esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Image Mask Correction Based on Skeleton Divergence
## 2 Materials and Methods
### Discrete Pixel Edge Similarity-Guided Merging
\[E_{S}(I_{P_{1}^{r}}(x,y),I_{P_{2}^{r}}(x,y))=\frac{\sqrt{I}\Big{(}1-\min\{ \frac{\overline{m}_{1}}{\overline{m}_{2}^{r}}\frac{\overline{m}_{1}}{ \overline{m}_{1}}\}\Big{)}}{\sqrt{\frac{1}{\overline{m}_{1}^{r}}\Big{(}\min\{ \frac{\overline{m}_{1}}{\overline{m}_{2}^{r}},1\}\Big{)}^{2}+\frac{1}{\overline {m}_{2}^{r}}\Big{(}\min\{\frac{\overline{m}_{2}}{\overline{m}_{1}},1\}\Big{)} ^{2}}}\, \tag{12}\]  
among it,  
\[\overline{m}_{1} =\frac{1}{\overline{m}_{1}^{r}}\sum_{P_{1}^{r}}I_{P_{1}^{r}}(x,y) \tag{13}\] \[\overline{m}_{2} =\frac{1}{\overline{m}_{2}^{r}}\sum_{P_{2}^{r}}I_{P_{2}^{r}}(x,y)\,\]  
where L is the apparent number of discrete single-channel images, \(\overline{m}_{1},\overline{m}_{2}\) represent the sample means of regions \(P_{1}^{r},P_{2}^{r}\), respectively, and \(\#P_{1}^{r},\#P_{1}^{r}\) represent the number of pixels in regions \(P_{1}^{r},P_{2}^{r}\), respectively. The deformation cost of the skeleton and boundary is measured using the Hausdorff distance [22]. The Hausdorff distance is a measure describing the degree of similarity between two sets of points. Considering that the deformation cost of the skeleton and boundary is tiny in many cases, and that the Hausdorff method divides the skeleton and the boundary into small segments, the degree of similarity is fine enough. At the same time, the processing of this study is only pixel operation, and no large external noise, so the Hausdorff distance was chosen. For the two connected discrete single-channel pixel regions \(P_{1}^{r}\), \(P_{2}^{r}\) to be merged, the vertex sets of the skeleton and boundary information before and after the region combination are respectively represented as: \(\Phi=\{l_{1},l_{2},\cdots,l_{n}\}\), \(\Psi=\{k_{1},k_{2},\cdots,k_{n}\}\), the distance is defined as:  
\[\Gamma_{S}(P_{1}^{r},P_{2}^{r})=H(\Phi,\Psi)=\max(h(\Phi,\Psi),h(\Psi,\Phi)), \tag{14}\]  
among them,  
\[ \begin{array}{l}h(\Phi,\Psi)=\max(l\in\Phi)\min(k\in\Psi)|l-k|\\ h(\Psi,\Phi)=\max(k\in\Psi)\min(l\in\Phi)|k-l|\end{array} , \tag{15}\]  
where \(|\cdot|\) is the Euclidean distance between the set of points \(\Phi\) and \(\Psi\). In order to avoid error accumulation in the process of merging discrete pixel regions guided by continuous edges, this study uses the TOPSIS method [15], which approximates the ideal solution. The TOPSIS method orders ideal merge strategies, providing additional point-cloud regularization constraint results. This method provides the optimal merge strategy while reducing the impact of cumulative error. At the same time, the decision process is used to determine the verification of the point-cloud regularization constraint, which reduces the amount of calculation required and improves system efficiency.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Experimental Setting  
In the pursuit of our objective to model 3D lung infection using 2D variants, it is crucial to ensure that all models are evaluated under the same settings to maintain fairness in the comparative analysis. For this purpose, the models were trained using a standardized configuration. They were compiled with the ADAM optimizer [57] using a learning rate of \(1\times 10^{-4}\). To assess the maximum capability of each architecture, a high number of epochs (5000) is set for both models, along with an early stopping mechanism. The early stopping operator monitors the validation loss with a patience of 250 epochs.  
In this study, a multi-loss strategy inspired by [58] is employed. This approach utilizes multiple loss functions to enhance the model's generalization capability by incorporating diverse theoretical motivations. The total loss used in this study consists of the focal loss and dice loss. The dice loss is commonly used in image segmentation tasks as it quantifies the overlap between the predicted segmentation mask and the ground truth mask. By penalizing the discrepancies between the predicted and ground truth masks, the dice loss encourages the model to generate accurate segmentations. In addition, the focal loss is applied to address the challenge of class imbalance, which is often encountered in segmentation tasks. It assigns higher weights to misclassified pixels, prioritizing the challenging examples and effectively mitigating the class imbalance issue. By combining the dice loss and focal loss, we can leverage the advantages of both. The dice loss helps to improve overall segmentation accuracy, while the focal loss addresses class imbalance and enhances the model's handling of challenging examples. Notably, we discovered through multiple experiments that the segmentation results achieved with the combination of dice loss and focal loss were better than those obtained with either one alone.  
Notably, all five models were executed without the utilization of any predefined backbones or pre-trained weights during training. This deliberate choice underscores the core objective of evaluating the pure learning capability of each model architecture, devoid of any external prior knowledge embedded within pre-trained weights. By initiating the models from scratch and allowing them to learn from the data without any pre-established biases, we aim to gain a comprehensive understanding of each architecture's innate ability to adapt to the specific task of image segmentation.  
Hold-out validation is utilized for the training and testing of models. The data are split, with 75% of the data allocated for model training and 25% for model testing. This study's models are all run on Google Colab Pro, which makes use of the GPU as a hardware accelerator and high RAM usage to maximize runtime efficiency. The modeling schemes used in this study are shown in Figure 6.
## 2 The IDIVS Concept and Application Domains
### Application Areas  
Given the output IDIVS can create, different services can be designed. As mentioned above, the output of the IDIVS is a virtual sensor value, typically representing a property of the environment. It can be a physical property as well as other states of the environment, e.g., related to human activities.  
Hence, services can be built using IDIVS output (single or multiple) as well as other data sources. A desired property of the service is that it supports the provision of feedback to the IDIVS, enabling learning to improve the accuracy of the IDIVS. Ideally, the feedback constitutes correctly labelled data provided by users of the service. If users provide feedback to improve accuracy, the service becomes interactive to a certain degree. Potentially, this opens up the door to new and richer services and to new areas of application. Next, we exemplify a few application areas. Obviously, IDIVS may be relevant for numerous applications where IoT is used or could be used (see, for instance, reviews of IoT and applications [15]).
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology  
In this study, a multimodal data semi-transfer fusion framework called \"Image + Text\" is proposed for identifying UFZs by fusing GF-7 imagery and POI data, as shown in Figure 4. The framework includes the following three steps: (1) data preprocessing, including the division of urban functional units, the construction of remote sensing image datasets, and POI text datasets; (2) multimodal data feature extraction and fusion, including extraction and fusion of remote sensing image features and POI semantic features; (3) identification and mapping of UFZs, including preferred classifiers to identify UFZs and accuracy evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Descriptions** \\ \hline Residential zones & Regular, well-equipped communities, such as apartments and high-rise residential areas \\ Commercial zones & Commercial retail, restaurants, financial, and media places, such as office buildings and malls \\ Shantytown & Dilapidated, old low-rise communities, such as villages within cities \\ Public service & Administrative, medical, sport, and cultural places, such as government, hospitals, and libraries \\ Development & A place to be developed or under construction \\ Education & Education and research places, such as schools, universities, and institutes \\ Green land & Park and greenspace places, such as parks, greenbelts, and water \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptions of UFZs categories schemes.  
Figure 4: The process flow of UFZs identification.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 3 Advanced Infrastructure for DC Fast Charging for Electric Vehicles
### Charging Standards
#### 3.1.3 Communication Protocols  
Communication protocols that enable data transmission between the charging station and the EV are also included in charging standards. These protocols allow for essential features to be controlled, including identification, control over power distribution, and real-time monitoring of charging conditions [125]. The Open Charge Point Protocol (OCPP) and ISO 15118 are two instances of communication protocols [126].
## 6 Simulations and Discussion
### Setting Cluster Parameters  
With the random walk mobility model, the long-term sensor spatial distribution \(F_{\mathcal{M}}\) is uniform, which suggests that the values of neighbors derived for stationary, uniformly distributed networks can be applied to characterize the reference cluster size \(z\). In reality, maintaining full connectivity in a mobile network is challenging. In this work, given \(\alpha=1\), a proper transmission range, and the network density, a possible choice for the reference cluster size as described in (6) yields  
\[z\simeq\alpha\left((n-1)\cdot\frac{\pi R_{\mathcal{M}}^{2}}{\ell^{2}}+1\right) \simeq 8, \tag{40}\]  
Figure 10: The impact of node mobility on cluster stability given the network density and the cluster formation parameters, \(LB=4\), \(z=8\), and \(UB=12\).  
Figure 9: Average number of clusters for considering different cluster constraints [\(LB,z,UB\)] with a given mean node speed (2.5 m/sec), appropriate transmission ranges and the number of sensors; \(R_{\mathcal{M}}^{(100)}=15.0\), \(R_{\mathcal{M}}^{(200)}=10.5\), \(R_{\mathcal{M}}^{(300)}=8.5\), \(R_{\mathcal{M}}^{(400)}=7.25\), and \(R_{\mathcal{M}}^{(500)}=6.25\).  
which coincides with the result presented in [9] that having a number of neighbors in the range \(6\sim 8\) (i.e. a cluster size in the range \(7\sim 9\)) may provide a reasonably connected communication graph in a mobile ad-hoc network.  
Accordingly, Figure 11 illustrates the impact of cluster parameter selection on network topology by investigating the parameters \(\alpha\), \(\beta\) and \(\gamma\) for determining _UB_ and _LB_ of cluster size (detailed in Section 4.1.). With \(\alpha=1\), \(\beta=(1+\triangle k)\alpha\), and \(\gamma=(1-\triangle k)\alpha\), the graph depicts the network performance for varying \(\triangle k\), where \(\triangle k=0.375\), 0.5, 0.625, and 0.75, respectively. Note that by configuring these cluster parameters, the network topology considerations can be clearly specified. When \(\triangle k\) is large, it may introduce a variation of the cluster size in the network. However, when \(\triangle k\) is small, the number of clusterhead changes may increase because of the process of frequent cluster reformation. Hence, the above results allow to elucidate the global network behavior and to set the appropriate parameters for shaping and controlling the network operation.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 6 Results
### Scalability and Topology Considerations  
The results and solutions that are presented in this study are influenced by the assumed topology and its implications. Nevertheless, scalability is a common concern when deploying a WSN, as increasing the number of nodes can greatly affect the network's performance.  
Although the proposed mechanism increases the traffic (the number of transmitted packets) with respect to the common solutions, it outperforms these solutions in terms of energy efficiency and connectivity success rate. On the other hand, for all shown mechanisms, (greatly) increasing the number of nodes means increasing the number of EBs in the network, which should enhance the probability of synchronization. Some articles have addressed the issue of scalability by increasing the number of advertiser nodes [21; 24; 28; 29]. However, for a topology in which the nodes are evenly distributed, such as the grid shown in Figure 6, the maximum number of neighbours of a node sending EB messages remains constant and the probability of success is not affected by the quality difference between the links, which allows us to see the improvements in the mechanism clearly. Irregular topologies have not been taken into account because these scenarios could generate nodes that are out of range and other undesirable characteristics that affect the reproducibility and usefulness of the results.  
Focusing only on the proposed mechanism, increasing the number of levels in the topology means increasing proportionally the simulation time based on the \"2 min per level\" limit (for instance, for 10 levels, the simulation would require 20 min). This statement is supported by the fact that the mechanism improves the network joining time of the nodes within the coverage area of a synchronized node. Therefore, nodes belonging to a certain level (i.e., hops to the root node) would require a maximum of 2 min more than the previous level, regardless of the chosen topology.
## 2 Results and Discussion
### Analysis of the Chemical Composition of Residual Oil from Babassu  
The analysis of the chemical composition of the residual oil from babassu was performed by gas chromatography (GC). The results with the free fatty acids present in the residual oil are shown in the chromatogram presented in Figure 1.  
The residual babassu oil chromatogram reveals a well-defined peak of lauric acid (C12:0), whose retention time (rt) is 19.68 min. The GC analysis identified others expressive peaks in the chemical composition of the residual babassu oil: caproic acid (C6:0), caprylic acid (C8:0), capric acid (C10:0), myristic acid (C14:0), palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C18:1n9c), linoleic acid (C18:2n6c). [PERSON] et al. (2019) [55], analyzed the fatty acid composition of babassu oil, the results confirming the high levels of lauric acid in babassu oil (47.40%). The difference in melting point did not influence the content of lauric acid. The results confirmed the high content of saturated fatty acids of babassu oil; this is mainly due to the high content of lauric acid. As can be seen in Table 2, lauric acid had a content of 41.55% in the composition of residual babassu oil.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 3 Imaging in BAV
### Elastography  
Elastography is an imaging modality that measures tissue stiffness, including the aortic wall. Increased aortic stiffness is a marker of vascular pathology and can indicate underlying aortopathy in BAV patients. Elastography can be performed using ultrasound or MRI, providing another layer of diagnostic information for risk assessment and management.  
Recent studies have demonstrated the utility of both ultrasound elastography and magnetic resonance elastography (MRE) for quantifying aortic stiffness. Ultrasound elastography uses sound waves to assess tissue stiffness and has been effectively used in various clinical settings, including cardiovascular imaging [42; 43]. MRE combines MRI with low-frequency vibrations to create a visual map of tissue stiffness, offering a non-invasive method to detect changes in the aortic wall associated with diseases such as BAV [42].
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
###### Abstract  
Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0-30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1-7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R\({}^{2}\)), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R\({}^{2}\) = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R\({}^{2}\) = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques.  
Keywords:Landsat 8; machine learning models; random forest; soil organic matter; XGBoost +
Footnote †: journal: Applied sciences
## 2 Materials and Methods  
### Theory  
When a part of the laser emission is reflected or scattered from an external target and then reinjected into the laser diode (LD) cavity, this feedback light provokes a fluctuation of the laser output power that carries information about the moving target. Using well-known three-mirror compound cavity models [19], the laser output power \(P_{f}\) can be expressed as:  
\[P_{f}(t)=P_{0}[1+m\text{-}cos(2\pi f_{d}t)] \tag{1}\]  
\[m=4\xi\frac{\tau_{p}}{\tau_{l}}=\ 2\frac{r_{ext}\big{(}1-r_{2}^{2}\big{)}\tau_{p}} {r_{2}L} \tag{2}\]  
where \(\lambda\) denotes the wavelength, \(m\) is the feedback modulation factor determining the feedback strength, which depends on the laser output mirror reflectivity \(r_{2}\), the external cavity length \(L\), the photon lifetime \(\tau_{p}\), and the feedback coupling efficiency \(r_{ext}\). \(f_{d}\) is the Doppler frequency shift which is proportional to the scalar product of the vector difference between incidence and scattering beam vector and the velocity vector \(\vec{V}\). The absolute value of \(f_{d}\) can be expressed under the following form [20]:  
\[\big{|}f_{d}\big{|}=\ \frac{\overrightarrow{|k\cdot V|}}{2\pi}=\ \frac{V|sin \theta_{sca}+sin\theta_{inc}|}{\lambda} \tag{3}\]  
In usual flow measurements with OFI, the optical system collects the scattered light from a large number of particles (scatterers), and the dispersion of the scatterer velocity \(\overrightarrow{V}\) and the scattering light direction \(\theta_{sca}\) induces a distribution of the Doppler frequency shifts \(f_{d}\) that impact the LD output power fluctuation (in Figure 1). To evaluate the feedback output power behavior in the complicated microfluidic context, \(P_{f}\) can be reconstructed as the sum of the feedback contributions from all particles in all the scattering directions:  
\[P_{f}(t)=\ P_{0}[1+\sum_{i}\sum_{k}m_{i}cos\big{(}2\pi f_{d,i}t\big{)}] \tag{4}\]  
The attenuation of the liquid during beam propagation and the effect of the scatterer density on the scattering light power must also be taken into account here. The attenuated power after propagation distance \(z\) can be derived by the usual extinction formula [21]:  
\[P=\ P_{0}exp(-\delta z) \tag{5}\]  
\[\delta=N_{p}\cdot\sigma \tag{6}\]  
where \(\delta\) is the extinction coefficient, \(N_{p}\) is the particle density and \(\sigma\) is the particle extinction cross-section.  
Considering that with higher particle density, more particles can be captured in the unit measurement period, the increase of density induces a higher OFI signal level. Thus, the particle density should also be added in the OFI signal expression. Finally, the OFI cosine function in Equation (4) can be corrected to reach the following complex form:  
\[P_{f}(t)=P_{0}[1+\sum_{i}\sum_{k}m_{i}N_{p}exp(-2\delta z_{i})cos\Big{(}2\pi f_{ di,k}t\Big{)}] \tag{7}\]  
Because of the round-trip light propagation in the OFI system, the radiation is attenuated twice through the solution; thus, a factor of 2 has been added in Equation (7).
## 4 Simulation Results  
Simulation experiments are conducted to validate the performance of signal detection and CFO estimation of the proposed method. The results and analyses are presented in this section. In addition, in order to show the superiority of the proposed method in signal detection and CFO estimation, performance comparisons between the published methods in [11; 12; 13; 14; 15] and ours are also made in this section under the same simulation conditions.  
The parameters used for simulation experiments are listed in Table 1. Moreover, the modulation type used in our simulation experiments is MSK. The signal channel is a narrowband interference channel under the condition of networked passive detection. The total length of the three-segment training sequence is 60 symbols, with 20 symbols for each subsequence.  
It is worth mentioning that the parameters listed in Table 1 are fixed ones that will not be changed in all simulation experiments. Those not listed such as \(E_{b}/N_{0}\) and SIR vary in different experiments and will be provided later.
# Smart Grid for Industry Using Multi-Agent Reinforcement Learning
## 4 Proposed MARL Approach
### Environment
#### 4.2.2 Ad-hoc Power Supply
* Compensating for a deficit of power availability and demand with energy from the public grid should be avoided, as this can incur high costs. Considering these key aspects, the control mechanism of the considered ad-hoc power supply elements works according to the following four guidelines, which are executed in every time step:  
1. Calculation of the charging/discharging power of the battery system based on RL (Battery Agent), whereas the total state of all elements in the power supply (battery, VPS, CPS, prior purchased power) and the current demand from production side is considered. 2. The gap between expected power consumption and available power from VPS, battery power as well as purchased electricity from the markets is settled by CPS as much as possible. 3. If there is more energy available than the expected demand, cut-off power generation of VPS to balance demand and supply as much as possible. 4. The remaining deficit is settled by the public grid. Consequently, the ad-hoc power consumption is based on a single RL agent used to control the battery. All other control operations are based on simple rules. The control system complexity can be significantly reduced in this way. However, in order to minimize total energy costs, the battery agent should learn to adapt the charging and discharging of the battery in a way that the CPS can still optimally be used. Thus, the battery agent indirectly controls the CPS power generation.