esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Data/Buildings Classification and Applied Clustering Algorithms  
Throughout the past decade, data classification and clustering algorithms have been applied extensively in the fields of biology, engineering, information science, medical sciences as well as behavioral and social sciences, to name a few. Furthermore, cluster analysis has been widely used in many applications such as business intelligence, image pattern recognition, web search, biology and security [7]. The need for, and advantageous uses of, cluster analysis probably stem from the rising amount of readily available and processable big data and the urge to generate interpretable structures and usable classifications and correlations out of seemingly unstructured or highly heterogeneous datasets.  
Concerning the classification and (technical) evaluation of buildings, various scientific studies have already been performed, mainly for energy consumption profiling [8; 9; 10; 11; 12; 13; 14] and assessing/improving energy efficiencies of different buildings [15; 16] as well as benchmarking buildings [17; 18; 19; 20; 21; 22; 23]. A comprehensive review of performed cluster analyses in the building sector can be found in [15].  
Throughout these performed studies (covering building topics), two methods for classification and clustering of data have proven themselves to be highly suitable and appropriate: hierarchical and k-means clustering algorithms. K-means is an iterative algorithm that divides a given dataset into k clusters by minimizing the sum of all squared Euclidian distances to the respective cluster centers (centroids to be chosen). Different 'cluster quality indices' are applied herein to obtain the optimal number of clusters, e.g., the 'Davies and Bouldin' and the 'Silhouette' indices. Grouping data into clusters so that objects within each specific cluster are very similar to each other but also very dissimilar to objects in other clusters requires distance measures (metrics based on indices of proximity) so that similarities and dissimilarities can be assessed correctly. Hierarchical data clustering results in a so-called '_dendrogram_' (graphical clustering tree structure), which represents groupings of objects at specific (dis-) similarity levels. Which clustering algorithm to choose is a matter of specific properties, i.e., type and scale of the given dataset, for example. As a matter of fact, HAC can yield impressive and rather easily readable results concerning clustering and classification of general building information/datasets. In the following, the effective application of HAC on inhomogeneous data of PUH-environments is presented, leading to the obvious suggestion that HAC could be far more intensely used for building classification tasks in urban planning. For a detailed description of a novel clustering algorithm (\(k\)-CMM), especially suited to mixed numerical and categorical data, see e.g., [PERSON] et al. [24].
# Identification of Allergenic Tropomyosins with Known and Unknown Sequences in Seafood Products
###### Abstract  
Invertebrate tropomyosins belong to the key food allergens. Several peptides likely to be released during proteolysis can be found in many sequences of proteins from this family. The aim of the present study was to evaluate the possibility of identifying tropomyosins with known and unknown amino acid sequences in unheated, boiled and fried seafoods. The workflow included in silico proteolysis simulation of tropomyosin sequences and analysis of the distribution of resulting peptides among proteins. The experiment entailed the proteolysis of unheated, boiled and fried products, containing crustaceans or mollusks, and the identification of resulting peptides using LC-MS/MS. Finally, taxonomic lineages of identified peptides were determined. Predicted peptides were identified in unheated samples. The boiling of seafoods resulted in an increase in the length of peptides containing predicted sequences. Some peptides from the boiled samples contained entire linear epitopes. The prediction of tropomyosin cleavage sites failed in the case of fried products. Peptides from the unheated and boiled samples were attributed to crustaceans, arthropod or molluscan tropomyosins. In turn, peptides from the fried samples possessed inconclusive taxonomic lineages. Our results show that bioinformatics analysis (especially using Unipept program) may be a viable tool supporting LC-MS/MS experiments aimed at the detection of allergens.  
allergens; crustaceans; mollusks; mass spectrometry; markers; peptides; seafood; boiling; frying; tropomyosins Article
## 4 Results
### Prediction Accuracy at the Validation sites  
The prediction accuracy results for the five study models are given in Table 3, indicating that KED-LN is clearly the best predictor of water depth, in this instance. For model choice, this result concurs with the simulation study of [PERSON] _et al._[37] using the same set of prediction models. Thus, it is important to cater for non-stationary relationship effects (at least between WD and SDRD.T, but also for WD and SEABED) and for spatial autocorrelation effects--both of which KED-LN does. GWRK also does both aspects, but clearly not as well. The poor performance of GWRK can be explained by its focus on modelling non-stationary relationship effects, rather than spatial autocorrelation effects (as shown by the well-behaved GWR bandwidth function in Figure 6c, combined with the weak structure of its residual variogram in Figure 6b). Considering the relatively good performance of KED-GN (our second best predictor), it is clear that, in this instance, accounting for spatial autocorrelation effects is much more important than accounting for non-stationary relationship effects. As expected, MLR is the poorest performer, and we observe also that GWRK only provides a marginal improvement over the simpler GWR fit (which again can be attributed to the observed weak structure in the GWRK residual variogram in Figure 6b).
# GPS-Based Indoor/Outdoor Detection Scheme Using Machine Learning Techniques
## 1 Introduction
A machine learning approach provides a simple solution, low-power costs, and high accuracy for I/O detection problems. The proposed scheme based on the GPS signal and machine learning methods require a very small volume of dataset, as only 850 training samples are enough for an accuracy classification. Furthermore, a mobile phone application can apply offline learning, which uses the trained model for the classification and updated if requested, so it does not require a training dataset. Therefore, the proposed scheme will be suitable for almost all mobile applications that require I/O environment information. The remainder of this paper is organized as follows. Section 2 describes related work. We discuss GPS signal characteristics and how we can take advantage of them in Section 3. Section 4 provides the working scheme for an I/O detection scheme, including the data collection process, preprocessing, and the training and testing phases. The supervised evaluation results will be discussed in Section 5. The paper will be concluded in Section 6.
## 4 Results and Discussion
### Kaplan-Meier Curve for Student Dropout  
The Kaplan-Meier curve [44] measures the nonparametric empirical distribution of the occurrence of events in ordered discrete occurrence times. Figure 2 represents the Kaplan-Meier curve for student dropout. The x-axis represents the survival time of dropout students, and the y-axis represents the remaining proportion or survival probability of dropout students. Assuming that the dropout students' proportion is one at the time of enrollment, it will gradually decrease to zero until the time of dropout. Therefore, the curve slope can indicate the rate of decrease for dropout students.  
Figure 1: The heat map based on (**A**) the mean value-based feature table, (**B**) the median value-based feature table, (**C**) the first-semester data-based feature table, and (**D**) the final semester data-based feature table.  
In detail, the curve shows that about 26% of students drop out in the first 12 months. After that, about 12% of students drop out in the next 12 months. The following 15% of students drop out between 24 and 36 months. The following 17% of students drop out between 36 and 48 months. The following 18% of students drop out in 48 and 60 months, and the remaining 12% leave in 60 months.  
In short, the distribution of dropout probability is relatively uniform, but the dropout probability in the first school year is relatively higher than in other school years. This result is inconsistent with previous studies claiming that nearly 50% of all dropouts left college between 6 and 18 months [45; 46; 47]. This result demonstrates the importance of dropout prediction for students in all semesters rather than for a specific semester [15; 16; 21]. Therefore, we extract features from student data of different lengths to predict dropouts in all academic years and quantitatively investigate the impact of different feature table generation methods on the performance of the prediction models. The results are reported in Section 4.3.
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.3.2 Network Structure  
As shown in Figure 4, the network structure of this model is mainly composed of backbone network, neck network and head network. The backbone network mainly uses CSP (cross stage partial) darknet network and spatial pyramid pooling (SPP) structure, the neck network uses PANet structure, and the head network uses 1 \(\times\) 1 Convolution.  
The model uses CSP darknet as the backbone network to extract rich information features from the input image. CSP Net solves the problem of gradient information repetition in network optimization in other large convolutional neural network frameworks, and integrates the change of gradient into the feature graph from head to tail. Therefore, it reduces the amount of parameters of the model, which not only ensures the reasoning speed and accuracy, but also reduces the size of the model.  
The neck network part of the model adopts PANet structure, and the neck network is mainly used to generate feature pyramid. The feature pyramid will enhance the detection of objects with different scaling scales, so that the same object with different sizes and scales can be recognized. PANet structure introduces bottom-up path augmentation structure on the basis of FPN. FPN mainly improves the effect of target detection by fusing high-level and low-level features, especially the detection effect of small-size targets. The bottom-up path enhancement structure can make full use of the shallow features of the network for segmentation. The network shallow feature information is very important for target detection, because target detection is a pixel level classification, and the shallow features are mostly edge shape and other features. PANet adds a bottom-up path enhancement structure on the basis of FPN, so that the top-level feature map can also share the rich location information provided by the bottom-level feature map, so as to improve the detection effect of large objects.  
Figure 3: Time frequency image interference monitoring results.  
The head network of the model is a \(1\times 1\) convolution structure, and has three groups of output with different image feature rates. Finally, the anchor box will be applied to the output feature map, and the final output vector with category probability, confidence score and bounding box will be generated. On the anchor, the model distinguishes the positive and negative samples of the anchor by matching rules across the grid.
# Decision-Refillable-Based Shared Feature-Guided Fuzzy Classification for Personal Thermal Comfort
## 3 The Proposed Fuzzy Classifier: SFG-TFC  
As mentioned above, building a thermal comfort model is not an easy task. In recent years, most of the work on thermal comfort models has been focused on machine learning algorithms. One representative work is the data-driven thermal comfort model based on the support vector machine algorithm [15]. This is a model with self-learning and self-tuning capabilities using the support vector machine (SVM) algorithm. This model focuses on variables such as indoor temperature, clothing insulation, metabolic rate, and wind speed. It is very similar to our study. More importantly, compared to SVM, the fuzzy system constructed by our model not only has good classification performance, but also has good interpretability.  
In our study, data are collected from specific people in specific climate regions and in specific scenarios. Subdatasets of M time nodes are obtained under laboratory simulation scenarios and are randomly ordered, and each subdataset contains shared features and extra features. For each time node, the collected datasets can be divided into a shared feature dataset and an extra feature dataset. As shown in Figure 1, the SFG-TFC training structure is divided into the following steps: First, the key shared features that greatly impact decision-making are obtained using the method of information gain, fuzzified, and output by rules. Second, the extra features of each subset are fuzzified and output by rules, and the rule output matrix of key shared features is reprojected to each independent base training unit. Finally, the output weights of the optimized model are integrated to improve the generalization performance of the model. In the modelling process, one of the difficulties and objectives of this study is to strengthen the guiding role of the key shared features in human thermal comfort while considering the enhancing effect of extra features on the final classification performance.
## 2 Background and Motivation
### Evaluation Criteria of PAPR Reduction Techniques  
There are different metrics used in the comparison of different PAPR reduction techniques. The main three metrics of them are; PAPR reduction gain, BER enhancement, and OOB-radiation reduction. PAPR reduction gain is the most famous metric at all. However, PAPR is a statistical quantity. Therefore, the probability of the OFDM symbol's PAPR is larger than or equal to a threshold \(\xi_{o}\) is described by aid of Complementary Cumulative Distribution Function (CCDF), which is expressed as:  
\[CCDF=\textbf{P}\{PAPR\geq\xi_{o}\} \tag{10}\]  
Theoretical CCDF of the PAPR of the original oversampled OFDM signal is given by [13; 16];  
\[CCDF_{c}=1-exp\bigg{(}\begin{smallmatrix}-N_{\mathrm{T}}\tau^{-\xi_{o}^{o}} \sqrt{\frac{\pi}{3}\log N_{\mathrm{T}}}\end{smallmatrix}\bigg{)} \tag{11}\]  
Oversampling by factor L (usually L = 4 is enough) is used in the simulation of continuous-time signal's PAPR [3; 13; 14].  
In contrast to CCDF, BER and OOB-radiation are affected by the HPA characteristics. EVM is another metric similar to BER, which is usually used when Forward Error Correction (FEC) is not considered. EVM evaluates the in-band distortion. The mathematical definition of EVM is represented as follows [3; 38]:  
\[EVM=\sqrt{\frac{\textbf{E}\big{\{}|\widehat{A_{k}}-A_{k}|^{2}\big{\}}}{\textbf {E}\big{\{}|A_{k}|^{2}\big{\}}}} \tag{12}\]  
Reduction in OOB-radiation may be measured by Adjacent Channel Power Ratio (ACPR), which is defined as adjacent Channel's power(out-of-band distortion) to the main Channel's power (in-band signal)ratio. ACPR is defined as follows [38]:  
Figure 1: General AM/AM characteristic of HPA.  
\[ACPR=10\log\left(\left(\int_{F_{out}}Y(f)df\right)\middle/\left(\int_{F_{in}}Y(f)df \right)\right) \tag{13}\]  
where \(F_{in}\) and \(F_{out}\) are defined as the limits of inband and outband, respectively. \(Y(f)\) is defined as the power spectral density (PSD) of HPA output (i.e., \(y(t)\)).  
Another important metric to be considered is the computational complexity. Computational Complexity Reduction Ratio (CCRR) between two techniques is formulated as follows [39];  
\[CCRR=\frac{CC\text{ of the conventional technique }-CC\text{ of the proposed technique}}{CC\text{ of the conventional technique}}\times 100\% \tag{14}\]  
\(CCRR\) is computed for both numbers of additions and multiplications.
# A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images
## 3 Results and Discussion
### Segmentation Architecture in Skeletal Muscle
(**D**) RAN architecture with two different layers for downsampling and upsampling for tracking the myotendinous junction. In a different skeletal muscle area, the concatenation layer without the deconvolution layer is possible for segmentation in skeletal muscles to preserve image details using the concatenation layer and convolutional layer with ReLu (Figure 5C). [PERSON] et al. (2020) used U-Net to find precisely identify a detailed location and see the longitudinal record effect on individual neck muscles. Deep learning with concatenation layers followed by a convolutional layer plus ReLu in upsampling U-Net network architecture was used to ensure image detail for the same injection site in [35]. In this case, the deep learning precision outcome is the main factor that represents image detail as clinical personnel can see the impact of health progress after continuous injection at an exact location. A concatenation layer is required to improve precision sharpness, which is followed by a convolution layer to acquire the final output to overcome the imaging problem [52]. This strategy can help increase the precision of feature identification and decrease the degree of information loss [35, 53, 54]. Two phases and skip connections are always used in the skeletal muscle segmentation (Figure 5). Two phases are used because the ultrasound image has a noise problem, while the area to be segmented is small and close to other muscles that move constantly. Additionally, the use of two phases is an effort to maintain the accuracy of segmentation for muscle identification supported by using a skip connection to eliminate singularities [55, 56]. Skip connections are additional connections between nodes in different layers of the neural network [57]. Skip connections can operate with very few training images and yield precise segmentation [36]. Skip connections are essential for preserving boundary details when dealing with limited image quality [58]; however, the resulting network architecture has different scenarios as the network architectures are specific for their given purposes and dataset; however, data imbalances and scarce images are common occurrences with skeletal muscles. The popular solution to solve data imbalances and limited numbers of images is to use an augmentation method with image rotation [30, 31, 33, 34, 35, 39] where the orientation ranges from \(5^{\circ}\) to \(180^{\circ}\). Apart from rotation and image flipping, transferred learning is used to solve image quality problems and data imbalances in skeletal muscle segmentation applications.
# Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation
## 3 Construction of Airspace Grid Model Based on Icosahedron Spherical Rhombus Subdivision
### Interchange of Coordinate Systems  
(1) Conversion of longitude and latitude coordinates to spherical Cartesian coordinates;  
The longitude and latitude coordinates of the airspace boundary points are transformed into spherical Cartesian coordinates. The longitude and latitude coordinates \(G\) (longitude \(\lambda\), latitude \(\varphi\)), the Earth's equatorial radius \(R\), and the corresponding spherical Cartesian coordinates are \(C\) (\(X\), \(Y\), \(Z\)), and the relationship from the longitude and latitude coordinates to the spherical Cartesian coordinates can be established by the spatial geometric relationship. As shown in Figure 5.  
\[X=R\cos\Bigl{(}\varphi\frac{\pi}{180}\Bigr{)}\cos\Bigl{(}\lambda\frac{\pi}{18 0}\Bigr{)} \tag{2}\]  
\[Y=R\cos\Bigl{(}\varphi\frac{\pi}{180}\Bigr{)}\sin\Bigl{(}\lambda\frac{\pi}{18 0}\Bigr{)} \tag{3}\]  
\[Z=R\sin\Bigl{(}\varphi\frac{\pi}{180}\Bigr{)} \tag{4}\]  
(2) Conversion of spherical Cartesian coordinates to longitude and latitude coordinates;  
Since trigonometric functions have one-to-many situations in the range of 0-360\({}^{\circ}\), there will be multiple solutions when converting from spherical Cartesian coordinates to longitude and latitude, so the specific coordinates need to be determined according to the positive and negative of spherical Cartesian coordinates during conversion. In addition to the north and south poles, the conversion relationship is as follows:  
\[\alpha=\arctan\bigl{(}\left|\frac{Y}{X}\right|\bigr{)}\cdot(\frac{180}{\pi}) \tag{5}\]  
\[\beta=\arctan(\frac{|Z|}{\sqrt{X^{2}+Y^{2}}})\cdot(\frac{180}{\pi}) \tag{6}\]  
When \(X>0\), \(Y>0\), \(\lambda\) is east longitude, \(\lambda=\alpha\);  
When \(X<0\), \(Y>0\), \(\lambda\) is east longitude, \(\lambda=\alpha+90^{\circ}\);  
When \(X>0\), \(Y<0\), \(\lambda\) is west longitude, \(\lambda=\alpha\);  
Figure 5: Schematic diagram of spherical Cartesian coordinates.  
When \(X<0\), \(Y<0\), \(\lambda\) is west longitude, \(\lambda=\alpha+90^{\circ}\)  
When \(Z>0\), \(\varphi\) is north latitude, \(\varphi=\beta\)  
When \(Z<0\), \(\varphi\) is south latitude, \(\varphi=\beta\)
## 4 Design and Experiment of Self-Rotating Cleaning Device
### Self-Rotating Device Test and Result Analysis  
The self-rotating cleaning device was manufactured and assembled, as shown in Figure 10. To evaluate the actual cleaning effect of the device, a test rig was established, as shown in Figure 11. The recoil force and the radius of cleaning area were also measured. The test rig was fixed on the pool by clamping to the wall with fixtures. The self-rotating cleaning device was horizontally mounted on the support rod which could rotate around the revolution axis. During the experiment, the self-rotating cleaning device scoured the test plate and generated a right thrust, causing the support rod to rotate counterclockwise at a small angle, thereby pulling the electronic tension meter. Then, the tension could be calculated according to principle of torque balance as:  
\[Thrust=\frac{Tension\cdot Length\ of\ tension\ arm}{Length\ of\ thrust\ arm} \tag{12}\]  
The experimental results showed that when the input water pressure was 30 Mpa, the reading of the electronic tension meter was 303.31 N (30.95 Kgf). Considering the thrust arm length to be in a 2:1 ratio with the tension arm length, the recoil force of a single rotating device was \(F_{anti}=151.7\) N. According to Equation (12), the actual reaction force \(F\) provided by a single nozzle can be calculated as follows:  
\[F\times\left(cos\ 30^{\circ}\right)^{2}\times 2+F\times cos\ 30^{\circ}\times 2 =F_{anti} \tag{13}\]  
The recoil force value of a single nozzle was calculated to be 47 N.  
In the experiment, the test plate was manufactured by pasting shells on a painted iron plate with waterproof glue to simulate the ship hull with biofouling, as shown in Figure 12a. The peel strength of the glue was about 1 MPa, which is equivalent to the peel strength of marine organisms such as barnacles. The test plate after cleaning is shown in Figure 12b. It can be seen that both the shells and glue were complete cleared. The cleaning area is a ring with an outer diameter of 270 mm and a width of 50 mm.  
Figure 9: Force analysis of the nozzles.  
In addition to laboratory testing, the self-rotating cleaning device was also tested in actual engineering applications. The self-rotating cleaning device was installed onto the underwater cleaning robot for in situ cleaning of ships at sea, as shown in Figure 13. According to the images transmitted back by the underwater cleaning robot, it can be observed that the ship's hull, previously covered by marine biofouling, was thoroughly cleaned. This test also demonstrated the effectiveness of the self-rotating cleaning device.  
Figure 11: Cleaning experiment of the self-rotating cleaning device.  
Figure 10: The self-rotating cleaning device.