esa-sceva/satcom-chunk-collection huggingface dataset:
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 3 Materials
PEG-based wound dressings provide a moist healing environment, promote cell adhesion, and can serve as carriers for bioactive molecules. Its anti-fouling properties also minimize bacterial adhesion, reducing the risk of infection [71]. Poly-N-vinylpyrrolidone (PVP) is a water-soluble polymer with excellent film-forming and moisture-retention properties, making it ideal for hydrogel-based wound dressings. It is often used in combination with other polymers to enhance mechanical strength and drug delivery capabilities. PVP-based dressings can also incorporate antimicrobial agents to improve wound care outcomes [72]. Polyurethane (PU) is a versatile polymer that provides excellent mechanical properties, breathability, and elasticity. It is commonly used in advanced wound dressings, including foam and film dressings, to provide protection and maintain an optimal healing environment. PU dressings can be engineered to have antibacterial properties and controlled permeability to regulate moisture levels [73]. Polyvinyl alcohol (PVA) is a biodegradable and hydrophilic polymer known for its high water retention and film-forming capabilities. PVA-based wound dressings can be designed as hydrogels or nanofibrous mats to facilitate tissue regeneration and drug delivery. Its compatibility with bioactive molecules makes it a promising candidate for multifunctional wound care solutions [74]. Figure 2: Representation of the chemical structures of some natural polymers commonly used in wound dressings. Poly-\(\epsilon\)-caprolactone (PCL) is a biodegradable polyester with excellent mechanical strength and long-term stability. It is frequently used in wound dressings for its ability to provide structural support and sustained drug release. PCL-based scaffolds are also utilized in tissue engineering applications to promote cellular proliferation and extracellular matrix deposition [70]. Figure 3 represents the chemical structure of some synthetic polymers used in wound dressings.
## 5 Connectivity Manager
### Vertical Handover Mechanism  
Vertical handover (VHO) means switching from one wireless radio technology to another, e.g., from the WiFi network to the LTE network or from a satellite connection to the mobile operator network. It is an essential part of the connectivity management, enabling connection to continue even when the vessel moves out from the coverage area of certain network or, e.g., when a certain frequency is intentionally jammed. The VHO procedure should be performed so that the required QoS level is maintained before the handover, during the handover, and after the handover. We will shortly describe how to perform VHO in the described integrated connectivity architecture. The proposed high-level logic is presented in Figure 5.  
First, a connection request including application and service requirements is made and the connectivity manager decides what technology and route are used to fulfill the requirements of the requesting service. The system is able to measure QoS parameters such as delay, round-trip time, and jitter over different routes to select the best technology to be used. Measurements are conducted over available routes between the ship and the remote operations center.  
There are two main options to conduct QoS measurements [47]. Active QoS measurements are performed by injecting traffic into the network, while observing the network behavior and performance. Contrastingly, passive measurements consist of monitoring traffic at one or more points in the network to acquire information of the traffic flow behavior while not affecting the traffic itself. The QoS  
Figure 4: Ship-to-ship maximum link distances according to antenna heights.  
parameters over an end-to-end link can be measured with a separate software that allows real-time measurements [47]. In our system, we assume that passive measurements are made with the software and that information is fed to the connectivity manager. The selection of the suitable interface is then made using measured QoS values, available bandwidth, and, e.g., power consumption of the interfaces if several possibilities exist. Further, economic considerations such as cost per megabyte can be included in the selection logic.  
There are many possible VHO strategies that can be used and actual signaling between entities of the network remains as future work. However, interested readers may refer to the thorough survey paper on the topic in [48]. The framework and a prototype implementation for improving the handover performance of Mobile IP based on a variety of cross-layer and cross-domain triggers that may use QoS measurements are presented in [49]. In addition, an example of how to use media-independent handover (MIH) in an integrated satellite-terrestrial network and related signaling is described in [50].  
When the optimized technology selection is made, the operation can continue using the selected interface as long as it is good enough to fulfill QoS requirements and there is data to transmit. It is also possible to use several interfaces in parallel and send data with different QoS requirements over different networks.
## 3 Simulation
### Impingement oil Film Spread Modes  
Assuming that the thickness of the oil film is much smaller than the curvature radius of the wall and the characteristic length of the wall surface, the flow of the oil film is in a laminar state and its velocity direction is tangent to the wall surface. The size of the film changes linearly along the wall surfaces normal within the oil film. The velocity of air above the oil film is much higher than that of the oil film.  
(1) Mass conservation equation. The main feature of the droplet impact wall process is the energy conversion based on the conservation of mass.  
Droplets impinge on different walls, heat conduction and energy dissipation are different, after touching the wall of the droplet size distribution, normal and tangential velocity components are also different.  
\[\frac{\partial\rho h}{\partial t}+A[\rho(\vec{u_{1}}-v_{w})h]=M\]  
where \(\rho\) is the liquid density, \(h\) is the oil film thickness, \(A\) is the surface gradient operator, \(\vec{u_{1}}\) is the average (absolute) velocity of the oil film, \(v_{w}\) is the velocity on the wall, and \(M\) is the mass source term per unit wall area.  
(2) Momentum conservation equation. For the calculation of the oil film speed \(\vec{u_{1}}\) based on the above assumptions, the following simplified momentum equation can be used to solve.  
\[\rho_{1}h\Big{\{}\frac{\partial\vec{u_{1}}}{\partial t}+[(\vec{u _{1}}-v_{w})A]\vec{u_{1}}\Big{\}}+hAp\] \[=\tau_{w}t-\mu_{1}(\vec{T_{1}})\frac{\vec{u_{1}}-v_{w}}{2}+P_{ imp}-\big{(}P_{imp}\cdot\vec{n}\big{)}n+M_{imp}[(v_{w}\cdot n)n-\vec{u_{1}}]\] \[+\delta p_{1}n+\rho_{1}ng\]  
where \(p_{1}\) is the average pressure in the oil film; \(\tau_{w}\) is the shear stress of the oil film surface; n is the unit normal vector, is the unit tangent vector in the same direction as \((\vec{u_{1}}-v_{w})\); \(\mu_{1}\) is the dynamic viscosity of the liquid, is the average oil film temperature; \(P_{imp}\) is the impingement momentum source term and \(M_{imp}\) is the Wall quality source term.
## 5 Discussion of the Noise Control Strategies
Additionally, the application of sound-absorbing materials such as acoustic panels and soundproof mats in tunnel and bridge areas can effectively reduce echoes and the reflection of environmental noise. To make a comprehensive noise control strategy, it is necessary to integrate advanced technological solutions with regular maintenance and operational optimization to address noise issues in the metro driver's cabin effectively. Implementing state-of-the-art monitoring systems that utilize real-time data analytics can enhance the effectiveness of noise mitigation measures by adapting strategies based on current conditions. For example, employing deep learning algorithms to predict high-noise events can enable pre-emptive actions, such as adjusting train speeds or modifying track conditions, thereby reducing noise at its source. Regular maintenance and inspections are essential to ensure that all components function optimally, preventing noise escalation due to wear and tear. By focusing on technological advancements and continuous maintenance efforts, these measures  
Figure 10: Comparison of rail grinding effects. (**a**) Before grinding; (**b**) after grinding. Figure 9: Rail grinding operations on Lanzhou Urban Rail Transit Line 1.  
not only improve the acoustic environment for drivers and passengers but also significantly protect the drivers' health and enhance safety within urban rail systems.
## 7 Conclusions  
In this paper, we propose a multisine PSK modulation and pre-equalization strategies to optimize the PCE for a SWIPT system with a realistic rectifier model. Compared to the state-of-the-art SWIPT systems, focusing on both amplitude and phase modulation, we show that our strategy improves PCE by more than a factor of two, at the cost of a lower spectral efficiency.  
This conclusion is based on a broad comparison of multiple pre-equalization strategies, verified using measurements. The main insights derived from the study is that PAPR is very important to optimize PCE, exploiting the rectifier non-linearity. The proposed transmission strategy, including a pre-equalization and modulation technique that boosts the PAPR, is hence able to achieve the maximum PCE. On the other hand, when the PAPR optimization is not considered, e.g., because of amplitude modulation or when using a simplified rectifier model, our analysis shows that boosting the sub-carrier with the best channel is optimal in terms of the power and capacity trade-off.  
We also show that the use of multiple tones can significantly improve PCE, while the impact on capacity is rather small and only when doing optimal water-filling. When using multisine PSK modulation, capacity is not improved when using multiple tones.  
We propose a practical balanced pre-equalizer that can balance the benefits of PAPR and received power optimization for multisine PSK modulation. Based on an empirical weighing factor \(p=0.1\), we can achieve the largest power-rate trade-off region for multisine PSK and for the rectifier we considered. A comparison for other rectifier models, or an analysis on how to determine the best balancing factor remains for future work.  
Conceptualization, N.P. and S.P.; methodology, N.P., M.R., S.C., D.S. and S.P.; software, N.P.; validation, N.P., M.R. and S.C.; formal analysis, N.P.; investigation, N.P., M.R. and S.C.; data curation, N.P.; writing-original draft preparation, N.P.; writing-review and editing, S.C., D.S. and S.P.; visualization, N.P.; supervision, D.S and S.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FWO-Flanders (Fonds voor Wetenschappelijk Onderzoek-Vlaanderen) and Hercules.  
The authors acknowledge [PERSON] from the University of Aveiro, Portugal for constructing the rectifier under test.  
The authors declare no conflict of interest.  
Figure 15: Received power as a function of the number of tones using the proposed pre-equalizers in a multisine SWIPT, \(\rho=0.5\), \(B=1\) MHz.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 4 Load Forecasting Methodology
### SVLM-FE Short-Time Power Load Forecasting Model  
This paper proposes a two-stage short-time forecasting method SSA-VMD-LSTM-MLR-FE (SVLM-FE) based on SSA-VMD and feature engineering (FE), where the key parameter combination \([K,\alpha]\) of the VMD is optimized using SSA, and we propose Equation (13) as the fitness function for this optimization process. The \(K\) IMFs and Residual are obtained by the SSA-VMD algorithm. In order to minimize signal loss, Res is also input into the forecasting model as an IMF in this paper. The low-frequency component is predicted using MLR, and the high-frequency component is input into the LSTM network for prediction to obtain \(K+1\) component forecasting values. ACF analysis is used to obtain the load values of moments with a high influence on the moments to be predicted; PCC and MIC are calculated to select the factors with a high influence and the first-stage component forecasting values. These three parts of the connection are fed into the fully connected layer as feature vectors to obtain the final forecasting values. A graphic of the model in this paper is shown in Figure 6.  
The SVLM-FE forecasting method proposed in this paper is divided into two parts: the original load data decomposition part and the forecasting part. Among these, the original load data decomposition uses the SSA-VMD algorithm. The forecasting part of this method is divided into two forecasting stages. In the first stage, the components obtained by SSA-VMD decomposition are predicted separately. In the second stage, the feature selection method is used to select the load-influencing factors, and the selected feature values are fused with the forecasting values of each load component from the first stage to construct a feature vector, which is input into the full connection layer for prediction, so as to complete the correction of the prediction error in the first stage. The output value of the full connection layer is the final forecasting value of the load.
## 4 Dynamic Leader Multi-Verge Optimizer (DLMVO)
### The Proposed DLMVO Algorithm  
The pseudo-code of the DLMVO algorithm is summarized as Algorithm 1. Compared with the traditional MVO, the additional computation in DLMVO is to calculate the speed factor(\(\alpha\)) and aggregation factor(\(\beta\)) of the population. The added computational complexity is \(O\) (\(2\times l\times n\)), where \(n\) is the population size, and \(l\) is the maximum number of iterations. Since the complexity of the basic MVO is \(O\) (\(l\times(n^{2}+n\times\log(n)\times D)\)), the total complexity of the DLMVO is \(O\) (\(l\times(n^{2}+n\times\log(n)\times D+2\times n)\)). Although DLMVO adds two new parameters to the calculation, the increase in calculation is minimal, and the two parameters are automatically generated without being user-defined, which makes the DLMVO easier to apply to various scenarios.  
```
1. Initially generate NP universes (\(U_{i,j}\))
2. Calculate fitness value of each universe
3. While NFEs < Max_NFEs
4. Identify the best universe in current population;
5. \(SUI\) = Sorted universes and \(U_{top}=SU\) (1: top:)
6. \(NI\) = Normalize the fitness value (the inflation rate) of the universes
7. // Black holes exchange objects through white holes by Equation (14) //
8. Update WEP and TDR by Equation (18) and Equation (21), respectively.
9. // Every universe has the probability to travel by Equation (22)//
10. Bring the new universes back if it goes out of the search space boundaries
11. Update fitness and the best universe
12. Accept the new solution if it is better than the old one
13. NFEs = NFEs + 1;
14. End while
```  
**Algorithm 1** The pseudo-code of the DLMVO algorithm.  
To more intuitively describe the mechanism of the DLMVO algorithm, we drew an abstract diagram of the DLMVO algorithm, as shown in the Figure 4. Specifically, in the primary iteration process, the top \(NP_{top}\) individuals are first identified as the temporary elite population. Then the dimensions of the good individuals are crossed over to the dimensions of the flawed individuals according to white hole and black hole models, and finally the wormhole model is used to guide the mutations through the temporary elite individuals. After the above process is executed, a better solution will be retained. Additionally, Figure 5 presents the flowchart of the DLMVO.  
Figure 4: An abstract demonstration of the process and mechanism of DLMVO.  
Figure 5: The flowchart of DLMVO algorithm.
###### Abstract
A total of 3246 and 1125 samples were used as training and test data set, respectively. The best test prediction accuracy obtained was 99.25%. [PERSON] et al. [22] conducted a study based on 1D-convolutional neural network integrated with hyperspectral images to detect apple bruises. The number of 20 wavelengths were determined to proper identification. The accuracy of system was 95.79% on the test set. Scientists believe that up to 35% of bruises occur during harvesting and transportation [23]. In order to minimize post-harvest losses, proper management, transportation, and storage techniques are needed. The bruised fruit does not only cause a decrease in marketable value, but also during the storage period, it is susceptible to various diseases due to peel/tissue damage and to the watery nature of the bruised tissue, thus making it necessary to separate bruised fruits from healthy (sound, undamaged) ones. Application of convolutional neural networks on images is widely used and proven to achieve state-of-the-art performance on image tasks like classification, object detection, noise reduction, etc. CNNs help in extracting information from different channels of input images (3 channels in the case of RGB) by considering the spatial relation of pixels and providing encoding features from the image. As it can be inferred from the literature review, studies on this field and topic can be divided into two categories: first category are those conducted on bruise detection after a few days (long period of time). In this case, the fruit piece is discolored and high accuracy will be obtained to distinguish between sound and bruised fruits. The second category are those conducted on fruits such as apples which will discolor in a rather short period of time, so it can be expected that the healthy and bruised samples will be classified with high accuracy only after a short period of time has passed from damage instant. Present paper deals on early identification of bruised lemons using 3D convolutional neural networks. Lemon is one of the fruits in which the visible symptoms of bruising appear at a later phase. Our methodology concatenates different spectra and create a 3D tensor image for each sample in input dataset. This can represent a 3D (volumetric) image similar to a human body medical imaging systems output. Furthermore, the 3D -NN layers are used to extract information from this 3D tensor image by considering the spatial information of the pixels in all three spatial dimensions. As a result, the relation of pixels in a single spectrum and its adjacent spectra values (similar wavelengths), as well as the near (local) spatial neighbor pixels in different spectrums, are considered while in the image encoding process of the CNN layers. Consequently, both local spectra and local spatial relevant information of different spectrums and spatial points, are considered and taken into account simultaneously in the final out classification decision of the automatic machine learning classification models here introduced.
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 2 Materials and Methods
### Optimal Array Signal Weighting Criterion  
In this section, we introduce the PI algorithm that does not need signal direction information and the MVDR algorithm that needs signal direction information.  
The PI algorithm aims to minimize the output power of the array antenna and achieve the effect of filtering interference. The objective function and constraint function of the algorithm are as follows [29].  
\[\left\{\begin{array}{l}\min:P=E\{|y(n)|_{2}\}=\mathbf{w}^{H}\mathbf{R}_{xx} \mathbf{w}\\ \mathbf{w}^{H}\mathbf{s}=1\end{array}\right. \tag{5}\]  
Here, \(P\) is the antenna array output signal power. \(\mathbf{R}_{xx}\) is the input signal autocorrelation matrix. \(\mathbf{w}\) is the antenna element weighting coefficient. \(\mathbf{s}=[1,0,\cdot\cdot\cdot,0]^{T}\) is the unit vector, which ensures that the weight response of the first reference element signal is constant to ensure that the weight value solved is effective.  
The optimal weight coefficient \(\mathbf{w}_{opt}\) and antenna array output signal power \(P_{PI}\) are as follows.  
\[\left\{\begin{array}{l}\mathbf{w}_{opt}=\frac{\mathbf{R}_{xx}^{-1}\mathbf{ s}}{\mathbf{s}^{H}\mathbf{R}_{xx}^{-1}\mathbf{s}}\\ P_{PI}=\frac{\mathbf{R}_{xx}^{-1}\mathbf{s}}{\mathbf{s}^{H}\mathbf{R}_{xx}^{-1 }\mathbf{s}}\end{array}\right. \tag{6}\]  
Figure 2: The structure of SAP and STAP. (**a**) SAP; (**b**) STAP.  
The MVDR algorithm forms the beam on the desired signal based on the minimum output power. However, the algorithm should obtain the satellite signal arrival information. The objective function and constraint function of the algorithm are as follows [30].  
\[\left\{\begin{array}{l}\min:P=E\Big{\{}|y(n)|^{2}\Big{\}}=\mathbf{w}^{H} \mathbf{R}_{xx}\mathbf{w}\\ \mathbf{w}^{H}\mathbf{a}(\theta_{0})=1\end{array}\right. \tag{7}\]  
Here, \(\mathbf{a}(\theta_{0})\) is the expected signal direction vector.  
The optimal weight coefficient \(\mathbf{w}_{opt}\) and antenna array output signal power \(P_{MVDR}\) are as follows.  
\[\left\{\begin{array}{l}\mathbf{w}_{opt}=\frac{\mathbf{R}^{-1}\mathbf{a}( \theta_{0})}{\mathbf{a}(\theta_{0})^{H}\mathbf{R}^{-1}\mathbf{a}(\theta_{0})} \\ P_{MVDR}=\frac{1}{\mathbf{a}(\theta_{0})^{H}\mathbf{R}^{-1}\mathbf{a}(\theta_{0 })}\end{array}\right. \tag{8}\]
# Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies
## 3 Methodology
### Evaluation  
From the literature review in Section 2 we observed that authors mostly used ME, MSE, RMSE, CAPE, MAPE, and R\({}^{2}\) metrics to measure the performance of the demand forecasting models related to the automotive industry. While ME, MSE, and RMSE are widely adopted, they all depend on the magnitude of the predicted and observed demands and thus cannot be used to compare groups of products with a different demand magnitude. This issue can be overcome with MAPE or CAPE metrics, though MAPE puts a heavier penalty on negative errors, preferring low forecasts--an undesired property in demand forecasting. Though R\({}^{2}\) is magnitude agnostic, it has been noticed that its value can increase when new features are added to the model [66].  
To evaluate the performance of our models, we consider two metrics: MASE and R\({}^{2}\)adj. MASE informs the ratio between the MAE of the forecast values against the MAE of the naive forecast, is magnitude agnostic, and not prone to distortions. R\({}^{2}\)adj, informs how well predictions adjust to target values. In addition, it weights the number of features used to make the prediction, preferring succinct models that use fewer features for the same forecasting performance.  
We compute an uncertainty range for each forecast, which illustrates possible bounds in which future demand values may be found. We also perform the Wilcoxon paired rank test [14] to assess if forecasts of a given model are significantly better than others.  
Summary metrics may not be enough to understand the goodness of fit of a particular model [67,68]. Therefore, based on experts' opinions described in Section 3, and available demand characterizations, we analyzed the proportion of products with forecasting errors below certain thresholds (5%, 10%, 20%, and 30%), and the proportion of forecasts that resulted in under-estimates.  
Though some research highlighted the importance of measuring forecast utility related to inventory performance (see, e.g., in [69,70]), this remains out of the scope of this work.