esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### MMC System
#### 2.3.2. Stator-Side MMC Controller  
The active power and the reactive power output of the PMSG is controlled by the stator-side MMC controller. The tree-phase current and the voltage of the stator winding are transformed into the _dq_-axis components using Park Transformation, where the position of the rotor angle (\(\theta_{r}\)) is obtained from the rotational speed of the PMSG. Details of the control scheme of the stator-side MMC controller is shown in Figure 8. The active power and reactive power are controlled independently by the \(q\)-axis current component and the \(d\)-axis current component, respectively. The PI controllers adjust the power loop and inner current loop controllers for each of the \(d\)-axis and the \(q\)-axis components. The generator (PMSG) usually operates in unity power factor operation, in which the active power output (\(P_{S}\)) is set according to power reference (\(P_{\textit{rf}}\)) tracking by the MPPT circuit, and the reactive power output (\(Q_{S}\)) is set at zero. The outer power loop controller generates the dq-axis reference currents (\(I_{S}^{*(dq)}\)). The inner current loop controller generates the dq-axis reference voltages (\((V_{S}^{*(dq)}\)). By using invers Park Transformation, the \(dq\)-axis reference voltages are transferred into sinusoidal three-phase reference voltages (\(+/-V_{S}^{*(abc)}\)) in which the minus (\(-\)) and plus (\(+\)) indicate the reference signal for the upper arm and the lower arm, respectively. The Park Transformation equations can be found in Appendix A. To increase the tracking capability of the controllers, the cross-coupling cancellation \(\omega_{r}\Big{(}L_{Sarn}/2+L_{S}+L_{S}^{(dq)}\Big{)}\) is added at the output of the inner loop of the current controller. \(L_{S}^{(dq)}\) denotes the \(dq\)-axis components of leakage inductance of the stator winding of the generator, and \(L_{Sarn}\) and \(L_{Sf}\) denote the arm inductance and reactor inductance of the stator-side MMC, respectively.
## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.5.4 Generalization Experiment  
MLNet was trained using DDR's training and validation dataset and tested on IDRiD's testing dataset to evaluate its generalization ability, and the results are shown in Table 3.  
According to Table 3, MLNet achieves the highest score in the mAUPR. Specifically, MLNet achieves an mAUPR of 59.44%, which is 1.11%, 19.56%, 17.39%, and 22.87% higher than M2 MRF, L-Seg, RTNet, and HEDNet, respectively. However, MLNet's performance in EX segmentation is slightly inferior to other methods. This discrepancy can be attributed to the training of MLNet using the DDR dataset, where EX-type lesions have a wide distribution and a larger number of pixels, resulting in a relatively lower weight assigned to EX-type lesions in the loss function. On the other hand, the distribution and pixel characteristics of EX-type lesions in the IDRiD dataset are different from those in the DDR dataset, leading to MLNet's relatively weaker performance in EX segmentation compared to other advanced methods. Nevertheless, it is important to note the improvements achieved by MLNet in other lesion types. In detail, MLNet demonstrates improvements of 5.47%, 5.88%, and 0.94% in the AUPR for MA, SE, and HE segmentation results, respectively, compared to the second-best method, M2 MRF. These results indicate that MLNet exhibits good generalization ability for DR lesion segmentation, showcasing its effectiveness across various lesion types.  
In summary, MLNet's impressive performance in the mAUPR highlights its superiority in overall DR lesion segmentation. While it may exhibit slightly weaker performance in EX segmentation due to dataset-specific characteristics, MLNet showcases its strength in accurately segmenting other lesion types, demonstrating its robust generalization ability for DR lesion segmentation.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 3 Proposed Scheme
### Origin-Based Continuous Object Tracking(Ocot)  
We assume for the design of OCOT as followings:  
* A two-dimensional sensor field is covered by a large number of homogeneous sensor nodes which communicate with each other through short-range radios. So, long distance message delivery is achieved by multi-hop transmission. All sensor nodes in the sensor field are aware of their own locations through either the GPS [27] or other location systems [28].
* The sensor nodes are periodically awoken and observe the targeted object. Once a stimulus appears or the message is forwarded at specific geographical location, the sensors surrounding it collectively process the signal and one of them becomes the source of the stimulus or the destination of the location.
* The scheme uses simple greedy forwarding [29] as underlying forwarding method.
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### Physical Network Setup for WSN-Based Clustering
#### 3.2.1 Leaf (End Device) and Coordinator Nodes' Main Components and Data Collection  
The same \"Contiki OS\"-generated codes that are used for compiling and configuring the physical nodes represented by TI CC2538 are used for compiling and constructing virtual nodes. As a result, the logical component of the physical environment's operational dynamics can be accurately reproduced virtually. This allows for flexibility in accessing the physical node code prior to implementation via the virtual more code. The main components that differ from the virtual more are the header files, channel allocation, and node ID. Figures 12 and 13 illustrate the pseudocodes for some of the key configuration components of the TI CC2538 leaf node as well as the cluster head (coordinator) node.  
Figure 12: Pseudocode for configuring TI CC2538 leaf node.  
Figure 13: Pseudocode for configuring TI CC2538 coordinator node.  
According to Figure 12, the RSSI sensing function, communication/sampling rate that can be set using the same function as for the virtual leaf mode, buffering, and the channel access method at the MAC layer (using the TDMA method in the leaf node as an example) are the main components that reflect the configuration of the leaf sensor node in the established testbed. It is worth noting that the variable \"transmit_flag\" is not accessible for transmitting the buffer while using the CSMA method.  
The declaration of data received from the nodes connected to the coordinator node is shown in an array format in Figure 13. Then, each received sensing datum as well as the node ID of the transmitter node is stored individually. Depending on the number of connected nodes, a counter can be set to print out the data received from the transmitter nodes. In addition, packet loss can be added to the coordinator node by setting any condition that can indicate the loss of a packet.  
For RSSI data logging, the data can be sent and received based on the sample codes shown in Figures 12 and 13. A sample of some of the data collected in Contiki in text format is shown in Figure 14. The data received by the coordinator node are displayed, represented by the connected leaf node ID and its RSSI value, along with the amount of packet loss experienced in each round.
# GNSS-Based Monitoring Methods for Mining Headframes
## 3 Monitoring Data Processing and Analysis
### Monitoring Data Cleaning
The formula for calculating the critical value is as follows:  
\[\lambda_{i}=t_{p,k-i}\sqrt{\frac{k-i}{\chi_{1-\alpha/2,k-i-1}^{2}}} \tag{5}\]  
where \(t_{p,x}\) refers to the \(100p\) percentile of the \(t\) distribution with \(v\) degrees of freedom, and \(\chi_{1-\alpha/2,v}^{2}\) refers to the \(1-\alpha/2\) percentile of the chi-squared distribution with \(v\) degrees of freedom, where \(\alpha\) is the significance level. Using Formulas (4) and (5), outliers in the dataset can be identified and subjected to outlier data cleaning. In terms of filtering and smoothing, this study selects Gaussian filtering for GNSS signal filtering. It aims to address Gaussian noise (also known as normal noise) in signals. As a common random noise, it is mainly caused by internal and external monitoring system factors. Internally, Gaussian noise often arises from receiver electronic component thermal motion, random noise in multipath effects, and internal receiver electronic noise (e.g., quantization noise, amplifier noise). Externally, atmospheric and ionospheric interference also contain Gaussian noise components. Gaussian filtering mitigates the impact of such noise. A Gaussian filter is a type of linear smoothing filter commonly used in image processing for smoothing and denoising operations. Its basic principle involves constructing a weight matrix using the Gaussian function and then performing a convolution operation on the image with this matrix to achieve a smoothing effect [31]. In practical applications, the core of the Gaussian filter is the Gaussian function, which is a widely used probability distribution function characterized by a bell-shaped curve. The expression for the Gaussian function is as follows:  
\[f(x,y)=\frac{1}{2\pi\delta^{2}}\exp(-\frac{x^{2}+y^{2}}{2\delta^{2}})=\frac{1} {2\pi\delta^{2}}\mathrm{e}^{(-\frac{x^{2}+y^{2}}{2\delta^{2}})} \tag{6}\]  
where _f(x,y)_ is the value of the Gaussian function on a two-dimensional plane, and \(\delta\) is the standard deviation of the Gaussian function, which determines the degree of smoothing of the filter. The Gaussian function peaks at the center point, with its value gradually decreasing as the distance from the center point increases. Specifically, the weight of the central pixel is the greatest, and the weights of the surrounding pixels gradually decrease. The deformation curves of some sites after the continuous 56-day monitoring data are cleaned are shown in Figure 4.  
From the results of the deformation curves, the data cleaning process conducted in this work is effective for all monitoring stations, eliminating abnormal data and effectively smoothing the monitoring data, especially at the lower sheave wheel monitoring station, where the cleaning effect is particularly noticeable.
## 1 Introduction
### Moisture damage mechanisms and common influential factors
[PERSON] and [PERSON] [25] studied the influence of asphalt mixes' volumetric and aggregate properties on the moisture susceptibility of different asphalt mixtures using the RSI. The major findings of their study indicate that increasing the air void contents and amount of friable materials in asphalt mixtures reduces the moisture resistance performance of the mixtures. Another critical parameter significantly influencing moisture damage is the mineral filler percentage in asphalt mixes [35,36,37]. Mineral fillers are the proportion of blended aggregates in asphalt mixtures that passes through sieve No. 200 (<0.075 mm) and is suspended in bitumen to form mastic [13,38]. Previous studies indicated that incorporating cementitious types of mineral fillers in asphalt mixes could improve the moisture resistance, stability, and deformation resistance of the mixes [13,15,37,38]. However, increasing the filler contents in asphalt mixes could result in moisture damage. [PERSON] et al. [36] studied asphalt mix performance by utilizing different types of filler materials at various contents ranging from 2% to 10%. Their results revealed that the moisture susceptibility of the mixes increases by increasing the filler contents due to the reduction in the asphalt binder content that forms the exact amount of mastic for lubricating the aggregate particles. [PERSON] et al. [4] and [PERSON] et al. [39] also stressed that moisture damage could happen due to the loss in cohesive strength of the asphalt-filler mastic coating the asphalt mix's particles. Overall, previous studies indicated that the progression of moisture damage in asphalt mixes is attributed to numerous factors. The materials and volumetric properties of asphalt mixes, including moisture absorption of aggregates, asphalt cement content, air void content, and proportion of mineral filler in the mixes, can be the most influential parameters that significantly control the moisture damage of the mixtures.
## 2 Geometry and Numerical Method
### Turbulence Model Validation  
For turbulent flow, it is essential to choose a proper turbulence model to investigate the skin friction coefficient and flow. The three commonly used turbulence models, realizable \(k\)-\(\varepsilon\), \(k\)-\(\omega\), and Reynolds stress model (RSM), are used to evaluate the reliability of the numerical simulation. The \(C_{\text{f}}\) obtained from [PERSON]'s experiment was selected as the benchmark for the validation, and the device's dimensions and experimental conditions used for verification are listed in Table 2[26]. It needs to be noted that the inner wall was stationary and the rotational speed of outer wall depends on \(Re\), which ranges from \(10^{3}\) to \(3.7\)\(\times\)\(10^{4}\). Moreover, the first cell height near the walls is varied to ensure that the maximum \(Y^{+}\) is in the range required by the turbulence models.  
Figure 4 compares the numerical results using different turbulence models with [PERSON]'s experimental data. It was found that the maximum relative deviation between the numerical results adopting the realizable \(k\)-\(\varepsilon\) model with enhanced wall treatment and the experimental data was only \(8.7\%\), which was minimal compared to the other deviations. This indicated that selecting the realizable \(k\)-\(\varepsilon\) model with enhanced wall treatment to predict \(C_{\text{f}}\) and simulate the flow characteristics in a shaft-type gap with rough walls was appropriate. In addition, the enhanced wall treatment, which required that the maximum \(Y^{+}\) was close to \(1\), was used to resolve the flow characteristics in more detail within the boundary layer. Therefore, the realizable \(k\)-\(\varepsilon\) model with enhanced wall treatment was adopted to study turbulent flow. For laminar flow with \(Re\)\(<\)\(10^{3}\), the laminar model was selected as viscous mode.
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 4 Antimicrobial Dressings
### Biquanide-Based Wound Dressings  
Polyhexamethylene biquanide (PHMB) is a widely used antiseptic commonly incorporated into wound dressings. As part of the biquanide family, PHMB consists of polymer mixtures and primarily targets microbial cell walls and membranes, disrupting their integrity. It can also interfere with intracellular structures, such as chromosomes, although this property is not unique to PHMB [77; 85]. PHM is known for its broad-spectrum activity, making it effective against a wide variety of microorganisms, including both Gram-positive and Gram-negative bacteria. PHMB is particularly notable for its effectiveness against resistant strains such as methicillin-resistant _Staphylococcus aureus_ (MRSA) and vancomycin-resistant Enterococci (VRE). In addition to its antibacterial properties, PHMB also exhibits antifungal activity, with effectiveness against _Candida_ and _Aspergillus_ species. Furthermore, PHMB demonstrates antimicrobial effects on amoeboid organisms like Acanthamoeba, as well as efficacy against both enveloped and non-enveloped viruses [86; 87]. Despite its broad-spectrum antimicrobial capabilities, no cases of microbial resistance to polyhexamethylene biquanide (PHMB) have been reported. PHMB is an effective antiseptic that targets a wide range of microorganisms, including bacteria, fungi, and viruses, by disrupting their cell membranes and inhibiting their growth. The lack of resistance development is particularly significant, as it highlights PHMB's unique mechanism of action, which differs from that of other commonly used antimicrobial agents. This absence of resistance makes PHMB a promising option for treating infections, especially in settings where antimicrobial resistance is a growing concern. Its continued effectiveness ensures it remains a valuable tool in wound care and other medical applications. Several commercially available wound dressings incorporate PHMB, including ActivHeal(r) PHMB, Excilon(tm) AMD, Telfa(tm) AMD, Kerlix(tm) AMD, and Kendall(tm) AMD. Research indicates that PHMB foam dressings can significantly reduce bacterial load, polymicrobial populations, wound pain, and wound size in ulcer treatments compared to non-antimicrobial foam dressings [77].
## 2 Computational Approach
### Semi-Empirical Tight-Binding Method  
The tight-binding method is a valuable approach for calculating a material's band structure. It employs atomic orbitals as the basis for expanding the single-electron wave functions of the system. The Hamiltonian matrix elements between these atomic orbitals are considered adjustable parameters [97], which are fitted to experimental or first-principles calculation results. The eigenvalues and eigenvectors are then obtained through the diagonalization of the Hamiltonian matrix. Despite its relative simplicity, the tight-binding model can offer good qualitative results with significantly lower computational costs than DFT calculations. However, its reliance on fitted parameters can result in poor transferability to other systems, thereby limiting its utility in some cases.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions  
This section surveys spaceborne SAR missions flown already or being planned. First, earlier SAR missions featuring satellite constellations or formation flight are covered in Section 3.1.
# Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment
## 4 MRI-Controlled SI and RDD--Performance and Compliance
### Electromagnetic Field Perturbations  
The field perturbations in a uniform electromagnetic field (EMF), due to the introduction of materials with specific characteristics, are governed by their induced EMF. The EMF equations (see the next section) are function of the next vector variables and parameters: \(\mathbf{H}\) and \(\mathbf{E}\) are the magnetic and electric fields, \(\mathbf{B}\) and \(\mathbf{D}\) are the magnetic and electric inductions, \(\mathbf{J}\) is the current density, \(\sigma\) is the electric material conductivity, and \(\omega\) is the angular frequency pulsation of the source field. The magnetic and electric comportment material laws between \(\mathbf{B}\)/\(\mathbf{H}\) and \(\mathbf{D}\)/\(\mathbf{E}\) are represented by the material permeability \(\mu\) and the permittivity \(\varepsilon\), respectively. Note that the conductor and dielectric behaviors of non-magnetic matters are dependent on the frequency following the relative values of \(\sigma\) and \(\omega.\varepsilon\) in the relation \(\mathbf{J}=\sigma\)\(\mathbf{E}\)\(+\)\(j\)\(\omega\)\(\mathbf{D}\) such that when \(\sigma>>\omega.\varepsilon\), the behavior is mainly a conductor, and for \(\sigma<<\omega.\varepsilon\), it is mainly dielectric. In the RF range, the above-mentioned electrodes in piezoelectric actuators belong to the first category and the piezoelectric material to the second, respectively.
# Balanced Team Formation Using Hybrid Graph Convolution Networks and MILP
## 4 Proposed GCN-MILP Model
### GNN Architecture for the Formation of Balanced Teams  
In this work, we consider the graph convolutional network (GCN) to be the most suitable GNN architecture due to its ability to capture the underlying structure of the candidates in the network.  
A GCN builds node embeddings by combining information from neighboring nodes. This is shown in Equation (1).  
\[H^{(l+1)}=\delta\Big{(}\bar{D}^{-\frac{1}{2}}\bar{A}\bar{D}^{-\frac{1}{2}}H^{(l )}W^{(l)}\Big{)}, \tag{1}\]  
where  
* \(\bar{A}=A+I\): adjacency matrix with self-loops.
* \(\bar{D}\): degree matrix of \(\bar{A}\).
* \(H^{(l)}\): node embeddings at layer \(l\) (\(H^{(0)}=X\)).
* \(W^{(l)}\): learnable weight matrix for layer \(l\).
* \(\sigma\): activation function (e.g., ReLU).  
Next, we explore the components of a GCN needed to solve the TFP:  
1. Input: * Each candidate is represented as a node with a feature vector containing attributes specific to the task; see the Experimental Work section. * The relationships among candidates are defined as an adjacency matrix.
2. First layer: * GCNConv(input_dim, hidden_dim): This layer propagates information along edges, aggregating information from neighboring nodes, which helps in capturing the higher-order relationships between candidates. * F.relu(x): This applies a ReLU activation function to introduce non-linearity.
3. Second layer: * GCNConv(hidden_dim, output_dim): In this layer, node embedding is generated for each candidate, capturing the importance of each candidate within the network. * F.relu(x): Another ReLU activation is applied.
4. Output: The final output is the node embeddings.  
Figure 2 shows the proposed model.  
After obtaining the node embeddings, we have to work around the possible clustering of imbalanced teams. The approach we apply to achieve this goal relies on constraint-based clustering, for which we employ Integer Programming (IP). Therefore, we formulate the last phase of team formation as an IP problem in which we can define several constraints, such as team size, minimum skill level, etc. Thus, constraint propagation and backtracking are necessary to find feasible solutions that satisfy these constraints.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 3 Research Design  
### Search Strategy and Data Source  
The United States is the largest business transaction market in the world and has a long history; its system development and data can be traced back to 1975 [30]. Therefore, this study selected data from the USPTO database to perform data analysis. The data were granted patents in the USPTO database from 2019 to 2020. This study used Derwent smart search to perform a patent search. Derwent smart search is a keyword searching method. Hundreds of experts read the official public patent data in the patent database, translated the data, rewrote the abstracts, corrected the contents, and normalized the assignees, after which they recorded the rewritten and normalized data back into the database and created the Derwent smart search. In order to find the intersection of machine learning and photonics, the search criteria were that the patent contained two topics, including machine learning and photonics. The search criteria of this study were as follows: (SSTO/Machine Learning) and (SSTO/photonics); smart search-topic (SSTO) refers to Derwent smart search. Data related to machine learning and photonics were extracted from patent documents; the technological data for both topics were explored through SSTO analysis. The search results obtained 821 patents.  
This framework is presented in Figure 1, and it describes the process of the study in detail. After the topics and patent search direction of this study were confirmed, the patent search began. The data acquired from the patent search were analyzed to identify mainstream technological trends. Subsequently, correspondence and clustering analyses were performed on these findings for patent portfolio and strategic group analyses (Figure 1).  
Figure 1: Analysis of patent portfolio of machine learning in photonics.
## 2 Materials and Methods
### Data Processing  
The mean values and standard deviations (SD) per group were calculated. The Kolmogorov-Smirnoff test was used to test the normality of data distribution. Since the normality of the variables was not confirmed, data were analyzed using non-parametric tests. The Kruskal-Wallis test, post hoc test for multiple comparisons, and Mann-Whitney U test were used for comparisons among the surface treatments. The statistical analysis was performed with SPSS 22.0 (SPSS Inc, Chicago, IL, USA) software at the Center for Data Processing of the Computing Service for Research Support of the Complutense University of Madrid. The level of significance was set at \(\alpha\) = 0.05.