esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Some ML techniques, such as convolutional neural networks (CNNs), are the most popular as they represent the closest technique to the learning process of human vision. CNNs are based on the mathematical operator of the convolution between the image and filters to extract information. Even though CNNs have provided successful results for image classification, their high computational cost, as well the non-invariance properties under rotation, scale, and translation of the convolution operation, make CNNs, in practice, an ineffective tool to implement in the identification or classification of some images, especially for those corresponding to skin lesions where vital information could be lost due to the low contrast that often exists between the lesion and healthy skin. This low contrast can mean that during the segmentation carried out by some filters, information on the lesion is not extracted correctly [18; 19]. In this context, other ML techniques, such as the support vector machine (SVM), k-nearest neighbor (k-NN), or ensemble classifiers, have shown acceptable performance [20; 21]. Although the SVM, k-NN, and ensemble classifiers have been successfully implemented to classify skin lesions, digital images under rotation, scaling, or translation were not considered. The application of the fractional radial Fourier transform in pattern recognition for digital images is invariant to translation, scale, and rotation, and the results obtained to classify phytoplankton species showed a high level of confidence (greater than 90%) [22]. In this paper, we implemented the Hilbert mask on the module of the Fourier-Mellin Transform to obtain a unique signature of the skin lesion called the radial Fourier-Mellin signature, which is invariant to rotation, scale, and translation. Additionally, we included as a signature the extraction of the uniform local binary pattern (LBP-U) image features. The classification task was obtained using the k-NN, SVM, and ensemble classifiers. Section 2 contains the description and image dataset pre-processing procedure and methods used to achieve its skin lesion classification. Section 3 presents the results using these supervised machine learning methods. In Section 4, we discuss the obtained results.
## 4 Verification of Spatial Geometric Characteristic
\[\mathbf{L}_{1i}=\big{(}\mathbf{r}_{\text{OB}_{i}}\times\hat{\mathbf{k}}\big{)}\times\hat{ \mathbf{k}},i=1\sim n. \tag{32}\]  
Substitute Equations (29) and (30) into Equation (28), the shortest distance between \(\mathbf{L}_{1i}\) and \(\mathbf{L}_{2i}\) is  
\[d_{i}=\frac{\mathbf{\Delta}_{i}\cdot(\mathbf{L}_{1i}\times\mathbf{L}_{2i})}{\|\mathbf{L}_{1i} \times\mathbf{L}_{2i}\|},i=1\sim n. \tag{33}\]  
In general, it is impossible for the perpendicular of any position in the track to the spin axis of the system to be parallel to or coincide with its curvature center axis. So, \(\|\mathbf{L}_{1i}\times\mathbf{L}_{2i}\|\
eq 0\), calculate \(\mathbf{\Delta}_{i}\cdot(\mathbf{L}_{1i}\times\mathbf{L}_{2i})\) to get  
\[ \begin{split}\mathbf{\Delta}_{i}\cdot(\mathbf{L}_{1i}\times\mathbf{L}_{2i})= R_{i}{}^{2}\big{[}-\cos(2\alpha_{i})\sin\phi_{1}\cos\phi_{1}\sin\phi_{2}+\sin \alpha_{i}\cos\alpha_{i}(\sin^{2}\phi_{1}-\cos^{2}\phi_{1}\sin^{2}\phi_{2}) \big{]}+\\ R_{i}\big{[}x_{C_{i}}\sin\alpha_{i}\cos\phi_{2}-y_{C_{i}}(\cos\alpha_{i} \cos\phi_{1}-\sin\alpha_{i}\sin\phi_{1}\sin\phi_{2})\big{]},i=1\sim n.\end{split}  \tag{34}\]  
For the research of the ABB vibration suppression system, most of the assumptions are that the balls in the track can freely rotate except for viscous damping and that the balls are in dynamic balance without any external forces acting in the tangential direction of the track. At the same time, the self-weight of the balls and the friction within the track are ignored. This study uses Equation (32) to examine various previous research on ABB under the aforementioned assumptions, confirming that the equilibrium positions of the balls in each system all satisfy Equation (32) equal to zero. This means that after any ball in the track is balanced and positioned, the radial vector \(\mathbf{L}_{1i}\) of its stable rotation intersects with the vector  
Figure 5: (**a**) Schematic diagram of spatial vectors and shortest distance; (**b**) Express the spatial vectors of the vibration suppression system. \(\mathbf{L}_{2}\) of the curvature center axis of that position in the track. In other words, the balance positioning of the balls in the track must satisfy the spatial-geometric relationship of _\"the perpendicular from the ball stationing position to the system spin axis intersects with the curvature center axis of the track at that position\"_. The establishment of this geometric relationship helps to understand and predict the possible positioning of ABB balls.
## 2 Materials and Methods
### ZnO-NPs Characterization
#### 2.3.4 X-ray Diffraction (XRD)  
The presence of crystalline phases in the sample was confirmed by obtaining an XRD pattern using a Brand diffractometer (Bruker Model D8 advance A25) equipped with Cu anode. The Debye-Scherrer formula, as outlined in earlier studies [26; 45], was utilized to calculate the crystalline size of ZS-ZnO-NPs and their crystallinity degree. The diffractograms were generated within the range of 20 (\({}^{\circ}\)) = 15-70.
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 5 Calculation Algorithm Possibilities in Commercial Treatment Planning Systems  
The development over the years of dose calculation algorithms aimed at reducing clinical treatment dose uncertainties and computation times. An overall dose uncertainty less than 5% is also recommended by ICRU Report 24 [85]; this means that for the dose calculation step, the necessary uncertainty level needed is on the order of 2%-3% [86].  
Algorithms used in commercial TPSs have advanced from homogeneous dose calculations such as pencil beam convolution (PBC) with an equivalent path length for inhomogeneity corrections (the so called type-A algorithms, according to the classification introduced by [PERSON] et al. in 2006 [87]); then to convolution/superposition algorithms such as collapsed cone convolution (CCC) and pencil beam algorithms, including lateral scaling for lateral electron transport (i.e., the AAA, classified as type B); and finally to the last generation dose calculation engines, represented by Monte Carlo (MC) and LBTE (type C) according to an updated classification [88], which model the dose distribution in heterogeneous materials with the highest accuracy. The latest type-C generation, which emerged with developments in information technology, have three main features: (1) improved secondary electron transport modeling, (2) the ability to compute dose deposition in biological tissues with high-\(Z\) materials, and (3) reporting of the dose as a dose-to-medium. Precisely for this reason, MC modeling today represents the arrival point for many treatment planning system manufacturers.  
Several products are currently available for clinical use. Some of them are in a basic format, providing an extremely simple and fast calculation, whereas others very complex and articulated with numerous applications and secondary tools.  
A list of the main commercial TPSs available and their respective algorithm options for photon, electron, proton and ion beams are reported in Table 1.
## 4 Choice of Occurrences
### Widening the Explored Space
#### 4.3.1 The Search Space  
The _mgp_ procedure permits us to resolve the problem of finding a smallest grammar given a fixed set of constituents. The Smallest Grammar Problem reduces then to find this good set of constituents. This is the idea behind the search space we will consider here.  
Consider the lattice \(\langle\mathcal{R}(s),\subseteq\rangle\), where \(\mathcal{R}(s)\) is the collection of all possible sets of repeated substrings in \(s\): \(\mathcal{R}(s)=2^{\text{repeats}(s)}\). Every node of this lattice corresponds to a set of repeats of \(s\). We then define a score function over the nodes of the lattice as \(score(\eta)=|\text{mgp}(\{s\}\cup\eta)|\).  
An algorithm over this search space will look for a local or global minimum. To define this, we first need some notation:  
**Definition 1**.: _Given a lattice \(\langle L,\preceq\rangle\):_  
1. \(\text{ancestors}(\eta)=\{\theta\
eq\eta\,\mid\,\eta\preceq\theta\wedge(\
ot \exists\kappa,\kappa\
eq\eta,\theta\,s.t.\,\eta\preceq\kappa\preceq\theta)\}\)__
2. \(\text{descendants}(\eta)=\{\theta\
eq\eta\,\mid\,\theta\preceq\eta\wedge(\
ot \exists\kappa,\kappa\
eq\eta,\theta\,s.t.\,\theta\preceq\kappa\preceq\eta)\}\)__  
The _ancestors_ of node \(\eta\) are the nodes exactly \"over\" \(\eta\), while the _descendants_ of node \(\eta\) are the nodes exactly \"under\" \(\eta\).  
Now, we are able to define a global and local minimum.  
**Definition 2**.: _Given a lattice \(\langle L,\preceq\rangle\) and an associate score function over nodes, \(g(\eta)\):_  
1. _A node_ \(\eta\) _is a_ global minimum _if_ \(g(\eta)\leq g(\theta)\) _for all_ \(\theta\in L\)_._
2. _A node_ \(\eta\) _is a_ local minimum _if_ \(g(\eta)\leq g(\theta)\) _for all_ \(\theta\in\text{ancestors}(\eta)\cup\text{descendants}(\eta)\)_._  
Unless otherwise noted the default score function for nodes will be \(score(\eta)\) (defined above). Finally, let \(\mathcal{SG}(s)\) be the set of all grammars of minimal size for the sequence \(s\). Similarly, we define \(\mathcal{MGP}(\{s\}\cup\eta)\) the set of minimal grammars with constituents \(\{s\}\cup\eta\). With this definition, we can state formally that this lattice is a \"good\" search space:  
**Theorem 2**.: \(\mathcal{SG}(s)=\bigcup_{\eta:\eta\text{ is global minimum of }(\mathcal{R}(s),\subseteq))}\mathcal{MGP}(\{s\}\cup\eta)\)__  
Proof.: To see the first inclusion (\(\subseteq\)), take a smallest grammar \(G^{*}\). All strings in \(cons(G^{*})\) have to be repeats of \(s\), so \(cons(G^{*})\setminus\{s\}\) corresponds to a node \(\eta\) in the lattice and \(G^{*}\) has to be in \(\mathcal{MGP}(\{s\}\cup\eta)\). Conversely (\(\supseteq\)), all grammars of the right expression have the same size, which is minimal, so they are all smallest grammars.  
Because of the NP-hardness of the problem, it is fruitless (supposing \(P\
eq NP\)) to search for an efficient algorithm to find a global minimum. We will present therefore an algorithm that looks for a local minimum on this search space.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 2 Description of Satellite Data Transmission  
### Ground Receiving System  
The receiving system of the ground station is an important part of the ground application system, which is cooperated with the satellite tracking subsystem and the data entry subsystem to complete the receiving and recording of the observation data transmitted by the satellite [14]. The antenna of the ground station is responsible for capturing the radio wave signal from the satellite. Through the feed network, the signal of the target frequency band (usually the L- or X-band) is sent to the corresponding processing link, and then amplified to the level that can be detected and processed by the low-noise amplifier (LNA). The received high-frequency signal is converted into a lower-frequency signal by the down converter (DC), and transmitted to the demodulator for demodulation and decoding. Finally, useful information is stored to a hard disk or other storage media through the recording device (RD) in the data processing subsystem [15].  
Satellite data reception requires the arrangement of antennas, channel subsystem links, and recording equipment in the data processing subsystem. The channel subsystem links include the first DC (FDC), a LNA, the second DC (SDC), and a demodulator [16]. The three hardware devices are connected by a radio frequency switch matrix (RFSM), intermediate frequency switch matrix (IFSM), and baseband three-stage switch matrix (BTSSM). A typical ground station receiving system structure is shown in Figure 1.  
The device represented by the dashed box represents multiple receiving links between the antenna device and the RFSM, and there is a receiving link that does not require the device to perform data reception.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
## 2 Methodology
### Object Tracking  
To calculate the distance from the remote object, we placed the three stationary anchors from the secluded tag by exchanging the messages. Then, it sends the information to the server through WiFi, as shown in Figure 1(b). The background service on the server gets the updated distances of the anchors and applies the mathematical model to find the location of the remote tag. According to the environment, it shows the location of the tag to the user. After that, it sends the information to the server through WiFi. This continuous cycle persistently updates the distances from the anchors of the tags on the database.  
Applying Trilateration Formula:  
\[\begin{array}{l}(r_{a})^{2}-(r_{b})^{2}\\ (r_{a})^{2}-(r_{c})^{2}\end{array} \tag{1}\]  
The Trilateration equation above is implemented with the help of the C++ language for distance calculation. It is apparently found to be an appropriate option to implement extensive procedures in order to manipulate and compute real-world values. Moreover, the embedded coding of the WiFi module esp8266 is conducted in the same language to provide the system with wireless communication between the anchors and the server. The Arduino IDE provides a platform to develop the embedded code that resides inside  
Figure 2: Flow diagram of the project.  
the microcontroller Atmega328p. PHP is used to implement the \"String query\" method on the server side in order to retrieve information. Then, that information is relayed to MySQL in order to be saved in the database [13; 14; 15]. Python and OpenCV facilitate, in conjunction, the development of the GUI in order to display the located items. Figure 2(a),2(b) shows the pictorial representation of a map of the research lab; two circles, blue and green, illustrate the tag and the anchors, respectively, while the circles show the real-time location of the tracked objects. The visualized items on a map show close-to-original pictures of the located objects at a position. Now, find out the distances d1, d2 and d3 from an object lying at point (_x_,_y_) to the anchor tags (_a_,_b_), (_c_,_d_) and (_e_,_f_), respectively, assuming the following coordinates of anchor tags as (_a_,_b_) = (0,0), (_c_,_d_) = (6,0) and (_e_,_f_) = (0,10). The distance between the coordinates (_x_,_y_) and (_a_,_b_) is:  
\[\begin{array}{c}d^{2}=(x-a)^{2}+(y-b)^{2}\\ =(x-0)^{2}+(y-0)^{2}\\ =x^{2}+y^{2}\end{array}\]  
Then, the distance between the coordinates (_x_,_y_) and (_c_,_d_) is:  
\[\begin{array}{c}d^{2}=(x-c)^{2}+(y-d)^{2}\\ =(x-6)^{2}+(y-0)^{2}\\ =x^{2}-12x+36+y^{2}\\ =x^{2}+y^{2}-12x+36\end{array} \tag{2}\]  
Now, the distance between the coordinates (_x_,_y_) and (_e_,_f_) is:  
\[\begin{array}{c}d^{2}=(x-e)^{2}+(y-f)^{2}\\ =(x-0)^{2}+(y-10)^{2}\\ =x^{2}+y^{2}-20y+100\end{array} \tag{3}\]  
By placing all the values at their respective positions in the above formulae, the coordinates of an object (_x_,_y_) = (4,6), in this example.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Labeling of Medical Videos  
During the labeling of medical videos, physicians or technicians usually only label the landmark markers at the selected informative frames. As a result, the video datasets suffer from two constraints: (1) the labeled frames are hugely biased towards specific points in time, i.e., only informative frames in each video are labeled, and (2) videos are sparsely labeled, i.e., a limited portion of frames in each video have ground-truth marker labels. Moreover, there is a lack of quantitative criteria for image annotation. Some frames show the ROI and uninformative features simultaneously, making the annotation process difficult. Adding uncertainty grading during the annotation process and integrating the uncertainty in the training process may improve model performance.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 4 Results
### Water Body Extraction Performance  
[PERSON]'s method can automatically determine the segmentation threshold based on the image histogram, which is widely used in water body extraction in combination with water indices [38]. The water body extraction results using [PERSON]'s method are shown in Figure 6. Panels (a), (d), (g), and (j) correspond to the NDWI water extraction results for Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake study areas, respectively. Similarly, panels (c), (f), (i), and (l) correspond to the SWI water extraction results for the four study areas. The blue regions of the four study areas in (c), (f), (i), and (l) correspond to the parts of the water bodies extracted by both the NDWI and SWI and the red regions correspond to the parts of the water bodies that the SWI extracted but were missed by the extraction performed with NDWI.  
Figure 5: Box plots of the NDWI and SWI values in the four study areas. ((**a–d**) show the box plots for Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, respectively).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & \multicolumn{3}{c}{**NDWI**} & \multicolumn{3}{c}{**SWI**} \\  & **Water** & **Nonwater** & **CV** & **Water** & **Nonwater** & **_CV_** \\ \hline Taihu Lake & 0.38 & \(-\)0.20 & 0.58 & 0.78 & \(-\)0.12 & 0.90 \\ Yangtze River & 0.22 & \(-\)0.23 & 0.45 & 0.89 & \(-\)0.09 & 0.98 \\ Chaka Salt Lake & 0.13 & \(-\)0.21 & 0.34 & 0.81 & \(-\)0.17 & 0.98 \\ Chain Lake & 0.34 & \(-\)0.26 & 0.60 & 0.84 & \(-\)0.31 & 1.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample means and contrast values.  
Figure 6a-c show that the SWI and NDWI can effectively extract most of the water bodies; however, the NDWI misses some water bodies in narrow rivers, which suggests that the NDWI easily confuses water bodies and nonwater bodies along these rivers. Figure 6d-f show that the NDWI cannot extract water bodies with high sediment concentrations because the NDWI easily misclassifies such water as buildings, bare soil, etc. However, the SWI can more effectively extract both turbid and nonturbid water in the Yangtze River and offshore. Figure 6g-i show that the NDWI has a poor extraction performance for saline water bodies, while the SWI can effectively extract the whole salt lake. Moreover, because the SWI is sensitive to water bodies in the SWIR band, a small amount of water bodies on bare land can also be effectively identified. Figure 6j-l show that both the NDWI and the SWI can extract most of the water bodies with floating ice. Due to the NDWI value difference between floating ice and the background being smaller than that of the SWI, the floating ice is easily confused with the high-reflectance bare ground in the NDWI results, while it is accurately extracted in the SWI results.
## 2 Materials and Methods
### Methodology  
The objective of this study was to make annual land cover datasets in the Qilian Mountains (1980-1990) with high accuracy, high spatial resolution, and excellent consistency to support the analysis of long-term climate, environmental, and ecological changes from the perspective of the regional scale or the river basin scale. However, several factors stopped us from making high-quality and excellent-consistency land cover datasets: (1) The absence of high spatial resolution satellite data and time series land cover products in early years. (2) The inter-annual inconsistency in annual classification and the huge workload in collecting training data. (3) In the previous study, all the pre-defined change pixels (30 m)  
Figure 3: Preprocessing rendering. (**a**) Quality check and cloud mask; (**b**) fill in missing values by temporal filter; (**c**) fill in missing values by spatial filter.  
within a coarse resolution pixel (8 km AVHRR or 250 m MODIS) had the same detected change results because of the downscaling of the coarse dataset [19], affecting the accuracy and consistency of land cover products.  
In order to solve the above problems, we proposed a new time series land cover mapping method based on deep learning and change detection by taking advantages of multiple satellite data and thereafter, validation. The procedure of the proposed method is illustrated in Figure 4 and described as follows: (1) Deep learning model selection and sample creation (Section 2.3.1); (2) Super-resolution generative adversarial network (Section 2.3.2); (3) Training process optimization (Section 2.3.3); (4) Change detection analysis and land cover updating (Section 2.3.4).
## 4 Discussion
### HIM Levels in Fish Samples
#### 4.1.2 Canned Products  
As previously reported in the Results section (Figure 2), on a total of 119 batches of canned samples, 98% of the samples contained HIM levels below 50 mg kg\({}^{-1}\), with 92% of the samples below 10 mg kg\({}^{-1}\)A number of 91 in a total of 101 canned tuna samples were processed by Spanish companies destined for fish transformation with raw tuna of EU and extra-EU origin. The majority of canned mackerel (9 samples out of 12 analyzed) were produced in Morocco. In this country, some critical issues in the canned product processing chain were verified over the past few years. Tunas and mackerels are often caught far from canning factories and so an inadequate interim conservation can lead to bacterial spoilage and HIM formation. Once formed, there is no method of fish preparation available, including thermic treatment, which can degrade the toxin. Therefore, a low content of HIM in the canned analyzed samples could indicate a general improvement in the production chain of canned products, coming from good quality of raw material, a correct maintenance of fish cold chain and adequate handling in the working place and in the processing [54]. No appreciable differences in HIM contamination (\(p<0.05\)) were noted between samples of tuna in different preserving liquid (in oil or natural) as already reported by [PERSON] et al. [55] Good quality canned tuna sold in Italy has already been reported in the literature [32; 37; 38]. Our results are comparable with those reported by [PERSON] et al. [56] with an HIM mean level for canned tuna products marketed in Brazil lower than 20 mg kg\({}^{-1}\). Similar results were obtained by [PERSON] et al. [57] that found an HIM ranging from 1 to 22.9 mg kg\({}^{-1}\) in 78.9% of 290 samples of canned products from Oman with an overall mean value of 3.18 mg kg\({}^{-1}\). HIM levels in the range 2.6-30.4 mg kg\({}^{-1}\) were reported by [PERSON] et al. [58] in Turkish canned tuna fish samples. Even worse, HIM content exceeding the limits of 50 mg kg\({}^{-1}\) set by the U.S. Food and Drug Administration, was found in 18.33% of canned fish samples marketed in Iran as reported by [PERSON] et al. [59], which also underlined the significant difference in HIM concentrations between canned tuna in oil and in brine. Among canned tuna, the only non-compliant sample with a concentration of 2219 mg kg\({}^{-1}\) was a \"residual of the meal\" associated with a scombroid poisoning case. This was a piece of tuna from a retail market that the seller had sold from a large open can and stored refrigerated for several days. Poisoning cases resulting from this common selling practice, already reported by [PERSON] et al. [37], may be a risk for consumers health. As shown in Table 1, the highest HIM level found among canned mackerel batches was \(105\pm 13\) mg kg\({}^{-1}\). This value, associated with a compliant sample, taking into account measurement uncertainty, was higher than the maximum values reported for canned mackerel (24 mg kg\({}^{-1}\) and 19.1 mg kg\({}^{-1}\) reported by [PERSON] et al. and [PERSON] et al., respectively [39; 60]).