esa-sceva/satcom-chunk-collection huggingface dataset:
# The Sun and Space Weather
## 3 CME Source Regions, Flares, and Filaments
The GOES X-ray intensity is higher by more than three orders of magnitude. CMEs also have similar morphology, but the speeds are very different. While the kinematics and energetics of CMEs may differ quantitatively, the basic mechanism of eruption seems to be the same in the two cases. The primary difference is therefore the soft X-ray flare intensity. Figure 6: **(top row)** CME–flare relationship, illustrated using the 6 October 1997 filament eruption event. The Nobeyama Radioheliograph (NoRH) image at 04:41 UT shows the filament located in the southeast quadrant (S54E46), while the image at 23:51 UT shows the filament has disappeared and ended up as the bright core of the SOHO/LASCO CME. **(bottom row)** The filament is also observed in the 04:33 UT EUV image obtained by SOHO/EIT at 195 Å. After the filament disappeared, a post-eruption arcade formed in the vicinity of the filament’s pre-eruption location. The PEA is so faint that it cannot be discerned in the GOES light curve. There can also be large differences in the mass emission in eruptions that have similar flare sizes. Figure 8 shows two soft X-ray flares with an X-ray flare size of X1.5. The flare on 9 March 2011 is a confined flare (no mass emission), while the eruptive flare on 2006 December 14 is accompanied by a CME that has an EUV disturbance on the solar disk and a shock in the corona. Confined flares are close to the neutral line compared to the eruptive flares [61; 62]. Since flares get their energy from nonthermal particles accelerated in the corona, both types of flares involve particle acceleration, but in confined flares, these particles do not escape from the Sun (no metric radio bursts or energetic particle events in space). However, these particles do precipitate to produce hard X-ray bursts and get trapped in closed-field lines to produce microwave bursts [63]. In many cases, a series of confined flares are followed by an eruptive flare, suggesting that the confined flares might facilitate the occurrence of eruptive flares. Figure 7: Information similar to that in Figure 6, but for the 4 August 2011 eruptive event involving a major flare. **(top row)** The Nobeyama Radioheliograph (NoRH) image at 03:20 UT shows the active region NOAA 11261 (N16W51) before the eruption. The 04:20 UT image shows the flare brightening in microwaves and the eruptive filament from the source active region. The associated CME is shown in a STEREO/COR1 image with a superposed EUV image **(top right)** showing the disturbance on the disk. **(bottom row)** The SDO/AIA images in the lower panels show the flare and filament eruption from the active region. The bright, compact PEA (marked “Flare”) shows up as an M9.3 flare in the GOES light curve.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 4 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Number** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline
1 & 94.52 & 71.46 & 81.39 \\
2 & 89.62 & 94.44 & 91.97 \\
3 & 85.33 & 91.69 & 88.39 \\
4 & 78.12 & 86.64 & 82.16 \\
5 & 75.97 & 99.08 & 86.00 \\
6 & 94.93 & 89.54 & 92.12 \\
7 & 96.60 & 73.68 & 83.60 \\
8 & 99.25 & 91.81 & 95.38 \\ Average & 89.29 & 87.29 & 87.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision, recall, and F-score of the proposed thermal bridge detection method. Figure 7: Experimental results: (a) thermal image, (b) clustering results, and (c) thermal bridge detection results.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 6 Clinical Utility
### Diagnostic Biomarkers  
The diagnostic capability of lncRNAs is one of the most promising aspects of their clinical utility. Numerous studies have demonstrated their strong association with LNM, distant organ metastasis, tumor size, and cancer stage [90; 93; 95; 97; 98; 99; 100; 101; 102; 104]. LncRNAs can be detected in various biological fluids, primarily blood, making them accessible through minimally invasive procedures. These biomarkers are particularly valuable for screening high-risk populations and monitoring disease recurrence in BC survivors. Research has shown that lncRNAs such as HOTAIR, MALAT1, and H19 exhibit promising diagnostic efficiency, often surpassing standard markers like CEA and CA153. Unlike protein markers, lncRNAs often exhibit tissue-specific expression patterns, which can lead to more accurate diagnoses. Techniques such as quantitative qRT-PCR and next-generation sequencing (NGS) have enabled the sensitive and specific detection of lncRNAs, making them viable candidates for routine clinical testing [80; 91].
## 5 Experimental Section and Results
### Experiment on PASCAL VOC Dataset
The right column is the detection result of our improved SSD network. In the first row, our method detected 2 more persons and 1 more ship than the original SSD method did. In the second row, we detected 2 more cows. In the third row, our method detected 3 more chairs. At the same time, the confidence of almost all kinds of objects were significantly improved in our method. \begin{table}
\begin{tabular}{c c c c} \hline
**Pruning Times** & **mAP (\%)** & **Model Size (M)** & **Time (ms)** \\ \hline
0 & 77.63 & 115 & 50 \\
1 & 77.49 & 80.6 & 43.3 \\
2 & 77.46 & 56.9 & 38.5 \\
3 & **76.82** & **40.7** & **35.5** \\
4 & 76.45 & 29.7 & 32.8 \\
5 & 75.55 & 21.9 & 30.5 \\
6 & 73.68 & 16.4 & 28.0 \\ \hline \end{tabular}
\end{table} 
Table 4: The mAP, model size and elapsed time with 6 prunings. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Class** & **Numbers** & \begin{tabular}{c} **Correct** \\ **Detection** \\ \end{tabular} & \begin{tabular}{c} **False** \\ **Detection** \\ \end{tabular} & \
\begin{tabular}{c} **Missed** \\ **Detection** \\ \end{tabular} &
\begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} \\ \hline Aero & 311 & 311 & 0 & 0 & 100\% \\ Bike & 389 & 375 & 1 & 13 & 96.4\% \\ Bird & 576 & 554 & 4 & 18 & 96.18\% \\ Boat & 393 & 392 & 0 & 1 & 99.75\% \\ Bottle & 657 & 640 & 2 & 15 & 97.41\% \\ Bus & 254 & 254 & 0 & 0 & 100\% \\ Car & 1541 & 1502 & 7 & 32 & 97.47\% \\ Cat & 370 & 368 & 0 & 2 & 99.46\% \\ Chair & 1374 & 1308 & 10 & 56 & 95.2\% \\ Cow & 329 & 314 & 4 & 11 & 95.44\% \\ Table & 299 & 298 & 0 & 1 & 99.67\% \\ Dog & 530 & 503 & 4 & 23 & 95.0\% \\ Horse & 395 & 387 & 3 & 5 & 97.97\% \\ Mbike & 369 & 364 & 1 & 4 & 98.64\% \\ Person & 5227 & 5174 & 20 & 33 & 98.98\% \\ Plant & 592 & 578 & 2 & 12 & 97.63\% \\ Sheep & 311 & 304 & 4 & 3 & 97.74\% \\ Sofa & 396 & 391 & 2 & 3 & 98.74\% \\ Train & 302 & 301 & 0 & 1 & 99.67\% \\ Tv & 361 & 361 & 0 & 0 & 100\% \\ Total & 14,976 & 14,679 & 64 & 233 & 98.01\% \\ \hline \end{tabular}
\end{table} 
Table 5: Experiment results of the PASCAL VOC test set.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 2 Related Work
### Swin Transformer  
Recently, the Transformer [29] model has attracted a lot of attention in the field of computer vision due to its excellent performance in the field of natural language processing (NLP) [30; 31; 32]. Similarly, in the field of remote sensing, Transformer has been widely used [33]. Transformer is uniquely suited to capture long-range dependencies in images [34; 35], which is particularly important for processing complex visual patterns. However, in low-level visual tasks such as image reconstruction, direct application of standard Transformer faces challenges in terms of computational efficiency and memory usage due to the large number of pixels involved. To address these issues, we introduce Swin Transformer [36], an innovative variant designed specifically for image processing. Swin Transformer retains the core strengths of Transformer and optimizes computational efficiency by introducing locality-awareness and hierarchical structure, making it more suitable for pixel-intensive image reconstruction tasks.
## 3 The Proposed Method
### PolSAR Data Pre-Processing
#### 3.1.1 Creating 6 Ch to Represent the Polarimetric Data  
Under the multi-look and reciprocity assumption, the single station PolSAR can be represented by the 3 \(\times\) 3 complex coherent matrix T which is symmetrical. To adapt the input format of the convolution neural network, it is necessary to convert the data into a real matrix. We create a 6 Ch to represent the polarimetric data, and each channel is obtained by Equation (7):  
\[\begin{split} A=& 10 ln(SPAN)\\ B=& T_{22}/SPAN\\ C=& T_{33}/SPAN\\ D=&|T_{12}|/\sqrt{T_{11}T_{22}}\\ E=&|T_{13}|/\sqrt{T_{11}T_{33}}\\ F=&|T_{23}|/\sqrt{T_{11}T_{33}}\\ \end{split} \tag{7}\]  
where \(T_{11}\), \(T_{22}\), \(T_{33}\) represent the diagonal elements of the matrix **T** and they are real numbers while \(T_{12}\), \(T_{13}\), \(T_{23}\) represent complex elements. \(A\) is the total scattering power in decibels, here \(SPAN=T_{11}+T_{22}+T_{33}\) ; \(B\) and \(C\) are normalized power of \(T_{22}\) and \(T_{33}\); \(D\), \(E\) and \(F\) are the relative correlation coefficients. Except \(A\), the remaining five parameters are normalized to [0, 1].  
Figure 2: The main procedures of the polarimetric synthetic aperture radar (PolSAR) images classification based on the dual-branch deep convolution neural network (Dual-CNN) model.  
Thus, the PolSAR data are converted into a 6 Ch to form a \(6\times m\times n\) dataset, where 6 represents the total number of the channels, i.e., \(A\), \(B\), \(C\), \(D\), \(E\) and \(F\); \(m\) and \(n\) represent the number of rows and columns in a single channel, respectively.
## 3 Results
### Design of Experiments Outcome
#### 3.3.2 Design of Experiment Outcome with aSPCE  
The DoE procedure was applied to optimize the DPV parameters used for revealing the oxidation peak of DCF. The sequence of experiments identified by the model was performed on three different days, using three different aSPCEs, and running the experimental series in a random way. All of the measurements were performed on a 0.1 mol L\({}^{-1}\) phosphate buffer solution at pH 2.0, and the analytical response used for DoE was the mean current value obtained for a solution 147 \(\mu\)g L\({}^{-1}\) DCF. The collected data were then processed with R software to estimate the model coefficients through Multiple Linear Regression (MLR) and generate the iso-response curves and response surfaces (Figure 5). As for the aGCE, the analysis of the confidence intervals of the model coefficients (Figure 5c), calculated with a confidence level of 95%, suggested that the terms containing the SC were not significant. The maximum response was obtained for parameter values located at the border of the investigated ranges (Figure 5d), but for high \(E_{\text{pulse}}\) values, the noise noticeably increases; therefore, it was chosen to work with a medium value of \(E_{\text{pulse}}\) and a low value of \(t_{\text{pulse}}\), as reported in Table 2 and shown in Figure S4.
## 3 Results
### The Sensitivities of TF-GPP to Droughs Occurring Different Seasons  
Figure 4 shows the GPP loss caused by drought in different seasons. Under the same drought duration and intensity, the \(\Delta\)GPP (\(\Delta\)GPP = GPP_Sta \(-\) GPP_Mod) change in HD is the most significant, and the \(\Delta\)GPP change in ER is the least significant. The GPP change in drought initiated in the dry season is more significant than drought initiated in the rainy season.  
**Drought initiation during the dry season**. The GPP impairment value is more affected by drought intensity than drought duration. As drought intensity increases, \(\Delta\)GPP also increases. When drought duration increases, \(\Delta\)GPP first increases and then decreases. Drought occurs in the CD, and GPP loss reaches the maximum (22.09 g C m\({}^{-2}\) month\({}^{-1}\)) after 5 months of continuous drought (Figure S1a). Drought occurs in the MD, and \(\Delta\)GPP reaches the maximum (31.31 g C m\({}^{-2}\) month\({}^{-1}\)) after 3 months of continuous drought (Figure S1b).  
**Drought initiation during the rainy season**. Drought duration has a more obvious impact on GPP than intensity, and \(\Delta\)GPP increases with the increase in drought intensity. Drought started in ER: \(\Delta\)GPP first decreases and then increases with the increase in drought duration. The turning point occurred when drought lasted 5 months. At this time, the \(\Delta\)GPP was 0.64 g C m\({}^{-2}\) month\({}^{-1}\), 0.77 g C m\({}^{-2}\) month\({}^{-1}\), 0.94 g C m\({}^{-2}\) month\({}^{-1}\), and 1.23 g C m\({}^{-2}\) month\({}^{-1}\) as drought intensity increased. (Figure S1c). When the drought lasted for 8 months or more, \(\Delta\)GPP increased significantly (Figure S1c), and the \(\Delta\)GPP was 2.49 g C m\({}^{-2}\) month\({}^{-1}\), 3.52 g C m\({}^{-2}\) month\({}^{-1}\), 4.68 g C m\({}^{-2}\) month\({}^{-1}\), and 5.25 g C m\({}^{-2}\) month\({}^{-1}\) as the drought intensity increased. Drought occurred in MLR, and \(\Delta\)GPP first increased with the increase in drought duration and then almost remained unchanged (Figure 4). The drought lasted more than 4 months, and GPP decreased significantly, and its values were 1.33 g C m\({}^{-2}\) month\({}^{-1}\), 2.11 g C m\({}^{-2}\) month\({}^{-1}\), 2.92 g C m\({}^{-2}\) month\({}^{-1}\), and 3.38 g C m\({}^{-2}\) month\({}^{-1}\) as the intensity increased (Figure S1d).  
Figure 4: Under SPI_9 (**a**) CD; (**b**) HD; (**c**) ER and (**d**) MLR initiate drought, the difference between the average monthly GPP in standard climate and the average GPP in arid climate month (\(\Delta\)GPP = GPP_Sta \(-\) GPP_Mod) varies with the duration and strengths of the drought.  
The sensitivities of TF-GPP to droughts occurring different seasons under SPI_1, SPI_3, SPI_6 and SPI_12 time scales are shown in Figures S2-S5.
## 4 SDR Implementation of FM-ACSK System Prototype
### Symbol Timing Synchronization Unit  
A timing synchronization unit is necessary for making decisions about the received symbols at optimal time instants. As was said before, the modem exploits chaotic synchronization for the despreading and demodulation of the encoded messages. In the case of the binary modulation, used in this research, there are two generators in the receiver and one of them is synchronized. At the output of RMS units in Figure 3, there is a gradually increasing signal when the respective slave chaos generator does not have chaotic synchronization. In contrast, when the generator is synchronized, its RMS unit's output is close to zero. The DM is carried out from the summary waveform, shown in Figure 9b, that is produced by the subtraction of signals from two RMS units. At the time instant when the summary waveform reaches the positive or negative peak, there is an optimal decision point for the best approximation of the received bit. The triangular waveform at the input of the DM unit, causes degradation of BER even at tiny symbol timing offset (STO). In terms of telecommunication terminology, it can be said that the eye diagram at the input of the DM unit is in the shape of a rhombus (see Figure 9a).  
Figure 8: Implementation of the chaotic generator and synchronization circuit in the ACSK demodulator.  
For the estimation of the received bits, two alternative detectors were employed. The first one--SD--punctured the triangular signal at the input of DM at the time instants governed by symbol timing (ST) unit shown in Figure 8(b). The second--ED--calculated the area of the waveform by summing all samples belonging to the symbol, i.e., around the decision point. The second detector was developed to mitigate the impact of the STO. The Simulink block scheme of ST unit is shown in Figure 10. The structure of ST unit resembles the classic loop synchronization scheme consisting of an offset estimator, loop filter and numerically controlled oscillator (NCO). The input signal is passed via the \"Delay line\" block which produces a series of windows shifted by one sample. \"Find delay\" finds the index of the maximum value in the particular window, which is selected by the symbol clock, and calculates offset from the midpoint. Error is sent to the loop filter which averages it over several symbol intervals and sends it to NCO which adjusts the clock to gradually decrease the offset.
# A Comparison of Faulty Antenna Detection Methodologies in Planar Array
## 2 Methodology  
The methodology consists of developing an 8 \(\times\) 8 element planar array, modelling and optimizing the developed model with four different optimization methods. This paper investigates a suitable optimization method to find failed elements in a planar array using failed radiation patterns, where the optimization is done in MATLAB through simulation. The simulations were conducted with a 64-bit operating system, Intel(R) Core (TM) i7-4790 CPU processor, and 16 GB installed memory. This work has computer capability and software limitations. This optimization simulation is for research purposes only. In real time, this procedure will be fast, because the procedure can be implemented with a Digital Signal Processor (DSP) with a parallel processing method. The suitable optimization method found in this study can be programmed with C language for DSP.  
Planar array modelling involves generating failed radiation patterns for a planar array with a set of random failed locations. Then, SNR is calculated for the failed radiation pattern to evaluate the gain performance. Later, the optimization algorithm will try to detect the same failed location from the planar array modelling part. To find the initial failed locations, the optimization algorithm will assign new random failed locations, generate the new failed radiation pattern from the failed location, and find the respective SNR. The error value is found by determining the difference between the new failed radiation pattern's SNR value and the initial radiation pattern's SNR value. Ideally, the error value of the correct failed location will be zero. The error value is then minimized by the optimization method selected (SA, PSO, and PS) until the correct failed location is found. The design methodology is summarized in Figure 1.
Moreover, based on the surface roughness of joints, the mechanical properties of rock masses are closer to those of natural rock masses. The above studies mainly focused on studying a single or single group of regular rough rock mass joint models; however, the actual natural rock mass joint network is more complicated, with joints possessing complex shapes such as multiple sets of curves and broken lines. However, only relatively few studies have been performed in this field. [PERSON] et al. [30] proposed a rough discrete fracture network (RDFN) model based on the roughness of the joint surface (Figure 2). Further, they constructed a sine curve, a triangular polyline, and a fractal fracture network model for indoor tests and numerical simulations based on three different geometric types of RDFN models and modelling methods, ultimately enabling the construction of complex structural surface networks of rock masses. This study sought to further propose an RDFN characterization model based on the Fourier transform principle, and also based on research of the rough discrete joint network model, to ultimately achieve a unified characterization of complex geometric joints by changing the Fourier transform series. Accordingly, a joint network RDFN model that can characterize more complex geometric shapes can be established. The particle flow discrete element method was used in this study to elucidate the Fourier-based RDFN model and the complex geometric RDFN numerical model modelling. Moreover, this study aimed to further present the anisotropic mechanical properties of the complex RDFN model under biaxial compression and also to present the influence of the joint network space transformation on the force chain distribution and mesoscopic failure to obtain the anisotropic strength distribution characteristics of the jointed rock mass. The findings of this study have important scientific significance for the refined and quantitative characterization of structural surfaces of complex rock masses, which will provide a reasonable and reliable mechanical model for the study of the mechanical properties of jointed rock masses.
# Telescopic Network of Zhulong for Orbit Determination and Prediction of Space Objects
## 4 Discussion
Brightness changes [41] can provide insights into the object's active state and changes. Measuring position and velocity can help us gain insight into the object's orbit and movement. In the future, with continued technological advancements, scientists anticipate the production of larger and more precise telescopes. These advanced instruments will offer enhanced insights into space objects, providing more detailed information. Additionally, with the advancement in data analysis technology and computer science, these data can be processed and analyzed more effectively to gain a deeper understanding of the mysteries of the universe.
## Carrier-Envelope Offset Stabilized Ultrafast Diode-Pumped Solid-State Lasers  
**[PERSON] * and [PERSON]**  
Laboratoire Temps-Frequence, Universite de Neuchatel, Neuchatel CH-2000, Switzerland;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +41-32-718-2917; Fax: +41-32-718-2901.  
Academic Editor: [PERSON]  
_Received: 24 August 2015 / Accepted: 10 September 2015 / Published: 14 October 2015_  
**Abstract:** Optical frequency combs have been revolutionizing many research areas and are finding a growing number of real-world applications. While initially dominated by Ti:Sapphire and fiber lasers, optical frequency combs from modelocked diode-pumped solid-state lasers (DPSSLs) have become an attractive alternative with state-of-the-art performance. In this article, we review the main achievements in ultrafast DPSSLs for frequency combs. We present the current status of carrier-envelope offset (CEO) frequency-stabilized DPSSLs based on various approaches and operating in different wavelength regimes. Feedback to the pump current provides a reliable scheme for frequency comb CEO stabilization, but also other methods with faster feedback not limited by the lifetime of the gain material have been applied. Pumping DPSSLs with high power multi-transverse-mode diodes enabled a new class of high power oscillators and gigahertz repetition rate lasers, which were initially not believed to be suitable for CEO stabilization due to the pump noise. However, this challenge has been overcome, and recently both high power and gigahertz DPSSL combs have been demonstrated. Thin disk lasers have demonstrated the highest pulse energy and average power emitted from any ultrafast oscillator and present a high potential for the future generation of stabilized frequency combs with hundreds of watts average output power.  
**Keywords:** optical frequency comb; carrier-envelope offset stabilization; modelocked laser; diode-pumped solid-state laser