esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Algorithm and Methodology
### Personalized Thresholds  
Considering the individual differences in eye and mouth sizes among drivers, using uniform average thresholds may reduce the accuracy of facial state detection. To enhance the adaptability and robustness of the system, a personalized threshold database was constructed based on \(\eta_{NAE}\) (Normalized Eye Area) and \(\eta_{NAM}\) (Normalized Mouth Area). This database adjusts threshold values according to the driver's facial features under normal conditions (i.e., eyes open and mouth closed), accommodating variations across different individuals. To improve the reliability of the threshold calibration, initial video frames are selected from the beginning of the driving task, when participants are typically in a rested and alert state. Additionally, subjective questionnaires and manual screening are employed to ensure that the selected frames reflect the driver's baseline (non-fatigued) condition.  
The \(\eta_{NAE}\) and \(\eta_{NAM}\) ranges used for threshold classification were derived from the publicly available WIDER FACE dataset. More than 600 representative facial images were selected from this dataset, covering a wide range of diversity in gender, ethnicity, facial structure, and the presence of accessories such as eyewear, facial hair, and masks. As WIDER FACE is a widely used benchmark in facial analysis with extensive demographic variability, the derived thresholds offer better generalizability and robustness across heterogeneous driver populations.  
Method for Determining Personalized Thresholds:  
1. Initial Data Collection:
* During the first interaction with the system, the initial data are collected by selecting the video frames where the eyes are open and the mouth is closed.
* The \(\eta_{NAE}\) and \(\eta_{NAM}\) values are calculated from these frames, and the sizes of the driver's eyes and mouth are assessed to determine whether they fall within the normal range.  
As shown in Table 1, the driver's eye and mouth sizes are evaluated using standardized indexes and categorized based on different facial feature ranges.  
2. Adaptive Threshold Adjustment:  
The personalized thresholds for each driver are set based on their individual eye and mouth sizes, determined from the initial measurements of \(\eta_{NAE}\) and \(\eta_{NAM}\). For example, if the driver's eye and mouth sizes fall into the \"Normal eyes, Undersize mouth\" category, the system sets the corresponding \(\eta_{NAE}\) and \(\eta_{NAM}\) thresholds. Other categories are similarly adjusted according to the driver's individual characteristics. The threshold divisions for eye sizes and mouth sizes are listed in Tables 2 and 3, respectively.
## 2 Materials and Methods
### Feature Selection  
To select the most relevant features for classification, three feature selection methods were investigated. Initially, the \(t\)-test [38] was employed to identify meaningful features that differentiate between \"no-stress\" and \"stress\" states. This statistical method evaluates the difference in means between two groups for each feature independently. Next, the Relief algorithm [39] was applied, yielding improved results by more effectively identifying relevant features for class separation. Unlike the \(t\)-test, Relief is a non-statistical method that assesses features based on their ability to distinguish between similar instances, considering the local structure of the data. The Relief method facilitated more effective prioritization of features, thereby enhancing the performance of machine learning models in comparison to the \(t\)-test approach. Finally, Random Forest (RF) [40] was used as a feature selection method. RF is a supervised ensemble learning technique that constructs multiple decision trees during training and outputs the mean prediction from each tree. It also includes built-in feature importance estimation methods, making RF a powerful tool not only for classification but also for feature selection.  
Since the set of features selected by RF ultimately demonstrated the best performance among all classification models, these features for the 37 individuals are presented in Tables A1, A2 and 5. Tables A1 and A2 cover the HRV features for the \"no stress\" stage and the \"stress\" stage, respectively. Table 5 comprises the statistical metrics for the \"no stress\" and \"stress\" stages to assess the statistical differences between the two groups, \"no stress\" and \"stress\". Metrics include the mean and standard deviation (SD), as well as p-values calculated using \(t\)-tests [38]. Table A3 shows the number of skin resistance peaks for the \"no stress\" and \"stress\" states that were selected by the experts as significant features.  
Figure 5: GSR peak detection.
## 4 Conclusions  
In this study, collagen/PEO nanofibrous membranes with varying crosslinking degrees were successfully fabricated by electrospinning and crosslinked by GA vapor for a series of time. Then, correlations between degree of crosslinking and their properties, such as water stability, mechanical  
Figure 7: Comparison of cell proliferation by culturing HUVECs on the GA crosslinked electrospun membranes and the controls of Tissue culture plates (TCP) substrate (Significant differences are marked by *** for \(p<0.001\); ** for \(p<0.01\); * for \(p<0.05\) compared to the TCP substrate).  
properties, hemostasis and cytocompatibility were explored. The results showed that water stability and mechanical properties of electrospun membranes were enhanced progressively as the degree of crosslinking increased. For the hemostatic property, it was obvious that the blood clotting capacity of crosslinked samples was enhanced by providing a stable enough physical support for platelet adhesion and clotting factor binding. However, there was no significant difference among the crosslinked (\(\geq\)30.8%) samples. In term of cytocompatibility, better-preserved nanofibrous morphology along with higher modulus provided by the higher degree of crosslinking would better resemble the ECM and provide better physical support, thus upgrading the cell proliferation. Hence, with their excellent performance in stability and mechanical properties under physiological condition, the electrospun collagen mats (60.6% crosslinked) also possessed better blood clotting formation and cell proliferation. All results suggested that the obtained collagen/PEO nanofibrous membrane has great application potential in wound dressing.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/8/1226/s1](http://www.mdpi.com/2076-3417/8/8/1226/s1)]([http://www.mdpi.com/2076-3417/8/8/1226/s1](http://www.mdpi.com/2076-3417/8/8/1226/s1)), Figure S1: SEM micrographs of non-crosslinked collagen/PEO nanofibers (**a**) and its diameter distribution (**b**), Figure S2: Macrographic image of the tensile testing (**a**), representative curves of the stress-strain of collagen/PEO nanofibrous membranes in the dry state (**b**) and in the hydrated state (**c**), Figure S3: DSC thermogram of PEO nanofibrous membranes and non-crosslinked collagen/PEO nanofibrous membranes, Figure S4: Water contact angle of collagen/PEO nanofibrous membranes with varied degrees of crosslinking.  
[PERSON] and [PERSON] conceived and performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] guided the whole experimental process. [PERSON] and [PERSON] wrote the paper.  
This research was funded by the Science & Technology Project for Major Disease Prevention and Control (No. 2017 ZX01001-S22) and Fundamental Research Funds for the Central Universities (No. 2232018G-01).  
The authors declare no conflict of interest.
## 1 Introduction
### Logistic Regression Model  
Logistic regression is a statistical method used to predict the likelihood of a dependent variable being in a certain category, based on one or more independent variables. For example, logistic regression can be used to estimate the probability of a patient having a disease, based on their symptoms, age, sex, etc. Logistic regression is based on the logistic function, which transforms input values into a range between 0 and 1, which is interpreted as the probability of belonging to the positive category. Logistic regression is applied to binary classification problems (where the dependent variable only has two possible values) or the multiclass (where the dependent variable has more than two possible values) [36; 37; 38; 39].  
The University of Valle de Mexico is home to accomplished Olympic athletes, professionals, and high-performance individuals. Annually, a massive event known as \"Interlines\" takes place, featuring various sports such as soccer, tennis, American football, touch football, basketball, swimming, animation, takewondo, gymnastics, and volleyball, involving a total of 3500 athletes in the year 2022 [40]. In the present work, a logistic regression model played a key role in the analysis and prediction of injuries in athletes within the framework of this research. We surveyed 500 athletes and took into consideration the different parameters of the individual, as presented in Section 2.  
The significance of these parameters has an impact on injury prediction, whether the individuals have or have not been injured.  
In this article, our contributions are:  
* A Cutting-Edge Predictive Model: We developed an accurate logistic regression model with an accuracy of 90.0%, standing out as a leading tool in predicting sports injuries.
* Identification of Determining Factors: We revealed significant associations, such as practicing sports with a risk of injury and kinesiophobia, providing crucial insights for early risk detection and personalized preventive strategies.
* A Comprehensive Performance Evaluation: We conducted a thorough analysis of various machine learning models, highlighting the versatility of the logistic regression model and supporting its practical utility and reliability in medical and sports environments.
* Detailed Performance Metrics: Beyond high accuracy, we provided a detailed analysis with metrics such as recall and precision, offering a comprehensive evaluation of the model's performance in crucial situations of accurate injury detection in athletes.
# Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning
## 4 Survey Results
### Other Methods
Thus, the MCA loss \(L_{MCA}\) is defined as the conditional entropy between \(p_{k}^{un}\) and \(p_{k}^{vc}\):  
\[L_{MCA}=-\frac{1}{M}\sum_{k=1}^{M}p_{k}^{vc}log(p_{k}^{vc})+p_{k}^{un}log(p_{k} ^{un}) \tag{31}\]where \(M\) is the size of \(E^{u}\). The goal is to maximize the difference between \(p_{k}^{vc}\) and \(p_{k}^{un}\) for the model to learn discriminative representations for unlabeled patches. The ACC loss, on the other hand, applies modifications to the walker loss in order to make the model robust to intra-class variation. This is achieved by penalizing association cycles that start and end at labeled embeddings belonging to the same image. The ACC loss was calculated as follows:  
\[L_{ACC}=-\frac{1}{N}\sum\
olimits_{i=1}^{N}\sum\
olimits_{j=1}^{N}\varphi \big{(}h_{i},h_{j}\big{)}P_{ij}^{T}log\Big{(}P_{ij}^{Intl}\Big{)} \tag{32}\]  
representing the cross-entropy between the association cycle probabilities \(P^{Intl}\) and uniform target distribution \(P^{T}\). The penalty factor \(\varphi\big{(}h_{i},h_{j}\big{)}\) is 1 if the association cycle does not start and end at the same image (\(h_{i}\
eq h_{j}\)) and 5 if it does. Finally, the total loss function of the model is:  
\[L_{\text{total }}=\lambda L_{BCE}+\gamma L_{ACC}+\beta L_{MCA} \tag{33}\]  
wherein \(L_{BCE}\) is the binary cross-entropy loss of the CNN model. \(\lambda\), \(\gamma\), and \(\beta\) are the weights of each loss which are set to 1, 1, and 0.75, respectively. This model was evaluated on the Bioimaging2015 and the Grand Challenge on BreAst Cancer Histology (BACH) datasets. The Bioimaging2015 dataset contains 249 training and 36 testing images of cancerous and non-cancerous histopathological images. The training set is split into 222 training and 27 validation images. The BACH dataset contains 400 cancerous and non-cancerous histopathological images split with the ratio 70:10:20 into the training, validation, and testing sets. After patch extraction, the size of the dataset is 8420, 1163, and 2369 patches for training, validation, and testing, respectively. 5-fold cross-validation was carried out on the BACH dataset, while the average of 5 runs was computed for the Bioimaging2015 dataset. For the BACH dataset, the model obtained error rates of 30.38, 24.87, and 15.46 for 20, 40, and 80 labeled images per class, respectively. For the Bioimaging2015 dataset, the model achieved error rates of 30.13, 28.39, and 25.23 for 20, 40, and 80 labeled images per class, respectively. The model proved to be more successful than state-of-the-art supervised models for histopathology image classification. A summary of the methods discussed in this section is provided in Table 4. Three methods have been reviewed: ACPL, FL with IRM, and Semi-HIC. For each model, the table contains the type of approach to SSL, the datasets used for testing, as well as the best performance score obtained on each dataset.
## 3 The Proposed Model  
In this article, a novel XAITO-DRGC technique was devised for the detection and classification of DR. Initially, the XAITO-DRGC model applies MF and contrast enhancement as a pre-processing step. In addition, the XAITO-DRGC model applies U-Net-based image segmentation and SqueezeNet-based feature extractor. Finally, AOA with BGRCU is exploited for DR detection and classification where the AOA assist in optimal hyperparameter tuning of the BGRCU model. Figure 1 depicts the block diagram of the XAITO-DRGC algorithm.  
Figure 1: Block diagram of the XAITO-DRGC approach.  
The proposed architecture comprises three basic mechanisms to empower an ophthalmologist in the telemedicine environment while collaborating with AI-based healthcare assistive expertise:  
1. A wearable head-mounted camera OphthoAI Internet of Medical Things (IoMT) headset with DL applications for DR disease severity diagnosis. This application enables us to take fresh retinal fundus images of eyes that are later transferred through the internet to a centralized position protected behindhand a firewall. The headset assists inference in the cloud backend with AI and data analytics services or local inference with an embedded-AI technique. User information is locally stored with encryption.
2. A cloud computing platform that serves and manages a connection to an AI engine for predicting disease progression, a single OphthoAI loMT headset, secured patients cloud storage drive, metering and monitoring of computing resources, secured communication of fundus representations, etc. The service can be managed by a cloud IoT system manager and IoT-assisted healthcare service directory.
3. An ophthalmologist dashboard with a secured multi-tenant cloud backend, providing privacy-aware role-based access control to resources and personal information. Secured multitenancy confirms users do not pose a risk to each other in terms of misuse, privacy violation, or data loss.
## 4 Coaxiality Error Analysis
The maximum value of the CE can reach 700 \(\upmu\)rad, far beyond the allowable limit of the APT pointing error. In satellite laser communication links, if only the CE is considered, the maximum value of the CE should not exceed the field-of-view angle of the fine-tracking CCD. However, the fine-tracking CCD work bandwidth is very high (Kilohertz level). Its pixels are limited to a small number (tens in value), resulting in a field of view that spans hundreds of microradians. The data in Tables 2-5 show that the dihedral angle error has less influence on coaxiality than the planeness error because the dihedral error can be treated as a small, peculiar planeness error that has a faster convergence speed. Therefore, in coaxiality measurements, a CCR with a small planeness error is preferred. Comparing the data in Tables 6 and 7, we find that when the CCR dihedral angle error and planeness error take different values, the coaxiality error steadily shows a downward trend as the assembly error increases, which indicates that there is a counteraction between the two errors; therefore, an appropriate CCR can be selected to reduce the coaxiality error. In practical cases, the PAM works in an open loop, and the CE will affect the communication link stability. However, the inter-satellite link or satellite-to-ground link is defined at a specific time; that is, the rotation angle of the APT has a certain value. Therefore, calibration can be performed to reduce the CE to an acceptable value at these special rotation angles  
the greater the impact on the pointing error before the intersection point. However, after this point, the opposite tendency was observed. This is related to the structural form of the APT system, where each assembly error not only increases the pointing error but may also decrease it; therefore, the pointing-error tendency is not static. The CE simulation results show that the dihedral angle error has less influence on the coaxiality than the planeness error, and there is a measure of counteraction between the errors. We can choose a smaller planeness error CCR in the actual testing process and find a counteracting point to reduce the CE as much as possible.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Bacteria
Especially magnetotactic bacteria are adept at building magnetosomes, which are suitable for producing magnetic radicals [52]. It is possible to control the geometry and composition of nanoparticles. Magnetosomes are organic-coated nanocrystals of iron oxide and iron sulphide, biosynthesised by both magnetotactic and non-magnetotactic bacteria. The magnetic properties are determined by species of bacteria, and the organic layer is the result of the bacterial phospholipid bilayer membrane [53]. Applications of magnetosomes include cancer therapy, molecular imaging [54] and toxicity assessment biosensor [55]. Bacteria are capable of synthesizing nanoparticles extracellularly or intracellularly. However, there are limitations of bacterial synthesis of nanoparticles. The main issues are purification of nanoparticles and difficulty in controlling the geometry due to a lack of full understanding of mechanisms [56]. It is challenging to control the particle shape and size, produce monodispersed particles and large-scale capability. Therefore, these methods require further study to improve the understanding of the mechanisms before they can be considered for industrial uses.
## 2 Biomedical Image Segmentation
### Challenges and Limitations of Traditional Methods
#### 2.2.1 Sensitivity to Image Noise  
Traditional segmentation methods are often sensitive to noise present in biomedical images. Noise can arise from various sources, including imaging modalities, acquisition processes, or inherent biological variability. High sensitivity to noise can lead to inaccuracies in segmentation results, affecting the precision of identified structures. Methods relying on pixel intensity thresholds, in particular, can be significantly impacted by noise, as they lack the sophistication to discriminate between noise and true anatomical features [31].
# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 2 Materials and Methods
### Performance Evaluation  
To evaluate the regular segmentation performance of the models, an in-house test was conducted with the separated test portion of CenterA dataset, with the ground truths and output segmentations from the models compared. Additionally, an external test was performed to evaluate the data compatibility in the models. Output segmentations for CT scans in external datasets (PDDCA, TCIA, and CenterB) were obtained and compared with the ground truths. The external test characterizes how the model can be utilized generally in varied data, which is common in clinical settings. In the absence of maxilla segmentations in the PDDCA and TCIA datasets, only mandible segmentations were considered. To quantitatively evaluate the segmentation performance of the models, we used the Dice  
Figure 4: Overall framework and block architectures of neural networks. (**a**) Overall framework. The numbers above the boxes refer to the channel number of the feature maps. The orange boxes for each neural network are represented in (**b–e**), with (**b**) block architecture of U-Net, (**c**) block architecture of Res-U-Net, (**d**) block architecture of mU-Net, and (**e**) block architecture of HPMR-U-Net.  
coefficient (DC), 95% Hausdorff distance (95 HD) and average surface distance (ASD) as evaluation metrics. Additionally, we qualitatively evaluated the segmentation results of the models by visualizing them in 3D.  
The DC measures the degree of volumetric overlap between two volumes. It is defined as  
\[\text{DC}=\frac{2|\text{GT}\cap\text{OUT}|}{|\text{GT}|+|\text{OUT}|}, \tag{1}\]  
where GT and OUT are the labeled voxel sets of the manual segmentation ground truth and output segmentation from the model, respectively.  
The 95 HD and ASD are distance-related metrics, with 95 HD being the 95 th percentile of the Hausdorff distance (HD) between the GT and OUT points. HD measures the distance of a point in the GT to the nearest point in the OUT. It is defined as  
\[\max_{\text{gt}\in\text{GT}}\min_{\text{out}\in\text{OUT}}\lVert\text{gt}- \text{out}\rVert. \tag{2}\]  
The 95 th percentile is used to eliminate the impact of outliers from a small subset of inaccurate points when evaluating the overall segmentation performance. ASD measures the average distance between the GT and the OUT, defined as:  
\[\text{ASD}\;=\frac{1}{2}\Bigg{\{}\frac{\sum_{\text{out}\in\text{OUT}}\text{ d}(\text{out, GT})}{|\text{OUT}|}+\frac{\sum_{\text{gt}\in\text{GT}}\text{d}(\text{gt, OUT})}{|\text{GT}|}\Bigg{\}}, \tag{3}\]  
where \(\text{d}(\text{out},\text{GT})\) is the minimum distance of a voxel on OUT to the voxels on GT, and \(\text{d}(\text{gt},\text{OUT})\) is the minimum distance of voxel gt on GT to the voxels on OUT.
## 3 The VSWT-PMSG Control System
### Stator Side Controller  
The aim of the stator side controller is to control the active and reactive power output of the PMSG. Details of the stator side controller system are presented in a block diagram shown in Figure 7. The rotor angle position (\(\theta_{r}\)) used in the transformation between abc and dq variables is obtained from the rotor speed of generator. The active power (\(P_{s}\)) and reactive power (\(Q_{s}\)) of the generator are controlled by the d-axis current (\(I_{sd}\)) and the q-axis current (\(I_{sq}\)), respectively. The value of active power reference (\(P_{rot}\)) is determined by MPPT method of the wind turbine characteristic as shown in Figure 2. For unity power factor operation, the reactive power reference (\(Q_{s}\)*) is set to zero. The cross couplings \(I_{sd}\omega_{e}L_{d}\) and \(I_{sd}\omega_{e}L_{q}\) should be compensated by the output of the current controllers in order to improve the tracking capability of the control system. Finally, \(V_{sd}\)* and \(V_{sq}\)* are voltage reference outputs of the current controller, which is used to generate the three phase reference voltage (\(V_{sa}\)*, \(V_{sb}\)*, \(V_{sc}\)*) to control stator currents of the PMSG.  
In order to analyze stability performance of the controller system shown in Figure 7, a block diagram of the current loop control shown in Figure 8 is used. In this study, the d-axis and the q-axis components are assumed identical, and hence the plant system can be modeled by using one axis component only as expressed by \(1/(R_{s}+L_{d}\mathcal{S})\). The controller is composed of a PI controller (PI 2), a processing delay, and a plant system. The time delay (\(T_{d}\)) is composed of one sample delay caused by switching frequency and one half sample delay caused by the dead time of PWM converter.  
Figure 8: Current control loop of SSC.  
Figure 7: Stator side controller system.