esa-sceva/satcom-chunk-collection huggingface dataset:
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
## 2 Related Work  
### Deep-Learning-Based Methods for Medical Image Segmentation  
Deep learning technology in the field of medical imaging is expanding rapidly, yet it faces challenges such as suboptimal accuracy. Numerous researchers are dedicated to refining neural networks to enhance segmentation precision. CNNs (Convolutional Neural Networks) [11] have demonstrated significant potential in the domain, offering the distinct advantage of automatically extracting image features, thereby eliminating the need for laborious manual feature extraction--a factor that has garnered substantial interest. Building upon CNNs, FCNs (Fully Convolutional Networks) [12] have been refined to accept images of any dimension and classify them at the pixel level, addressing semantic segmentation challenges. The U-Net architecture, specifically tailored for medical image segmentation, has evolved rapidly in the field and has become a classic model for image segmentation tasks. As the U-Net has been widely adopted, a variety of networks inspired by its design have come to light. In 2016, [PERSON] et al. [10] introduced a 3D version of the U-Net to leverage volumetric information and enhance the efficacy of medical image segmentation, achieving promising outcomes. In 2021, [PERSON] et al. [13] developed a streamlined version of U-Net known as Half U-Net. This architecture standardizes the channel, integrates features across various scales, and introduces a novel ghost module. Extensive experimental validation has revealed that Half U-Net matches the segmentation precision of U-Net while substantially reducing the number of parameters and computational complexity. Moving forward to 2023, [PERSON] et al. [14] enhanced U-Net by integrating residual blocks, an approach which led to more efficient feature extraction. They also introduced skip connections that facilitate the integration of low-level details with high-level features across different scales, significantly boosting the segmentation accuracy of retinal vasculature images. In 2024, [PERSON] et al. [15] introduced RR-UNet, a U-Net-derived model which leverages residual learning to capture more abstract and informative representations. This innovation notably improved the segmentation outcomes for brain tumors. The enhanced U-Net framework, ResUNet++, was put forward by [PERSON] et al. [16]. This framework replaced the standard convolutional blocks in both the encoder and intermediate stages with 3D dense convolutional blocks and the ResNet50 architecture. Additionally, the convolutional layers in the decoder stage of the original U-Net were replaced with transposed convolution layers, resulting in a notable enhancement over the conventional U-Net. While U-Net and its derivatives have garnered considerable acclaim, there is still untapped potential for enhancing the precision of brain tumor segmentation. We are committed to exploring innovative approaches that can elevate the performance of these models, striving to unlock new levels of accuracy in this vital domain.
## 4 Dispersing the Light
### Arrayed Waveguide Gratings (AWGs)
#### 4.1.3 Challenges and the Future  
While the concept of AWGs has matured over the last three decades, there are some specific issues that need to be addressed in the context of astrophotonic AWGs. The most important requirements are high-throughput over broad bands, polarization independence, and high coupling efficiency with  
Figure 4: Analogy between a conventional grating spectrograph (left) and arrayed waveguide gratings (right), reprinted with permission from [62].  
SMFs. Solutions to these issues exist in standalone devices or material platforms [46; 72]. It is critical to bring these together to build an astrophotonic AWG spectrograph with better performance than existing conventional astronomical spectrographs, such as volume phase holographic (VPH) gratings.  
Astrophotonic AWGs have great potential to be deployed on space telescopes. They can work as low-resolution filters or high-resolution spectrographs. By stacking the AWGs and feeding light from different sections of an extended source to separate AWGs, a compact integral field unit (IFU) spectrograph can be constructed. Such a stack can also be combined with automated fiber positioners (e.g., _starbugs_[73]) to make rapidly reconfigurable multi-object spectrographs.
## 5 Experiments and Results
### Results Analysis  
To further study the performance of our proposed method and verify its effectiveness, we employed several state-of-the-art benchmark methods to compare results on the xBD dataset. Six state-of-the-art UNet-based segmentation networks were considered to assess the performance of the proposed localization method (see Figure 13 and Table 1). For damage assessment method evaluation, the two-stage framework was kept unchanged, but four UNet-based architectures were used to compare the results. Figure 13 shows the fundamental structures of networks that we used for comparisons. The predefined evaluation metrics were used for statistical assessments, and the localization and classification maps were further visually investigated to better understand the performances.
## 4 Discussion
### Discussion of the Results of the Analysis for a Variable Weight of the Satellite
Conclusions:  
* The increase in the weight of the satellite limits the potential increase in orbital velocity, which means that heavier satellites require more efficient propulsion systems or larger amounts of fuel;
* Higher specific impulses result in higher effective exhaust velocity, which improves the efficiency of fuel consumption;
* Heavier satellites require more time to perform the manoeuvres, which may limit the number of operations during the mission and increase the demand for energy;
* The satellite's mass is positively correlated with observational capability, not directly due to the mass itself, but because of the ability to implement more advanced observation systems (e.g., sensors with greater swath width);
* The sensor's swath width increases on average by 0.5-1 km for every additional kilogram of the satellite's mass, reflecting the possibility of installing better optical systems;
* Observation time (pass over the Area of Interest, AOI) increases with swath width and decreases with the rise in orbital velocity, which in turn, depends on orbital altitude;
* The duration of the satellite's flight over the given area is directly related to the angle of inclination of the trajectory in reference to the equator \(\theta\) and the swath width L.  
The conducted analyses allow for the determination that the flight duration of 13.256 s (and hence, the possibility to make only five passes over the area of interest) is not a fully satisfactory result from the operational point of view. The altitude of the orbit of 482 km was selected as a compromise between orbital stability, minimising the influence of atmospheric drag, and ensuring the appropriate resolution of imaging for the EO sensor. Due to that, research was conducted on the possibility to perform orbital manoeuvres that may potentially increase the frequency of flights over the target area, thus improving the efficiency of the mission.
### Mechanical Wood Processing and Surface Quality  
In their work, [PERSON] et al. (Paper 17) applied an artificial neural network modelling technique and response surface methodology to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and drilling torque based on drill tip angle, tooth bite, and drill type. It was found that the drill type significantly influenced the force during drilling, and the most significant factor affecting the drilling torque during the drilling of plywood was the tooth bite. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process.  
Paper 18, ([PERSON] et al.) investigates the possibility of reproducing a precise copy of a carved wood object of medium size and considerable cultural value, which could continue to be used in its original location or could be exhibited in a museum space. 3D scanning, CNC machining and traditional wood carving handwork were applied and discussed. The current work transfers the experience of these reproduction techniques as applied to a 17 th-century \"Bema Door\" element, sharing significant information and details on the tools, materials, process steps, etc., with the aim of being an inspiration for the preservation or reproduction of other highly important cultural heritage monuments.  
[PERSON] et al. (Paper 19) focus their work on 3D printed connectors for a chair leg-stretcher joint. Larch (_Larix decidua_ Mill.) wood was used for the chair components and selective laser sintering (SLS) technology was employed for the 3D printed connector designed to assemble the chair elements. Diagonal tensile and compression tests were performed on the L-type joints with 3D printed connectors, along with the bending moments and micrographs taken of the broken areas of the 3D printed connectors. The results show that the SLS printing technology applied to build the connector does not ensure enough strength for the assembly, and further research on changes in printing parameters, in terms of laser power and scan speed, is needed.  
The last paper of this section, written by [PERSON] and [PERSON] (Paper 20), is focused on the surface quality of beech wood obtained by profiled milling. In this study, the surface quality was analysed across stratified levels of irregularities differentiating between waviness, tool marks, fuzzy grain, and accidental surface gaps overlapped onto the deep anatomical cavities of the wood, such as pores from earlywood. The findings of the work show that the processing quality measured at different depths does not differ statistically, in spite of local differences in cutting speed. The statistical analysis helped the researchers understand the hierarchy of influencing factors and their significance. In this respect, the most important factor affecting the surface quality is the feed speed.
## 2 Materials and Methods
### Modeling and Verification  
As shown in Figure 2, the four points distributed along the edge were removed, and 120 soil samples were taken as the research object. Based on the randomness of sample data, the 120 samples were divided into two parts, 90 of which were used for modeling, with the rest intended for model validation. To prevent the impact of moisture on soil nutrient (N, P, and K) prediction, the prediction models of main soil nutrients N, P, and K were constructed through a combination of partial least squares (PLS), the simultaneous partial least squares (Si-PLS), the support vector regression (SVR), and the simultaneous support vector regression (Si-SVR) methods.  
Figure 3: Spectral data acquisition system flowchart.  
The prediction models were validated against various criteria, including root mean square error of calibration (_RMSEC_), root mean square error of prediction (_RMSEP_), modeling coefficient of determination (_Rc_), prediction set correlation coefficient (_Rp_), and relative percent deviation (_RPD_). More specifically, _RMSEC_ and _RMSEP_ must be minimized as much as possible. In addition, the closer _RMSEC_ and _RMSEP_ are to each other, the more consistently the model performs and the greater its predictive power is. _RPD_ can be calculated by using Equation (3).  
\[RPD=SD/RMSEP \tag{3}\]  
where _SD_ represents the standard deviation of verification samples. When _RPD_\(\geq\) 2.0, the model is considered as being capable to resist the interference of soil moisture with spectra; when 1.4 \(<RPD\)\(\leq\) 2.0, the model is regarded as applicable for rough sample estimation; and when _RPD_\(\leq\) 1.4, the model is deemed inaccurate.
## 3 Experimental Results and Analysis
### Cone Tip Resistance (\(q_{\text{c}}\))  
The relationship between \(T\), \(\omega\), degree of \(K\), and \(q_{\text{c}}\) is depicted in Figure 3. It is observed that under consistent \(T\) and \(\omega\), the \(q_{\text{c}}\) increases with the increase in degree of \(K\). Additionally, as the degree of \(K\) increases, the rate of increase in \(q_{\text{c}}\) tends to slow down.  
As shown in Figure 3a, at room temperature with a moisture content of 20%, the \(q_{\text{c}}\) of the soil sample is relatively low. This low resistance is due to water molecules adsorbed on the surfaces of soil particles, forming intermolecular adhesive forces that reduce the cohesion on the surface of the soil particles, thereby diminishing the soil's strength. When the degree of \(K\) is constant, the \(q_{\text{c}}\) increases as the \(T\) decreases. Above \(-2.5\)\({}^{\circ}\)C, for every \(1\)\({}^{\circ}\)C decrease in temperature, the \(q_{\text{c}}\) increases by approximately 30%. Below \(-2.5\)\({}^{\circ}\)C, the \(q_{\text{c}}\) tends to stabilize with changes in temperature. This stabilization occurs because, as the temperature decreases, the content of unfrozen water decreases, the degree of ice saturation increases, and the strength of the ice enhances. However, as the temperature continues to drop, the ice content and strength gradually stabilize, leading to an initial increase and subsequent stabilization of the cone tip resistance (\(q_{\text{c}}\)).  
Figure 3b illustrates that under sub-zero conditions, with a constant degree of \(K\), the \(q_{\text{c}}\) increases as the \(\omega\) increases. When the moisture content is below 40%, the \(q_{\text{c}}\) continuously increases, with the largest growth occurring as the moisture content approaches the liquid limit. When the moisture content exceeds 40%, the \(q_{\text{c}}\) tends to stabilize. This is because the ice content increases with increasing moisture content. When the degree of ice saturation approaches 1, the strength of the permafrost fully develops. The use of ice sand, which does not cause volumetric expansion, prevents damage to the soil skeleton and hence avoids a reduction in soil strength. Therefore, as the \(\omega\) increases, the soil sample's \(q_{\text{c}}\) first increases and then stabilizes.  
Figure 3: Variation in soil sample \(q_{\text{c}}\) with temperature and moisture content: (**a**) variation in \(q_{\text{c}}\) with temperature for a sample with 40% moisture content; (**b**) variation in \(q_{\text{c}}\) with moisture content for a sample at \(-2.1\)\({}^{\circ}\)C.
# Neural Network Approach to MPPT Control and Irradiance Estimation
## 2 Theoretical Background
### Overview of MPPT Algorithms  
The MPPT tracker system is usually composed of the MPPT algorithm, power converter, and control unit (Figure 4). The MPPT algorithm aims to provide the MPP voltage (\(V_{mpp}\)), which is used as the reference value in the power converter control loop. Due to simplicity, it can be assumed that the power converter is perfectly controlled.  
One of the simplest MPPT algorithms is the Perturb and Observe (P&O) [20]. The P&O is the model-free algorithm that iteratively changes the output voltage towards the MPP, wherein the direction of change is determined by using the voltage and current measurements. The main limitations of the model-free algorithms are slow tracking speed and steady-state oscillations about the MPP [20]. The MPPT algorithms that rely on the photovoltaic model can be used to deal with these issues [28; 29]. The model-based algorithms are fast and accurate, but also more complex and require irradiance sensors for implementation. The irradiance sensors are costly and difficult to calibrate. However, it has been shown that the irradiance sensors can be effectively replaced by irradiance estimators [29].  
On the other hand, as far as the model-based algorithms are concerned, not only are there MPPT algorithms based on the equivalent circuit models, but there are also many of them based on neural networks. Comprehensive review of the NN-based MPPT algorithms can be found in [30]. The NN-based algorithms provide the \(V_{mpp}\) voltage at the output, while their inputs can be datasheet information [31; 32; 33], irradiance and temperature [34; 35; 36], voltage and current measurements [37; 38]. Moreover, some authors focuses on the design of both the MPPT algorithm and control unit [42; 43]. Hybrid approaches that combine the NN-based algorithm with model-free algorithms have also been proposed [44].  
Among the proposed algorithms, the most precise are those that use irradiance and temperature as input information [34; 35; 36]. Figure 5a shows an example of the feedforward NN in which \(G\) and \(T\) measurement are used to predict \(V_{mpp}\). It should be noted that other types of networks, such as a radial basis function NNs, can be used in the same way. The NN from Figure 5a provides the \(V_{mpp}\) instantaneously, which enables fast tracking speed under fast changes of the environmental conditions. However, the drawback of this algorithm is that the irradiance information needs to be known. In [38], a cascade NN-based MPPT (CNNMPPT) the technique is proposed where two NNs are utilized to estimate \(V_{mpp}\). The first NN estimates the irradiance from the voltage and current measurements (Figure 5b), whereas the other NN determines the MPP voltage from the temperature and estimated irradiance (Figure 5a). The proposed technique can provide accurate results, but at the cost of the increased computational complexity. The algorithm that predict the MPP voltage by using the multilayer feedforward NN is proposed in [33].  
Figure 4: Schematic arrangement for the PV system.
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 3 System Model and Problem Formulation  
We assume that there are \(m\) VMs with the same configuration in the cloud computing system, and let \(\mathcal{M}=\{1,2,\ldots,M\}\) be the set of VMs. Each VM has \(k\) types of hardware resources, such as CPU, memory, and disk. Let \(\mathcal{R}=\{1,2,\ldots,M\}\) be the type set of hardware resources, and let vector \(c=(c_{1},c_{2},\ldots,c_{k})\) represent the resource capacity of each VM. Meanwhile, there are \(n(n\leq m)\) tasks waiting to be deployed in the cloud computing system. Let \(\mathcal{J}=\{1,2,\ldots,n\}\) represent the set of tasks. For each task \(j(j\subseteq\mathcal{J})\), let \(d_{j}=(d_{j1},d_{j2},\ldots,d_{jk})\) represent the resource requirement vector of task \(j\), where \(d_{jr}\) represents the requirement of the \(j\)th task for the \(r\)th resource. In a cloud computing environment, there may be interference between tasks. If mutually interfering tasks are deployed on the same VM, their performance may be affected. Therefore, when scheduling tasks, it is important to avoid scheduling mutually interfering tasks to the same VM. The conflict interference relationship between tasks is characterized by a conflict graph \(G=(V,E)\). Each vertex in vertex set \(V\) corresponds to each task in task set \(J\), and the conflict relationship between tasks corresponds to edge set \(E\). For any two vertices \(j,j^{\prime}\in V\), if there is \((j,j^{\prime})\in E\), it indicates that there is a conflict relationship between task \(j\) and task \(j^{\prime}\); thus, they cannot be deployed on the same VM. The optimal solution for task scheduling of this problem is to use as few VMs as possible. The aforementioned problem can be described by the following integer programming model:  
\[z=\min\sum_{i\in\mathcal{M}}y_{i} \tag{1}\]  
\[\text{s.t.}\ \ \sum_{j\in\mathcal{J}}d_{jr}\cdot x_{ij}\leq c_{r}\cdot y_{i} \forall i\in\mathcal{M},\forall r\in\mathcal{R} \tag{2}\]  
\[\sum_{i\in\mathcal{M}}x_{ij}=1,\ \forall j\in\mathcal{J} \tag{3}\]  
\[x_{ij}+x_{ij^{\prime}}\leq 1,\ \forall(j,j^{\prime})\in E,\ \forall i\in \mathcal{M} \tag{4}\]  
\[x_{ij},y_{i}\in\{0,1\},\ \forall j\in\mathcal{J},\ \forall i\in\mathcal{M} \tag{5}\]  
The objective \(z\) is to minimize the number of cloud computing VMs deployed. The decision variable \(x_{ij}\) indicates whether task \(j\) is scheduled to VM \(i\), where \(x_{ij}=1\) indicates that task \(j\) is executed on VM \(i\), and \(x_{ij}=0\) indicates that it is not. The decision variable \(y_{i}\) indicates whether there exists a task executed on the \(i\)th VM, where \(y_{i}=1\) indicates that there is and \(y_{i}=0\) otherwise. Formula (2) indicates that the resource used of any VM cannot exceed the capacity of any type of resource. Formula (3) ensures that each task has to be executed on exactly one VM. Formula (4) ensures that conflicting tasks cannot be executed on the same VM. Formula (5) ensures that the values of decision variables \(x_{ij}\) and \(y_{i}\) can only be 0 or 1.
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
## 2 Synthetic Strategies of Mn-Zn Ferrites
### Microemulsion Method  
The microemulsion method uses water droplets as nanoreactors in a continuous phase (oil) in the presence of surfactant molecules. Nanoparticles with a controlled size are obtained by the simple mixing of two water-in-oil microemulsions: one containing a salt or a complex of metal and the other containing a reducing agent [34]. The microdroplets will continuously collide, coalesce, and break again, and, finally, produce a precipitate which is finally separated by extraction with polar solvents. Due to their small size, the droplets are subject to Brownian motion resulting in the formation of dimers and other aggregates with a short life, which rapidly disintegrate into droplets of the original size. The main drawback of this method concerns the inefficient scale-up, with lower yields of SPIONs compared to other techniques and the large volume of solvents necessary for the synthesis of significant amounts of magnetic material.  
[PERSON] et al. reported the synthesis of Mn-Zn ferrite nanoparticles by inducing the co-precipitation of hydroxides of Mn, Zn and Fe ions in a microemulsion system, consisting of an aqueous phase, n-hexanol as the oil phase, and cetyltrimethylammonium bromide (CTAB) as the surfactant. By this method, the authors obtained Mn0.5 Zn0.5 Fe2O4 spinel-ferrite nanoparticles with a size ranging from 2 to 15 nm. Moreover, the distribution of the cations over the two sublattices of the spinel structure of the nanoparticles was deeply investigated in comparison to the bulk standard [35].