esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 3 Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
### Principles of Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
The total number of data reads and writes \(\widetilde{A}_{m1}\) for the data store is as follows:  
\[\tilde{A}_{m1}=2 MKN_{a}, \tag{16}\]  
In the angle estimation data reading step, it is necessary to read the velocity dimensional data of all virtual antenna channels corresponding to each target distance from memory I. Assuming that the number of detected targets is \(C\), the number of data readings \(\widetilde{A}_{m2}\) is calculated as follows:  
\[\tilde{A}_{m2}=CN_{a}K, \tag{17}\]  
If the processing time unit utilized by the system is \(T\), then the processing time \(\widetilde{T}_{s}\) of the system can be expressed as follows:  
\[\tilde{T}_{s}=(\tilde{A}_{m1}+\tilde{A}_{m2})T+T_{o}, \tag{18}\]  
where \(T_{o}\) is the algorithmic elapsed time outside the data stream. Integrating Formulas (16)-(18) yields the following derived equation:  
\[\tilde{T}_{s}=(2 MKN_{a}+CN_{a}K)T+T_{o}, \tag{19}\]  
where \(T_{o}\) is the algorithm time consumption outside the data stream. Given the limited number of targets, where \(C\ll M\), the proposed system substantially decreases the usage of storage resources. Consequently, the system processing time \(\widetilde{T}_{s}\) is notably reduced compared to \(T_{s}\), thereby significantly enhancing the real-time performance.
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 4 Results and Discussion
### Coherence Magnitude Analyses  
The low coherence result destroys the unwrapped result (Figure 7b), which is why a high coherence area from the IW2 sub-swath is extracted by a polygon (Figure 7c). The mean coherence is relatively low over the low coherence area in VH polarization (Figure 8c) and VV polarization (Figure 8d), typically with values below 0.26.  
The inner boundary of landfast ice zone or a high coherence area (Figure 7c) is located between Pirtikari and Ykskuusi with mean coherence value slightly higher than 0.3. This high coherence level gives us an acceptable unwrapped result. There are a few low coherence lines across the zone that are on-ice traffic routes between islands made and used by local people. It is interesting to see that coherence in the near-shore zone is lower. The outer boundary of the high coherence zone is sharp that reflects the location of the landfast ice edge (Figure 2).  
Figure 6: Methodology steps: A schematic block diagram for producing an interferogram and displacement map.  
The coherence level in this study may have decreased for several reasons. Firstly, there were two storm events, which may have caused mechanical ice displacements. Secondly, there were warm (air temperature is above 0 \({}^{\circ}\)C) periods, when minor melting in the surface layer and consequent melt-freeze metamorphosis may have taken place. Thirdly, there were cold periods that could have increased ice thickness by 5-10 cm in the study region [2]; however, no significant change was seen in Ajos ice station data [22]. Ice growth can lower coherence by increasing freeboard as implied by the Archimedes law. Fourthly, the low coherence can be caused by random movements of scatterers in snow and ice as well. Such movements can be caused by snow metamorphosis or dynamics of brine inclusions in sea ice.  
Our observations showed that the accumulated sums of rain and snowfall were approximately 44 mm during the period between overflights, 38.8 mm solid and 5.2 mm in the liquid phase (Figure 4b, precipitation). The rainfall affected the surface characteristics and reduced coherence. If the weather is cold (air temperature is below 0 \({}^{\circ}\)C), snow on the ice is dry, and the interference with the radar is small [11]. In our case, several periods of melting might have reduced coherence (Figure 4a). If the surface layer becomes wet, then the backscatter suffers and will lose coherence. Melting-freezing cycles were other important causes of losing coherence. In addition, some factors contributed to high coherence over the polygon area. One of these factors was setting the polygon in the shallowest area in the Bay of Bothnia based on bathymetry information in the Baltic Sea [22]. Setting the polygon in the shallowest area increased the possibility of having grounding ridges. Islands serve as support points and so do grounded ridges, so these increase fast ice stability as well.
Various material models representing different types of concrete have been proposed for impact analysis [15; 16; 17]. However, they often require many input parameters, some of which have no clear physical meaning and cannot be estimated by traditional material tests [18]. So far, there is a dearth of numerical studies on the behavior of ECCs under dynamic loading. For instance, [PERSON] [19] utilized the LS-DYNA code in modeling the behavior of hybrid-fiber ECC panels under drop-weight impact loading. It was observed that the Finite Element (FE) model gave a reasonable prediction of the local and global responses of the ECC panels and closely predicted the impact load-time histories of the impactor. Likewise, [PERSON] and [PERSON] [2] performed a numerical simulation of ECC panels under high-velocity impact loading using the LS-DYNA code. Their resultsdemonstrated the effectiveness of the proposed model in simulating the dynamic behavior of ECC materials. Recently, [PERSON] et al. [20] conducted an FE study on ECC beams under impact loading using the commercial ABAQUS software package to observe the stress distribution in beams and attain an accurate and verified model capable of simulating such loading conditions. Their analytical results were in good agreement with the experimentally measured data in terms of maximum displacement and load values. In addition, they observed that the stress distribution through the beams was in reasonable agreement with the experimental crack distribution. Hence, it appears that accurate simulation and prediction of the structural response under dynamic load can be a convenient way to decrease the cost and efforts related to testing new materials and applications. The availability of powerful FE modeling codes with the flexibility to incorporate new material models developed by the user has stimulated such efforts. In the present study, experimental testing and numerical simulation of a novel hybrid fiber-reinforced engineered cementitious composite incorporating SMA and PVA short fibers (HECC-SMAF) under impact loading were carried out, and the findings are discussed below. All test results and curves present the average values of the tested specimen in triplicate for each test.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 3 Methodology
### Image Encoder
#### 3.1.1 Backbone Network  
The input image \(I\in R^{H\times W\times C}\) is first fed into a Swin Transformer backbone, which extracts multi-scale hierarchical features. It is partitioned into non-overlapping patches, which are projected into an embedding space via a convolutional layer:  
\[X=W_{p}*I+b_{p} \tag{1}\]  
where \(W_{p}\) and \(b_{p}\) are the learnable weight and bias parameters of a convolutional layer with a \(P\times P\) kernel size. \(P\) represents the patch size and \(D\) is the embedding dimension.  
The backbone architecture employs two key attention mechanisms: Window-based Multi-Head Self-Attention (W-MSA), which models local dependencies within each window, and Shifted Window Multi-Head Self-Attention (SW-MSA), which enables interactions across neighboring windows by shifting the window partitioning. The output at each layer is computed as follows:  
\[\begin{split}& X^{\prime}=\text{W-MSA}(\text{LN}(X))+X\\ & X^{\prime\prime}=\text{SW-MSA}\big{(}\text{LN}\big{(}X^{\prime} \big{)}\big{)}+X^{\prime}\\ & Y=\text{MLP}\big{(}\text{LN}\big{(}X^{\prime\prime}\big{)}\big{)} +X^{\prime\prime}\end{split} \tag{2}\]  
Here, \(X\) denotes the input embedding at each stage. After each attention operation, the intermediate features are passed through a multilayer perceptron (MLP), which improves feature expressiveness while preserving spatial structure. Layer normalization (LN) is applied before each module to stabilize training and improve convergence. The final refined representation at each stage is denoted by \(Y\).  
Through this hierarchical process, the Swin Transformer backbone generates a set of multi-scale feature maps \(F=\{F_{1},F_{2},F_{3},F_{4}\}\), where each \(F_{i}\) corresponds to a different resolution level. These feature maps are passed to the RAOE and SAGE modules, which independently extract object-centric and globally distributed visual features, respectively.
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
## 4 Results  
The preceding sections describe the design development up until the preliminary design review. This section details the further design development stage in which the overall design and components are integrated into a full system design. Details of the later integration stage testing and completed development and integration of the payload will be addressed in a follow-up publication.
## 2 Methods
### Statistical Analysis  
The paired \(t\)-test was performed to compare the SSQ score before the participants watched the VR video with that after. Two-way repeated-measures analysis of variance (rmANOVA) was performed. Time (before and after the participants watched the VR video) and region (anterior, middle, posterior, and global) were used as the within-subject factors of EEG frequency band power. Two-way repeated-measures analysis of variance (ANOVA) was performed. Time (before and after the participants watched the VR video) and region (angular gyrus, anterior cingulate, cingulate gyrus, cuneus, extra nuclear, fusiform gyrus, inferior frontal gyrus, inferior occipital gyrus, inferior parietal lobule, inferior temporal gyrus, insula, lingual gyrus, medial frontal gyrus, middle frontal gyrus, middle occipital gyrus, middle temporal gyrus, orbital gyrus, paracentral lobule, parahippocampal gyrus, postcentral gyrus, posterior cingulate, precentral gyrus, precuneus, rectal gyrus, subcallosal gyrus, sub gyral, superior frontal gyrus, superior occipital gyrus, superior parietal lobule, superior temporal gyrus, supramarginal gyrus, transverse temporal gyrus, and uncus) were used as the within-subject factors of brain source activity. When a significant effect was found, post hoc comparisons were performed using the paired \(t\)-test. Since many cortical areas were analyzed simultaneously as dependent variables, Bonferroni correction for cases of multiple comparison was used. Adjusted \(p\)-value was calculated by dividing 0.05 by the number of brain regions (i.e., adjusted \(p\)-value for frequency band power analysis: 0.05/4, \(p_{adj}<0.0125\); adjusted \(p\)-value for source activation analysis: 0.05/33, \(p_{adj}<0.00151\)). Pearson correlation analysis was performed to examine the relationship between SSQ score and brain source activation. Statistical analyses were performed using SPSS 21 (SPSS Inc., Chicago, IL, USA).
## 3 Results and Discussion
### Synthesis of Silicalite-1 Zeolites
The thermal stability of the organic cations within the zeolite porosity was determined by the thermogravimetric analyses, shown in Figure 4. The TPA-MFI zeolite shows a unique 12.5 wt.% weight loss around 430 \({}^{\circ}\)C, whereas, for the TPP-MFI sample, its 10.5 wt.% weight loss occurs at approximately 510 \({}^{\circ}\)C, demonstrating the higher thermal stability of the phosphorous cations inside the zeolite. The TPX-MFI\({}_{25}\) zeolite presents a 11.5 wt.% weight loss, with the main peak at 435 \({}^{\circ}\)C, and a less intense peak between 505-510 \({}^{\circ}\)C, while the TPX-MFI\({}_{45}\) sample exhibits a 11 wt.% weight loss with the main peak at 455 \({}^{\circ}\)C followed by a peak around 505 \({}^{\circ}\)C, that is, the thermal decomposition of TPA shifts to higher temperatures due to the presence of TPP, indicating a higher thermal stability. In addition, the weight loss decreases as TPP content increases due to the phosphorous oxide remaining inside the materials upon calcination. The combustion of the organic material is an exothermic and complex process, and it is possible that the heat produced during burning of TPA helps in the combustion of the closer TPP cations (occurring at higher temperature), leading to a lower temperature in the TGA-DTG. The shifts of the TPA peak towards higher temperatures and of the TPP peak towards lower temperatures suggests the coexistence of both TPA and TPP inside the ZSM-5 channels. Figure 3: FESEM images of silicalite-1 samples synthesized using TPA, TPP, and mixtures of them as OSDAs. Scale bar stands for all images. These results allow concluding that the synthesis of silicalite-1 zeolites using the dual-template procedure with TPA and TPP cations is feasible and the crystal size of the zeolites decreases when phosphorous agents are used under the synthesis conditions studied.
## 1 Introduction  
Colorectal cancer (CRC) represents the third most common cause of cancer mortality worldwide. Experimental evidence supports the role of gut microbiota as an important contributor in the carcinogenic process [1]. The idea concerning the role of microbes in carcinogenesis is not new, but it received compelling evidence over the last two decades, mainly thanks to the studies on _Helicobacter pylori_[2]. Human colon contains the largest population of bacteria in the body (in excess of \(10^{11}\) organisms per gram of wet weight) and the majority of these organisms are anaerobes. Among this population, 25% are _Bacteroides_, bile-resistant, gram negative bacilli [3]. Statistically, the relative abundance of five genera, _Bacteroides_, _Roseburia_, _Alistipes_, _Eubacterium_ and _Parasanterella_ has been identified in human gut microbiota [4]. A healthy balance between host and microorganism is kept for the optimal performance of metabolic and immune functions and to prevent disease development in intestinal and extra-intestinal regions, including the central nervous system [5]. Indeed, gut microbiota plays a pivotal immune function against pathogenic bacteria colonization and invasion and contributes to the maintenance of the intestinal epithelium integrity [6].  
Microbiota variations could be principally due to host-related factors including body mass index (BMI), exercise frequency, ethnicity, cultural and dietary habits. As far as the last aspect, recent studies have shown that diet has an important role in shaping the structure of gut microbiota [7; 8; 9], which anaerobically ferments dietary components that are not completely digested and absorbed in the upper gastrointestinal tract [10]. Metabolites and antigens produced by gut microbiota may have significant roles in influencing the risk of developing colon cancer through their interactions with host metabolism and immunity [11; 12].  
It is widely accepted that there is no single causative agent identified in CRC [13]. However, there is evidence that a reduction of \"protective\" bacteria and an increase in some bacteria such as _Fusobacterium_ sp. or _Bacteroides/Prevotella_, as well as age-related changes in microbiota have an impact on adenoma and cancer development [13]. [PERSON] et al. [4] reported structural fecal bacterial segregations between CRC patients and healthy volunteers. [PERSON] et al. demonstrated a higher abundance of Proteobacteria and a lower abundance of Bacteroidetes in CRC cases compared to healthy controls [14].  
Most colorectal carcinomas develop from pre-cancerous lesions, namely sporadic colorectal adenoma/polyp (SCA/P), considered to have malignant potential [15]. At our knowledge, this is the first study comparing the gut microbiota anaerobic components in stool specimens from patients with SCA/P and subjects without precancerous lesions (controls).
# A PSF-Shape-Based Beamforming Strategy for Robust 2D Motion Estimation in Ultrafast Data
## Materials and Methods
### Rotating Disk Experiments
#### 2.4.1 Rotational Flow  
After RF data beamforming and prior to velocity estimation, clutter filtering was performed. Although the phantom does not contain actual blood or tissue, including clutter filtering will give a more realistic picture of the performance of the methods for velocity estimation. A finite impulse response (FIR) filter with an order of 46 was used in combination with a temporal sliding window, not sacrificing frames for filter initialization. The \(-3\) dB velocity cut-off point was equal to 0.068 times the Nyquist velocity. By down sampling the beamformed RF data in temporal direction, a range of PRFs (8, 4, 2 and 1 kHz) was studied. The resulting inter-frame displacements are summarized in Table 2. The same filter characteristics were used for all PRFs and estimated velocities were median averaged over 40 temporal frames.
## 3 Results and Discussion
### Downscaling of MODIS LST with ASTER and ETM+ VNIR Data
(**c2**) The ASTER LST image of area C. (**c3**) The downscaled result of the MODIS LST image of area C by the OBD approach with the ETH+ VNIR data. artificial surfaces, like urban areas, and a river flowing through the region. The PBA method yields a lower overall ME value for the vegetation area, reduced by 4.28 K, due to the high NDVI and vegetation component \(F_{i}\) values in this region. Subsequent calculations using the OBD method led to a decrease in the ME to 1.34 K, with the ME increased by 2.94 K. In urban areas, the high NDBI and impermeable component values contributed to an overall ME that is 2.14 K higher. After recalculating with the OBD method, the ME decreased to 0.86 K, with the ME increased by 1.26 K. Additionally, the ME value in water bodies increased from 1.32 K to 1.08 K. In region C, the predominant landscape is characterized by natural surfaces, such as vegetation, with much more artificial surfaces, like urban areas, than region B, and few water bodies. The PBA method yields a lower overall ME value for the vegetation area, reduced by 2.52 K, due to the high NDVI and vegetation component \(F_{i}\) values. Subsequent calculations using the OBD method led to a decrease in the ME to 1.16 K with the ME increased by 1.36 K. In urban areas, the high NDBI and impermeable component values contributed to an overall ME that is 3.08 K higher. After recalculating with the OBD method, the ME decreased to 0.84 K, with the ME increased by 2.22 K. Additionally, the ME value in water bodies increased from 1.22 K to 0.48 K. As shown in Table 3, the ME and the RMSE of the OBD method are consistently lower than those of the PBA method across all the landcover types, including natural terrain, urban surfaces, and water bodies. Notably, the OBD method shows a significant improvement in ME. This improvement is primarily attributed to the principle of maintaining thermal radiance unchanged after decomposition, which reduces the uncertainty of LST changes when using the NDVI, the NDBI, and other remote sensing indices (RSIs) to estimate the PBA temperature for the subpixels. At the same time, the improvement effect on the STD is relatively small. This is primarily because the weights of the subpixels within the same object are derived from the initial temperature calculations. A significant estimation error in the initial temperature will consequently lead to a substantial estimation error in the weights. Figure 6: Performance comparison: (**a**) A scatter plot of the OBD LST images and the ASTER LST. (**b**) The LST error statistic chart of the OBD LST images and the ASTER LST.
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation  
In this section, we evaluate the pipeline in terms of real-time operation capability and the quality of the HDR video output. First, we explain the implementation details of the pipeline and discuss the results from our real-time performance assessment. Then, we discuss the three experiments conducted to assess HDR video quality: (1) objective evaluation of LDR-to-HDR converted videos obtained from our pipeline and two state-of-the-art methods, (2) subjective evaluation of LDR-to-HDR converted videos obtained from our pipeline versus original LDR content, and (3) subjective evaluation of HDR-to-xHDR converted videos using an alternative application of our pipeline.  
Figure 8: Results of the experiment to investigate the most adequate color saturation enhancement parameter value (\(s\)) used in [PERSON]’s formulation. Each dot represents a particular scene. The horizontal axis shows the mid-level out parameter (\(m_{o}\)) decided by our proposed dynamic range expansion function for that scene. The vertical axis shows the color saturation value (\(s\)) an expert user selected as most desirable. Green dots indicate scenes where the expert deemed it unnecessary to increase saturation, while black dots are those where the expert preferred increased saturation.  
We implemented our entire pipeline in Quasar, a development environment designed to facilitate heterogeneous programming across both CPUs and GPUs [50; 51]. Utilizing this environment enabled us to harness the GPU's inherent parallel processing capabilities, resulting in a substantial acceleration of our processing speed, optimizing performance, and demonstrating the real-time capabilities of our approach.  
We meticulously chose parameter values to optimize the time efficiency of our pipeline. Each pipeline step was fine-tuned with a dual objective in mind: achieving high-quality results while ensuring time efficiency. For instance, rather than processing the entire frame, we computed the image features used to estimate the middle-gray tone value for the dynamic range expansion operation (\(m_{\text{o}}\)) from a smaller region of interest (ROI). This approach not only sped up our pipeline but also avoided complications that might arise from video sequences containing vertical/horizontal stripes, advertisements, or captions. Likewise, we limited the number of iterations in the dequantization step to five and set the sub-sampling ratio for the fast-guided filters to four. The specific parameter values used in the experiments are detailed in Table 1.