esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review on Image Captioning
## 4 Discussion
### Model Architecture and Computational Resources
#### 4.1.2 Decoder--LSTM  
LSTM (long-short-term memory) was developed from RNN, with the intention to work with sequential data. It is now considered as the most popular method for image captioning due to its effectiveness in memorizing long term dependencies through a memory cell. Undoubtedly this requires a lot of storage and is complex to build and maintain. There have been intentions to replace it with CNN [52], but as we can see from the number of times this method is used in most of the articles found during this SLR (68 of 78), scientists always come back to LSTM. LSTM works by generating a caption by making one word at every time step conditioned on a context vector, together with the previous hidden state and the earlier generated words.  
Computational speed not only depends on the feature detection model, but also on the size of the vocabulary--each new word added consumes more time. Just recently [73] scientists have tried to solve the image captioning task by resizing the vocabulary dictionary. Usually the vocabulary size might vary from 10,000 to 40,000 words, while their model relies on 258 words. The decrease is quite sharp--reduced by 39 times if compared to 10,000, but the results are high, with some space for improvements.
# Solar Radio Emissions and Ultralight Dark Matter
## 6 Summary  
Solar plasma is an ideal place for detecting ultralight axions or dark photons. When axion dark matter or dark photon dark matter particles cross the solar corona or solar wind, they can resonantly convert into standard model photons when their mass equals the plasma frequency. The plasma frequency of the solar corona and solar wind spans from 1 GHz to 0.1 MHz for the distance extending up to 1 AU. Therefore, searching for radio signals converted from ultralight axions or dark photons in the plasma provides a complementary method to other haloscope studies, to detect these dark matter particles in the mass range \(\sim\)10\({}^{-6}\)-10\({}^{-10}\) eV and set stringent constraints on the parameter space of these dark matter models. Based on this mechanism, radio telescopes, such as LOFAR and SKA, perform well in searching for ultralight dark matter, especially for the dark photon case. Future projected SKA and upgraded FAST are expected to obtain solar radio data with much higher precision. In addition to the terrestrial telescopes, we can use space solar probes, such as STEREO and Parker, to search for the ultralight dark matter. We conclude that searching for resonant-conversion radio signals from solar plasma is an efficient method, which is competitive and complementary compared to other direct detection experiments [8; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104] aiming for the ultralight dark matter.  
The work of [PERSON] is supported in part by the National Key R&D Program of China under Grant No. 2021 YFC2203100 and 2017 YFA0402204, the NSFC under Grant No. 11975134, and the Tsinghua University Initiative Scientific Research Program. The work of [PERSON] is supported by NSFC under Grant No. 12247147, the International Postdoctoral Exchange Fellowship Program, and the Boya Postdoctoral Fellowship of Peking University. The work of [PERSON] is supported by NSFC under Grant No. 12075005, 12235001.  
The data presented in this work is available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Three-Dimensional Finite Element Model  
To verify the validity of the full-scale field test monitoring data and further study the influence of the thermal stress on the steel assembled supporting structure of the foundation pit, a three-dimensional numerical model matching the full-scale field test was established by using ABAQUS 6.14/Standard module. In the process of the finite element simulation, the surface temperature of the supporting structure was considered to be uniformly distributed, and the influence of the soil rheology and groundwater was not taken into account.
## 2 Materials and Methods  
### Measurements, Data Collection, and Model Type Selection  
Photovoltaics is developing extremely fast and nowadays we can talk about many different types of models and approaches, depending on the final application of energy yield forecasts. The forecasts can refer to a single PV plant, a group of plants over a larger geographical area, or different time horizons [40]. 0-6 h ahead forecasts and day-ahead forecasts are crucial for successful grid integration [41]. Physical weather forecasting methods use weather forecasts, PV models, and characteristics, while statistical methods rely mainly on past data and numerical methods, with little or no dependence on PV models [42]. The increase in the number of PV power plants connected to the power system has led to increased interest in the development of various PV power forecasting models. The models can be based on advanced techniques such as artificial neural networks (ANNs) [43,44], as well as support vector machines [45,46] or various hybrid models [47,48]. In this study, the ANN model was used.  
In choosing a working plant suitable for the study, two main factors were considered. The first one is related to weather conditions, and these are directly related to the location of the installation. In the case considered in this study, the PV system was installed in western Poland, in the Wielkopolskie vovodeship, in Poznan County, Komorniki municipality. According to the research conducted by the Institute of Meteorology and Water Management in Poland, the sunlight conditions in this area are similar to the average sunlight conditions in Poland [8]. Thus, it can be concluded that the results of the analysis will be similar to the average results obtained in Poland, which is important for the objectivity of the study conducted. The second crucial factor in selecting a suitable photovoltaic installation for the study was a location as close as possible to the meteorological station and the ability to obtain data from the station to conduct the analysis.  
The installation was tailored to meet the energy consumption needs of a single-family household. Due to the roof mounting system, the position of the installation with respect to the earth's poles depends on the orientation of the roof of the building. One of the roof pitches was conveniently positioned for energy production by the photovoltaic panel. Its azimuth was about 200\({}^{\circ}\). The roof pitch angle was about 30\({}^{\circ}\) and PV modules were installed at the same angle. The roof area allowed the installation of 22 modules with a total peak power of 8.8 kWp. There were shading elements in the vicinity of the panel; however, their characteristics were constant and did not affect the accuracy of the study.
## 3 Proposed Strongest AoA Components Estimation Algorithm  
### Problem Formulation  
According to (6), \(p_{i}=\Pr\{|y_{k,i}|>\tau\}\). Clearly, the stronger the effective channel after beamforming, i.e., \(|h_{k,i}|\), the higher value is \(p_{i}\). Hence, the best beam identification problem in (3) becomes to identify the beam with the highest value of \(p_{i}\).  
Suppose \(L\) observations have been made by the BS at each RF chain: \(\{\widehat{y}_{k,i}\}_{n=1}^{L}\). Denote \(\mathcal{I}_{+}=\{K|(\widehat{y}_{k,i})_{i}=1,j=1,\ldots,K\}\) and \(\mathcal{I}_{-}=\{L-K|(\widehat{y}_{k,i})_{t}=0,t=1,\ldots,L-K\}\), respectively, then \(\widehat{p}_{i}\) can be estimated as  
\[\widehat{p}_{i}=\frac{K}{L}, \tag{8}\]  
where \(\widehat{p}_{i}\) can be obtained as close as \(p_{i}\) provided that the number of observations \(L\) is sufficiently large. Therefore, in principle, the best beam can be identified by choosing the beam that has the largest \(\widehat{p}_{i}\), \(i\in\{1,\ldots,M\}\), regardless of the quantization threshold \(\tau\). However, the value of \(\tau\) will affects the discrimination between the best beam and others, i.e., affecting the differences of \(p_{i}\), thus will have a significant impact on the number of observations needed to identify the best beam.  
Unfortunately, it is difficult to directly obtain the optimal threshold \(\tau_{\text{opt}}\) as well as the minimum estimation overhead without the precise knowledge of the noise variance, which is known to be hard to obtain with 1-bit A/Ds [38]. As an alternative way, one can determine the threshold \(\tau\) by counting the minimum number of pilots \(L\) under different values of \(\tau\).  
Therefore, in the remaining parts of this section, we focus on the problem of minimizing the overall AoA estimation overhead while maintaining reasonable estimation accuracy. We begin by considering how to design proper quantization thresholds to reduce the overhead in low SNR scenarios.
## 3 Materials and Methods
### Methods  
In this study, we propose both segmentation and classification approaches. In segmentation, we used the GWO metaheuristic algorithm to segment the optic disc from the retinal fundus images. Two approaches are proposed for classification (Figure 2): one-stage and two-stage classifications.  
In the one-stage classification approach, whole images were used to train pre-trained CNN and ST models without any cropping. In the two-stage classification, we used the YOLO detection algorithm. The process comprises two phases. In the first phase, the YOLO detection algorithm is used to locate and crop the optic disc region. In the second phase, the classification is performed by feeding the cropped images of the optical disc into the DenseNet201, VGG19, ResNet50, InceptionV3 CNN, and Swin Transformer models. In the RF model, the features extracted from these pre-trained CNN models are then fed into a Random Forest (RF) classifier for classification. Both the cropped and the uncropped approaches are evaluated using RF classifiers. This comprehensive framework allows us to evaluate the performance of different models and approaches in distinguishing individuals with glaucoma from healthy individuals using retinal fundus images.  
The proposed method for optic disc segmentation and glaucoma classification offers several distinctive characteristics compared to previous studies. This study is distinguished by its introduction of a novel metaheuristic algorithm, GWO, for optic disc segmentation. Unlike traditional segmentation methods, GWO mimics the hunting behavior of grey wolves to optimize the segmentation process, providing robust and accurate results even in challenging retinal images. The use of a state-of-the-art Swin Transformer for glaucoma  
Figure 2: Overview of the classification framework.  
classification represents an approach not used in previous studies. Also, this study provides an in-depth evaluation and comparison of one-stage and two-stage approaches for glaucoma classification. A two-stage approach harnesses the complementary strengths of different methodologies, leading to superior performance compared to methods relying solely on traditional algorithms or deep learning models. Many previous studies have often focused on individual methods. The remainder of this section contains information on GWO and YOLO, ST.
## 2 Morphological Reconstruction and Multi-feature Analysis for TIR Ship Images
### Ship Target Verification Based on Contour Description and Shape Constraint
#### 2.4.1 Contour Description of TIR Ship Based on Eigenvalue Analysis of Structure Tensor
The threshold \(Thr^{*}\) is experimentally set to 0.2094 for most scenarios, and the threshold selection will be discussed in detail in the Effects of Parameters part. Figure 9 illustrates the calculation process of STAEM of a connected region. Figure 9a is the computed contour of the connected region by grouping normalized large eigenvalues \(\lambda_{\text{large}}\), and its binary contour \(D_{\text{can}}\) is obtained by dilation with three-pixel disk-shaped structuring element, as shown in Figure 9d. Figure 10 provides the final detection results according to STAEM calculation. The red boxes have enlarged true ship targets, and the purple boxes have enlarged small-size false targets. First row is the normalized large eigenvalue maps of structure tensor of pre-processed images in Figure 5d-f, then, second row displays the binarized final saliency maps and the computed STAEM maps simultaneously, and the third row gives the final detection results after the threshold division of STAEM values. As shown in Figure 10d-f, for these three complex infrared images, the STAEM values of ship targets are much larger than those of sea clutter, hence, the STAEM calculation can be used to distinguish candidate infrared ship targets from residual clutter by thresholding operation. The reason can be illustrated by the crucial differences between infrared ship targets and sea clutter in terms of the thermodynamic character and the imaging procedure. In TIR remote sensing images, the pixel  
Figure 9: The illustration of the calculation process of STAEM around TIR ship targets. (**a**) is the contour computed by grouping normalized large eigenvalues, (**b**) binary ship target and its computed contour, (**c**) the dilated binary ship target, (**d**) binary contour obtained by dilation of ship targets and its true computed contour. intensities of small ship targets have high uniformity, and their solid contours appear almost totally closed and have larger eigenvalues in structure tensor map. While for sea clutter, due to their pixel intensities are uneven, their contours present a semi-open form after saliency map fusion based on gray-level morphological reconstruction.
# Observing APOD with the AuScope VLBI Array
###### Abstract  
The possibility to observe satellites with the geodetic Very Long Baseline Interferometry (VLBI) technique is vividly discussed in the geodetic community, particularly with regard to future co-location satellite missions. The Chinese APOD-A nano satellite can be considered as a first prototype--suitable for practical observation tests--combining the techniques Satellite Laser Ranging (SLR), Global Navigation Satellite Systems (GNSS) and VLBI on a single platform in a Low Earth Orbit (LEO). Unfortunately, it has hardly been observed by VLBI, so major studies towards actual frame ties could not be performed. The main reason for the lack of observations was that VLBI observations of satellites are non-standard, and suitable observing strategies were not in place for this mission. This work now presents the first serious attempt to observe the satellite with a VLBI network over multiple passes. We introduce a series of experiments with the AuScope geodetic VLBI array which were carried out in November 2016, and describe all steps integrated in the established process chain: the experiment design and observation planning, the antenna tracking and control scheme, correlation and derivation of baseline-delays, and the data analysis yielding delay residuals on the level of 10 ns. The developed procedure chain can now serve as reference for future experiments, hopefully enabling the global VLBI network to be prepared for the next co-location satellite mission.  
VLBI; APOD; AuScope; satellite tracking +
Footnote †: journal: Accepted: 11 May 2018; Accepted: 11 May 2018; Published: 16 May 2018
## 4 DPS: Grid-Based Dual Phantom Node Source Location Privacy Protection Scheme
### The Phantom Node Determination Phase  
(1) The first step in the PN determination phase is the same as that of the SPS scheme;  
(2) After the SN receives the request packet from the SoN, it randomly selects \(2M\) grids from the grids outside the visible region of the SoN, and randomly selects a node from each grid to form two PNCSs: The SPNCS and the RPNCS. The SPNCS is composed of nodes selected from \(M\) grids close to the SoN, and the RPNCS is composed of nodes selected from \(M\) grids far from the SoN;  
(3) The SN sends the two PNCSs and the grid number of each node in them to the SoN;  
(4) Once receiving the PNCSs, the SoN randomly selects a node from the SPNCS and a node from the RPNCS as the SPN and the RPN, respectively.
## 2 Materials and Methods  
### Data Overview  
#### 2.1.1 GOSAT Level 1b Radiance Data  
The GOSAT satellite, launched on 23 January 2009, operates on a sun-synchronous, descending orbit, ensuring an overpass time of 13:00 LT at the Equator. Each sounding is acquired by the satellite with a footprint of approximately 10.5 km \(\times\) 10.5 km. Onboard the satellite is the Thermal and Near-infrared Sensor--Fourier Transform Spectrometer (TANSO-FTS), equipped with four bands. Specifically, the FTS-1 band spans the wavelength range of 757 to 775 nm, falling within the frequency range of SIF emission. The spectral resolution of FTS-1 is 0.025 nm, with a sampling interval of 0.012 nm [15].  
The high spectral resolution of GOSAT, coupled with its suitability for SIF retrieval, provides an ideal operational space for refining the retrieval window. With a revisit period of 3 days, GOSAT facilitates global coverage in a relatively short timeframe. The high-precision SIF products obtained through GOSAT are valuable for conducting data analyses at high temporal resolutions.
## 3 New Numerical Parametric Models  
This chapter consists of computational analysis to evaluate the fire resistance of wooden support elements. Based on the previous validation of the numerical model M4, it becomes possible to study the fire behavior, in different parametric models, considering the same assumptions.  
In this way, the variation of geometric parameters, such as the distance between the centers, the height, and width of the wooden beams, as well as the thickness of the gypsum board, allows determining the influence of each variable. Therefore, the present study focuses on determining the temperature range at the control points of the different parametric models, the char layer in the wooden beams, the calculation of the respective residual cross-section, and the fire resistance of the models under analysis, based on the application of the thermal insulation criterion, according to EN 1363-1:2020 [20].  
The work is, thus, subdivided into four iterative studies, each evaluating the influence of geometric parameters. The first study consists of varying the distance between the centers of the wooden beams (D), the second on changing the height (W), the third affected the width of the beams (H) and, finally, the fourth evaluated the impact of gypsum board thickness (Tg).  
The typical constructive model, with the identification of geometric parameters, is represented in Figure 5. Control points A, E, and F, as well as the 13 nodes located on the surface not exposed to fire to allow the calculation of the thermal insulation criterion, are represented in Figure 5.  
Regarding the studies, an iterative and sequential methodology was adopted, with each study consisting of three geometric parameter variations (D, H, W, and Tg), as represented in Table 3, which consists of determining the value relating to the geometric parameter that allows obtaining better performance, that is, greater fire resistance.  
The study consists of the development of nine computational analyses using the finite element program ANSYS(r) 2022R2. From the study of the distance between centers (D), one of the parametric models is repeated in the following study, depending on the best performance. Regarding materials, the use of glued laminated wood GL32h and gypsum board type F was considered, which have the best characteristics for obtaining the greatest fire resistance. In each numerical model different results were obtained, the temperature field at control points A, E, and F, the char layer of the wooden beams and respective residual cross-section, and the fire resistance, through the application of the criterion thermal insulation, by EN 1363-1:2020 [20].  
Figure 5: Constructive model [H \(\times\) W \(\times\) Tg] \(\times\) D, control points and node location.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 4 Methodology
### Iterative Feedback Mechanism  
A new and more complete iCFG can be constructed by adding the discovered indirect jump edges \((b_{src},b_{dst})\) to the old iCFG. We observed that the combined iCFG contains new structure information of the target program and can be used to optimize the DGF-based test case generation (Section 4.2). Therefore, we propose an iterative feedback mechanism to achieve a virtuous cycle between CFG combination and input generation and continuously improve the completeness of CFGs.  
The key problem of iterative feedback mechanism is how to recalculate the seed distance on the new iCFG. If we simply use the approach in Section 4.2.1 to recalculate the distance, it will bring redundant time overhead. To minimize the overhead, we develop an incremental calculation to update the distance.  
Figure 6 shows an example of updating the distance adopting the incremental calculation. The node \(t\) is the target basic block. The solid lines represent the jump edges in the old iCFG and the dash line \(b\to c\) represents the newly discovered indirect jump edge. Because the new edge changes the structure of the iCFG, the basic block-level distance needs to be updated.  
Figure 6: Example of updating the distance incrementally.  
Through observation, it can be found that the new edge only affects the distance of the basic blocks before the edge on iCFG, but not the distance of the basic blocks after the edge. Therefore, we only need to calculate the distance of the precursors of the source basic block of the edge. In the example, the distances of \(a\) and \(b\) need to be recalculated, while the distances of \(c\) and \(d\) do not. The updated results of the example are shown in Table 2.
# Grammar-Supervised End-to-End Speech Recognition with Part-of-Speech Tagging and Dependency Parsing
## 4 Experiments
### Experiment Results
#### 4.4.2 The Auxiliary Text-to-Grammar Downstream Task (AT2G)  
Since mismatch is a long-standing and complex problem, we take the joint training of the ASR task and text-to-grammar task as a new configuration. In this experiment, the loss of the text-to-grammar falls at a high speed, and the joint training tasks have a fast convergence rate and good stability in the training process.  
As reported in Table 3, the AT2G slightly reduces the CER and GDD on both datasets. For TRANS-M, the CER and GDD of AT2G are 17.73% and 23.06%, respectively, representing a relative CER reduction of 2.26% and a relative GDD reduction of 4.00% over the baseline. However, AT2G adds extra parameters and the cost of model training, and weakens the constraints of the hidden features in the ASR decoder.