \(P_{k}^{i}\) for \(i=1\ldots I\) and \(k=1\ldots K\). The power of an SU shall be measured in mW. The aim of optimization will be to minimize the total power of the entire network.
3. Delay: This is the amount of time required to transfer all the bits of a packet data of size \(Ls\) through the channel. The transmission delay for the \(k\)th SU on the \(i\)th channel shall be denoted by \(\tau_{k}^{i}\) for \(i=1\ldots I\) and \(k=1\ldots K\). The transmission delay will be measured in Bps.
4. Channel Selection: We assume that at any given instant, an SU is either ON a channel or OFF the channel. Thus, we use the binary variable \(\gamma\) to represent if a particular channel is selected by an SU or not: \[\gamma_{k}^{i}=\left\{\begin{array}{lcl}1&:&\text{if SU $k$ selects channel $i\;\forall\;k=1\ldots K$, $i=1\ldots I$}\\ 0&:&\text{Otherwise}\end{array}\right.\] (1)
## 2 Leakage Model of Shield Joints Based on the Persson Model
### Critical State of Leakage
#### 2.3.1 Critical Percolation Channel  
As the magnification increases, unconnected areas become more and larger. Therefore, on a certain scale, many non-contact regions (micro-pores and short fractures) are connected for the first time to form a hole penetrating the contact interface. The hole is defined as a critical percolation channel, and the scale is called critical magnification (\(\zeta_{c}\)). Leakage is thought to occur only through the critical percolation channel, because the cross-section of the percolation channel and the pressure gradient are the largest at the moment. Critical percolation channels are shown in Figure 5; the gray area indicates the surface that has been contacted, and the green area is the unconnected surface that is observed during the increase of the magnification from \(\zeta_{c}-\Delta\zeta\) to \(\zeta_{c}\). As the magnification approaches the critical observation scale, the non-contact region penetrates the two sides of the contact surface for the first time (circle marks in Figure 5).  
Then the average height of the critical percolation channel \(u_{c}\) can be expressed by the average interfacial separation and the contact area [31].  
\[u_{c}=\overline{u}(\zeta_{c})+\overline{u}^{\prime}(\zeta_{c})\frac{A(\zeta_{ c})}{A^{\prime}(\zeta_{c})} \tag{7}\]  
Figure 5: Gray areas represent the contact region, white areas are the non-contact region, and green areas are the observed new non-contact region during the increase of the magnification from \(\zeta_{c}-\Delta\zeta\) to \(\zeta_{c}\), which leads to the first connection of the sealing interface. Lines with arrows represent critical percolation channels, and its average diameter is \(u_{c}\).  
The relative contact area at this point is given by the percolation theory; thus, the relative contact area \(P(\zeta_{c})=A(\zeta)/A_{0}\approx 1-P_{c}\), where \(P_{c}\) is the so called site percolation threshold. The literature suggests taking \(P_{c}\approx 0.6\) so that \(A(\zeta)/A_{0}\approx 0.4\) will determine the critical magnification \(\zeta=\zeta_{c}\)[33].
# Call Model and Test-Verification Methods for PS-LTE Core Equipment
## 4 Measurement of Wireless Coverage and Quality
### Discussion  
This chapter presents detailed scenarios for voice call quality measurement for coverage and disaster safety communication network services and scenarios for end-to-end testing in the same environment as actual users. In addition, test items and results for mobile base stations used as main communication methods in mountains and maritime areas increase success rate for late-entry construction organizations.
# Integrating Textural and Spectral Features to Classify Silicate-Bearing Rocks Using Landsat 8 Data
## 1 Introduction
Few studies have focused on assessing the ability of different textural vectors and their combinations to aid spectral features in image classification. For many years, Landsat satellites have acquired global moderate-resolution images of the earth's terrestrial and polar regions at visible, near-infrared, and short wave infrared wavelengths that have been widely used in geological remote sensing for mineral exploration and lithological mapping [37; 38; 39; 40; 41; 42; 43; 44]. In recent years, the Landsat 8 platform (launched on 11 February 2013) has provided enhanced lithological identification capabilities due to a new spectral band in the blue and two new spectral bands in the thermal infrared, as well as improved performance in sensor signal-to-noise ratio and in radiometric resolution [45]. A panchromatic band (_band_ 8) with a spatial resolution of 15 m is included in Landsat 7 and Landsat 8 data. By fusing the multiple spectral bands with the panchromatic band, all the images can be resampled to 15 m without geometrical misregistration. Moreover, Landsat 8 data can be freely obtained from the United States Geological Survey (USGS) and Earth Resources Observation and Science (EROS) data center [46]. Hence, in this paper, Landsat 8 Operational Land Imager (OLI) data were used to derive texture images and for classification procedures. Image classification is one of the most important approaches to identify surface objects from remote sensing imagery [26; 47]. Support vector machine (SVM), a popular approach used in classification, is proficient at fusing effective and distinctive image features with spectral features to improve classification accuracy [27; 48]. In this paper, we attempt to integrate multiple spectral and textural features for lithological classification. In these circumstances, SVM is an appropriate choice as the classifier. This paper aims to assess the ability of different textural vectors and combinations of them to aid spectral features in image classification. The study area is located in the northern Qinghai Province of China, on the northern edge of the Qinghai-Tibet Plateau. In this region, silicate-bearing rocks,including gabbro, serpentinite, granite, and quartzose rocks are well exposed. Landsat 8 imagery resampled to 15 m using the panchromatic band was used to derive texture images and for classification procedures. To select optimal combinations of spectral and textural vectors, the Jeffries-Matusita (J-M) distance was calculated for pairwise rocks. A large J-M distance value indicates a higher class of separability. Then, textural and spectral features were integrated using the SVM model to improve lithological classification. The results were evaluated based on the confusion matrix.
## 3 Results
### Accuracy Verification of the LUE Model with Optimal Structure  
We checked the performance of the LUE model with optimal structure (the combination of the ECM-LUE model on cloudy days and the modified GLO-PEM model based on PWSI on sunny days) using data from July-September 2021 and May-June 2022 (Figures 8 and A3). The optimal structure model can predict the magnitudes and seasonal variations of the measured GPP well in the cork oak plantation, with the R\({}^{2}\), IA, and RMSE being 0.6726, 0.8925, 1.0309 g C m\({}^{-2}\) day\({}^{-1}\), respectively, and the fitting curve of the simulated values and measured values was very close to the 1:1 line. However, the performance of the seven original models was worse than the optimal structure model, with the RMSE and IA ranging from 1.2128 to 2.6834 g C m\({}^{-2}\) day\({}^{-1}\) and 0.4786 to 0.8432,  
Figure 7: Comparison of daily gross primary production (GPP) simulated by MOD17\({}_{\rm pwsi}\) (**a,b**), MODTEM\({}_{\rm pwsi}\) (**c,d**), EC-LUE\({}_{\rm pwsi}\) (**e,f**), ECM-LUE\({}_{\rm pwsi}\) (**g.h**), SM-LUE\({}_{\rm pwsi}\) (i,j), GLO-PEM\({}_{\rm pwsi}\) (**k,l**), and Wang\({}_{\rm pwsi}\) (**m,n**) models versus observed GPP on cloudy days. The subscript plant water stress index (PWSI) indicates that PWSI was used to modify the water stress scalar in the LUE model. Solid dots represent cloudy days, and the open circle dots represent sunny days. Data from June-September 2020 and May–June 2021. The red line is the fitting curve.  
respectively, and these models explained 13-57% of the daily GPP variation in the cork oak plantation. Moreover, the performance of all models in validation data was similar to that in evaluation data.
# A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra
## 2 Materials and Methods  
### Experimental Materials  
In the experiment, pesticide solution with gradient concentrations of 100, 10, 1, 10\({}^{-1}\), 10\({}^{-2}\) and 10\({}^{-3}\) mg\(\cdot\)kg\({}^{-1}\) were made by deionized water and commercially available chlorrypyrifos pesticides and acetamiprid pesticide with concentrations of 4800 mg\(\cdot\)kg\({}^{-1}\) and 2000 mg\(\cdot\)kg\({}^{-1}\) respectively. Before the detection, 50 mL of the solution was placed in a small mist spray gun. After washed with deionized water and dried naturally, apple samples were sprayed to simulate the spraying process of field pesticides to prepare experimental samples.  
60 Fuji apples were bought as samples from the local market with different shape and color. 40 apples were chosen as calibration set and the others formed validation set. Experiment samples were prepared with the method of spray simulation as pesticide spraying process in the farmland. Before collecting Raman spectroscopy, 2 \(\upmu\)L acetone was dropped on the surface of apple sample with diameter of 2 mm. Then 4 \(\upmu\)L silver sol treated with centrifuge enrichment was dropped on the acetone drop rapidly with diameter of 3 mm. At last 1 \(\upmu\)L nitric acid solution with concentration of 40 mmol\(\cdot\)L\({}^{-1}\) was dropped on the mixture drop. Adding of acetone solution would extract pesticide in peel and disperse the pesticide molecule in the acetone solution, which made the silver nanoparticles and pesticide molecule fully mixed. Nitric acid shortened the distance between the pesticide molecules and the silver nanoparticles, which made the Raman signal enhanced. Each detection point was successively applied with acetone, silver sol and nitric acid solution. And then Raman spectra signal was collected. In order to get accurate Raman spectral information of apple samples, several detection points were detected from each apple sample. And the detection points were located over the entire surface of the sample.  
After detecting the Raman spectra, the spectra of samples with same concentration were averaged as original spectrum at that concentration. Raman spectra of single pesticide and pesticide mixture with same concentration were detected from the same apple to reduce interference from apple background.
## 4 Results
### Analysis of Simulation Versus Experimental Results
(**a**) Overlay of the experimentally determined coverage area onto the simulated Received Signal Strength Indicator (RSSI) map. (**b**) Color-coded overlay of (red) O-FDTD simulation only, (green) experimental only, and (yellow) overlapping coverage areas (CA). Spatial overlap of 84% between the simulated and experimental coverage areas is identified. (**c**) Histogram of covered distances and (**d**) circular coverage within the experimental (green) and simulated (red) coverage areas. The red, yellow, and green shades relate directly to the regions identified in (**b**). and be used as a quantitative descriptor of the coverage prediction quality, where  
\[\Delta r(\theta)=\int\Bigl{(}C^{\prime}_{sim}(\theta)-C^{\prime}_{exp}(\theta) \Bigr{)}\mathrm{d}r \tag{16}\]  
N = 360\({}^{\circ}\)/d\(\theta\) is the number of angular points. We chose an angular resolution of d\(\theta\) = 0.2\({}^{\circ}\). We found that a radius of 180 m leads to the minimal RMSD of around 41 m for the path-loss circular model. Similarly, we plotted the spatial-resolved differences between prediction and experimental results in Figure 10c. Experimentally, we found an increased signal coverage towards North-West directions, exceeding the path-loss model predictions, while towards the South-East directions, the path-loss model overestimates the signal coverage, not being able to take the presence of a dense building arrangement into account. Figure 10d shows the angle-dependent deviations \(\delta r(\theta)\) for the optimized circle and O-FDTD simulations. These deviations result in a net RMSD of 41 m, while the Google Maps building arrangement-supported O-FDTD simulations achieve a net RMSD value of 24 m, thereby showing the strength of the proposed model. Figure 10: Correlation between the edges of the simulation and experimental coverage areas. (**a**) Color-coded overlay of O-FDTD simulation (red), experimental (green) coverage differences. (**b**) Minimization of the Root Mean Squared Differences (RMSD) between the experimental coverage area and a path-loss circular model. (**c**) Color-coded overlay of optimized circular model (blue), experimental (green) coverage differences. (**d**) Deviations of the O-FDTD (red) and optimized circle (blue) models from the experimentally determined coverage in dependence of the detection angle, with a 0.2 o resolution. Effective RMSD of 24 and 41 m are determined, respectively.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Bus Capacitance and Voltage Perturbation
Replacing the previous values into \(\max(i_{k})\), and adding the ripple \(\delta i_{m}\) given in (17) to calculate the peak value of \(i_{m}\), it is possible to obtain (45):  
\[\max(i_{k})=\max\left(\frac{i_{m}}{n}\right)=\frac{i_{m}+\delta i_{m}}{n} \tag{45}\]  
The net change on \(i_{Cdc}=i_{k}-i_{dc}\) that causes the voltage deviation, as observed in Figure 4, is:  
\[\Delta i_{k}=\max(i_{k})-i_{dc(f)} \tag{46}\]Since the leakage current takes \(T_{d}\) seconds to reach the \(i_{Cdc}=0\)\([A]\) condition, i.e., \(i_{k}=i_{dc(f)}\), the charge \(Q_{Cdc}\) introduced into the bus capacitor \(C_{dc}\) during this perturbation is:  
\[Q_{Cdc}=\int_{0}^{T_{d}}i_{k}-i_{dc(f)}\ dt=\frac{1}{2}\cdot T_{d}\cdot\Delta i _{k} \tag{47}\]  
\(T_{d}\) is calculated from the condition \(i_{k}=i_{dc(f)}\), considering the relation \(i_{k}=\frac{i_{m}}{n}\) for \(u=0\) given in (8), and using the derivative of \(i_{m}\) for \(u=0\) given in (3) to calculate the derivative of \(i_{k}\):  
\[T_{d}=\frac{\max(i_{k})-i_{dc(f)}}{\frac{v_{dc}}{n^{2}\cdot L_{q}}} \tag{48}\]  
Now, taking into account that the charge (47) introduced in \(C_{dc}\) produces the voltage deviation \(\Delta v_{dc}=\frac{Q_{Cdc}}{C_{dc}}\), and using the previous expressions for \(T_{d}\) given in (48), \(Q_{Cdc}\) given in (47), \(\Delta i_{k}\) given in (46), \(\max(i_{k})\) given in (45), and \(\delta i_{m}\) given in (17), the voltage deviation caused by a step-like DC current perturbation is:  
\[\Delta v_{dc}=\frac{n^{2}\cdot L_{q}}{2\cdot v_{dc}\cdot C_{dc}}\cdot\left[ \frac{i_{dc(0)}}{1-d}+\frac{v_{b}}{2\cdot F_{sw}\cdot n\cdot L_{m}}-i_{dc(f)} \right]^{2} \tag{49}\]  
Finally, the capacitance \(C_{dc}\) of the DC bus must be calculated from (49) to ensure a maximum \(\Delta v_{dc}\) value that guarantees a safe operation of the devices connected to the bus. Since the bus capacitor collects the output capacitances of the sources and the input capacitances of the loads connected to the bus, \(C_{dc}\) could be increased to the desired value by adding an external capacitor. However, if the required \(C_{dc}\) is lower than the collected capacitance of sources and loads, the voltage deviation will be lower than the maximum safe value and a correct operation is achieved. Figure 4: Example of a bus voltage deviation due to fast current perturbations.
# An Efficient Dropout for Robust Deep Neural Networks
## 1 Introduction  
In recent years, deep learning has become an indispensable tool across various domains such as computer vision, speech recognition, and natural language processing. Despite its effectiveness, a recurrent difficulty in training deep neural networks (DNNs) is overfitting, when a model performs well on training data but fails to generalize effectively to unseen data. To address this issue, several regularization strategies have been developed to improve model robustness and generalization. Among them, dropout [1] has been a commonly utilized method due to its simplicity and effectiveness, particularly in deep networks.  
Dropout works by randomly deactivating a part of neurons during training, thereby preventing complicated co-adaptations and indirectly training an ensemble of subnetworks [1, 2, 3, 4]. However, standard dropout applies this deactivation equally, without considering each neuron's importance or contribution. This indiscriminate strategy may unduly block important neurons and generate instability during training, especially in deeper or more intricate models [5, 6].  
To confront these restrictions, numerous adaptive dropout variations have been proposed, where dropout probabilities are adjusted based on weight magnitudes, neuron activations, or learned priors [5; 7]. While these approaches increase the method's selectivity, they often come at the cost of increased computational complexity or require architectural redesign and tuning.  
This paper introduces a novel regularization method that combines adaptive sigmoidal dropout, neuron recovery, and weight amplification. Unlike standard dropout algorithms, the proposed technique alters dropout behavior dynamically based on neuron activity, weight distributions, and historical inputs. A new neuron recovery mechanism is integrated to restore important neuron activations that would otherwise be blocked. Additionally, weight amplification selectively elevates the most influential weights during training, accelerating learning and enhancing feature extraction, particularly successful in high-complexity datasets.  
Importantly, a fundamental practical advantage of the proposed method comes in its implementation flexibility: it may be easily applied to dynamically constructed, pre-trained, or saved models without requiring any architectural changes. This makes it perfect for modern deep learning processes where model structures may vary or be reused across several jobs.  
This flexibility, combined with its adaptivity and recovery properties, puts the proposed method as a feasible and robust alternative for regularizing deep networks in real-world applications.  
The remainder of this article is organized as follows. Section 2 evaluates related dropout strategies and discusses their strengths and drawbacks in comparison to the proposed method. Section 3 introduces the details of the suggested method. Section 4 discusses the experimental setup, displays the assessment results, and examines the performance across multiple network setups and datasets. Finally, Section 5 concludes the paper.