esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Dysprosium Selenide Films  
Dysprosium selenide (Dy\({}_{2}\)Se\({}_{3}\)) thin films were deposited onto glass substrates [118]. These materials could be used for electrochemical energy storage. Experimental findings showed the specific capacitance was 92 F/g, when the current density was 2.85 A/g (1M lithium perchlorate (LiClO\({}_{4}\)) electrolyte), and successfully retained 85% over 5000 cycles, as indicated in the galvanostatic charge-discharge technique. On the other hand, specific capacitance (83 F/g) and specific energy (18 Wh/kg) at specific power (2.7 kW/kg) were highlighted in the Dy\({}_{2}\)Se\({}_{3}\)/LiClO\({}_{4}\)- polyvinyl alcohol (PVA)/manganese dioxide or manganese (IV) oxide (MnO\({}_{2}\)) flexible hybrid electrochemical capacitor. Lastly, they concluded that this device successfully retained 92% when the ending angle was 160\({}^{\circ}\). [PERSON] and co-workers [119] reported on the preparation of thin films onto stainless steel substrates. Orthorhombic phase and spherical grains could be observed based on the XRD and SEM analyses. The obtained films showed specific surface areas of about 48 m\({}^{2}\)/g and hydrophilic nature (with a contact angle of 50\({}^{\circ}\)). Experimental findings confirmed excellent specific capacitance (273 F/g) when the scan rate was 5 mV/s and a 1 M sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) electrolyte.
## 1 Introduction
Over the past two decades, machine learning and deep learning approaches have proven their superiority in several fields such as image processing [6; 7], object detection and localization [8], and NLP (Natural Language Processing) tasks [9], etc., and text analysis is no exception. Additionally, the use of machine learning models has been made to analyze the text in several different languages including Turkish, Lithuanian, and French, other than English [10; 11; 12]. Bearing in mind that machine learning and deep learning approaches can be leveraged for text annotation, clustering, and classification, this study utilizes machine learning approaches for annotation while amachine and deep learning approach for sentiment classification. To put it in a nutshell, the primary goal of the study is to address the following:  
* The analysis of the effectiveness of the e-learning system to achieve the desired learning outcome through sentiment analysis on stakeholders' tweets. * To analyze the thoughts and experiences of learners and teachers about the transition from face-to-face education to online education. * To find the gap between traditional education and online education by leveraging NLP approaches for text processing, feature selection for sentiment analysis, and machine learning models for sentiment classification. * To find the problems associated with online education in terms of technology, social setup, and interaction by employing topic modeling. * To analyze the performance of various machine learning and deep learning models for sentiment analysis using different annotation approaches such as TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet, as well as the efficacy of TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) feature extraction approaches. The rest of the paper is organized as follows: Section 2 discusses several research works related to the current study. Section 3 contains the description of data collection, feature extraction, proposed methodology, and machine learning algorithms. Results and discussions are provided in Section 4, followed by the conclusions and future work in Section 5.
## 3 Converter Topology for the Power Modules
### Double Input Single Output (DISO) Mode  
The converter operates in double input single output (DISO) mode when the load demand exceeds the power capability of the PV-module. Therefore, the extra power is supplied by the battery module which means that the current in L1 is negative (opposite to the direction indicated by the arrow). For this mode, the operating modes of the converter are as:  
1. _Stage I_ is illustrated in Figure 6a: switches S3 and S1 are turned ON and switch S2 is OFF which means that the PV is shorted via L2 forcing the diode D1 to be reverse biased (OFF) and only the battery provides current to the load. This means that the voltage across inductor L1 is negative (\(\mathrm{V_{O}}\)-\(\mathrm{V_{batt}}\)) and its current raises and the voltage across inductor L2 is (+\(\mathrm{V_{PV}}\)) forcing its current to increase.
2. _Stage II_ is illustrated in Figure 6b: switch S3 is turned OFF while switch S1 is still in conduction. This means the PV will contribute current towards the load and the diode D1 will conduct. Therefore, the voltage across inductor L1 remains (\(\mathrm{V_{O}}\)-\(\mathrm{V_{batt}}\)) and the voltage across inductor L2 is (\(\mathrm{V_{PV}}\)-\(\mathrm{V_{O}}\)) forcing its current to decrease.  
Figure 5: Typical waveforms for single input double output mode for continuous conduction mode.  
3. _Stage III_ is illustrated in Figure 6c: switches S1 and S3 are turned OFF while switch S2 is conducting through its body diode. Therefore, the voltage across inductor L1 is (V\({}_{\text{O}}\)) causing the amplitude of its current to decrease. The voltage across inductor L2 remains negative (V\({}_{\text{PV}}\)-V\({}_{\text{O}}\)) and it current continues to decrease.  
Figure 7 shows the typical waveforms of the converter during DISO mode. As can be observed, the operation of the PV-converter remained unchanged and consequently it is governed by Equation (8). For the battery converter, applying the voltage balance principle to inductor L1, the following equations can be obtained:  
\[(\text{V}_{\text{O}}-\text{V}_{\text{batt}})\text{d}_{1}+\text{V}_{\text{O}}( 1-\text{d}_{1})=0 \tag{12}\]  
Therefore,  
\[\text{V}_{\text{O}}=\text{d}_{1}\text{V}_{\text{batt}} \tag{13}\]  
Figure 6: Current flow paths of operating stages in DISO mod: (**a**) Stage I; (**b**) Stage II; (**c**) Stage III.  
Figure 7: Typical waveforms in DISO mode at CCM.
## 3 Design of Reinforced Concrete Corrosion Testing  
### Corrosion Method  
The corrosion methods for reinforced concrete specimens currently include natural corrosion, artificial climate simulation, and accelerated corrosion using applied current [28].  
Natural corrosion involves exposing concrete specimens to real environmental conditions over an extended period to induce corrosion in the reinforcing steel, thereby assessing its durability. This method accounts for factors such as climate variation, humidity, temperature, and CO\({}_{2}\) concentration. However, due to the long experimental period required, it is rarely used in research [29; 30].  
Artificial climate simulation is conducted in multi-functional climate chambers where parameters such as temperature, humidity, oxygen content, light duration, CO\({}_{2}\) concentration, and chloride ion concentration are controlled to accelerate corrosion [28]. This method involves two stages: initiating corrosion and accelerating it. For instance, [PERSON] et al. [31] induced and accelerated corrosion by subjecting specimens to a dry-wet cycle for 32 days followed by a humid environment. Compared to natural corrosion, artificial simulation offers a shorter corrosion duration and better simulates actual corrosion environments.  
Figure 1: Process of reinforcement corrosion reaction.  
Accelerated corrosion using applied current is based on electrochemical principles, where an external current is applied to speed up the corrosion process. In this method, the reinforcing steel acts as the positive electrode, while a counter electrode serves as the negative electrode, with an electrolyte solution facilitating the corrosion [7]. The steel connected to the positive terminal undergoes oxidation, while the steel connected to the negative terminal undergoes reduction. This method, which forces electrochemical reactions in the specimen's steel, has relatively low requirements for the experimental environment and demonstrates good adaptability [32]. Among the three corrosion testing methods, this study proposes using accelerated corrosion with applied current for testing short columns and bridge piers.
## 4 Conclusions  
This study successfully produces ferrite magnets [Mn\({}_{0.54}\)Zn\({}_{0.46}\)]Fe\({}_{2}\)O\({}_{4}\) using waste batteries. When the sintering temperature exceeds 600 \({}^{\circ}\)C, Fe atoms are thermally dissociated into \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) and the saturated magnetization decreases. Sintering in a N\({}_{2}\) environment increases thermal dissociation. In a N\({}_{2}\) environment, the optimal sintering temperature is 800 \({}^{\circ}\)C, at which temperature the saturated magnetization is 42.36 M (emu/g). For sintering temperatures greater than 800 \({}^{\circ}\)C, excessively long sintering in N\({}_{2}\) has a negative effect. The difference between the maximum and the minimum values for the saturated magnetization is 32.14 M (emu/g). These values are obtained at different sintering temperatures. The XRD diffractograms and the SEM images show that the sintering temperature affects the strength of the crystal phase. The higher the sintering temperature, the stronger is the crystal phase with a spinel structure. The average particulate diameter also increases and the higher the sintering temperature, the greater is the average particulate diameter.  
The VSM images for Mn\({}_{\rm x}\)Zn\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\) with different pH values show that the optimal pH value is 9. When the pH value exceeds 9, the saturated magnetization decreases because Zn ions do not precipitate completely at higher pH values. The XRD diffractograms for different pH values for recycled batteries that are used to produce [Mn\({}_{\rm x}\)Zn\({}_{1-x}\)]Fe\({}_{2}\)O\({}_{4}\) show that when the pH value is less than 9, insufficient alkaline content in the crystal phase results in incomplete precipitation of metallic ions. The VSM images show that the optimal pH value is 10. At pH values that are greater than 10, the saturated magnetization decreases because Zn ions do not precipitate completely.  
The SEM images show that there is a difference between unsintered Mn\({}_{\rm x}\)Zn\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\) and Mn\({}_{\rm x}\)Zn\({}_{1-x}\)Fe\({}_{2}\)O\({}_{4}\) that is sintered at 900 \({}^{\circ}\)C. The average particulate diameter is affected by sintering temperature, and varies between 10 nm and 100 nm.  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
This research received no external funding.  
The financial support from the Ministry of Science and Technology under grant numbers MOST 105-2221-E-274-003 and MOST 106-2221-E-274-004 is gratefully acknowledged.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 9: The effect of forming sintering on the \(\mu_{0}\) and R values for different systems.
## 2 Principle of RCDMC  
As shown in Figure 1, the proposed structure of RCDMC consists of two PMs, one center yoke, two outer yokes and two end yokes. The two PMs are magnetized horizontally in opposite directions, thus the magnetic poles of the two PMs in contact with the center yoke are of the same polarity, which is the south pole in Figure 1. In this way, in the proposed RCDMC there are only one pole pair (two PMs) and one stator tooth pair, which are protrusive in the middle of outer yokes. Two long air gaps (LAGs) are formed between the stator tooth pair and the center yoke. In this structure, magnetic flux lines can form a set of closed loops through the center yoke, air gaps, outer yokes and one end yoke. Uniform MFD distribution is therefore formed in the two LAGs, along which the Al\({}_{2}\)O\({}_{3}\) ceramic coil skeleton with coil winding wrapped can be moved with driving current flowing in coil winding.  
Figure 1: Schematic diagram of the rectangular closed double magnetic circuit (RCDMC): **(a)** 3D model; **(b)** 2D model, side view with magnetic flux lines. Reproduced with permission from [19], IEEE, 2019.
## 2 Technical Background
### Machine Learning Models
#### 2.3.3 K-Nearest Neighbors Regression  
The K-Nearest Neighbors Regression is an instance-based algorithm primarily applied to solve regression problems. The core idea of the algorithm is quite simple: the predicted target value for a new instance is calculated by searching for the most similar samples in the dataset--the so-called neighbors--and computing the mean or median target value associated with them. Therefore, the only mathematics required are the calculus of distance metrics to find the neighbors of the query instance. Several distance measures can be used, such as the Euclidean (19), Manhattan (20), and Minkowski distances (21):  
* Euclidean \[d(x_{i},x_{j})=\sqrt{\Big{(}\ a_{r}(x_{i})-a_{r}(x_{j})\Big{)}^{2}}\] (19)
* Manhattan \[d(x_{i},x_{j})=|a_{r}(x_{i})-a_{r}(x_{j})|\] (20)
* Minkowski \[d(x_{i},x_{j})=\Big{(}\ |\ a_{r}(x_{i})-a_{r}(x_{j})|^{p}\Big{)}^{\frac{1}{p}},\] (21)  
where \(a_{r}(x)\) denotes the value of the \(r_{th}\) feature. The most popular of them is the Euclidean distance.  
The value of \(k\), the number of neighbors to be considered, is the only hyperparameter of the model. It can be defined using hyperparameter tuning techniques, and typical values range from 1 to 21. Since the model relies on calculations conducted with the entire training data, its computational complexity considerably increases for large datasets [43].
## 3 MTR Algorithm Based on Multi-Layer Sparse Structure
### Alternating Optimization
#### 3.4.2 Fix \(\mathbf{F}\) and \(\mathbf{A}\) update \(\mathbf{S}\)  
Given the fixed \(\mathbf{F}\) and \(\mathbf{A}\), Equation (10) becomes  
\[\min_{\mathbf{S},\mathbf{F}}\frac{1}{N}tr\Big{(}(\mathbf{Y}-\mathbf{S}( \mathbf{A}\mathbf{K}+\mathbf{F}))^{\top}(\mathbf{Y}-\mathbf{S}(\mathbf{A} \mathbf{K}+\mathbf{F}))\Big{)}+\lambda_{2}tr\Big{(}\mathbf{S}^{\top}\mathbf{D} \mathbf{S}\Big{)} \tag{16}\]  
Taking the derivative of the objective function (15) with respect to \(\mathbf{F}\), we have  
\[\frac{\partial J}{\partial\mathbf{S}}=-\frac{1}{N}(\mathbf{Y}-\mathbf{S}( \mathbf{A}\mathbf{K}+\mathbf{F}))(\mathbf{A}\mathbf{K}+\mathbf{F})^{\top}+ \lambda_{2}\mathbf{S}\mathbf{D} \tag{17}\]  
which lead to  
\[\mathbf{S}(\mathbf{A}\mathbf{K}+\mathbf{F})(\mathbf{A}\mathbf{K}+\mathbf{F})^{ \top}+\lambda_{2}N\mathbf{S}\mathbf{D}=\mathbf{Y}(\mathbf{A}\mathbf{K}+\mathbf{ F})^{\top} \tag{18}\]and hence \(\mathbf{S}\) can be iteratively solved by  
\[\mathbf{S}=\mathbf{Y}(\mathbf{AK}+\mathbf{F})^{\top}\Big{[}(\mathbf{AK}+\mathbf{ F})(\mathbf{AK}+\mathbf{F})^{\top}+\lambda_{2}\text{N}\mathbf{D}\Big{]}^{-1} \tag{19}\]  
In each iteration, \(\mathbf{S}\) is calculated with the current \(\mathbf{D}\) and then \(\mathbf{D}\) is updated based on the newly calculated \(\mathbf{S}\). In summary, the alternative optimization algorithm is described in Algorithm 1.  
```
Input: data matrix \(\mathbf{X}\) associated with corresponding targets matrix \(\mathbf{Y}\);  Regularization parameter \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\). Output: the regression coefficient matrix \(\mathbf{A}\);  the structure matrix \(\mathbf{S}\);  the latent feature matrix \(\mathbf{F}\).
```  
**Algorithm 1** The alternative optimization algorithm to solve MTR-MLS.
# Solid-State Transformer-Based DC Power Distribution Network for Shipboard Applications
## 2 DC Distribution Systems Based on Conventional and Solid-State Transformers
Moreover, it has been reported that the line frequency transformer-based system has efficiency between 88 and 92% in the traction applications, but the same system, when being replaced by an SST, can achieve an efficiency of more than 95% while being more compact [31]. From a commercial perspective, ABB introduced its first 1.2 MVA SST and reported an efficiency of 95% in 2012. This SST is advantageous because it allows more space for passengers and goods on shipboard and reaches a power density of 0.75 kVA/kg, which is significantly smaller and lighter than the line frequency transformer [32]. It has been proved that SSTs have lower weight per kVA than their line frequency transformer counterparts, where the 500 kVA line frequency transformer volume and weight are 3.43 m\({}^{3}\) and 2590 kg, respectively, while the 500 kVA SST volume and weight are 1.1 m\({}^{3}\) and 1270 kg, respectively [16]. SST is considered expensive at an early stage of deployment due to the overall system components, including controllers, gate drivers, filters, semiconductor devices, communications modules, and passive elements. The cost of SST is expected to experience significant reductions in the coming years due to continuous reductions in the components, packaging, and manufacturing costs [27].
# Automatic Segmentation of Histological Images of Mouse Brains
## 2 Materials and Methods
### Implementation  
The proposed method was integrated into existing analytical pipelines to allow neuroanatomists to continue annotating regions of interest in the mouse brain albeit more efficiently. Using a command line, the end user is now able to pre-analyze the images automatically and then run the existing analytical pipeline made of ImageJ macros to validate or edit the deep-learning generated regions of interest. For the end user, this results in faster (6 to 10X) image annotation times. Figure 4 shows the final pipeline of the model.  
In Figure 5, an example of standard workflow for all the regions within the mouse brain is shown. As inputs we have, on the one hand, the masks of each of the regions of interest (up to 24 per brain), and on the other hand, about 2000 histological images of the mouse brain associated with these masks. Of the total number of target images, 85% are used for training and validation tasks. The remaining 15% were destined for testing. The regions of interest are converted to a format that can be used in the deep learning model (ROI format to PNG format) and then reduced in size to the size set for the study. In the same way, the size of the histological images is reduced. Afterwards, with both images reduced in size, we proceed to train the deep learning model (in this case, an Attention U-Net architecture). Once the model has been trained, the predicted masks are then stored and evaluated with the aforementioned evaluation metrics.  
Once the network is correctly trained and with favorable responses for the segmentation of the regions of the mouse brain, we proceed to the implementation of the system that will be put into operation in the NeuroGeMM laboratory. Figure 4 shows the final pipeline implemented to segment 24 region of interest within the mouse brain. This system accepts high-definition histological images as input and delivers up to 24 regions of interest found in the input image as output. This process is performed by the automatic segmentation of the regions thanks to the previous trained network. The main constituent parts to fulfill this purpose are: size reduction, region prediction, size increase, and finally the selection of points of interest for storage in ROI format.  
Figure 4: Overall pipeline of the proposed method. Top: Input (high-resolution images) and Output (24 regions of interest, ROIs) used or generated by the deep learning model. The process of automated segmentation is delineated into several steps: downscaling of images, pretraining based on the specific group to which each ROI belongs, upscaling of the mask, and conversion of the mask to landmarks.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 6 Discussion  
Stacked-LSTM has three advantages: first, compared with Conv-TasNet, it only requires fewer stacked LSTM blocks to achieve the same separation accuracy as the former or even surpass the former in the case of half of the mixed signals. In addition, the model is more concise. Second, it is superior to Stacked-TCN in the potential to improve separation accuracy. This is because the input of the Bi-LSTM block can be segmented and overlapped to form a 3-D tensor as input, and the longest possible sequence can be used to improve  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Frame Duration** & **CPU Computing Time** \\ \hline Stacked-LSTM time domain mask method & 0.032 & \(7.9\times 10^{-3}\) \\ Time-frequency domain mask method & 0.256 & 1.24 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Times per frame (ms).  
the accuracy of separation of long-term dependent effects, while the receiving domain of stacked-TCN block cannot be too large. Third, GAP is embedded in the Bi-LSTM block of Stacked-LSTM instead of the FC layer. At the same time, the attention mechanism of the SE module can be used to determine the importance of different channel features by capturing global information to dynamically adjust the network. Although Stacked-LSTM has three advantages, there is still room for improvement in separation speed.
# Development and Application of MAGIC-f Gel in Cancer Research and Medical Imaging
## 1 Introduction  
Gel dosimeters are composed of radiation-sensitive chemicals that experience a fundamental change in their properties following ionizing radiation, due to the absorbed dose [1, 2]. These features of the polymeric gel dosimeters can store dose distribution information in three dimensions, an additional benefit compared to other dosimeters that provide only one-dimensional dose distribution, such as ionization chambers and films in a point, or two-dimensional dose distribution [2, 3]. This benefit is especially important for emerging technologies relating to radiation, where there is a substantial incidence of high-dose gradients [4]. There are various kinds of gel dosimeters that have been discovered so far, but two types, with the names Fricke and polymer gel, have been extensively used. These polymer gels are usually analyzed by using various diagnostic imaging techniques, such as sonography, optical computer tomography (OCT), X-ray computer tomography (X-ray CT), and magnetic resonance imaging (MRI) [5].  
A radiation dosimetry process with MAGIC-f (Methacrylic Ascorbic Acid in Gelatin Caused by Copper-Added Formaldehyde) gel consists of steps involving gel preparation, irradiation with ionizing radiation, taking images of irradiated gels using some imaging techniques and finally analyzing the images to calculate the dose distribution. A typical polymer gel dosimetry process involves three stages known as fabrication, irradiation, and evaluation stages, as shown in Figure 1. Therefore, development process of MAGIC-f gel dosimetry consists of development of all these three processes as well. Thus, in this review, besides the discussion of development trends of MAGIC-f gel from the development of a precursor form of polymer gel such as dye made up of methylene, development of each step of MAGIC-f dosimetry is also discussed. Hence, this review briefly examines the current knowledge of dosimetry using MAGIC-f gel to emphasize its structure, manufacture, imaging, calibration, and application to medical physics research.