esa-sceva/satcom-chunk-collection huggingface dataset:
# Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
## 3 Results  
### Training  
The experimental hardware and software conditions are detailed in Table 1. Table 2 encompasses the parameters of the enhanced woodland-fire-classification detection model, which were initially configured through a preliminary exploration of the default settings of YOLOv8 and subsequently adjusted during the experimental process. Initially, the YOLOv8 model's learning rate was initialized to 0.01, and the training epochs were adaptively modified depending on the model's performance on the validation set, with 200 epochs determined as the optimal selection. The dataset for forest fire classification was partitioned into training, validation, and test sets in an 8:1:1 ratio.
# Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection
## 4 Experiment
### Comparison with State-of-the-Art Methods
#### 4.4.2 38-Cloud Dataset  
Table 5 and Figure 8 show the performance of our model and other methods on the 38-cloud dataset. From the data in Table 5, we can see that Cloudformer has obtained similar results to Cloud-Net+ on the 38-Cloud dataset. However, Cloudformer only inputs the visible light spectrum band data, and Cloud-Net+ inputs the near-infrared spectrum data at the same time as the visible light spectrum band data. This means that Cloudformer has higher versatility and can be applied to remote-sensing satellites equipped with only a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MIoU (\%)** & **FwIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline Deeplabv3+ [40] & 89.76 & 90.02 & 94.62 & 95.08 \\ CDNet [39] & 91.83 & 93.04 & 97.45 & 96.72 \\ DABNet [38] & 92.08 & 93.25 & 97.69 & 98.43 \\ Cloudformer (ours) & **96.56** & **98.17** & **98.29** & **99.07** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different cloud-detection methods on AIR-CD.  
Figure 8: Comparison of different cloud-detection methods on the AIR-CD dataset. (**a**) Image. (**b**) Ground truth. (**c**) Cloudformer. (**d**) DABNet. (**e**) Deeplabv3+. (**f**) CDNet.  
few spectral band sensors. Figure 9 shows the visual mask of the detection results of each model. Visually, the detection results of Cloudformer are excellent.
# Innovation and Application of the Type III Secretion System Inhibitors in Plant Pathogenic Bacteria
## 3 Discovery of T3 SS Inhibitors in Plant Pathogens and Their Regulatory Mechanisms  
Small molecules that could specifically inhibit the synthesis or functionality of the T3 SS are referred to as T3 SS inhibitors. Unlike traditional antibiotics that often target bacterial survival, T3 SS inhibitors display negligible effects on bacterial growth, thus reducing the selective pressure for the development of resistance [113; 114; 112]. In plant pathogenic bacteria, extensive studies have led to the discovery of a group of plant-derived compounds and several chemically synthesized compounds that modulate the expression of T3 SS in major plant pathogens (Table 1). Further studies detailed the mode of action of these compounds and investigated their potential for disease management.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound** & **Structure** & **Known Modes of Action on T3 SS in Plant Pathogens** & **References** \\ \hline  & & Induce T3 SS via the RsmA/RsmB-HrpL pathway in D. _adandtii_; inhibitor T3 SS in _E. amylovora_ & [32; 115; 116] \\ \(\alpha\)-coumaric acid & & Inhibit T3 SS via the HrpC-HrpX regulatory cascade. \\ \(t\)-cinnamic acid & & Inhibit T3 SS via the HrpC-HrpL pathway in D. _adandtii_. & [117] \\ \(p\)-coumaric acid & & Inhibit T3 SS via the HrpC-HrpL pathway in D. _adandtii_. & [118] \\ \(t\)-cinnamic acid & & Inhibit T3 SS via the HrpC-HrpL pathway in D. _adandtii_. & [115] \\ \(t\)-cinnamic acid & & Inhibit T3 SS via the HrpC-HrpL pathway in D. _adandtii_. & [117] \\ \(t\)-cinnamic acid & & Inhibit T3 SS via the RpN-HrpL pathway, \\ \(t\)-chnamic acid & & HrpX/HrpY-HrpS-HrpL pathway, and the RsmB-HrpL pathway in D. _adandtii_. & [118] \\ Benzoic acid & & Inhibit T3 SS via targeting HrpS in _E. amylovora_. & [115] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compounds that regulate T3 SS in some major plant pathogens.
## 3 Experiment and Result Analysis
### Binary Fitting of Twisting Tension to Spindle Angular Speed and Yarn Fineness  
The spindle angular speed of the direct-twisting machine and the fineness of the industrial yarn were the key factors that affect the twisting tension. In order to comprehensively investigate the influence of these two factors on the tension, different twisting components were selected, and the spindle angular velocity and yarn fineness were changed, so as to obtain the experimental data of twisting tension under the conditions of different spindle angular speed and yarn fineness. For example, when the configuration of the twisting tray/yarm storage tray/spindle can was: 220, 120, and 266 mm, and the fineness of the industrial yarn were: 930, 1100, 1440, 1670, and 2200 dtex, the twisting tension was measured under a series of spindle angular velocity, as shown in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Yarn Fineness** & **Fitting Equation** & \(\mathbf{R^{2}}\) \\ \hline
930 dtex & \(P_{\omega 1}=0.00438\omega^{2}-6.44385\omega+3493.50\) & 0.99152 \\
1100 dtex & \(P_{\omega 2}=0.00326\omega^{2}-3.91692\omega+2422.11\) & 0.99676 \\
1440 dtex & \(P_{\omega 3}=0.00467\omega^{2}-5.72722\omega+3032.47\) & 0.99837 \\
1670 dtex & \(P_{\omega 4}=0.00131\omega^{2}-0.20779\omega+1393.84\) & 0.99963 \\
2200 dtex & \(P_{\omega 5}=0.00283\omega^{2}-0.75535\omega+1075.35\) & 0.99410 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics table of fitting equation for twisting tension and spindle angular velocity under different yarn fineness. Figure 7: Fitting curves of balloon yarn tension and spindle angular velocity under different yarn fineness. According to the data in Table 3, the twisting tension increased with the increase of spindle angular speed and yarn fineness, and the variation of twisting tension had obvious regularity. In order to fit the binary equation of the twisting tension \(P_{d}\), the spindle angular velocity \(\omega\) and the yarn fineness \(T\) of the direct-twisting machine, the Levenberg-Marquardt algorithm of the origin software was used to non-linearly fit the curved surface. The spindle angular velocity \(\omega\) and yarn fineness \(T\) were selected as the independent variables. Twisting tension \(P_{d}\) was selected as the dependent variable. According to the measurement data in Table 3 above, various nonlinear fitting equations were obtained, which are numbered 1-8, as shown in Table 4. \(P_{d1}\), \(P_{d2}\), \(P_{d3}\), \(P_{d4}\), \(P_{d5}\), \(P_{d6}\), \(P_{d7}\), and \(P_{d8}\) represent the fitting values of twisting tension under different binary fitting equations. The unit of \(P_{d1}\)-\(P_{d8}\) is cN. \(m_{1}\) and \(m_{2}\) are the intermediate variables of the transition. The accuracy of the fitting equations was measured by the coefficient of determination \(R^{2}\), which is also called the goodness of fit. For a set of sample spaces containing \(N\)-ordered pairs of real numbers, (\(x_{i}\), \(y_{i}\)) (\(i\) = 1, 2, 3,..., \(N\)) is the sample points, \(\overline{y}\) is the average of the
# A Method for Generation of a Sizing System and Representative Models for a Facial Mask Design
## 3 Comparative Selection of RFMs for a Pilot Oxygen Mask for KAF Pilots
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**WSED Calculation**} & \multicolumn{4}{c}{**NED of Representative Face Per Sizing Category**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SD**} \\ \cline{3-4} \cline{6-9}  & & **Small** & **Medium** & **Medium** & & **Large** & \\  & & **Narrow** & **Narrow** & **Wide** & **Wide** & & \\ \hline \multirow{2}{*}{1} & Measured value \& & 0.23 & 0.23 & 0.37 & **0.13** & & \\  & Equal weight & (260) & (165) & (240) & **(177)** & & & \\ \multirow{2}{*}{2} & Measured value \& & **0.20** & **0.21** & **0.14** & 0.15 & & \\  & Unequal weight & **(11)** & **(307)** & **(189)** & (139) & **0.18** & **0.04** \\ \multirow{2}{*}{3} & Normalized value \& & 0.46 & 0.46 & 0.53 & 0.58 & & \\  & Equal weight & (54) & (128) & (189) & (177) & & 0.51 & 0.06 \\ \multirow{2}{*}{4} & Normalized value \& & 0.58 & 0.49 & 0.53 & 0.66 & & \\  & Unequal weight & (32) & (128) & (189) & (139) & & & \\ \hline \end{tabular} Bold texts: the smallest values among the results of WSED calculation conditions.  
\end{table} 
Table 4: Normalized Euclidean distance (NED) of the identified representative face by the weighted sum of Euclidean distance (WSED) calculation method (unit: mm; pilot identification numbers are indicated in parentheses). \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**WSED Calculation Condition**} & \multicolumn{2}{c}{**Pilot Identification Numbers Showing the Smallest WSED**} \\ \cline{2-6}  & **Type of Measurement** & & **Small** & **Medium** & **Medium** & **Large** \\  & **Value** & & **Narrow** & **Narrow** & **Wide** & **Wide** \\ \hline
1 & measured & equal & 260 & 165 & 240 & 177 \\
2 & measured & unequal & 11 & 307 & 189 & 139 \\
3 & normalized & equal & 54 & 128 & 189 & 177 \\
4 & normalized & unequal & 32 & 128 & 189 & 139 \\ \hline \end{tabular}
\end{table} 
Table 3: Representative pilot identification numbers showing the smallest weighted sum of Euclidean distance (WSED) for each sizing category for different WSED calculation conditions.
# Spam Review Detection Techniques: A Systematic Literature Review
## 2 Unsupervised Learning
### Twice-Clustering Technique  
Twice-clustering is used to improve the precision and diversity of an unsupervised learning method [64; 65; 66]. Twice-clustering works in a series of steps. First, the original dataset is divided by using k-fold cross-validation. Second, all the training data to cluster is chosen for the first time to form a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper ID** & **Dataset** & **Method** & **Accuracy** \\ \hline
[60] & Vietnamese mobile phone, product review & K-nearest neighbors (KNN) classification & 72\% \\
[61] & Product reviews from 360 buy.com & Twice-clustering & 66\% \\
[62] & Chinese reviews from the web & K-means & Precision 71\% \\
[63] & Real-life Amazon review dataset & Constrained k-means clustering (COF K-mean) & Recall 61\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of different Unsupervised Learning Techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper ID** & **Dataset** & **Learner** & **Accuracy** \\ \hline
[35] & Amazon.com & Logistic Regression (LR) & 78\% \\
[27] & Epinions.com & Naive Bayes (NB) & 63\% \\
[15] & Hotel reviews through Amazon & Support Vector Machine (SVM) & 89.9\% \\  & Mechanical Turk (AMT) & Support Vector Machine (SVM) & 71\% \\
[36] & Amazon.com & Support Vector Machine (SVM) & 86.1\% \\
[4] & Yelp’s real-life data & Support Vector Machine (SVM) & 84\% \\
[33] & Hotel reviews through Amazon & Support Vector Machine (SVM) & 84\% \\  & Mechanical Turk (AMT) & Support Vector Machine (SVM) & 84\% \\
[34] & Arabic reviews from & [PERSON] (NB) & 99\% \\  & Tripadvisor.com and Booking.com & Decision Tree (DT) & 75\% \\
[49] & IMDb movie & Random Forest (RF) & 65\% \\
[51] & Chinese Language micro-blog & Logistic Regression (LR) & 79\% \\  & & [PERSON] (NB) & 72\% \\
[52] & Yelp restaurant reviews & Random Forest (RF) & 76\% \\  & Support Vector Machine (SVM) & 78\% \\
[53] & Amazon product reviews & Random Forest (RF) & 91\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of different Supervised Learning Techniques.  
cluster subclass and then clustering is applied to each subclass to form a sample subset of each subclass. The sample subset of each subclass may be introducing some biasness. Therefore, to overcome this problem, it was observed by literature review that non-uniform random sampling is a good approach to form a sample subset of each subclass [67]. Finally, a subset of each subclass is selected to construct a training set to train an unsupervised learner. [PERSON] et al. [61] used the twice-clustering method on a product review dataset from 360 buy.com and reported 66% accuracy in the detection of spam reviews.
# The Refined Simulation Methods of Fluctuating Wind and Aerodynamic Forces Relative to Moving Trains
## 5 The Aerodynamic Force Simulation of Moving Trains
### The Unsteady Aerodynamic Forces of Moving Trains  
The unsteady aerodynamic forces of the moving train under four different wind environments were simulated using Equation (33), as shown in Figure 8. Considering the inhomogeneous and non-stationary characteristics of the wind environment, the unsteady aerodynamic force of the moving train is significantly different from those in the homogeneous and stationary wind environment, which indicates that the proposed method in this paper could effectively simulate the aerodynamic force of the moving train under different wind environments. Similar to the results of the simulated wind fields in Figure 5, the aerodynamic force of the train is almost unchanged around \(t=14.4\) s with consideration of inhomogeneous characteristics. In comparison, it remains unchanged in the range around \(t=30\) s with consideration of non-stationary characteristics.  
Figure 8: The aerodynamic forces of the moving train under different wind environments: (**a**) inhomogeneous and non-stationary wind environment; (**b**) inhomogeneous and stationary wind environment; (**c**) homogeneous and non-stationary wind environment; and (**d**) homogeneous and stationary wind environment.  
Figure 8 illustrates the difference in aerodynamic forces of moving trains under different wind environments. The peak values of aerodynamic forces decrease significantly as the inhomogeneous and non-stationary characteristics are considered, and the inhomogeneous characteristic has a more significant influence on the aerodynamic force of the train. Therefore, in the dynamic response analysis of moving trains under complex wind environments, especially in canyons, it is necessary to carefully consider the wind load acting on the train with the proposed framework.
## Materials and Methods
### Overall Workflow  
The overall workflow is shown in Figure 3, which includes the following four parts: Image preprocessing, image classification, classification results, and sensibility analysis. Image preprocessing includes filtering Sentinel-2 data in a specific phenological period based on the GEE, and obtaining  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land Cover** & **Abbreviated** & **Description** & **Samples** \\ \hline Wheat & WHE & Wheat pixels during the observation period & 910 \\ Vegetation & VEG & Other crops, urban greening, and other & 900 \\  & & & evergreen vegetation & \\ Urban & URB & Land for construction, such as for houses & 480 \\  & & and roads & \\ Water & WAT & Water bodies such as rivers, lakes, & 210 \\  & & and swamps & \\ Others & OTH & Bare, idle, or deserted land during the & 290 \\  & Total & & observation period & 2790 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample data selected in this study.  
high-quality cloudless images in the study area through steps such as minimum cloud cover, cloud mask, splicing, and clipping. For image classification, three machine learning algorithms, namely, SVM, RF, and CART, were employed. After dividing all sample data into training and validation sets, the training set served as the input data for these algorithms, and the validation set was used to validate the classification accuracy. In the classification results section, the three classification results were compared to the officially published wheat area data in order to analyze the classification differences. The OA, user's accuracy (UA), producer's accuracy (PA), and kappa coefficient (Kappa) were calculated by error matrixes, and these classification assessment indicators were compared to the three machine-learning algorithms. Meanwhile, to explore the generalization ability and the mechanisms of the three algorithms, different training and validation data sets were created and inputted into the three algorithms for classification. Finally, those algorithms with robust classification effects and generalization ability were chosen. In the sensitivity analysis section, various algorithm parameters were iterated to explore their effect on the accuracy score and classification residuals.
## 4 DT Use Cases and Application in Healthcare
### Medical Devices  
Healthcare organizations rely on a wide range of critical assets, including monitoring and imaging devices, respirators, and surgical tools, which must always be ready for patient care. Digital twin (DT) technology leverages data from IoT sensors embedded in these devices to monitor their configuration and maintenance history. DT solutions offer real-time insights into device performance, utilization, and maintenance needs. Furthermore, DTs enable the development of customized and intelligent medical devices, optimizing both efficiency and costs. For example, DTs can precisely simulate the positioning, alignment, and dimensions of devices like stents, pacemakers, and implants. This allows healthcare providers to determine the most effective device type and positioning for each patient, thereby minimizing complications and reducing the necessity for follow-up procedures [66].
# Application in Nanomedicine of Manganese-Zinc Ferrite Nanoparticles
###### Abstract  
Besides the study and the medical application of iron oxide nanoparticles, ferrites produced with zinc and manganese are of particular interest for their properties. The introduction of these elements into the crystalline structure of the magnetic particle generates some changes in the material properties, enhancing their potential use in the transostic applications. This review will cover the most important aspects of the preparation of these materials, taking into account the different methods of synthesis, and will analyze the most promising results in their use in MRI, magnetic hyperthermia and other emerging applications.  
magnetic nanoparticles; Mn-Zn ferrites; nanomedicine; MRI; magnetic hyperthermia +
Footnote †: journal: Applied Physics Letters
# Sparse Indoor Camera Positioning with Fiducial Markers
## 4 Experiments
### Corridor Dataset  
In this experiment, our methodology was evaluated within a real 50 m\({}^{2}\) corridor (refer to Figure 5a). The setup included six fixed cameras positioned linearly along the ceiling without overlapping views. Each camera boasted a resolution of \(3840\times 2160\) pix and a focal length of approximately 2201 pix. The experiment required two groups to reconstruct the corridor with the aid of a moving camera and 50 ArUco [34] markers. The moving camera operated at a resolution of \(1920\times 1080\) pix and a focal length of approximately 1339 pix. This setup was chosen to evaluate the accuracy of our method in preserving spatial continuity across cameras that do not share fields of view. Ground truth data were generated through manual annotation. Upon completion of each dataset, our method is not run in real-time but is instead initiated after the dataset has been fully recorded. An overview summarizing the datasets used in this work, including the number of markers, fixed cameras, frames, and groups required for reconstruction, can be found in Table 1.
## 1 Introduction  
Enhancing the internal quantum efficiency (IQE) of GaInN-based light-emitting diodes (LEDs) has been discussed in the literature for potential solid-state lighting applications [1; 2; 3]. There are multiple reports to improve the IQE of LEDs mainly by using nonpolar substrates [4; 5] and bandgap engineering [6; 7; 8; 9]. Salient bandgap engineering approaches include triangular-shaped quantum wells [10; 11], staggered quantum wells [12], graded quantum wells [8; 13], polarization-matched quaternary barriers [14; 15], quaternary electron blocking layer (EBL) [16], modification of EBL [17; 18], InGaN barriers [19; 20], as well as last quantum barrier [21], coupled quantum wells [22], and recently proposed all-quaternary device with and without electron-blocking layer [23; 24]. In comparison to the conventional rectangular quantum wells, it has been shown using triangular, staggered, dip-shaped, and trapezoidal wells that the electron-hole wavefunction overlap is significantly improved in the quantum wells (QWs), which leads to improved optoelectronic output of the device [11; 25; 26; 27]. In addition, indium fluctuations in the GaN-based LEDs are known to influence the optoelectronic properties of the device [28; 29; 30; 31; 32]. Most of the reported work on bandgap engineering is based on LEDs in the blue emission range [11; 22; 27]. However, because of the 'green gap' challenge, it is of great interest to improve the performance of green LEDs [32; 33]. Therefore, in this work, we present bandgap-engineered green W-shaped quantum well and discuss its influence on the improved optoelectronic performance of the green light-emitting diode by numerical simulation.
# Modal Verification and Strength Analysis of Bladed Rotors of Turbine in Rated Working Conditions
## 2 Materials and Methods
### Analysis of Centrifugal Force of TRT Rotor
#### 2.2.1 Analytical Solution  
The TRT rotor working speed is 3600 rpm (60 rps). To obtain the tensile stress distribution on the blade due to centrifugal force, the centrifugal force (\(F_{r}\)) shall be calculated which is expressed as follows:  
\[F_{r}=m\omega^{2}l \tag{4}\]  
where \(m\) is the object mass; \(\omega\) is the angular velocity of an object rotating along the shaft; \(l\) is the shortest distance between the object centroid and rotation axis. According to the above equation, the magnitude of centrifugal force is correlated with the rotational speed of the object and related to the mass and centroid position of the object. As the length direction of the blade is normal to the direction of centrifugal force, there are different centrifugal forces in different length positions of the blade, expressed as follows:  
\[F_{r}=\int_{r-R_{Hub}}^{R_{Tip}-R_{Hub}}\rho A_{\kappa}\omega^{2}D_{r}dx \tag{5}\]  
where \(R_{Tip}\) is the distance from the center of circle \(\mathbf{O}\) to the blade tip; \(R_{Hub}\) is the distance from the center of circle \(\mathbf{O}\) to the blade root; \(r\) is the distance from the center of circle \(\mathbf{O}\) to the blade section of the desired centrifugal force; \(\rho\) is the blade material density; \(\omega\) is the angular velocity of rotation of the rotor; \(A_{\kappa}\) is the function of the locational relation between the cross-section area and blade length; \(D_{r}\) is the function of the locational relation between the centroid position and blade length. The aforesaid symbols and blade diagram are shown in Figure 3. The centrifugal force on the section of arbitrary blade length positions can be obtained through the aforesaid procedure.  
Figure 2: Centrifugal force resulting from rotor blades of TRT rotor in operation.  
When the centrifugal force in each length position of the blade is obtained, the tensile stress (\(\sigma_{r}\)) in various positions of the blade can be calculated which is expressed as follows:  
\[\sigma_{r}=\frac{F_{r}}{A_{r}} \tag{6}\]  
where \(\sigma_{r}\) is the tensile stress resulting from centrifugal force; \(F_{r}\) is the centrifugal force; and \(A_{r}\) is the cross-sectional area. The tensile stress resulting from the centrifugal force in any radius position of the blade can be obtained by Equations (4)-(6).