esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Preliminary Evaluation Results
#### 5.4.2 Per-Band Absolute Error
The frequency distribution of absolute errors using Pre-F calibration (Figure 9c) indicates that 54.6% of the samples present an error of up to 5%, and 45.4% of the sample is in the range between 5% and 10%. Once the radiometric calibration parameters have been updated, 46.1% of the sample has a maximum absolute error of 2%; 48.5% is between 2% and 5%; and just 5.4% of the samples is in the order of 5%-10%. Lastly, for B4, the RMSE dropped from 7.8% down to 3.56%, MAPE from 7.52% down to 3.05%, and the maximum error diminished from 12.66% down to 9.6%. The median value of absolute errors changed from 7.5% down to 3.04%, once I\({}_{0}\) coefficients are applied to the DN samples. In relation to the distribution of errors (Figure 9d), prior to the B4 cross-calibration, 9.9% of the sample presented absolute errors of up to 5%; 77.1% was in the range 5%-10%; and 13% presented errors equal to or higher than 10%. The results obtained using the I\({}_{0}\) gain revealed that 33.1% of the sample contained errors of up to 2%; 51.5% has errors in the range of 2%-5%; and 15.4% has errors of the order of 5%-10%. Figure 9: Frequency distribution of the absolute percentage errors for FASat-C evaluation samples, \(\Delta\)(R-F), when applying the Pre-F and I\({}_{0}\) gains. The I\({}_{0}\) results are shown in blue for B1 (**a**), green for B2 (**b**); red for B3 (**c**) and purple for B4 (**d**), while the Pre-F results are shown in grey. Cumulative percentage curves, Cum. Pre-Flight and Cum. Cross-Cal, are shown in grey and black, respectively.
# Review of the Research Progress in Combat Simulation Software
## 2 Top-Level Specification of Simulation Software
### Hla  
In order to solve the interoperability among simulation applications and the reusability of simulation components, the High Level Architecture (HLA) [10] was proposed by DMSO in 1995 and is still widely utilized in various simulation scenarios [16; 17]. In 2011, the American Computer Society issued the distributed simulation engineering and execution process standard IEEE 1730 [18], which unified the engineering and execution processes related to distributed simulation such as DIS and HLA.  
The HLA is divided into three levels: _federation_, _federate_, and _object_. The _federation_ is a simulation system to achieve specific simulation objectives, and it usually consists of multiple _federates_, each of which is composed of several _objects_. Different _federates_ cooperate to complete the simulation objectives of the federation.  
The HLA is composed of rules, interface specifications, and object model templates, and the relevant contents have formed IEEE 1516 series standards. HLA rules ensure that all _federates_ can communicate and interact correctly when the _federation_ is running. The HLA interface specification determines all the service functions of the runtime environment (RTI) and the callback functions that the relevant _federates_ should provide. The HLA object model template is a description of the _Federation Object Mode_ file, providing a common method and model standard for recording information during the running process of the federation.  
As shown in Figure 3, the HLA provides general and relatively independent services through an RTI, separating the simulation application from the underlying support environment. Concretely speaking, the HLA separates the specific simulation function implementation, simulation operation management, and the underlying communication transmission, so that each part can be developed relatively independently and make full use of the advanced technology in their respective fields.  
Figure 3: Construction of the federation in an HLA.
# Automatic Medical Image Segmentation with Vision Transformer
## 2 Related Works
### Transformers in Vision  
Originally developed for machine translation [37], transformer architectures have achieved state-of-the-art results on numerous NLP tasks [38]. More recently, transformer models have been adapted to computer vision tasks [39], such as image classification [40], saliency detection [41], and semantic segmentation [42], demonstrating their strong potential in this domain.  
To apply transformers to image data, the authors of [43] employed self-attention locally instead of globally on image regions. In [44], the authors used approximations for scalable self-attention computation. The vision transformer (ViT) [45] directly applies global self-attention transformers on full-sized images, matching the performance of CNN on the ImageNet classification task for large-scale visual recognition. Our medical segmentation framework builds upon the ViT architecture. Transformers show significant potential for medical image analysis, especially for segmentation tasks, due to their better handling of long-range dependencies, flexibility in input size, opportunities for transfer learning, improved global context modeling, enhanced interpretability, and efficient training mechanisms. Subsequently, our work represents an initial effort to integrate transformers and their benefits of global context modeling into the domain of medical image segmentation.
# DTI Histogram and Texture Features as Early Predictors of Post-Radiotherapy Cognitive Decline
###### Abstract  
Radiotherapy for brain tumors can induce cognitive decline, yet most studies examine white matter (WM) damage six months post-treatment, overlooking early microstructural changes. This study investigated whether early WM changes, as measured by diffusion tensor imaging (DTI) histogram and texture features, can predict later cognitive deficits. **Methods**: Nineteen adults with brain metastases underwent DTI before and immediately after radiotherapy. Ten features--eight histogram-based and two texture-based--were extracted from normal-appearing WM of major DTI indices. Changes (\(\Delta\)) in these features, if any, were analyzed via multiple linear regression, correlating them with cognitive performance at four months after therapy. **Results**: Out of 40 features, four exhibited significant post-radiotherapy changes. These were the mean (AD\({}_{\text{mean}}\)) and skewness (AD\({}_{\text{skewness}}\)) of axial diffusivity and the kurtosis of mean diffusivity (MD\({}_{\text{kurtosis}}\)) and radial diffusivity (RD\({}_{\text{kurtosis}}\)). Regression identified \(\Delta\)AD\({}_{\text{mean}}\) (\(\beta=-3.303\times 10^{4}\), \(p=0.002\)) as negatively and \(\Delta\)AD\({}_{\text{skewness}}\) (\(\beta=4.642\), \(p=0.006\)) and \(\Delta\)RD\({}_{\text{kurtosis}}\) (\(\beta=-1.505\), \(p=0.027\)) as positively associated with semantic fluency. **Conclusions**: Early WM microstructural disruptions--particularly axonal damage and heterogeneous injury--correlate with declines in semantic fluency. DTI histogram and texture features may be promising as early non-invasive biomarkers for cognitive risk following radiotherapy.  
Keywords:brain; radiotherapy; cognitive function; diffusion tensor imaging; white matter; histogram; texture +
Footnote †: journal: Applied Sciences
# Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions
## 1 Introduction
### Development of Strategies for Pavement Maintenance in Agencies with Limited Data and Resources  
Data Analytics has been considered as an effective way of handling pavement condition data and relating predicting pavement conditions [26]. However, in many instances, many large databases do not provide particularly useful information to allow for efficient decisions to be made with many of the databases having subjective data [37]. There have been attempts at creating datasets that will help boost deep learning activities in the field of civil engineering where datasets of different infrastructures have been set up [38]. However, these studies have tended to focus on image-based datasets. Image-based analysis can be very effective and recent studies have shown how low-cost models can be developed to detect distresses within a network using smartphones [39]. However, even within these systems, the question of where to direct the image survey and how to understand the feature characteristics of the network still needs to be addressed for them to be practically applied.  
There is one large database that includes pavement features and factors called the Long-Term Pavement Performance (LTPP)database [40] which is an enormous database developed and maintained by the Federal Highway Administration in the U.S. It has allowed for the study and development of many different algorithms and decision-based studies for predicting different future conditions of pavements and developing maintenance strategies, with reviews showing that the majority of research using ANN for predictions is done using the LTPP database [41]. If similar databases could be set up for all agencies, it would be a great help in their planning processes. However, in most cases, road agencies do not have access or funds to create such a large database and the associated algorithms and models developed for road maintenance. This means the systems and predictions cannot be applied to their cities and Whilst it can be reasoned that more data can produce higher accuracies in models, the costs of acquiring the additional data must be amply considered by the road authority. Therefore, there should be a balance between available budgets and information pursuit [42]. The use of feature selection has been considered useful to help increase the accuracy of models by utilizing fewer feature characteristics but those of the most significant importance [42]. To this end, machine learning approaches have been used to obtain the most important features within a dataset with conclusions that these approaches could potentially be effective for cases where there is limited data available.
## 4 Numerical Analysis
### Critical Buckling Load Affected by the Core Layer's Thickness  
The core layer's thickness of the honeycomb reinforced composite laminated structure directly influences the structure's dynamic effective equivalent stiffness, and the stiffness further affects the buckling load of the composite structures. In this section, the thickness of the honeycomb reinforcement and viscoelastic material composed layer is analyzed numerically. The geometrical, physical and load parameters are: \(t_{1}=t_{3}=2.5\) mm, \(d_{t}=1.5t_{1}\), \(k_{n}=k_{a}=\)1 and \(a=b\), and the numerical data are provided in Table 3. Similar to that which has been researched, the thickness of the honeycomb and viscoelastic material composed  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(a\) & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 & 0.6 & 0.7 & 0.8 & 0.9 & 1 \\ \hline \multirow{6}{*}{\(k_{a}\)} & 2 & 553.83 & 138.46 & 61.540 & 34.61 & 22.15 & 15.38 & 11.30 & 8.65 & 6.84 & 5.54 \\  & 4 & 1198.17 & 299.54 & 133.13 & 74.89 & 47.93 & 33.28 & 24.45 & 18.72 & 14.79 & 11.98 \\ \cline{1-1}  & 6 & 2193.74 & 548.44 & 243.75 & 137.11 & 87.75 & 60.94 & 44.77 & 34.28 & 27.08 & 21.94 \\ \cline{1-1}  & 8 & 357.48 & 893.72 & 397.21 & 223.43 & 143.00 & 99.30 & 72.96 & 55.86 & 44.13 & 35.75 \\ \cline{1-1}  & 10 & 5346.98 & 1336.75 & 594.11 & 334.19 & 213.88 & 148.53 & 109.12 & 83.55 & 66.01 & 53.47 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Buckling loads vs. \(k_{a}\).  
Figure 6: Change rate of buckling loads affected by geometrical parameter \(k_{a}\) and the dimensions.  
layer are defined as \(t_{2}=t_{R}t_{1}\). \(t_{R}=\{\)2, 4, 6, 8, 12, 16\(\}\). The buckling load of is sharply decreased by increasing the composite laminate's geometrical dimensions, which is the same as the trend displayed in Figure 8. Moreover, it can be observed from Figures 8 and 9 that the buckling load of the structure is increased by increasing the honeycomb reinforced layer's thickness. To display the buckling load's increasing rate on the honeycomb layer's thickness, Figure 9 is provided. In the figure, the buckling load of the composite honeycomb laminate at \(t_{R}=2\) is considered as the reference value.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 6 Conclusions  
This paper proposed the optimization techniques for the object tracking algorithms in the OpenCV library. We targeted two object tracking algorithms, object detection with the Haar/LBP classifier and Farneback optical flow, which are affordable solutions to resource-constrained computing systems, such as smart devices. Based on careful profiling of the algorithms implemented in the OpenCV library, we proposed and applied various optimization techniques. The evaluation results demonstrated that the proposed optimization techniques successfully improved the performance (throughput) of the object tracking algorithms.  
The constraint of our research is that programmers should manually locate the algorithm's performance bottleneck and apply an appropriate optimization scheme. This may undermine the advantage of OpenCV's transparent API introduced for the programmer's convenience. Therefore, our future work is developing a profiler that automatically profiles OpenCV/OpenCL applications and applies such optimization strategies to them.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON], [PERSON] and [PERSON]; software, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; investigation ; resources, [PERSON], [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF- 2020R1A2C2102406), by the MSIT (Ministry of Science and ICT), Korea under the ICT Creative Consilience program (IITP-2020-0-01821).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} OpenCV & Open-Source Computer Vision \\ OpenCL & Open Computing Language \\ CE & Consumer Electronics \\ APU & Accelerated Processing Unit \\ ALU & Arithmetic Logical Unit \\ VGPR & Vector General Purpose Register \\ SGPR & Scalar General Purpose Register \\ LDS memory & Local Data Share Memory \\ CU & Compute Unit \\ \end{tabular}
# A Point Crack Source Location Method without Velocity Information in Anisotropic Plates
## 4 Results
### Robustness on Noise  
In this section, we present the numerical experimental results to test the noise robustness of the source location method with the concept of ASNR defined in Equation (7). The numerical experiment settings were explained in Section 3.1, and the result figures are shown in Figure 6.  
The estimated location with the SDMM applied are depicted in Figure 6a-d. In the figure, the red dot is the source location, the blue plus sign (+) and the blue cross (x) indicate the predicted crack locations when \(\lambda=0\) and \(\lambda\
eq 0\), respectively. As can be seen in the figure, the estimated location when \(\lambda\
eq 0\), marked by a blue cross (x), found the source location better as the ASNR increased, whereas the estimated location when \(\lambda=0\), denoted with a blue plus sign (+), did not find the source location well regardless of the ASNR value. Therefore, when \(\lambda=0\) (i.e., when there was no regularization term), the estimated location hardly moved to the crack location, and when \(\lambda\
eq 0\) (i.e., when there was a regularization term), the estimated location moved to the crack location. For quantitative analysis, the relative error (distance error for the diameter of a circle with the same area as the experimental area) was calculated as follows:  
\[\text{relative error}=\frac{\text{distance error}}{2\sqrt{\text{domain area}/\pi}} \tag{9}\]  
The relative error according to the ASNR value of the noise can be seen in Figure 6e, and it can be seen that the relative error was less than 15% when the noise in the TOA was ASNR \(\geq 30\).  
Figure 6: Numerical experiment results with noise ASNRs 10, 30, and 50 and without noise. Here, ASNR is defined as in Equation (7). For simplicity of notation, the ASNR in the absence of noise is expressed as \(\infty\). The estimated location is represented in (**a–d**), where ASNR is 10, 30, 50, and \(\infty\), respectively. The black dots are for sensor positions, while red dots represent crack positions. The blue plus sign (+) and blue cross mark (x) denote the estimated crack positions for \(\lambda=0\) and \(\lambda\
eq 0\), respectively. The relative error for noise is shown in (**e**) with a blue plus sign (+) when \(\lambda\) is zero and blue cross mark (x) when \(\lambda\) is non-zero.
## 3 Results and Discussion
### Effect of LED Light Treatments on Photosynthetic Pigments  
Following exposure to light, microgreens undergo photomorphogenesis and inevitably synthesize photosynthetic pigments, such as chlorophylls and carotenoids [15]. Additionally, the color of microgreens is important for the visual appearance of the product and may influence the choice of customers. They are also very important plant-derived components of the human diet. The beneficial health effects of carotenoids are not only related to their role as vitamin A precursors, but they may also reduce the risk of developing degenerative chronic diseases such as age-related macular degeneration, type 2 diabetes, obesity, certain types of cancer, and cardiovascular diseases, among others [34]. Chlorophyll and its derivatives can stimulate the immune system, act against sinusitis, fluid accumulation, and skin rashes, eliminate toxins from the body, prevent cancer, normalize blood pressure, and fight bad odors and bad breath [35]. The results summarized in Table 3 indicate that supplementation of white light with red or blue light has negative effects on the photosynthetic pigment content of both cabbage microgreens. Significant differences (\(p<0.05\)) were found between the content of pigments in microgreens grown under different light treatments. The highest total carotenoids (689 mg 100 g\({}^{-1}\) DW), total chlorophylls (1312 mg 100 g\({}^{-1}\) DW), chlorophyll \(a\) (445 mg 100 g\({}^{-1}\) DW), and chlorophyll \(b\) (870 mg 100 g\({}^{-1}\) DW) content was observed in red cabbage microgreens under white light. Generally, red cabbage microgreens were characterized by higher levels of chlorophyll and carotenoids compared to white cabbage microgreens at the same light treatments.  
Figure 2: Contribution (%) of soluble and insoluble fiber fraction components of white and red cabbage microgreens under white, white with red, and white with blue LED lights.  
In all cases, a higher content of chlorophyll \(b\) than chlorophyll \(a\) was found. [PERSON] et al. demonstrated that amber LED was involved in the regulatory mechanism of carotenoid biosynthesis, and generally carotenoid content increased under dose-dependent increasing amber-blue light and decreasing red light [18]. The content of chlorophylls and carotenoids in 13-day-old red cabbage microgreens grown in a glasshouse under red, blue, or combined red and blue (1:1) lights was favorably influenced by the blue spectrum. The lowest contents were obtained when a combination of red and blue lighting was used [14]. The study by [PERSON] et al. observed that microgreens of green and red cabbage grown under red:blue (20:80) LEDs had the highest \(\beta\)-carotene content, compared to those grown under red:blue (80:20) LEDs, and when green light was added to the mixture of red and blue lights [16]. According to [PERSON] et al., the blue light percentage (from 5 to 30%) supplied from LEDs did not affect chlorophyll \(a\) and \(b\) concentrations or carotenoid concentrations in red cabbage microgreens [19].
## 3 Using Classifiers to Predict Relevant Change
### Classification Algorithms
#### 3.2.2 Support Vector Machines  
The use of support vector machines (SVMs) has been widely discussed as a means of performing nonlinear two-class classification. Originally developed by [PERSON] and [PERSON] [22], SVMs are capable of performing efficient classification of data not otherwise linearly separable by employing the \"kernel trick\" to project data into a high-dimensional feature space.  
For each tile in the training set, we can define feature vector \(x_{i}\in\mathcal{R}_{d}\) and assignment membership to it based on whether it was marked as being relevant change (_i.e._, let \(y_{i}=1\)) or either not-relevant change or no change (_i.e._, let \(y_{i}=-1\)). If we let \(\mathbf{w}\) represent the vector normal to the hyperplane that divides the two sets, then we can solve the classification problem using quadratic programming. We must optimize:  
\[\operatorname*{arg\,min}_{(\mathbf{w},b)}\frac{1}{2}||\mathbf{w}||^{2} \tag{2}\]  
subject to:  
\[y_{i}(\mathbf{w}*\mathbf{x_{i}}-b)\geq 1 \tag{3}\]  
for all values of \(i\). The resulting classification is the one which maximizes the margin, or separation between the two classes, in the high dimensional space used by the chosen kernel.  
Classification is then performed by projecting each new data point into the same high dimensional space and determining on which side of the hyperplane it falls.
## 1 Introduction  
Physical exercise is one of the main recommendations included in clinical guidelines for people with type 1 diabetes (T1D) because it is associated with substantially lower cardiovascular and overall mortality risks [1; 2]. In addition, a minimum of 150 min of moderate-intensity physical activity per week [3] results in improvements in insulin sensitivity, body composition, endothelial function, and blood lipid profile [4].  
Despite these benefits, adherence to regular physical activity is low in the T1D population due to factors like time limitations, no access to appropriate equipment at home, or economic costs [5]. In addition, some people with T1D tend to be less physically active due to fear of hypoglycemia when performing sport activities [6]. High-intensity interval training (HIIT) has been proposed as a time-efficient methodology, consisting of sessions of short duration and high intensity exercises. Safety and efficacy of HIIT in T1D has usually been analyzed using continuous glucose monitoring (CGM) to determine the influence of exercise on glycemic control [7; 8; 9]. From these studies, HIIT demonstrated to have a lower impact on blood glucose homeostasis than moderate-intensity continuous training [7], and is associated with low risk of hypoglycemia [8].  
In previous studies, cycle ergometer was the HIIT training method most commonly used in studies that evaluate its impact on glycemic control [7; 8; 9; 10]. HIIT sessions have also been carried out on a treadmill [11] under strictly controlled conditions. While those HIIT methods have been associated with glucose levels within a safe range [8; 11] and an improvement on cardio-metabolic health [7], their application in a clinical setting would be limited because of the dedicated equipment required. An easier and cheaper training alternative for HIIT is exercising with elastic bands, either in a clinic or at home [12]. Elastic bands have been used in people with type 2 diabetes at low-moderate intensity conditions [13], increasing the strength of the lower limbs [13] and reducing the prevalence of frailty in this type of patient [14]. Even so, training with elastic bands in T1D in combination with the HIIT method has not been investigated, and therefore, neither has its safety nor efficacy. Thus, the main objective of this study was to examine the impact of HIIT using elastic bands on glycemic control in people with T1D.