esa-sceva/satcom-chunk-collection huggingface dataset:
# Immunohistochemical Expression of ABCB5 as a Potential Prognostic Factor in Uveal Melanoma
## 3 Results
### Correlations between ABCB5 Expression and Clinico-Pathological Factors in Uveal Melanomas  
In the whole group (\(n=55\)) the median ABCB5 value was 4. ABCB5 expression was high in 27 (49.1%) melanomas (Figure 2), and low in 28 (50.9%) melanomas (Figure 3).  
In 32 primary uveal melanomas without metastasis, ABCB5 IS was strong/moderate in 14 cases (43.7%) and weak in 10 cases (31.3%). 8 cases (25%) were totally negative; ES was \(>\)50% in 14 cases (43.7%), variable between 5-30% in 10 cases (31.3%). Only 13/32 cases (40.6%) showed H-IRS, while the remaining 19 cases showed L-IRS (59.4%) ([PERSON]'s exact test, \(p=0.026\), Table 4). In 23 primary uveal melanomas with metastasis, ABCB5 IS was strong/moderate in 15 cases (65.2%) and weak in 7 cases (30.5%). Only 1 case (4.3%) was completely negative. ES was \(>\)75% in 3 cases (13.1%), \(>\)50% in 11 cases (47.8%), 30-50% in 7 cases (30.5%), \(<\)30% in 1 case (4.3%); lack of expression of ABCB5 was observed in only 1 case (4.3%). 14/23 cases (60.9%) showed H-IRS, while only 9 cases (39.1%) had L-IRS ([PERSON]'s exact test, \(p=0.026\), Table 4).  
Figure 3: ABCB5 in uveal melanoma. Mild and heterogeneous cytoplasmic positivity in mixed cell type uveal melanoma at medium (**a**) and high magnification (**b**) (Immunoperoxidase stain; original magnification 100\(\times\) (**a**) and 200\(\times\) (**b**)).  
Figure 2: ABCB5 in uveal melanoma. Strong and diffuse cytoplasmic positivity in mixed cell type uveal melanoma at medium (**a**) and high magnification (**b**) (Immunoperoxidase stain; original magnification 100\(\times\) (**a**) and 200\(\times\) (**b**)).  
Factors related to the presence of metastasis at univariate analysis on a Cox proportional hazards regression model were: age (\(p\) = 0.053), tumor greater diameter (\(p\) = 0.009), pT stage (\(p\) = 0.016), epithelioid cell type (\(p\) = 0.011) and ABCB5 expression (\(p\) = 0.047); at multivariate analysis tumor greater diameter (\(p\) = 0.010), ABCB5 expression (\(p\) = 0.003) and epithelioid cell type (\(p\) = 0.026) were significant.  
No correlation was found between the histological type and ABCB5 expression (Spearman's rho \(p\) = 0.334).  
Figure 4 shows the results of Kaplan-Meier survival analyses in patients with uveal melanomas with low and high ABCB5 expression. The estimated survival times free from metastasis (SE, with 95% CI) were respectively: 101.1 (10.0) (CI: 81.6 to 120.7) and 64.4(10.5) (CI: 43.9 to 85.0).  
The log-rank test showed a significant difference (\(p\) = 0.039) between the two groups in ABCB5 expression.
## 4 Machine Learning-Based Label Quality Assurance
### Machine Learning-Based Label Quality Assurance Method  
In this section, we outline the algorithmic steps for creating a machine learning-based label quality assurance model that can be utilized to streamline the annotation process and improve label quality for various computer vision tasks.  
1. Collect a dataset that accurately represents the problem to be solved. This dataset should be large enough to train a machine learning model for the primary task (e.g., object detection).
2. Split the dataset into two parts: a _small set_ (approximately 5% of the full dataset) and a _large set_ (approximately 95% of the full dataset).
3. Carefully annotate the _small set_ with high-quality labels. This can be done by assigning multiple annotators to each sample and checking inter-annotator agreement or by assigning one or more annotators per sample, followed by reviewers checking the quality of all labels.
4. Generate large amounts of good and bad labels for every sample in the _small set_ based on the ground truth label information.
5. Train a machine learning-based label quality assurance model using the generated good and bad labels from the _small set_.
6. Use the trained label quality assurance model to classify the quality of labels from the _large set_, which are created by a machine learning model that generates labels, a program or a human. Notify annotators to pay more attention to samples classified as having bad labels.
7. If there are false-negative or false-positive predictions from the model, which occur when the model incorrectly predicts the presence or absence of certain labels, have annotators confirm that they have carefully inspected the sample. Their confirmation serves as a strong signal that the sample is outside the _small set_ distribution.
8. Use the _small set_ as a validation set for the primary task and the _large set_ as a training set.  
These steps outline the process for creating a machine learning-based label quality assurance model that can be adapted and extended to various computer vision tasks and other machine learning subfields with appropriate label representation and uncertainty region ranges. By implementing this approach, researchers can enhance the efficiency and quality of the data labeling process, ultimately improving the performance of their primary task models.
# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 5 Customized Experimental Setups
Measured rain attenuation is much higher than the predicted one (ITU model) and that would be equal. The wet antenna effect was also investigated. [PERSON] et al. presented in their work the feasibility of detecting signal through a cellphone signal [85]. Data were captured from 8 June to 17 July 2016, on University Campus in Nanjing China. The path link was 504 m with no clear line of sight. The measurement setup consisted of a signal generator (Tx side) and a spectrum analyzer (Rx side). The spectrum analyzer was used to measure the received power in the receiver. The resolution was 0.01 dBm, and the sampling of the received signal was about 183 times per minute and the operating frequency was 2 GHz. An OTT Parsivl dichrometer exhibiting 1 min resolution was used for temporal validation. A detection method to classify dry/rainy periods presented and trained using the C4.5 algorithm. [PERSON] et al. used a unique experimental setup [86] that is a successor to their previous work to analyze the wet antenna effect and determine its effect on the rain rate. Their unique instrument provided measurements derived from the antenna reflection coefficient. In their work, 6 independent Commercial microwave link antennas were used with frequencies of 18, 22.235, 25.375, 28.500, 34.800, 38.500 GHz. The test site was at Fendt near Weilheim in Upper Bavaria, Germany. A detailed analysis of the used hardware was also presented. The heart of the instrument was a standard VNA from Anritsu, while a Raspberry Pi 3 was used as a main central processing unit (CPU). Two disdrometers, a weather station near the test site, and a video camera were used for further validation of the attenuation measurements.
# Federated Learning for Secure In-Vehicle Communication
## 5 Results Analysis
### Centralized Learning-Based Intrusion Detection Performance  
In the centralized evaluation, we merge the three datasets by aligning the rows. The model is trained and tested independently on each dataset corresponding to the three clients (Hyundai, Chevrolet, and Kia). For each dataset, 80% is used for training and 20% for testing. This allows us to evaluate the performance of the model in a non-federated setting on each vehicle-specific dataset separately. During training, the model learns how to classify CAN messages using the Adam optimizer. The loss function employed is categorical crossentropy, given the multi-class nature of the classification task. The  
Figure 14: Confusion Matrix of LSTM-5E Architecture.  
Figure 15: ROC of LSTM-5E Architecture.  
evaluation phase measures the model's accuracy on the test set after each epoch to assess its generalization performance.  
Figure 16 depicts the accuracy history using the traditional centralized learning as a function of the number of epochs, using a-BiLSTM and LSTM [10]. The three sub-datasets (i.e., Hyundai, Chevrolet and Kia) are used to train the model on the same node. Note that early stopping is applied to determine the optimal number of epochs and prevent overfitting. We can note that:  
* Our a-BiLSTM model always performs better than the LSTM [10] model for all numbers of epochs and for the three testing sub-datasets.
* When testing using the Sonata dataset, our a-BiLSTM learns faster than the LSTM model [10] during the first 10 epochs and achieves higher accuracy. We can also observe that the LSTM model performs well in learning patterns in some epochs but does not generalize in others (i.e., epoch 3) due to sensitivity to the training data. This can be explained by the fact that the model memorizes noise or spurious correlations rather than generalizable features where the model fits perfectly to some batches but generalizes poorly to others. In the Spark and Soul datasets, both models perform well, but a-BiLSTM converges more quickly.  
Figure 17 illustrates the loss history when training using the traditional centralized approach across epochs. Early stopping is applied to determine the optimal number of epochs and prevent overfitting. We can notice that:  
* In the Sonata dataset, a-BiLSTM achieves a lower loss compared to LSTM, indicating better convergence. In the Spark and Soul datasets, both models perform well, but a-BiLSTM reaches a lower loss value more quickly.
* LSTM approach has a peak during epoch 3. As already explaied, this can be due to the fact that the model does not memorize well the generalizable features where it fits perfectly to some batches but generalizes poorly to others.  
Figure 16: Accuracy Training History of Centralized Learning.
###### Abstract  
The total allowable catch system (TACs) is a basic, widely used system for maintaining marine fishery resources. The vessel monitoring system (VMS) provides a superior method to monitor fishing activities that serve TACs project management. However, few studies have been conducted on this topic. Here, an artificial neural network was used to identify vessel position states based on BeiDou VMS data and fishing logs of vessels under the TACs project for _Acetes chinensis_ in the Yellow Sea in 2021. Furthermore, fishing behaviors and intensity were explored. The results showed significant differences in the speed of vessels in different states (_p_ < 0.01). Casting occurred during the day, and the azimuth of fishing nets for shrimp ranged from 60 to 90\({}^{\circ}\) or 240 to 270\({}^{\circ}\). The length of the fishing nets of each vessel was mostly between 3500 and 4500 m. In addition, the fishing efforts of the vessels showed an obvious aggregated distribution. The main area was at 120\({}^{\circ}\)04\({}^{\prime}\)-120\({}^{\circ}\)16\({}^{\prime}\) E, 34\({}^{\circ}\)42\({}^{\prime}\)-34\({}^{\circ}\)46\({}^{\prime}\) N, whereas fishing intensity ranged from 120,000 to 280,000 m\({}^{2}\)-h/km\({}^{2}\). Finally, this study provides a scientific basis for TACs project management and a VMS data mining and application expansion standard.  
vessel monitoring system; total allowable catch system; fishing intensity; _Acetes chinensis_; Yellow Sea
## 3 Optimization Problem
### Objective Function  
It is required to optimize the weight of the links to implicitly optimize the power consumption and the costs associated to the structure. The objective function in Equation (9) corresponds to the total weight, including a sum term for each link, where \(l\) and \(d\) are the link length and diameter, respectively, and \(g\) is the gravitational acceleration. Expression (9) presents the design vector, where \(x_{1},x_{2}\), and \(x_{3}\) are the material type, thickness, and width of link \(1\), respectively, while \(x_{4},x_{5}\), and \(x_{6}\) are the same parameters for link \(2\).  
\[\min f(\vec{x})=x_{1}gx_{2}\Big{(}lx_{3}+\frac{\pi}{4}x_{3}^{2}- \frac{\pi}{2}d^{2}\Big{)}+x_{4}gx_{5}\Big{(}lx_{6}+\frac{\pi}{4}x_{6}^{2}- \frac{\pi}{2}d^{2}\Big{)} \tag{8}\] \[\vec{x}=[x_{1},x_{2},x_{3},x_{4},x_{5},x_{6}]^{T}=[\rho_{1},t_{1},W_{1},\rho_{2},t_{2},W_{2}]^{T} \tag{9}\]  
The material type is a discrete variable that must guarantee the device will not fail due to loading. This discards materials without a defined fatigue limit, such as polymers, ceramics or compounds. There are studies establishing the endurance limits for metal alloys [42; 47]. Alloys in Table 1 were selected because they met the design requirements, are used in medical non invasive applications, and are commercially available. They can be changed in type and/or number to meet specific design requirements.  
Figure 2: Specification of links.
# Wound Dressing Materials: Bridging Material Science and Clinical Practice
## 4 Antimicrobial Dressings
### Plant-Derived Natural Compounds  
Essential oils, derived from plants, are increasingly being incorporated into antimicrobial wound dressings due to their wide-ranging therapeutic effects. These oils exhibit antibacterial, antiviral, antifungal, analgesic, anti-inflammatory, and antioxidant properties, making them highly beneficial in wound care [96; 97]. Each essential oil also has unique attributes that enhance its effectiveness in specific applications. Oregono oil, known for its antimutagenic properties, is integrated into cellulose acetate fibers to boost the antimicrobial efficiency of dressings [98]. Tea tree oil is used in Burnaid(r) hydrogel dressings for burn treatment in several countries outside the U.S. [99]. Additionally, St. John's Wort supports skin re-epithelialization, while laverd promotes faster granulation tissue formation when applied topically [100]. A key advantage of essential oils is their low likelihood of promoting microbial resistance, making them a valuable tool in combating multidrug-resistant bacteria in wound infections. This quality positions them as potential alternatives or complements to traditional antibiotic therapies [101].  
Polyphenols, another class of plant-derived compounds, also exhibit antimicrobial activity against a broad range of bacteria and fungi [102]. These compounds, which include flavonoids, function through multiple mechanisms, such as enhancing the effectiveness of antibiotics, directly eliminating microbes, and reducing their virulence [103]. Together, these plant-derived natural compounds offer promising solutions in modern wound care.
# Estimating and Analyzing Long-Term Multi-GNSS Inter-System Bias Based on Uncombined PPP
## 5 Estimation and Analysis of Inter-system Biases
### Correlation between ISB and Receiver Type  
According to the analysis of Equation (8), the ISB parameters were related to both the receiver clock and receiver hardware delay. Thus, it is necessary to further analyze the correlation between the parameters of inter-system bias and receiver type. According to the types of receivers shown in Table 1, the receivers of the eight stations in this experiment were divided into two types, i.e., the allic and Ihaz stations have the Leica gr25 receiver type, and the karr, kiri, kxn2, laut, pngm and xmis stations have the Trimble netr9 receiver. The relationship between the ISB parameters and different receiver types is shown in Figure 12.  
In Figure 12, the y axis represents the difference values of the three groups of ISB parameters of two different stations, respectively, and the x axis indicates the target day. Corresponding to three different types of ISB parameters, it should select two stations with the largest or smallest mean value of the single-day solution of the ISB parameter for each receiver type, and, in the process, due to the missing observation values on certain days from some stations, for fairness, it is considered that the ISB parameters of the corresponding type of measuring station are still the largest or smallest in the subsequent missing days. Blue triangles in Figure 12 represent the difference between the maximum and minimum ISB mean values of stations equipped with Leica type receivers in each day, and red Squares represent the difference between the maximum and minimum ISB mean the values of stations equipped with Trimble type receivers in each day. The yellow pentagonal star in Figure 12 shows the minimum difference between ISB mean values of stations equipped with Trimble type receivers and ISB mean values of stations equipped with LECIA type receivers in each day. As shown in Figure 12, apart from the graph for the ISB_GE parameter, the maximum change in amplitude of the mean single-day ISB value between two stations with the same type of receiver were all less than 9 ns, but the minimum change between two mean values of the single-day solutions corresponding to two  
Figure 11: Jump of the mean ISB parameters of G-R, G-E and G-C in adjacent days.  
stations equipped with two different kinds of receivers were all greater than 15 ns. Even considering the ISB_GE parameter, the minimum difference value of the mean values of the single-day solutions of the ISB parameters between two stations equipped with different kinds of receivers was far greater than those equipped with the same receiver. Based on the analysis shown in Figure 12, the mean value of the single-day solutions of ISB parameters between two stations equipped with the same kind of receiver demonstrates a small change range, and the change is relatively stable; however, it varies significantly for two stations equipped with different types of receivers. Therefore, we conclude that there is a clear relationship between the ISB parameters and receiver type.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 4 Results
### GS Controller and MRAC: Simulation  
During the drilling operation of oil wells, as mentioned in previous chapters, disturbances eventually arise that cause pressure fluxes. The pipe connection operation, which involves a continuous procedure when reaching greater depths is desired, can be cited as sources of disturbance. Also, drilling in geological zones with greater pressure can create influences and circulation losses, among others [2]. For the purpose of testing the performance of the GS controller proposed in this study, the responses of the oil well drilling system were evaluated by simulating real-problem situations present during operation and compared with the presented MRAC controller (Figure 8).
## 3 Results and Discussion
### Properties of Sintered Aggregates
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multicolumn{2}{c}{**Preheating Stage**} & \multicolumn{2}{c}{**Sintering Stage**} & \multirow{2}{*}{**Loss on**} & \multicolumn{2}{c}{**Aggregate Property**} \\ \cline{2-2} \cline{4-6}  & **Preheating** & **Preheating** & **Sintering** & **Sintering** & **Ignition (\%)** & **24 h Water** & **Particle** \\  & **Temp. (°C)** & **Time (min)** & **Temp. (°C)** & **Time (min)** & **Absorption Rate (\%)** & **Density (g/cm\({}^{3}\))** \\ \hline \multirow{2}{*}{WPS} & \multirow{2}{*}{500} & \multirow{2}{*}{5} & \(1150\) & \multirow{2}{*}{12.5} & \(5.72\) & \(10.91\) & \(1.77\) \\  & & & \(1175\) & & \(5.77\) & \(8.84\) & \(1.92\) \\  & & & \(1200\) & & \(5.81\) & \(5.07\) & \(1.90\) \\ \hline \multirow{2}{*}{A1} & \multirow{2}{*}{500} & \multirow{2}{*}{5} & \(1150\) & \multirow{2}{*}{12.5} & \(11.14\) & \(10.70\) & \(1.10\) \\  & & & \(1175\) & & \(10.88\) & \(9.18\) & \(0.85\) \\  & & & \(1200\) & & \(10.97\) & \(13.29\) & \(0.54\) \\ \hline \multirow{2}{*}{A2} & \multirow{2}{*}{500} & \multirow{2}{*}{5} & \(1150\) & \multirow{2}{*}{12.5} & \(19.92\) & \(12.93\) & \(1.09\) \\  & & & \(1175\) & & \(12.5\) & \(15.52\) & \(10.65\) & \(0.90\) \\  & & & \(1200\) & & \(15.74\) & \(12.14\) & \(0.58\) \\ \hline \multirow{2}{*}{A3} & \multirow{2}{*}{500} & \multirow{2}{*}{5} & \(1150\) & \multirow{2}{*}{12.5} & \(21.17\) & \(16.21\) & \(1.08\) \\  & & & \(1175\) & & \(20.47\) & \(13.40\) & \(0.81\) \\  & & & \(1200\) & & \(21.19\) & \(10.37\) & \(0.54\) \\ \hline \multirow{2}{*}{B1} & \multirow{2}{*}{500} & \multirow{2}{*}{5} & \(1150\) & \multirow{2}{*}{12.5} & \(10.88\) & \(14.29\) & \(1.00\) \\  & & & \(1175\) & & \(10.21\) & \(15.33\) & \(0.82\) \\ \hline \multirow{2}{*}{B1} & \multirow{2}{*}{500} & \multirow{2}{*}{5} & \(1200\) & \multirow{2}{*}{12.5} & \(10.91\) & \(16.49\) & \(0.60\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Properties of the sintered aggregates under different sintering temperatures. Figure 9: Three-phase diagram of the chemical composition of the water purification sludge and the Sample B sludge after blending.
## 1 Introduction  
Medical images have multiple modalities, such as Magnetic Resonance Angiography (MRA), Computed Tomography Angiography (CTA), Positron Emission Tomography (PET), ultrasound imaging, and more. In clinical diagnosis and treatment, the segmentation technology of medical images affects the reliability of diagnosis results to a great extent. Moreover, medical image segmentation technology is the first step of many medical image processing technologies, such as visualization, 3d reconstruction. Therefore, its development will affect the evolution of other related technologies in medical image processing.  
With the rapid development of deep learning, a large number of intelligent methods based on neural networks for medical images segmentation have emerged in recent years. Nevertheless, medical image segmentation has some limitations, including data scarcity and class imbalance [1]. To get the results we want through deep learning training, we often need many medical images as a data set. For solving the problem of sparse data sets, different data transformation or enhancement techniques are sometimes used to increase the number of available labeled samples, such as the automatic data augmentation method proposed by Google in 2018 [2], or use a patch-based approach to resolve class imbalances.  
To reduce large data sets required for network training and improve the efficiency of the work, U-Net [3] was proposed, so that we can complete the network training with very few data sets and accurately segment the region of interest. However, U-Net has some defects, such as the accuracy of the model is limited by the batch size, and the correlation between the channels is not fully considered.  
To address these issues, we propose an improved de-normalized residual block. It uses a new initialization method, which solves the problem of gradient explosion and gradient disappearance by properly adjusting initialization parameters at the beginning of training. During the training process, it can converge faster. The attention mechanism, because of its low model complexity, can be calculated in parallel, and the advantage of being able to obtain critical information is also increasingly used in medical image segmentation tasks. This paper proposes a new channel attention module called SCA, which can calculate the feature mapping value in each channel, to explore the correlation of channel.  
The contributions of our work can be summarized as below:  
1. We propose a de-normalized channel attention network, which consists of a new channel attention module SCA and an improved de-normalized block.
2. We construct two brain vascular data sets, which uses multiple angles of CTA and MRA images from various patients, called the ELE and CORO data sets, respectively. The labeling process results brain vascular experts.
3. We perform experiments on the public retinal vascular data set DRIVE [4] and brain vascular data sets ELE and CORO, compare the results with the state-of-the-art methods, and obtain high accuracy. The effects of de-normalized structure on vascular segmentation were tested on DRIVE, ELE, and CORO data sets, respectively.