esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Big Data Archives in Time-Domain Astronomy
## 3 Time-Domain Astronomy
#### Science Project and Virtual Observatory in the Era of Big Data in Astronomy
A follow-up study is needed to gather more information on the detected transients, such as ultraviolet-optical-infrared light curves and hues, spectroscopic categorization, X-ray and radio observations for non-thermal emission, and a complete multi-wavelength follow-up survey [39]. After years of development, the Zwicky Transient Facility (ZTF) was launched in 2018 with the largest instantaneous field of view for any camera on a telescope with an aperture bigger than 0.5 m using a 48-inch Schmidt Telescope [40]. The ZTF observing system provides high-speed, wide-field-of-view, multi-band optical imagery for time-domain astrophysics analysis [20,41]. The work of ZTF expands our understanding of the temporal and dynamic sky. This category includes Near-Earth Asteroids (NEAs), unusual and rapidly developing flux transients, and all sorts of galactic variable sources. Managing data are transferred from the P48, raw data ingestion, all processing pipelines, long-term archiving and curation of data products, user interfaces for data retrieval and access management, near-real-time distribution of flux-transient alerts and potentially new solar system objects (SSOs), generation of quality assurance (QA) metrics for the project, analysis and trending, and maintenance of all software, hardware fundamental areas of ZTF [42]. Astronomical observatories in the modern era are building work on the Legacy Survey of Space and Time (LSST), which is now taking place in conjunction with the Vera C. Rubin Observatory. Images and data items of approximately 500 petabytes are sent via the LSST [18]. The Rubin Observatory includes an 8.4-m primary mirror, the world's largest digital camera, a complex data processing system, and an online education platform. The 8.4-m Simonyi Survey Telescope, which boasts a unique three-mirror construction and an incredibly wide field of vision, takes only three nights to survey the entire sky [43]. LSST will shed light on the unseen components of the universe by tracking the motion of billions of galaxies and analyzing how they distort space and time. For example, variable stars, supernovas, and black holes will be studied in unprecedented detail owing to the LSST. New classes of transient occurrences were discovered. The telescope will reveal the motions of millions of stars and provide a three-dimensional image of our galaxy, which is 1000 times larger than that of earlier surveys. Over 90% of the potentially hazardous asteroids larger than 140 m in diameter were investigated using LSST. Beyond Neptune, it should be able to pick up an additional 40,000 bodies [44].
## 3 Candidate Universal Decoders for URLLC
### Ordered Statistics Decoding (OSD)  
For linear block codes, ordered statistics decoding (OSD) is a soft-decision decoder. In successive cancellation (SC) decoding, a single data bit cannot be decoded until all of its preceding bits have been received, which increases the data dependency. That is to say, when the block length increases, the decoding latency unavoidably rises. Ordered statistics decoding (OSD) was proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2017) to prevent such data dependency. OSD works by parallelizing the bit flips and using the most dependable bits to get the most likely codeword. In the first step, the received vector \(\mathbf{y}\) is sorted in order of descending confidence values \(\alpha_{i}\). In OSD, two permutations are performed over the received signal vector \(\mathbf{y}\) and the generator matrix \(\mathbf{G}\) before decoding. Applying the first permutation \(p_{1}\), the columns of \(\mathrm{G}\) and \(\mathbf{y}\) are sorted in the decreasing order of the confidence values \(\alpha_{i}=|y_{i}|\). On applying the permutation \(p_{1}\) to \(\mathbf{G}\), \(G^{\prime}\) is obtained. Now, Gaussian elimination is applied to \(G^{\prime}\) to get the augmented systematic matrix \(G=[I_{k}|P]\), in which \(I_{k}\) is a k-dimensional identity matrix and \(P\) is the parity sub-matrix. Using the permuted generator matrix, the channel output's \(k\) most reliable values are converted into a codeword. The Algorithm 3 is developed based on reference [PERSON] and [PERSON] (2017).  
```
1 Variable declaration
2y: channel output vector
3G: \(K\times N\) generator matrix
4\(p\) : permutation function
5 Step I Reordering
6 Input:y
7 Output:r
8 Sort \(\mathbf{y}\) in descending order of reliability (absolute LLRs)
9 Obtain the resultant sequence \(\mathbf{r}\)
10 Generate permutation \(p_{1}\)
11 Step II Matrix operation
12 Input:\(G\)
13 Output:\(\tilde{G}\)
14 Permute columns of \(G\) as \(p_{1}G=G^{\prime}\) having first \(K\) columns as most reliable first \(K\) columns of \(G\)
15 Construct \(G^{\prime\prime}\) by permuting \(G^{\prime}\) as \(G^{\prime\prime}=p_{2}(p_{1}(G))\)
16 Obtain systematic form of \(G^{\prime\prime}\) as \(\tilde{G}\)
17 Step III MRI sequence
18 Input:\(\mathbf{r}\), \(\tilde{G}\)
19 Output:\(\tilde{c}\)
20 Generate most reliable independent (MRI) sequence \(z^{K}=p_{2}(p_{1}(\mathbf{r}))\)
21 Perform hard decisions on the \(K\) MRI symbols of \(z^{K}\) to get \(a^{K}\)
22 Obtain \(\tilde{c}=p_{1}^{-1}(p_{2}^{-1}(a^{K}\tilde{G}))\)
```  
**Algorithm 3**Ordered statistics decoding (OSD)
## 2 OFDM Based Multicarrier Modulation Schemes
### Filter Bank Multicarrier Modulation  
Spectral leakage in conventional OFDM due to large side lobes degrades the performance of OFDM-based cognitive radio systems. Therefore, null sub-carriers or guard bands are inserted at the frequency boundaries of OFDM systems in order to avoid adjacent channel interference. Null sub-carriers cause loss of spectral efficiency. Moreover, cyclic prefix in conventional OFDM contains redundant information which adds to loss of spectral efficiency. To overcome these drawbacks, OFDM-offset quadrature modulation (OFDM/OQAM) has been proposed to be a good alternative in [6]. It does not use any CP and also it offers the possibility to use different prototype filters that eliminate the need for guard bands. Thus, filter bank based OFDM-OQAM maximizes the secondary users' spectral efficiency in a DSA network. Cosine modulated multitone (CMT)-based Cognitive Radio (CMTCR) and filtered multitone (FMT)-based Cognitive Radio (FMT-CR) systems have been studied in [32]. CMT allows overlap of adjacent bands and thus, it maximizes bandwidth efficiency. By limiting the overlaps to adjacent bands, the filter design is simplified. In CMT, PAM symbol sequences are transmitted using vestigial sideband (VSB) modulation. Filtered multitone (FMT) is another multicarrier modulation technique that uses subcarrier bands with no overlap, unlike OFDM-OQAM and CMT which allow for overlapping of adjacent subcarrier bands. Data symbols are quadrature amplitude modulated (QAM). Guard bands are used to separate subcarrier bands, which results in loss in bandwidth efficiency compared to OFDM-OQAM and CMT. Filter bank (FB) based spectrum analyzer has been proposed in [6], as an alternative to FFT based spectrum sensing. It was shown that, the FB spectrum analyzer can provide higher spectral dynamic range for spectrum sensing and hence, it can be used for detecting even low power primary users.
## 2 Materials and Methods
### Training and Internal Testing of the Model
#### 2.2.1 Segmentation of Aortic Arch and Supra-Aortic Vessels  
_Semiautomatic segmentation_. The aortic arch was initially segmented from the CT images with a semiautomatic active contour segmentation algorithm. Segmented volume was then manually modified by one expert human to correct segmentation errors. This procedure was adopted for all CT images. Prior to the segmentation procedure, all CT images with slice thicknesses greater than 1.5 mm were resampled to reduce the thickness to 1.5 mm.  
_Fully automatic segmentation of aortic arch and supra-aortic vessels_. A semantic-segmentation algorithm was developed and used for all CT examinations for training (\(k\)-fold cross-validation) and internal testing of the classification model in order to implement a fully automatic process. For this purpose, the 3D Dense U-Net [22] was trained from scratch in a hold-out fashion using half images of the dataset from Center 1 for training (80%, 40% CILCA and 40% NON-CILCA) and validation phase (20%) and the remaining half for internal testing. This type of network is known to be able to generalize well on relatively small datasets leading to acceptable results [23; 24]. Due to hardware limitations, the CT scans were not passed entirely as input to the network but were preprocessed and serialized in batches of several slices. In particular, each CT scan and the relative segmentation mask underwent the following preprocessing steps: (i) selection of the slices pertaining only to the aortic arch in both CT scan and segmentation mask generated via semiautomatic segmentation; (ii) thresholding of pixel intensities between \(-1000\) HU and +400 HU in the CT scan where a contrast medium was used, otherwise the threshold range \(-500\) HU +100 HU was used; and (iii) resampling of the CT scans and relative segmentation masks to \(128\times 128\times 80\) matrices.  
The preprocessed CT scans and relative segmentation masks were passed to the network for the training stage in batches of size 1, where each batch was composed of 16 slices. The binary cross-entropy loss and Adam optimizer [25] were used for training the network, while the Dice coefficient [26] was monitored as a metric to evaluate the performance on both the training and validation set. The training was stopped if there were no improvements for 20 consecutive epochs on the Dice coefficient evaluated on the validation set. As for the data augmentation techniques used, only the rotations in the range (\(-15^{\circ}\), \(+15^{\circ}\)) were seen to be effective on the regularization of the training phase. In order to improve the quality of the segmentation results, the model was then fine-tuned, with the same procedure described above, only on the few final slices of the scan where the arteries start to detach from the aortic arch.  
In the testing phase, the predicted segmentation masks were resampled to their original shape and then refined in the upper part with the fine-tuned model. As the last step, the predictions were cleaned from spurious findings, keeping only the biggest connected component segmented.
# A Rule-Based Language and Verification Framework of Dynamic Service Composition
## 3 Declarative Formalism for the Service Composition
### A Formal Syntax and Semantic of the Service Composition Language: GSLang
#### 3.2.3 Action  
An **action** is a send message, a receive message or a silent interpretation of the service instances.  
\[act ::= vp\;\overline{\pi}\;\langle M,p\rangle\] \[| \alpha(M,p)\] \[| p(M)\] \[| \overline{p}\;\langle M\rangle\] \[| r\]  
The **actions** define the communication to be performed between the services execution spaces:  
* \(vp\;\overline{\pi}\;\langle M,p\rangle\) allows, firstly, the creation of a variable representing the private port (p), then send a message \(M\) and \(p\) to the public port \(\alpha\) of a remote space;
* \(\alpha(M,p)\) receiving a message M and a variable p on the public port \(\alpha\);
* \(p(M)\) receive a message M on a private port p;
* \(\overline{p}\;\langle M\rangle\) sending a message M on a private port p;
* \(r\) silent action which consists of interpreting the services parameters using semantic rules. No communication with the environment.
###### Abstract  
In order to scientifically and rationally evaluate the susceptibility to landslide hazards in Tekes County, Yili State. This paper takes Tekes County in Xinjiang as an example, on the basis of a comprehensive analysis of the regional geological environment conditions and the distribution pattern and formation conditions of geological disasters, using the data of geological disaster points (landslide center points), and through the correlation matrix calculation of the evaluation factors, the nine evaluation factors with larger absolute values of correlation coefficients were determined to construct the evaluation system of the susceptibility to landslide geological hazards in Tekesi County. Combining the information quantity method and the entropy value method, using the weights determined by the entropy value method, the information quantity method is used to calculate the information quantity value of each factor within the factor, calculate the susceptibility index of landslide geological disasters within the territory of Tekes County, and then carry out the landslide susceptibility evaluation. The susceptibility of landslide disasters was evaluated by ArcGIS. The results show that the landslide disaster susceptibility level in Tekes County can be divided into four levels: high susceptibility, medium susceptibility, low susceptibility, and not susceptible, with areas of 491.3276 km\({}^{2}\), 1181.5171 km\({}^{2}\), 1674.7609 km\({}^{2}\) and 5295.2976 km\({}^{2}\) accounting for 5.68%, 13.67%, 19.38% and 61.27% of the total area of Tek County, respectively. The AUC number obtained by the success curve method (ROC) is 0.8736, reflecting the evaluation accuracy of 87.36%, indicating that the model method used in this paper is effective. The results are expected to provide practical data support for landslide disaster control in Tekes County and provide a reference for geological disaster monitoring, early warning and engineering prevention and control deployment in Yili Valley.  
ArcGIS; geological hazards; evaluation of susceptibility; information quantity model; Tekes County +
Footnote †: journal: Applied Sciences
# Multi-Target Detection Algorithm for Fusion Images Based on an Attention Mechanism
## 3 Dataset Creation
### Image Collection
#### 3.1.1 From the Web  
The datasets used in this paper are mostly from existing open-source datasets on the Internet, including TNO [19], Roadscene [20], Multispectral [21], 3 FD [22], LLVIP [23], etc. Table 2 lists an overview of these datasets, including image resolution, shooting environment, scene category, etc., Figure 1 shows the infrared and visible-light image data pairs collected in some dark environments in the LLVIP dataset.
## 2 Related Work
### LiDAR Based Method  
The point cloud output from the LiDAR provides feedback on the distance of the object and reflectivity information. Using prior calibration information and the characteristics of the track presented in the point cloud, it is also possible to determine the tracked area and identify intruders within the tracked area [19; 24; 25]. However, methods based on LiDAR input are more dependent on the quality of the point cloud. In situations where the point cloud quality is suboptimal, the performance of such methodologies experiences a pronounced decline.
# Vibration and Reliability Analysis of Non-Uniform Composite Beam under Random Load
## 2 Theoretical Model
### Natures (Nature Frequencies and Mode Shapes) of Non-Uniform Composite Beam
\(W_{i}(x)\) is written in series form,  
\[W_{i}(x)=\sum_{k=0}^{\infty}W_{k_{i}}(x), \tag{10}\]  
where \(k\) is the number of terms in series form. The larger the value of \(k\), the more accurate the solution. A precise solution is often obtained with very small values of \(k\)[50]. Substituting Equation (10) into Equation (9), we have,  
\[ \begin{split}\sum\limits_{k=0}^{\infty}W_{k_{i}}(x)& =C_{1}+C_{2}x+C_{3}\frac{x^{2}}{2!}+C_{4}\frac{x^{3}}{3!}\\ &\quad-L_{x}^{-1}\bigg{[}\frac{2\frac{d}{dx}[E(x)I(x)]}{E(x)I(x) }\sum\limits_{k=0}^{\infty}W_{k_{i}}^{\prime\prime}(x)\\ &\quad+\frac{\frac{d^{2}}{dx^{2}}[E(x)I(x)]}{E(x)I(x)}\sum \limits_{k=0}^{\infty}W_{k_{i}}^{\prime\prime}(x)-\omega_{i}^{2}\frac{\rho(x)A (x)\sum\limits_{k=0}^{\infty}W_{k_{i}}(x)}{E(x)I(x)}\bigg{]}.\end{split}  \tag{11}\]  
For each term in the series, we can have,  
\[W_{0_{i}}(x)=C_{1}+C_{2}x+C_{3}\frac{x^{2}}{2!}+C_{4}\frac{x^{3}}{3!}, \tag{12}\]  
\[ \begin{split} W_{k+1_{i}}(x)=-L_{x}^{-1}\Bigg{[}\frac{2\frac{d}{ dx}[E(x)I(x)]}{E(x)I(x)}W_{k_{i}}^{\prime\prime\prime}(x)+\frac{\frac{d^{2}}{dx^{2}}[E( x)I(x)]}{E(x)I(x)}W_{k_{i}}^{\prime\prime}(x)-\omega_{i}^{2}\frac{\rho(x)A(x)W_{k_{i }}(x)}{E(x)I(x)}\Bigg{]}k\geq 0.\end{split}  \tag{13}\]  
The natural frequencies can be obtained by introducing the mode functions into the boundary conditions and solving the eigenvalue problem. The detailed solving process of the mode shape functions and nature frequencies of the non-uniform composite beam can be found in references [41; 42; 43] and is hence not provided here.
## 3 Results and Discussion
### Neutron Tomography
It is known, the experimental data on the chemical and mineral composition obtained from the surface of massive object may differ dramatically from the distribution of chemical components in its volume. In this case, the use of non-destructive methods with high penetration into the bulk objects looks justified. The neutron diffraction method provides information about the phase composition of the studied object and neutron radiography and tomography allows the obtaining of the pattern of the spatial distribution of internal components. The fundamental difference in the nature of neutron interactions with matter compared to X-rays provides additional benefits including sensitivity to light elements, a notable difference in contrast between neighboring elements. All these features make neutron methods a highly growing tool in archaeology science applications. However, it seems that the main value of this study was precisely in the coordinated diverse research of the mosaic fragments. Additionally, the capabilities of various methods were demonstrated and these will provide a basis for future work on the classification and notification of numerous mosaics remains from other cultural centers.
# Theory and Design of Blink Jamming
## 2 Analysis of Formation Jamming
### Jammer-to-Signal Ratio Effects  
Angle electronic attacks are typically applied in scenarios where a range-gate or velocity-gate attack has been used first to pull the tracking gate off the actual target. This is done to create the very high jammer-to-signal ratio (JSR) required for most angle deception techniques to be effective, such as cross-eye or cross-polarization jamming. To understand formation jamming in the context of JSR, the analysis discussed so far needs to be expanded.  
Figure 4: Two amplitude modulated blink jamming signals with a signal-to-noise ratio of 20 dB are simulated using (11), demonstrating the noise effects in the indicated angle as the two signals approach zero amplitude.  
Extending the analysis in (9), each sum signal can be expressed as superposition of the jammer and skin return:  
\[s_{n}=p_{n}+j_{n} \tag{12}\]  
where \(p_{n}\) is the power of the skin return and, \(j_{n}\) is the power of the jammer.  
Substituting (12) into (9) results in:  
\[\theta_{i}=\frac{\theta_{a}(p_{a}+j_{a})+\theta_{b}(p_{b}+j_{b})}{p_{a}+j_{a}+ p_{b}+j_{a}} \tag{13}\]  
which can be framed in terms of the JSR. Where the JSR is:  
\[\chi_{n}=\frac{j_{n}}{p_{n}}. \tag{14}\]  
Combining the Equations (13) and (14), we arrive at an expression for the indicated angle given the power of two jammers, and two radar targets, framed in terms of the JSR:  
\[\theta_{i}=\frac{\theta_{a}p_{a}(\chi_{a}+1)+\theta_{b}p_{b}(\chi_{b}+1)}{p_{ a}(\chi_{a}+1)+p_{b}(\chi_{b}+1)} \tag{15}\]  
where \(\chi_{a}\) is the JSR from target \(a\) and \(\chi_{b}\) is the JSR from target \(b\).  
Simplifying this expression to the common blink jamming scenario, where only one target is jamming but the skin returns are present from both, the assumption is made that the skin returns are equal (\(p=p_{a}=p_{b}\)). This changes (15) to  
\[\theta_{i}=\frac{\theta_{a}(\chi_{a}+1)+\theta_{b}}{\chi_{a}+2} \tag{16}\]  
The critical question from (16) is at what JSR level does the jammer induced error become negligible? To evaluate this problem several MATLAB simulations were conducted, comparing the indicated versus true angle to the JSR for several different target separations. The results, shown in Figure 5, consistently demonstrate convergence at a JSR of 20 dB, and negligible effects of the skin return at 25 \"\"  
Figure 5: The measured and true angles for target separations of 2, 4, 6, 8, 10, and 12 degrees, plotted against the jammer-to-signal-ratio.
# Testing of Indoor Obstacle-Detection Prototypes Designed for Visually Impaired Persons
## 2 Materials and Methods
### Interpretation of Results  
For the interpretation of the test results, the confusion matrix [15; 16] is the chosen method. A confusion matrix is a table that is often used to evaluate the performance of a classification model. It summarizes the performance of a classification algorithm by displaying the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions made by the model.  
Here is a breakdown of the components of a confusion matrix:  
* **True positive** (TP): Instances that were correctly predicted as positive.
* **True negative** (TN): Instances that were correctly predicted as negative.
* **False positive** (FP): Instances that were incorrectly predicted as positive.
* **False negative** (FN): Instances that were incorrectly predicted as negative.  
In order to calculate the matrix components, the following equations are used:  
\[TP=\sum_{i=1}^{n}\ \mathbb{1}\ \left(model[i]=1\ and\ reality[i]=1\right) \tag{4}\]  
\[TN=\sum_{i=1}^{n}\ \mathbb{1}\ \left(model[i]=0\ and\ reality[i]=0\right) \tag{5}\]  
\[FP=\sum_{i=1}^{n}\ \mathbb{1}\ \left(model[i]=1\ and\ reality[i]=0\right) \tag{6}\]  
\[FN=\sum_{i=1}^{n}\ \mathbb{1}\ \left(model[i]=0\ and\ reality[i]=1\right) \tag{7}\]  
where:  
* \(\mathbb{1}\)--indicator function that returns 1 if the condition is met and 0 in all the other cases;
* model--the measured distance by the prototype;
* reality--the real distance measured using a measuring tape.  
Considering this, the values of the confusion matrix should correspond to the following sets of data:  
* **TP**: results \(\in[\)_realDistance_ \(-\) 2 cm, _realDistance_\(]\).
* **FP**: results \(\in[\)_realDistance_ \(-\) 5 cm, _realDistance_ \(-\) 2 cm].
* **FN**: results \(\in[\)_realDistance_ \(+\) 1 cm, _realDistance_ \(+\) 5 cm].
* **TN**: any other result.  
where _realDistance_ is the distance measured with a tape between the prototype and the obstacle  
For example, if the measured distance between the prototypes and the obstacle is 70 cm, then the values for the confusion matrix are as follows:  
* **TP**: if the prototype reading is between 68 and 70 cm and the user hears a \"STOP\" message;
* **FP**: if the prototype reading is between 65 and 67 cm and the user hears the \"STOP\" message;
* **FN**: if the prototype reading is between 71 and 75 cm and the user hears the \"DANGER\" message;
* **TN**: any other prototype reading.