esa-sceva/satcom-chunk-collection huggingface dataset:
# Interconnected Government Services: An Approach toward Smart Government
## 4 Results--Model Evaluation  
We evaluated the proposed IGS solution in two ways: by practically implementing the solution in a test environment and by creating a mathematical model based on weighted digraphs. The proposed model is evaluated in the example of the application for obtaining child benefits in the Republic of Serbia. By this model/example, the problem regarding applying the current solutions in public administration is perfectly illustrated. Developing a practical solution helped us verify the proposed solution's feasibility in controlled laboratory conditions. Integrating autonomous databases is a justified and logical step in upgrading public administration procedures. Furthermore, in addition to the proof of the practical implementation feasibility, the advantage and improvements brought by the proposed solution can be seen very clearly through a mathematical model that deals with weighted digraphs [37; 38]. In that way, we emphasize the difference in terms of the communication complexity and the delays of the traditional documentation-obtaining model compared to the proposed IGS model.
# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 1 Introduction
We propose an object tracking algorithm based on Transformer feature pyramid and add a response map enhancement module. Compared with other trackers, the accuracy and robustness of our tracker are greatly improved when dealing with the challenges of similar object and occlusion. 2. Feature pyramid based on Transformer proposed in this paper establishes global information interaction, efficiently integrates feature information of low and high layers, and effectively improves tracking performance; the proposed response map enhancement module makes full use of the context information of the response map, which improves the ability of the model to distinguish the object from the background, and effectively improve the ability to deal with the interference of similar targets and other complex environments. 3. Extensive experiments and ablation experiments are conducted on many challenge benchmarks, these prove our proposed method has competitive performance, not only in accuracy but also in speed. When different backbone networks are selected, the frame rate of the algorithm in this paper can reach 45-130 FPS, meeting the real-time requirements.
## 3 Materials and Methods
### Machine Learning Model  
[PERSON] and [PERSON] developed a Fully Convolutional Neural Network with a Dilated Kernel (FCN-DK) specifically for the purpose of slum mapping [23]. Among the various models within this network framework, the Fully Convolutional Network (FCN) has demonstrated a higher level of effectiveness relative to its counterparts. Consequently, the FCN model is employed for the identification of slum areas utilizing remote sensing imagery (RSI) alongside a combination of RSI and street view imagery (SVI). Prior studies have investigated urban land use classification by merging RSI with SVI through the FCN-VGG16 architecture; however, this approach is hindered by significant computational demands when compared to the standard FCN [14]. Therefore, this research focuses exclusively on the implementation of FCN and FCN-DK models.  
The FCN-DK architecture employs a dilated convolution method, which effectively broadens the receptive field while simultaneously reducing the number of learnable parameters, thus mitigating the risk of over-fitting [24]. This characteristic makes FCN-DK particularly appealing for applications that require efficient learning from complex datasets. Despite its advantages, the application of FCN-DK in integrating datasets from varying perspectives--specifically, aerial views from RSI and ground-level images from SVI--remains under-explored.  
This research gap presents a valuable opportunity to investigate the potential of FCN-DK in synthesizing these distinct types of data for enhanced urban analysis. By leveraging the strengths of both aerial and ground-level imagery, this study aims to contribute to a more nuanced understanding of urban environments, particularly in identifying and analyzing slum areas. The findings could pave the way to more effective urban planning and policymaking, addressing the challenges associated with urbanization and its impact on marginalized communities.
## 2 Materials and Methods
### Oversampling  
Oversampling is a technique used in AI to address the quite common issue of imbalanced datasets. This occurs in classification scenarios, where one class of data is significantly underrepresented compared to the rest. It involves increasing the number of instances of the minority class by randomly generating synthetic samples. Oversampling can help to improve the performance of machine learning models, particularly in scenarios where the minority class is of high importance, such as in medical diagnosis. There are several common oversampling methods, including Random Oversampling [32], the Synthetic Minority Oversampling Technique (SMOTE) [33], and Adaptive Synthetic Sampling (ADASYN) [34]. Studies have shown that oversampling techniques can significantly improve the accuracy of machine learning models in imbalanced datasets [33], demonstrating the importance of considering this technique in the development of AI models.  
All of the abovementioned oversampling techniques were considered and tested with the particular dataset. Nevertheless, random oversampling gave the most balanced accuracy. In the current classification scenario, all the employed prediction models gave the most equally accurate predictions for both classes (adenoma/MG), when they were trained on datasets modified by random oversampling. Since a prediction model can easily be trained to be accurate for the prevalent class, generating a model that is equally accurate for both classes is of utmost importance. The holistic approach of this study, along with all its steps, is showcased in Figure 1.  
Random oversampling [32] is one of the most straightforward and widely used oversampling methods. It involves randomly duplicating instances of the minority class until it reaches a certain threshold, thus balancing the classes. Although it has limitations and is prone to overfitting, random oversampling is a simple and effective way to address imbalanced datasets. The method was first introduced by [PERSON] et al. in 2002 [33] and has been widely used in various domains, including finance, healthcare, and cybersecurity. For example, random oversampling has been used to improve the accuracy of fraud detection models in credit card transactions [35], to predict early signs of Alzheimer's disease [36], and to predict depression among subjects [37].  
Figure 1: Current study’s methodology.
## 5 Experimental Verification
### Experimental Results
#### 5.3.1 PID Control Comparison Experiment  
Figure 6 shows the experimental results of PID and ILC-ASMC controllers. The ILC -ASMC control effect is significantly better than the PID control effect. As shown in Table 1, the control performance is improved by 89.46% and 89.78%, respectively, which is not only reflected in the tracking accuracy of the reference signal, but also in the convergence speed. Therefore, it can be shown that the proposed controller has strong robustness to system disturbance and greatly improves the operating accuracy of FTS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller** & **PID** & **SMC** & **ASMC** & **ILC-ASMC** \\ \hline \(e_{\text{max}}\) (nm) & 806.24 & 874.85 & 511.84 & 84.95 \\ Percentage error & 4.03\% & 4.37\% & 2.56\% & 0.42\% \\ \hline \(e_{\text{rms}}\) (nm) & 549.84 & 366.09 & 228.91 & 56.21 \\ Percentage error & 2.75\% & 1.83\% & 1.14\% & 0.28\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Controller performance comparison.  
Figure 6: PID and ILC-ASMC tracking results of sinusoidal trajectory at 40 Hz.
## 3 Test Results and Discussion
### Connectors and Insulation
However, a strong reduction in bond strength, whose main governing parameter is the temperature near the resin \(\mathrm{T_{g}}\)[31], has a stronger influence than surface finishing and diameter, as reported in [32]. Although, at the end of fire exposure, the temperatures in the exposed wythe must have been much higher than the \(\mathrm{T_{g}}\) of the connectors' resin, no signs of pull-out and no premature collapse of the facing wythe due to the debonding of the GFRP connectors were observed. This finding agrees with the results obtained in [31; 33], as the bonds of the ribbed bars from the same manufacturer were less affected than the sand-coated GFRP bars reported in [34]. In Test 2, the exposure of the facing wythe (in this case, the 70 mm thickness was further reduced due to concrete spalling) to fire resulted in much more severe damage of the connectors in position B. In this case, the resins of the connectors melted up to ca. 50 mm of their free length and over the length to which they were embedded in the fire-exposed wythe. This was noted during the examination of the cooled specimen, when the resin in this region was not present and only the unprotected continuous intact glass fibres were visible. Although the connectors experienced some heat-induced damage over their entire free length, substantial carbonation was observed up to about half of their free length. Therefore, it could be concluded that the melted connectors in all the inner rows lost their entire load-bearing capacity and the facing wythe was supported solely by the upper and bottom rows of the unmelted connectors (position A). Sudden and high increases in temperature in the concrete layer led to excessive pore pressure and compressive stress on the fire-exposed side, leading to explosive spalling. In the tested fire-exposed wythes, the temperature could have been larger than would have been the case for single wythes of the same thickness, as a result of the thermal insulation preventing the heat transfer outside the furnace. The spalling of the fire-exposed layer of concrete occurred in Tests 2 and 3 up to depths of \(\sim\)40 mm within 15 to 20 min of the furnace ignition. The lack of spalling in Specim 1 could have been due to differences in the moisture content of the concrete, which was itself probably caused by different conditions during the curing and drying times, which were not recorded. The influence of the connectors on the spalling behaviour of the concrete wythes was analysed. No spalling-initiating effect caused by the presence of the connectors could be identified. An examination of the post-fire surface revealed that in Test 2, the areas surrounding the locations of the connectors showed smaller or no spalling comparing to other areas on the same specimen. This could have been caused by lower pore pressure around the empty holes left by the melted connectors' sections, where moisture could escape the concrete.
## 3 Results and Discussions
### Effect of Nanoparticles on Hydraulic Conductivity  
Figure 3 displays the relationship between hydraulic conductivity over permeation time for untreated and nanoparticles-treated soil samples. It is observed that the hydraulic conductivity of untreated and treated samples decreased with testing duration. The hydraulic conductivity of NS decreased from \(4.96\times 10^{-7}\) cm/s to \(1.26\times 10^{-7}\) cm/s after seven weeks of testing. It is quite likely that after long-term permeation, the voids between the soil particles are fully occupied by the water molecules. As a result, the movement of water along the compacted soil samples slows down and reduces the hydraulic conductivity of NS. The hydraulic conductivity of nanoparticles-blended soil reduced to almost one order of magnitude after seven weeks of permeation. The percentage reduction of the hydraulic conductivity values after seven weeks of permeation for the magnesium and calcium hydroxide nanoparticles-treated samples relative to the first week were approximately 81% and 99%, respectively. The observed percentage reduction in the hydraulic conductivity of nanoparticles-treated soils was more significant than that of untreated soil (75%). This variation can be due to the clogging of soil pores by the formation of cementing gels from the reactions between magnesium and calcium nanoparticles and the soil matrix. Among the two nanoparticles-treated samples, CS showed better hydraulic performance compared to MS, as evident by their percentage reductions. This phenomenon can be attributed to the effectiveness of the cementing gels formed for the CS relative to the MS. Likewise, the higher OMC of CS compared to MS provides a more convoluted flow path for water to follow. Consequently, a further decrease in the hydraulic conductivity was observed. It is interesting to note that the recorded hydraulic conductivity values for the treated soil specimens satisfied the permeability requirements for landfill liner materials (\(\leq\)\(1\times 10^{-7}\) cm/s) specified by [32; 33; 34].  
Figure 2: Compaction curves for untreated and nanoparticles-treated soil specimens.
## 3 Results and Discussion
### Zeta Potential of PMC  
The zeta potential of the test water at varying PMC-based coagulant doses was determined to investigate the mechanism of turbidity and algae removal. With increasing PMC dose from 0 to 70 mg/L, the zeta potential was increased from \(-\)26.3 mV to +27.1 mV (Figure 4). This could be explained by the highly positive charge of the PMC stock solution (+44.86 mV). The isoelectric point was found to be around 20-40 mg/L PMC dose, which range is close to the optimum PMC dose for the algae and turbidity removal, i.e., 20 mg/L (Figure 4). This result indicates that the charge neutralization could be the major removal mechanism of algae and turbidity in coagulation process [28]. At the PMC dose higher than 20 mg/L, the reversal of the solution charge from negative to positive could lead to restabilization of the particles. The particles could repel each other, owing to strong electrostatic repulsion forces caused by the adsorbed polycations [22]. Moreover, algae bio-colloids are known to carry negative surface charges at most pH levels [29]. Thus, the high algae removal efficiencies of the PMC can be attributed to the mechanisms of charge neutralization and mutual attraction between the negatively charged algae and the positively charged components of the PMC-based coagulant (i.e., zeolite, barley stone, and loess) [10].  
Figure 4: Effect of PMC-based coagulant dose (0, 5, 10, 20, 40, and 70 mg/L) on Zeta potential.  
Figure 3: Removal of UV\({}_{254}\) and dissolved organic carbon (DOC) at the laboratory-scale using varying doses of the PMC-based coagulant (0, 5, 10, 20, 40, and 70 mg/L).
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 3 Methodology
### Framework of LTS-SEI  
Figure 2 shows the framework of the proposed LTS-SEI method. The framework mainly comprises four modules: (1) A data-preprocessing module to process source domain signals and target domain signals; (2) An extractor to learn the shallow RFFs of source domain samples and target domain samples; (3) A classifier to learn the deep RFFs and predict the labels of source domain samples and target domain samples; and (4) A discriminator to implement adversarial learning and a gradient reversal layer (GRL). In the LTS-SEI framework, the source domain classification model and the target domain classification model share the same feature extractor and classifier. By adding reasonable constraints, LTS-SEI tends to learn features with interclass separation, domain invariance, and intraclass compactness. Additionally, LTS-SEI adopts an unsupervised learning mechanism. The target domain samples and source domain samples are used to jointly train the entire network, and, ultimately, the classifier provides the prediction results of the target domain sample labels.
# Thin-Walled CFST Columns for Enhancing Seismic Collapse Performance of High-Rise Steel Frames
## 2 Modeling and Analysis Methods
### Thin-Walled Steel Columns  
The width-to-thickness ratio, \(\lambda\), has been classified to control the occurrence of local buckling of flexural H-shaped steel members [25]. The \(\lambda\) of H-shaped steel beams is classified as a compact cross section (FA represents a compact cross section) when \(\lambda\) is equal to or less than \(9\sqrt{235/Fy}\), and the non-compact cross section (FB represents a non-compact cross section) when \(\lambda\) is equal to or less than \(11\sqrt{235/Fy}\) for flanges, where \(F_{y}\) is the nominal yield strength of steel materials. For square hollow steel sectional columns, the critical values of the compact and non-compact cross sections are given as \(33\sqrt{235/Fy}\) and \(37\sqrt{235/Fy}\). Note that the above \(\lambda\) are classified on the basis of the various types of steel materials. Both of the above advantages contribute to the ductile behavior of the CFST column due to the delay of local-buckling and the confined effect on the in-filled concrete. The compact and thin-walled CFST columns are employed in high-rise CFST building models in order to be analogous to the steel moment-resisting frame (SMRF) building models with various sectional compactness.  
According to the Recommendations for the Design and Construction of Concrete Filled Steel Tubular Structures [26]--different from the ranks of the width-thickness ratio (FA, FB) on HSS members by the Building Standards Act of Japan [25]--the ranks of the width-thickness ratio (\(B/t\)) of CFST members is required differently, such as \(R_{u}\geq 0.02\) for the compact FA section; and \(R_{u}\geq 0.02\) for the thin-walled FB section. Rectangular CFST members can be \(R_{u}\) calculated as  
\[R_{u}=\frac{\gamma_{r}}{0.15+3.79\frac{N}{N_{0}}}\cdot\frac{t}{B}\cdot{}_{R}\alpha \tag{1}\]  
where \(B\) is the width of the rectangular steel tube, \(t\) is the thickness of the steel tube, \(F_{c}\) is the nominal design strength of the concrete, \(N\) is axial loading, \(N_{0}\) is the yield strength of the CFST columns which is approximately equal to \(A_{s}f_{y}+A_{c}f_{c}\), \(\gamma_{r}\) is a coefficient defined by the buckling length \(l_{k}\) of the CFST members, (if \(l_{k}/D\leq 10\), \(\gamma_{r}=1.0\), if \(l_{k}/D>10\), \(\gamma_{r}=0.8\)), and \({}_{R}\alpha\) is defined as follows:  
\[{}_{R}\alpha=1.0-\frac{F_{c}-40.3}{566},\text{if}\;_{R}\alpha\geq 1,{}_{R}\alpha=1 \tag{2}\]
## 5 Tracking Performance Verification
### Simulator-Based Testing and Verification
Comparing Line 2 and Line 3, the static tracking error of the INS-aided PLL is slightly worse (not more than one degree) than that of the normal PLL, because of the INS bias-type errors, which is consistent with the analysis result shown in Figure 6. The dynamic tracking error of the PLL aided by the MEMS INS is almost the same as that aided by the medium-grade INS under vehicle dynamic conditions, which agrees with the model analysis results in Figures 8 and 9. All test results are consistent with the analysis results of the models, which verifies the feasibility of the proposed models in Section 3.
## 2 Input Modalities: CT or X-ray
### Artificial Intelligence Perspective  
From the perspective of using artificial intelligence algorithms, the most significant difference between CT and X-ray images is the input shape. As shown in Figure 3, a CT image contains a series of slices; therefore, the input has multiple channels. However, there are also some methods that ignore the multiple channels and treat each slice independently, leading to the loss of spatial contextual information. Comparatively, X-ray images only have a single channel.  
Figure 2: Typical CT (**left**) [30] and chest X-ray (**right**) [32] images of COVID-19-infected patients. The right subfigure a–f represents different cases, please refer to [32] for more details.  
Despite the difference in data dimension, there are not many other differences between CT and X-ray images from the artificial intelligence perspective, since most existing methods do not utilize any explicit domain knowledge. Therefore, the number of CT-based methods and X-ray based methods are approximately equal (49% vs. 53 (some methods are compatible with both CT and X-ray images)) among the covered papers in this review. However, we found that, among CT-based and X-ray based methods, the proportion of classification methods and segmentation methods is different. CT-based segmentation methods account for 20%, whereas X-ray-based segmentation methods account for only 3%. The reason might be the difficulty of the X-ray-based segmentation task. Meanwhile, it can also be partially explained by the imbalance in the dataset type: the proportion of the CT-based segmentation dataset is more than that of the X-ray-based segmentation dataset (40% vs. 29%).
# Internal Curing Effect of Waste Glass Beads on High-Strength Cement Composites
###### Abstract
analyzed the influence of LWA particle diameter on the internal curing efficiency and reported that hydration increases as diameter decreases [20; 21; 22]. [PERSON] et al. proposed a mechanism to improve mechanical properties via the shrinkage reduction in concrete using rice husk ash as pre-wetted LWA because it can delay the relative humidity reduction inside UHPC and promote the continuous hydration of cement [23]. [PERSON] et al. examined an effective method of mitigating the autogenous shrinkage of UHPC using coral aggregates [24]. [PERSON] et al. conducted research to increase the internal relative humidity and decrease autogenous shrinkage by adding zeolite into cement paste with a low moisture content [25]. The degree of hydration of UHPC through the internal curing effect of LWA has been primarily analyzed using TGA, X-ray diffraction (XRD), and SEM-EDS; however, studies on the hydration degree of the individual elements in paste, such as Si, in high-strength cement composites (HSCCs), such as UHPC, remain limited. In previous studies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], the hydration effect of various porous materials was examined to improve the internal hydration of HSC and UHPC. However, as the studies on hydration analysis using LWA have not performed thermodynamic modeling based on the hydration degree of certain elements formed in the HSCC paste, they have failed to identify the hydration tendency by age. Therefore, this study examined whether non-hydrated reactants inside HSCCs can be reduced using waste glass beads (WGBs) as LWAs based on the internal hydration mechanism of high-density concrete. In addition, WGB was subjected to pre-wetting to examine the improvement in internal hydration by water-containing aggregates, and the hydration tendency by age was identified via thermodynamic modeling. The specimens prepared by adding WGBs at ratios of 5%, 10%, and 20% were subjected to steam curing and room temperature curing. For each test group, the internal hydration effect of HSCCs was examined via uniaxial compression testing, XRD/Rietveld-based quantitative phase analysis, TGA, solid-state \({}^{29}\)Si magic-angle spinning nuclear magnetic resonance (MAS NMR), and thermodynamic modeling. In addition to understanding the hydration level of the paste, the internal state of WGB pores was confirmed through a visual inspection of the cross-section of a test specimen mixed with WGBs and through SEM-EDS analysis.