esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 2 Shearer Cutting Unit Detection in Infrared Thermal Images
### The Basic Theory of Mathematical Morphology  
In image processing, morphology takes the morphological features of an image as its processing object, and describes the relationships among elements and parts in the image. When dealing with the related morphological features, the image region can be operated logically by using a structured element that can be segmented and extracted for various morphologies, making it easier to detect and identify the active parts in the image. As a field operation, the morphology of the image combines the structural elements with the pixels by logic operation. The output image is the result of the interaction between the structural element and the original image. In the morphology algorithm, corrosion and swell are two basic operations, as shown in Figure 1.  
The corrosion operation reduces or refines the objects in the image, and can be regarded as a morphological filter. Some unimportant image details (smaller than a structural element) are removed so that key information elements can be retained. The swell operation will increase or coarsen the image objects, so that the key elements can be amplified to obtain more complete details.  
Image morphology operations can be represented by mathematical set theory. Assuming that set \(B\) is a structural element, the corrosion of set \(B\) to set \(A\) can be defined as:  
\[A-B=\left\{z|(B)_{z}\subseteq A\right\} \tag{1}\]  
where \(z\) is a point in set \(A\) and \((B)_{z}\) is the translation of the central point of set \(B\) according to point \(z=(z_{1},z_{2})\). If \(B\) is completely contained by \(A\), then this point is marked as a point of the new set; otherwise, the point will not belong to the new set.  
Analogously, the swell of set \(B\) to set \(A\) can be defined as:  
\[A\oplus B=\left\{z|(\hat{B})_{z}\cap A\
eq\varnothing\right\} \tag{2}\]  
where \(\hat{B}\) is the reflection of set \(B\), which can be obtained by the transformation from \((x,y)\) to \((-x,-y)\) of each point in set \(B\). \((\hat{B})_{z}\) is also the translation of the central point of set \(\hat{B}\) according to point \(z=(z_{1},z_{2})\). Some points covered by the translation of set \(\hat{B}\) will form a new set; this process is the swell of \(B\) to \(A\).
## 3 Results and Discussion
### Transcriptional Levels of Defense-Related Genes
In the current study, the detected down-regulation of PR-1 and PAL genes in T2 plants could be attributed to the suppressing activities of TMV against the tomato immune defense system, which resulted in a failure in the development of SAR activity. Flavonoids, secondary metabolites, compounds play important roles in plant resistance against pathogenic microorganisms, including viruses [69; 70; 71]. Once infection occurs, flavonoids transport to the infection sites, induce hypersensitivity reactions and activate programmed cell death [72; 73]. Chalcone synthase, CHS, encodes the first enzyme of flavonoid pathway that synthesizes naringenin chalcones, is strictly needed in various plant tissues for flavonoid production [74; 75]. Compared to control (T1), the transcriptional levels of CHS in treatments of T2, T3, T4, and T5 were up-regulated with relative expression levels of 1.273-, 2.38-, 1.848-, and 3.556-fold increase, respectively (Figure 7). These results showed that TMV infection slightly induced the expression of CHS in tomato tissues, while ZnO NPs treatments (T3, T4, and T5) exhibited more accumulation of CHS inside the tomato tissues. In line with the obtained results, foliar application of the biologically synthesized ZnO NPs may increase flavonoid production in plant tissues that resulted in the stimulation of the defense system against viral infection. Reactive oxygen species (ROS) promotes programmed cell death at plant cell infection sites and plays an important role as a signaling molecule for the activation of antioxidant activity and the induction of the genes coding for pathogenesis-related proteins (PRs) [76; 77; 78; 79]. It was reported that the increased activity of peroxidase (POD), alternative producers of ROS, was correlated with the increasing plant defense against pathogens [78; 80]. In the current study, we observed that the application of the biologically synthesized ZnO NPs significantly increased the transcriptional levels of POD by 2.641-, 2.175-, and 3.177-fold change higher than control in T3, T4, and T5, respectively (Figure 7). Conversely, the POD of T2 plants was down-regulated with a relative expression level of 0.672-fold change lower than control (Figure 7). It was observed that the induction of POD resulted in an increase of chlorophyll content and enhanced plant resistance against mungbean yellow mosaic virus and TMV [81; 82]. In addition, foliar application of Ag NPs exhibited an improvement in releasing ROS and increasing antioxidant enzymes in virally infected plant tissues [36; 37]. Moreover, MgO NPs, ZnO NPs, and SiO2 NPs treatments induced ROS generation and suppressed TMV infection in tomato and tobacco plants [32; 83]. Therefore, the foliar applications of the biosynthesized ZnO NPs may enhance SAR by releasing ROS and stimulating the expression of PR genes and SA accumulation [32].
## 2 Field Measurement of Rainfall from OEL
### Validation of Rainfall Inversion  
During the plum rain season in 2020, the continuous rainfall has caused serious floods in the Yangtze River basin of China. An extreme rainfall event occurred from June 13 to 16, 2020 and the maximum rain intensity has exceeded 200 mm/h. Next, we will analyze the performance of OEL in these days. The data used here are measured by our established OEL. The comparisons between SNR from receiving terminal and rain intensity from OTT are shown in upper part of each subplot of Figure 1. Due to the limitation of OTT, the temporal resolution of SNR is changed to 1 min. It can be clearly observed that the SNR are negatively correlated with rain intensity and the correlation coefficient (CC) between them is approximately \(-\)0.615. Moreover, the inversed rain intensity from OEL is also shown in the lower part of each subplot. In comparison with OTT measurements, the results of OEL show a good agreement, especially for June 13 and June 15. For the given four examples, the temporal correlations between OTT and OEL are higher than 0.68 and root mean square error (_RMSE_) is lower than 12 mm/h. Although the above results cannot accurately describe the actual performance of OEL, they indicate the potential of OEL being used to reconstruct the rainfall field. Moreover, we will evaluate the rainfall inversion by OEL based on a year of data in Section 5.  
Figure 1: An extreme rainfall event occurred from June 13 to 16, 2020: (**a**) June 13, (**b**) June 14, (**c**) June, 15 and (**d**) June 16. The black line is the signal to noise ratio (SNR), the dark red dotted line is the reference level, the blue- and green line are the rain intensity measured by OTT-Parsivel (OTT) and earth-space links (OELs), respectively.
## 3 Results and Discussion
### Dry Matter, Total Fat and Fatty Acid Profile of Teff Varieties
All teff varieties did not reveal significant differences (_p_ > 0.05) in omega 3 fatty acid (\(\alpha\)-linolenic acid (C18:3, all _cis_-9,12,15)) content. The content of linoleic acid in the studied teff varieties was lower than that reported for barley and wheat but comparable with that reported for sorghum and oat. On the other hand, teff had higher \(\alpha\)-linolenic acid content than barley, sorghum, millet, wheat, rice, maize and oat [23; 24; 27]. Alpha linoleic acid, which is an omega 3 fatty acid, is a precursor for the higher n-3 polyunsaturated fatty acids (PUFAs) (eicosapentaenoic acid, docosapentaenoic and docosahexanoic acid). Thus, the presence of higher \(\alpha\)-linolenic acid in staple foods will protect consumers from the risk of cardiovascular disease, atherosclerosis and hypertension [28]. Table 3 shows the proportion of fatty acid groups, along with the atherogenic and thrombogenic indices of teff varieties. The total saturated fatty acid level ranged from 22.35 to 24.33%; the lowest was obtained in DZ-Cr-387 variety and the highest in DZ-01-1278. Total unsaturated fatty acids ranged from 75.45 to 76.83%, but no statistical difference among the varieties was found, and approximately two-thirds of total unsaturated fatty acids were PUFAs. The lowest percentage of PUFAs was found in DZ-Cr-387 and the highest was found in DZ-01-2423. The presence of a high proportion of PUFAs in oils is a desirable attribute for application in healthy food product development [29]. The ratio of omega 6 to omega 3 fatty acids was in the range of 5.86 to 7.04; the highest was for DZ-01-2675 and the lowest was for DZ-01-1681. The result was lower than that reported for other cereals such as oat, barley, wheat, sorghum, rice and buckwheat (-14-39), which is beneficial, taking into consideration the optimal recommendation of the Mediterranean diet (1-2:1) [23; 24; 30] to reduce cardiovascular diseases. However, according to the European Food Safety Authority opinion, there is no strict recommendation, but it was advised that the intake of n-6 and n-3 should be 4 and 0.5%, respectively, of the total dietary energy [31]. The thrombogenicity index (TI) and atherogenicity index (AI) of teff were in the range from 0.38 to 0.41 and 0.23 to 0.25, respectively (Table 3). The TI of teff was higher than quinoa (0.20), sunflower oil (0.20), rapeseed (0.10), peanut, sesame and olive (0.33-0.34) and oat (0.03-0.34), but lower than rice bran (0.49), buckwheat (0.52), _Amaranthus hypochondicus_ and _Amaranthus cruentus_ (0.65-0.71), palm oil (1.92) and coconut oil (3.98). On the other hand, the AI of teff was higher than that of sunflower (0.08) and slightly higher than oat (0.17-0.19), but lower than _Amaranthus hypochondriacus_ (0.31), palm oil (0.97) and coconut oil (14.71) [30; 32].
## 4 Prospective
### Challenges of Existing Optimization Methodologies
#### 4.1.3 Data Sample Issue  
Training a fully supervised DNN as a forward model takes around 10,000 data samples, as examples in Section 3.2 suggested. These data samples are collected by time-consuming EM simulations, which may cost even more time than iterative optimization algorithms. Therefore, DNNs-based forward models could only show their advantages in scenarios where piles of similar devices are needed. Data dimensionality reduction methods like PCA and Fourier transform have been applied to ease the burden of data samples collection but the effect is not satisfactory. For PCA, optimized data samples are required to find the lower-dimensional sub-space. For Fourier transform, some information will be discarded by high-frequency filters.  
Inverse models project low-dimensional data labels to high-dimensional data features, which require even more data samples compared to forward model using traditional DNNs. The number of data samples can be reduced to a few thousand or hundreds by using semi-supervised DNNs, such as CVAE or CGAN. However, these inverse models require optimized or semi-optimized data samples. Without high-performance training data samples, the generative neural networks are hard to generate devices with high optical performance. Lowering the demand on the number and quality of training data samples is a good direction for future research. Unsupervised learning for inverse models totally solves the problems of massive data samples collection. However, an EM-simulator must merge into the training process of unsupervised generative neural networks to guarantee that the unsupervised DNN obeys physical rules. Apart from the time-consuming training process where EM-simulator has to be involved, inverse models based on unsupervised DNNs are not as general as those based on supervised DNNs. In other words, a well-trained DNN with the help of an EM-simulator may not be suitable for devices with diverse optical properties because such DNN is trained towards the direction of a target optical response. Improving the versatility of unsupervised-learning-based inverse models is also an interesting and meaningful topic for further study.
## 3 Materials and Methods
### K-Means++ Approach  
The K-means++ algorithm [30] is an improvement over the K-means algorithm, specifically addressing the issue of the dependency on the initial centroids. The process of the K-means++ algorithm is as follows:  
1. From the given dataset samples \(S=\left\{s_{1},s_{2},...,s_{p}\right\}\), randomly select one sample as the initial cluster center \(c_{1}\).
2. Calculate the Euclidean distance between each sample \(s_{i}(i=1,2,\cdots,p)\) in the dataset and the initialized cluster centers. Select the shortest distance and denote it as \(D(s_{i})\).
3. To calculate the probability \(P(s_{i})\) of each sample \(s_{i}(i=1,2,\cdots,p)\) being selected as the next cluster center and choose the sample with the highest probability as the new cluster center, use the expression given by Equation (5). Repeat the process until K clusters are determined, and their corresponding cluster centers are denoted as \(C=\left\{C_{1},C_{2},\cdots,C_{k}\right\}\). \[P(s_{i})=\frac{D^{2}(s_{i})}{\sum_{s_{i}\in S}D^{2}(s_{i})}\] (5) where \(D^{2}(s_{i})\) represents the Euclidean distance between the sample \(s_{i}\) and the current cluster center \(C\).
4. Compute the Euclidean distance between each sample \(s_{i}\) in the dataset and the K cluster centers, and then assign each sample \(s_{i}\) to the cluster corresponding to the closest cluster center.
5. For each cluster \(C_{i}\), recompute the cluster center \(c_{i}\) (i.e., the centroid of all samples belonging to that cluster) using the following formula (Equation (3)): \[c_{i}=\frac{1}{|C_{i}|}{\sum_{s\in C_{i}}s}\] (6) where \(|C_{i}|\) represents the total number of samples in cluster \(C_{i}\), \(s\) represents the samples in cluster \(C_{i}\).
6. Repeat steps (4) and (5) until the positions of the cluster centers no longer change.
# Additive Manufacturing for Surgical Planning and Education: A Review
## 3 Educational Applications  
### Models of Human Organs  
Medical professionals are in constant need of better tools that help them to save lives. 3D printing can be used to improve patient care, reduce costs, and increase the speed of every step in the medical value chain. Positive medical outcomes are usually decided by several factors: Well-briefed and prepared surgeons, efficient completion of the surgical procedure within the shortest possible timeframe, and an understanding of patient-specific risks to avoid complications during surgery.  
All of these factors benefit greatly from using high-fidelity anatomical models made with 3D-printing technologies. As surgeons work in a real 3D world, it makes sense for them to analyze patient-specific 3D-printed models, particularly when complex pathologies are involved. This allows procedures to be refined during pre-surgery, meaning fewer complications, shorter procedures, and faster patient recovery times.  
Anatomical models have found extensive applications in various surgical specialties, including maxillofacial surgery, cardiovascular surgery, vascular neurosurgery, dental surgery, general surgery, cranial/orbital surgery, orthopedics, and spinal surgery, as indicated by other studies [8; 10; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. In maxillofacial surgery specifically, some studies [18] have highlighted the utility of anatomical models for implant shaping. Among these studies, some papers emphasized time reduction as a significant advantage. Furthermore, some studies confirmed that printed models offered accurate anatomical representations, leading to improved surgical outcomes, while some studies pointed out potential exposure to ionizing radiation and increased costs [18]. One study found the anatomical model to be an effective representation of the actual pathology without discussing associated costs, while another study acknowledged increased costs due to utilizing an anatomical model [19].  
In our research, de-identified MRI data representing five common ventricular septal defect (VSD) subtypes (infundibular, membranous, inlet, muscular, and atrioventricular types) were retrieved from a radiology archive. Following the isolation of the heart in each image set, 3D volume rendering was applied to generate a digital heart model (Figure 7). After standard post-processing, synthetic models representing each VSD subtype were successfully produced.  
These 3D heart models were used to develop a simulation-based educational curriculum focusing on the anatomy and clinical management of VSDs for pediatric residents. Furthermore, we successfully adapted the printing material to produce a heart model with a soft texture, facilitating the creation of surgical incisions and the placement of sutures within the models.
# Wireless Remote Control for Underwater Vehicles
## 2 Underwater Communication Technologies
### Modern Selection and Considerations
Another reason why MF and HF modems are used more often in small AUVs and ROVs than LF modems is because the integration of LF modems in small vehicles can be complex or even impossible due to the large size of their transducer, that has a diameter of at least 12 cm and a total weight that can easily exceed 5 kg. MF and HF modems, instead, usually have a total weight of less than 2.5 kg and a transducer diameter of less than 6 cm. The acoustic modems selected for the wireless remote control designed in this paper are the Subnero WNC and the EvoLogics S2C HS, both equipped with an omnidirectional transducer, the former operating in the MF bandwidth with LBL capabilities and the latter operating in the HF bandwidth.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 2 Materials and Methods
### Unscented Kalman Filter  
The Unscented Kalman Filter (UKF) is another implementation of the Kalman Filter for non-linear systems. However, instead of linearizing the non-linear system around a point, it computes the statistics of the state random variables, using a function called the Unscented Transform (UT), in order to approximate the state distribution of the non-linear function [18]. As such, the only difference between the EKF and UKF lies in its observations model, keeping the entire dynamics model from the EKF.  
Let \(\mathbf{f}(\mathbf{x}_{k})\) be a non-linear function, where \(\mathbf{x}\) is a vector of Gaussian random variables with an expected value; i.e., mean, \(\hat{\mathbf{x}}_{k}\) and covariance matrix \(\mathbf{P}_{k}\). The UT of this function consists in taking a set of samples from the random variable \(\mathbf{x}_{k}\), applying the non-linear function to this set of points, and then obtaining a new set of statistics for the transformed random variable, which will also be Gaussian.  
The points used in the UT are called sigma points and together form the sigma vector, \(\mathcal{X}\). For a given iteration \(k\), these points are given by [18]  
\[\begin{split}\mathcal{X}_{0,k}&=\hat{x}_{k}\\ \mathcal{X}_{i,k}&=\hat{x}_{k}+\sqrt{n+\tau}\Big{(} \sqrt{\mathbf{P}_{k}}\Big{)}_{i}\qquad\text{with }i=1,\ldots,n\\ \mathcal{X}_{i+n,k}&=\hat{x}_{k}-\sqrt{n+\tau}\Big{(} \sqrt{\mathbf{P}_{k}}\Big{)}_{i}\end{split} \tag{20}\]  
where \(n\) is the size of \(\mathbf{x}\), \(\tau\) is the scale factor of the sampling, \(\big{(}\sqrt{\mathbf{P}_{k}}\big{)}_{i}\) designates the \(i\)-th line of \(\sqrt{\mathbf{P}_{k}}\) and \(\sqrt{\mathbf{P}_{k}}\) can be obtained by the Cholesky decomposition of \(\mathbf{P}_{k}\). The resulting sigma vector is then a matrix of \(n\times(2n+1)\), where each line is a set of sigma points for a given element of \(\mathbf{x}_{k}\). The scaling factor \(\tau\) is used to adjust the spacing between sample points and their weight in the statistics of the transformation and can be either positive or negative. For the symmetrical sampling case [18],  
\[n+\tau=3 \tag{21}\]  
After the sampling, the sigma points are propagated through \(\mathbf{f}\)  
\[\mathcal{Y}_{i},k=\mathbf{f}(\mathcal{X}_{i,k})\qquad\text{with }i=0,\ldots,2n \tag{22}\]  
and then the statistics of the resulting vector can be obtained by means of a weighted average [18], from which the mean and covariance matrix can be determined using  
\[\begin{split}\mathbf{\hat{y}}_{k}&=\sum_{i=0}^{2n} W_{i}\mathcal{X}_{i,k}\\ \mathbf{P}_{yy}&=\sum_{i=0}^{2n}W_{i}(\mathcal{Y}_{i,k}-\mathbf{\hat{y}}_{k})(\mathcal{Y}_{i,k}-\mathbf{\hat{y}}_{k})^{\text{T}} \end{split} \tag{23}\]where the weight values \(W_{i}\) are given by  
\[\begin{split} W_{0}&=\frac{\tau}{n+\tau}\\ W_{i}&=\frac{1}{2(n+\tau)}\end{split} \tag{24}\]
## 5 Conclusions  
In this study, the risk evaluation of roadway collapse disaster susceptibility in the Guangxi Zhuang Autonomous Region is conducted using the ResNet18 model, a convolutional neural network (CNN). By collecting and processing multi-source data, a collapse risk zoning map of the region is generated, integrating various natural and anthropogenic factors, including slope, lithology, precipitation, and vegetation cover. The findings demon  
Figure 13: Distribution of the weights of factors in the risk evaluation of collapse hazards.  
strate that high-risk areas are predominantly located in regions characterized by steep slopes, loose lithology, and high precipitation. The model's predictions exhibit a high degree of rationality and accuracy, providing a scientific basis for the prevention and management of roadway disasters in Guangxi.  
The innovation of this study lies in applying the CNN model to assess collapse disaster susceptibility, overcoming the limitations of traditional statistical approaches, and enhancing the precision of multi-factor interaction analysis. Additionally, the integration of multi-source data enables a more comprehensive evaluation of the combined effects of natural and anthropogenic factors, resulting in a more targeted risk assessment framework. Nevertheless, the study has certain limitations, such as the low spatial resolution of some factors and the model's limited generalization ability, which may impact prediction accuracy across diverse geographic environments.  
Future research will focus on enhancing data accuracy and model adaptability by incorporating higher-resolution data and advanced deep learning techniques to optimize model performance. Furthermore, the study will explore trends in collapse hazards by integrating dynamic factors such as climate change and aim to combine deep learning with other geohazard assessment methods. These efforts seek to provide more robust technical support for disaster prevention and mitigation in Guangxi and regions with similar geomorphic characteristics.  
Conceptualization, Y.L.; data curation, C.L., Z.L., Y.H., Z.D. and C.H.; methodology, C.L., Z.L., Y.H., Y.L. and Z.D.; project administration, Y.L.; supervision, C.L. and Y.L.; writing-original draft, C.L., Z.L. and C.H.; writing-review and editing, C.L. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Project of High Resolution Earth Observation System of China (No. GFZX0404130304); the Zibo City Social Science Planning Research Project of China (No. 2023 ZBSK041); and the Shandong Province Culture and Tourism Research Project of China (No. 23 WL(Y)53).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Guojiao Spatial Information Technology (Beijing) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.